diff --git a/.gitignore b/.gitignore
index c27c7a6a5f6..73bba6cb364 100644
--- a/.gitignore
+++ b/.gitignore
@@ -29,9 +29,6 @@
 # IPython notebook checkpoints
 .ipynb_checkpoints
 
-# CMake generated files
-*.gen.cmake
-
 # Editor temporaries
 *.swp
 *~
@@ -44,6 +41,9 @@
 *.*project
 .settings
 
+# QtCreator files
+*.user
+
 # OSX dir files
 .DS_Store
 
@@ -71,6 +71,8 @@ distribute/*
 *.testbin
 *.bin
 python/caffe/proto/
+cmake_build
+.cmake_build
 
 # Generated documentation
 docs/_site
diff --git a/CMakeLists.txt b/CMakeLists.txt
index d6b08470a7f..adea37be565 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,177 +1,65 @@
-cmake_minimum_required(VERSION 2.8.8)
-project( Caffe )
-
-###    Build Options     ##########################################################################
-
-option(CPU_ONLY "Build Caffe without GPU support" OFF)
-option(BUILD_PYTHON "Build Python wrapper" OFF)
-option(BUILD_MATLAB "Build Matlab wrapper" OFF)
-option(BUILD_EXAMPLES "Build examples" ON)
-option(BUILD_SHARED_LIBS "Build SHARED libs if ON and STATIC otherwise" OFF)
-
-if(NOT BLAS)
-    set(BLAS atlas)
+cmake_minimum_required(VERSION 2.8.7)
+
+# ---[ Caffe project
+project(Caffe C CXX)
+
+# ---[ Using cmake scripts and modules
+list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake/Modules)
+
+include(cmake/Utils.cmake)
+include(cmake/Targets.cmake)
+include(cmake/Misc.cmake)
+include(cmake/Summary.cmake)
+include(cmake/ConfigGen.cmake)
+
+# ---[ Options
+caffe_option(CPU_ONLY  "Build Caffe wihtout CUDA support" OFF) # TODO: rename to USE_CUDA
+caffe_option(USE_CUDNN "Build Caffe with cuDNN libary support" ON IF NOT CPU_ONLY)
+caffe_option(BUILD_SHARED_LIBS "Build shared libraries" ON)
+caffe_option(BUILD_python "Build Python wrapper" ON)
+caffe_option(BUILD_matlab "Build Matlab wrapper" OFF IF UNIX OR APPLE)
+caffe_option(BUILD_docs   "Build documentation" ON IF UNIX OR APPLE)
+
+# ---[ Dependencies
+include(cmake/Dependencies.cmake)
+
+# ---[ Flags
+if(UNIX OR APLE)
+  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -Wall")
 endif()
 
-if(NOT CUDA_TEST_DEVICE)
-    set(CUDA_TEST_DEVICE -1)
+if(USE_libstdcpp)
+  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -stdlib=libstdc++")
+  message("-- Warning: forcing libstdc++ (controlled by USE_libstdcpp option in cmake)")
 endif()
 
-#    Install Prefix
-if (CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT)
-    set (CMAKE_INSTALL_PREFIX "${CMAKE_BINARY_DIR}/install" CACHE PATH "Default install path" FORCE )
-endif()
-
-###    Configuration    ###########################################################################
-#    Compiler Flags
-set(CMAKE_CXX_COMPILER_FLAGS ${CMAKE_CXX_COMPILER_FLAGS} -Wall)
-set(CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS} -fPIC) # set global flags
-set(CMAKE_CXX_FLAGS_DEBUG ${CMAKE_CXX_FLAGS_DEBUG}) # set debug flags
-set(CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) # set release flags
-
-#    Link Flags
-if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")  # clang++
-  set(CAFFE_STATIC_LINK -Wl,-force_load $(STATIC_NAME))
-elseif ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")  # g++
-  set(CAFFE_STATIC_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive)
-endif()
-
-#    Global Definitions
-if(CPU_ONLY)
-    add_definitions(-DCPU_ONLY)
-endif()
-
-#    Include Directories
-set(${PROJECT_NAME}_INCLUDE_DIRS ${CMAKE_SOURCE_DIR}/include)
-include_directories(${${PROJECT_NAME}_INCLUDE_DIRS})
-include_directories(${CMAKE_SOURCE_DIR}/src)
-
-#    CMake Scripts dir
-set(CMAKE_SCRIPT_DIR ${CMAKE_SOURCE_DIR}/CMakeScripts)
-
-#	 CMake module path for custom module finding
-set( CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${CMAKE_SCRIPT_DIR})
-
-###    Dependencies    ##########################################################################
-
-#    Boost
-find_package(Boost 1.46 COMPONENTS system thread REQUIRED)
-include_directories(${Boost_INCLUDE_DIR})
-link_directories(${Boost_LIBRARY_DIRS})
-
-#    CUDA
-if(NOT CPU_ONLY)
-    find_package(CUDA 5.5 REQUIRED)
-    include_directories(${CUDA_INCLUDE_DIRS})
-
-    set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS}
-            -gencode arch=compute_20,code=sm_20
-            -gencode arch=compute_20,code=sm_21
-            -gencode arch=compute_30,code=sm_30
-            -gencode arch=compute_35,code=sm_35
-    )
-
-    # https://github.com/ComputationalRadiationPhysics/picongpu/blob/master/src/picongpu/CMakeLists.txt
-    # work-arounds
-    if(Boost_VERSION EQUAL 105500)
-        # see https://svn.boost.org/trac/boost/ticket/9392
-        message(STATUS "Boost: Applying noinline work around")
-        # avoid warning for CMake >= 2.8.12
-        set(CUDA_NVCC_FLAGS
-          "${CUDA_NVCC_FLAGS} \"-DBOOST_NOINLINE=__attribute__((noinline))\" ")
-    endif(Boost_VERSION EQUAL 105500)
-endif()
-
-#    Threads
-find_package(Threads REQUIRED)
-
-#	 Google-glog
-find_package(Glog REQUIRED)
-include_directories(${GLOG_INCLUDE_DIRS})
-
-#    Google-gflags
-find_package(GFlags REQUIRED)
-include_directories(${GFLAGS_INCLUDE_DIRS})
+add_definitions(-DGTEST_USE_OWN_TR1_TUPLE)
 
-#    BLAS
-if(BLAS STREQUAL "atlas")
+# ---[ Warnings
+caffe_warnings_disable(CMAKE_CXX_FLAGS -Wno-sign-compare -Wno-uninitialized)
 
-    find_package(Atlas REQUIRED)
-    include_directories(${Atlas_INCLUDE_DIR})
-    set(BLAS_LIBRARIES ${Atlas_LIBRARIES})
+# ---[ Config generation
+configure_file(cmake/Templates/caffe_config.h.in "${PROJECT_BINARY_DIR}/caffe_config.h")
 
-elseif(BLAS STREQUAL "open")
-
-    find_package(OpenBLAS REQUIRED)
-    include_directories(${OpenBLAS_INCLUDE_DIR})
-    set(BLAS_LIBRARIES ${OpenBLAS_LIB})
-
-elseif(BLAS STREQUAL "mkl")
-
-    find_package(MKL REQUIRED)
-    include_directories(${MKL_INCLUDE_DIR})
-    set(BLAS_LIBRARIES ${MKL_LIBRARIES})
-
-endif()
-
-#    HDF5
-find_package(HDF5 COMPONENTS HL REQUIRED)
-include_directories(${HDF5_INCLUDE_DIRS})
-
-#    LevelDB
-find_package(LevelDB REQUIRED)
-include_directories(${LEVELDB_INCLUDE})
-if(LEVELDB_FOUND)
-    find_package(Snappy REQUIRED)
-    include_directories(${SNAPPY_INCLUDE_DIR})
-    set(LEVELDB_LIBS
-        ${LEVELDB_LIBS}
-        ${SNAPPY_LIBS}
-    )
-endif()
-
-#    LMDB
-find_package(LMDB REQUIRED)
-include_directories(${LMDB_INCLUDE_DIR})
-
-#    OpenCV
-find_package(OpenCV REQUIRED)
-include_directories(${OpenCV_INCLUDE_DIRS})
-
-###    Subdirectories    ##########################################################################
+# ---[ Includes
+set(Caffe_INCLUDE_DIR ${PROJECT_SOURCE_DIR}/include)
+include_directories(${Caffe_INCLUDE_DIR} ${PROJECT_BINARY_DIR})
+include_directories(BEFORE src) # This is needed for gtest.
 
+# ---[ Subdirectories
 add_subdirectory(src/gtest)
 add_subdirectory(src/caffe)
 add_subdirectory(tools)
+add_subdirectory(examples)
+add_subdirectory(python)
+add_subdirectory(matlab)
+add_subdirectory(docs)
 
-if(BUILD_EXAMPLES)
-    message(STATUS "Examples enabled")
-    add_subdirectory(examples)
-endif()
-
-if(BUILD_PYTHON)
-    message(STATUS "Python enabled")
-    add_subdirectory(python)
-endif()
-
-if(BUILD_MATLAB)
-    message(STATUS "Matlab enabled")
-    add_subdirectory(matlab)
-endif()
-
-###    Lint Target Setup    ##########################################################################
-
-set(LINT_TARGET lint)
-set(LINT_SCRIPT ${CMAKE_SCRIPT_DIR}/lint.cmake)
-add_custom_target(
-    ${LINT_TARGET}
-    COMMAND ${CMAKE_COMMAND} -P ${LINT_SCRIPT}
-)
-
-###    Install    #################################################################################
-
-#    Install Includes
-file(GLOB folders ${${PROJECT_NAME}_INCLUDE_DIRS}/*)
-install(DIRECTORY ${folders} DESTINATION include)
+# ---[ Linter target
+add_custom_target(lint COMMAND ${CMAKE_COMMAND} -P ${PROJECT_SOURCE_DIR}/cmake/lint.cmake)
 
+# ---[ Configuration summary
+caffe_print_configuration_summary()
 
+# ---[ Export configs generation
+caffe_generate_export_configs()
diff --git a/CMakeScripts/FindLMDB.cmake b/CMakeScripts/FindLMDB.cmake
deleted file mode 100644
index e615f542335..00000000000
--- a/CMakeScripts/FindLMDB.cmake
+++ /dev/null
@@ -1,28 +0,0 @@
-# Try to find the LMBD libraries and headers
-#  LMDB_FOUND - system has LMDB lib
-#  LMDB_INCLUDE_DIR - the LMDB include directory
-#  LMDB_LIBRARIES - Libraries needed to use LMDB
-
-# FindCWD based on FindGMP by:
-# Copyright (c) 2006, Laurent Montel, <montel@kde.org>
-#
-# Redistribution and use is allowed according to the terms of the BSD license.
-
-# Adapted from FindCWD by:
-# Copyright 2013 Conrad Steenberg <conrad.steenberg@gmail.com>
-# Aug 31, 2013
-
-if (LMDB_INCLUDE_DIR AND LMDB_LIBRARIES)
-  # Already in cache, be silent
-  set(LMDB_FIND_QUIETLY TRUE)
-endif (LMDB_INCLUDE_DIR AND LMDB_LIBRARIES)
-
-find_path(LMDB_INCLUDE_DIR NAMES "lmdb.h" HINTS "$ENV{LMDB_DIR}/include")
-find_library(LMDB_LIBRARIES NAMES lmdb HINTS $ENV{LMDB_DIR}/lib )
-MESSAGE(STATUS "LMDB lib: " ${LMDB_LIBRARIES} )
-MESSAGE(STATUS "LMDB include: " ${LMDB_INCLUDE} )
-
-include(FindPackageHandleStandardArgs)
-FIND_PACKAGE_HANDLE_STANDARD_ARGS(LMDB DEFAULT_MSG LMDB_INCLUDE_DIR LMDB_LIBRARIES)
-
-mark_as_advanced(LMDB_INCLUDE_DIR LMDB_LIBRARIES)
diff --git a/CMakeScripts/FindLevelDB.cmake b/CMakeScripts/FindLevelDB.cmake
deleted file mode 100644
index f3386f26dbf..00000000000
--- a/CMakeScripts/FindLevelDB.cmake
+++ /dev/null
@@ -1,37 +0,0 @@
-# - Find LevelDB
-#
-#  LEVELDB_INCLUDE - Where to find leveldb/db.h
-#  LEVELDB_LIBS    - List of libraries when using LevelDB.
-#  LEVELDB_FOUND   - True if LevelDB found.
-
-get_filename_component(module_file_path ${CMAKE_CURRENT_LIST_FILE} PATH)
-
-# Look for the header file.
-find_path(LEVELDB_INCLUDE NAMES leveldb/db.h PATHS $ENV{LEVELDB_ROOT}/include /opt/local/include /usr/local/include /usr/include DOC "Path in which the file leveldb/db.h is located." )
-mark_as_advanced(LEVELDB_INCLUDE)
-
-# Look for the library.
-# Does this work on UNIX systems? (LINUX)
-find_library(LEVELDB_LIBS NAMES leveldb PATHS /usr/lib $ENV{LEVELDB_ROOT}/lib DOC "Path to leveldb library." )
-mark_as_advanced(LEVELDB_LIBS)
-
-# Copy the results to the output variables.
-if (LEVELDB_INCLUDE AND LEVELDB_LIBS)
-  message(STATUS "Found leveldb in ${LEVELDB_INCLUDE} ${LEVELDB_LIBS}")
-  set(LEVELDB_FOUND 1)
-  include(CheckCXXSourceCompiles)
-  set(CMAKE_REQUIRED_LIBRARY ${LEVELDB_LIBS} pthread)
-  set(CMAKE_REQUIRED_INCLUDES ${LEVELDB_INCLUDE})
- else ()
-   set(LEVELDB_FOUND 0)
- endif ()
-
- # Report the results.
- if (NOT LEVELDB_FOUND)
-   set(LEVELDB_DIR_MESSAGE "LEVELDB was not found. Make sure LEVELDB_LIBS and LEVELDB_INCLUDE are set.")
-   if (LEVELDB_FIND_REQUIRED)
-     message(FATAL_ERROR "${LEVELDB_DIR_MESSAGE}")
-   elseif (NOT LEVELDB_FIND_QUIETLY)
-     message(STATUS "${LEVELDB_DIR_MESSAGE}")
-   endif ()
- endif ()
\ No newline at end of file
diff --git a/CMakeScripts/FindMKL.cmake b/CMakeScripts/FindMKL.cmake
deleted file mode 100644
index eb2d9f8868b..00000000000
--- a/CMakeScripts/FindMKL.cmake
+++ /dev/null
@@ -1,113 +0,0 @@
-# - Find Intel MKL
-# Find the MKL libraries
-#
-# Options:
-#
-#   MKL_STATAIC       :   use static linking
-#   MKL_MULTI_THREADED:   use multi-threading
-#   MKL_SDL           :   Single Dynamic Library interface
-#
-# This module defines the following variables:
-#
-#   MKL_FOUND            : True if MKL_INCLUDE_DIR are found
-#   MKL_INCLUDE_DIR      : where to find mkl.h, etc.
-#   MKL_INCLUDE_DIRS     : set when MKL_INCLUDE_DIR found
-#   MKL_LIBRARIES        : the library to link against.
-
-
-include(FindPackageHandleStandardArgs)
-
-set(INTEL_ROOT "/opt/intel" CACHE PATH "Folder contains intel libs")
-set(MKL_ROOT ${INTEL_ROOT}/mkl CACHE PATH "Folder contains MKL")
-
-# Find include dir
-find_path(MKL_INCLUDE_DIR mkl.h
-    PATHS ${MKL_ROOT}/include)
-
-# Find include directory
-#  There is no include folder under linux
-if(WIN32)
-    find_path(INTEL_INCLUDE_DIR omp.h
-        PATHS ${INTEL_ROOT}/include)
-    set(MKL_INCLUDE_DIR ${MKL_INCLUDE_DIR} ${INTEL_INCLUDE_DIR})
-endif()
-
-# Find libraries
-
-# Handle suffix
-set(_MKL_ORIG_CMAKE_FIND_LIBRARY_SUFFIXES ${CMAKE_FIND_LIBRARY_SUFFIXES})
-
-if(WIN32)
-    if(MKL_STATAIC)
-        set(CMAKE_FIND_LIBRARY_SUFFIXES .lib)
-    else()
-        set(CMAKE_FIND_LIBRARY_SUFFIXES _dll.lib)
-    endif()
-else()
-    if(MKL_STATAIC)
-        set(CMAKE_FIND_LIBRARY_SUFFIXES .a)
-    else()
-        set(CMAKE_FIND_LIBRARY_SUFFIXES .so)
-    endif()
-endif()
-
-
-# MKL is composed by four layers: Interface, Threading, Computational and RTL
-
-if(MKL_SDL)
-    find_library(MKL_LIBRARY mkl_rt
-        PATHS ${MKL_ROOT}/lib/ia32/)
-
-    set(MKL_MINIMAL_LIBRARY ${MKL_LIBRARY})
-else()
-    ######################### Interface layer #######################
-    if(WIN32)
-        set(MKL_INTERFACE_LIBNAME mkl_intel_c)
-    else()
-        set(MKL_INTERFACE_LIBNAME mkl_intel)
-    endif()
-
-    find_library(MKL_INTERFACE_LIBRARY ${MKL_INTERFACE_LIBNAME}
-        PATHS ${MKL_ROOT}/lib/ia32/)
-
-    ######################## Threading layer ########################
-    if(MKL_MULTI_THREADED)
-        set(MKL_THREADING_LIBNAME mkl_intel_thread)
-    else()
-        set(MKL_THREADING_LIBNAME mkl_sequential)
-    endif()
-
-    find_library(MKL_THREADING_LIBRARY ${MKL_THREADING_LIBNAME}
-        PATHS ${MKL_ROOT}/lib/ia32/)
-
-    ####################### Computational layer #####################
-    find_library(MKL_CORE_LIBRARY mkl_core
-        PATHS ${MKL_ROOT}/lib/ia32/)
-    find_library(MKL_FFT_LIBRARY mkl_cdft_core
-        PATHS ${MKL_ROOT}/lib/ia32/)
-    find_library(MKL_SCALAPACK_LIBRARY mkl_scalapack_core
-        PATHS ${MKL_ROOT}/lib/ia32/)
-
-    ############################ RTL layer ##########################
-    if(WIN32)
-        set(MKL_RTL_LIBNAME libiomp5md)
-    else()
-        set(MKL_RTL_LIBNAME libiomp5)
-    endif()
-    find_library(MKL_RTL_LIBRARY ${MKL_RTL_LIBNAME}
-        PATHS ${INTEL_RTL_ROOT}/lib)
-
-    set(MKL_LIBRARY ${MKL_INTERFACE_LIBRARY} ${MKL_THREADING_LIBRARY} ${MKL_CORE_LIBRARY} ${MKL_FFT_LIBRARY} ${MKL_SCALAPACK_LIBRARY} ${MKL_RTL_LIBRARY})
-    set(MKL_MINIMAL_LIBRARY ${MKL_INTERFACE_LIBRARY} ${MKL_THREADING_LIBRARY} ${MKL_CORE_LIBRARY} ${MKL_RTL_LIBRARY})
-endif()
-
-set(CMAKE_FIND_LIBRARY_SUFFIXES ${_MKL_ORIG_CMAKE_FIND_LIBRARY_SUFFIXES})
-
-find_package_handle_standard_args(MKL DEFAULT_MSG
-    MKL_INCLUDE_DIR MKL_LIBRARY MKL_MINIMAL_LIBRARY)
-
-if(MKL_FOUND)
-    set(MKL_INCLUDE_DIRS ${MKL_INCLUDE_DIR})
-    set(MKL_LIBRARIES ${MKL_LIBRARY})
-    set(MKL_MINIMAL_LIBRARIES ${MKL_LIBRARY})
-endif()
diff --git a/CMakeScripts/FindNumPy.cmake b/CMakeScripts/FindNumPy.cmake
deleted file mode 100644
index baf21541e63..00000000000
--- a/CMakeScripts/FindNumPy.cmake
+++ /dev/null
@@ -1,103 +0,0 @@
-# - Find the NumPy libraries
-# This module finds if NumPy is installed, and sets the following variables
-# indicating where it is.
-#
-# TODO: Update to provide the libraries and paths for linking npymath lib.
-#
-#  NUMPY_FOUND               - was NumPy found
-#  NUMPY_VERSION             - the version of NumPy found as a string
-#  NUMPY_VERSION_MAJOR       - the major version number of NumPy
-#  NUMPY_VERSION_MINOR       - the minor version number of NumPy
-#  NUMPY_VERSION_PATCH       - the patch version number of NumPy
-#  NUMPY_VERSION_DECIMAL     - e.g. version 1.6.1 is 10601
-#  NUMPY_INCLUDE_DIRS        - path to the NumPy include files
-
-#============================================================================
-# Copyright 2012 Continuum Analytics, Inc.
-#
-# MIT License
-#
-# Permission is hereby granted, free of charge, to any person obtaining
-# a copy of this software and associated documentation files
-# (the "Software"), to deal in the Software without restriction, including
-# without limitation the rights to use, copy, modify, merge, publish,
-# distribute, sublicense, and/or sell copies of the Software, and to permit
-# persons to whom the Software is furnished to do so, subject to
-# the following conditions:
-#
-# The above copyright notice and this permission notice shall be included
-# in all copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
-# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
-# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
-# OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
-# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
-# OTHER DEALINGS IN THE SOFTWARE.
-#
-#============================================================================
-
-# Finding NumPy involves calling the Python interpreter
-if(NumPy_FIND_REQUIRED)
-    find_package(PythonInterp REQUIRED)
-else()
-    find_package(PythonInterp)
-endif()
-
-if(NOT PYTHONINTERP_FOUND)
-    set(NUMPY_FOUND FALSE)
-    return()
-endif()
-
-execute_process(COMMAND "${PYTHON_EXECUTABLE}" "-c"
-    "import numpy as n; print(n.__version__); print(n.get_include());"
-    RESULT_VARIABLE _NUMPY_SEARCH_SUCCESS
-    OUTPUT_VARIABLE _NUMPY_VALUES_OUTPUT
-    ERROR_VARIABLE _NUMPY_ERROR_VALUE
-    OUTPUT_STRIP_TRAILING_WHITESPACE)
-
-if(NOT _NUMPY_SEARCH_SUCCESS MATCHES 0)
-    if(NumPy_FIND_REQUIRED)
-        message(FATAL_ERROR
-            "NumPy import failure:\n${_NUMPY_ERROR_VALUE}")
-    endif()
-    set(NUMPY_FOUND FALSE)
-    return()
-endif()
-
-# Convert the process output into a list
-string(REGEX REPLACE ";" "\\\\;" _NUMPY_VALUES ${_NUMPY_VALUES_OUTPUT})
-string(REGEX REPLACE "\n" ";" _NUMPY_VALUES ${_NUMPY_VALUES})
-# Just in case there is unexpected output from the Python command.
-list(GET _NUMPY_VALUES -2 NUMPY_VERSION)
-list(GET _NUMPY_VALUES -1 NUMPY_INCLUDE_DIRS)
-
-string(REGEX MATCH "^[0-9]+\\.[0-9]+\\.[0-9]+" _VER_CHECK "${NUMPY_VERSION}")
-if("${_VER_CHECK}" STREQUAL "")
-    # The output from Python was unexpected. Raise an error always
-    # here, because we found NumPy, but it appears to be corrupted somehow.
-    message(FATAL_ERROR
-        "Requested version and include path from NumPy, got instead:\n${_NUMPY_VALUES_OUTPUT}\n")
-    return()
-endif()
-
-# Make sure all directory separators are '/'
-string(REGEX REPLACE "\\\\" "/" NUMPY_INCLUDE_DIRS ${NUMPY_INCLUDE_DIRS})
-
-# Get the major and minor version numbers
-string(REGEX REPLACE "\\." ";" _NUMPY_VERSION_LIST ${NUMPY_VERSION})
-list(GET _NUMPY_VERSION_LIST 0 NUMPY_VERSION_MAJOR)
-list(GET _NUMPY_VERSION_LIST 1 NUMPY_VERSION_MINOR)
-list(GET _NUMPY_VERSION_LIST 2 NUMPY_VERSION_PATCH)
-string(REGEX MATCH "[0-9]*" NUMPY_VERSION_PATCH ${NUMPY_VERSION_PATCH})
-math(EXPR NUMPY_VERSION_DECIMAL
-    "(${NUMPY_VERSION_MAJOR} * 10000) + (${NUMPY_VERSION_MINOR} * 100) + ${NUMPY_VERSION_PATCH}")
-
-find_package_message(NUMPY
-    "Found NumPy: version \"${NUMPY_VERSION}\" ${NUMPY_INCLUDE_DIRS}"
-    "${NUMPY_INCLUDE_DIRS}${NUMPY_VERSION}")
-
-set(NUMPY_FOUND TRUE)
-
-
diff --git a/CMakeScripts/FindProtobuf.cmake b/CMakeScripts/FindProtobuf.cmake
deleted file mode 100644
index 0f94f498197..00000000000
--- a/CMakeScripts/FindProtobuf.cmake
+++ /dev/null
@@ -1,152 +0,0 @@
-# Locate and configure the Google Protocol Buffers library.
-# Defines the following variables:
-#
-#   PROTOBUF_FOUND - Found the Google Protocol Buffers library
-#   PROTOBUF_INCLUDE_DIRS - Include directories for Google Protocol Buffers
-#   PROTOBUF_LIBRARIES - The protobuf library
-#
-# The following cache variables are also defined:
-#   PROTOBUF_LIBRARY - The protobuf library
-#   PROTOBUF_PROTOC_LIBRARY   - The protoc library
-#   PROTOBUF_INCLUDE_DIR - The include directory for protocol buffers
-#   PROTOBUF_PROTOC_EXECUTABLE - The protoc compiler
-#
-#  ====================================================================
-#  Example:
-#
-#   find_package(Protobuf REQUIRED)
-#   include_directories(${PROTOBUF_INCLUDE_DIRS})
-#
-#   include_directories(${CMAKE_CURRENT_BINARY_DIR})
-#   PROTOBUF_GENERATE_CPP(PROTO_SRCS PROTO_HDRS foo.proto)
-#   add_executable(bar bar.cc ${PROTO_SRCS} ${PROTO_HDRS})
-#   target_link_libraries(bar ${PROTOBUF_LIBRARY})
-#
-# NOTE: You may need to link against pthreads, depending
-# on the platform.
-#  ====================================================================
-#
-# PROTOBUF_GENERATE_CPP (public function)
-#   SRCS = Variable to define with autogenerated
-#          source files
-#   HDRS = Variable to define with autogenerated
-#          header files
-#   ARGN = proto files
-#
-#  ====================================================================
-
-
-#=============================================================================
-# Copyright 2009 Kitware, Inc.
-# Copyright 2009 Philip Lowman <philip@yhbt.com>
-# Copyright 2008 Esben Mose Hansen, Ange Optimization ApS
-#
-# Distributed under the OSI-approved BSD License (the "License");
-# see accompanying file Copyright.txt for details.
-#
-# This software is distributed WITHOUT ANY WARRANTY; without even the
-# implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-# See the License for more information.
-#=============================================================================
-# (To distributed this file outside of CMake, substitute the full
-#  License text for the above reference.)
-
-function(PROTOBUF_GENERATE_PYTHON SRCS)
-  if(NOT ARGN)
-    message(SEND_ERROR "Error: PROTOBUF_GENERATE_PYTHON() called without any proto files")
-    return()
-  endif(NOT ARGN)
-
-  set(${SRCS})
-  foreach(FIL ${ARGN})
-    get_filename_component(ABS_FIL ${FIL} ABSOLUTE)
-    get_filename_component(FIL_WE ${FIL} NAME_WE)
-    
-    
-    list(APPEND ${SRCS} "${CMAKE_CURRENT_BINARY_DIR}/${FIL_WE}_pb2.py")
-
-    add_custom_command(
-      OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/${FIL_WE}_pb2.py"
-      COMMAND  ${PROTOBUF_PROTOC_EXECUTABLE}
-      ARGS --python_out  ${CMAKE_CURRENT_BINARY_DIR} --proto_path ${CMAKE_CURRENT_SOURCE_DIR} 
-${ABS_FIL}
-      DEPENDS ${ABS_FIL}
-      COMMENT "Running Python protocol buffer compiler on ${FIL}"
-      VERBATIM )
-  endforeach()
-
-
-  set_source_files_properties(${${SRCS}} PROPERTIES GENERATED TRUE)
-  set(${SRCS} ${${SRCS}} PARENT_SCOPE)
-endfunction()
-
-
-function(PROTOBUF_GENERATE_CPP SRCS HDRS)
-  if(NOT ARGN)
-    message(SEND_ERROR "Error: PROTOBUF_GENERATE_CPP() called without any proto files")
-    return()
-  endif(NOT ARGN)
-
-  set(${SRCS})
-  set(${HDRS})
-  foreach(FIL ${ARGN})
-    get_filename_component(ABS_FIL ${FIL} ABSOLUTE)
-    get_filename_component(FIL_WE ${FIL} NAME_WE)
-    
-    list(APPEND ${SRCS} "${CMAKE_CURRENT_BINARY_DIR}/${FIL_WE}.pb.cc")
-    list(APPEND ${HDRS} "${CMAKE_CURRENT_BINARY_DIR}/${FIL_WE}.pb.h")
-
-    add_custom_command(
-      OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/${FIL_WE}.pb.cc"
-             "${CMAKE_CURRENT_BINARY_DIR}/${FIL_WE}.pb.h"
-      COMMAND  ${PROTOBUF_PROTOC_EXECUTABLE}
-      ARGS --cpp_out  ${CMAKE_CURRENT_BINARY_DIR} --proto_path ${CMAKE_CURRENT_SOURCE_DIR} 
-${ABS_FIL}
-      DEPENDS ${ABS_FIL}
-      COMMENT "Running C++ protocol buffer compiler on ${FIL}"
-      VERBATIM )
-  endforeach()
-
-  set_source_files_properties(${${SRCS}} ${${HDRS}} PROPERTIES GENERATED TRUE)
-  set(${SRCS} ${${SRCS}} PARENT_SCOPE)
-  set(${HDRS} ${${HDRS}} PARENT_SCOPE)
-endfunction()
-
-
-find_path(PROTOBUF_INCLUDE_DIR google/protobuf/service.h)
-
-# Google's provided vcproj files generate libraries with a "lib"
-# prefix on Windows
-if(WIN32)
-    set(PROTOBUF_ORIG_FIND_LIBRARY_PREFIXES "${CMAKE_FIND_LIBRARY_PREFIXES}")
-    set(CMAKE_FIND_LIBRARY_PREFIXES "lib" "")
-endif()
-
-find_library(PROTOBUF_LIBRARY NAMES protobuf
-             DOC "The Google Protocol Buffers Library"
-)
-find_library(PROTOBUF_PROTOC_LIBRARY NAMES protoc
-             DOC "The Google Protocol Buffers Compiler Library"
-)
-find_program(PROTOBUF_PROTOC_EXECUTABLE NAMES protoc
-             DOC "The Google Protocol Buffers Compiler"
-)
-
-mark_as_advanced(PROTOBUF_INCLUDE_DIR
-                 PROTOBUF_LIBRARY
-                 PROTOBUF_PROTOC_LIBRARY
-                 PROTOBUF_PROTOC_EXECUTABLE)
-
-# Restore original find library prefixes
-if(WIN32)
-    set(CMAKE_FIND_LIBRARY_PREFIXES "${PROTOBUF_ORIG_FIND_LIBRARY_PREFIXES}")
-endif()
-
-include(FindPackageHandleStandardArgs)
-FIND_PACKAGE_HANDLE_STANDARD_ARGS(PROTOBUF DEFAULT_MSG
-    PROTOBUF_LIBRARY PROTOBUF_INCLUDE_DIR)
-
-if(PROTOBUF_FOUND)
-    set(PROTOBUF_INCLUDE_DIRS ${PROTOBUF_INCLUDE_DIR})
-    set(PROTOBUF_LIBRARIES    ${PROTOBUF_LIBRARY})
-endif()
diff --git a/CMakeScripts/FindSnappy.cmake b/CMakeScripts/FindSnappy.cmake
deleted file mode 100644
index d769b442812..00000000000
--- a/CMakeScripts/FindSnappy.cmake
+++ /dev/null
@@ -1,33 +0,0 @@
-# Find the Snappy libraries
-#
-# The following variables are optionally searched for defaults
-#  Snappy_ROOT_DIR:    Base directory where all Snappy components are found
-#
-# The following are set after configuration is done:
-#  Snappy_FOUND
-#  Snappy_INCLUDE_DIRS
-#  Snappy_LIBS
-
-find_path(SNAPPY_INCLUDE_DIR
-    NAMES snappy.h
-    HINTS ${SNAPPY_ROOT_DIR}
-          ${SNAPPY_ROOT_DIR}/include
-)
-
-find_library(SNAPPY_LIBS
-    NAMES snappy
-    HINTS ${SNAPPY_ROOT_DIR}
-          ${SNAPPY_ROOT_DIR}/lib
-)
-
-include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(Snappy
-    DEFAULT_MSG
-    SNAPPY_LIBS
-    SNAPPY_INCLUDE_DIR
-)
-
-mark_as_advanced(
-    SNAPPY_LIBS
-    SNAPPY_INCLUDE_DIR
-)
diff --git a/Makefile b/Makefile
index 81c67efeaf4..29827270baf 100644
--- a/Makefile
+++ b/Makefile
@@ -69,8 +69,8 @@ EMPTY_LINT_REPORT := $(BUILD_DIR)/.$(LINT_EXT)
 NONEMPTY_LINT_REPORT := $(BUILD_DIR)/$(LINT_EXT)
 # PY$(PROJECT)_SRC is the python wrapper for $(PROJECT)
 PY$(PROJECT)_SRC := python/$(PROJECT)/_$(PROJECT).cpp
-PY$(PROJECT)_HXX_SRC := python/$(PROJECT)/_$(PROJECT).hpp
 PY$(PROJECT)_SO := python/$(PROJECT)/_$(PROJECT).so
+PY$(PROJECT)_HXX := include/$(PROJECT)/python_layer.hpp
 # MAT$(PROJECT)_SRC is the matlab wrapper for $(PROJECT)
 MAT$(PROJECT)_SRC := matlab/$(PROJECT)/mat$(PROJECT).cpp
 ifneq ($(MATLAB_DIR),)
@@ -123,10 +123,8 @@ TEST_CU_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
 TEST_CXX_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
 		$(foreach obj,$(TEST_CXX_OBJS),$(basename $(notdir $(obj))))))
 TEST_BINS := $(TEST_CXX_BINS) $(TEST_CU_BINS)
-# TEST_ALL_BIN is the test binary that links caffe statically.
+# TEST_ALL_BIN is the test binary that links caffe dynamically.
 TEST_ALL_BIN := $(TEST_BIN_DIR)/test_all.testbin
-# TEST_ALL_DYNINK_BIN is the test binary that links caffe as a dynamic library.
-TEST_ALL_DYNLINK_BIN := $(TEST_BIN_DIR)/test_all_dynamic_link.testbin
 
 ##############################
 # Derive compiler warning dump locations
@@ -245,6 +243,11 @@ ifeq ($(OSX), 1)
 	COMMON_FLAGS += -DGTEST_USE_OWN_TR1_TUPLE=1
 	# boost::thread is called boost_thread-mt to mark multithreading on OS X
 	LIBRARIES += boost_thread-mt
+	# we need to explicitly ask for the rpath to be obeyed
+	DYNAMIC_FLAGS := -install_name @rpath/libcaffe.so
+	ORIGIN := @loader_path
+else
+	ORIGIN := \$$ORIGIN
 endif
 
 # Custom compiler
@@ -285,6 +288,12 @@ ifeq ($(CPU_ONLY), 1)
 	COMMON_FLAGS += -DCPU_ONLY
 endif
 
+# Python layer support
+ifeq ($(WITH_PYTHON_LAYER), 1)
+	COMMON_FLAGS += -DWITH_PYTHON_LAYER
+	LIBRARIES += $(PYTHON_LIBRARIES)
+endif
+
 # BLAS configuration (default = ATLAS)
 BLAS ?= atlas
 ifeq ($(BLAS), mkl)
@@ -343,7 +352,6 @@ endif
 LDFLAGS += $(foreach librarydir,$(LIBRARY_DIRS),-L$(librarydir)) $(PKG_CONFIG) \
 		$(foreach library,$(LIBRARIES),-l$(library))
 PYTHON_LDFLAGS := $(LDFLAGS) $(foreach library,$(PYTHON_LIBRARIES),-l$(library))
-DYNAMIC_LDFLAGS := -l$(PROJECT) -Wl,-rpath,\$$ORIGIN/../lib
 
 # 'superclean' target recursively* deletes all files ending with an extension
 # in $(SUPERCLEAN_EXTS) below.  This may be useful if you've built older
@@ -419,10 +427,11 @@ py$(PROJECT): py
 
 py: $(PY$(PROJECT)_SO) $(PROTO_GEN_PY)
 
-$(PY$(PROJECT)_SO): $(PY$(PROJECT)_SRC) $(STATIC_NAME) $(PY$(PROJECT)_HXX_SRC)
-	@ echo CXX $<
+$(PY$(PROJECT)_SO): $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX) | $(DYNAMIC_NAME)
+	@ echo CXX/LD -o $@ $<
 	$(Q)$(CXX) -shared -o $@ $(PY$(PROJECT)_SRC) \
-		$(STATIC_LINK_COMMAND) $(LINKFLAGS) $(PYTHON_LDFLAGS)
+		-o $@ $(LINKFLAGS) -l$(PROJECT) $(PYTHON_LDFLAGS) \
+		-Wl,-rpath,$(ORIGIN)/../../build/lib
 
 mat$(PROJECT): mat
 
@@ -440,9 +449,8 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 			CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \
 			CXXLIBS="\$$CXXLIBS $(STATIC_LINK_COMMAND) $(LDFLAGS)" -output $@
 
-runtest: $(TEST_ALL_BIN) $(TEST_ALL_DYNLINK_BIN)
-	$(TEST_ALL_BIN) $(TEST_GPUID) --gtest_shuffle $(TEST_FILTER) && \
-	$(TEST_ALL_DYNLINK_BIN) $(TEST_GPUID) --gtest_shuffle $(TEST_FILTER)
+runtest: $(TEST_ALL_BIN)
+	$(TEST_ALL_BIN) $(TEST_GPUID) --gtest_shuffle $(TEST_FILTER)
 
 pytest: py
 	cd python; python -m unittest discover -s caffe/test
@@ -479,7 +487,7 @@ $(ALL_BUILD_DIRS): | $(BUILD_DIR_LINK)
 
 $(DYNAMIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
 	@ echo LD -o $@
-	$(Q)$(CXX) -shared -o $@ $(OBJS) $(LINKFLAGS) $(LDFLAGS)
+	$(Q)$(CXX) -shared -o $@ $(OBJS) $(LINKFLAGS) $(LDFLAGS) $(DYNAMIC_FLAGS)
 
 $(STATIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
 	@ echo AR -o $@
@@ -506,38 +514,33 @@ $(BUILD_DIR)/cuda/%.o: %.cu | $(ALL_BUILD_DIRS)
 		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
 
-$(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_NAME) \
-		| $(TEST_BIN_DIR)
-	@ echo CXX/LD -o $@ $<
-	$(Q)$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_LINK_COMMAND) \
-		-o $@ $(LINKFLAGS) $(LDFLAGS)
-
-$(TEST_ALL_DYNLINK_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(DYNAMIC_NAME) \
-		| $(TEST_BIN_DIR)
+$(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
+		| $(DYNAMIC_NAME) $(TEST_BIN_DIR)
 	@ echo CXX/LD -o $@ $<
 	$(Q)$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
-		-o $@ $(LINKFLAGS) $(LDFLAGS) $(DYNAMIC_LDFLAGS)
+		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(ORIGIN)/../lib
 
 $(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CU_BUILD_DIR)/%.o \
-	$(GTEST_OBJ) $(STATIC_NAME) | $(TEST_BIN_DIR)
+	$(GTEST_OBJ) | $(DYNAMIC_NAME) $(TEST_BIN_DIR)
 	@ echo LD $<
-	$(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_LINK_COMMAND) \
-		-o $@ $(LINKFLAGS) $(LDFLAGS)
+	$(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) \
+		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(ORIGIN)/../lib
 
 $(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CXX_BUILD_DIR)/%.o \
-	$(GTEST_OBJ) $(STATIC_NAME) | $(TEST_BIN_DIR)
+	$(GTEST_OBJ) | $(DYNAMIC_NAME) $(TEST_BIN_DIR)
 	@ echo LD $<
-	$(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_LINK_COMMAND) \
-		-o $@ $(LINKFLAGS) $(LDFLAGS)
+	$(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) \
+		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(ORIGIN)/../lib
 
 # Target for extension-less symlinks to tool binaries with extension '*.bin'.
 $(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR)
 	@ $(RM) $@
 	@ ln -s $(abspath $<) $@
 
-$(TOOL_BINS) $(EXAMPLE_BINS): %.bin : %.o $(STATIC_NAME)
-	@ echo LD $<
-	$(Q)$(CXX) $< $(STATIC_LINK_COMMAND) -o $@ $(LINKFLAGS) $(LDFLAGS)
+$(TOOL_BINS) $(EXAMPLE_BINS): %.bin : %.o | $(DYNAMIC_NAME)
+	@ echo CXX/LD -o $@
+	$(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(PROJECT) $(LDFLAGS) \
+		-Wl,-rpath,$(ORIGIN)/../../lib
 
 proto: $(PROTO_GEN_CC) $(PROTO_GEN_HEADER)
 
diff --git a/Makefile.config.example b/Makefile.config.example
index 9d13b45715b..7a8aafd7c9f 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -57,6 +57,9 @@ PYTHON_INCLUDE := /usr/include/python2.7 \
 PYTHON_LIB := /usr/lib
 # PYTHON_LIB := $(ANACONDA_HOME)/lib
 
+# Uncomment to support layers written in Python (will link against Python libs)
+# WITH_PYTHON_LAYER := 1
+
 # Whatever else you find you need goes here.
 INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
 LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
diff --git a/README.md b/README.md
index 9a9b3ee36eb..ebec286d550 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,34 @@
 # Caffe
 
-Caffe is a deep learning framework developed with cleanliness, readability, and speed in mind.<br />
-Consult the [project website](http://caffe.berkeleyvision.org) for all documentation.
+Caffe is a deep learning framework made with expression, speed, and modularity in mind.
+It is developed by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and community contributors.
 
+Check out the [project site](http://caffe.berkeleyvision.org) for all the details like
 
-Please ask usage questions and how to model different tasks on the [caffe-users mailing list](https://groups.google.com/forum/#!forum/caffe-users).
+- [DIY Deep Learning for Vision with Caffe](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.p)
+- [Tutorial Documentation](http://caffe.berkeleyvision.org/tutorial/)
+- [BVLC reference models](http://caffe.berkeleyvision.org/model_zoo.html) and the [community model zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo)
+- [Installation instructions](http://caffe.berkeleyvision.org/installation.html)
 
+and step-by-step examples.
+
+[![Join the chat at https://gitter.im/BVLC/caffe](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/BVLC/caffe?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+
+Please join the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users) or [gitter chat](https://gitter.im/BVLC/caffe) to ask questions and talk about methods and models.
+Framework development discussions and thorough bug reports are collected on [Issues](https://github.com/BVLC/caffe/issues).
+
+Happy brewing!
+
+## License and Citation
+
+Caffe is released under the [BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/LICENSE).
+The BVLC reference models are released for unrestricted use.
+
+Please cite Caffe in your publications if it helps your research:
+
+    @article{jia2014caffe,
+      Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
+      Journal = {arXiv preprint arXiv:1408.5093},
+      Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
+      Year = {2014}
+    }
diff --git a/cmake/ConfigGen.cmake b/cmake/ConfigGen.cmake
new file mode 100644
index 00000000000..c82047dcc5f
--- /dev/null
+++ b/cmake/ConfigGen.cmake
@@ -0,0 +1,93 @@
+
+################################################################################################
+# Helper function to fetch caffe includes which will be passed to dependent projects
+# Usage:
+#   caffe_get_current_includes(<includes_list_variable>)
+function(caffe_get_current_includes includes_variable)
+  get_property(current_includes DIRECTORY PROPERTY INCLUDE_DIRECTORIES)
+  caffe_convert_absolute_paths(current_includes)
+
+  # remove at most one ${PROJECT_BINARY_DIR} include added for caffe_config.h
+  list(FIND current_includes ${PROJECT_BINARY_DIR} __index)
+  list(REMOVE_AT current_includes ${__index})
+
+  caffe_list_unique(current_includes)
+  set(${includes_variable} ${current_includes} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Helper function to get all list items that begin with given prefix
+# Usage:
+#   caffe_get_items_with_prefix(<prefix> <list_variable> <output_variable>)
+function(caffe_get_items_with_prefix prefix list_variable output_variable)
+  set(__result "")
+  foreach(__e ${${list_variable}})
+    if(__e MATCHES "^${prefix}.*")
+      list(APPEND __result ${__e})
+    endif()
+  endforeach()
+  set(${output_variable} ${__result} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Function for generation Caffe build- and install- tree export config files
+# Usage:
+#  caffe_generate_export_configs()
+function(caffe_generate_export_configs)
+  set(install_cmake_suffix "share/Caffe")
+
+  # ---[ Configure build-tree CaffeConfig.cmake file ]---
+  caffe_get_current_includes(Caffe_INCLUDE_DIRS)
+
+  set(Caffe_DEFINITIONS "")
+  if(NOT HAVE_CUDA)
+    set(HAVE_CUDA FALSE)
+    list(APPEND Caffe_DEFINITIONS -DCPU_ONLY)
+  endif()
+
+  if(NOT HAVE_CUDNN)
+    set(HAVE_CUDNN FALSE)
+  else()
+    list(APPEND DEFINITIONS -DUSE_CUDNN)
+  endif()
+
+  if(BLAS STREQUAL "MKL" OR BLAS STREQUAL "mkl")
+    list(APPEND Caffe_DEFINITIONS -DUSE_MKL)
+  endif()
+
+  configure_file("cmake/Templates/CaffeConfig.cmake.in" "${PROJECT_BINARY_DIR}/CaffeConfig.cmake" @ONLY)
+
+  # Add targets to the build-tree export set
+  export(TARGETS caffe proto FILE "${PROJECT_BINARY_DIR}/CaffeTargets.cmake")
+  export(PACKAGE Caffe)
+
+  # ---[ Configure install-tree CaffeConfig.cmake file ]---
+
+  # remove source and build dir includes
+  caffe_get_items_with_prefix(${PROJECT_SOURCE_DIR} Caffe_INCLUDE_DIRS __insource)
+  caffe_get_items_with_prefix(${PROJECT_BINARY_DIR} Caffe_INCLUDE_DIRS __inbinary)
+  list(REMOVE_ITEM Caffe_INCLUDE_DIRS ${__insource} ${__inbinary})
+
+  # add `install` include folder
+  set(lines
+     "get_filename_component(__caffe_include \"\${Caffe_CMAKE_DIR}/../../include\" ABSOLUTE)\n"
+     "list(APPEND Caffe_INCLUDE_DIRS \${__caffe_include})\n"
+     "unset(__caffe_include)\n")
+  string(REPLACE ";" "" Caffe_INSTALL_INCLUDE_DIR_APPEND_COMMAND ${lines})
+
+  configure_file("cmake/Templates/CaffeConfig.cmake.in" "${PROJECT_BINARY_DIR}/cmake/CaffeConfig.cmake" @ONLY)
+
+  # Install the CaffeConfig.cmake and export set to use wuth install-tree
+  install(FILES "${PROJECT_BINARY_DIR}/cmake/CaffeConfig.cmake" DESTINATION ${install_cmake_suffix})
+  install(EXPORT CaffeTargets DESTINATION ${install_cmake_suffix})
+
+  # ---[ Configure and install version file ]---
+
+  # TODO: Lines below are commented because Caffe does't declare its version in headers.
+  # When the declarations are added, modify `caffe_extract_caffe_version()` macro and uncomment
+
+  # configure_file(cmake/Templates/CaffeConfigVersion.cmake.in "${PROJECT_BINARY_DIR}/CaffeConfigVersion.cmake" @ONLY)
+  # install(FILES "${PROJECT_BINARY_DIR}/CaffeConfigVersion.cmake" DESTINATION ${install_cmake_suffix})
+endfunction()
+
+
diff --git a/cmake/Cuda.cmake b/cmake/Cuda.cmake
new file mode 100644
index 00000000000..ff58d31c166
--- /dev/null
+++ b/cmake/Cuda.cmake
@@ -0,0 +1,254 @@
+if(CPU_ONLY)
+  return()
+endif()
+
+# Known NVIDIA GPU achitectures Caffe can be compiled for.
+# This list will be used for CUDA_ARCH_NAME = All option
+set(Caffe_known_gpu_archs "20 21(20) 30 35 50")
+
+################################################################################################
+# A function for automatic detection of GPUs installed  (if autodetection is enabled)
+# Usage:
+#   caffe_detect_installed_gpus(out_variable)
+function(caffe_detect_installed_gpus out_variable)
+  if(NOT CUDA_gpu_detect_output)
+    set(__cufile ${PROJECT_BINARY_DIR}/detect_cuda_archs.cu)
+
+    file(WRITE ${__cufile} ""
+      "#include <cstdio>\n"
+      "int main()\n"
+      "{\n"
+      "  int count = 0;\n"
+      "  if (cudaSuccess != cudaGetDeviceCount(&count)) return -1;\n"
+      "  if (count == 0) return -1;\n"
+      "  for (int device = 0; device < count; ++device)\n"
+      "  {\n"
+      "    cudaDeviceProp prop;\n"
+      "    if (cudaSuccess == cudaGetDeviceProperties(&prop, device))\n"
+      "      std::printf(\"%d.%d \", prop.major, prop.minor);\n"
+      "  }\n"
+      "  return 0;\n"
+      "}\n")
+
+    execute_process(COMMAND "${CUDA_NVCC_EXECUTABLE}" "--run" "${__cufile}"
+                    WORKING_DIRECTORY "${PROJECT_BINARY_DIR}/CMakeFiles/"
+                    RESULT_VARIABLE __nvcc_res OUTPUT_VARIABLE __nvcc_out
+                    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+    if(__nvcc_res EQUAL 0)
+      string(REPLACE "2.1" "2.1(2.0)" __nvcc_out "${__nvcc_out}")
+      set(CUDA_gpu_detect_output ${__nvcc_out} CACHE INTERNAL "Returned GPU architetures from caffe_detect_gpus tool" FORCE)
+    endif()
+  endif()
+
+  if(NOT CUDA_gpu_detect_output)
+    message(STATUS "Automatic GPU detection failed. Building for all known architectures.")
+    set(${out_variable} ${Caffe_known_gpu_archs} PARENT_SCOPE)
+  else()
+    set(${out_variable} ${CUDA_gpu_detect_output} PARENT_SCOPE)
+  endif()
+endfunction()
+
+
+################################################################################################
+# Function for selecting GPU arch flags for nvcc based on CUDA_ARCH_NAME
+# Usage:
+#   caffe_select_nvcc_arch_flags(out_variable)
+function(caffe_select_nvcc_arch_flags out_variable)
+  # List of arch names
+  set(__archs_names "Fermi" "Kepler" "Maxwell" "All" "Manual")
+  set(__archs_name_default "All")
+  if(NOT CMAKE_CROSSCOMPILING)
+    list(APPEND __archs_names "Auto")
+    set(__archs_name_default "Auto")
+  endif()
+
+  # set CUDA_ARCH_NAME strings (so it will be seen as dropbox in CMake-Gui)
+  set(CUDA_ARCH_NAME ${__archs_name_default} CACHE STRING "Select target NVIDIA GPU achitecture.")
+  set_property( CACHE CUDA_ARCH_NAME PROPERTY STRINGS "" ${__archs_names} )
+  mark_as_advanced(CUDA_ARCH_NAME)
+
+  # verify CUDA_ARCH_NAME value
+  if(NOT ";${__archs_names};" MATCHES ";${CUDA_ARCH_NAME};")
+    string(REPLACE ";" ", " __archs_names "${__archs_names}")
+    message(FATAL_ERROR "Only ${__archs_names} architeture names are supported.")
+  endif()
+
+  if(${CUDA_ARCH_NAME} STREQUAL "Manual")
+    set(CUDA_ARCH_BIN ${Caffe_known_gpu_archs} CACHE STRING "Specify 'real' GPU architectures to build binaries for, BIN(PTX) format is supported")
+    set(CUDA_ARCH_PTX "50"                     CACHE STRING "Specify 'virtual' PTX architectures to build PTX intermediate code for")
+    mark_as_advanced(CUDA_ARCH_BIN CUDA_ARCH_PTX)
+  else()
+    unset(CUDA_ARCH_BIN CACHE)
+    unset(CUDA_ARCH_PTX CACHE)
+  endif()
+
+  if(${CUDA_ARCH_NAME} STREQUAL "Fermi")
+    set(__cuda_arch_bin "20 21(20)")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Kepler")
+    set(__cuda_arch_bin "30 35")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Maxwell")
+    set(__cuda_arch_bin "50")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "All")
+    set(__cuda_arch_bin ${Caffe_known_gpu_archs})
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Auto")
+    caffe_detect_installed_gpus(__cuda_arch_bin)
+  else()  # (${CUDA_ARCH_NAME} STREQUAL "Manual")
+    set(__cuda_arch_bin ${CUDA_ARCH_BIN})
+  endif()
+
+  # remove dots and convert to lists
+  string(REGEX REPLACE "\\." "" __cuda_arch_bin "${__cuda_arch_bin}")
+  string(REGEX REPLACE "\\." "" __cuda_arch_ptx "${CUDA_ARCH_PTX}")
+  string(REGEX MATCHALL "[0-9()]+" __cuda_arch_bin "${__cuda_arch_bin}")
+  string(REGEX MATCHALL "[0-9]+"   __cuda_arch_ptx "${__cuda_arch_ptx}")
+  caffe_list_unique(__cuda_arch_bin __cuda_arch_ptx)
+
+  set(__nvcc_flags "")
+  set(__nvcc_archs_readable "")
+
+  # Tell NVCC to add binaries for the specified GPUs
+  foreach(__arch ${__cuda_arch_bin})
+    if(__arch MATCHES "([0-9]+)\\(([0-9]+)\\)")
+      # User explicitly specified PTX for the concrete BIN
+      list(APPEND __nvcc_flags -gencode arch=compute_${CMAKE_MATCH_2},code=sm_${CMAKE_MATCH_1})
+      list(APPEND __nvcc_archs_readable sm_${CMAKE_MATCH_1})
+    else()
+      # User didn't explicitly specify PTX for the concrete BIN, we assume PTX=BIN
+      list(APPEND __nvcc_flags -gencode arch=compute_${__arch},code=sm_${__arch})
+      list(APPEND __nvcc_archs_readable sm_${__arch})
+    endif()
+  endforeach()
+
+  # Tell NVCC to add PTX intermediate code for the specified architectures
+  foreach(__arch ${__cuda_arch_ptx})
+    list(APPEND __nvcc_flags -gencode arch=compute_${__arch},code=compute_${__arch})
+    list(APPEND __nvcc_archs_readable compute_${__arch})
+  endforeach()
+
+  string(REPLACE ";" " " __nvcc_archs_readable "${__nvcc_archs_readable}")
+  set(${out_variable}          ${__nvcc_flags}          PARENT_SCOPE)
+  set(${out_variable}_readable ${__nvcc_archs_readable} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Short command for cuda comnpilation
+# Usage:
+#   caffe_cuda_compile(<objlist_variable> <cuda_files>)
+macro(caffe_cuda_compile objlist_variable)
+  foreach(var CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_RELEASE CMAKE_CXX_FLAGS_DEBUG)
+    set(${var}_backup_in_cuda_compile_ "${${var}}")
+
+    # we remove /EHa as it generates warnings under windows
+    string(REPLACE "/EHa" "" ${var} "${${var}}")
+
+  endforeach()
+
+  if(UNIX OR APPLE)
+    list(APPEND CUDA_NVCC_FLAGS -Xcompiler -fPIC)
+  endif()
+
+  if(APPLE)
+    list(APPEND CUDA_NVCC_FLAGS -Xcompiler -Wno-unused-function)
+  endif()
+
+  cuda_compile(cuda_objcs ${ARGN})
+
+  foreach(var CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_RELEASE CMAKE_CXX_FLAGS_DEBUG)
+    set(${var} "${${var}_backup_in_cuda_compile_}")
+    unset(${var}_backup_in_cuda_compile_)
+  endforeach()
+
+  set(${objlist_variable} ${cuda_objcs})
+endmacro()
+
+################################################################################################
+# Short command for cuDNN detection. Believe it soon will be a part of CUDA toolkit distribution.
+# That's why not FindcuDNN.cmake file, but just the macro
+# Usage:
+#   detect_cuDNN()
+function(detect_cuDNN)
+  set(CUDNN_ROOT "" CACHE PATH "CUDNN root folder")
+
+  find_path(CUDNN_INCLUDE cudnn.h
+            PATHS ${CUDNN_ROOT} $ENV{CUDNN_ROOT} ${CUDA_TOOLKIT_INCLUDE}
+            DOC "Path to cuDNN include directory." )
+
+  get_filename_component(__libpath_hist ${CUDA_CUDART_LIBRARY} PATH)
+  find_library(CUDNN_LIBRARY NAMES libcudnn.so # libcudnn_static.a
+                             PATHS ${CUDNN_ROOT} $ENV{CUDNN_ROOT} ${CUDNN_INCLUDE} ${__libpath_hist}
+                             DOC "Path to cuDNN library.")
+
+  if(CUDNN_INCLUDE AND CUDNN_LIBRARY)
+    set(HAVE_CUDNN  TRUE PARENT_SCOPE)
+    set(CUDNN_FOUND TRUE PARENT_SCOPE)
+
+    mark_as_advanced(CUDNN_INCLUDE CUDNN_LIBRARY CUDNN_ROOT)
+    message(STATUS "Found cuDNN (include: ${CUDNN_INCLUDE}, library: ${CUDNN_LIBRARY})")
+  endif()
+endfunction()
+
+
+################################################################################################
+###  Non macro section
+################################################################################################
+
+find_package(CUDA 5.5 QUIET)
+find_cuda_helper_libs(curand)  # cmake 2.8.7 compartibility which doesn't search for curand
+
+if(NOT CUDA_FOUND)
+  return()
+endif()
+
+set(HAVE_CUDA TRUE)
+message(STATUS "CUDA detected: " ${CUDA_VERSION})
+include_directories(SYSTEM ${CUDA_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS ${CUDA_CUDART_LIBRARY}
+                              ${CUDA_curand_LIBRARY} ${CUDA_CUBLAS_LIBRARIES})
+
+# cudnn detection
+if(USE_CUDNN)
+  detect_cuDNN()
+  if(HAVE_CUDNN)
+    add_definitions(-DUSE_CUDNN)
+    include_directories(SYSTEM ${CUDNN_INCLUDE})
+    list(APPEND Caffe_LINKER_LIBS ${CUDNN_LIBRARY})
+  endif()
+endif()
+
+# setting nvcc arch flags
+caffe_select_nvcc_arch_flags(NVCC_FLAGS_EXTRA)
+list(APPEND CUDA_NVCC_FLAGS ${NVCC_FLAGS_EXTRA})
+message(STATUS "Added CUDA NVCC flags for: ${NVCC_FLAGS_EXTRA_readable}")
+
+# Boost 1.55 workaround, see https://svn.boost.org/trac/boost/ticket/9392 or
+# https://github.com/ComputationalRadiationPhysics/picongpu/blob/master/src/picongpu/CMakeLists.txt
+if(Boost_VERSION EQUAL 105500)
+  message(STATUS "Cuda + Boost 1.55: Applying noinline work around")
+  # avoid warning for CMake >= 2.8.12
+  set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} \"-DBOOST_NOINLINE=__attribute__((noinline))\" ")
+endif()
+
+# disable some nvcc diagnostic that apears in boost, glog, glags, opencv, etc.
+foreach(diag cc_clobber_ignored integer_sign_change useless_using_declaration set_but_not_used)
+  list(APPEND CUDA_NVCC_FLAGS -Xcudafe --diag_suppress=${diag})
+endforeach()
+
+# setting default testing device
+if(NOT CUDA_TEST_DEVICE)
+  set(CUDA_TEST_DEVICE -1)
+endif()
+
+mark_as_advanced(CUDA_BUILD_CUBIN CUDA_BUILD_EMULATION CUDA_VERBOSE_BUILD)
+mark_as_advanced(CUDA_SDK_ROOT_DIR CUDA_SEPARABLE_COMPILATION)
+
+# Handle clang/libc++ issue
+if(APPLE)
+  caffe_detect_darwin_version(OSX_VERSION)
+
+  # OSX 10.9 and higher uses clang/libc++ by default which is incompartible with old CUDA toolkits
+  if(OSX_VERSION VERSION_GREATER 10.8)
+    # enabled by default if and only if CUDA version is less than 7.0
+    caffe_option(USE_libstdcpp "Use libstdc++ instead of libc++" (CUDA_VERSION VERSION_LESS 7.0))
+  endif()
+endif()
diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
new file mode 100644
index 00000000000..aa2dcbe1d0d
--- /dev/null
+++ b/cmake/Dependencies.cmake
@@ -0,0 +1,125 @@
+# This list is required for static linking and exported to CaffeConfig.cmake
+set(Caffe_LINKER_LIBS "")
+
+# ---[ Boost
+find_package(Boost 1.46 REQUIRED COMPONENTS system thread)
+include_directories(SYSTEM ${Boost_INCLUDE_DIR})
+list(APPEND Caffe_LINKER_LIBS ${Boost_LIBRARIES})
+
+# ---[ Threads
+find_package(Threads REQUIRED)
+list(APPEND Caffe_LINKER_LIBS ${CMAKE_THREAD_LIBS_INIT})
+
+# ---[ Google-glog
+find_package(Glog REQUIRED)
+include_directories(SYSTEM ${GLOG_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS ${GLOG_LIBRARIES})
+
+# ---[ Google-gflags
+find_package(GFlags REQUIRED)
+include_directories(SYSTEM ${GFLAGS_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS ${GFLAGS_LIBRARIES})
+
+# ---[ Google-protobuf
+include(cmake/ProtoBuf.cmake)
+
+# ---[ HDF5
+find_package(HDF5 COMPONENTS HL REQUIRED)
+include_directories(SYSTEM ${HDF5_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS ${HDF5_LIBRARIES})
+
+# ---[ LMDB
+find_package(LMDB REQUIRED)
+include_directories(SYSTEM ${LMDB_INCLUDE_DIR})
+list(APPEND Caffe_LINKER_LIBS ${LMDB_LIBRARIES})
+
+# ---[ LevelDB
+find_package(LevelDB REQUIRED)
+include_directories(SYSTEM ${LEVELDB_INCLUDE})
+list(APPEND Caffe_LINKER_LIBS ${LevelDB_LIBRARIES})
+
+# ---[ Snappy
+find_package(Snappy REQUIRED)
+include_directories(SYSTEM ${Snappy_INCLUDE_DIR})
+list(APPEND Caffe_LINKER_LIBS ${Snappy_LIBRARIES})
+
+# ---[ CUDA
+include(cmake/Cuda.cmake)
+if(NOT HAVE_CUDA)
+  if(CPU_ONLY)
+    message("-- CUDA is disabled. Building without it...")
+  else()
+    message("-- CUDA is not detected by cmake. Building without it...")
+  endif()
+
+  # TODO: remove this not cross platform define in future. Use caffe_config.h instead.
+  add_definitions(-DCPU_ONLY)
+endif()
+
+# ---[ OpenCV
+find_package(OpenCV QUIET COMPONENTS core highgui imgproc imgcodecs)
+if(NOT OpenCV_FOUND) # if not OpenCV 3.x, then imgcodecs are not found
+  find_package(OpenCV REQUIRED COMPONENTS core highgui imgproc)
+endif()
+include_directories(SYSTEM ${OpenCV_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS ${OpenCV_LIBS})
+message(STATUS "OpenCV found (${OpenCV_CONFIG_PATH})")
+
+# ---[ BLAS
+if(NOT APPLE)
+  set(BLAS "Atlas" CACHE STRING "Selected BLAS library")
+  set_property(CACHE BLAS PROPERTY STRINGS "Atlas;Open;MKL")
+
+  if(BLAS STREQUAL "Atlas" OR BLAS STREQUAL "atlas")
+    find_package(Atlas REQUIRED)
+    include_directories(SYSTEM ${Atlas_INCLUDE_DIR})
+    list(APPEND Caffe_LINKER_LIBS ${Atlas_LIBRARIES})
+  elseif(BLAS STREQUAL "Open" OR BLAS STREQUAL "open")
+    find_package(OpenBLAS REQUIRED)
+    include_directories(SYSTEM ${OpenBLAS_INCLUDE_DIR})
+    list(APPEND Caffe_LINKER_LIBS ${OpenBLAS_LIB})
+  elseif(BLAS STREQUAL "MKL" OR BLAS STREQUAL "mkl")
+    find_package(MKL REQUIRED)
+    include_directories(SYSTEM ${MKL_INCLUDE_DIR})
+    list(APPEND Caffe_LINKER_LIBS ${MKL_LIBRARIES})
+    add_definitions(-DUSE_MKL)
+  endif()
+elseif(APPLE)
+  find_package(vecLib REQUIRED)
+  include_directories(SYSTEM ${vecLib_INCLUDE_DIR})
+  list(APPEND Caffe_LINKER_LIBS ${vecLib_LINKER_LIBS})
+endif()
+
+# ---[ Python
+if(BUILD_python)
+  # disable Python 3 search
+  find_package(PythonInterp 2.7)
+  find_package(PythonLibs 2.7)
+  find_package(NumPy 1.7.1)
+  find_package(Boost 1.46 COMPONENTS python)
+
+  if(PYTHONLIBS_FOUND AND NUMPY_FOUND AND Boost_PYTHON_FOUND)
+    set(HAVE_PYTHON TRUE)
+  endif()
+endif()
+
+# ---[ Matlab
+if(BUILD_matlab)
+  find_package(MatlabMex)
+  if(MATLABMEX_FOUND)
+    set(HAVE_MATLAB TRUE)
+  endif()
+
+  # sudo apt-get install liboctave-dev
+  find_program(Octave_compiler NAMES mkoctfile DOC "Octave C++ compiler")
+
+  if(HAVE_MATLAB AND Octave_compiler)
+    set(Matlab_build_mex_using "Matlab" CACHE STRING "Select Matlab or Octave if both detected")
+    set_property(CACHE Matlab_build_mex_using PROPERTY STRINGS "Matlab;Octave")
+  endif()
+endif()
+
+# ---[ Doxygen
+if(BUILD_docs)
+  find_package(Doxygen)
+endif()
diff --git a/cmake/Misc.cmake b/cmake/Misc.cmake
new file mode 100644
index 00000000000..608a5f13a79
--- /dev/null
+++ b/cmake/Misc.cmake
@@ -0,0 +1,47 @@
+# ---[ Configurations types
+set(CMAKE_CONFIGURATION_TYPES "Debug;Release" CACHE STRING "Possible configurations" FORCE)
+mark_as_advanced(CMAKE_CONFIGURATION_TYPES)
+
+if(DEFINED CMAKE_BUILD_TYPE)
+  set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS ${CMAKE_CONFIGURATION_TYPES})
+endif()
+
+# --[ If user doesn't specify build type then assume release
+if("${CMAKE_BUILD_TYPE}" STREQUAL "")
+  set(CMAKE_BUILD_TYPE Release)
+endif()
+
+if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
+  set(CMAKE_COMPILER_IS_CLANGXX TRUE)
+endif()
+
+# ---[ Solution folders
+caffe_option(USE_PROJECT_FOLDERS "IDE Solution folders" (MSVC_IDE OR CMAKE_GENERATOR MATCHES Xcode) )
+
+if(USE_PROJECT_FOLDERS)
+  set_property(GLOBAL PROPERTY USE_FOLDERS ON)
+  set_property(GLOBAL PROPERTY PREDEFINED_TARGETS_FOLDER "CMakeTargets")
+endif()
+
+# ---[ Install options
+if(CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT)
+  set(CMAKE_INSTALL_PREFIX "${PROJECT_BINARY_DIR}/install" CACHE PATH "Default install path" FORCE)
+endif()
+
+# ---[ RPATH settings
+set(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE CACHE BOOLEAN "Use link paths for shared library rpath")
+set(CMAKE_MACOSX_RPATH TRUE)
+
+# ---[ Funny target
+if(UNIX OR APPLE)
+  add_custom_target(symlink_to_build COMMAND "ln" "-sf" "${PROJECT_BINARY_DIR}" "${PROJECT_SOURCE_DIR}/build"
+                                     COMMENT "Adding symlink: <caffe_root>/build -> ${PROJECT_BINARY_DIR}" )
+endif()
+
+# ---[ Set debug postfix
+set(Caffe_DEBUG_POSTFIX "-d")
+
+set(CAffe_POSTFIX "")
+if(CMAKE_BUILD_TYPE MATCHES "Debug")
+  set(CAffe_POSTFIX ${Caffe_DEBUG_POSTFIX})
+endif()
diff --git a/CMakeScripts/FindAtlas.cmake b/cmake/Modules/FindAtlas.cmake
similarity index 63%
rename from CMakeScripts/FindAtlas.cmake
rename to cmake/Modules/FindAtlas.cmake
index 27657a6c7d7..6e1564351c7 100644
--- a/CMakeScripts/FindAtlas.cmake
+++ b/cmake/Modules/FindAtlas.cmake
@@ -23,14 +23,14 @@ set(Atlas_LIB_SEARCH_PATHS
   $ENV{Atlas_ROOT_DIR}/lib
 )
 
-find_path(Atlas_CBLAS_INCLUDE_DIR NAMES cblas.h PATHS ${Atlas_INCLUDE_SEARCH_PATHS})
+find_path(Atlas_CBLAS_INCLUDE_DIR   NAMES cblas.h   PATHS ${Atlas_INCLUDE_SEARCH_PATHS})
 find_path(Atlas_CLAPACK_INCLUDE_DIR NAMES clapack.h PATHS ${Atlas_INCLUDE_SEARCH_PATHS})
-find_library(Atlas_CBLAS_LIBRARY NAMES ptcblas_r ptcblas cblas_r cblas PATHS ${Atlas_LIB_SEARCH_PATHS})
-find_library(Atlas_BLAS_LIBRARY NAMES atlas_r atlas PATHS ${Atlas_LIB_SEARCH_PATHS})
-find_library(Atlas_LAPACK_LIBRARY NAMES alapack_r alapack lapack_atlas PATHS ${Atlas_LIB_SEARCH_PATHS})
 
-set(LOOKED_FOR
+find_library(Atlas_CBLAS_LIBRARY NAMES  ptcblas_r ptcblas cblas_r cblas PATHS ${Atlas_LIB_SEARCH_PATHS})
+find_library(Atlas_BLAS_LIBRARY NAMES   atlas_r   atlas                 PATHS ${Atlas_LIB_SEARCH_PATHS})
+find_library(Atlas_LAPACK_LIBRARY NAMES alapack_r alapack lapack_atlas  PATHS ${Atlas_LIB_SEARCH_PATHS})
 
+set(LOOKED_FOR
   Atlas_CBLAS_INCLUDE_DIR
   Atlas_CLAPACK_INCLUDE_DIR
 
@@ -43,19 +43,10 @@ include(FindPackageHandleStandardArgs)
 find_package_handle_standard_args(Atlas DEFAULT_MSG ${LOOKED_FOR})
 
 if(ATLAS_FOUND)
-
+  set(Atlas_INCLUDE_DIR ${Atlas_CBLAS_INCLUDE_DIR} ${Atlas_CLAPACK_INCLUDE_DIR})
+  set(Atlas_LIBRARIES ${Atlas_LAPACK_LIBRARY} ${Atlas_CBLAS_LIBRARY} ${Atlas_BLAS_LIBRARY})
   mark_as_advanced(${LOOKED_FOR})
 
-  set(Atlas_INCLUDE_DIR
-    ${Atlas_CBLAS_INCLUDE_DIR}
-    ${Atlas_CLAPACK_INCLUDE_DIR}
-  )
-
-  set(Atlas_LIBRARIES
-    ${Atlas_LAPACK_LIBRARY}
-    ${Atlas_CBLAS_LIBRARY}
-    ${Atlas_BLAS_LIBRARY}
-  )
-
+  message(STATUS "Found Atlas (include: ${Atlas_CBLAS_INCLUDE_DIR}, library: ${Atlas_BLAS_LIBRARY})")
 endif(ATLAS_FOUND)
 
diff --git a/CMakeScripts/FindGFlags.cmake b/cmake/Modules/FindGFlags.cmake
similarity index 79%
rename from CMakeScripts/FindGFlags.cmake
rename to cmake/Modules/FindGFlags.cmake
index f93c57136a1..146e8455a50 100644
--- a/CMakeScripts/FindGFlags.cmake
+++ b/cmake/Modules/FindGFlags.cmake
@@ -38,11 +38,13 @@ else()
     find_library(GFLAGS_LIBRARY gflags)
 endif()
 
-find_package_handle_standard_args(GFLAGS DEFAULT_MSG
-    GFLAGS_INCLUDE_DIR GFLAGS_LIBRARY)
+find_package_handle_standard_args(GFLAGS DEFAULT_MSG GFLAGS_INCLUDE_DIR GFLAGS_LIBRARY)
 
 
 if(GFLAGS_FOUND)
     set(GFLAGS_INCLUDE_DIRS ${GFLAGS_INCLUDE_DIR})
     set(GFLAGS_LIBRARIES ${GFLAGS_LIBRARY})
+    message(STATUS "Found gflags  (include: ${GFLAGS_INCLUDE_DIR}, library: ${GFLAGS_LIBRARY})")
+    mark_as_advanced(GFLAGS_LIBRARY_DEBUG GFLAGS_LIBRARY_RELEASE
+                     GFLAGS_LIBRARY GFLAGS_INCLUDE_DIR GFLAGS_ROOT_DIR)
 endif()
diff --git a/CMakeScripts/FindGlog.cmake b/cmake/Modules/FindGlog.cmake
similarity index 70%
rename from CMakeScripts/FindGlog.cmake
rename to cmake/Modules/FindGlog.cmake
index 0dc30abdbf5..56c76434897 100644
--- a/CMakeScripts/FindGlog.cmake
+++ b/cmake/Modules/FindGlog.cmake
@@ -34,15 +34,15 @@ if(MSVC)
 else()
     find_library(GLOG_LIBRARY glog
         PATHS ${GLOG_ROOT_DIR}
-        PATH_SUFFIXES
-            lib
-            lib64)
+        PATH_SUFFIXES lib lib64)
 endif()
 
-find_package_handle_standard_args(GLOG DEFAULT_MSG
-    GLOG_INCLUDE_DIR GLOG_LIBRARY)
+find_package_handle_standard_args(GLOG DEFAULT_MSG GLOG_INCLUDE_DIR GLOG_LIBRARY)
 
 if(GLOG_FOUND)
-    set(GLOG_INCLUDE_DIRS ${GLOG_INCLUDE_DIR})
-    set(GLOG_LIBRARIES ${GLOG_LIBRARY})
+  set(GLOG_INCLUDE_DIRS ${GLOG_INCLUDE_DIR})
+  set(GLOG_LIBRARIES ${GLOG_LIBRARY})
+  message(STATUS "Found glog    (include: ${GLOG_INCLUDE_DIR}, library: ${GLOG_LIBRARY})")
+  mark_as_advanced(GLOG_ROOT_DIR GLOG_LIBRARY_RELEASE GLOG_LIBRARY_DEBUG
+                                 GLOG_LIBRARY GLOG_INCLUDE_DIR)
 endif()
diff --git a/CMakeScripts/FindLAPACK.cmake b/cmake/Modules/FindLAPACK.cmake
similarity index 100%
rename from CMakeScripts/FindLAPACK.cmake
rename to cmake/Modules/FindLAPACK.cmake
diff --git a/cmake/Modules/FindLMDB.cmake b/cmake/Modules/FindLMDB.cmake
new file mode 100644
index 00000000000..8a817fd6f10
--- /dev/null
+++ b/cmake/Modules/FindLMDB.cmake
@@ -0,0 +1,28 @@
+# Try to find the LMBD libraries and headers
+#  LMDB_FOUND - system has LMDB lib
+#  LMDB_INCLUDE_DIR - the LMDB include directory
+#  LMDB_LIBRARIES - Libraries needed to use LMDB
+
+# FindCWD based on FindGMP by:
+# Copyright (c) 2006, Laurent Montel, <montel@kde.org>
+#
+# Redistribution and use is allowed according to the terms of the BSD license.
+
+# Adapted from FindCWD by:
+# Copyright 2013 Conrad Steenberg <conrad.steenberg@gmail.com>
+# Aug 31, 2013
+
+find_path(LMDB_INCLUDE_DIR NAMES  lmdb.h PATHS "$ENV{LMDB_DIR}/include")
+find_library(LMDB_LIBRARIES NAMES lmdb   PATHS "$ENV{LMDB_DIR}/lib" )
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(LMDB DEFAULT_MSG LMDB_INCLUDE_DIR LMDB_LIBRARIES)
+
+if(LMDB_FOUND)
+  message(STATUS "Found lmdb    (include: ${LMDB_INCLUDE_DIR}, library: ${LMDB_LIBRARIES})")
+  mark_as_advanced(LMDB_INCLUDE_DIR LMDB_LIBRARIES)
+
+  caffe_parse_header(${LMDB_INCLUDE_DIR}/lmdb.h
+                     LMDB_VERSION_LINES MDB_VERSION_MAJOR MDB_VERSION_MINOR MDB_VERSION_PATCH)
+  set(LMDB_VERSION "${MDB_VERSION_MAJOR}.${MDB_VERSION_MINOR}.${MDB_VERSION_PATCH}")
+endif()
diff --git a/cmake/Modules/FindLevelDB.cmake b/cmake/Modules/FindLevelDB.cmake
new file mode 100644
index 00000000000..97f08ac9349
--- /dev/null
+++ b/cmake/Modules/FindLevelDB.cmake
@@ -0,0 +1,44 @@
+# - Find LevelDB
+#
+#  LevelDB_INCLUDES  - List of LevelDB includes
+#  LevelDB_LIBRARIES - List of libraries when using LevelDB.
+#  LevelDB_FOUND     - True if LevelDB found.
+
+# Look for the header file.
+find_path(LevelDB_INCLUDE NAMES leveldb/db.h
+                          PATHS $ENV{LEVELDB_ROOT}/include /opt/local/include /usr/local/include /usr/include
+                          DOC "Path in which the file leveldb/db.h is located." )
+
+# Look for the library.
+find_library(LevelDB_LIBRARY NAMES leveldb
+                             PATHS /usr/lib $ENV{LEVELDB_ROOT}/lib
+                             DOC "Path to leveldb library." )
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(LevelDB DEFAULT_MSG LevelDB_INCLUDE LevelDB_LIBRARY)
+
+if(LEVELDB_FOUND)
+  message(STATUS "Found LevelDB (include: ${LevelDB_INCLUDE}, library: ${LevelDB_LIBRARY})")
+  set(LevelDB_INCLUDES ${LevelDB_INCLUDE})
+  set(LevelDB_LIBRARIES ${LevelDB_LIBRARY})
+  mark_as_advanced(LevelDB_INCLUDE LevelDB_LIBRARY)
+
+  if(EXISTS "${LevelDB_INCLUDE}/leveldb/db.h")
+    file(STRINGS "${LevelDB_INCLUDE}/leveldb/db.h" __version_lines
+           REGEX "static const int k[^V]+Version[ \t]+=[ \t]+[0-9]+;")
+
+    foreach(__line ${__version_lines})
+      if(__line MATCHES "[^k]+kMajorVersion[ \t]+=[ \t]+([0-9]+);")
+        set(LEVELDB_VERSION_MAJOR ${CMAKE_MATCH_1})
+      elseif(__line MATCHES "[^k]+kMinorVersion[ \t]+=[ \t]+([0-9]+);")
+        set(LEVELDB_VERSION_MINOR ${CMAKE_MATCH_1})
+      endif()
+    endforeach()
+
+    if(LEVELDB_VERSION_MAJOR AND LEVELDB_VERSION_MINOR)
+      set(LEVELDB_VERSION "${LEVELDB_VERSION_MAJOR}.${LEVELDB_VERSION_MINOR}")
+    endif()
+
+    caffe_clear_vars(__line __version_lines)
+  endif()
+endif()
diff --git a/cmake/Modules/FindMKL.cmake b/cmake/Modules/FindMKL.cmake
new file mode 100644
index 00000000000..d2012db579a
--- /dev/null
+++ b/cmake/Modules/FindMKL.cmake
@@ -0,0 +1,110 @@
+# Find the MKL libraries
+#
+# Options:
+#
+#   MKL_USE_SINGLE_DYNAMIC_LIBRARY  : use single dynamic library interface
+#   MKL_USE_STATIC_LIBS             : use static libraries
+#   MKL_MULTI_THREADED              : use multi-threading
+#
+# This module defines the following variables:
+#
+#   MKL_FOUND            : True mkl is found
+#   MKL_INCLUDE_DIR      : unclude directory
+#   MKL_LIBRARIES        : the libraries to link against.
+
+
+# ---[ Options
+caffe_option(MKL_USE_SINGLE_DYNAMIC_LIBRARY "Use single dynamic library interface" ON)
+caffe_option(MKL_USE_STATIC_LIBS "Use static libraries" OFF IF NOT MKL_USE_SINGLE_DYNAMIC_LIBRARY)
+caffe_option(MKL_MULTI_THREADED  "Use multi-threading"   ON IF NOT MKL_USE_SINGLE_DYNAMIC_LIBRARY)
+
+# ---[ Root folders
+set(INTEL_ROOT "/opt/intel" CACHE PATH "Folder contains intel libs")
+find_path(MKL_ROOT include/mkl.h PATHS $ENV{MKL_ROOT} ${INTEL_ROOT}/mkl
+                                   DOC "Folder contains MKL")
+
+# ---[ Find include dir
+find_path(MKL_INCLUDE_DIR mkl.h PATHS ${MKL_ROOT} PATH_SUFFIXES include)
+set(__looked_for MKL_INCLUDE_DIR)
+
+# ---[ Find libraries
+if(CMAKE_SIZEOF_VOID_P EQUAL 4)
+  set(__path_suffixes lib lib/ia32)
+else()
+  set(__path_suffixes lib lib/intel64)
+endif()
+
+set(__mkl_libs "")
+if(MKL_USE_SINGLE_DYNAMIC_LIBRARY)
+  list(APPEND __mkl_libs rt)
+else()
+  if(CMAKE_SIZEOF_VOID_P EQUAL 4)
+    if(WIN32)
+      list(APPEND __mkl_libs intel_c)
+    else()
+      list(APPEND __mkl_libs intel gf)
+    endif()
+  else()
+    list(APPEND __mkl_libs intel_lp64 gf_lp64)
+  endif()
+
+  if(MKL_MULTI_THREADED)
+    list(APPEND __mkl_libs intel_thread)
+  else()
+     list(APPEND __mkl_libs sequential)
+  endif()
+
+  list(APPEND __mkl_libs core cdft_core)
+endif()
+
+
+foreach (__lib ${__mkl_libs})
+  set(__mkl_lib "mkl_${__lib}")
+  string(TOUPPER ${__mkl_lib} __mkl_lib_upper)
+
+  if(MKL_USE_STATIC_LIBS)
+    set(__mkl_lib "lib${__mkl_lib}.a")
+  endif()
+
+  find_library(${__mkl_lib_upper}_LIBRARY
+        NAMES ${__mkl_lib}
+        PATHS ${MKL_ROOT} "${MKL_INCLUDE_DIR}/.."
+        PATH_SUFFIXES ${__path_suffixes}
+        DOC "The path to Intel(R) MKL ${__mkl_lib} library")
+  mark_as_advanced(${__mkl_lib_upper}_LIBRARY)
+
+  list(APPEND __looked_for ${__mkl_lib_upper}_LIBRARY)
+  list(APPEND MKL_LIBRARIES ${${__mkl_lib_upper}_LIBRARY})
+endforeach()
+
+
+if(NOT MKL_USE_SINGLE_DYNAMIC_LIBRARY)
+  if (MKL_USE_STATIC_LIBS)
+    set(__iomp5_libs iomp5 libiomp5mt.lib)
+  else()
+    set(__iomp5_libs iomp5 libiomp5md.lib)
+  endif()
+
+  if(WIN32)
+    find_path(INTEL_INCLUDE_DIR omp.h PATHS ${INTEL_ROOT} PATH_SUFFIXES include)
+    list(APPEND __looked_for INTEL_INCLUDE_DIR)
+  endif()
+
+  find_library(MKL_RTL_LIBRARY ${__iomp5_libs}
+     PATHS ${INTEL_RTL_ROOT} ${INTEL_ROOT}/compiler ${MKL_ROOT}/.. ${MKL_ROOT}/../compiler
+     PATH_SUFFIXES ${__path_suffixes}
+     DOC "Path to Path to OpenMP runtime library")
+
+  list(APPEND __looked_for MKL_RTL_LIBRARY)
+  list(APPEND MKL_LIBRARIES ${MKL_RTL_LIBRARY})
+endif()
+
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(MKL DEFAULT_MSG ${__looked_for})
+
+if(MKL_FOUND)
+  message(STATUS "Found MKL (include: ${MKL_INCLUDE_DIR}, lib: ${MKL_LIBRARIES}")
+endif()
+
+caffe_clear_vars(__looked_for __mkl_libs __path_suffixes __lib_suffix __iomp5_libs)
diff --git a/cmake/Modules/FindMatlabMex.cmake b/cmake/Modules/FindMatlabMex.cmake
new file mode 100644
index 00000000000..28ae65e7cbb
--- /dev/null
+++ b/cmake/Modules/FindMatlabMex.cmake
@@ -0,0 +1,48 @@
+# This module looks for MatlabMex compiler
+# Defines variables:
+#    Matlab_DIR    - Matlab root dir
+#    Matlab_mex    - path to mex compiler
+#    Matlab_mexext - path to mexext
+
+if(MSVC)
+  foreach(__ver "9.30" "7.14" "7.11" "7.10" "7.9" "7.8" "7.7")
+    get_filename_component(__matlab_root "[HKEY_LOCAL_MACHINE\\SOFTWARE\\MathWorks\\MATLAB\\${__ver};MATLABROOT]" ABSOLUTE)
+    if(__matlab_root)
+      break()
+    endif()
+  endforeach()
+endif()
+
+if(APPLE)
+  foreach(__ver "R2014b" "R2014a" "R2013b" "R2013a" "R2012b" "R2012a" "R2011b" "R2011a" "R2010b" "R2010a")
+    if(EXISTS /Applications/MATLAB_${__ver}.app)
+      set(__matlab_root /Applications/MATLAB_${__ver}.app)
+      break()
+    endif()
+  endforeach()
+endif()
+
+if(UNIX)
+   execute_process(COMMAND which matlab OUTPUT_STRIP_TRAILING_WHITESPACE
+                   OUTPUT_VARIABLE __out RESULT_VARIABLE __res)
+
+   if(__res MATCHES 0) # Suppress `readlink` warning if `which` returned nothing
+     execute_process(COMMAND which matlab  COMMAND xargs readlink
+                     COMMAND xargs dirname COMMAND xargs dirname COMMAND xargs echo -n
+                     OUTPUT_VARIABLE __matlab_root OUTPUT_STRIP_TRAILING_WHITESPACE)
+   endif()
+endif()
+
+
+find_path(Matlab_DIR NAMES bin/mex bin/mexext PATHS ${__matlab_root}
+                     DOC "Matlab directory" NO_DEFAULT_PATH)
+
+find_program(Matlab_mex    NAMES mex    mex.bat    HINTS ${Matlab_DIR} PATH_SUFFIXES bin NO_DEFAULT_PATH)
+find_program(Matlab_mexext NAMES mexext mexext.bat HINTS ${Matlab_DIR} PATH_SUFFIXES bin NO_DEFAULT_PATH)
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(MatlabMex DEFAULT_MSG Matlab_mex Matlab_mexext)
+
+if(MATLABMEX_FOUND)
+  mark_as_advanced(Matlab_mex Matlab_mexext)
+endif()
diff --git a/cmake/Modules/FindNumPy.cmake b/cmake/Modules/FindNumPy.cmake
new file mode 100644
index 00000000000..a671494caba
--- /dev/null
+++ b/cmake/Modules/FindNumPy.cmake
@@ -0,0 +1,58 @@
+# - Find the NumPy libraries
+# This module finds if NumPy is installed, and sets the following variables
+# indicating where it is.
+#
+# TODO: Update to provide the libraries and paths for linking npymath lib.
+#
+#  NUMPY_FOUND               - was NumPy found
+#  NUMPY_VERSION             - the version of NumPy found as a string
+#  NUMPY_VERSION_MAJOR       - the major version number of NumPy
+#  NUMPY_VERSION_MINOR       - the minor version number of NumPy
+#  NUMPY_VERSION_PATCH       - the patch version number of NumPy
+#  NUMPY_VERSION_DECIMAL     - e.g. version 1.6.1 is 10601
+#  NUMPY_INCLUDE_DIR         - path to the NumPy include files
+
+unset(NUMPY_VERSION)
+unset(NUMPY_INCLUDE_DIR)
+
+if(PYTHONINTERP_FOUND)
+  execute_process(COMMAND "${PYTHON_EXECUTABLE}" "-c"
+    "import numpy as n; print(n.__version__); print(n.get_include());"
+    RESULT_VARIABLE __result
+    OUTPUT_VARIABLE __output
+    OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+  if(__result MATCHES 0)
+    string(REGEX REPLACE ";" "\\\\;" __values ${__output})
+    string(REGEX REPLACE "\r?\n" ";"    __values ${__values})
+    list(GET __values 0 NUMPY_VERSION)
+    list(GET __values 1 NUMPY_INCLUDE_DIR)
+
+    string(REGEX MATCH "^([0-9])+\\.([0-9])+\\.([0-9])+" __ver_check "${NUMPY_VERSION}")
+    if(NOT "${__ver_check}" STREQUAL "")
+      set(NUMPY_VERSION_MAJOR ${CMAKE_MATCH_1})
+      set(NUMPY_VERSION_MINOR ${CMAKE_MATCH_2})
+      set(NUMPY_VERSION_PATCH ${CMAKE_MATCH_3})
+      math(EXPR NUMPY_VERSION_DECIMAL
+        "(${NUMPY_VERSION_MAJOR} * 10000) + (${NUMPY_VERSION_MINOR} * 100) + ${NUMPY_VERSION_PATCH}")
+      string(REGEX REPLACE "\\\\" "/"  NUMPY_INCLUDE_DIR ${NUMPY_INCLUDE_DIR})
+    else()
+     unset(NUMPY_VERSION)
+     unset(NUMPY_INCLUDE_DIR)
+     message(STATUS "Requested NumPy version and include path, but got instead:\n${__output}\n")
+    endif()
+  endif()
+else()
+  message(STATUS "To find NumPy Python interpretator is required to be found.")
+endif()
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(NumPy REQUIRED_VARS NUMPY_INCLUDE_DIR NUMPY_VERSION
+                                        VERSION_VAR   NUMPY_VERSION)
+
+if(NUMPY_FOUND)
+  message(STATUS "NumPy ver. ${NUMPY_VERSION} found (include: ${NUMPY_INCLUDE_DIR})")
+endif()
+
+caffe_clear_vars(__result __output __error_value __values __ver_check __error_value)
+
diff --git a/CMakeScripts/FindOpenBLAS.cmake b/cmake/Modules/FindOpenBLAS.cmake
similarity index 100%
rename from CMakeScripts/FindOpenBLAS.cmake
rename to cmake/Modules/FindOpenBLAS.cmake
diff --git a/cmake/Modules/FindSnappy.cmake b/cmake/Modules/FindSnappy.cmake
new file mode 100644
index 00000000000..eff2a864a7b
--- /dev/null
+++ b/cmake/Modules/FindSnappy.cmake
@@ -0,0 +1,28 @@
+# Find the Snappy libraries
+#
+# The following variables are optionally searched for defaults
+#  Snappy_ROOT_DIR:    Base directory where all Snappy components are found
+#
+# The following are set after configuration is done:
+#  SNAPPY_FOUND
+#  Snappy_INCLUDE_DIR
+#  Snappy_LIBRARIES
+
+find_path(Snappy_INCLUDE_DIR NAMES snappy.h
+                             PATHS ${SNAPPY_ROOT_DIR} ${SNAPPY_ROOT_DIR}/include)
+
+find_library(Snappy_LIBRARIES NAMES snappy
+                              PATHS ${SNAPPY_ROOT_DIR} ${SNAPPY_ROOT_DIR}/lib)
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(Snappy DEFAULT_MSG Snappy_INCLUDE_DIR Snappy_LIBRARIES)
+
+if(SNAPPY_FOUND)
+  message(STATUS "Found Snappy  (include: ${Snappy_INCLUDE_DIR}, library: ${Snappy_LIBRARIES})")
+  mark_as_advanced(Snappy_INCLUDE_DIR Snappy_LIBRARIES)
+
+  caffe_parse_header(${Snappy_INCLUDE_DIR}/snappy-stubs-public.h
+                     SNAPPY_VERION_LINES SNAPPY_MAJOR SNAPPY_MINOR SNAPPY_PATCHLEVEL)
+  set(Snappy_VERSION "${SNAPPY_MAJOR}.${SNAPPY_MINOR}.${SNAPPY_PATCHLEVEL}")
+endif()
+
diff --git a/cmake/Modules/FindvecLib.cmake b/cmake/Modules/FindvecLib.cmake
new file mode 100644
index 00000000000..9600da43647
--- /dev/null
+++ b/cmake/Modules/FindvecLib.cmake
@@ -0,0 +1,34 @@
+# Find the vecLib libraries as part of Accelerate.framework or as standalon framework
+#
+# The following are set after configuration is done:
+#  VECLIB_FOUND
+#  vecLib_INCLUDE_DIR
+#  vecLib_LINKER_LIBS
+
+
+if(NOT APPLE)
+  return()
+endif()
+
+set(__veclib_include_suffix "Frameworks/vecLib.framework/Versions/Current/Headers")
+
+find_path(vecLib_INCLUDE_DIR vecLib.h
+          DOC "vecLib include directory"
+          PATHS /System/Library/${__veclib_include_suffix}
+                /System/Library/Frameworks/Accelerate.framework/Versions/Current/${__veclib_include_suffix}
+                /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.9.sdk/System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/)
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(vecLib DEFAULT_MSG vecLib_INCLUDE_DIR)
+
+if(VECLIB_FOUND)
+  if(vecLib_INCLUDE_DIR MATCHES "^/System/Library/Frameworks/vecLib.framework.*")
+    set(vecLib_LINKER_LIBS -lcblas "-framework vecLib")
+    message(STATUS "Found standalone vecLib.framework")
+  else()
+    set(vecLib_LINKER_LIBS -lcblas "-framework Accelerate")
+    message(STATUS "Found vecLib as part of Accelerate.framework")
+  endif()
+
+  mark_as_advanced(vecLib_INCLUDE_DIR)
+endif()
diff --git a/cmake/ProtoBuf.cmake b/cmake/ProtoBuf.cmake
new file mode 100644
index 00000000000..8946d66c57b
--- /dev/null
+++ b/cmake/ProtoBuf.cmake
@@ -0,0 +1,90 @@
+# Finds Google Protocol Buffers library and compilers and extends
+# the standart cmake script with version and python generation support
+
+find_package( Protobuf REQUIRED )
+include_directories(SYSTEM ${PROTOBUF_INCLUDE_DIR})
+list(APPEND Caffe_LINKER_LIBS ${PROTOBUF_LIBRARIES})
+
+# As of Ubuntu 14.04 protoc is no longer a part of libprotobuf-dev package
+# and should be installed  separately as in: sudo apt-get install protobuf-compiler
+if(EXISTS ${PROTOBUF_PROTOC_EXECUTABLE})
+  message(STATUS "Found PROTOBUF Compiler: ${PROTOBUF_PROTOC_EXECUTABLE}")
+else()
+  message(FATAL_ERROR "Could not find PROTOBUF Compiler")
+endif()
+
+if(PROTOBUF_FOUND)
+  # fetches protobuf version
+  caffe_parse_header(${PROTOBUF_INCLUDE_DIR}/google/protobuf/stubs/common.h VERION_LINE GOOGLE_PROTOBUF_VERSION)
+  string(REGEX MATCH "([0-9])00([0-9])00([0-9])" PROTOBUF_VERSION ${GOOGLE_PROTOBUF_VERSION})
+  set(PROTOBUF_VERSION "${CMAKE_MATCH_1}.${CMAKE_MATCH_2}.${CMAKE_MATCH_3}")
+  unset(GOOGLE_PROTOBUF_VERSION)
+endif()
+
+# place where to generate protobuf sources
+set(proto_gen_folder "${PROJECT_BINARY_DIR}/include/caffe/proto")
+include_directories(SYSTEM "${PROJECT_BINARY_DIR}/include")
+
+set(PROTOBUF_GENERATE_CPP_APPEND_PATH TRUE)
+
+################################################################################################
+# Modification of standard 'protobuf_generate_cpp()' with output dir parameter and python support
+# Usage:
+#   caffe_protobuf_generate_cpp_py(<output_dir> <srcs_var> <hdrs_var> <python_var> <proto_files>)
+function(caffe_protobuf_generate_cpp_py output_dir srcs_var hdrs_var python_var)
+  if(NOT ARGN)
+    message(SEND_ERROR "Error: caffe_protobuf_generate_cpp_py() called without any proto files")
+    return()
+  endif()
+
+  if(PROTOBUF_GENERATE_CPP_APPEND_PATH)
+    # Create an include path for each file specified
+    foreach(fil ${ARGN})
+      get_filename_component(abs_fil ${fil} ABSOLUTE)
+      get_filename_component(abs_path ${abs_fil} PATH)
+      list(FIND _protoc_include ${abs_path} _contains_already)
+      if(${_contains_already} EQUAL -1)
+        list(APPEND _protoc_include -I ${abs_path})
+      endif()
+    endforeach()
+  else()
+    set(_protoc_include -I ${CMAKE_CURRENT_SOURCE_DIR})
+  endif()
+
+  if(DEFINED PROTOBUF_IMPORT_DIRS)
+    foreach(dir ${PROTOBUF_IMPORT_DIRS})
+      get_filename_component(abs_path ${dir} ABSOLUTE)
+      list(FIND _protoc_include ${abs_path} _contains_already)
+      if(${_contains_already} EQUAL -1)
+        list(APPEND _protoc_include -I ${abs_path})
+      endif()
+    endforeach()
+  endif()
+
+  set(${srcs_var})
+  set(${hdrs_var})
+  set(${python_var})
+  foreach(fil ${ARGN})
+    get_filename_component(abs_fil ${fil} ABSOLUTE)
+    get_filename_component(fil_we ${fil} NAME_WE)
+
+    list(APPEND ${srcs_var} "${output_dir}/${fil_we}.pb.cc")
+    list(APPEND ${hdrs_var} "${output_dir}/${fil_we}.pb.h")
+    list(APPEND ${python_var} "${output_dir}/${fil_we}_pb2.py")
+
+    add_custom_command(
+      OUTPUT "${output_dir}/${fil_we}.pb.cc"
+             "${output_dir}/${fil_we}.pb.h"
+             "${output_dir}/${fil_we}_pb2.py"
+      COMMAND ${CMAKE_COMMAND} -E make_directory "${output_dir}"
+      COMMAND ${PROTOBUF_PROTOC_EXECUTABLE} --cpp_out    ${output_dir} ${_protoc_include} ${abs_fil}
+      COMMAND ${PROTOBUF_PROTOC_EXECUTABLE} --python_out ${output_dir} ${_protoc_include} ${abs_fil}
+      DEPENDS ${abs_fil}
+      COMMENT "Running C++/Python protocol buffer compiler on ${fil}" VERBATIM )
+  endforeach()
+
+  set_source_files_properties(${${srcs_var}} ${${hdrs_var}} ${${python_var}} PROPERTIES GENERATED TRUE)
+  set(${srcs_var} ${${srcs_var}} PARENT_SCOPE)
+  set(${hdrs_var} ${${hdrs_var}} PARENT_SCOPE)
+  set(${python_var} ${${python_var}} PARENT_SCOPE)
+endfunction()
diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake
new file mode 100644
index 00000000000..3f7dff6b6e0
--- /dev/null
+++ b/cmake/Summary.cmake
@@ -0,0 +1,166 @@
+################################################################################################
+# Caffe status report function.
+# Automatically align right column and selects text based on condition.
+# Usage:
+#   caffe_status(<text>)
+#   caffe_status(<heading> <value1> [<value2> ...])
+#   caffe_status(<heading> <condition> THEN <text for TRUE> ELSE <text for FALSE> )
+function(caffe_status text)
+  set(status_cond)
+  set(status_then)
+  set(status_else)
+
+  set(status_current_name "cond")
+  foreach(arg ${ARGN})
+    if(arg STREQUAL "THEN")
+      set(status_current_name "then")
+    elseif(arg STREQUAL "ELSE")
+      set(status_current_name "else")
+    else()
+      list(APPEND status_${status_current_name} ${arg})
+    endif()
+  endforeach()
+
+  if(DEFINED status_cond)
+    set(status_placeholder_length 23)
+    string(RANDOM LENGTH ${status_placeholder_length} ALPHABET " " status_placeholder)
+    string(LENGTH "${text}" status_text_length)
+    if(status_text_length LESS status_placeholder_length)
+      string(SUBSTRING "${text}${status_placeholder}" 0 ${status_placeholder_length} status_text)
+    elseif(DEFINED status_then OR DEFINED status_else)
+      message(STATUS "${text}")
+      set(status_text "${status_placeholder}")
+    else()
+      set(status_text "${text}")
+    endif()
+
+    if(DEFINED status_then OR DEFINED status_else)
+      if(${status_cond})
+        string(REPLACE ";" " " status_then "${status_then}")
+        string(REGEX REPLACE "^[ \t]+" "" status_then "${status_then}")
+        message(STATUS "${status_text} ${status_then}")
+      else()
+        string(REPLACE ";" " " status_else "${status_else}")
+        string(REGEX REPLACE "^[ \t]+" "" status_else "${status_else}")
+        message(STATUS "${status_text} ${status_else}")
+      endif()
+    else()
+      string(REPLACE ";" " " status_cond "${status_cond}")
+      string(REGEX REPLACE "^[ \t]+" "" status_cond "${status_cond}")
+      message(STATUS "${status_text} ${status_cond}")
+    endif()
+  else()
+    message(STATUS "${text}")
+  endif()
+endfunction()
+
+
+################################################################################################
+# Function for fetching Caffe version from git and headers
+# Usage:
+#   caffe_extract_caffe_version()
+function(caffe_extract_caffe_version)
+  set(Caffe_GIT_VERSION "unknown")
+  find_package(Git)
+  if(GIT_FOUND)
+    execute_process(COMMAND ${GIT_EXECUTABLE} describe --tags --always --dirty
+                    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE
+                    WORKING_DIRECTORY "${PROJECT_SOURCE_DIR}"
+                    OUTPUT_VARIABLE Caffe_GIT_VERSION
+                    RESULT_VARIABLE __git_result)
+    if(NOT ${__git_result} EQUAL 0)
+      set(Caffe_GIT_VERSION "unknown")
+    endif()
+  endif()
+
+  set(Caffe_GIT_VERSION ${Caffe_GIT_VERSION} PARENT_SCOPE)
+  set(Caffe_VERSION "<TODO> (Caffe doesn't declare its version in headers)" PARENT_SCOPE)
+
+  # caffe_parse_header(${Caffe_INCLUDE_DIR}/caffe/version.hpp Caffe_VERSION_LINES CAFFE_MAJOR CAFFE_MINOR CAFFE_PATCH)
+  # set(Caffe_VERSION "${CAFFE_MAJOR}.${CAFFE_MINOR}.${CAFFE_PATCH}" PARENT_SCOPE)
+
+  # or for #define Caffe_VERSION "x.x.x"
+  # caffe_parse_header_single_define(Caffe ${Caffe_INCLUDE_DIR}/caffe/version.hpp Caffe_VERSION)
+  # set(Caffe_VERSION ${Caffe_VERSION_STRING} PARENT_SCOPE)
+
+endfunction()
+
+
+################################################################################################
+# Prints accumulatd caffe configuration summary
+# Usage:
+#   caffe_print_configuration_summary()
+
+function(caffe_print_configuration_summary)
+  caffe_extract_caffe_version()
+  set(Caffe_VERSION ${Caffe_VERSION} PARENT_SCOPE)
+
+  caffe_merge_flag_lists(__flags_rel CMAKE_CXX_FLAGS_RELEASE CMAKE_CXX_FLAGS)
+  caffe_merge_flag_lists(__flags_deb CMAKE_CXX_FLAGS_DEBUG   CMAKE_CXX_FLAGS)
+
+  caffe_status("")
+  caffe_status("******************* Caffe Configuration Summary *******************")
+  caffe_status("General:")
+  caffe_status("  Version           :   ${Caffe_VERSION}")
+  caffe_status("  Git               :   ${Caffe_GIT_VERSION}")
+  caffe_status("  System            :   ${CMAKE_SYSTEM_NAME}")
+  caffe_status("  C++ compiler      :   ${CMAKE_CXX_COMPILER}")
+  caffe_status("  Release CXX flags :   ${__flags_rel}")
+  caffe_status("  Debug CXX flags   :   ${__flags_deb}")
+  caffe_status("  BUILD_SHARED_LIBS :   ${BUILD_SHARED_LIBS}")
+  caffe_status("  Build type        :   ${CMAKE_BUILD_TYPE}")
+  caffe_status("  BUILD_python      :   ${BUILD_python}")
+  caffe_status("  BUILD_matlab      :   ${BUILD_matlab}")
+  caffe_status("  BUILD_docs        :   ${BUILD_docs}")
+  caffe_status("  CPU_ONLY          :   ${CPU_ONLY}")
+  caffe_status("")
+  caffe_status("Dependencies:")
+  caffe_status("  BLAS              : " APPLE THEN "Yes (vecLib)" ELSE "Yes (${BLAS})")
+  caffe_status("  glog              :   Yes")
+  caffe_status("  gflags            :   Yes")
+  caffe_status("  protobuf          : " PROTOBUF_FOUND THEN "Yes (ver. ${PROTOBUF_VERSION})" ELSE "No" )
+  caffe_status("  lmdb              : " LMDB_FOUND THEN "Yes (ver. ${LMDB_VERSION})" ELSE "No")
+  caffe_status("  Snappy            : " SNAPPY_FOUND THEN "Yes (ver. ${Snappy_VERSION})" ELSE "No" )
+  caffe_status("  LevelDB           : " LEVELDB_FOUND THEN  "Yes (ver. ${LEVELDB_VERSION})" ELSE "No")
+  caffe_status("  OpenCV            :   Yes (ver. ${OpenCV_VERSION})")
+  caffe_status("  CUDA              : " HAVE_CUDA THEN "Yes (ver. ${CUDA_VERSION})" ELSE "No" )
+  caffe_status("")
+  if(HAVE_CUDA)
+    caffe_status("NVIDIA CUDA:")
+    caffe_status("  Target GPU(s)     :   ${CUDA_ARCH_NAME}" )
+    caffe_status("  GPU arch(s)       :   ${NVCC_FLAGS_EXTRA_readable}")
+    if(USE_CUDNN)
+      caffe_status("  cuDNN             : " HAVE_CUDNN THEN "Yes" ELSE "Not found")
+    else()
+      caffe_status("  cuDNN             :   Disabled")
+    endif()
+    caffe_status("")
+  endif()
+  if(HAVE_PYTHON)
+    caffe_status("Python:")
+    caffe_status("  Interpreter       :" PYTHON_EXECUTABLE THEN "${PYTHON_EXECUTABLE} (ver. ${PYTHON_VERSION_STRING})" ELSE "No")
+    caffe_status("  Libraries         :" PYTHONLIBS_FOUND  THEN "${PYTHON_LIBRARIES} (ver ${PYTHONLIBS_VERSION_STRING})" ELSE "No")
+    caffe_status("  NumPy             :" NUMPY_FOUND  THEN "${NUMPY_INCLUDE_DIR} (ver ${NUMPY_VERSION})" ELSE "No")
+    caffe_status("")
+  endif()
+  if(BUILD_matlab)
+    caffe_status("Matlab:")
+    caffe_status("  Matlab            :" HAVE_MATLAB THEN "Yes (${Matlab_mex}, ${Matlab_mexext}" ELSE "No")
+    caffe_status("  Octave            :" Octave_compiler THEN  "Yes (${Octave_compiler})" ELSE "No")
+    if(HAVE_MATLAB AND Octave_compiler)
+      caffe_status("  Build mex using   :   ${Matlab_build_mex_using}")
+    endif()
+    caffe_status("")
+  endif()
+  if(BUILD_docs)
+    caffe_status("Documentaion:")
+    caffe_status("  Doxygen           :" DOXYGEN_FOUND THEN "${DOXYGEN_EXECUTABLE} (${DOXYGEN_VERSION})" ELSE "No")
+    caffe_status("  config_file       :   ${DOXYGEN_config_file}")
+
+    caffe_status("")
+  endif()
+  caffe_status("Install:")
+  caffe_status("  Install path      :   ${CMAKE_INSTALL_PREFIX}")
+  caffe_status("")
+endfunction()
+
diff --git a/cmake/Targets.cmake b/cmake/Targets.cmake
new file mode 100644
index 00000000000..e3ad872313b
--- /dev/null
+++ b/cmake/Targets.cmake
@@ -0,0 +1,169 @@
+################################################################################################
+# Defines global Caffe_LINK flag, This flag is required to prevent linker from excluding
+# some objects which are not addressed directly but are registered via static constructors
+if(BUILD_SHARED_LIBS)
+  set(Caffe_LINK caffe)
+else()
+  if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
+    set(Caffe_LINK -Wl,-force_load caffe)
+  elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
+    set(Caffe_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive)
+  endif()
+endif()
+
+################################################################################################
+# Convenient command to setup source group for IDEs that support this feature (VS, XCode)
+# Usage:
+#   caffe_source_group(<group> GLOB[_RECURSE] <globbing_expression>)
+function(caffe_source_group group)
+  cmake_parse_arguments(CAFFE_SOURCE_GROUP "" "" "GLOB;GLOB_RECURSE" ${ARGN})
+  if(CAFFE_SOURCE_GROUP_GLOB)
+    file(GLOB srcs1 ${CAFFE_SOURCE_GROUP_GLOB})
+    source_group(${group} FILES ${srcs1})
+  endif()
+
+  if(CAFFE_SOURCE_GROUP_GLOB_RECURSE)
+    file(GLOB_RECURSE srcs2 ${CAFFE_SOURCE_GROUP_GLOB_RECURSE})
+    source_group(${group} FILES ${srcs2})
+  endif()
+endfunction()
+
+################################################################################################
+# Collecting sources from globbing and appending to output list variable
+# Usage:
+#   caffe_source_group(<output_variable> GLOB[_RECURSE] <globbing_expression>)
+function(caffe_collect_sources variable)
+  cmake_parse_arguments(CAFFE_COLLECT_SOURCES "" "" "GLOB;GLOB_RECURSE" ${ARGN})
+  if(CAFFE_COLLECT_SOURCES_GLOB)
+    file(GLOB srcs1 ${CAFFE_COLLECT_SOURCES_GLOB})
+    set(${variable} ${variable} ${srcs1})
+  endif()
+
+  if(CAFFE_COLLECT_SOURCES_GLOB_RECURSE)
+    file(GLOB_RECURSE srcs2 ${CAFFE_COLLECT_SOURCES_GLOB_RECURSE})
+    set(${variable} ${variable} ${srcs2})
+  endif()
+endfunction()
+
+################################################################################################
+# Short command getting caffe sources (assuming standard Caffe code tree)
+# Usage:
+#   caffe_pickup_caffe_sources(<root>)
+function(caffe_pickup_caffe_sources root)
+  # put all files in source groups (visible as subfolder in many IDEs)
+  caffe_source_group("Include"        GLOB "${root}/include/caffe/*.h*")
+  caffe_source_group("Include\\Util"  GLOB "${root}/include/caffe/util/*.h*")
+  caffe_source_group("Include"        GLOB "${PROJECT_BINARY_DIR}/caffe_config.h*")
+  caffe_source_group("Source"         GLOB "${root}/src/caffe/*.cpp")
+  caffe_source_group("Source\\Util"   GLOB "${root}/src/caffe/util/*.cpp")
+  caffe_source_group("Source\\Layers" GLOB "${root}/src/caffe/layers/*.cpp")
+  caffe_source_group("Source\\Cuda"   GLOB "${root}/src/caffe/layers/*.cu")
+  caffe_source_group("Source\\Cuda"   GLOB "${root}/src/caffe/util/*.cu")
+  caffe_source_group("Source\\Proto"  GLOB "${root}/src/caffe/proto/*.proto")
+
+  # source groups for test target
+  caffe_source_group("Include"      GLOB "${root}/include/caffe/test/test_*.h*")
+  caffe_source_group("Source"       GLOB "${root}/src/caffe/test/test_*.cpp")
+  caffe_source_group("Source\\Cuda" GLOB "${root}/src/caffe/test/test_*.cu")
+
+  # collect files
+  file(GLOB test_hdrs    ${root}/include/caffe/test/test_*.h*)
+  file(GLOB test_srcs    ${root}/src/caffe/test/test_*.cpp)
+  file(GLOB_RECURSE hdrs ${root}/include/caffe/*.h*)
+  file(GLOB_RECURSE srcs ${root}/src/caffe/*.cpp)
+  list(REMOVE_ITEM  hdrs ${test_hdrs})
+  list(REMOVE_ITEM  srcs ${test_srcs})
+
+  # adding headers to make the visible in some IDEs (Qt, VS, Xcode)
+  list(APPEND srcs ${hdrs} ${PROJECT_BINARY_DIR}/caffe_config.h)
+  list(APPEND test_srcs ${test_hdrs})
+
+  # collect cuda files
+  file(GLOB    test_cuda ${root}/src/caffe/test/test_*.cu)
+  file(GLOB_RECURSE cuda ${root}/src/caffe/*.cu)
+  list(REMOVE_ITEM  cuda ${test_cuda})
+
+  # add proto to make them editable in IDEs too
+  file(GLOB_RECURSE proto_files ${root}/src/caffe/*.proto)
+  list(APPEND srcs ${proto_files})
+
+  # convet to absolute paths
+  caffe_convert_absolute_paths(srcs)
+  caffe_convert_absolute_paths(cuda)
+  caffe_convert_absolute_paths(test_srcs)
+  caffe_convert_absolute_paths(test_cuda)
+
+  # propogate to parent scope
+  set(srcs ${srcs} PARENT_SCOPE)
+  set(cuda ${cuda} PARENT_SCOPE)
+  set(test_srcs ${test_srcs} PARENT_SCOPE)
+  set(test_cuda ${test_cuda} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Short command for setting defeault target properties
+# Usage:
+#   caffe_default_properties(<target>)
+function(caffe_default_properties target)
+  set_target_properties(${target} PROPERTIES
+    DEBUG_POSTFIX ${Caffe_DEBUG_POSTFIX}
+    ARCHIVE_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/lib"
+    LIBRARY_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/lib"
+    RUNTIME_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/bin")
+endfunction()
+
+################################################################################################
+# Short command for setting runtime directory for build target
+# Usage:
+#   caffe_set_runtime_directory(<target> <dir>)
+function(caffe_set_runtime_directory target dir)
+  set_target_properties(${target} PROPERTIES
+    RUNTIME_OUTPUT_DIRECTORY "${dir}")
+endfunction()
+
+################################################################################################
+# Short command for setting solution folder property for target
+# Usage:
+#   caffe_set_solution_folder(<target> <folder>)
+function(caffe_set_solution_folder target folder)
+  if(USE_PROJECT_FOLDERS)
+    set_target_properties(${target} PROPERTIES FOLDER "${folder}")
+  endif()
+endfunction()
+
+################################################################################################
+# Reads lines from input file, prepends source directory to each line and writes to output file
+# Usage:
+#   caffe_configure_testdatafile(<testdatafile>)
+function(caffe_configure_testdatafile file)
+  file(STRINGS ${file} __lines)
+  set(result "")
+  foreach(line ${__lines})
+    set(result "${result}${PROJECT_SOURCE_DIR}/${line}\n")
+  endforeach()  
+  file(WRITE ${file}.gen.cmake ${result})
+endfunction()
+
+################################################################################################
+# Filter outs all files that are not inlcuded in selected list
+# Usage:
+#   caffe_leave_only_selected_tests(<filelist_variable> <selected_list>)
+function(caffe_leave_only_selected_tests file_list)
+  if(NOT ARGN)
+    return() # blank list means leave all
+  endif()
+  string(REPLACE "," ";" __selected ${ARGN})
+  list(APPEND __selected caffe_main)
+
+  set(result "")
+  foreach(f ${${file_list}})
+    get_filename_component(name ${f} NAME_WE)
+    string(REGEX REPLACE "^test_" "" name ${name})
+    list(FIND __selected ${name} __index)
+    if(NOT __index EQUAL -1)
+      list(APPEND result ${f})
+    endif()
+  endforeach()
+  set(${file_list} ${result} PARENT_SCOPE)
+endfunction()
+
diff --git a/cmake/Templates/CaffeConfig.cmake.in b/cmake/Templates/CaffeConfig.cmake.in
new file mode 100644
index 00000000000..a4b03d961e0
--- /dev/null
+++ b/cmake/Templates/CaffeConfig.cmake.in
@@ -0,0 +1,58 @@
+# Config file for the Caffe package.
+#
+# Note:
+#   Caffe and this config file depends on opencv,
+#   so put `find_package(OpenCV)` before searching Caffe
+#   via `find_package(Caffe)`. All other lib/includes
+#   dependencies are hard coded int the file
+#
+# After successful configuration the following variables
+# will be defined:
+#
+#   Caffe_INCLUDE_DIRS - Caffe include directories
+#   Caffe_LIBRARIES    - libraries to link against
+#   Caffe_DEFINITIONS  - a list of definitions to pass to compiler
+#
+#   Caffe_HAVE_CUDA    - signals about CUDA support
+#   Caffe_HAVE_CUDNN   - signals about cuDNN support
+ 
+
+# OpenCV dependency
+
+if(NOT OpenCV_FOUND)
+  set(Caffe_OpenCV_CONFIG_PATH "@OpenCV_CONFIG_PATH@")
+  if(Caffe_OpenCV_CONFIG_PATH)
+    get_filename_component(Caffe_OpenCV_CONFIG_PATH ${Caffe_OpenCV_CONFIG_PATH} ABSOLUTE)
+
+    if(EXISTS ${Caffe_OpenCV_CONFIG_PATH} AND NOT TARGET opencv_core)
+      message(STATUS "Caffe: using OpenCV config from ${Caffe_OpenCV_CONFIG_PATH}")
+      include(${Caffe_OpenCV_CONFIG_PATH}/OpenCVModules.cmake)
+    endif()
+
+  else()
+    find_package(OpenCV REQUIRED)
+  endif()
+  unset(Caffe_OpenCV_CONFIG_PATH)
+endif()
+
+# Compute paths
+get_filename_component(Caffe_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
+set(Caffe_INCLUDE_DIRS "@Caffe_INCLUDE_DIRS@")
+
+@Caffe_INSTALL_INCLUDE_DIR_APPEND_COMMAND@
+ 
+# Our library dependencies
+if(NOT TARGET caffe AND NOT caffe_BINARY_DIR)
+  include("${Caffe_CMAKE_DIR}/CaffeTargets.cmake")
+endif()
+
+# List of IMPORTED libs created by CaffeTargets.cmake
+set(Caffe_LIBRARIES caffe)
+
+# Definitions
+set(Caffe_DEFINITIONS "@Caffe_DEFINITIONS@")
+
+# Cuda support variables
+set(Caffe_CPU_ONLY @CPU_ONLY@)
+set(Caffe_HAVE_CUDA @HAVE_CUDA@)
+set(Caffe_HAVE_CUDNN @HAVE_CUDNN@)
diff --git a/cmake/Templates/CaffeConfigVersion.cmake.in b/cmake/Templates/CaffeConfigVersion.cmake.in
new file mode 100644
index 00000000000..cbfa514f1a6
--- /dev/null
+++ b/cmake/Templates/CaffeConfigVersion.cmake.in
@@ -0,0 +1,11 @@
+set(PACKAGE_VERSION "@Caffe_VERSION@")
+ 
+# Check whether the requested PACKAGE_FIND_VERSION is compatible
+if("${PACKAGE_VERSION}" VERSION_LESS "${PACKAGE_FIND_VERSION}")
+  set(PACKAGE_VERSION_COMPATIBLE FALSE)
+else()
+  set(PACKAGE_VERSION_COMPATIBLE TRUE)
+  if ("${PACKAGE_VERSION}" VERSION_EQUAL "${PACKAGE_FIND_VERSION}")
+    set(PACKAGE_VERSION_EXACT TRUE)
+  endif()
+endif()
diff --git a/cmake/Templates/caffe_config.h.in b/cmake/Templates/caffe_config.h.in
new file mode 100644
index 00000000000..6039e8f6b21
--- /dev/null
+++ b/cmake/Templates/caffe_config.h.in
@@ -0,0 +1,32 @@
+/* Sources directory */
+#define SOURCE_FOLDER "${PROJECT_SOURCE_DIR}"
+
+/* Binaries directory */
+#define BINARY_FOLDER "${PROJECT_BINARY_DIR}"
+
+/* NVIDA Cuda */
+#cmakedefine HAVE_CUDA
+
+/* NVIDA cuDNN */
+#cmakedefine HAVE_CUDNN
+#cmakedefine USE_CUDNN
+
+/* NVIDA cuDNN */
+#cmakedefine CPU_ONLY
+
+/* Test device */
+#define CUDA_TEST_DEVICE ${CUDA_TEST_DEVICE}
+
+/* Temporary (TODO: remove) */
+#if 1
+  #define CMAKE_SOURCE_DIR SOURCE_FOLDER "/src/"
+  #define EXAMPLES_SOURCE_DIR BINARY_FOLDER "/examples/"
+  #define CMAKE_EXT ".gen.cmake"
+#else
+  #define CMAKE_SOURCE_DIR "src/"
+  #define EXAMPLES_SOURCE_DIR "examples/"
+  #define CMAKE_EXT ""
+#endif
+
+/* Matlab */
+#cmakedefine HAVE_MATLAB
diff --git a/cmake/Utils.cmake b/cmake/Utils.cmake
new file mode 100644
index 00000000000..a56c7c300c0
--- /dev/null
+++ b/cmake/Utils.cmake
@@ -0,0 +1,381 @@
+################################################################################################
+# Command alias for debugging messages
+# Usage:
+#   dmgs(<message>)
+function(dmsg)
+  message(STATUS ${ARGN})
+endfunction()
+
+################################################################################################
+# Removes duplicates from list(s)
+# Usage:
+#   caffe_list_unique(<list_variable> [<list_variable>] [...])
+macro(caffe_list_unique)
+  foreach(__lst ${ARGN})
+    if(${__lst})
+      list(REMOVE_DUPLICATES ${__lst})
+    endif()
+  endforeach()
+endmacro()
+
+################################################################################################
+# Clears variables from lsit
+# Usage:
+#   caffe_list_unique(<variables_list>)
+macro(caffe_clear_vars)
+  foreach(_var ${ARGN})
+    unset(${_var})
+  endforeach()
+endmacro()
+
+################################################################################################
+# Removes duplicates from string
+# Usage:
+#   caffe_string_unique(<string_variable>)
+function(caffe_string_unique __string)
+  if(${__string})
+    set(__list ${${__string}})
+    separate_arguments(__list)
+    list(REMOVE_DUPLICATES __list)
+    foreach(__e ${__list})
+      set(__str "${__str} ${__e}")
+    endforeach()
+    set(${__string} ${__str} PARENT_SCOPE)
+  endif()
+endfunction()
+
+################################################################################################
+# Prints list element per line
+# Usage:
+#   caffe_print_list(<list>)
+function(caffe_print_list)
+  foreach(e ${ARGN})
+    message(STATUS ${e})
+  endforeach()
+endfunction()
+
+################################################################################################
+# Function merging lists of compiler flags to single string.
+# Usage:
+#   caffe_merge_flag_lists(out_variable <list1> [<list2>] [<list3>] ...)
+function(caffe_merge_flag_lists out_var)
+  set(__result "")
+  foreach(__list ${ARGN})
+    foreach(__flag ${${__list}})
+      string(STRIP ${__flag} __flag)
+      set(__result "${__result} ${__flag}")
+    endforeach()
+  endforeach()
+  string(STRIP ${__result} __result)
+  set(${out_var} ${__result} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Converts all paths in list to absolute
+# Usage:
+#   caffe_convert_absolute_paths(<list_variable>)
+function(caffe_convert_absolute_paths variable)
+  set(__dlist "")
+  foreach(__s ${${variable}})
+    get_filename_component(__abspath ${__s} ABSOLUTE)
+    list(APPEND __list ${__abspath})
+  endforeach()
+  set(${variable} ${__list} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Reads set of version defines from the header file
+# Usage:
+#   caffe_parse_header(<file> <define1> <define2> <define3> ..)
+macro(caffe_parse_header FILENAME FILE_VAR)
+  set(vars_regex "")
+  set(__parnet_scope OFF)
+  set(__add_cache OFF)
+  foreach(name ${ARGN})
+    if("${name}" STREQUAL "PARENT_SCOPE")
+      set(__parnet_scope ON)
+    elseif("${name}" STREQUAL "CACHE")
+      set(__add_cache ON)
+    elseif(vars_regex)
+      set(vars_regex "${vars_regex}|${name}")
+    else()
+      set(vars_regex "${name}")
+    endif()
+  endforeach()
+  if(EXISTS "${FILENAME}")
+    file(STRINGS "${FILENAME}" ${FILE_VAR} REGEX "#define[ \t]+(${vars_regex})[ \t]+[0-9]+" )
+  else()
+    unset(${FILE_VAR})
+  endif()
+  foreach(name ${ARGN})
+    if(NOT "${name}" STREQUAL "PARENT_SCOPE" AND NOT "${name}" STREQUAL "CACHE")
+      if(${FILE_VAR})
+        if(${FILE_VAR} MATCHES ".+[ \t]${name}[ \t]+([0-9]+).*")
+          string(REGEX REPLACE ".+[ \t]${name}[ \t]+([0-9]+).*" "\\1" ${name} "${${FILE_VAR}}")
+        else()
+          set(${name} "")
+        endif()
+        if(__add_cache)
+          set(${name} ${${name}} CACHE INTERNAL "${name} parsed from ${FILENAME}" FORCE)
+        elseif(__parnet_scope)
+          set(${name} "${${name}}" PARENT_SCOPE)          
+        endif()
+      else()
+        unset(${name} CACHE)
+      endif()
+    endif()
+  endforeach()
+endmacro()
+
+################################################################################################
+# Reads single version define from the header file and parses it
+# Usage:
+#   caffe_parse_header_single_define(<library_name> <file> <define_name>)
+function(caffe_parse_header_single_define LIBNAME HDR_PATH VARNAME)
+  set(${LIBNAME}_H "")
+  if(EXISTS "${HDR_PATH}")
+    file(STRINGS "${HDR_PATH}" ${LIBNAME}_H REGEX "^#define[ \t]+${VARNAME}[ \t]+\"[^\"]*\".*$" LIMIT_COUNT 1)
+  endif()
+
+  if(${LIBNAME}_H)
+    string(REGEX REPLACE "^.*[ \t]${VARNAME}[ \t]+\"([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_MAJOR "${${LIBNAME}_H}")
+    string(REGEX REPLACE "^.*[ \t]${VARNAME}[ \t]+\"[0-9]+\\.([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_MINOR  "${${LIBNAME}_H}")
+    string(REGEX REPLACE "^.*[ \t]${VARNAME}[ \t]+\"[0-9]+\\.[0-9]+\\.([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_PATCH "${${LIBNAME}_H}")
+    set(${LIBNAME}_VERSION_MAJOR ${${LIBNAME}_VERSION_MAJOR} ${ARGN} PARENT_SCOPE)
+    set(${LIBNAME}_VERSION_MINOR ${${LIBNAME}_VERSION_MINOR} ${ARGN} PARENT_SCOPE)
+    set(${LIBNAME}_VERSION_PATCH ${${LIBNAME}_VERSION_PATCH} ${ARGN} PARENT_SCOPE)
+    set(${LIBNAME}_VERSION_STRING "${${LIBNAME}_VERSION_MAJOR}.${${LIBNAME}_VERSION_MINOR}.${${LIBNAME}_VERSION_PATCH}" PARENT_SCOPE)
+
+    # append a TWEAK version if it exists:
+    set(${LIBNAME}_VERSION_TWEAK "")
+    if("${${LIBNAME}_H}" MATCHES "^.*[ \t]${VARNAME}[ \t]+\"[0-9]+\\.[0-9]+\\.[0-9]+\\.([0-9]+).*$")
+      set(${LIBNAME}_VERSION_TWEAK "${CMAKE_MATCH_1}" ${ARGN} PARENT_SCOPE)
+    endif()
+    if(${LIBNAME}_VERSION_TWEAK)
+      set(${LIBNAME}_VERSION_STRING "${${LIBNAME}_VERSION_STRING}.${${LIBNAME}_VERSION_TWEAK}" ${ARGN} PARENT_SCOPE)
+    else()
+      set(${LIBNAME}_VERSION_STRING "${${LIBNAME}_VERSION_STRING}" ${ARGN} PARENT_SCOPE)
+    endif()
+  endif()
+endfunction()
+
+########################################################################################################
+# An option that the user can select. Can accept condition to control when option is available for user.
+# Usage:
+#   caffe_option(<option_variable> "doc string" <initial value or boolean expression> [IF <condition>])
+function(caffe_option variable description value)
+  set(__value ${value})
+  set(__condition "")
+  set(__varname "__value")
+  foreach(arg ${ARGN})
+    if(arg STREQUAL "IF" OR arg STREQUAL "if")
+      set(__varname "__condition")
+    else()
+      list(APPEND ${__varname} ${arg})
+    endif()
+  endforeach()
+  unset(__varname)
+  if("${__condition}" STREQUAL "")
+    set(__condition 2 GREATER 1)
+  endif()
+
+  if(${__condition})
+    if("${__value}" MATCHES ";")
+      if(${__value})
+        option(${variable} "${description}" ON)
+      else()
+        option(${variable} "${description}" OFF)
+      endif()
+    elseif(DEFINED ${__value})
+      if(${__value})
+        option(${variable} "${description}" ON)
+      else()
+        option(${variable} "${description}" OFF)
+      endif()
+    else()
+      option(${variable} "${description}" ${__value})
+    endif()
+  else()
+    unset(${variable} CACHE)
+  endif()
+endfunction()
+
+################################################################################################
+# Utility macro for comparing two lists. Used for CMake debugging purposes
+# Usage:
+#   caffe_compare_lists(<list_variable> <list2_variable> [description])
+function(caffe_compare_lists list1 list2 desc)
+  set(__list1 ${${list1}})
+  set(__list2 ${${list2}})
+  list(SORT __list1)
+  list(SORT __list2)
+  list(LENGTH __list1 __len1)
+  list(LENGTH __list2 __len2)
+
+  if(NOT ${__len1} EQUAL ${__len2})
+    message(FATAL_ERROR "Lists are not equal. ${__len1} != ${__len2}. ${desc}")
+  endif()
+
+  foreach(__i RANGE 1 ${__len1})
+    math(EXPR __index "${__i}- 1")
+    list(GET __list1 ${__index} __item1)
+    list(GET __list2 ${__index} __item2)
+    if(NOT ${__item1} STREQUAL ${__item2})
+      message(FATAL_ERROR "Lists are not equal. Differ at element ${__index}. ${desc}")
+    endif()
+  endforeach()
+endfunction()
+
+################################################################################################
+# Command for disabling warnings for different platforms (see below for gcc and VisualStudio)
+# Usage:
+#   caffe_warnings_disable(<CMAKE_[C|CXX]_FLAGS[_CONFIGURATION]> -Wshadow /wd4996 ..,)
+macro(caffe_warnings_disable)
+  set(_flag_vars "")
+  set(_msvc_warnings "")
+  set(_gxx_warnings "")
+
+  foreach(arg ${ARGN})
+    if(arg MATCHES "^CMAKE_")
+      list(APPEND _flag_vars ${arg})
+    elseif(arg MATCHES "^/wd")
+      list(APPEND _msvc_warnings ${arg})
+    elseif(arg MATCHES "^-W")
+      list(APPEND _gxx_warnings ${arg})
+    endif()
+  endforeach()
+
+  if(NOT _flag_vars)
+    set(_flag_vars CMAKE_C_FLAGS CMAKE_CXX_FLAGS)
+  endif()
+
+  if(MSVC AND _msvc_warnings)
+    foreach(var ${_flag_vars})
+      foreach(warning ${_msvc_warnings})
+        set(${var} "${${var}} ${warning}")
+      endforeach()
+    endforeach()
+  elseif((CMAKE_COMPILER_IS_GNUCXX OR CMAKE_COMPILER_IS_CLANGXX) AND _gxx_warnings)
+    foreach(var ${_flag_vars})
+      foreach(warning ${_gxx_warnings})
+        if(NOT warning MATCHES "^-Wno-")
+          string(REPLACE "${warning}" "" ${var} "${${var}}")
+          string(REPLACE "-W" "-Wno-" warning "${warning}")
+        endif()
+        set(${var} "${${var}} ${warning}")
+      endforeach()
+    endforeach()
+  endif()
+  caffe_clear_vars(_flag_vars _msvc_warnings _gxx_warnings)
+endmacro()
+
+################################################################################################
+# Helper function get current definitions
+# Usage:
+#   caffe_get_current_definitions(<definitions_variable>)
+function(caffe_get_current_definitions definitions_var)
+  get_property(current_definitions DIRECTORY PROPERTY COMPILE_DEFINITIONS)
+  set(result "")
+
+  foreach(d ${current_definitions})
+    list(APPEND result -D${d})
+  endforeach()
+
+  caffe_list_unique(result)
+  set(${definitions_var} ${result} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Helper function get current includes/definitions
+# Usage:
+#   caffe_get_current_cflags(<cflagslist_variable>)
+function(caffe_get_current_cflags cflags_var)
+  get_property(current_includes DIRECTORY PROPERTY INCLUDE_DIRECTORIES)
+  caffe_convert_absolute_paths(current_includes)
+  caffe_get_current_definitions(cflags)
+
+  foreach(i ${current_includes})
+    list(APPEND cflags "-I${i}")
+  endforeach()
+
+  caffe_list_unique(cflags)
+  set(${cflags_var} ${cflags} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Helper function to parse current linker libs into link directoris, libflags and osx frameworks
+# Usage:
+#   caffe_parse_linker_libs(<Caffe_LINKER_LIBS_var> <directories_var> <libflags_var> <frameworks_var>)
+function(caffe_parse_linker_libs Caffe_LINKER_LIBS_variable folders_var flags_var frameworks_var)
+
+  set(__unspec "")
+  set(__debug "")
+  set(__optimized "")
+  set(__framework "")
+  set(__varname "__unspec")
+
+  # split libs into debug, optimized, unspecified and frameworks
+  foreach(list_elem ${${Caffe_LINKER_LIBS_variable}})
+    if(list_elem STREQUAL "debug")
+      set(__varname "__debug")
+    elseif(list_elem STREQUAL "optimized")
+      set(__varname "__optimized")
+    elseif(list_elem MATCHES "^-framework[ \t]+([^ \t].*)")
+      list(APPEND __framework -framework ${CMAKE_MATCH_1})
+    else()
+      list(APPEND ${__varname} ${list_elem})
+      set(__varname "__unspec")
+    endif()
+  endforeach()
+
+  # attach debug or optimized libs to unspecified according to current configuration
+  if(CMAKE_BUILD_TYPE MATCHES "Debug")
+    set(__libs ${__unspec} ${__debug})
+  else()
+    set(__libs ${__unspec} ${__optimized})
+  endif()
+
+  set(libflags "")
+  set(folders "")
+
+  # convert linker libraries list to link flags
+  foreach(lib ${__libs})
+    if(TARGET ${lib})
+      list(APPEND folders $<TARGET_LINKER_FILE_DIR:${lib}>)
+      list(APPEND libflags -l${lib})
+    elseif(lib MATCHES "^-l.*")
+      list(APPEND libflags ${lib})
+    elseif(IS_ABSOLUTE ${lib})
+      get_filename_component(name_we ${lib} NAME_WE)
+      get_filename_component(folder  ${lib} PATH)
+
+      string(REGEX MATCH "^lib(.*)" __match ${name_we})
+      list(APPEND libflags -l${CMAKE_MATCH_1})
+      list(APPEND folders    ${folder})
+    else()
+      message(FATAL_ERROR "Logic error. Need to update cmake script")
+    endif()
+  endforeach()
+
+  caffe_list_unique(libflags folders)
+
+  set(${folders_var} ${folders} PARENT_SCOPE)
+  set(${flags_var} ${libflags} PARENT_SCOPE)
+  set(${frameworks_var} ${__framework} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Helper function to detect Darwin version, i.e. 10.8, 10.9, 10.10, ....
+# Usage:
+#   caffe_detect_darwin_version(<version_variable>)
+function(caffe_detect_darwin_version output_var)
+  if(APPLE)
+    execute_process(COMMAND /usr/bin/sw_vers -productVersion
+                    RESULT_VARIABLE __sw_vers OUTPUT_VARIABLE __sw_vers_out
+                    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+    set(${output_var} ${__sw_vers_out} PARENT_SCOPE)
+  else()
+    set(${output_var} "" PARENT_SCOPE)
+  endif()
+endfunction()
diff --git a/CMakeScripts/lint.cmake b/cmake/lint.cmake
similarity index 94%
rename from CMakeScripts/lint.cmake
rename to cmake/lint.cmake
index 04df3409e84..585babb3587 100644
--- a/CMakeScripts/lint.cmake
+++ b/cmake/lint.cmake
@@ -1,10 +1,12 @@
 
-set(CMAKE_SOURCE_DIR ../)
+set(CMAKE_SOURCE_DIR ..)
 set(LINT_COMMAND ${CMAKE_SOURCE_DIR}/scripts/cpp_lint.py)
 set(SRC_FILE_EXTENSIONS h hpp hu c cpp cu cc)
 set(EXCLUDE_FILE_EXTENSTIONS pb.h pb.cc)
 set(LINT_DIRS include src/caffe examples tools python matlab)
 
+cmake_policy(SET CMP0009 NEW)  # supress cmake warning
+
 # find all files of interest
 foreach(ext ${SRC_FILE_EXTENSIONS})
     foreach(dir ${LINT_DIRS})
diff --git a/docs/CMakeLists.txt b/docs/CMakeLists.txt
new file mode 100644
index 00000000000..ae47e461736
--- /dev/null
+++ b/docs/CMakeLists.txt
@@ -0,0 +1,106 @@
+# Building docs script
+# Requirements:
+#   sudo apt-get install doxygen texlive ruby-dev
+#   sudo gem install jekyll execjs therubyracer
+
+if(NOT BUILD_docs OR NOT DOXYGEN_FOUND)
+  return()
+endif()
+
+#################################################################################################
+# Gather docs from <root>/examples/**/readme.md
+function(gather_readmes_as_prebuild_cmd target gathered_dir root)
+  set(full_gathered_dir ${root}/${gathered_dir})
+
+  file(GLOB_RECURSE readmes ${root}/examples/readme.md ${root}/examples/README.md)
+  foreach(file ${readmes})
+    # Only use file if it is to be included in docs.
+    file(STRINGS ${file} file_lines REGEX "include_in_docs: true")
+
+    if(file_lines)
+      # Since everything is called readme.md, rename it by its dirname.
+      file(RELATIVE_PATH file ${root} ${file})
+      get_filename_component(folder ${file} PATH)
+      set(new_filename ${full_gathered_dir}/${folder}.md)
+
+      # folder value might be like <subfolder>/readme.md. That's why make directory.
+      get_filename_component(new_folder ${new_filename} PATH)
+      add_custom_command(TARGET ${target} PRE_BUILD
+        COMMAND ${CMAKE_COMMAND} -E make_directory ${new_folder}
+        COMMAND ln -sf ${root}/${file} ${new_filename}
+        COMMENT "Creating symlink ${new_filename} -> ${root}/${file}"
+        WORKING_DIRECTORY ${root} VERBATIM)
+    endif()
+  endforeach()
+endfunction()
+
+################################################################################################
+# Gather docs from examples/*.ipynb and add YAML front-matter.
+function(gather_notebooks_as_prebuild_cmd target gathered_dir root)
+  set(full_gathered_dir ${root}/${gathered_dir})
+
+  if(NOT PYTHON_EXECUTABLE)
+    message(STATUS "Python interpeter is not found. Can't include *.ipynb files in docs. Skipping...")
+    return()
+  endif()
+
+  file(GLOB_RECURSE notebooks ${root}/examples/*.ipynb)
+  foreach(file ${notebooks})
+    file(RELATIVE_PATH file ${root} ${file})
+    set(new_filename ${full_gathered_dir}/${file})
+
+    get_filename_component(new_folder ${new_filename} PATH)
+    add_custom_command(TARGET ${target} PRE_BUILD
+      COMMAND ${CMAKE_COMMAND} -E make_directory ${new_folder}
+      COMMAND ${PYTHON_EXECUTABLE} scripts/copy_notebook.py ${file} ${new_filename}
+      COMMENT "Copying notebook ${file} to ${new_filename}"
+      WORKING_DIRECTORY ${root} VERBATIM)
+  endforeach()
+
+  set(${outputs_var} ${outputs} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+########################## [ Non macro part ] ##################################################
+
+# Gathering is done at each 'make doc'
+file(REMOVE_RECURSE ${PROJECT_SOURCE_DIR}/docs/gathered)
+
+# Doxygen config file path
+set(DOXYGEN_config_file ${PROJECT_SOURCE_DIR}/.Doxyfile CACHE FILEPATH "Doxygen config file")
+
+# Adding docs target
+add_custom_target(docs COMMAND ${DOXYGEN_EXECUTABLE} ${DOXYGEN_config_file}
+                       WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}
+                       COMMENT "Launching doxygen..." VERBATIM)
+
+# Gathering examples into docs subfolder
+gather_notebooks_as_prebuild_cmd(docs docs/gathered ${PROJECT_SOURCE_DIR})
+gather_readmes_as_prebuild_cmd(docs docs/gathered  ${PROJECT_SOURCE_DIR})
+
+# Auto detect output directory
+file(STRINGS ${DOXYGEN_config_file} config_line REGEX "OUTPUT_DIRECTORY[ \t]+=[^=].*")
+if(config_line)
+  string(REGEX MATCH "OUTPUT_DIRECTORY[ \t]+=([^=].*)" __ver_check "${config_line}")
+  string(STRIP ${CMAKE_MATCH_1} output_dir)
+  message(STATUS "Detected Doxygen OUTPUT_DIRECTORY: ${output_dir}")
+else()
+  set(output_dir ./doxygen/)
+  message(STATUS "Can't find OUTPUT_DIRECTORY in doxygen config file. Try to use default: ${output_dir}")
+endif()
+
+if(NOT IS_ABSOLUTE ${output_dir})
+  set(output_dir ${PROJECT_SOURCE_DIR}/${output_dir})
+  get_filename_component(output_dir ${output_dir} ABSOLUTE)
+endif()
+
+# creates symlink in docs subfolder to code documentation built by doxygen
+add_custom_command(TARGET docs POST_BUILD VERBATIM
+                   COMMAND ln -sfn "${output_dir}/html" doxygen
+                   WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/docs
+                   COMMENT "Creating symlink ${PROJECT_SOURCE_DIR}/docs/doxygen -> ${output_dir}/html")
+
+# for quick launch of jekyll
+add_custom_target(jekyll COMMAND jekyll serve -w -s . -d _site --port=4000
+                         WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/docs
+                         COMMENT "Launching jekyll..." VERBATIM)
diff --git a/docs/_layouts/default.html b/docs/_layouts/default.html
index 73c6d5873d3..b8efe60bc3b 100644
--- a/docs/_layouts/default.html
+++ b/docs/_layouts/default.html
@@ -11,6 +11,8 @@
       Caffe {% if page contains 'title' %}| {{ page.title }}{% endif %}
     </title>
 
+    <link rel="icon" type="image/png" href="/images/caffeine-icon.png">
+
     <link rel="stylesheet" href="/stylesheets/reset.css">
     <link rel="stylesheet" href="/stylesheets/styles.css">
     <link rel="stylesheet" href="/stylesheets/pygment_trac.css">
@@ -34,8 +36,16 @@
       <header>
         <h1 class="header"><a href="/">Caffe</a></h1>
         <p class="header">
-          Deep learning framework developed by <a class="header name" href="http://daggerfs.com/">Yangqing Jia</a> / <a class="header name" href="http://bvlc.eecs.berkeley.edu/">BVLC</a>
+          Deep learning framework by the <a class="header name" href="http://bvlc.eecs.berkeley.edu/">BVLC</a>
         </p>
+        <p class="header">
+          Created by
+          <br>
+          <a class="header name" href="http://daggerfs.com/">Yangqing Jia</a>
+          <br>
+          Lead Developer
+          <br>
+          <a class="header name" href="http://imaginarynumber.net/">Evan Shelhamer</a>
         <ul>
           <li>
             <a class="buttons github" href="https://github.com/BVLC/caffe">View On GitHub</a>
diff --git a/docs/development.md b/docs/development.md
index dfed3308eeb..fe54864bd35 100644
--- a/docs/development.md
+++ b/docs/development.md
@@ -1,10 +1,10 @@
 ---
 title: Developing and Contributing
 ---
-# Development
+# Development and Contributing
 
 Caffe is developed with active participation of the community.<br>
-The [BVLC](http://bvlc.eecs.berkeley.edu/) maintainers welcome all contributions!
+The [BVLC](http://bvlc.eecs.berkeley.edu/) brewers welcome all contributions!
 
 The exact details of contributions are recorded by versioning and cited in our [acknowledgements](http://caffe.berkeleyvision.org/#acknowledgements).
 This method is impartial and always up-to-date.
@@ -21,7 +21,7 @@ If a contributor wants to further mark their specific copyright on a particular
 
 ### Documentation
 
-This website, written with [Jekyll](http://jekyllrb.com/), functions as the official Caffe documentation -- simply run `scripts/build_docs.sh` and view the website at `http://0.0.0.0:4000`.
+This website, written with [Jekyll](http://jekyllrb.com/), acts as the official Caffe documentation -- simply run `scripts/build_docs.sh` and view the website at `http://0.0.0.0:4000`.
 
 We prefer tutorials and examples to be documented close to where they live, in `readme.md` files.
 The `build_docs.sh` script gathers all `examples/**/readme.md` and `examples/*.ipynb` files, and makes a table of contents.
@@ -33,79 +33,77 @@ Other docs, such as installation guides, are written in the `docs` directory and
 We strive to provide provide lots of usage examples, and to document all code in docstrings.
 We absolutely appreciate any contribution to this effort!
 
-### The release cycle
+### Versioning
 
-- The `dev` branch receives all new development, including community contributions.
-We aim to keep it in a functional state, but large changes do occur, and things do get broken every now and then.
-Use only if you want the "bleeding edge".
-- BVLC maintainers will periodically update the `master` branch with changes from `dev`, giving it a release tag ([releases so far](https://github.com/BVLC/caffe/releases)).
-Use this if you want more stability.
+The `master` branch receives all new development including community contributions.
+We try to keep it in a reliable state, but it is the bleeding edge, and things do get broken every now and then.
+BVLC maintainers will periodically make releases by marking stable checkpoints as tags and maintenance branches. [Past releases](https://github.com/BVLC/caffe/releases) are catalogued online.
 
-### Issues & Pull Request Protocol
+#### Issues & Pull Request Protocol
 
-Use Github Issues to report [bugs], propose features, and ask development [questions].
-Large-scale development work is guided by [milestones], which are sets of Issues selected for concurrent release (integration from `dev` to `master`).
+Post [Issues](https://github.com/BVLC/caffe/issues) to propose features, report [bugs], and discuss framework code.
+Large-scale development work is guided by [milestones], which are sets of Issues selected for bundling as releases.
 
 Please note that since the core developers are largely researchers, we may work on a feature in isolation for some time before releasing it to the community, so as to claim honest academic contribution.
 We do release things as soon as a reasonable technical report may be written, and we still aim to inform the community of ongoing development through Github Issues.
 
-When you are ready to start developing your feature or fixing a bug, follow this protocol:
+**When you are ready to develop a feature or fixing a bug, follow this protocol**:
 
-- Develop in [feature branches] with descriptive names.
-    - For new development branch off `dev`.
-    - For documentation and fixes for `master` branch off `master`.
-- Bring your work up-to-date by [rebasing] onto the latest `dev` / `master`.
-(Polish your changes by [interactive rebase], if you'd like.)
-- [Pull request] your contribution to `BVLC/caffe`'s `dev` / `master` branch for discussion and review.
+- Develop in [feature branches] with descriptive names. Branch off of the latest `master`.
+- Bring your work up-to-date by [rebasing] onto the latest `master` when done.
+(Groom your changes by [interactive rebase], if you'd like.)
+- [Pull request] your contribution to `BVLC/caffe`'s `master` branch for discussion and review.
   - Make PRs *as soon as development begins*, to let discussion guide development.
   - A PR is only ready for merge review when it is a fast-forward merge, and all code is documented, linted, and tested -- that means your PR must include tests!
 - When the PR satisfies the above properties, use comments to request maintainer review.
 
-Below is a poetic presentation of the protocol in code form.
+The following is a poetic presentation of the protocol in code form.
 
 #### [Shelhamer's](https://github.com/shelhamer) “life of a branch in four acts”
 
-Make the `feature` branch off of the latest `bvlc/dev`
+Make the `feature` branch off of the latest `bvlc/master`
 ```
-git checkout dev
-git pull upstream dev
+git checkout master
+git pull upstream master
 git checkout -b feature
 # do your work, make commits
 ```
 
-Prepare to merge by rebasing your branch on the latest `bvlc/dev`
+Prepare to merge by rebasing your branch on the latest `bvlc/master`
 ```
-# make sure dev is fresh
-git checkout dev
-git pull upstream dev
-# rebase your branch on the tip of dev
+# make sure master is fresh
+git checkout master
+git pull upstream master
+# rebase your branch on the tip of master
 git checkout feature
-git rebase dev
+git rebase master
 ```
 
-Push your branch to pull request it into `dev`
+Push your branch to pull request it into `BVLC/caffe:master`
 ```
 git push origin feature
-# ...make pull request to dev...
+# ...make pull request to master...
 ```
 
-Now make a pull request! You can do this from the command line (`git pull-request -b dev`) if you install [hub](https://github.com/github/hub).
+Now make a pull request! You can do this from the command line (`git pull-request -b master`) if you install [hub](https://github.com/github/hub). Hub has many other magical uses.
 
-The pull request of `feature` into `dev` will be a clean merge. Applause.
+The pull request of `feature` into `master` will be a clean merge. Applause.
 
 [bugs]: https://github.com/BVLC/caffe/issues?labels=bug&page=1&state=open
-[questions]: https://github.com/BVLC/caffe/issues?labels=question&page=1&state=open
 [milestones]: https://github.com/BVLC/caffe/issues?milestone=1
 [Pull request]: https://help.github.com/articles/using-pull-requests
 [interactive rebase]: https://help.github.com/articles/interactive-rebase
 [rebasing]: http://git-scm.com/book/en/Git-Branching-Rebasing
 [feature branches]: https://www.atlassian.com/git/workflows#!workflow-feature-branch
 
+**Historical note**: Caffe once relied on a two branch `master` and `dev` workflow.
+PRs from this time are still open but these will be merged into `master` or closed.
+
 ### Testing
 
 Run `make runtest` to check the project tests. New code requires new tests. Pull requests that fail tests will not be accepted.
 
-The `googletest` framework we use provides many additional options, which you can access by running the test binaries directly. One of the more useful options is `--gtest_filter`, which allows you to filter tests by name:
+The `gtest` framework we use provides many additional options, which you can access by running the test binaries directly. One of the more useful options is `--gtest_filter`, which allows you to filter tests by name:
 
     # run all tests with CPU in the name
     build/test/test_all.testbin --gtest_filter='*CPU*'
@@ -119,7 +117,7 @@ To get a list of all options `googletest` provides, simply pass the `--help` fla
 
 ### Style
 
-- Follow [Google C++ style](http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml) and [Google python style](http://google-styleguide.googlecode.com/svn/trunk/pyguide.html) + [PEP 8](http://legacy.python.org/dev/peps/pep-0008/).
+- **Run `make lint` to check C++ code.**
 - Wrap lines at 80 chars.
+- Follow [Google C++ style](http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml) and [Google python style](http://google-styleguide.googlecode.com/svn/trunk/pyguide.html) + [PEP 8](http://legacy.python.org/dev/peps/pep-0008/).
 - Remember that “a foolish consistency is the hobgoblin of little minds,” so use your best judgement to write the clearest code for your particular case.
-- **Run `make lint` to check C++ code.**
diff --git a/docs/images/caffeine-icon.png b/docs/images/caffeine-icon.png
new file mode 100644
index 00000000000..88b4a002bb0
Binary files /dev/null and b/docs/images/caffeine-icon.png differ
diff --git a/docs/index.md b/docs/index.md
index ccc8f750eef..932b3b58d1d 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -4,31 +4,33 @@ title: Deep Learning Framework
 
 # Caffe
 
-Caffe is a deep learning framework developed with cleanliness, readability, and speed in mind.
-It was created by [Yangqing Jia](http://daggerfs.com) during his PhD at UC Berkeley, and is in active development by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and by community contributors.
+Caffe is a deep learning framework made with expression, speed, and modularity in mind.
+It is developed by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and by community contributors.
+[Yangqing Jia](http://daggerfs.com) created the project during his PhD at UC Berkeley.
 Caffe is released under the [BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/LICENSE).
 
 Check out our web image classification [demo](http://demo.caffe.berkeleyvision.org)!
 
-## Why use Caffe?
+## Why Caffe?
 
-**Clean architecture** enables rapid deployment.
-Networks are specified in simple config files, with no hard-coded parameters in the code.
-Switching between CPU and GPU is as simple as setting a flag -- so models can be trained on a GPU machine, and then used on commodity clusters.
+**Expressive architecture** encourages application and innovation.
+Models and optimization are defined by configuration without hard-coding.
+Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.
 
-**Readable & modifiable implementation** fosters active development.
-In Caffe's first year, it has been forked by over 600 developers on Github, and many have pushed significant changes.
+**Extensible code** fosters active development.
+In Caffe's first year, it has been forked by over 1,000 developers and had many significant changes contributed back.
+Thanks to these contributors the framework tracks the state-of-the-art in both code and models.
 
-**Speed** makes Caffe perfect for industry use.
-Caffe can process over **40M images per day** with a single NVIDIA K40 or Titan GPU\*.
-That's 5 ms/image in training, and 2 ms/image in test.
-We believe that Caffe is the fastest CNN implementation available.
+**Speed** makes Caffe perfect for research experiments and industry deployment.
+Caffe can process **over 60M images per day** with a single NVIDIA K40 GPU\*.
+That's 1 ms/image for inference and 4 ms/image for learning.
+We believe that Caffe is the fastest convnet implementation available.
 
 **Community**: Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia.
-There is an active discussion and support community on [Github](https://github.com/BVLC/caffe/issues).
+Join our community of brewers on the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users) and [Github](https://github.com/BVLC/caffe/).
 
 <p class="footnote" markdown="1">
-\* When files are properly cached, and using the ILSVRC2012-winning [SuperVision](http://www.image-net.org/challenges/LSVRC/2012/supervision.pdf) model.
+\* With the ILSVRC2012-winning [SuperVision](http://www.image-net.org/challenges/LSVRC/2012/supervision.pdf) model and caching IO.
 Consult performance [details](/performance_hardware.html).
 </p>
 
@@ -46,7 +48,7 @@ Tested on Ubuntu, Red Hat, OS X.
 BVLC suggests a standard distribution format for Caffe models, and provides trained models.
 * [Developing & Contributing](/development.html)<br>
 Guidelines for development and contributing to Caffe.
-* [API Documentation](/doxygen/)<br>
+* [API Documentation](/doxygen/annotated.html)<br>
 Developer documentation automagically generated from code comments.
 
 ### Examples
@@ -56,7 +58,7 @@ Developer documentation automagically generated from code comments.
 - <div><a href="{{page.url}}">{{page.title}}</a><br>{{page.description}}</div>
 {% endfor %}
 
-### Notebook examples
+### Notebook Examples
 
 {% assign notebooks = site.pages | where:'category','notebook' | sort: 'priority' %}
 {% for page in notebooks %}
@@ -77,6 +79,17 @@ Please cite Caffe in your publications if it helps your research:
 If you do publish a paper where Caffe helped your research, we encourage you to update the [publications wiki](https://github.com/BVLC/caffe/wiki/Publications).
 Citations are also tracked automatically by [Google Scholar](http://scholar.google.com/scholar?oi=bibs&hl=en&cites=17333247995453974016).
 
+## Contacting Us
+
+Join the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users) to ask questions and discuss methods and models. This is where we talk about usage, installation, and applications.
+
+Framework development discussions and thorough bug reports are collected on [Issues](https://github.com/BVLC/caffe/issues).
+
+Contact [caffe-dev](mailto:caffe-dev@googlegroups.com) if you have a confidential proposal for the framework *and the ability to act on it*.
+Requests for features, explanations, or personal help will be ignored; post to [caffe-users](https://groups.google.com/forum/#!forum/caffe-users) instead.
+
+The core Caffe developers offer [consulting services](mailto:caffe-coldpress@googlegroups.com) for appropriate projects.
+
 ## Acknowledgements
 
 The BVLC Caffe developers would like to thank NVIDIA for GPU donation, A9 and Amazon Web Services for a research grant in support of Caffe development and reproducible research in deep learning, and BVLC PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for guidance.
@@ -85,20 +98,9 @@ The BVLC members who have contributed to Caffe are (alphabetical by first name):
 [Eric Tzeng](https://github.com/erictzeng), [Evan Shelhamer](http://imaginarynumber.net/), [Jeff Donahue](http://jeffdonahue.com/), [Jon Long](https://github.com/longjon), [Ross Girshick](http://www.cs.berkeley.edu/~rbg/), [Sergey Karayev](http://sergeykarayev.com/), [Sergio Guadarrama](http://www.eecs.berkeley.edu/~sguada/), and [Yangqing Jia](http://daggerfs.com/).
 
 The open-source community plays an important and growing role in Caffe's development.
-Check out the Github [project pulse](https://github.com/BVLC/caffe/pulse) for recent activity, and the [contributors](https://github.com/BVLC/caffe/graphs/contributors) for a sorted list.
+Check out the Github [project pulse](https://github.com/BVLC/caffe/pulse) for recent activity and the [contributors](https://github.com/BVLC/caffe/graphs/contributors) for the full list.
 
 We sincerely appreciate your interest and contributions!
 If you'd like to contribute, please read the [developing & contributing](development.html) guide.
 
 Yangqing would like to give a personal thanks to the NVIDIA Academic program for providing GPUs, [Oriol Vinyals](http://www1.icsi.berkeley.edu/~vinyals/) for discussions along the journey, and BVLC PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for advice.
-
-## Contacting us
-
-All questions about usage, installation, code, and applications should be searched for and asked on the [caffe-users mailing list](https://groups.google.com/forum/#!forum/caffe-users).
-
-All development discussion should be carried out at [GitHub Issues](https://github.com/BVLC/caffe/issues).
-
-If you have a proposal that may not be suited for public discussion *and an ability to act on it*, please email us [directly](mailto:caffe-dev@googlegroups.com).
-Requests for features, explanations, or personal help will be ignored; post such matters publicly as issues.
-
-The core Caffe developers may be able to provide [consulting services](mailto:caffe-coldpress@googlegroups.com) for appropriate projects.
diff --git a/docs/install_apt.md b/docs/install_apt.md
new file mode 100644
index 00000000000..89bc9a00aef
--- /dev/null
+++ b/docs/install_apt.md
@@ -0,0 +1,44 @@
+---
+title: Installation: Ubuntu
+---
+
+# Ubuntu Installation
+
+**General dependencies**
+
+    sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev
+
+**Remaining dependencies, 14.04**
+
+    sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler
+
+**Remaining dependencies, 12.04**
+
+    # glog
+    wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
+    tar zxvf glog-0.3.3.tar.gz
+    cd glog-0.3.3
+    ./configure
+    make && make install
+    # gflags
+    wget https://github.com/schuhschuh/gflags/archive/master.zip
+    unzip master.zip
+    cd gflags-master
+    mkdir build && cd build
+    export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
+    make && make install
+    # lmdb
+    git clone git://gitorious.org/mdb/mdb.git
+    cd mdb/libraries/liblmdb
+    make && make install
+
+Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.
+
+**CUDA**: Install via the NVIDIA package instead of `apt-get` to be certain of the library and driver versions.
+Install the library and latest driver separately; the driver bundled with the library is usually out-of-date.
+
+**BLAS**: install ATLAS by `sudo apt-get install libatlas-base-dev` or install OpenBLAS or MKL for better CPU performance.
+
+**Python** (optional): if you use the default Python you will need to `sudo apt-get install` the `python-dev` package to have the Python headers for building the pycaffe interface.
+
+Continue with [compilation](installation.html#compilation).
diff --git a/docs/install_osx.md b/docs/install_osx.md
new file mode 100644
index 00000000000..55b098731fc
--- /dev/null
+++ b/docs/install_osx.md
@@ -0,0 +1,128 @@
+---
+title: Installation: OS X
+---
+
+# OS X Installation
+
+We highly recommend using the [Homebrew](http://brew.sh/) package manager.
+Ideally you could start from a clean `/usr/local` to avoid conflicts.
+In the following, we assume that you're using Anaconda Python and Homebrew.
+
+**CUDA**: Install via the NVIDIA package that includes both CUDA and the bundled driver. **CUDA 7 is strongly suggested.** Older CUDA require `libstdc++` while clang++ is the default compiler and `libc++` the default standard library on OS X 10.9+. This disagreement makes it necessary to change the compilation settings for each of the dependencies. This is prone to error.
+
+**Library Path**: We find that everything compiles successfully if `$LD_LIBRARY_PATH` is not set at all, and `$DYLD_FALLBACK_LIBRARY_PATH` is set to to provide CUDA, Python, and other relevant libraries (e.g. `/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib`).
+In other `ENV` settings, things may not work as expected.
+
+**General dependencies**
+
+    brew install --fresh -vd snappy leveldb gflags glog szip lmdb
+    # need the homebrew science source for OpenCV and hdf5
+    brew tap homebrew/science
+    hdf5 opencv
+
+If using Anaconda Python, a modification to the OpenCV formula might be needed
+Do `brew edit opencv` and change the lines that look like the two lines below to exactly the two lines below.
+
+      -DPYTHON_LIBRARY=#{py_prefix}/lib/libpython2.7.dylib
+      -DPYTHON_INCLUDE_DIR=#{py_prefix}/include/python2.7
+
+If using Anaconda Python, HDF5 is bundled and the `hdf5` formula can be skipped.
+
+**Remaining dependencies, with / without Python**
+
+    # with Python pycaffe needs dependencies built from source
+    brew install --build-from-source --with-python --fresh -vd protobuf
+    brew install --build-from-source --fresh -vd boost
+    # without Python the usual installation suffices
+    brew install protobuf boost
+
+**BLAS**: already installed as the [Accelerate / vecLib Framework](https://developer.apple.com/library/mac/documentation/Darwin/Reference/ManPages/man7/Accelerate.7.html). OpenBLAS and MKL are alternatives for faster CPU computation.
+
+**Python** (optional): Anaconda is the preferred Python.
+If you decide against it, please use Homebrew.
+Check that Caffe and dependencies are linking against the same, desired Python.
+
+Continue with [compilation](installation.html#compilation).
+
+## libstdc++ installation
+
+This route is not for the faint of heart.
+For OS X 10.10 and 10.9 you should install CUDA 7 and follow the instructions above.
+If that is not an option, take a deep breath and carry on.
+
+In OS X 10.9+, clang++ is the default C++ compiler and uses `libc++` as the standard library.
+However, NVIDIA CUDA (even version 6.0) currently links only with `libstdc++`.
+This makes it necessary to change the compilation settings for each of the dependencies.
+
+We do this by modifying the Homebrew formulae before installing any packages.
+Make sure that Homebrew doesn't install any software dependencies in the background; all packages must be linked to `libstdc++`.
+
+The prerequisite Homebrew formulae are
+
+    boost snappy leveldb protobuf gflags glog szip lmdb homebrew/science/opencv
+
+For each of these formulas, `brew edit FORMULA`, and add the ENV definitions as shown:
+
+      def install
+          # ADD THE FOLLOWING:
+          ENV.append "CXXFLAGS", "-stdlib=libstdc++"
+          ENV.append "CFLAGS", "-stdlib=libstdc++"
+          ENV.append "LDFLAGS", "-stdlib=libstdc++ -lstdc++"
+          # The following is necessary because libtool likes to strip LDFLAGS:
+          ENV["CXX"] = "/usr/bin/clang++ -stdlib=libstdc++"
+          ...
+
+To edit the formulae in turn, run
+
+    for x in snappy leveldb protobuf gflags glog szip boost boost-python lmdb homebrew/science/opencv; do brew edit $x; done
+
+After this, run
+
+    for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
+    brew uninstall protobuf; brew install --build-from-source --with-python --fresh -vd protobuf
+    brew install --build-from-source --fresh -vd boost boost-python
+
+If this is not done exactly right then linking errors will trouble you.
+
+**Homebrew versioning** that Homebrew maintains itself as a separate git repository and making the above `brew edit FORMULA` changes will change files in your local copy of homebrew's master branch. By default, this will prevent you from updating Homebrew using `brew update`, as you will get an error message like the following:
+
+    $ brew update
+    error: Your local changes to the following files would be overwritten by merge:
+      Library/Formula/lmdb.rb
+    Please, commit your changes or stash them before you can merge.
+    Aborting
+    Error: Failure while executing: git pull -q origin refs/heads/master:refs/remotes/origin/master
+
+One solution is to commit your changes to a separate Homebrew branch, run `brew update`, and rebase your changes onto the updated master. You'll have to do this both for the main Homebrew repository in `/usr/local/` and the Homebrew science repository that contains OpenCV in  `/usr/local/Library/Taps/homebrew/homebrew-science`, as follows:
+
+    cd /usr/local
+    git checkout -b caffe
+    git add .
+    git commit -m "Update Caffe dependencies to use libstdc++"
+    cd /usr/local/Library/Taps/homebrew/homebrew-science
+    git checkout -b caffe
+    git add .
+    git commit -m "Update Caffe dependencies"
+
+Then, whenever you want to update homebrew, switch back to the master branches, do the update, rebase the caffe branches onto master and fix any conflicts:
+
+    # Switch batch to homebrew master branches
+    cd /usr/local
+    git checkout master
+    cd /usr/local/Library/Taps/homebrew/homebrew-science
+    git checkout master
+
+    # Update homebrew; hopefully this works without errors!
+    brew update
+
+    # Switch back to the caffe branches with the forumlae that you modified earlier
+    cd /usr/local
+    git rebase master caffe
+    # Fix any merge conflicts and commit to caffe branch
+    cd /usr/local/Library/Taps/homebrew/homebrew-science
+    git rebase master caffe
+    # Fix any merge conflicts and commit to caffe branch
+
+    # Done!
+
+At this point, you should be running the latest Homebrew packages and your Caffe-related modifications will remain in place.
diff --git a/docs/install_yum.md b/docs/install_yum.md
new file mode 100644
index 00000000000..478e7d952cc
--- /dev/null
+++ b/docs/install_yum.md
@@ -0,0 +1,45 @@
+---
+title: Installation: RHEL / Fedora / CentOS
+---
+
+# RHEL / Fedora / CentOS Installation
+
+**General dependencies**
+
+    sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel
+
+**Remaining dependencies, recent OS**
+
+    sudo yum install gflags-devel glog-devel lmdb-devel
+
+**Remaining dependencies, if not found**
+
+    # glog
+    wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
+    tar zxvf glog-0.3.3.tar.gz
+    cd glog-0.3.3
+    ./configure
+    make && make install
+    # gflags
+    wget https://github.com/schuhschuh/gflags/archive/master.zip
+    unzip master.zip
+    cd gflags-master
+    mkdir build && cd build
+    export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
+    make && make install
+    # lmdb
+    git clone git://gitorious.org/mdb/mdb.git
+    cd mdb/libraries/liblmdb
+    make && make install
+
+Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.
+
+**CUDA**: Install via the NVIDIA package instead of `yum` to be certain of the library and driver versions.
+Install the library and latest driver separately; the driver bundled with the library is usually out-of-date.
+    + CentOS/RHEL/Fedora:
+
+**BLAS**: install ATLAS by `sudo yum install atlas-devel` or install OpenBLAS or MKL for better CPU performance. For the Makefile build, uncomment and set `BLAS_LIB` accordingly as ATLAS is usually installed under `/usr/lib[64]/atlas`).
+
+**Python** (optional): if you use the default Python you will need to `sudo yum install` the `python-devel` package to have the Python headers for building the pycaffe wrapper.
+
+Continue with [compilation](installation.html#compilation).
diff --git a/docs/installation.md b/docs/installation.md
index c2b16a23244..16575b54029 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -4,28 +4,36 @@ title: Installation
 
 # Installation
 
-Prior to installing, it is best to read through this guide and take note of the details for your platform.
-We have installed Caffe on Ubuntu 14.04, Ubuntu 12.04, OS X 10.10, 10.9, and 10.8.
+Prior to installing, have a glance through this guide and take note of the details for your platform.
+We install and run Caffe on Ubuntu 14.04 and 12.04, OS X 10.10 / 10.9 / 10.8, and AWS.
+The official Makefile and `Makefile.config` build are complemented by an automatic CMake build from the community.
 
 - [Prerequisites](#prerequisites)
 - [Compilation](#compilation)
-- [Hardware questions](#hardware_questions)
+- [Hardware](#hardware)
+- Platforms: [Ubuntu guide](install_apt.html), [OS X guide](install_osx.html), and [RHEL / CentOS / Fedora guide](install_yum.html)
+
+When updating Caffe, it's best to `make clean` before re-compiling.
 
 ## Prerequisites
 
-Caffe depends on several software packages.
+Caffe has several dependencies.
+
+* [CUDA](https://developer.nvidia.com/cuda-zone) is required for GPU mode.
+    * library version 7.0 and the latest driver version are recommended, but 6.* is fine too
+    * 5.5, and 5.0 are compatible but considered legacy
+* [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) via ATLAS, MKL, or OpenBLAS.
+* [Boost](http://www.boost.org/) >= 1.55
+* [OpenCV](http://opencv.org/) >= 2.4 including 3.0
+* `protobuf`, `glog`, `gflags`
+* IO libraries `hdf5`, `leveldb`, `snappy`, `lmdb`
 
-* [CUDA](https://developer.nvidia.com/cuda-zone) library version 6.5 (recommended), 6.0, 5.5, or 5.0 and the latest driver version for CUDA 6 or 319.* for CUDA 5 (and NOT 331.*)
-* [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) (provided via ATLAS, MKL, or OpenBLAS).
-* [OpenCV](http://opencv.org/) (>= 2.4)
-* [Boost](http://www.boost.org/) (>= 1.55, although only 1.55 and 1.56 are tested)
-* `glog`, `gflags`, `protobuf`, `leveldb`, `snappy`, `hdf5`, `lmdb`
-* For the Python wrapper
-    * `Python 2.7`, `numpy (>= 1.7)`, boost-provided `boost.python`
-* For the MATLAB wrapper
-    * MATLAB with the `mex` compiler.
+Pycaffe and Matcaffe interfaces have their own natural needs.
 
-**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic.
+* For Python Caffe:  `Python 2.7`, `numpy (>= 1.7)`, boost-provided `boost.python`
+* For MATLAB Caffe: MATLAB with the `mex` compiler.
+
+**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. For now cuDNN v1 is integrated but see [PR #1731](https://github.com/BVLC/caffe/pull/1731) for v2.
 
 **CPU-only Caffe**: for cold-brewed CPU-only Caffe uncomment the `CPU_ONLY := 1` flag in `Makefile.config` to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.
 
@@ -37,13 +45,9 @@ To install CUDA, go to the [NVIDIA CUDA website](https://developer.nvidia.com/cu
 For best performance, Caffe can be accelerated by [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). Register for free at the cuDNN site, install it, then continue with these installation instructions. To compile with cuDNN set the `USE_CUDNN := 1` flag set in your `Makefile.config`.
 
 Caffe requires BLAS as the backend of its matrix and vector computations.
-There are several implementations of this library.
-The choice is yours:
+There are several implementations of this library. The choice is yours:
 
 * [ATLAS](http://math-atlas.sourceforge.net/): free, open source, and so the default for Caffe.
-    + Ubuntu: `sudo apt-get install libatlas-base-dev`
-    + CentOS/RHEL/Fedora: `sudo yum install atlas-devel`
-    + OS X: already installed as the [Accelerate / vecLib Framework](https://developer.apple.com/library/mac/documentation/Darwin/Reference/ManPages/man7/Accelerate.7.html).
 * [Intel MKL](http://software.intel.com/en-us/intel-mkl): commercial and optimized for Intel CPUs, with a free trial and [student](http://software.intel.com/en-us/intel-education-offerings) licenses.
     1. Install MKL.
     2. Set `BLAS := mkl` in `Makefile.config`
@@ -51,7 +55,7 @@ The choice is yours:
     1. Install OpenBLAS
     2. Set `BLAS := open` in `Makefile.config`
 
-### Python and/or MATLAB wrappers (optional)
+### Python and/or MATLAB Caffe (optional)
 
 #### Python
 
@@ -59,15 +63,9 @@ The main requirements are `numpy` and `boost.python` (provided by boost). `panda
 
 You can install the dependencies with
 
-    for req in $(cat requirements.txt); do sudo pip install $req; done
-
-but we highly recommend first installing the [Anaconda](https://store.continuum.io/cshop/anaconda/) Python distribution, which provides most of the necessary packages, as well as the `hdf5` library dependency.
+    for req in $(cat requirements.txt); do pip install $req; done
 
-For **Ubuntu**, if you use the default Python you will need to `sudo apt-get install` the `python-dev` package to have the Python headers for building the wrapper.
-
-For **Fedora**, if you use the default Python you will need to `sudo yum install` the `python-devel` package to have the Python headers for building the wrapper.
-
-For **OS X**, Anaconda is the preferred Python. If you decide against it, please use Homebrew -- but beware of potential linking errors!
+but we suggest first installing the [Anaconda](https://store.continuum.io/cshop/anaconda/) Python distribution, which provides most of the necessary packages, as well as the `hdf5` library dependency.
 
 To import the `caffe` Python module after completing the installation, add the module directory to your `$PYTHONPATH` by `export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH` or the like. You should not import the module in the `caffe/python/caffe` directory!
 
@@ -77,170 +75,7 @@ To import the `caffe` Python module after completing the installation, add the m
 
 Install MATLAB, and make sure that its `mex` is in your `$PATH`.
 
-*Caffe's MATLAB interface works with versions 2012b, 2013a/b, and 2014a.*
-
-### The rest of the dependencies
-
-#### Linux
-
-On **Ubuntu**, most of the dependencies can be installed with
-
-    sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev
-
-and for **Ubuntu 14.04** the rest of the dependencies can be installed with
-
-    sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler
-
-Keep reading to find out how to manually build and install the Google flags library, Google logging library and LMDB on **Ubuntu 12.04**.
-
-On **CentOS / RHEL / Fedora**, most of the dependencies can be installed with
-
-    sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel
-
-The Google flags library, Google logging library and LMDB already made their ways into newer versions of **CentOS / RHEL / Fedora** so it is better to first attempt to install them using `yum`
-
-    sudo yum install gflags-devel glog-devel lmdb-devel
-
-**Finally** in case you couldn't find those extra libraries mentioned above in your distribution's repositories, here are the instructions to follow for manually building and installing them on **Ubuntu 12.04 / CentOS / RHEL / Fedora** (or practically on any Linux distribution)
-
-    # glog
-    wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
-    tar zxvf glog-0.3.3.tar.gz
-    cd glog-0.3.3
-    ./configure
-    make && make install
-    # gflags
-    wget https://github.com/schuhschuh/gflags/archive/master.zip
-    unzip master.zip
-    cd gflags-master
-    mkdir build && cd build
-    export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
-    make && make install
-    # lmdb
-    git clone git://gitorious.org/mdb/mdb.git
-    cd mdb/libraries/liblmdb
-    make && make install
-
-Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.
-
-#### OS X
-
-On **OS X**, we highly recommend using the [Homebrew](http://brew.sh/) package manager, and ideally starting from a clean install of the OS (or from a wiped `/usr/local`) to avoid conflicts.
-In the following, we assume that you're using Anaconda Python and Homebrew.
-
-To install the OpenCV dependency, we'll need to provide an additional source for Homebrew:
-
-    brew tap homebrew/science
-
-If using Anaconda Python, a modification is required to the OpenCV formula.
-Do `brew edit opencv` and change the lines that look like the two lines below to exactly the two lines below.
-
-      -DPYTHON_LIBRARY=#{py_prefix}/lib/libpython2.7.dylib
-      -DPYTHON_INCLUDE_DIR=#{py_prefix}/include/python2.7
-
-**NOTE**: We find that everything compiles successfully if `$LD_LIBRARY_PATH` is not set at all, and `$DYLD_FALLBACK_LIBRARY_PATH` is set to to provide CUDA, Python, and other relevant libraries (e.g. `/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib`).
-In other `ENV` settings, things may not work as expected.
-
-**NOTE**: There is currently a conflict between boost 1.56 and CUDA in some configurations. Check the [conflict description](https://github.com/BVLC/caffe/issues/1193#issuecomment-57491906) and try downgrading to 1.55 or upgrading to 1.57.
-
-#### 10.8-specific Instructions
-
-Simply run the following:
-
-    brew install --build-from-source boost boost-python
-    brew install --with-python protobuf
-    for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew install $x; done
-
-Building boost from source is needed to link against your local Python (exceptions might be raised during some OS X installs, but **ignore** these and continue). If you do not need the Python wrapper, simply doing `brew install boost` is fine.
-
-**Note** that the HDF5 dependency is provided by Anaconda Python in this case.
-If you're not using Anaconda, include `hdf5` in the list above.
-
-#### 10.10- and 10.9-specific Instructions
-
-In OS X 10.9+, clang++ is the default C++ compiler and uses `libc++` as the standard library.
-However, NVIDIA CUDA (even version 6.0) currently links only with `libstdc++`.
-This makes it necessary to change the compilation settings for each of the dependencies.
-
-We do this by modifying the Homebrew formulae before installing any packages.
-Make sure that Homebrew doesn't install any software dependencies in the background; all packages must be linked to `libstdc++`.
-
-The prerequisite Homebrew formulae are
-
-    boost snappy leveldb protobuf gflags glog szip lmdb homebrew/science/opencv
-
-For each of these formulas, `brew edit FORMULA`, and add the ENV definitions as shown:
-
-      def install
-          # ADD THE FOLLOWING:
-          ENV.append "CXXFLAGS", "-stdlib=libstdc++"
-          ENV.append "CFLAGS", "-stdlib=libstdc++"
-          ENV.append "LDFLAGS", "-stdlib=libstdc++ -lstdc++"
-          # The following is necessary because libtool likes to strip LDFLAGS:
-          ENV["CXX"] = "/usr/bin/clang++ -stdlib=libstdc++"
-          ...
-
-To edit the formulae in turn, run
-
-    for x in snappy leveldb protobuf gflags glog szip boost boost-python lmdb homebrew/science/opencv; do brew edit $x; done
-
-After this, run
-
-    for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
-    brew uninstall protobuf; brew install --build-from-source --with-python --fresh -vd protobuf
-    brew install --build-from-source --fresh -vd boost boost-python
-
-**Note** that `brew install --build-from-source --fresh -vd boost` is fine if you do not need the Caffe Python wrapper.
-
-**Note** that the HDF5 dependency is provided by Anaconda Python in this case.
-If you're not using Anaconda, include `hdf5` in the list above.
-
-**Note** that in order to build the Caffe Python wrappers you must install `boost` and `boost-python`:
-
-    brew install --build-from-source --fresh -vd boost boost-python
-
-**Note** that Homebrew maintains itself as a separate git repository and making the above `brew edit FORMULA` changes will change files in your local copy of homebrew's master branch. By default, this will prevent you from updating Homebrew using `brew update`, as you will get an error message like the following:
-
-    $ brew update
-    error: Your local changes to the following files would be overwritten by merge:
-      Library/Formula/lmdb.rb
-    Please, commit your changes or stash them before you can merge.
-    Aborting
-    Error: Failure while executing: git pull -q origin refs/heads/master:refs/remotes/origin/master
-
-One solution is to commit your changes to a separate Homebrew branch, run `brew update`, and rebase your changes onto the updated master. You'll have to do this both for the main Homebrew repository in `/usr/local/` and the Homebrew science repository that contains OpenCV in  `/usr/local/Library/Taps/homebrew/homebrew-science`, as follows:
-
-    cd /usr/local
-    git checkout -b caffe
-    git add .
-    git commit -m "Update Caffe dependencies to use libstdc++"
-    cd /usr/local/Library/Taps/homebrew/homebrew-science
-    git checkout -b caffe
-    git add .
-    git commit -m "Update Caffe dependencies"
-
-Then, whenever you want to update homebrew, switch back to the master branches, do the update, rebase the caffe branches onto master and fix any conflicts:
-
-    # Switch batch to homebrew master branches
-    cd /usr/local
-    git checkout master
-    cd /usr/local/Library/Taps/homebrew/homebrew-science
-    git checkout master
-
-    # Update homebrew; hopefully this works without errors!
-    brew update
-
-    # Switch back to the caffe branches with the forumlae that you modified earlier
-    cd /usr/local
-    git rebase master caffe
-    # Fix any merge conflicts and commit to caffe branch
-    cd /usr/local/Library/Taps/homebrew/homebrew-science
-    git rebase master caffe
-    # Fix any merge conflicts and commit to caffe branch
-
-    # Done!
-
-At this point, you should be running the latest Homebrew packages and your Caffe-related modifications will remain in place.
+*Caffe's MATLAB interface works with versions 2014a/b, 2013a/b, and 2012b.*
 
 #### Windows
 
@@ -248,8 +83,7 @@ There is an unofficial Windows port of Caffe at [niuzhiheng/caffe:windows](https
 
 ## Compilation
 
-Now that you have the prerequisites, edit your `Makefile.config` to change the paths for your setup (you should especially uncomment and set `BLAS_LIB` accordingly on distributions like **CentOS / RHEL / Fedora** where ATLAS is installed under `/usr/lib[64]/atlas`)
-The defaults should work, but uncomment the relevant lines if using Anaconda Python.
+Now that you have the prerequisites, edit your `Makefile.config` to change the paths for your setup The defaults should work, but uncomment the relevant lines if using Anaconda Python.
 
     cp Makefile.config.example Makefile.config
     # Adjust Makefile.config (for example, if using Anaconda Python)
@@ -257,24 +91,22 @@ The defaults should work, but uncomment the relevant lines if using Anaconda Pyt
     make test
     make runtest
 
-To compile with cuDNN acceleration, you should uncomment the `USE_CUDNN := 1` switch in `Makefile.config`.
-
-If there is no GPU in your machine, you should switch to CPU-only Caffe by uncommenting `CPU_ONLY := 1` in `Makefile.config`.
+- For cuDNN acceleration, you should uncomment the `USE_CUDNN := 1` switch in `Makefile.config`.
+- For CPU-only Caffe, uncomment `CPU_ONLY := 1` in `Makefile.config`.
 
 To compile the Python and MATLAB wrappers do `make pycaffe` and `make matcaffe` respectively.
 Be sure to set your MATLAB and Python paths in `Makefile.config` first!
 
-*Distribution*: run `make distribute` to create a `distribute` directory with all the Caffe headers, compiled libraries, binaries, etc. needed for distribution to other machines.
+**Distribution**: run `make distribute` to create a `distribute` directory with all the Caffe headers, compiled libraries, binaries, etc. needed for distribution to other machines.
 
-*Speed*: for a faster build, compile in parallel by doing `make all -j8` where 8 is the number of parallel threads for compilation (a good choice for the number of threads is the number of cores in your machine).
+**Speed**: for a faster build, compile in parallel by doing `make all -j8` where 8 is the number of parallel threads for compilation (a good choice for the number of threads is the number of cores in your machine).
 
 Now that you have installed Caffe, check out the [MNIST tutorial](gathered/examples/mnist.html) and the [reference ImageNet model tutorial](gathered/examples/imagenet.html).
 
-### Compilation using CMake (beta)
+### CMake Compilation
 
-In lieu of manually editing `Makefile.config` to tell Caffe where dependencies are located, Caffe also provides a CMake-based build system (currently in "beta").
-It requires CMake version >= 2.8.8.
-The basic installation steps are as follows:
+In lieu of manually editing `Makefile.config` to configure the build, Caffe offers an unofficial CMake build thanks to @Nerei, @akosiorek, and other members of the community. It requires CMake version >= 2.8.7.
+The basic steps are as follows:
 
     mkdir build
     cd build
@@ -282,21 +114,14 @@ The basic installation steps are as follows:
     make all
     make runtest
 
-#### Ubuntu 12.04
-
-Note that in Ubuntu 12.04, Aptitude will install version CMake 2.8.7 by default, which is not supported by Caffe's CMake build (requires at least 2.8.8).
-As a workaround, if you are using Ubuntu 12.04 you can try the following steps to install (or upgrade to) CMake 2.8.9:
-
-    sudo add-apt-repository ppa:ubuntu-sdk-team/ppa -y
-    sudo apt-get -y update
-    sudo apt-get install cmake
+See [PR #1667](https://github.com/BVLC/caffe/pull/1667) for options and details.
 
-## Hardware Questions
+## Hardware
 
-**Laboratory Tested Hardware**: Berkeley Vision runs Caffe with K40s, K20s, and Titans including models at ImageNet/ILSVRC scale. We also run on GTX series cards and GPU-equipped MacBook Pros. We have not encountered any trouble in-house with devices with CUDA capability >= 3.0. All reported hardware issues thus-far have been due to GPU configuration, overheating, and the like.
+**Laboratory Tested Hardware**: Berkeley Vision runs Caffe with K40s, K20s, and Titans including models at ImageNet/ILSVRC scale. We also run on GTX series cards (980s and 770s) and GPU-equipped MacBook Pros. We have not encountered any trouble in-house with devices with CUDA capability >= 3.0. All reported hardware issues thus-far have been due to GPU configuration, overheating, and the like.
 
 **CUDA compute capability**: devices with compute capability <= 2.0 may have to reduce CUDA thread numbers and batch sizes due to hardware constraints. Your mileage may vary.
 
 Once installed, check your times against our [reference performance numbers](performance_hardware.html) to make sure everything is configured properly.
 
-Refer to the project's issue tracker for [hardware/compatibility](https://github.com/BVLC/caffe/issues?labels=hardware%2Fcompatibility&page=1&state=open).
+Ask hardware questions on the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users).
diff --git a/docs/model_zoo.md b/docs/model_zoo.md
index 358bbb7fc91..ad30d0acd55 100644
--- a/docs/model_zoo.md
+++ b/docs/model_zoo.md
@@ -7,10 +7,10 @@ Lots of people have used Caffe to train models of different architectures and ap
 To lower the friction of sharing these models, we introduce the model zoo framework:
 
 - A standard format for packaging Caffe model info.
-- Tools to upload/download model info to/from Github Gists, and to download trained `.caffemodel` binaries.
+- Tools to upload/download model info to/from Github Gists, and to download trained `.caffemodel` parameters.
 - A central wiki page for sharing model info Gists.
 
-## Where to get trained models
+## BVLC Reference Models
 
 First of all, we provide some trained models out of the box.
 Each one of these can be downloaded by running `scripts/download_model_binary.py <dirname>` where `<dirname>` is specified below:
@@ -20,7 +20,11 @@ Each one of these can be downloaded by running `scripts/download_model_binary.py
 - **BVLC Reference R-CNN ILSVRC-2013** in `models/bvlc_reference_rcnn_ilsvrc13`: pure Caffe implementation of [R-CNN](https://github.com/rbgirshick/rcnn). (Trained by Ross Girshick @rbgirshick)
 - **BVLC GoogleNet** in `models/bvlc_googlenet`: GoogleNet trained on ILSVRC 2012, almost exactly as described in [GoogleNet](http://arxiv.org/abs/1409.4842). (Trained by Sergio Guadarrama @sguada)
 
-User-provided models are posted to a public-editable [wiki page](https://github.com/BVLC/caffe/wiki/Model-Zoo).
+
+## Community Models
+
+The publicly-editable [Caffe Model Zoo wiki](https://github.com/BVLC/caffe/wiki/Model-Zoo) catalogues user-made models.
+Refer to the model details for authorship and conditions -- please respect licenses and citations.
 
 ## Model info format
 
@@ -36,7 +40,7 @@ A caffe model is distributed as a directory containing:
     - License information.
 - [optional] Other helpful scripts.
 
-## Hosting model info
+### Hosting model info
 
 Github Gist is a good format for model info distribution because it can contain multiple files, is versionable, and has in-browser syntax highlighting and markdown rendering.
 
diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 5f8f519cdc4..34bb48050e8 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -451,6 +451,26 @@ The `CONCAT` layer is a utility layer that concatenates its multiple input blobs
 
 The `SLICE` layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices.
 
+* Sample
+
+     layers {
+       name: "slicer_label"
+       type: SLICE
+       bottom: "label"
+       ## Example of label with a shape N x 3 x 1 x 1
+       top: "label1"
+       top: "label2"
+       top: "label3"
+       slice_param {
+           slice_dim: 1
+           slice_point: 1
+           slice_point: 2
+       }
+     }
+
+`slice_dim` indicates the target dimension and can assume only two values: 0 for num or 1 for channel; `slice_point` indicates indexes in the selected dimension (the number of indexes must be equal to the number of top blobs minus one). 
+
+
 #### Elementwise Operations
 
 `ELTWISE`
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 055f4ef0d35..f29fc7e5522 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -1,16 +1,31 @@
-project( Examples )
+file(GLOB_RECURSE examples_srcs "${PROJECT_SOURCE_DIR}/examples/*.cpp")
 
-file(GLOB_RECURSE EXAMPLES_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
-
-foreach(source ${EXAMPLES_SOURCES})
-    #    get file name
-    get_filename_component(name ${source} NAME_WE)  
+foreach(source_file ${examples_srcs})
+  # get file name
+  get_filename_component(name ${source_file} NAME_WE)
     
-    #get folder name
-    get_filename_component(path ${source} PATH)      
-    get_filename_component(folder ${path} NAME_WE)      
+  # get folder name
+  get_filename_component(path ${source_file} PATH)
+  get_filename_component(folder ${path} NAME_WE)
     
-    add_executable(${name} ${source})
-    target_link_libraries(${name} caffe)
-    set_target_properties(${name} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/${folder})
-endforeach(source)
+  add_executable(${name} ${source_file})
+  target_link_libraries(${name} ${Caffe_LINK})
+  caffe_default_properties(${name})
+
+  # set back RUNTIME_OUTPUT_DIRECTORY
+  set_target_properties(${name} PROPERTIES
+    RUNTIME_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/examples/${folder}")
+
+  caffe_set_solution_folder(${name} examples)
+
+  # install
+  install(TARGETS ${name} DESTINATION bin)
+
+  if(UNIX OR APPLE)
+    # Funny command to make tutorials work
+    # TODO: remove in future as soon as naming is standartaized everywhere
+    set(__outname ${PROJECT_BINARY_DIR}/examples/${folder}/${name}${CAffe_POSTFIX})
+    add_custom_command(TARGET ${name} POST_BUILD
+                       COMMAND ln -sf "${__outname}" "${__outname}.bin")
+  endif()
+endforeach()
diff --git a/examples/classification.ipynb b/examples/classification.ipynb
index 6b6c5488d36..6f8fa4252e6 100644
--- a/examples/classification.ipynb
+++ b/examples/classification.ipynb
@@ -4,7 +4,7 @@
   "example_name": "ImageNet classification",
   "include_in_docs": true,
   "priority": 1,
-  "signature": "sha256:2caae2c1fe3e282b8f836d380a45622351c91db18a1591e4f2fa67faba9ab72c"
+  "signature": "sha256:918b797b1b7d78125c8f1e3c84756b0679120cbe1071ce7fee7aeafef0fbae55"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -64,10 +64,9 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "caffe.set_phase_test()\n",
       "caffe.set_mode_cpu()\n",
       "net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n",
-      "                       mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy'),\n",
+      "                       mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1),\n",
       "                       channel_swap=(2,1,0),\n",
       "                       raw_scale=255,\n",
       "                       image_dims=(256, 256))"
@@ -237,7 +236,7 @@
       "# Resize the image to the standard (256, 256) and oversample net input sized crops.\n",
       "input_oversampled = caffe.io.oversample([caffe.io.resize_image(input_image, net.image_dims)], net.crop_dims)\n",
       "# 'data' is the input blob name in the model definition, so we preprocess for that input.\n",
-      "caffe_input = np.asarray([net.preprocess('data', in_) for in_ in input_oversampled])\n",
+      "caffe_input = np.asarray([net.transformer.preprocess('data', in_) for in_ in input_oversampled])\n",
       "# forward() takes keyword args for the input blobs with preprocessed input arrays.\n",
       "%timeit net.forward(data=caffe_input)"
      ],
diff --git a/examples/detection.ipynb b/examples/detection.ipynb
index d05c0c22052..2ccf21f09eb 100644
--- a/examples/detection.ipynb
+++ b/examples/detection.ipynb
@@ -3,7 +3,8 @@
   "description": "Run a pretrained model as a detector in Python.",
   "example_name": "R-CNN detection",
   "include_in_docs": true,
-  "priority": 3
+  "priority": 3,
+  "signature": "sha256:5d53dc49c9b6b93c1a2714c99043a763029ec98aebfb44acfa8d9e61781c9499"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -37,7 +38,7 @@
      "input": [
       "!mkdir -p _temp\n",
       "!echo `pwd`/images/fish-bike.jpg > _temp/det_input.txt\n",
-      "!../python/detect.py --crop_mode=selective_search --pretrained_model=models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel --model_def=models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt --gpu --raw_scale=255 _temp/det_input.txt _temp/det_output.h5"
+      "!../python/detect.py --crop_mode=selective_search --pretrained_model=../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel --model_def=../models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt --gpu --raw_scale=255 _temp/det_input.txt _temp/det_output.h5"
      ],
      "language": "python",
      "metadata": {},
@@ -47,52 +48,60 @@
        "stream": "stdout",
        "text": [
         "WARNING: Logging before InitGoogleLogging() is written to STDERR\r\n",
-        "I0610 10:12:49.299607 25530 net.cpp:36] Initializing net from parameters: \r\n",
+        "I0218 20:43:25.383932 2099749632 net.cpp:42] Initializing net from parameters: \r\n",
         "name: \"R-CNN-ilsvrc13\"\r\n",
-        "layers {\r\n",
+        "input: \"data\"\r\n",
+        "input_dim: 10\r\n",
+        "input_dim: 3\r\n",
+        "input_dim: 227\r\n",
+        "input_dim: 227\r\n",
+        "state {\r\n",
+        "  phase: TEST\r\n",
+        "}\r\n",
+        "layer {\r\n",
+        "  name: \"conv1\"\r\n",
+        "  type: \"Convolution\"\r\n",
         "  bottom: \"data\"\r\n",
         "  top: \"conv1\"\r\n",
-        "  name: \"conv1\"\r\n",
-        "  type: CONVOLUTION\r\n",
         "  convolution_param {\r\n",
         "    num_output: 96\r\n",
         "    kernel_size: 11\r\n",
         "    stride: 4\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"relu1\"\r\n",
+        "  type: \"ReLU\"\r\n",
         "  bottom: \"conv1\"\r\n",
         "  top: \"conv1\"\r\n",
-        "  name: \"relu1\"\r\n",
-        "  type: RELU\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"pool1\"\r\n",
+        "  type: \"Pooling\"\r\n",
         "  bottom: \"conv1\"\r\n",
         "  top: \"pool1\"\r\n",
-        "  name: \"pool1\"\r\n",
-        "  type: POOLING\r\n",
         "  pooling_param {\r\n",
         "    pool: MAX\r\n",
         "    kernel_size: 3\r\n",
         "    stride: 2\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"norm1\"\r\n",
+        "  type: \"LRN\"\r\n",
         "  bottom: \"pool1\"\r\n",
         "  top: \"norm1\"\r\n",
-        "  name: \"norm1\"\r\n",
-        "  type: LRN\r\n",
         "  lrn_param {\r\n",
         "    local_size: 5\r\n",
         "    alpha: 0.0001\r\n",
         "    beta: 0.75\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"conv2\"\r\n",
+        "  type: \"Convolution\"\r\n",
         "  bottom: \"norm1\"\r\n",
         "  top: \"conv2\"\r\n",
-        "  name: \"conv2\"\r\n",
-        "  type: CONVOLUTION\r\n",
         "  convolution_param {\r\n",
         "    num_output: 256\r\n",
         "    pad: 2\r\n",
@@ -100,56 +109,57 @@
         "    group: 2\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r",
+        "\r\n",
+        "  name: \"relu2\"\r\n",
+        "  type: \"ReLU\"\r\n",
         "  bottom: \"conv2\"\r\n",
         "  top: \"conv2\"\r\n",
-        "  name: \"relu2\"\r\n",
-        "  type: RELU\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"pool2\"\r\n",
+        "  type: \"Pooling\"\r\n",
         "  bottom: \"conv2\"\r\n",
         "  top: \"pool2\"\r\n",
-        "  name: \"pool2\"\r\n",
-        "  type: POOLING\r\n",
         "  pooling_param {\r\n",
         "    pool: MAX\r\n",
         "    kernel_size: 3\r\n",
         "    stride: 2\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"norm2\"\r\n",
+        "  type: \"LRN\"\r\n",
         "  bottom: \"pool2\"\r\n",
         "  top: \"norm2\"\r\n",
-        "  name: \"norm2\"\r\n",
-        "  type: LRN\r\n",
         "  lrn_param {\r\n",
         "    local_size: 5\r\n",
         "    alpha: 0.0001\r\n",
         "    beta: 0.75\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"conv3\"\r\n",
+        "  type: \"Convolution\"\r\n",
         "  bottom: \"norm2\"\r\n",
         "  top: \"conv3\"\r\n",
-        "  name: \"conv3\"\r\n",
-        "  type: CONVOLUTION\r\n",
         "  convolution_param {\r\n",
         "    num_output: 384\r\n",
         "    pad: 1\r\n",
         "    kernel_size: 3\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"relu3\"\r\n",
+        "  type: \"ReLU\"\r\n",
         "  bottom: \"conv3\"\r\n",
         "  top: \"conv3\"\r\n",
-        "  name: \"relu3\"\r\n",
-        "  type: RELU\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"conv4\"\r\n",
+        "  type: \"Convolution\"\r\n",
         "  bottom: \"conv3\"\r\n",
         "  top: \"conv4\"\r\n",
-        "  name: \"conv4\"\r\n",
-        "  type: CONVOLUTION\r\n",
         "  convolution_param {\r\n",
         "    num_output: 384\r\n",
         "    pad: 1\r\n",
@@ -157,17 +167,17 @@
         "    group: 2\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"relu4\"\r\n",
+        "  type: \"ReLU\"\r\n",
         "  bottom: \"conv4\"\r\n",
         "  top: \"conv4\"\r\n",
-        "  name: \"relu4\"\r\n",
-        "  type: RELU\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"conv5\"\r\n",
+        "  type: \"Convolution\"\r\n",
         "  bottom: \"conv4\"\r\n",
         "  top: \"conv5\"\r\n",
-        "  name: \"conv5\"\r\n",
-        "  type: CONVOLUTION\r\n",
         "  convolution_param {\r\n",
         "    num_output: 256\r\n",
         "    pad: 1\r\n",
@@ -175,205 +185,265 @@
         "    group: 2\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"relu5\"\r\n",
+        "  type: \"ReLU\"\r\n",
         "  bottom: \"conv5\"\r\n",
         "  top: \"conv5\"\r\n",
-        "  name: \"relu5\"\r\n",
-        "  type: RELU\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"pool5\"\r\n",
+        "  type: \"Pooling\"\r\n",
         "  bottom: \"conv5\"\r\n",
         "  top: \"pool5\"\r\n",
-        "  name: \"pool5\"\r\n",
-        "  type: POOLING\r\n",
         "  pooling_param {\r\n",
         "    pool: MAX\r\n",
         "    kernel_size: 3\r\n",
         "    stride: 2\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"fc6\"\r\n",
+        "  type: \"InnerProduct\"\r\n",
         "  bottom: \"pool5\"\r\n",
         "  top: \"fc6\"\r\n",
-        "  name: \"fc6\"\r\n",
-        "  type: INNER_PRODUCT\r\n",
         "  inner_product_param {\r\n",
         "    num_output: 4096\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"relu6\"\r\n",
+        "  type: \"ReLU\"\r\n",
         "  bottom: \"fc6\"\r\n",
         "  top: \"fc6\"\r\n",
-        "  name: \"relu6\"\r\n",
-        "  type: RELU\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"drop6\"\r\n",
+        "  type: \"Dropout\"\r\n",
         "  bottom: \"fc6\"\r\n",
         "  top: \"fc6\"\r\n",
-        "  name: \"drop6\"\r\n",
-        "  type: DROPOUT\r\n",
         "  dropout_param {\r\n",
         "    dropout_ratio: 0.5\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"fc7\"\r\n",
+        "  type: \"InnerProduct\"\r\n",
         "  bottom: \"fc6\"\r\n",
         "  top: \"fc7\"\r\n",
-        "  name: \"fc7\"\r\n",
-        "  type: INNER_PRODUCT\r\n",
         "  inner_product_param {\r\n",
         "    num_output: 4096\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"relu7\"\r\n",
+        "  type: \"ReLU\"\r\n",
         "  bottom: \"fc7\"\r\n",
         "  top: \"fc7\"\r\n",
-        "  name: \"relu7\"\r\n",
-        "  type: RELU\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"drop7\"\r\n",
+        "  type: \"Dropout\"\r\n",
         "  bottom: \"fc7\"\r\n",
         "  top: \"fc7\"\r\n",
-        "  name: \"drop7\"\r\n",
-        "  type: DROPOUT\r\n",
         "  dropout_param {\r\n",
         "    dropout_ratio: 0.5\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"fc-rcnn\"\r\n",
+        "  type: \"InnerProduct\"\r\n",
         "  bottom: \"fc7\"\r\n",
         "  top: \"fc-rcnn\"\r\n",
-        "  name: \"fc-rcnn\"\r\n",
-        "  type: INNER_PRODUCT\r\n",
         "  inner_product_param {\r\n",
         "    num_output: 200\r\n",
         "  }\r\n",
         "}\r\n",
-        "input: \"data\"\r\n",
-        "input_dim: 10\r\n",
-        "input_dim: 3\r\n",
-        "input_dim: 227\r\n",
-        "input_dim: 227\r\n",
-        "I0610 10:12:49.300204 25530 net.cpp:77] Creating Layer conv1\r\n",
-        "I0610 10:12:49.300214 25530 net.cpp:87] conv1 <- data\r\n",
-        "I0610 10:12:49.300220 25530 net.cpp:113] conv1 -> conv1\r\n",
-        "I0610 10:12:49.300283 25530 net.cpp:128] Top shape: 10 96 55 55 (2904000)\r\n",
-        "I0610 10:12:49.300294 25530 net.cpp:154] conv1 needs backward computation.\r\n",
-        "I0610 10:12:49.300302 25530 net.cpp:77] Creating Layer relu1\r\n",
-        "I0610 10:12:49.300308 25530 net.cpp:87] relu1 <- conv1\r\n",
-        "I0610 10:12:49.300314 25530 net.cpp:101] relu1 -> conv1 (in-place)\r\n",
-        "I0610 10:12:49.300323 25530 net.cpp:128] Top shape: 10 96 55 55 (2904000)\r\n",
-        "I0610 10:12:49.300328 25530 net.cpp:154] relu1 needs backward computation.\r\n",
-        "I0610 10:12:49.300335 25530 net.cpp:77] Creating Layer pool1\r\n",
-        "I0610 10:12:49.300341 25530 net.cpp:87] pool1 <- conv1\r\n",
-        "I0610 10:12:49.300348 25530 net.cpp:113] pool1 -> pool1\r\n",
-        "I0610 10:12:49.300357 25530 net.cpp:128] Top shape: 10 96 27 27 (699840)\r\n",
-        "I0610 10:12:49.300365 25530 net.cpp:154] pool1 needs backward computation.\r\n",
-        "I0610 10:12:49.300372 25530 net.cpp:77] Creating Layer norm1\r\n",
-        "I0610 10:12:49.300379 25530 net.cpp:87] norm1 <- pool1\r\n",
-        "I0610 10:12:49.300384 25530 net.cpp:113] norm1 -> norm1\r\n",
-        "I0610 10:12:49.300393 25530 net.cpp:128] Top shape: 10 96 27 27 (699840)\r\n",
-        "I0610 10:12:49.300400 25530 net.cpp:154] norm1 needs backward computation.\r\n",
-        "I0610 10:12:49.300406 25530 net.cpp:77] Creating Layer conv2\r\n",
-        "I0610 10:12:49.300412 25530 net.cpp:87] conv2 <- norm1\r\n",
-        "I0610 10:12:49.300420 25530 net.cpp:113] conv2 -> conv2\r\n",
-        "I0610 10:12:49.300925 25530 net.cpp:128] Top shape: 10 256 27 27 (1866240)\r\n",
-        "I0610 10:12:49.300935 25530 net.cpp:154] conv2 needs backward computation.\r\n",
-        "I0610 10:12:49.300941 25530 net.cpp:77] Creating Layer relu2\r\n",
-        "I0610 10:12:49.300947 25530 net.cpp:87] relu2 <- conv2\r\n",
-        "I0610 10:12:49.300954 25530 net.cpp:101] relu2 -> conv2 (in-place)\r\n",
-        "I0610 10:12:49.300961 25530 net.cpp:128] Top shape: 10 256 27 27 (1866240)\r\n",
-        "I0610 10:12:49.300967 25530 net.cpp:154] relu2 needs backward computation.\r\n",
-        "I0610 10:12:49.300974 25530 net.cpp:77] Creating Layer pool2\r\n",
-        "I0610 10:12:49.300981 25530 net.cpp:87] pool2 <- conv2\r\n",
-        "I0610 10:12:49.300987 25530 net.cpp:113] pool2 -> pool2\r\n",
-        "I0610 10:12:49.300994 25530 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0610 10:12:49.301000 25530 net.cpp:154] pool2 needs backward computation.\r\n",
-        "I0610 10:12:49.301007 25530 net.cpp:77] Creating Layer norm2\r\n",
-        "I0610 10:12:49.301013 25530 net.cpp:87] norm2 <- pool2\r\n",
-        "I0610 10:12:49.301019 25530 net.cpp:113] norm2 -> norm2\r\n",
-        "I0610 10:12:49.301026 25530 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0610 10:12:49.301033 25530 net.cpp:154] norm2 needs backward computation.\r\n",
-        "I0610 10:12:49.301041 25530 net.cpp:77] Creating Layer conv3\r\n",
-        "I0610 10:12:49.301048 25530 net.cpp:87] conv3 <- norm2\r\n",
-        "I0610 10:12:49.301054 25530 net.cpp:113] conv3 -> conv3\r\n",
-        "I0610 10:12:49.302455 25530 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0610 10:12:49.302467 25530 net.cpp:154] conv3 needs backward computation.\r\n",
-        "I0610 10:12:49.302477 25530 net.cpp:77] Creating Layer relu3\r\n",
-        "I0610 10:12:49.302484 25530 net.cpp:87] relu3 <- conv3\r\n",
-        "I0610 10:12:49.302490 25530 net.cpp:101] relu3 -> conv3 (in-place)\r\n",
-        "I0610 10:12:49.302496 25530 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0610 10:12:49.302503 25530 net.cpp:154] relu3 needs backward computation.\r\n",
-        "I0610 10:12:49.302510 25530 net.cpp:77] Creating Layer conv4\r\n",
-        "I0610 10:12:49.302515 25530 net.cpp:87] conv4 <- conv3\r\n",
-        "I0610 10:12:49.302521 25530 net.cpp:113] conv4 -> conv4\r\n",
-        "I0610 10:12:49.303639 25530 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0610 10:12:49.303650 25530 net.cpp:154] conv4 needs backward computation.\r\n",
-        "I0610 10:12:49.303658 25530 net.cpp:77] Creating Layer relu4\r\n",
-        "I0610 10:12:49.303663 25530 net.cpp:87] relu4 <- conv4\r\n",
-        "I0610 10:12:49.303670 25530 net.cpp:101] relu4 -> conv4 (in-place)\r\n",
-        "I0610 10:12:49.303676 25530 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0610 10:12:49.303683 25530 net.cpp:154] relu4 needs backward computation.\r\n",
-        "I0610 10:12:49.303691 25530 net.cpp:77] Creating Layer conv5\r\n",
-        "I0610 10:12:49.303697 25530 net.cpp:87] conv5 <- conv4\r\n",
-        "I0610 10:12:49.303704 25530 net.cpp:113] conv5 -> conv5\r\n",
-        "I0610 10:12:49.304410 25530 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0610 10:12:49.304420 25530 net.cpp:154] conv5 needs backward computation.\r\n",
-        "I0610 10:12:49.304427 25530 net.cpp:77] Creating Layer relu5\r\n",
-        "I0610 10:12:49.304433 25530 net.cpp:87] relu5 <- conv5\r\n",
-        "I0610 10:12:49.304440 25530 net.cpp:101] relu5 -> conv5 (in-place)\r\n",
-        "I0610 10:12:49.304446 25530 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0610 10:12:49.304471 25530 net.cpp:154] relu5 needs backward computation.\r\n",
-        "I0610 10:12:49.304478 25530 net.cpp:77] Creating Layer pool5\r\n",
-        "I0610 10:12:49.304484 25530 net.cpp:87] pool5 <- conv5\r\n",
-        "I0610 10:12:49.304491 25530 net.cpp:113] pool5 -> pool5\r\n",
-        "I0610 10:12:49.304498 25530 net.cpp:128] Top shape: 10 256 6 6 (92160)\r\n",
-        "I0610 10:12:49.304504 25530 net.cpp:154] pool5 needs backward computation.\r\n",
-        "I0610 10:12:49.304512 25530 net.cpp:77] Creating Layer fc6\r\n",
-        "I0610 10:12:49.304517 25530 net.cpp:87] fc6 <- pool5\r\n",
-        "I0610 10:12:49.304523 25530 net.cpp:113] fc6 -> fc6\r\n"
+        "I0218 20:43:25.385720 2099749632 net.cpp:336] Input 0 -> data\r\n",
+        "I0218 20:43:25.385769 2099749632 layer_factory.hpp:74] Creating layer conv1\r\n",
+        "I0218 20:43:25.385783 2099749632 net.cpp:76] Creating Layer conv1\r\n",
+        "I0218 20:43:25.385790 2099749632 net.cpp:372] conv1 <- data\r\n",
+        "I0218 20:43:25.385802 2099749632 net.cpp:334] conv1 -> conv1\r\n",
+        "I0218 20:43:25.385815 2099749632 net.cpp:105] Setting up conv1\r\n",
+        "I0218 20:43:25.386574 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\r\n",
+        "I0218 20:43:25.386610 2099749632 layer_factory.hpp:74] Creating layer relu1\r\n",
+        "I0218 20:43:25.386625 2099749632 net.cpp:76] Creating Layer relu1\r\n",
+        "I0218 20:43:25.386631 2099749632 net.cpp:372] relu1 <- conv1\r\n",
+        "I0218 20:43:25.386641 2099749632 net.cpp:323] relu1 -> conv1 (in-place)\r\n",
+        "I0218 20:43:25.386649 2099749632 net.cpp:105] Setting up relu1\r\n",
+        "I0218 20:43:25.386656 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\r\n",
+        "I0218 20:43:25.386663 2099749632 layer_factory.hpp:74] Creating layer pool1\r\n",
+        "I0218 20:43:25.386675 2099749632 net.cpp:76] Creating Layer pool1\r\n",
+        "I0218 20:43:25.386682 2099749632 net.cpp:372] pool1 <- conv1\r\n",
+        "I0218 20:43:25.386690 2099749632 net.cpp:334] pool1 -> pool1\r\n",
+        "I0218 20:43:25.386699 2099749632 net.cpp:105] Setting up pool1\r\n",
+        "I0218 20:43:25.386716 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\r\n",
+        "I0218 20:43:25.386725 2099749632 layer_factory.hpp:74] Creating layer norm1\r\n",
+        "I0218 20:43:25.386736 2099749632 net.cpp:76] Creating Layer norm1\r\n",
+        "I0218 20:43:25.386744 2099749632 net.cpp:372] norm1 <- pool1\r\n",
+        "I0218 20:43:25.386803 2099749632 net.cpp:334] norm1 -> norm1\r\n",
+        "I0218 20:43:25.386819 2099749632 net.cpp:105] Setting up norm1\r\n",
+        "I0218 20:43:25.386832 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\r\n",
+        "I0218 20:43:25.386842 2099749632 layer_factory.hpp:74] Creating layer conv2\r\n",
+        "I0218 20:43:25.386852 2099749632 net.cpp:76] Creating Layer conv2\r\n",
+        "I0218 20:43:25.386865 2099749632 net.cpp:372] conv2 <- norm1\r\n",
+        "I0218 20:43:25.386878 2099749632 net.cpp:334] conv2 -> conv2\r\n",
+        "I0218 20:43:25.386899 2099749632 net.cpp:105] Setting up conv2\r\n",
+        "I0218 20:43:25.387024 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\r\n",
+        "I0218 20:43:25.387042 2099749632 layer_factory.hpp:74] Creating layer relu2\r\n",
+        "I0218 20:43:25.387050 2099749632 net.cpp:76] Creating Layer relu2\r\n",
+        "I0218 20:43:25.387058 2099749632 net.cpp:372] relu2 <- conv2\r\n",
+        "I0218 20:43:25.387066 2099749632 net.cpp:323] relu2 -> conv2 (in-place)\r\n",
+        "I0218 20:43:25.387075 2099749632 net.cpp:105] Setting up relu2\r\n",
+        "I0218 20:43:25.387081 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\r\n",
+        "I0218 20:43:25.387089 2099749632 layer_factory.hpp:74] Creating layer pool2\r\n",
+        "I0218 20:43:25.387097 2099749632 net.cpp:76] Creating Layer pool2\r\n",
+        "I0218 20:43:25.387104 2099749632 net.cpp:372] pool2 <- conv2\r\n",
+        "I0218 20:43:25.387112 2099749632 net.cpp:334] pool2 -> pool2\r\n",
+        "I0218 20:43:25.387121 2099749632 net.cpp:105] Setting up pool2\r\n",
+        "I0218 20:43:25.387130 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0218 20:43:25.387137 2099749632 layer_factory.hpp:74] Creating layer norm2\r\n",
+        "I0218 20:43:25.387145 2099749632 net.cpp:76] Creating Layer norm2\r\n",
+        "I0218 20:43:25.387152 2099749632 net.cpp:372] norm2 <- pool2\r\n",
+        "I0218 20:43:25.387161 2099749632 net.cpp:334] norm2 -> norm2\r\n",
+        "I0218 20:43:25.387168 2099749632 net.cpp:105] Setting up norm2\r\n",
+        "I0218 20:43:25.387176 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0218 20:43:25.387228 2099749632 layer_factory.hpp:74] Creating layer conv3\r\n",
+        "I0218 20:43:25.387249 2099749632 net.cpp:76] Creating Layer conv3\r\n",
+        "I0218 20:43:25.387258 2099749632 net.cpp:372] conv3 <- norm2\r\n",
+        "I0218 20:43:25.387266 2099749632 net.cpp:334] conv3 -> conv3\r\n",
+        "I0218 20:43:25.387276 2099749632 net.cpp:105] Setting up conv3\r\n",
+        "I0218 20:43:25.389375 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0218 20:43:25.389408 2099749632 layer_factory.hpp:74] Creating layer relu3\r\n",
+        "I0218 20:43:25.389421 2099749632 net.cpp:76] Creating Layer relu3\r\n",
+        "I0218 20:43:25.389430 2099749632 net.cpp:372] relu3 <- conv3\r\n",
+        "I0218 20:43:25.389438 2099749632 net.cpp:323] relu3 -> conv3 (in-place)\r\n",
+        "I0218 20:43:25.389447 2099749632 net.cpp:105] Setting up relu3\r\n",
+        "I0218 20:43:25.389456 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0218 20:43:25.389462 2099749632 layer_factory.hpp:74] Creating layer conv4\r\n",
+        "I0218 20:43:25.389472 2099749632 net.cpp:76] Creating Layer conv4\r\n",
+        "I0218 20:43:25.389478 2099749632 net.cpp:372] conv4 <- conv3\r\n",
+        "I0218 20:43:25.389487 2099749632 net.cpp:334] conv4 -> conv4\r\n",
+        "I0218 20:43:25.389497 2099749632 net.cpp:105] Setting up conv4\r\n",
+        "I0218 20:43:25.391810 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0218 20:43:25.391856 2099749632 layer_factory.hpp:74] Creating layer relu4\r\n",
+        "I0218 20:43:25.391871 2099749632 net.cpp:76] Creating Layer relu4\r\n",
+        "I0218 20:43:25.391880 2099749632 net.cpp:372] relu4 <- conv4\r\n",
+        "I0218 20:43:25.391888 2099749632 net.cpp:323] relu4 -> conv4 (in-place)\r\n",
+        "I0218 20:43:25.391898 2099749632 net.cpp:105] Setting up relu4\r\n",
+        "I0218 20:43:25.391906 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0218 20:43:25.391913 2099749632 layer_factory.hpp:74] Creating layer conv5\r\n",
+        "I0218 20:43:25.391923 2099749632 net.cpp:76] Creating Layer conv5\r\n",
+        "I0218 20:43:25.391929 2099749632 net.cpp:372] conv5 <- conv4\r\n",
+        "I0218 20:43:25.391937 2099749632 net.cpp:334] conv5 -> conv5\r\n",
+        "I0218 20:43:25.391947 2099749632 net.cpp:105] Setting up conv5\r\n",
+        "I0218 20:43:25.393072 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0218 20:43:25.393108 2099749632 layer_factory.hpp:74] Creating layer relu5\r\n",
+        "I0218 20:43:25.393122 2099749632 net.cpp:76] Creating Layer relu5\r\n",
+        "I0218 20:43:25.393129 2099749632 net.cpp:372] relu5 <- conv5\r\n",
+        "I0218 20:43:25.393138 2099749632 net.cpp:323] relu5 -> conv5 (in-place)\r\n",
+        "I0218 20:43:25.393148 2099749632 net.cpp:105] Setting up relu5\r\n",
+        "I0218 20:43:25.393157 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0218 20:43:25.393167 2099749632 layer_factory.hpp:74] Creating layer pool5\r\n",
+        "I0218 20:43:25.393175 2099749632 net.cpp:76] Creating Layer pool5\r\n",
+        "I0218 20:43:25.393182 2099749632 net.cpp:372] pool5 <- conv5\r\n",
+        "I0218 20:43:25.393190 2099749632 net.cpp:334] pool5 -> pool5\r\n",
+        "I0218 20:43:25.393199 2099749632 net.cpp:105] Setting up pool5\r\n",
+        "I0218 20:43:25.393209 2099749632 net.cpp:112] Top shape: 10 256 6 6 (92160)\r\n",
+        "I0218 20:43:25.393218 2099749632 layer_factory.hpp:74] Creating layer fc6\r\n",
+        "I0218 20:43:25.393226 2099749632 net.cpp:76] Creating Layer fc6\r\n",
+        "I0218 20:43:25.393232 2099749632 net.cpp:372] fc6 <- pool5\r\n",
+        "I0218 20:43:25.393240 2099749632 net.cpp:334] fc6 -> fc6\r\n",
+        "I0218 20:43:25.393249 2099749632 net.cpp:105] Setting up fc6\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "I0218 20:43:25.516396 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0218 20:43:25.516445 2099749632 layer_factory.hpp:74] Creating layer relu6\r\n",
+        "I0218 20:43:25.516463 2099749632 net.cpp:76] Creating Layer relu6\r\n",
+        "I0218 20:43:25.516470 2099749632 net.cpp:372] relu6 <- fc6\r\n",
+        "I0218 20:43:25.516480 2099749632 net.cpp:323] relu6 -> fc6 (in-place)\r\n",
+        "I0218 20:43:25.516490 2099749632 net.cpp:105] Setting up relu6\r\n",
+        "I0218 20:43:25.516497 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0218 20:43:25.516505 2099749632 layer_factory.hpp:74] Creating layer drop6\r\n",
+        "I0218 20:43:25.516515 2099749632 net.cpp:76] Creating Layer drop6\r\n",
+        "I0218 20:43:25.516521 2099749632 net.cpp:372] drop6 <- fc6\r\n",
+        "I0218 20:43:25.516530 2099749632 net.cpp:323] drop6 -> fc6 (in-place)\r\n",
+        "I0218 20:43:25.516538 2099749632 net.cpp:105] Setting up drop6\r\n",
+        "I0218 20:43:25.516557 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0218 20:43:25.516566 2099749632 layer_factory.hpp:74] Creating layer fc7\r\n",
+        "I0218 20:43:25.516576 2099749632 net.cpp:76] Creating Layer fc7\r\n",
+        "I0218 20:43:25.516582 2099749632 net.cpp:372] fc7 <- fc6\r\n",
+        "I0218 20:43:25.516589 2099749632 net.cpp:334] fc7 -> fc7\r\n",
+        "I0218 20:43:25.516599 2099749632 net.cpp:105] Setting up fc7\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "I0218 20:43:25.604786 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0218 20:43:25.604838 2099749632 layer_factory.hpp:74] Creating layer relu7\r\n",
+        "I0218 20:43:25.604852 2099749632 net.cpp:76] Creating Layer relu7\r\n",
+        "I0218 20:43:25.604859 2099749632 net.cpp:372] relu7 <- fc7\r\n",
+        "I0218 20:43:25.604868 2099749632 net.cpp:323] relu7 -> fc7 (in-place)\r\n",
+        "I0218 20:43:25.604878 2099749632 net.cpp:105] Setting up relu7\r\n",
+        "I0218 20:43:25.604885 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0218 20:43:25.604893 2099749632 layer_factory.hpp:74] Creating layer drop7\r\n",
+        "I0218 20:43:25.604902 2099749632 net.cpp:76] Creating Layer drop7\r\n",
+        "I0218 20:43:25.604908 2099749632 net.cpp:372] drop7 <- fc7\r\n",
+        "I0218 20:43:25.604917 2099749632 net.cpp:323] drop7 -> fc7 (in-place)\r\n",
+        "I0218 20:43:25.604924 2099749632 net.cpp:105] Setting up drop7\r\n",
+        "I0218 20:43:25.604933 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0218 20:43:25.604939 2099749632 layer_factory.hpp:74] Creating layer fc-rcnn\r\n",
+        "I0218 20:43:25.604948 2099749632 net.cpp:76] Creating Layer fc-rcnn\r\n",
+        "I0218 20:43:25.604954 2099749632 net.cpp:372] fc-rcnn <- fc7\r\n",
+        "I0218 20:43:25.604962 2099749632 net.cpp:334] fc-rcnn -> fc-rcnn\r\n",
+        "I0218 20:43:25.604971 2099749632 net.cpp:105] Setting up fc-rcnn\r\n",
+        "I0218 20:43:25.606878 2099749632 net.cpp:112] Top shape: 10 200 1 1 (2000)\r\n",
+        "I0218 20:43:25.606904 2099749632 net.cpp:165] fc-rcnn does not need backward computation.\r\n",
+        "I0218 20:43:25.606909 2099749632 net.cpp:165] drop7 does not need backward computation.\r\n",
+        "I0218 20:43:25.606916 2099749632 net.cpp:165] relu7 does not need backward computation.\r\n",
+        "I0218 20:43:25.606922 2099749632 net.cpp:165] fc7 does not need backward computation.\r\n",
+        "I0218 20:43:25.606928 2099749632 net.cpp:165] drop6 does not need backward computation.\r\n",
+        "I0218 20:43:25.606935 2099749632 net.cpp:165] relu6 does not need backward computation.\r\n",
+        "I0218 20:43:25.606940 2099749632 net.cpp:165] fc6 does not need backward computation.\r\n",
+        "I0218 20:43:25.606946 2099749632 net.cpp:165] pool5 does not need backward computation.\r\n",
+        "I0218 20:43:25.606952 2099749632 net.cpp:165] relu5 does not need backward computation.\r\n",
+        "I0218 20:43:25.606958 2099749632 net.cpp:165] conv5 does not need backward computation.\r\n",
+        "I0218 20:43:25.606964 2099749632 net.cpp:165] relu4 does not need backward computation.\r\n",
+        "I0218 20:43:25.606971 2099749632 net.cpp:165] conv4 does not need backward computation.\r\n",
+        "I0218 20:43:25.606976 2099749632 net.cpp:165] relu3 does not need backward computation.\r\n",
+        "I0218 20:43:25.606982 2099749632 net.cpp:165] conv3 does not need backward computation.\r\n",
+        "I0218 20:43:25.606988 2099749632 net.cpp:165] norm2 does not need backward computation.\r\n",
+        "I0218 20:43:25.606995 2099749632 net.cpp:165] pool2 does not need backward computation.\r\n",
+        "I0218 20:43:25.607002 2099749632 net.cpp:165] relu2 does not need backward computation.\r\n",
+        "I0218 20:43:25.607007 2099749632 net.cpp:165] conv2 does not need backward computation.\r\n",
+        "I0218 20:43:25.607013 2099749632 net.cpp:165] norm1 does not need backward computation.\r\n",
+        "I0218 20:43:25.607199 2099749632 net.cpp:165] pool1 does not need backward computation.\r\n",
+        "I0218 20:43:25.607213 2099749632 net.cpp:165] relu1 does not need backward computation.\r\n",
+        "I0218 20:43:25.607219 2099749632 net.cpp:165] conv1 does not need backward computation.\r\n",
+        "I0218 20:43:25.607225 2099749632 net.cpp:201] This network produces output fc-rcnn\r\n",
+        "I0218 20:43:25.607239 2099749632 net.cpp:446] Collecting Learning Rate and Weight Decay.\r\n",
+        "I0218 20:43:25.607255 2099749632 net.cpp:213] Network initialization done.\r\n",
+        "I0218 20:43:25.607262 2099749632 net.cpp:214] Memory required for data: 62425920\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "E0218 20:43:26.388214 2099749632 upgrade_proto.cpp:618] Attempting to upgrade input file specified using deprecated V1LayerParameter: ../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "I0610 10:12:49.364333 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0610 10:12:49.364372 25530 net.cpp:154] fc6 needs backward computation.\r\n",
-        "I0610 10:12:49.364387 25530 net.cpp:77] Creating Layer relu6\r\n",
-        "I0610 10:12:49.364420 25530 net.cpp:87] relu6 <- fc6\r\n",
-        "I0610 10:12:49.364429 25530 net.cpp:101] relu6 -> fc6 (in-place)\r\n",
-        "I0610 10:12:49.364437 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0610 10:12:49.364444 25530 net.cpp:154] relu6 needs backward computation.\r\n",
-        "I0610 10:12:49.364455 25530 net.cpp:77] Creating Layer drop6\r\n",
-        "I0610 10:12:49.364461 25530 net.cpp:87] drop6 <- fc6\r\n",
-        "I0610 10:12:49.364467 25530 net.cpp:101] drop6 -> fc6 (in-place)\r\n",
-        "I0610 10:12:49.364480 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0610 10:12:49.364487 25530 net.cpp:154] drop6 needs backward computation.\r\n",
-        "I0610 10:12:49.364495 25530 net.cpp:77] Creating Layer fc7\r\n",
-        "I0610 10:12:49.364501 25530 net.cpp:87] fc7 <- fc6\r\n",
-        "I0610 10:12:49.364507 25530 net.cpp:113] fc7 -> fc7\r\n",
-        "I0610 10:12:49.391316 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0610 10:12:49.391350 25530 net.cpp:154] fc7 needs backward computation.\r\n",
-        "I0610 10:12:49.391361 25530 net.cpp:77] Creating Layer relu7\r\n",
-        "I0610 10:12:49.391369 25530 net.cpp:87] relu7 <- fc7\r\n",
-        "I0610 10:12:49.391377 25530 net.cpp:101] relu7 -> fc7 (in-place)\r\n",
-        "I0610 10:12:49.391384 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0610 10:12:49.391391 25530 net.cpp:154] relu7 needs backward computation.\r\n",
-        "I0610 10:12:49.391398 25530 net.cpp:77] Creating Layer drop7\r\n",
-        "I0610 10:12:49.391427 25530 net.cpp:87] drop7 <- fc7\r\n",
-        "I0610 10:12:49.391433 25530 net.cpp:101] drop7 -> fc7 (in-place)\r\n",
-        "I0610 10:12:49.391440 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0610 10:12:49.391446 25530 net.cpp:154] drop7 needs backward computation.\r\n",
-        "I0610 10:12:49.391454 25530 net.cpp:77] Creating Layer fc-rcnn\r\n",
-        "I0610 10:12:49.391459 25530 net.cpp:87] fc-rcnn <- fc7\r\n",
-        "I0610 10:12:49.391466 25530 net.cpp:113] fc-rcnn -> fc-rcnn\r\n",
-        "I0610 10:12:49.392812 25530 net.cpp:128] Top shape: 10 200 1 1 (2000)\r\n",
-        "I0610 10:12:49.392823 25530 net.cpp:154] fc-rcnn needs backward computation.\r\n",
-        "I0610 10:12:49.392829 25530 net.cpp:165] This network produces output fc-rcnn\r\n",
-        "I0610 10:12:49.392850 25530 net.cpp:183] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0610 10:12:49.392868 25530 net.cpp:176] Network initialization done.\r\n",
-        "I0610 10:12:49.392875 25530 net.cpp:177] Memory required for Data 41950840\r\n"
+        "I0218 20:43:27.089423 2099749632 upgrade_proto.cpp:626] Successfully upgraded file specified using deprecated V1LayerParameter\r\n"
        ]
       },
       {
@@ -381,21 +451,39 @@
        "stream": "stdout",
        "text": [
         "GPU mode\r\n",
-        "Loading input...\r\n",
-        "selective_search_rcnn({'/home/shelhamer/caffe/examples/images/fish-bike.jpg'}, '/tmp/tmpo7yOum.mat')\r\n"
+        "Loading input...\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "selective_search_rcnn({'/Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg'}, '/var/folders/bk/dtkn5qjd11bd17b2j36zplyw0000gp/T/tmpakaRLL.mat')\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Processed 1570 windows in 35.012 s.\r\n",
-        "/home/shelhamer/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py:2446: PerformanceWarning: \r\n",
+        "Processed 1570 windows in 102.895 s.\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "/Users/shelhamer/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py:2453: PerformanceWarning: \r\n",
         "your performance may suffer as PyTables will pickle object types that it cannot\r\n",
         "map directly to c-types [inferred_type->mixed,key->block1_values] [items->['prediction']]\r\n",
         "\r\n",
-        "  warnings.warn(ws, PerformanceWarning)\r\n",
-        "Saved to _temp/det_output.h5 in 0.035 s.\r\n"
+        "  warnings.warn(ws, PerformanceWarning)\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Saved to _temp/det_output.h5 in 0.298 s.\r\n"
        ]
       }
      ],
@@ -432,12 +520,12 @@
        "stream": "stdout",
        "text": [
         "(1570, 5)\n",
-        "prediction    [-2.64547, -2.88455, -2.85903, -3.17038, -1.92...\n",
+        "prediction    [-2.62247, -2.84579, -2.85122, -3.20838, -1.94...\n",
         "ymin                                                     79.846\n",
         "xmin                                                       9.62\n",
         "ymax                                                     246.31\n",
         "xmax                                                    339.624\n",
-        "Name: /home/shelhamer/caffe/examples/images/fish-bike.jpg, dtype: object\n"
+        "Name: /Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg, dtype: object\n"
        ]
       }
      ],
@@ -481,37 +569,37 @@
        "stream": "stdout",
        "text": [
         "name\n",
-        "accordion      -2.645470\n",
-        "airplane       -2.884554\n",
-        "ant            -2.859026\n",
-        "antelope       -3.170383\n",
-        "apple          -1.924201\n",
-        "armadillo      -2.493925\n",
-        "artichoke      -2.235427\n",
-        "axe            -2.378177\n",
-        "baby bed       -2.757855\n",
-        "backpack       -2.160120\n",
-        "bagel          -2.715738\n",
-        "balance beam   -2.716172\n",
-        "banana         -2.418939\n",
-        "band aid       -1.604563\n",
-        "banjo          -2.329196\n",
+        "accordion      -2.622471\n",
+        "airplane       -2.845788\n",
+        "ant            -2.851219\n",
+        "antelope       -3.208377\n",
+        "apple          -1.949950\n",
+        "armadillo      -2.472935\n",
+        "artichoke      -2.201684\n",
+        "axe            -2.327404\n",
+        "baby bed       -2.737925\n",
+        "backpack       -2.176763\n",
+        "bagel          -2.681061\n",
+        "balance beam   -2.722538\n",
+        "banana         -2.390628\n",
+        "band aid       -1.598909\n",
+        "banjo          -2.298197\n",
         "...\n",
-        "trombone        -2.531519\n",
-        "trumpet         -2.382109\n",
-        "turtle          -2.378510\n",
-        "tv or monitor   -2.777433\n",
-        "unicycle        -2.263807\n",
-        "vacuum          -1.894700\n",
-        "violin          -2.797967\n",
-        "volleyball      -2.807812\n",
-        "waffle iron     -2.418155\n",
-        "washer          -2.429423\n",
-        "water bottle    -2.163465\n",
-        "watercraft      -2.803971\n",
-        "whale           -3.094172\n",
-        "wine bottle     -2.830827\n",
-        "zebra           -2.791829\n",
+        "trombone        -2.582361\n",
+        "trumpet         -2.352853\n",
+        "turtle          -2.360859\n",
+        "tv or monitor   -2.761043\n",
+        "unicycle        -2.218467\n",
+        "vacuum          -1.907717\n",
+        "violin          -2.757079\n",
+        "volleyball      -2.723689\n",
+        "waffle iron     -2.418540\n",
+        "washer          -2.408994\n",
+        "water bottle    -2.174899\n",
+        "watercraft      -2.837425\n",
+        "whale           -3.120338\n",
+        "wine bottle     -2.772960\n",
+        "zebra           -2.742913\n",
         "Name: 0, Length: 200, dtype: float32\n"
        ]
       }
@@ -542,22 +630,22 @@
        "output_type": "pyout",
        "prompt_number": 4,
        "text": [
-        "<matplotlib.text.Text at 0x4e2c090>"
+        "<matplotlib.text.Text at 0x114f15f90>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
        "text": [
-        "<matplotlib.figure.Figure at 0x4d008d0>"
+        "<matplotlib.figure.Figure at 0x114254b50>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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lkkT/l19+GblcTuwRHA6HpDmDwUBYeYTfOp2O5NapVEq6sSz2AEiwaLfbiMVi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+9rdtp/vggw90d3env/zLv9Rv/uZv/oc/A89ggmDAc0EHgOSoAdEJXl/fpzoBRWFFRVpU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\nM+YdVreO42h9fd0eDqix7mEPDTcbMp1Om78HHO6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4HA4kEgncv39fMGZN05DJZOD1euFyuUTUFAqF0G634ff7YTKZ4PF44PV65X0F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V1fX1cul9PNzY22trZ0dnZmhgGqG77++muzMaH7WFm5c5Dxc6RSKYMbh8OhhTs6c6pJ0/+t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\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zj9o3yJlgigksRhlDLSvJO+twOLRhIT8b2RcRx/A0JJmOiWK90+mYlonMC3Sm0+lYiAAa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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x4d00a10>"
+        "<matplotlib.figure.Figure at 0x114254ad0>"
        ]
       }
      ],
@@ -586,16 +674,16 @@
        "stream": "stdout",
        "text": [
         "name\n",
-        "person          1.883164\n",
-        "bicycle         0.936994\n",
-        "unicycle        0.016907\n",
-        "banjo           0.013019\n",
-        "motorcycle     -0.024704\n",
-        "electric fan   -0.193420\n",
-        "turtle         -0.243857\n",
-        "cart           -0.289637\n",
-        "lizard         -0.307945\n",
-        "baby bed       -0.582180\n",
+        "person          1.835771\n",
+        "bicycle         0.866110\n",
+        "unicycle        0.057080\n",
+        "motorcycle     -0.006122\n",
+        "banjo          -0.028209\n",
+        "turtle         -0.189831\n",
+        "electric fan   -0.206788\n",
+        "cart           -0.214235\n",
+        "lizard         -0.393519\n",
+        "helmet         -0.477942\n",
         "dtype: float32\n"
        ]
       }
@@ -651,20 +739,20 @@
        "text": [
         "Top detection:\n",
         "name\n",
-        "person             1.883164\n",
-        "swimming trunks   -1.136701\n",
-        "rubber eraser     -1.251888\n",
-        "plastic bag       -1.286928\n",
-        "snowmobile        -1.304962\n",
+        "person             1.835771\n",
+        "swimming trunks   -1.150371\n",
+        "rubber eraser     -1.231106\n",
+        "turtle            -1.266037\n",
+        "plastic bag       -1.303265\n",
         "dtype: float32\n",
         "\n",
         "Second-best detection:\n",
         "name\n",
-        "bicycle     0.936994\n",
-        "unicycle   -0.372841\n",
-        "scorpion   -0.812350\n",
-        "lobster    -1.041506\n",
-        "lamp       -1.118889\n",
+        "bicycle     0.866110\n",
+        "unicycle   -0.359139\n",
+        "scorpion   -0.811621\n",
+        "lobster    -0.982891\n",
+        "lamp       -1.096808\n",
         "dtype: float32\n"
        ]
       },
@@ -673,15 +761,15 @@
        "output_type": "pyout",
        "prompt_number": 6,
        "text": [
-        "<matplotlib.patches.Rectangle at 0x4f59f10>"
+        "<matplotlib.patches.Rectangle at 0x118576a90>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+/ud/Dmsds/mMqqz5a1/4HPNyDgiUiuHtwzuPePz4MW+99RZt2/LBnTt84xt/\nwuc/c4t33nmXt958nRs3rlE1x8zmE+7e+4BelrO3t4cxnRcRmSnRkmZxoOiwz2VoIxPzJCTljRDo\n0OJT8gpiLq87JnrkdvHCKu9wc4dzkA9yZKZwwtFfH5IPe9y9+yFX8iuoQlO1NaPNdap5SQies9kY\nIxUqz9jb3QWt6a+PUEqxNlrj2dEho/V1ennBeDJma3eXtrV4a1F5xu6VSwSt+PDD99jY2uG9D96n\nMIaHDx/ggDc/93k213q8duszfOOb3yYz/ajgN9c52z/g5UuXMULjAow2NmmnNcoLBv0NxvWYZycH\n3LhxGbO2hbMBrw3T4LA2oHSORCC9Z3s0oj/I0SpHEPDO4mxNmzwUkRJkSkqk0QgReDo7IlctlYdx\n2MQMPL51kVI62CTb2XvhPCp6hlQNTaY1Ugh6eUGuNME6JtUk4o5aLRw36xrm1QwhFdYFhBbUrmJW\nnuF8RsgMMQYPqJ7idHzM5s6rHB2VFIMe07KklymkELjE99faoFWs9kXEPhoq8ddDwtGF1tH79J6m\nboDYm2Y6HYMQ+GCRQaCANlha5whCkBUGKUlKNypeJwR53uPDD+/xxms3CK5AKclsNiMzBds7O2xt\n7fHh3fsM+mu88fotfuqnfholNd/97ncZDg1SxgpK68DaZumNpzzRx4m3drF2lrAFtO3CPi0ghpgb\n6crUA0XH90886YguLPHw9MvxPOGj3mesll8qvuhJx4Rlt84777iDOEIXlQePcLGFQOiuQeR/CwJK\nisiOWTlHCDFJHxONUScptawYDcQEq/ceS5d7iQnT+CaX43GOUbOi+FeVuJB6obQ7qu0Pk0+PhaI1\n3jmGgwHOOfq9gjzPca2LnO+6RmlN6x1Xr15la2uL4XCID5Yie4vXX3+VjY1NPnP7Ns5WjM+O2Nvb\nY39/n/l8zrDXp6qqBHXEx5SJXihEV53YvRSVoJuOKw1K6iVJ3yWvJ2l6n5IdMSmauKzJy/eyK4mP\ni01KifURg7fWkuUxoemBzZ1txmdn2OGAd999lyLPaUPDpZcu4VrLrC7ZubTHo4ePKKuKV197HaXi\ndM97BQHY2t2laltO53Pe+/ADdvb26I1G7Fy+ihSKz9++xdnJCc+ODnj25AHHBwfsXblC2zS0TcCF\nwAdPHiLLOWtNiapmjLbXeHZ6zPRkyuXRJlpLvvC5L8I7hrVeIBcZbSMIRY+mLMm0wbhA4Ry+KkGB\nzgxB5TFTvBYYAAAgAElEQVSKsQKnIQRH09QEEUPQxltCa6OxzHtU3uEHa9x5OqWZH1J4S9E2DHsF\nwb0YC7Qqlj57AUiFl2B1IAiHx2GMWhRltG0bvSytMXkW+echLspMaG68cpPabvBwf0LwDlMYziZj\nNguYNi1vv3eHvPcyIi+SJxdi0VRqchIbhHVsCEfrGgIBJaM3a5SKRVEhtgJTSrO7u8PP/uzf4s6d\n92mammo8RspUi+BdrA5MHn50HPyCTdPMZgyGQ57uP4tJzEGP9bU1rl67xvHJKccnZ1y/9jI/uHOH\n27c/y6XLLzGdTNjY2EjOCgsqW9eASimF0pG9VVf1C8cc4RcUPFIic6HQkzJWMrFDnD+nuGZVGesM\ntI4sE+dAdD1/YtWm1Cqu048o8LDgmSNWWkOFjyb7BOC6uoEUeUeDI841Iuv+FiIm4Rc9vAIgovOh\npIxJd++SAhcxkd0paBl7x0ixwnDy/px33p1SJwMmvMdz3tsOKbJaJHjDeVjoRfKpKXCTZXGStw1C\nQJEbBLE3B0GiJQTfYvI8FpBohfOWTCmGwyGbm5vYpuXNN19nPp9jjAEzYmMj9mkgeStZltE0ybsI\nKyFWWIY3biVh08Et3i85prFZjVx4HEpLMiWxNqCNWGCVIQRswt6jCKx3GK8Y9fu0zrGxvo7SKnlt\nOnK2TcaN69fJ85zjoyPaJja8apuWpmmoq5osy7n52uvs7+/z/p33uHrtGlVVYXoFwmg2dza5fO0l\nLl2+Qq/fY3J8BCrjn/7BH/HTn/8Ma8M1KhcLMKbjCVubm0xnDUJJdvZ2+e7jb/PSzVfYevkK33v7\nB0zmNW/euk0dYP/ZYyZ2ytMnD8muDJm3giwf0TrPwdERl69eZ1AU9J0Do5BZDDtdEw2jVwbnmlim\njMRkBmSX8Y/0yjPrKfrrjF2Gc4I6CFzjcC0gBNWz6QvnUdlWNLZFZiYpcUntXaTFtRacS55WXKRK\nZ3GuxE9QUmDbNjVm2mQyc3jXIkSND47cZHg54PGzklkjcbrFGDg6mSGQaGPITIF1NbP5PLVuiIks\nnZg5wnu89QtsW6lIw7TeUbcNX/ziFymKgsePH+M3t/HeUXuHI7YYiPSN5OkFaL2ncY48y7l25Qqj\nYZ/t7S1wlvHZKd995x3Gkykb65s8Oz7ijTffIisK6trS6w+SYxG9SkTkTxeZwXbFKVIQcOjsxUaz\nw5YJIVULurQGll5wxNW7IhhSYleAkbTOxo6bSuL90hBEIwBCReWvknqK+Hb8l0oY9QLBTpc04jl+\neMKb41oPizyoECwqILt5Ef936TOR5krUF1rE/kUkZpIPKXLwXT/OpZ5QybCKxJ5Jg5SSqUteeAhR\nkXdR/5L3HmE+JZd01L9IPjUF7oNLnUWXdBmlBFoLmqZevKgmZaKVjoPWJqvW1g0Bx2w+jdza0OLa\nZVtImf7umCixoGRp2ZAvtmzncLE0uBYBruvURge7xZ4NYtlJLdZ7xWOllLFCUkf6mQ8BYxQhNZTy\nRPrWYHdvwakWQjDs9Rc4fV3VSCm5cvkyARgORwzXRmxsbTAajbj/4AFaa6z3bAnBeHKGygwQIY2T\n08eYrMeHdx+wtb3NK6++zu61G3znnfcgSH7qC58nMzmHx/vk/YKj2Zj7D+7TlxLnYf/RQyrfEmzD\ng/ffZrPI+c6TdxlkQ/7mz/zLfO/Oh7RCcPBsn0s33+Tpe+9x9PQhl65eYt7M2WCd7e0tirwg6w8o\nyxm9jS1GayOkjuX0J8enjKdTdp2mGAz5vT/7AfZMQK4wON689gpb23v84N6Dj74sIEsrM1hPSB6d\nlDlt7XCtQyHpD/psbGyk5klxXt2/f4/5bA4hUBQF45Mz9o9raqvxrsTkDbPZGTuXXseVgSf7FabY\niQlhOaPo9ZEythiYtzXBO4SCspnTNA0mM4sk+iDLCM5hsgwpNWVVUbcNznmKvBdpqHkeDbIyeEg0\nvdiPpisj9z5CKiiNBUbra0ymY8bTM/JeRq/IebT/JPa6kYLB+hr7Tw/4N/6tf5tZVaMmUzY31qKT\nQcSGJS41VnJIufRSwaaE5kfFtTUR4lSEhccYo5El/CgWSbsOb5dCILWIfX2URGFiR8iOHRLASwHB\ngQeXiHuRyhlZJL6tEwwa16IUAuc9WpgVTDnE4jEpkzFdMlhiZ8ZVOCOtZUGCZmCBw0OCwKKSF4kC\nK0JA6dRasrM6AYJzIGJ7gRU1AcSWXEJKtIrQWwOJl+6XXPk4qvH6Cabxf4ES/9QU+FIbpp+Cw/mu\nS98yYSBFZ9nP91yGEPtjLxlG50KP1efuoIwO3xJCLEpnu9/r/r3wLlgqcKXMuZdxTsmnCb8IkrwD\nn1qfrhyvUhMfIcSivDhi8hFnVzJSp7qozFrFcDCMdMTNzZjokZKd3R3e4A28d7z+xuuUZYVMkMCf\nf/8d8l6fpmp4VD/k9mc/w+z0JBYRa0XjA+ubG2xvbzEra8YnRxQmYysv8FXDMM/IQkBZx8bmBpUU\nNFrx6itv8vYfTZjuP8PmNbOjMfc/fB8jJcG2nB4+42Gmefu7f8rGoODtd454uP+Qv/3X/xWGvo3t\nR1WP2rb40jOrSobDIRvbW/RHa+SDAX014vD4lPe/8w7N+lXMZkYWWvYGQ15+6RW+/WfvvXAWfeH1\nN9k/eMa0aZjVNbroc3h0Sr8YoIXheDphuLHDzt41JpMJQghefuVldncvc3ZyxJ333mN8cowSgsu7\nO8xmLboYEPSYfu8l+mYX6bcpy1OUNGRmgFMt1TzOTaUlQQVaV2NyjVEK2UQ83KgMYyTSA6jY6yZX\n1G0svFI6QmGTyYTf+do/AWB6NkdlGc7HyDSTEtla1vMeTVmRDYfMRcCsjdjff8Jar08InrsffMDW\nzmZUcgJ6g36skgyWz37uszw7OKG2guPjY0KI7V4DltFohHOWum1RqaGZtdG58qF94ZiLlGAlJW1b\n18RwH5M6YupE3/V4B3rZQpCgJcJ13PtkqETXW6SrwI1FP9bHYpc8z7CNix50UqgdZONFhMojxLSk\nA3cKYBE5LxT1eT0i07qLHvCy93f3KwsnLemjRSS0wNVTS+BU57CINFbW/mqyUohYIKeERITY0ji2\nxY5ObG7Uwqn1K4nQj5NPTYEvlK0g9WBfNoRJWjt5tF0GmOjfrmSbY9lsp0z9IsxaNQ7d4IXQpSqi\nhOe+7yQ8p9jjZ35xXSlEDPFYWtjV46Vggd11iGC8o7A4VwcdumR5l2MSG/DI1Lfah9ibuGxriqJY\nhFghxK5ufd1jOBpS1RVBKG7deoODg0N2tzYptCRTmoMnRUzsDXr0N9YYjEb8zO4OIkSj0cwq3v/B\n+1xa3+CtV26QIdH9EVnW47Sco3oGd3ZCOz2jlyuGV65Qn5UcPH3C9kvX2RkOsULibc21q5cZ9AoO\nz4740ms/Q2s0h+2cXGgmZUPAU0hB2zRMZo4jW9E0LcoYtnqeOnisCMyqM9ZDTpEJ7rzzbY7PptQf\n0wdlqz9i79Yu87blaDrlez+4g2tA5BlFr09fKHTWZzKrqJqIZ47PJjx6cJc3X3uVyeYRbjalX2R4\nA6GsGfWH6GKEEIZyNuP4+Ii7d9/DWU0xGJH1WuomVtCNRgXrG31m81MIDZk2DAZDNta3GPQHnJ3V\nlPOafn8Qe013fV+EQGnNg0cP+Z2v/k6ERpqG4dqI1raoYMBZVNuwYXJuX3uZPMt478E9xtMxta8I\nXlAdn2AyzfRUcnTwFKUVg8EQ51ru37tHkfc4PjyKdQ8eyrqhsU1skdxaTk6fxWIak6O8QSpNr5ej\nM/WxykOnsnNIkKMwiwRrdFsimyvCB1172LTfh5ULTnVXLdvljyK2HT3PqBB1gjRTq6kQECEmCbvW\nscYYuj73MQpIbWQFC5ZNSP/F3JfAtZ1vD8LZzv1eruUUVUOkK3f4fFzmKXErRbp/GRt0+VhlutTd\nXQ+mwKpjGWGl+J3zsfOgNgaZvotoQSqo8izG6OPk08PAV9ghMXBIuE/6JKSEkEhtRjuIolOkfjEw\n3cvtWpGmqidWMOyUUOnOGy/7Yo5r562f8/Z9AFw0FiJO3g6/gjh1FlZckMJCcU6xq+escvd5WFkk\nPoRlQsx7HI6qjQyXejpFaRW9meCRTiy55qm3cZHnrI2GHB4esre3xeuvvcbx4TFN08bmVc4ijOL6\nlUsREnIOJTW9tTVGm0N+6rOfoWkb1jc3OTkbcxXB6dEhjx98SNbT/PQXfpqgFQ/v3KUpa65f3eO4\nrtm6fIkHd++RZXGDjI2dLXavvcRs2kTFG+B0PEMQyExG8J7cOtq02Uae54znJceHp7g8cPtzr2H6\nOUfvv81r125wevQMZ18cSj7e32dtcwPynD/78+/z4eNn9PrrzO2Y6XSfxwcPee21VyN1U8emZNV0\nwt7WBm1dcvPGKxhX8dnbb/E///Z/S09vsdPbQmSSe3efsLV5nVNbUc+PUXqd+ank4f2nOKnY29vE\nGAdnU87O9tHKU1cltvV88Qv/Ar/0C7+AUQX7T495dnBIVVVMy4rJbE5mMsq65vf/4P/ibDqFEAt1\nynoePXspEL5hbzTi89dfwY1nbA/7DN94HfngA+6eHjHsjXC1o9CaXr/AOsvO1jb37j9ga3uXpmrJ\npOH9937AjTc+g0NgMo33Lnax1GDQICAvCua1pa5LXHD4eXsuGj0nnUOUvOaOBrhaGRlCOMcF7/jx\nPkhkDJwTH19SVfWyaZNIvUMALQIST9u26G79J2jEGLlI6IYFLbGrCCU6hKn3yOruRFpqnG/pKms6\nHRGCT3z7pbceo291br12P8de+GqhyIUQCNe1RF42pGKFj766K1OEp1Ljr1TkJUJXHfpReuHHyafX\njXBlcgiRFKxPr0jIheXuwIuYJZYLxZseH4gZ4NXsrZQSufL8q2FTh3/Fc533tFePXfXclUi7ZXQw\nToJtQlgeg48RgBcyNc8J55Rz97ydIZHd1mJySWfERxw3RgoCB2RFvnJ/ISZTfCy9bRqLVir1ZVCA\nZ2trk+GgR24kR8cHaG0odIHqZTTexU5sKhCcRakAwrH7ylWuv3QJ0bQEDXMVWLu2i5tV7G2tcXl7\nxCuvXaGyNZc2dhG1Y3NtnfW9XfZyg8gydtbWkK1nXpUczyb0Nte4tJXHyUpMyHrbIkJndBTT6YSg\nDArNzLcM14d89rNv0WaSujrlpe0B2z3D+mCXCdUL59HjowOOm5JgMsSgD3nOSVMzm02YTecoBM8O\nDplNJ4yGQ+aTKbvrI/7ml77Ile3XOHjyiG9944852n/M7OwhP/Plz3N6WvP44SMe3n3Exuf3wM6R\nVPSyDdpaE2ogD/R6BXhLXVYMepqXXtpmd3uLo4MTgqs4fPqIQX+dra1t1tbW2btyhSf7z/jz73+f\n9z74kHfffY+nT5/GJLsXC29SK0EvU2zvrHN1uIZoG67vbLP/dJ/Na5fJhWeQK4RzFEqz1uuTFbFq\nM5OKzdGIyekJvd6Qcj7jH//2P+Lv/f03aJ2greYURUZbTanaGkLsHWRtC4k62zQV5xHc89LRZiNu\nK9B5nKPW2kVNRYxko9KyzmJM3OEJlwJsBME5nG9RUpDJPEalziYFp9BaxrYWQqRdrNJWZYuNFEgM\nI0vWW/aisdYS5FKRdjvsIGJRVNwCMPVTIZbRd5Cm6OAcHyN2n+CcBafb2eSNp60THQulrdIGJ89H\n9V0ODpbOW5cYjdHG0gmMx/glhLMCM79IPr1KTLviheIiwL9QcqB15wXn0Zte8YpXYY4lDWfpUUea\n1XmPdzmeUQF3Gwc8T9ORckVZpsRM3E8yTemkgF3o2k+e9+zl6gsUy89VakbfNeVxKxns7h1JocCn\nl9tBRe6j0EEHOWmdRUvuli1dvW0p8jyOodALb0ECuRAEG6gn5WKcpHSI9ox5x3tv4lj7po1N7pVk\nsLfH6PJlIKCVZvf6azR1vRgPBDBIGF4zYJOd2OgreT6dB7RKO+siqpBYANY7pNTcfHnGvK7xDsp5\nxXQ2padnvOHGL5xHp9Mpfe8RwFaWMby6y6ycs37zClJrRKUJUvDo8SO8FBwbyUtpk+157bn/+IiZ\nzXn//imN3cCpIa0Yc3TaYEPO02fPODmtgE2CzJnZCQ0BoUusL5FlgLphcjZht93h6MGE09M5LpSc\nXp/Tu7rNZBZ4/95jfnbjTbb2tuDuIWF4yJ39e7heoK1a+mqIagucEdhQMhCKlwc9LucKWbecTI/I\ndtdohznDvW3c+0cU/T7aGy5vbvDSxhqElqPxCZsvbfHdD+5yVo4JMqc5OOTb3/9Tfvqvf4GD/cfM\nZ4fkucZ6cDqjUVBNzhj1CjIDta1otYAXQ+CccEYmDaKNPb6diiyvum7o9wa0dYN3gaoqUUqR93qE\nEPeMdNojnUSjUVKljUUCXtSpTzgIJ8E6moTVa61j06jcIJXBhlil2DQWpRX90YCmGaNCdAYykcU+\nLMpR+TleggqauvJoMmxoIpYePAmnQMvU6AqBTAlYFQRGZyAVzsd9cxGpgti1CJ8i/2RYmp5Iyc64\nfZuzEVLxTfTkpZIxTRYChRVRgYuA9JLYvTHu2BNbBEQvccloe7F8agq8244Llgp5YTHFShlsp7hX\nwrTnk4+rv/f8Md1xq/KiCqjn5dzviKUdXC3PPZ8MOR9uvuiaL7p2hHvOH7usYhMrVvmjnNBlyHr+\nTzzH0kB2/V2e9wJWr/fciRe8WpcazK+K1nrRuKnbtaT7vLvfzBiclB+5Vnfe5w2w8T5us6YNGyH1\ns/CBumnJ8myxe8vzcvPmzUV/6g62ap2lqipm5Zx26rn28nU2t2/jQ+Dg+JCNzU2UVNy7/wGnZ8ds\nbI4Y9Ae4oHl6cEA1bzAmj0wDESuEjTFx30UR/bI1M0LWksGwx8lpCRju3n9EXc7o5316gx5HB4/Z\n3lnjB3fv81tf/V3M5jpvffYLPNs/5Zt/9GdMj2rmB1O2+iPacUmRLYs4hr0eW6M1ckAieXb4hPJ0\nzJXREG8t68MRs3lFv5/TyzVagRGG9X6fw9mUz92+xfuP9jk8m1HNT/nDf/p/cHT4iEs721Eh6gzb\nVEgR33G/l0EI1I2NFYvBL1hbz4sKkVVhnUPpDO/j/rBKCZq2IuCROvbxDyGOn0xFMs62sXDIKaRQ\nSAnCQUitDUTasEUiQMZdn4SMnrh1bexL71zc11LGPVvLqsXaikzEjUwiNBmwocVhoyMlBEWWk6kc\nZ9u0a0/CuhXobj662CIBYpRv/XLPXYh5uM45W01whhD3yfV+SUeM+7Dq2G7BxW6E3ZruNmSXIrak\n7RAEIbrNpiP88iObxOyKazo6T6cMup+7MGxBAVyBIJ5XZquVSy9ilKwqxO6756+/8JQ7ov1HPPyl\nPG8ofti9nGsrugqjiPNcz+cz16vHLeGcj95H99lqVny1kfzqOVdLe1cNyhKrPH/d1bF5/v5W+8W8\naCy6jo7Pj9nzxmZhcFJYvWj9RiyS6joYFvmQF8n2zlbci1MbmraltW2ke/noWck2shyqpo6dF3sw\nHk+Yty0Kwdp6j+Ar5rMzLl/dpm0dp+MzTs6OOTx8RlXNMJnGyIDCs7ezwfbWRlzkPtDOLNWsoZ7P\nGfRyrIf++oBiWLA/ecaTP37KBwdTJtUJ/WGfb37rT/m93/2/Maxz88bnCZVncvSMtWGPcnaGVobM\naDb7AwqTEeqatnUMRmsUJqOfF2wN1xmPp6ie4NL2iLVBgSKgBfSzjDVfcHJywutXr/CFz22xtrFD\nluesb23S6w2YTOd4HyuOtXPgW9rSYtHorA/Wo+WyAOl50c4jVCySsq5FCUFZzhFCYkzsCWJtu4jO\nYoXpssOhkOBdi3cWnaC/RV0GDic0PmiCa1OiL4pROU1TLXJFtqljj3mtMZkmWE/TVEihUUrQhhon\n476dwTVk0tH6BoRP3UVBp775PsQoDgFCpV4zKGoXveiuEtb5uEmMlDolO2MeLoSACQIhY0fC2Ioj\nIJ2LmycnnF7IuCuRMjp57p2uS7k32RmLZbuOHyafajOrhQe3sri77zp5sXfpFwrx474PISy2J1pV\nJJ2iWY0AnlfCH+f5Pq8UVw1DpwBfpKiev/bzyvL5c79I+a7ez+r5n++3sJqA/WeJMpYJmedpmufv\n5XzOQpwrtV41Iqv3tVodt3ovq4Zs8X23ozgSrSKX3WSKXBpsaz82lFxfX4+hZ+vQRpHlJiajSDRO\nZVE69jQRUnJ5sMelS7sopbBNSzUvqcuSqqxAB4zKGAxGTKdzdna2WN8YMpmMmc3n7Gxv8PrNN5hO\nS+49eYBtPPVkzq2bN9kYrfGtb36LtmnRxTo3P/dZ8rUeTw6ecvW1Ta6+epM/+MM/4MO7h8zmjsOj\nEzY2CtZ3rxOM5Nn+fUabBe5sxt5wm77JsE2LAiyBN966xcNnh5RlRTmZUQhF3i/YHOYYLMKBkTlG\n5zjjaV1Ae8fZ0yf83X/tK9z54IPIU5/NaRtAKQga4Vu0tRR5RuMMQQ1wbY1y7mN3g8mBqv5/mHvz\nINuO+77v0322u86+vXk78ABiIwASgLiIFCmRICVTosX8IZUsJ1JVLCvlpRynUlYqVbGqnNimSxWX\n7TiJU7JUoeQsFmXtkiVKpkhxByEQ+/Y2vHVm3qx3P2t3/uhz7vQ9c+48yIoLatTDnXvPOX16/fZv\n/yUoR+HX6ujkMPxyFJlgX3nOYRMYShiHNpWa2OEmWJYJL6Cy1ABcniokz82DIEWSxx7PLUBG4eDQ\n0UgbvZfrSFxHMRiOcKWHFIbijaIUJTK8wHACWZahs5gsjfFrAQpl/C+SFFIAjczFIUKIccpAJYyn\nrud5xoIlK9zsc/wYK9sUvnTzta/JpOEGyKl1E6Aq3+sopDDOfEkaj/UFtt7BEFbOdEVyXt5xCrws\nKoDJzW5TxGVqvbjXBrFynTZA2ddssLEPkeL3sv1mUZf93X6uaEsVJQ0QFDG0rX9vp4wtXSyQtPsx\n7XAp2lhVXxlwy1Y39sFQ1FNct8G+PD72IQLV3My0fiXG5dIYjmbZmFXV2WGC2erxyfLFLnFyp6ks\n3+QKnYeZVShh2HqVt09rDZ7D7Gwbb2GewWCADDySOGFhYZn7LtxHGI7wXGMiOQyHSOFQC5rsHXQ5\nc2GRbm/AsBPiizrX39rgoSe+i/4wpjMc8IWvvkDQqqEcwfLqPFtbV+l1Unx/ASFc5mbm6Hf3EAjq\nXpOzp89xZ/sWJxYXmW+0kBqG4QjhucjAY/uggwx8RnHMsD+i7gf4Acw2arhK4wkHqYzMdqHu02i0\nGKYJjz/6btJhj5majxs06G51uXpzmzhVnD+1wplza4SDPRzX58rNPXZHMTLLWG0JqNcrx9xRTh4L\nXjAcDJjJk1s4roPreSRZShwnJmm2pYuSovCGBhw5FtFkOjHGBYLxPyFMVFedKVKVmYNAgutI0iQl\nzaKccpaM4hC35qMzRaYiY68fOCRZNo4Q6eSUr+9J4+0NFLlcC52MMSDIuU5lAk8pKQy3ok3i8yzN\nP3OPayM6MlhmTA5l7p2au83nJpdCFvvFKCldz0U6Ild1HebWjNMEpQVK5bb6d4GJd9CRZ5LtLjKd\nFMBYdKgsAzoOuMogVvXdpiiPA5ZyO21gt3+zKWIb5KcB67SDqvy+qu82OBalKuB7AXhVIpfyeFVx\nCVU6hDIlXu6HfQCWOaRp7574u7iux9w0UkjiMDX2wFPsYY3XrovQEOeZawovW+k6qMyMme8H+IFP\nnCRjGWOz0SSJIhwhmZ2dRblGbBMPIxqNBvN6liQdMuwpavUZVAa+X6fZaBI0YrSQJHgkNLj3KcVe\nL+Hn//UvMRiE6EQTXz8ApXiNN3Fch3CYMtvq0T0Y8NQTT6GbLmkSIplhf/8O7oxm1hfUfJc0yxim\nMVnmGBGBDEmUYhgmLC0tEUZDHJmi4wSECb6llMZzXXzPY6buk/mCx971EL1kxOLcLFHmkmU9VtdO\nI72A0+tLLK/UiXoutza2uLOzz0C38YVk+fwpgvnZyjGv19ooP0UJk/jj3NIyvV6Pg26HJE3Q2oS9\ncFwvtxQxDjmO5+bx2CXaMVYpQmh84SByCjwTAi1ckJIsjcaZjjJVuLJrHM8Ze4kak0KXOPcD0UKR\nZhEqVSaAmANaOSbSYpbiublllOOgEGOlopHVp8ZTRB/GTUq1SUQ8NgjIHXYK+XucJQiVixGjdJzB\nHiBTcpzUW0h56KCTAULmoK5IktSIc1zXiGucw/g6Ysq6L8o7CuAFUINpfFnUUSV/LYsSit9sCtC+\nXgUa0+SwcEiVTiv2M+V7y8FrbM7Czmht11VFJU8TZVT9bh9wR5SjFaKYqvfcDejv1rYxRQsT82nP\nRXlejrRhHD7i0GQTbUK/cowxlR/kclqliwrGTlNKpYjMGTtMJMmIgqvVWpOEqXHTFrlLtjCUf71R\nBzRSKfygycJcO6eIVJ4tXEIWkWnQXo1LNzb5w69+m1cvvcUwTGnWmvT6ezipwhcObtjAdSRtoRjt\n7dJ2XZ79+h+yuLSMdB0WlhfpJ4qZpVMMtq7iqJhm4FL3PTJHMEzicYquVGfoVBHUAnzhIrUg8AM8\n7VJ36qAlizPztObmmD+5zMFuh8hRaMehn8Cbl6/QS32iNOP29QbtjzxB0/N49dU32dwZoeuCeDji\nUpAxs7ZWOeauX2dn4xavX7+OAm61WqysrLC8uoIWgp29fa5cvsTMzIw5CGfnSOKYOFE0aj5xkpu9\nSgcpFDJToDPCKEJ4PplICeoN0iwkiqOcElUEvgnXGgQBXuAaRWWa4HsuMk0RQpssU55nQlGkKcJ1\n0ZlmZWGB2UaL7t4e6XCA4wii3IrFxE3ReI5jXNvTw3hGQinjXSpMfJtROsLLfRlMWjeTyUdKiRN4\nY2s5g10ZSEgzRc2r5TGZjJw7jAb5etVkeTYeotxIS5iE4n+hlZhl4JpGjZblpTZI29fLisoqKt0G\nn9o5RAoAACAASURBVEI5YB8IhdK0qhwnH57WfptyH7vzWyKdIltQVZ+rKNcqwC23t+rgKve5fK2K\ny5k2H3Z7ymIm+55ppSpUARSAbf7SaIQlfz22PlFY2OTst9a5hYH5LLIp2e+0zTu11iblmxDEoYnx\nUchCpQCtUzrdEbVaDc8LQPomiH/7FCrT/NEX/ohvfes59vY7xPs9sihmc2RsrcM4ZPncWVYbZ7jx\n1jXCpIvfDuhFHeZOztAZdjh16h46o5DW4gqjUYjyPLSjidE4WhvFbJLhIDDpEPL4JdrkWxRaEsUJ\n9WYDMN+Xllaot5voDJJEMVAhiQC/PceTT7wXEcwyjGJmmuZwTJVmGMVGpKEV954/w4MP3cOLb7xZ\nPeiuy62bG/huDS00/d6I++9f5GC/x2tvvM7eQZcTJ05w8sQZ9nf3efWlNxgNR9QCn729O3i1Bh/6\n2Me4ePkKuztbtDyPc6fXWVtZYRCF3NzeZmZeMNeu0+l0aDSahMMRtTyVohCCvQMTtdFxHKTnGlk2\nJsbKYNgjTRVuUGM4GBGNElq1JmdOrHP21En+5JlvAZpazZgIIgSD/gjHddGOcSAsOD4nt/JK05RM\nZQSeR5bEuXmjf+gk5HmkwliwiJx6l46xynJ8I87TDmQqNUnJHdcS/x6KgV3fy6NAGo1AkvwFVWJO\nE2+UlXTl61ByipnGlnOoEqgCmGnxjqdRrdPaUgaHSWXf+MrE75PWGZPvL6jDKnAsf7ffVz60qgDY\n84rpPgqIZdn82xHvFO2pEsGAbVPPWJZnc0qHdWmUEuO/y8qc40qW5V5s5CZhgrG8UQhAVRzgRV/H\noyFAQGDSS4CWuaw2A+FQ95oI4TCKM4TI2D8Y8I1vX2Ljxm1e/tafku11cUcJT548TbvdINQRI5kS\nuhleO6Axf5L3Pno/X/nmH3Ft6zLNWZ+D/h1qTo2rV69w78l7qGlJO2jSC5ps3nmL9ZUFkA5pFON6\nHkprXFeaaIbSQzqaOI5NUg2jj0QI8B2PK9cusbZ+CtmoEyzO4SnPZBHyfJLOAZ3dPaSUrM6dZn9n\nh9Goz/LKMqnuolDsbd3gZXosLlbHYP/d3/89ao0m8wvLbGxssLK2wsrKCv/+93+fOElxpMPm7S1c\n4bO9eQdXSk6trKPSDM916A5HXLl6jWE4QmtzcF56/U0unD3Hd154kds7O5y9D1RcZ2FhgSiKqNXb\njKKMnZ0darU6vh8QpwkLc/OMwpCZoEm/f0CWJQS+B2RkmUYKj9u3r7PQnuUb3/gW66tLNJt1dnf3\nQEjCKMHza2RKoQWozIhylKNwhMSRGVGU4DpGNOJ6HiqXfxu+0M2zPQmkw2GyakeCUGgK88LDPe0H\nHmkUjx39TKgnY2aYpibKYWGC6ExJJViUPxeAnzt3jpmZmbEw/5lnnmFvb48f/dEf5dq1a5w7d45f\n+ZVfYW5u7sizVdSxzYpXsfF3A1dbsZn/cQTkis9Ca15lWleu/4hrPdODrU+rxxa5TCpEqwGqrOic\nRhVXiXDKvxcly6qBsepgPA7AjzvAbKXyZDsn21iu38GlCM8JMrcLtt47JXrkuM2asey7ZHmeHx56\nfDiORVelNshC9unkmcx1ztK6DnGSUWvN8Pf//j/izJl7GcoZXvnO8ywFDe5/7DzeMCQ66PDouXNk\nIuPS7evsjbqkachcS9O9s82HHn6EL+7dorvfYbY9g4oFMpPsbWzx1GPv4frly5w6uU63f4c00ya4\nlHBQUYr0PTwpkdrFc0x2IymNzFc4ApwM6XlkKubWjdvs9zroep3v/uQnWVhdJpMa4UhOrq+jlWI0\nGhFGIf5ck6Rdx3VdTp44RTSI0I6AQBrbzoqytDxPt9uns7XFqYVFWq0GGxu3EEKTZQkC48Qz6PUI\nh0OWF5aYbc7S6RwQjobs7e3jtJqkaBr1GjOBz5zvsb66Qu/ggIW5WW7fvIF78hSLCyucOb3OYBQy\nGA5ZWHS4ees26+snEXikyuHdjz3JPUuL7GxvsLl1gzs7dwgcF78xwyuvXWJ754DGEzOcP3OKvZ0t\nkIIkSajVGwiZEkURfi2gVqvT63VRaYZ0oFarmexEGDPCMIyIotwnIA8VHHgeWkMURQSeb9wJc+OG\nQrfjB7WxyW0URXksJY1w8ngwooibQm777pBlmiSNMNY208ufC8CFEHzpS19iYWFh/NtnP/tZnn76\naf7e3/t7/JN/8k/47Gc/y2c/+9mpz1eJN/5j2iGEOOIkUmzUqsNiYiNTLVuvorptADsO9Kv6Uz5g\n7AOrqk/2Z7neae+dJtawr5X7acvyy4fhcaXc/wLAyxY7Ve0ot8FkqRc5iBs5eKFs1MKOr1wutkhm\n8vDRGqQogpyNW52LsZKjY4rJZ6hUbudrjIIZDke0ZuZIleSv/1d/g69+7Rm+/m9/m1NrJzizMo9S\nQ1QNPvrpj5MNQgIc/tLHPsnO5ja3rt1geHOElgHDwZAHn/gEz1x8ieevXkY3WnTjCC8I+L1vfZEP\nfOC72N/dZP3ESUadPVzh4gZ1Yj3KwxQrmrUAk6pNmaQPaBxfMoyGxNGQulPj5OlVHnrkca7f2aPR\nboIjSVWKjmOkSgmEptnyyVo+odZIP2BtbQ1fOgRCsN/rcJAltLxG5YgHdY9TjVXmGrOEoxDtCPqD\nHufOn2Vnd49+f0i9VqNWC5hptU1Qta07dDoHKDdlcWkeITSNep10OCBo1phpt0jCkPNnTrHZ7TJf\nn6Pb7fPCCy9x6vQZwjhieXmV1bWT7B/0+c4LLxGGJhJntx/SeOzdNJs1lheXuXj5IlGSUW9rXn75\nVdCCq1ffou6eJ4pCHrj/YTKl6Q8G1GsNojjh4KBDr9en0ajTnmkbZ6EswdG5NYnWLC4u5kpPRb1W\nJ01TkjglThJqtRpxNMwToZuojqKgAKSJ2hjU6ibhtuOS+ApwUCod7z/XdUmyBCFNjl2lhOECjyl/\nbhFKeZP+1m/9Fl/+8pcB+Imf+Ak++tGPTgXwMohCBWWeE6ki/8+60bAuwvyqlR7v8eOUedOo+jLl\nPwkEk6BuU6xVlHJVP6v6WPW9yoLlOIq4XEdxb1nBepx8v+r3Ktm8/a7yOFS1uUoEZLdnQsmpDoHb\nCKkd8unFQOuUIkFrlcdWPoxnbeSQxvmCkreqiVhnt8FQQFHQQycObhrgZR4uPkooajWPMMl45Y3X\n+Bf/y79ie7fLDzzyYe69517q7QY3tjZYOrHGcGmJjtzj3ecu8PWXX8Tb79DKFPefbtCcm2F/MMDH\n4wG3iRqmfGWwQTbrwyhiXvk8/+KrSBKeevBB4sGIZr1BPNgnjAeIwCfRUHNcyIyVj1+v40kX8IhV\nxsr58+x1u3z4kz/A0toJTksXHBdkngUoD5qkVRHiwESHVEqjaooszZAI2s4cXhQdxkMulQ89+SSO\n41IPTFaoRKUMhyMylXH+1DL7e/vGS1M6JPEC4Sik2+0iA4dbmyGnVs+wuLLMxUuX8D2X/W4HL/B5\n7uKbqEad9dkZFhYX89gqCuG4rDSWSTPNxtYGw+HIRNPEgwiuvnaF2288y8LCIucv3EukXLb3OoQb\n+6AypNDsHdxhtzPD9WtXeeqhdzOjXXb3h+yPQi7duM0wSZhbXGammdKqZywvLrK2ehan1WF1ZZ2N\n65sMuxHJKGFuZpaHH3yIU6fWybTiT597nheefwGphybmv+saRScCrQSLiyeYnVshSyXbdzrMzy8h\ng4wwGuE4gu07t43OJOyjB12kznA9CR4k8ZR4Bnn5c1PgH//4x3Ech5/+6Z/mp37qp9ja2mJ1dRWA\n1dVVtra2Kp91LDaj2NhVFiBVVHoVYIAJSlNVqqhmG6CqZLnlusu/VVHzVdeK9x+XYWMa93EcNX1c\nKU7z4w6i8nvKfx/HAUwoH4UYBzea1v+i2GKnMtCb2GLTubBpbXdycQfOZNuK3JEFeNuesXZ95n7z\n6crABEFyJCJTZCRoZeI939rY5F/+8/+VcKh47IFHOXXmNJ1uh1EyIotj6rUat27d5vSp06yfO4cb\nhji7u9Drcae/SzYa4TcaLNUbPHji3Xy0qXnhC79JJ4zQqYvrBnhKoIRkFMW02m1jm56mtNpNtBSk\nQjAajXCEIHA9klQRNFyk4xFFEfe+6wE+8cCDDOKYQZoYD8g8NaAx5TskmEzCFKfaWzYnlaYd4gvz\n84eEgtYEjk+r2RiP8emT6yRJMjEXYRgSRTGjgbH39gOfZt1nMBxy0O3SajfJVMbW5iaDwYD5hXlW\nV5dZXV3LY2XHNIIGURhysLvNXKuBN+vjCJcwDHFkgzSTbG916XdTrl3bNiFpqTEa9Ni6tctce552\na4ntzS22Njd5+aUXGSqBqDdxpMuoP2B5cYmZ2VkeePBB5tttvMU+InO4kWW89sYbzDVm6ex3WFpc\nxPM9avUajUaLldUTjJJ9er0e9Xabudk5ksREE818j14Sc+vGFufO3cfVqze4sXGd9z7xXqLhiKUT\n5xBC0VIJnidAp2xt3Wam3aTbq44BVJQ/F4B/7Wtf48SJE2xvb/P000/zwAMPTFyvEjEU5XP/5vPj\na48/+jCPP/ZwpYeeHY+hDJK2ss9QXNXyojJYCCGOyss5Kt+1f7ffWf4sA1pV3wuzujKAVSkB/yxl\nGsVvW73c7RCYFm/F7kN5rG0ALIuCjvu7fACU+1A1fsetoyKIV7mdVeIpmzOp4qJUaFzSHaGRbgak\nKCGo11p89h//HE7m8fR3fwiUw2DUYzTsc+fKJsuLS1x58SVOnj3H1ltv8auvvMZ84HPv6jL33n+e\nkXeKg36XLM2Im3M0W3P86KOf4a3Nm/zRM99k6CgGMkYkDp5nvA5d10VkgiCooUmN40seE8RxXdNv\nBGGaUa9L/HqD2fl5hONQq9eMAjTVFHbETq7gPTzcVC5KyibGAg5toKeVNdu8UJi41kopE68kt3Qq\nTPmK9R4EgTG7mwGEIIpjZi7cg3BMCjKNseO/cP40aZKiBcYt3pHGOkdpRlFI0h9y5sQSruPS63ZZ\nmJ/l8Uc/SK0WENTqvPjSq2zd3iUcjBAIhqMh7WabYW/E1u1t7rv3HpZPrNKPI+4fDkkdj94oIkFw\nZ3uP7Y1b3Ll1najfYWdnm3MPzjI3s8DuVofAdUyYg9l5vv3tZ/ja177C6TNnqNUaXLlyhbkT88ws\nrdIdDLhz/RbD4YizZ88R6oxubw9dh9qCx7Kap68H+K06wnf49X//e2it+cj3fA8vPP8dhErZun2L\n2dlZRqPh1HmAPyeAnzhxAoDl5WU+85nP8Mwzz7C6usrm5iZra2tGQ71Srcn+L3/yx6ZSeUURwnhu\n3Y2yK8rd3E6rAGnyerWZW0HNTqvj7YCuvWkKECvs16vaOA0Eq/pU9VtZbHNcmTauZWAte2eWAbFq\n/or2H9eH8gFZ/q1437RS5SVbPFt4/MLReDHltgXpLELGaDlCiRGJTsi0RxrH/Ozf/5/4g9/8Q+qq\nTsML2Nx7i62NWzRcj/Bgh3Z7HicMEdLh4Ycf4PIbbzBwBM9du0zSkDz4wP28+6FHePX117i8ucn6\nQYOfeP9HePaP/4iwHpD4PouNNrdu32JjpsZjF87S2xqSJLGJX60dwjTF9QKE4xvRBZokjnB0k7/1\nd/9rRnFClCZI1wUtcBwBuXM6eZIQe2zLa8wei+wYjnEwGk4o5AvK2/d96vX6BIFkrxmlFPEwJFMZ\ncRQb1/skzhV8hmNo1zxqsy283IEnyVKiyNhraw1hGGLIOoFKtbFAygaoLGbj9i3euvISYdhlZbGR\nKxY9lBpyYm2WXmeDdutetvf3eP3iG4zCmLnFFYJ6jdOnz7F55w7n772Hfr+H40gOVueI1R10EjNT\nrzNkwLPPfJP3PvYedra3GQz7JGnCQw8/SrvdpuW2+MLv/CGf+sEfZHZ9lkajYTgmR7LR22Cm5vOn\n3/wSDz/8EOfOrnL79hUuvnmR97/vvdTrTaT0mJ1bwPcDzl94hGazhZCS3/31fzd17f9HA/hwOCTL\nMto5q/eFL3yBn/3Zn+XTn/40n/vc5/iZn/kZPve5z/HDP/zDlc9XnfxFsTd+kd26fK18fyH7rCpV\nyrsqsUr53mniA7vecj32tXIpu5bbFIpdt31gHOljRd+qSpWn6ds5aKY9Y4N02Wmq3L7jxrp8f/Gv\nYNfLzxSf09j5wkFqWh/SimiKdl/sdrl4CKFQjjSBsKSL0jU8Z443XnuVutsm3uvQ795mkO3QdGG2\n4ZMMQ+JBh41b15DNFi++8QrNVos7e3do1XzCfo/NV67w1gsX+ej3fy86HMGtDo1On3/4k3+bv/6L\n/5TYrTP0AuoNI4qZDxzarmK21WYU9nFqAdFgiHYkaZ7Sq96oUWvWac7Ps9/rETSaoDITkhTj/TjO\nYGWcxCfGwdaLlOenypO3KLVabSyfBiYIkyRJxtxfeb6llLieiZrYatVJEiPfLaJb2rFXTMx7U1/s\nxZj8oxnteo3RaITv+4RhSLPRpNfrE6f7zM15fOz7niLLNL1en/5gwO7uLuFoSBgO8fwF5mZdtAtn\n7z1HEqcMhxHb27t88Q9+l7WTJ9m4cYVUZ9x74R5qjTrnT15g2I84SPucP3OasydOkyQxg0GPXr/D\n6sqKSUKcZdx88xLnVtZ54/mXSeKUubl5PM9lb3+H+cVZZudaPHz+HKszTRzVYf3COo/df4Z+b8jM\n7DyZgosv95iba9Pr7nLrrSv/6Rx5tra2+MxnPgMYB5Ef//Ef5xOf+ARPPvkkP/IjP8Iv/MIvcO6c\nMSOsKrbFyDTRBUxSSDZAlMEQGAcxKpcqC5MqxV35XfZGP05+bV8rFm554G3gK1PIZeCv5ESmjM+0\nCb6bVU25/vL1qsOr6vkqh6zyfNn6jqpxLIBkUi799kRLNoAfx42U+1JF0TuORrsaLRSJFiTaQzot\nvvH1l9G6TuDW2d6/QtrbwQlSap6LTGMcMuJkyJkT9zMULptvXeED77qPmUaD02trfOP/+U0W77mX\nP/3tL/Dis9/ifR98kkfn13nu1Zd58v0f4C899UF++8rLjLKI1uwMmzeuMQoj1teXUdmQ//a/+xlW\n1k9yY2ODq29d5+rFS8a6JRri1Os89uSTzC0tc9DtIB3XxFnXhV4pMx6p0hkn97XH0ngHTreqqirF\nfJVjAVWt+bFddH5fmItZ6jVjWue6Lmkcj/eDK/N9LfWYQCyeD4Igl+dnDEd9pJwjThLqDZ9MNegc\n9BCOII4TmrUaZClJo8E9Z04TBD6u6+IHLol2ac/OgoZ1z+ddF+5Fqafo9ru4NZ8ojtjvdpht11ld\nWEQseHzt2je4dPEaS4sr3HPPPSDWqNVNiOFu54BWs0Gr5rK2eoLZ2Xl6/RGzcwvs7Oyy3FvG9eCF\nF57j4qXXidMIJ00YDkYEQUCt0aTTHdBuz+F4Pjf7B3R7A777uz9MEAT8n8fMhdB/VqHr/w9FCMF/\n+Pf/tnLBlL+70juyocsgOd6Q8iilXbB4xwFZFWjb91ZRmnadVaBVLlXXp1HuVWMy7RB5O+KVMgdR\nbm8VmNliifIBWxZX3O1wK3M1/7H9ePSpjx259vy3/vBtiWfKlH1ln1OHTCYEDZdRmuDV5vnKV1/i\nxlv7LLdXuP7qi+xffx2ZdpkJTBabwHfpRyNay8ssnD5DfWUNt9ZEaMH64jJXX3+DR9wZbrzxBo88\n8SgvXXuDG9s3ePjcBd6zfg9f/sIXyRbn+N3XX+TV3g7tmXl2bm1wZnWeR+49zQP3neMHf+hTxCiU\ndBDCwZcuvuMQpREJCikFaWbs3Z1cJ1BkiwIjcMhtbY6M9d3W63d9+FNHrn37q7931+fL7yF/f5Z7\nyaK1EYQIYUQ7Oje5s9aWIg8ljcids8iVo+owUl8u1/eEb0ImaJOEPE1T4ig2BzzaRAPExD5PXE2W\npMaFPslyW29jwjocDclQOJ7LYDQg8ASeEyBwSWMTQ3xmpsX5e8+DEOzuHfDyy6/RH4Sk7ojeYMjc\nzCKu66O1Q6PeojfoE0Uj2jNNmq06UTiiHhtHob2DffYPTAyZwSji2o0btGdn6fX7JvFDrcGvff7X\npq7xd9SV3mbFp57+enKR2KxZVWCnsrleQQUU38vsfxXreKQJUwDHpiyqnH3K90+jEO222u8rA7D9\nzNvZOOWDwXZemuaEVK6j6qCy33+32DHlequ+279VteG4/hZJJGy5drmU48UUgdPKJRUhruPQ6Y6Q\nssZv/cbv4Mg5XOXy1qVLpOGAxaVZ9rZ2GQ4ktZrHMIyot5osra1w+dY1Hj9zmkzA1sYWbgrbW9v8\n6cFlVpcWefnam6yfO8WZh+/lmeee5anv+wh/5bG/wz//uX/G6blFeoFkexBxYv0UKh2yvHqCT3z/\nD+D4HkJlaGGyl0dZaoJGCY2WGJGJKJTyApBoWxxRJAjIl9Hb0Y1MO2iLMSyLEsul0jQYgUmfmD8j\n8nSC+f8MfhuPSLRGuCZImTl8QCgT7U8IE3Nc58GtNJq0yEsmwFMaMEGuigBR5Gaj+D6zrZqJGKgU\nOlVkqQlYpbRiNmsTpzFpltJsmNSEaaJwnBpZavZNvV6j09lDAeEoZHFhjnY7I6bDqfU1pPRIU00U\nJbTbdYTOiBxBlihatRaNoElTBGxvbzO7uM7pex5kGI3o9vpEwtiQu802tUadg07n2Hl6R13py+BV\nxcrp7Hi59wTI6aOiCptSt2V1VZYXQoiJGCk2tV117zSX/re7oG1ALG+Iqg1U9Y7jREFVwFn0qxxC\n165fiKNmlvbYHScOqSrlcasqx8n63i44HwfyBXALYXJPlusH0I4mTTKW59d47tsv06RGPWjyypuv\n0z84oOlrgobHwvIKmzf3caRxcT977jx7/T4ukp2tO9z30GPc2bhDvdmgPjPD7u5thp2IlbU1hmnM\nyfYKf+1v/E02D/a5s32bT/3VH0PWa/z43/2b0J7Hr9fo9w544cWX+Vt/86fpDTrGu09KE2NDaaQC\npMil2kfBtOzV/HZA1xpIDGFcPZ7OWDGcc2jqaF32+w7nyAQOK9Lojf07pMiDdTHOcgOg83ag87Db\nUuZKaRMVUGHs/wEckpyQ12RZXDTPrAnHmK9q8wODbmL8a6Q01jmORLoCV4N06jSFiaDoOCYjk4nW\nYCJVFvb0cRKTqRSlUmZnGsRxCpnxrEySjHqryUiGOFoxv7RAp9vF9+uIFPr9EWquiV9rcdA5YL+3\ngRYQJiELSys4rkOmNZ7vc/LUGT4/fabeOQAvW2TAJPhWBXSyN7IdybB41nf9SnB0x2zl0QVcBsUq\nK5Qq0UH597sBlF1sICy7r1dRuXZ7y9/vBnx2mRZ6ttzmQgll96cssnq7fa269zgKvOq342zo7Yzj\nVXUUnIddZ/nwGbfRcWl5bb74B3/M6y9chMSh2dijlg1w65DEI7KkhhQt3EWHg9GQhy5coNsbkSUp\nbVlD9SPatTrLC4uEWUptdoYdEpLRkN7VHpvXb7B78w4rqye5/6GH+OLWV1lbnKPp1/nJv/Kf83/9\n3hcYDEcEtTpaw1e+9jUefuwhk78SjRaZyeiSKbQSFLlTTf8P489MjpcmjxZTKUIrF6X1ONFvVRlT\n9IWDVIXuqbyex/s2fy5HV9O6PLmw1noMyEAegtWkdhuvciXzw0Uj0YxjeavCPNgA/7h6IYwCNBcg\nCSFxkjyonDIBqgwVD1opk+cTSRanoEG5Cs8LyDKN63o4rgn1G3geUnoIDa5TI0kyCGM8zyOKQpOQ\nOTCha4XjUnNnyTJNOEpMbtq4y0xDUvPahFGE43lId4Fef0Cr3UK6Hr1ez6SmO6a8owAOkwBSlq3C\nIQU+dj6ooByLeqpibVfJt48Do+NEGcVvd6M+pwHJ2733OKucsvhomihkGvVe1Y5pfS63rUzVHdcH\nuxwHFkUpKzHtZ48ToZRDEJeLzU2UzQ3Lc6+F5K0rN/jal7/B2eUzzM/NcvnSRQbDLotL87iuYDSK\nqfkN/IUGS42ThGmMSFIabkCcKtL+iLg74OaNm8jAZxiFdDu7qMGAht8gDjWjm3tsvrXBX/sH/wPf\n/fGPc3NrkzdfeZ3HHnmUL3z9OfajASLMOHv+Xg46XTwvIExGJpE2hlqV0jggUazHPGQuAjKdWf00\nYhYpJI446rRTGc4YTFCvu8zV+B132QuyoJy1CRaVXzicN8eylLHrFaqIoG3MiQGUSQeuDyVCZEIj\nqJNkysQi0XqCd1AyD9OQdynIk30IKVHCHASpUiZ6IBJXCIQXIBGMVIQUzjiWSZalKJ2QpiYhsk4V\nUoSgJa7TIM0y8DwyBxw/INMmRHF7dpYshTnpkmYamfQOxy0PqjUKI5ZbCyYPbM1jzp8jreBu7PIO\nplQrFoIN3DnrJg6T8sp84lOtyJLDzBVjFi9fQI4UZCIYD4oQh/dopcdKEIkYv8fcVwYGWzRTbPCi\n2sLd265DMFmF7dWZUxoUz2vrHlNvlk2mUyruk/KwHWXAmaYHKAPfNKAat3QM6od9LNpi3mv+Np9m\nx1TFlCo8944UOenhetwhonUZpPV4DVBde2WfjhYX8uzjBeVm2GCjkDKgLkFIaqLN137/85yYWaVW\nb0BDolSXJgNkJJGNGQ7ChDl8zrzrXSzPNrn47DdpexIdDUmHIf3dHa48+yyNMCY+6LIQePQywVy9\nTcPxaK/OkvRD4s4u/8c//kf8F//9f0MzCBhubbAVDlk5sch8Os+tm5d45PFHeM8T7yZTGT6OiY2R\n98GMaz5fOeWqC+ASh2OsNehMo4UmE9WmmNM4vWljKzR5ECYzT1pUg0wVBT4xw3nbx8Qak2t9/Lf9\nbgqZeBFRUiPzT12IVHSuYxtvctCYUK9Ka+I8DZou1LrCJGrO1ZxkKn+PEHh5th3Hc8bvEyLI94sy\nCCoEAkmcJOPWxXEKuTxfShPyV2tzMAoh8DAhDZxcJOT6DrP1GbSGpoUfd9NXvKMAnmWHWW6K4IMm\nrgAAIABJREFUBWnK4YZPJ9KhmUtK5yeflIj8OaUlKrcrteXcQhgZG9oAQlakQ3JdhBRjlnOyHUVD\nCjGFDcimfQVHULxv3PIpYFkVhe/wnUfHJ7MyWJuDTlFkvC47R1QBs/3PftfkHBSHZiEumbx2eHBZ\nnBFHuSXG9g2TxYb142LBgElHVd6qxQF5XJl2gBVFjTHLpKkqMr84noOUDmhJmmZ4rsev/Jt/y/bG\nDqfXz9EfDrl09XXmAhcnE+hoiKjVjRt7q8m5lRNcfu0l9m7dQjkZvopNtEflmABNnk+apgSuj5IO\nvU6PRrtN4Ls89sRjiE7C9YM9vvSrv8EHPvZxagd9nEDQDjy6Wcj6iVV+6Zc/x1/+9P/Nzs4dfM9H\nawPgCI0y6GfmlUmK84iY6BgirsoRayxKnEZZlwgF+732vNj1VXFXBcUgintL3N/4GfvVh2/L/2/6\n7zrG2/SI6KjAAq3R0qRJG6/aon6lj65JYah0aYmdwHipGjm7rRMycb4Fh3H+fc8d96EQ89n7JVWY\niJN5QuQkSY4kQXk75R1VYtqnbVmmPS5j4GA8GFLKsSJFZRk6U5ApEwCGYmoNpSURSHFYt5b6MG60\nNWnl9Gi2mKbs7GCbMdoAWfxWtainTUgVNV2mpI0zylGTveNk7+XDaJoYYhoBO60tjjjal2l9s+WZ\nd7NWkZKcoqw60O9epopZNBg5scIEXNGgjOItiiJ8v04SZdy8fo3tWzssrp+kE43wFLiDhGHaZ36u\nTpokxAdd3v/4h2isnOLmyy+x+eYrOIO+SZzsOdT8AKWgF3ZZWT3H3p0BC4szzKwumrUbp+ze3uTS\nIOI9Zx7gZH2GVHhsv/oaC27Ar/+/v8rB/BzXewfcd98ZnnziPYTRiGazTprmYiDhoIWJ8yKFHLu8\nTwPJ8m/l+6aFQbjbfB2XBMQWW9l1ThNzFtfsz2mlSjRYvLuov8AJ+3pZJl+1Xqo4huL3ci7YKuyo\nEu3a7Z30AzGeq8V1P882VG7L3co7BuBlxdIhUIkJxw8p5Ri8i4Etci6OBzOXoUXjcI4eUkiT9kib\nwPeu547j+Jrs0kc9/4q22KBrvxMOvcayLKNWq01MOkyRKVqTXQWoxfNFUChb3n94XRUEy0SZBqZC\nHN24VcWM8dFNUyV+EUKAVkcW8dSFpifd1ct1lttxnKx7Wrkbi+m4EoPi+X3C/KkVzLTbdA56rK6c\n4Hd++/dpN+c4c9/9pErztd/8XZpRgu86HPS6NGt1/CQj7h4QLC7R690hSfok6ZBYK9q1JlmW4Dse\n8aCHpzVhr0c4GhHUayyfPkt4a4PN7Q10GPP12/v4rRlOrj7Jk5/8XmQ/4lc//3lIErI45PbN63zu\nl36e7Tu38XxjlWD4cZELqW2O8CjRUbXmqkrZU/XtUID2Xq0KB1EFsva18vVibxX1FM5Zx4U0vlv/\nCoefMlgXh0t5vR1yuvrIeh1zJKU+lA+tQzHoZP9sM9eir1l2eHjaxJ/drrcT0vkdFKEcletWZppX\neqz1nvgnZU5c5QtIg+scatqVSscg7Xk+kKdFyjNGa2XL06sdaop2FZ/lhVJ4sdmWM1WAOu20L05u\n27QPzIJOkmSql1x54RXFSBwsKWFps1QtdMO5Vsmkj1LwAJLDhXs3sJ226Kuey7I0v6d85c9GkR15\nWpixGDPf2sTRcF2X4SBitj3P88++wN6dfR57+H2EaPb291lcWkTu7KHUgCTKGGQjslHGxTffoD4a\nMLfUIozn2Mu6KJUxUimtepM01ogkI+oOqEmP4W6PWnOWSy+/xpNnzvPkB9+PQuOOMvx6k+35Gq/2\ndki6PT7ymU9zcWuTV//gd6jVFHE0wvMd4iRC5vFPtM45iEKUqI7Oa3nu7HE6Mp8V6/puIO55XqX1\n1NudlyqAm7b/yqXc1mlth+qIp8X7yhyJHauoqFdKOWHhVEWETK7rSee34t4ysSiEsLJjHbZ7WnrE\n48o7CuA2eBXFBkQAR7rGHVgXuSXzGA9SGiWEEGQYBtnVGYULsSMkvu/nIJ0rS0XOSAuRJ7o9fKe9\nsMugV7j82jksbZbHXgRlkK06zYu/C7GRfW3c75wamLaoy20sDrUyG3c3qlYIxmM2rVRxGHa/ppnw\npRalU66vuh1VIH/8Ir6bbL3IOViuJ4oSmvU2WaJ57tvPszS/zLXtLWZmZ7n51nVklrG4vEgWuTix\nw/buDkK61Fs+rWYNT0CrUWczShA6w1XgOCna8dnvdWknMbW5RWrtGebOruCmmu3tA8TWLvc/dD9z\nXpMoTNALbS686wFGB112vv4aO5ubJHHI3/nbP8Ogd4DjO0jHMS7nGfm6zYEgN6Yur5Oy+KusfzmO\nej2OSypKmqbj9ZkV6cVKADftYLDbaq/7Mni+3VJ+n71/j9t35UPHFosWbazCJnvdl+8vnKfKeDKO\n9ZJLDorfC3GwvY/uFvukXN5RGXjZBMweuOKeIpmo1gXFnOuKs8zYxUqQrkecpiAkSmX4vk8SJbi6\nsGbJlaU5gBfvFhxl78ptLP6VvfeKwbfbW1XKis4q4LVPf631OIJeFfVQ3ih2VDjbfttWBBf5+OBo\n6FjD+UwPB1uUww1W1F9stkJNUbXpTCadwprEWAYdPbAmWcjJw69s7lkuQhzPZoqShYTWGoSDIyFJ\nUp795nPs7uzyyIOPsh+49O/skXY6NDyXXhYyPzMLfcHqqQZDCc3lNXZ2d5n1JGtLS2zXZxkc7NOL\nYhyvgRt4LJ0+zamH3oVoz3H74AB3aRlncZfhbpftToeNr3+Tk3PLPPXwe6k7LeR+zHIwy0Mn7+Xf\n/fZv8ImnP8aTTzxJt78LGGJBI3CFRGgHpTMQ2liDUA3G5fUmxNGQEgXHU/xkni/AdTqQeFaiaFvx\nBkcP+KPzUS3uqHrmbmuyXMrUbxUAw2FkUPu5she31kZM5HneRB3FPrCBuzjAbOw9HH9DLBRj6+S5\nNYv22fVWjcXd+v2OAXgx8ceddsV9IhMonSFE7qggQCnT2TevXOXC/Q/g1RuEgwPqQQ3H9RA4ZHFM\no1YnjiJjfiUETsEOZZlRZsKE7L38/vLvdilkbMWnTUGUB75YCNMWsE0VlKnvYrJt2+9iAdnhUu2F\nVR7X4jAsAN+mIMpcQ9V8jDe/mqSiimtV/SpsicvjYo/P4cEwGdP77Ra77qo2OMLJg5wZsBLC2FJr\nIXGQfOfZ59CJorN3QPvCSbZ3t1lq1El1ykgpdnodAi1IXZd7H383J8+f48WvfpvR3jYH/SHN+SWi\nROOi8Jtt5hYXiFyHURjSnHcZxSmDMCZ1HYZa4WSaDMXNzi6LGzd54vRpFoIW/WjIzOoK3/f0x3n8\ne99HWqTpyhKyLCOoNQynpMkzpmfIPKxqFXCVqU8zNpMs/sRcTRALmuMIwTLHacty7Xm267b/Lq9T\nOzKpPY9VhNG0tWG/s0wplyn7Ko/sKmKpXJcdPbSqbZM6q+pD7W7F3kvTQl7Y5R135Jk20UVJUwPc\nRRJRKQXScUzOOgTf+va3+d/+9S/y4e/5CE9/30fQSUamJaSKml8jihMzKDJfaKKwNXfHIgwbPKsi\nqpUBzQZau91VwFRFedvfiwPCXmxVGu84NoF5HOlQWNAUoCxyBw4DTEeDNVVtNrsNZQ/N8mYYAy/g\n5naxZRCvWpzSmXQ2suuqal/VGE+ruyhVm3Hyem67O2ZMBGgT2e7NVy4y7A9YXz3Nwe4+lzevwMGA\nRb+O6zu4boOD6IA407QXlmkvrtEbJqyfOcfS+x5n4/ZtTjx4Py8/+x1knJIOe3iOi8gU4c4uyysn\nCcKUWqqJfJ+e5+JLgfI1sSt4c+8Ws9cuIa8ssvDAOcR9q5zpPsD8/CJSapIUavUGnu/RH4Sm+WZA\nAI2QhwkbyvNnz+MhZX1UzmyP9+E1s8+mjftxhJf9Xrst5XbZXGZ5rqv2iv3OqlIGX5tQKBM1tvXH\nUY5kEo/KxFnR3yqv5ipiyH7H21nP9mFWhUfl8o6KUMpyq/IAQKGIAIQxfNfabMhUKbx6Ay+o8ehj\nj+MGAb/4uX/DyZPrfP/HnmZ+pk2WJBYI5gOC8e5yOPT8cxxnTJ1WUeHFSWifpEqpcUyNqgkqn+42\n5VtmwYpP+31lADdONLntMhYYMrlRit8KkCzaUQSysttjL8qi3XfbnGWTqWmLGQoZ7aScsopTMd/F\nOK+pNg+BKCzJp8vxbQ6kqmiloHgPhoMQKFSW8cd//CUW5xfZ29llYW6RwaXr+BoOHJd6o452PZr1\nJgMlWDl/D1FoUl216nWaOiN1JWtnz3L50lvs37yJpxSDbocg8FGjAW3X4cT8DHXPR9RrdH0JwyH9\n0ZCOqxlmLt/6431wBPe3PeRsg8yFtRPr7He2yYQJtJSpBN8PjBJH6NyxXKKURumj4FW1j6rEV/ZB\nWp77Ki5yPK8Wl2avicrxr6D0CyKlTCGXjQru5q1drrf8OQ30px0E5XeUwdx+rixHt9tbftc0orCq\n2Jm7qix8jtx/7NX/hCXJwbUAl2kssJHdAaKgOiRRmjA3N0d3NKLVajNKDlhYWmZxaYmrV67wT//F\nv+AHP/FJ3v/Ek7jSRWcpaEOFVclEywBj3iuO/G5PhJRyHJDeXnhlNso+JKoA3r7P5giKUt4cxUFT\nbIB6vW6NVb54pcBx5MT9xYFTRRWUuYEClMuHlmnQ0Q03bVNplR3ZUPbhYffNdTyUIS/tGo4ATbmU\nD8ojbTA+eGMvPaU1rnB58aWXuH3zFveduw+n5dLvdFnMBCEpOILe9g4aCQuLeOsn8WZm0LEgHWYs\nnDvB3u2rvPSd55l16pxaPcFo6w6OSun1DmjNrOI1XCId0k9H9G73iKIBO9u3qe91UIEgrEObFq1h\nRvzSRdQ9Z5BnV/nAd72P559/nlOnT5hs5/WA3rCP5wW5LtZ4lUoykC5CHuUCq6henespqoCxDCiG\nQ60cbjNXOciUQdP+rcoO3AamIopkmdq0/9l9sa3Bpq2DKsq9eMa+bsu77cPuz3qAldd9Wd9lj3P5\nMJhW7sZplMs7BuCeV2hmjYLyaHAq8+kKRaI0WZYi0gzhClzfJ+708TyHhTOrXNvdYtnxiLXmvfc/\nyGP3vYuXX3mFi5cv8QOf/CTzczO4UiKyDM910VmGTBMTa1ypwzgITrEwc9vhEpjb8i9b9GKXIpph\n8VwVNVHOj1mesCrxUvG9eLdN/dv/bHZROg6Ok1PhQk6IZgpwLDZ10cdyNMYy0OtM556tjDPB573N\n+2e39yiVX6bsx4s9O6qsNNyEifMxbT2X21seNylMUoNiDlwnYDRSfPE/fIO1tbP0O33m6nX29jZo\n1uqEww5CJzhuSqoEne42p86fYtA9IOyOOHNqFRGP2H3zMsEw5Lkvf4lPfeoH2Z1vkw0EiQthmNIJ\nt9HBG3TjhKgbEngeTibRjk/d9UjiEVqHRAh2d7YY7uwj5xs0GnNs3bzN2vmTyFRCnOLVA1IBfgau\ncvLYHsayqvDYtQ++o+BazPfh+JZlv0fX2/GsexmUymv1boSMTYSUwaogJOw6beuNae2BoxyiXcr7\npPit6v12H6cBehnAq2TdNgFiH2BVVH5Ve/7CilCyLC0BxyQ1W/zL4oTYNex44LggzWZ3YxCOw8nz\nZ/jDP/kSQZRQb9Q56HbJHIfVtTXa83P8/C//Eh/8wAf4/qc/zrDTxUPiOg5SZ7gSUl3EaRN5PAnz\nLq0y4+VpnYhObs5VUA+FnTZUK0GqJrIAkgJMy5poW1ZdOPNIKckyRZqkE3WB4WSKsTqMjX24sW2O\nI80S0jTNQU/jOAW1fzjexbvL7LFZSGL8DltmPWlOaYGyPmpDO21Bpmk68X4hbJv+470CizZUiQ68\nwCEZxSwuLtDpDEjijK2tfbLMAxnQagQM9u4Q9fZItKReq4GKGKiYfhwzu3qCulbcunyRXn9I04ft\nzdskG3dYnZuhH4e88tJztFo1Nnd36B/0yZJ9XM+jFjSYW5hlMxzh+D5Bs0WaQnOuxawLaTxk2I9I\n65J6s0nm1/HdGm6ikNLBlx5kEdqDBBOF0FUOGkGWW2Q51hjYn+U1aMb/qNiuWhwBx4mtinIci3+U\nqj8UnUy7v8xtVnF3ZdPdKi7AJnSqSvkwqKKO7QPHNhqw31eu3+Zk7T5XpfUrE3F222xc/AsrQrEp\n7rKSAUxH0jRFZpIUcB0nl2UqMpWRKUGWSRYXFkijmFES0XbnWV2fJWg0WM8yBlHI937047z6ysv8\n4z/9Of6zH/oh7r9wgeGgT81xifJMHMKRJFliNPyuiUgmHAcsKsIG5zhPAWWLOgozw6Jf5Qn2fR84\nqiEvStVkTrCTiLH82y52bOvDcRzXOgbUYjHU63WLCirEJJNUkVImw3hRDkGVsbdoAZZVqe3KfSjX\nY/et+F4VVdA+PI6rv3yI2Idpt9un0ahx/fotGvU2Qa3Jc9/5Eo+8+zG2rt9CpDE3N27jIvAAFCRp\nRqM5i2woTp4+z15/yLAzYHVxkZ0bN7h++SLNLCVVCW6jxq2bN1mYn6fX64LKiEchUkr6nQOWVpdw\n2jXkTI1GOk8vjdECVleXGO7vIzKXpNfnha9/i/ec+EFUmrF94xbXn3uZC4/cz0gJvEQjPAflmsh6\njpbo3PRTlPprj18Z3KqVvEcPVDM31UBr13PcgVzmWo8TDRRtLwiDMgDah5C9fuxni1IcEsVn1fuq\nYo5UHYA20VGAflWy5moO5vCaLXKq8va021DloX5ceUeVmDAp4y3Lw7XWuHh4eZ+E0djgCg+hNY6U\n+EjmZ2a5vnmbh5bOsHcwQPZGjOKYRquJ5wQ89cT70VrxK7/263zgfe/jI9/zYeIkRgY+QmtAIaUJ\nRamUOSBAgDRKwyxLkeIwXksBmoVDQ6FIK07bApxsd+M4jicUNDbFe9z4FAtIZWoMzOVFVlZWFr/b\ni9v0K52ggqR0cJzJWOnFM7ZycEIUlE3K7+02loHEpl7sOa0Sq9jgPu7zMexwUfr9/ri9ZbESQFCv\n0+32WFhcIU00X/2Tb7CzvUt9fYZz58+xefUKrZkFwu4+WTQkUikKTa87YGZ5Bc9v4EYj5lqSQGk2\nbt3Aj2McKfCkpB4ExCozLvOBj8o0sl4jCHyiUWjEdUqRRiGuI4njyCQgFhqymNHeAXqUcStJ2fwP\nAR/8wIf4/o9/gn/4P/4Dfu6X/3cO7vRpCgcnhaEHsdD4mTI+EGjQRw/P8lxWXSuvs8lS7VVsP2NT\nzMdlsbLXPFTHpC/fb7erChzL7SqAv/jbFtOU94HdBntt2RyJ/c8myMrjWrVv7LVnr/syx2oTPmUO\n4G5iE7u84wBeli+VOyOlA05+4klMlnotEFITqwwnlXzofR/gyvOv0On3MHF5XWYaAY1ag1Qr9g/2\niNOID3/oIzz3/LPc2trg0z/0l6n5PkKleMI4Sag4xMuTPyBchOOCkHkQ+pIsWOuxUrCgvrMsG3ts\n2hSq67pjWa1NiUxj4WwN9KG3JibtkzrMLmSX4ntBIZfZVziM41LcU4C/zS4Wi6zgMsptlGKSUi4v\nevu9aTYpQimUvnYZi8pKXptFXXdjIQslbln+WGyuMAmpN5qMhhFxmPGtbz7LmTMXUJkiimM27tyh\n3moRuJJ6Umdvf59hrBC1Jq3FNbb2+vQGQ86sryPjEBGFBDpDKMGw36MfD5GBh1aaU6trDNQBKToP\ng5jRPzhAxhlpqo1ZZarQGWzevsPTn/wY1y5ehkwSNgKClUVudnY5e9+7uf+e+2jUGtQaDWSS4klJ\n4mhSYZTUXm466jAd4Mpc3t1EIpP3Tb+3WGu2B2a5TLVMOoYCL9pcBd7l9Vyux14nNsdRRVmX13QV\nN1z8s8Ufdl/L1jv2b2XlZdX42ARK1fW7Ud5FeUftwG0xSplVKj5TnZHlAXrtcJGO6yBR1JCcXT/F\nN770FZ5eO0Gn26XuB+b5JCEJQ5qeTyPwCVXI448/zubWJp/9n/8ZP/zpT/PUex9j2NnFUSmtekAS\nRwZ43SKTh9kwAnGkrWXgKmvWC9bLlrmVJ6aYSDjcEIWd6iQlSq5XnZzwshNEVRvBLK5iMRaedLYY\nw67TXoDlTaoyNUHBVHmNjvuvJzebzX6WWdDi0LOTL9gUy3Gsul3HEQrKd8gShe/UuPjWNeZmF1ha\nWERlcP3KFYbDAUIKZpot3BRmfRdGMfW5BZzmDLu3N5htt9E64+rli0iVEAQuUZyisoyD/S6J0Pi+\nz/L8Ao1mk16UEIURbj1g0O1Rm19mq9dlYX2NmbUTuP2QhuOwdvIcZ8/ciyt9Xrl5g3e95wlubNwm\n9iXvfuoJRNBgFEbU2m3SXh+hQPkQIqkpgdCQVeBsmYr9s4K4Gbu732uvk3KxvTWr9vbd3n+c+KCK\nW6uyIJvGfVQpG6tM/WxCa4ILzddplQjYHo9p41esbZuCtwkPuw93K+8gBe5g2mqLTyZlxwU4ucg8\nl17eUa2JtcYNfESqObG4jK65CB1DFpPGGgn4ns/S2iqdfg/pS7xggV44YGVpiQsPPs7v/s5vc7C/\ny8c+/EF8oRkN+8bLTZms1U6u2DTB3gUZagIcgiDIe2MBapqZwyanvG1q4CgoV2u1wzAcy9OBCfar\nTJW6FXLxgoLKsixnscG4XhvPvSI0iCNzbkFnlSaGxQE7uUAVjutNaNXLsTDGC1kddYcvgLYIiFTc\nPw4TbMXYKAcBqirFuGRZNjZNnWiL0uhU4Em4evEqJ1dOEPYHLCws0OvsIXXGaBjiC43nZERCUl9Y\n4MIjjzGIU+ZThacVne4BcTxCxzGe7+J5PqM0zg/GmJrnkagMMvCDANd16fZ77O0qnvqu99Pdus2J\nhx5E1xr0Lt9CRSkvvPgqZ0+eZGl2gYcvvIvl1iLLT57k6rWrvOuDT3Hl9TfwA4+N/T1mHReZgBYQ\nuxm11GTY0e5RRZrNYd0NTKrEHyaZyHRosL2oodpqyrbGsuepvMaqqNRizVS1/W4Ua/GOKiVhFeVt\n13kcF2PvubKosri3kL1Pa5tdEit3gf15N46zXN4xAIejsbDLi0wIkwTVQRhzMAFCCySCVBgvTV84\nxCrDazXYvrNJu9UmiSKCWoPhcECn0yGo12h5LToHHfrDPkhB4tT4vo98lDffeI1/+S//FX/1x36E\ntZUlHAGjQZ+gViOMQgM2rpfLoCdP2KqFK4QwZnZMUp9JkkxYmNjP2RSHLXqZpuW2FXdlBx075Vzx\nTFF/prIj9Womw1zaIFs1J/ZGLK4Vm7WKWrEB1W53MR5l0Lap9eLvaRsDmLAEKo+D67okKqZeryOV\ny5U3L3P+3H2MhkN6eztIHbO80EaHPqNen76OiJGcPn2exvwCvd097n/oAer/H3NvHmRZdtd3fs65\n+1vz5Z6VVdVVXVW9L1W9qUGrbQlkFoFgRrLkAEaYAYNNhGE8hhk7iBjP2BIzEx4HYTDMGIwAG5DZ\nJCMJLEC0JCSrpZbUa3V37Uvu29vvfu/8cfPkO+/my5Zw2NGciIzMvO++e8/yO7/z/X1/v/M7huDZ\nz3+WLE+xPYcwivFsSRbnmK6DEeXUphqEaUK/O6BmObiOQ9M0sBwHKSTHjh0nBFLboT0MyLKca7dX\n6HS7PPbweVxTEl+7xvEL95NYJmu9Pe47eSc7u5uEjlecypNkZHFCLCBNsuIosNLRZ6p/y3JZVnB6\n35aLEPspK44oZX/HpORQR1lMZeAyiRqZZDmU21EuR+UfLyPZ8vv0RU5Refp3y+2YtOBMUrpH1V1X\n1JOskqP+Pqq87gc6qAFVZsWhzjYlZIVA5YbAzAQyF/vn4AmcTJKaBrPHj7G+vs78Q/OEUcR2e48w\niKjWaqTkbO3sEYQh1VqNWs1jzx+yvdfh7rPnMOVd/Pwv/r/8vR/7UVzLZGlpnl57j2Zziijwiy3k\n2or+Wp2cZhl5engnpa5kJilqXVkqJFk24VT/lFG9eu5RpuOozmJiznFl7pbRRHm3XLk+qv0KTU9S\nDnq79bEuR52UKTXlBFbvPCofjU4X6E7TMAyLyWdkZGHO5z/9aVrNFi89+zwnTxzn1SsXsYwco+Iy\nXW1Qn25xq7/LwuwiZ++5j+3+kO7QZ35uhu7GbeIoYG5+lv5ehyBKSf0ADJNh6FOZmmJmYZH25jaW\n4+BV6kw1mliWxU57j/WLV5i75yzbez08w8JxbIwkJSFhSMSN7VWOeSeZn64yPT3F1KADYczWzRUq\n9Sq7/S5W1SY3JIaQOFIiTUiTw1n8ykpnkhI4KmZ+XGaOVh66LByFiHUZ1b83aR4dhX4nWQ1/GUtC\nPaustCf1j6qTDq6UT2tSvfSFQZf58r1HXZtkBXy9708qr6sCn0QJ6MIhpSTJQeSF1a9Oj87yDGGa\nWEIi/CIM7vidd/DCR/6Uk6dOEaUp1akpWo6HNEwMwyRNMlr7ccXSECzPVVmYbrG9vUuYJDzy6Bv4\nvY9+nEcvnKdar2M5LmEYEEcBruUVZ+9pnGxZoaliyCJni1Ik5fAjhSwnIeWy17w84GWkq/elHk6o\nKJ3RUWn7aF2aY3XPsqw4hJVxwVVKuTxW5fFTReVZL1/XJ4uqYxnpqzaWJ6CO7CbRTaqUI1mU3Bxw\n82aKiC3++I/+iEcfeILZqRaD9h5GGuMPupihzXB7i7pXJXU95heP4XpVdq7fplavEAU+N69dJYtD\nTMvGq9SIoozA75CbKcI0WDi2RJDEZIbEcR229vZI4oSlpSWyPKe3us7pc2cIHZNmZYpkfoZgZ4eM\nFD8ecvnGKzx/8WvctXqF75qbpW7YZAJWbl1lulKlOdMgyiQDIoQAK0wJFMWQfX30V1ZiOlqdrBCL\nmPGjlGXZGV2mR4QQB/6WcgjopLBSXXEeZR2UraxymcRrq++VQeGkCJtJkSZlZD3pHZPol3LRLZZy\nvcr1+3qLb7l8XQX+gz/4g3z84x9nfn6e559/HoDd3V3e+973cuPGDU6dOsVHPvIRpqadZLylAAAg\nAElEQVSmAPjgBz/Ir/zKr2AYBj/3cz/Ht3zLt0x8rsqnrZDWeMTFSIFLaWClOYnISNh3HmSQiKQ4\nIDQFYUruvOscH7/5q/R8H69SR9ouRqWCadqs3Fpj0OsjhaDmVZhqNGnWbIRlsHDvPex2h8wtLrNw\n7AR//tlP41Uczt5xHJIQ15bESUE+6lZDlmUHsd2gKxIQ+wpEHxz1uTqooewAKm88UMKvwhYn0Qnl\nvhsJSE7xZ4ae/yIMw7HJlqbFOaGuZx9C+XrmNX1i6u/R+cGjkE75PEA9ckdfrFSETBl96VbKpFLm\n3/WQSiEEUTJkdXWVeqWKZZiYnsflSzeQMsHIE2zTJgpDht2YCAspDVZWVvFcl7mZWbZXrtPd3YU4\nIswyWq0ZvEqTtmmw3W3TnJpCGEW+8EalRqXW4Oalawz6A6qNBgiJ40lSkWI4NjEJO51tBptrGDIn\ny3ysJMHI4MXPPMWVKyv8yI/9AxZmZ6ifvYtP/f7H+Pbv/k7aZsJeHlLJwQhi+jLFsEzMNJ+oWPQF\nsmyZfSNmuhBHf/ZaTu/yc8vjqfPz6jPHcSb6eCY996jQ26OiXtT39faX01ro95StxEkcuvpe2a+l\nj8NRfV1eiMr10EOSv5HydRX4Bz7wAX78x3+c7//+7z+49qEPfYh3vOMd/KN/9I/42Z/9WT70oQ/x\noQ99iJdeeonf/u3f5qWXXmJlZYW3v/3tvPrqqxMnn65Mys46vWRZSrYfAeIaJqaZQ0aBEIDMKg4U\nXXJqTN2xyF6vg2U7DPwBq2vrVKwKqTSYmp/DSFJcIWl39tjZ7SKkJE4zDMulUq+TZzlvfONb+MJ/\n/jJJknDfvefIDUE+DCHLSCXE+7sLDdMgz/JiJ5wouOTcEJBn6Id/6wOjlLRCt/ogT0K4SvnpDo+y\ncEwSCCnVJCg266h7bMfeP8AVhDQwLQF5XmQLKU0q3drQ0a2OvnQlKxi5csuUi46udCtDv6+MUiZx\nmpNKlucHp6SLfe5WGoI0jcmyHNuu8NKzLxEHCbVmg2cvPo2VRthGTq1RhyjCzGCYJtQdpzhVfnWN\n5dOniAc23d0tHCmxvToizfCHIdK0aB07hmzWaczN0g2GOFaFilNhY20dyzXJSdhtb3Ps+DJRHPHi\nyiWGWyYVwyPa2aOaSvI0QUoLBwNERi8N2fZ7fPjDv8o9J0/y1779W3no8QtYQULFsbFyi5SMTIJp\nMMrykqbkKRiGSZKDYZtkosjGbgDGftKrhHGL8SgUqETxqG4f7fIFEPv55Mt3KcWo5KGQTwV69MX7\nG40yKlvqepH7lu/+t7Q2TKZjJlm4xXPGd0br9+n36zI9escIxKEdBD5aMPKD30dZDPq1/yoI/M1v\nfjPXr18fu/axj32Mp556CoAf+IEf4G1vexsf+tCH+OhHP8r73vc+LMvi1KlTnD17lqeffponn3zy\n0HN1pKcPZtnUsO3CnCTLyZKEfL/hxv6A+maKQDDdjXnL97yDi3/+Je4+fYauHzJdqVGTHruhz8bu\nBk3TolFtsLg0jds4SRIVyq3f77OxuUW/3ycVOXNLx1nd7fHUr3+EH/nRH6Yet7EFhLLY8m+5DkYK\nRAkkRZKkmIxYgG0ZWKXIECnlwZZ3xTmrAdIdm0qZRVGEZVmFEy6OD1Z7mExnqAkwQjD6ST+jjRb6\nTtHie6NTQnTqQX9HGWXo7ywr+7KAT4ok0BcJ/beShfIEUp8fZc5neeETwZBF9klTEsUxSRrhujbk\nFV594Sp3n72fvU4Hv99j3hSwH+duppALE7NiM1urEvfamOmQuL/NxWdvcOvKNebqU1TcGkmaEMUx\naRAR1iwWzpxh+dhJXnzhRWqeQzQY0u90ybMYYRn4YZdO1yHuBfSzBHdmBrc5Q6tRox13SHKBmUAl\ntzCzlCTPGNoW59/wGK986Ut0M5/5B8+x9uoKRlalMdVgK9rDtC0qSU4WxwjHRiRFfkLbdvFsl0EU\nkJFBniLSFJmm5EIc7CyeZP0p+VD/jyvE8ZKmhy2zctEXa/X/JOriGyn6Qq/L4/i79GujxWP093gY\n4CS5VP2iRzapz3SLZhJFVTxLB5+HfXtFKgN1tq0YW/S+HjI/qvwXceAbGxssLCwAsLCwwMbGBgCr\nq6tjyvr48eOsrKxMfIa+cUM3y8t0gNrBqDpORReUhSFJEh5/+GG++PE/Yau9SRKDIyrk1QrzrTkW\nqi6eadBZ3yDo9Vjd3CoGPcuZmZnljhOnkIZEGJJ+MMCPAra3t/mNX/0N3vcd72Cq6iERWHkKQYIw\nJKZrQ150op0X+bKjNJk4CLp5pq7pik9XUio8UffuT1KAqr8UBVFWpDrvnuf5WGiXclIKIQ4OZ9Yd\nrOWNNXp7VJkk/KpORyGqcly5TgeVfQDluO7JpZiMSZJg2TZRFJAmCdVKlTTN+MOPfpLFO89ippJw\nt42R5wR5gityzCQlNwxwDE4sLTFz7DivXr1MqzUDWcbO2hqEIWHewa7VsKVFmKb0gpDclNSnW3SD\nIYbnMbcwx+XnnydJEsxcINKcZBiyt77NdGozb5q4aXGuZTZbZ6u/g5NJ3DRDmDa1xOJsbZYb3ZQX\nv/wlHnzTk9QaLbBd2ia4kU8QpzBTYbizw/HaFGHoE+c5oQGGaxHmKZnfw8rFwdmlqSEJjCIM1uQw\natSjoyZZRpOKrmzUnNSvq890pa0sSSkPW5JKdsvPKKNitchPkj0dcY+UMyjUq7dHD+/V31NW6opC\nKVMjZfn9ev00+Z7CZi3TjmVf1H8TBV6u5FHoSH3+WtfVoJQjENRnumLXV1ClYHTFY6YSt+YSi4S5\n6TlM4TDwI/prbQzLAgmNWpWKXWFx+QQbG1v0en06nS6b61vYrkOtXqVaq4Fh88gD59lt7/Hrv/c7\n/PAHfgAnBQuJa1oMo5CQnFwKDARmLrDSDMc0ye3x9LO6wE/Kg6zapwf4R1F0IGiKStFD+3SqQSk7\nwzAOdlqqZ6r36uhFoXpdSer1UEUX0skIJD9AEeU6jU+k0RgeFeKmK/ZJMnOUIFtq70DO/lFjAsv2\nGAYpt27eZnd7QGVmihPzx3jhyqc5uXSM3mAb/C55GNMVEVmlwjHTYc9PiIXB/MIimxsrDPf2qBoG\njsjobG1Sb7YwHQeHjNbyEnatwo0bt6k0qhi2heGYuJUKcbcD+wt6nMZsNUw8I6fl+8wzTavSoNmM\n8Ve3sKTD/B1LREnE3NJx8ueu8MCb34rzxoe4sr7DmeU600sLyJ02tm3wqc8/xVsfe5Ir19dYmJvB\nTwMSU5KLCFMaWAaYcYqRCxKRk4icQBZUn7nfhbrDWM+FX6a4jipj1FmJkpk0XkoxKYVaVlq6zKrv\n6nI2OrIs0541rhxN86j83Idluzz39O+UgY7ePv39Xy90UAjG5sP+HWP1OUrOv1HlDf+FCnxhYYH1\n9XUWFxdZW1tjfn4egOXlZW7dunVw3+3bt1leXp74jH/7a791UMnzD93P+YcfOBAcHYWXHQ66o0sp\nuANFNfQ5e+4sl69fxT3pQG7iTM9yYn4OmQhwTQahT7/bp9u/jR8E2LZDqzGF47hUq1U6nTbtdodu\nr4NpGcS+zyNveAO/8R8+wv/4vu8jGoSEWYxj20QyJxM5aZ5jZClZlhOFyRjXWF5VdWWrt0t3+Akh\nDlC4cpbqQqsLled5B1EgCsWq56vv6+/WkbGaODrSLYc16spbf79+feIGmgkL2GtZEjrS0ZFdmZop\nFyFyhFDflQjhEAwTms1Znv3Kpzi+fI6doMsXvvCfmdq3VKbnZ4n3DIJulyAJ8ZqzyEqTyxsbeE6F\n1bVNtm7domHZ1C2TNAgI/CFBFJI7DnMn7uDcXXfTHw4RUlJt1NheXSNOE2qNOsI0ycOI0A/JowQx\nZ2BZDnGWIpOcumWzePIUndzl8vVrCMvi/JOPYedQw+SLn3uKU02Xu+69AJnB9OlTXLz1pzR3Qh7q\nmXz69z7Jo9/2TlZFjiMsyBOMqMgT3ut1qXkeEYIEiDNJJgWmNMg4HM1j2/bYnBsh2q9/Ik/ZijrK\n7NfnrWGMsuzpllb52brslt85WfmOEliNZG3E55cXGV2mywuPDnjKdVL1mozAdbpmnLr5y5QvP/Ms\nX3rma9/Qvf9FCvxd73oXH/7wh/mpn/opPvzhD/Pd3/3dB9ff//7385M/+ZOsrKxw6dIlnnjiiYnP\n+KEP/O0xISlzW6rhk05uLq/ASgisNOPkiVM8/YWv8Ni5R/DDlK4/xO+HmEFON4tILEkjN3A9E9d1\nSJKUIAqI45C99g6ObTM302K61SBNYgbDPt0o4Nzd9/Erv/br/MB730/QH+AJQZ6m5Psn/OTkZIbA\nNm0cbSKotpU3nOim4KQMhoZhEMfxQfpafVKpZ+toSadLVCn3pe4sKi8Iql7qmeWddOq+SV5y/fvq\n2WUnjT7Ok5R4eUKU23pUyfIEFK+IhWl41GtVvvaVF6lWW4XPJIzpbe/iCoGZR4gsR3o10tym7rk0\nFo+zOozpBCGu57Hb3mbYG1AlgTzFyDOkY9OLA+I8oxEFtLe26Q2GnF4+DnnOWuCj9r5U6nVSJyKl\n2G08ZzcRScowDNjw2+xZKSCpH6vR79hs9jpEX3yGvDNkeXkWM8/YuniFWnUev9Nn4dgMtm0Rv3SV\nN3gtNsWAf/2bv8m9D97LX7twgTm3gdEfYCYxVqVKKjJikZFSnPvp5BJSiLP4kGKK4/iAltT7/rWU\njj6W+rjrY6isrcMoOjmInlKflfcRqL+Vw7wst2U5URTa4YUjQ8Wzlz/T31derBSYkFKOgSodFE1a\nRMaPoTscvaJbIEcdGi2E4InHL/DE4xcOrv3i//drE++Fb0CBv+997+Opp55ie3ubEydO8E//6T/l\np3/6p3nPe97DL//yL3PqVBFGCHDffffxnve8h/vuuw/TNPmFX/iFIwVBrch6GJyOwHUkYNv2gRJT\npRyLmuc5ru1y99m7WV3bZOgHeLVphOvCMCEM+mzsbGM0q2S5ybTl4LgO080WhmHS6/YY9HtsBwFp\nHFOpuszNznBsYYFZK+f2+ioPnH+Uf/Vvf4Uf/5EfJvIDXNNGZmkxKJYkTGOyJIJoZBoqpVc2AcsI\ntbztXs/zrSuyskJTE1D1qa5MdeeUCt/TI2D0sSnH3OrPUTnEC6pLHDxPxXWXQ/dGZuYod7h6vk5/\nqXFTyEwItQNwvF7qwI9JpYg3zkFKRJ4hhEHFqfGHH/sEj154AscxSfb2aFkm/rCHZwqiro+o1dk1\nXI6fuId77j/PF59/idOtBmG3zebaOjUErmlhmwLhWHT9gJScqekW1XqNl7/2HIZpcWx6jtW1Nfx2\nB1cIiGOwDIRt47YspOXhVlxWN9dwKh63tjd58uydzB1b4uKVy5iLLba32hgZSAlBEvCON76J33/u\nBU7Ygmi3w3Btm/OP3s3ndj5Hfc7loZNnGcwt8J++9DS3L1/jR9//PmwEluXgVTyCxCfLMiwhkalE\nRClxmpLKdKIcqjQESt4KZZu/hpLR07qO88c6ANAd9kqOFDLVKUNdtvVka0rGdH5cp3t0+UnT4v44\njsdCVYu0AOMnZcXxSCnryrvs0Pf9ENu2D2TXNMd5+1F/jOabAj4qHa+Sf/WZOikrz8dDX1XdyvP3\nqPDZg3fn3wjR8l+5CCH4sz/6nUOoTKdO9FW3HDtc5oLVIIRRSl6r8gcf/wTB7pB7H7pAKCy83KQm\nXKxGHW+mSdIZEEY7JGlMGBaHHBjSwHNdKq6LlIIkjgj8AWmSIk0L6ViEIiXKY/x+j7/xpjch+kNk\nHCFETiRTIpFhYWDkh8+51AVWtywm56KQB23V0UiWZWMOn0ncnY5e1T06fVJWtDo6L1tA+nip3wpB\nqWfrVoDevuLHOFRXvYyjnmSsv0bvGyXcuvf8Ww4944Wv/CnIYkJU3Qa9js/Tn3+WSy9fx5QO9Zk6\nvavXSXpdcIEgRmYmN30fFo9x7s77SWJBR2ScP1HnyksvsnXtCnlvl3zQpeaaZCIjyiEyTO686z5i\nBFubm9TqdUzDYmtzE9cycE1R5AOPQzJpEiRw90MPc2xpnhcvXSTOE7wk5/47zpLZkt0s5MUXX6KR\nmniZSZynGOmAx+56gFtrbYzpBe6+9z7Czi6Lx6c5eWKJ5//4cyw0l7hlGlyqJryydYvuzhbv/d7v\nZrrqYZNhZFkReZLkRW6fPEeYBmEWHwJUhZIelxeFFPM858KTh/dxfOlznzig6YpxH6H3o9SJem+a\njucAgcOZBHUrTZfLMngZr3Mxj/Ssn0UI7XhIbPHDWP3V/bplq4rjOERRdLAxScm6slh0+daVdXmh\nUqhbSgXsjDEFrc873fo1DIMLb3jHkf36uu3EHCWCOhzFoJtdMM65qgarFV6PRmnUmgzThG97+7fw\nr37+l7jHABfB5toGoVujc/smzdlZXNel3jQwTYN6cwbXcQnCkG63y9ZuGwFUKw6t1hzVaoXdrT1i\nitSkO70ec7PT/E8//dP84r/4F3Q3NsjiCGEbGKYxFgOu2qLqP4lb0zn9SU5HPT/6pET1k7IRqnth\n3ON+tPALTcgOh/aNFgEOJoR6no5s1Lio/w3DOmSelp2VIyH+y2VhOyiyOPTaMCz6vSFxmHHj6g3S\nKMayTfrbq2zduoqIQmqzDbI4g9ym1pqmcfIOhmHAzsYup88/yJWXv8rayk3qtonRaDBMYvw8IUwz\ngjRjfnmBxvQ0K6vrVCsVbCnZ3tygs7FJbJnU5qexTEm1VmP+jtMMMsk3vfWtLMxMc/9jj9Cam+bT\nH/2PrK+tc9f5B+j2drBqHmk3RBoSI4f1wOd3P/UJ/uaJR9j66tMkx+apP3SKThzQswyWT5+h88wV\nzj76MFEtZmlpgZW9Lf7JP/sgH/zg/07LqeDvtpmv1kizAD/wqbYaBFF46BDuQn4mxViPO//KxXEc\nkiQhiiLStKApdABmGAaO4xygbj2iSQc0qujzQ6dX1Nwugx+9jBRf4cBXiLl4poE6hUhZGYXesA7p\nnPKzy9SMbp2o58G4vOsWb56nY0AHimMkDUMeLCL66VYKmEIReed53ti1o8rreKRaOqZs8jwfM0uU\nIJS3aeuTXxX1t+8PsEyL2WYDu2ozDAYQ9JmbaVKrT9GYatDZ69DPIwbDbL/zdsmFQAhJtVLBq7ao\nei5C5AyDkG5/lzSISbIMwzE5c+wUcR7zHe96N//yl36Jv/W976biVsjiCJlJTCkxStygapcSUBV1\nM0lZKaSg7ld5ufXPdYU96RAD9ZnO3+n9XUa6uhNSr5OUcmwBKK5nY2Om7lPf052z+t/qczVxyvH/\nlnX4DMWj6De9SGkipInIBdPT03zqk39KHEUcW1wgjVJuXrmEYWY0PJeg1yFKMyLpUZ9fYL5WY2tn\njyeffIiBP+D66g1EEpIJgW1a2LUae702wzTH9CpYtQY3bq+SJynN6RrDXo9Br0PNtXHynO7GBouL\nC9xx/ARTC4sYUzPU6g2eu3aVE8tLtDd2sDFIwoi1azepTlV56+Nv4ItP/QVbu7t0dtvYlmCq2kRW\nTPaCLVZuXuL0nS1cu0Kn78NMjc2aIN9cRYYue1shtmfxdz/ww3ziE59ieXGRNz56ns2hj5NnOFWP\nOImI4hDbcg4hX30x1vv7tRZRIQSe52lyMR5RlmUZg8FgTDZGyu1wtInuOFfXy45snfKYVMqyWMif\nhb4QqXvSdARO9M/KCdbUe/XFRg8W0GV0ksyq7424dPWTkaaj9yv6SjENKnR60lFs5fK6H+gAk8+d\nUx2ittzrdIK+cuqDarkmmR/R39vl3ofu5fbaLS7ceR87ux38OESGKfeeu4eeTDBFlShKWF9fZ3t3\nF0MaDPrFBpqZVos0jQqe27EwEaRJQhbFhEOf2EhIgZnjy/yfv/jzfPBnfgaGQ/KBP5YbrswlK9ML\nRocr6CabGjD9JB+dJ1TP1JWczg+Wkbj6nuIA9WgTHVmXn6HqUEb3xViNx5mrZ8P4xCub1GUrAsqh\nk+mhtultOkqhRHGCACzTYX1tk7/47F9w7o672NpYZ3Z6lrzfIyXCj3MalokvJf0soVZxuP3yS2Su\nReBPEaxtksc+rmES+T6W4yIcF8+YIYl9qo0GcS7odzo0vArdQY8wHFJsosywpcTCREQxW7du4yc5\nT977ILZhgjQIewFf/YsvMNdq8E1veQt/8Ae/xzd90xsI99oIKQiigEpuYPQiao0qz29d4/H3vp3P\nff4L3HHuNFMnzrC32mbh/nNMf6tDqxvjRDntnW2kdOh1Qs7f9SDtcMAv/Ma/5/vf/x6kJTGSGCuO\nqToucTaeFlWn1vQ+L/5+DcexRmup8ddTL0gpcV33kGVXzOeRE1yX/fI4l61BJQvl6K3RwpCPpa8Y\nIexJETJy4rN1WkS3Csp1KwcAqDoEQaDRKCP+OwzD/ecWNIoQAtt2DuaDGgM9IiyKorFjDY8qr5sC\n100HhfR0haMaVY4pLq98egfHgU/VcoiyhDvvvIPr1z7D3Ow0ruuRCAMZprzyykVC18IfpFimjVep\n8OAD9yOkQZbl7O3uEIQ+3W4P0hQha6RSUql4mFKCyAnSAM+tsnTqOMKU/JsPf5j3fue7aNo2JClx\nlJBLQIAhJNn+IcIYBrkAspwkKlZdYYwjj/KipStzGD+FfVJO4rKFovdn4TBWiFcJ+2QHpp6hUL9e\nbFEfn3j6GOh1VY6ao3wb6v5iHCn66OAEdf0IuaNzXSRRAlISRCG/+5Hfh9Rie7dLr9/HQOL3fWqz\nTfKgR5pmRGlMvdXCtSXbazuYzSovfuVLhJubeI6JbQssz8XEJApCpOUwMz/L8TtOsXF7hdwYYrg1\n+sMttm+vMO141CseMkupSJcsTLByg87OHq+8fBGj0WRuYZHrL7xE06vw8qVXWL7vFN/zA+/jU5/4\nBDPT09iWQRaEtKSF7XnkSUqfmNXeDkG3xwuff5raO2dZPH4Hu9t7nL7wAGvPXqS/sUfVtugFPlXP\no7PbIxcpjz78KP/Pz/8Cf/u9/z1n5mZxnSrx0MdybNI8RxiiWDCzHLJsPx2EAVLsW6MC+ZpOTLXz\nMdsfq8n+HB0sjOb64fSrR/1fzseTZdnBHgb9HYVit5BSHNA5I+V7mKIp7h3NO112lVJV8tZoNMmy\nlCQuHPlJOpp/WZZBrhy5OdVqtZDJJCHLVOI5tau1sD5Urn/LisboUdU+ZXEra2A8Sd3h8ropcF0x\nqDKJo9X/102K8iBKKbFESiRjMCVztTpGknJr9TYyMTCFzczsAubxCsJxiJMBw8GAIPC5fvUFHMeh\nUZ+i6pq06g3mZup0Oz0GwwF+kuJnAY7tMFVr0KhUgRzRy3n03AWei77CMy+/zMOPPEwjSbFNi2Ee\nY7lF2lBbSKRhEEoYJjFSCFyK/BFlZagrYN2E1IWsHFZV5vNUn+iCUTyvCF8b70OBYZiH+luvjz5O\nI8R8OHPi4WRUI6RT5jD1cS8moIoyUly7PPS+ScXGwnBr/KennubKq9vMeE1qzSVWdttsX71KNhSE\nWwNyGbFnZAiRs1yvQejjmDlTlsn27i7dzg62MMhnWniNJgwynMxiEOcYlSbG1CxeL8aymgyJGa7d\npB4KqlGAWUuZPb1MHmZE611MXJZPnKLj94l6bYL2NrdXbhJEAVdvX+MDp3+IKA5ZPrXM7sYm040K\n7bpJP4xoRQZJkiIcm1cuXub07Al6V1dZ29nAeOwcwncIr+5gTi2SBhF22MepSFIX7CSjkZv4uyHv\nePBNvPTsZT7ff4b3vOvdTFcqJNEAYUuiLAKRYYoMgxwDCbkgxSDBKM7azGImiMT+mJhjIXWGMXnj\n2uh+pSzzAwpFt6bL1J8qkw4UKTvxR5FZCWk67icbAYJRHRQC1wGieq+u1A/ql6ZkeYY0JI7hYOf2\nwVmk6X6kSZ7lJKk6IHx8J7lhgJTpActgmvY+Hx+NUUdpmh4gbmXR6FbIUeV1PdChvOGkvBKWzb2y\n0tcRvJQSS7qESYpTdZlqOJw8foytrXUeP/8Y6ysbvHrpBSzPI8xTpptTOLbD3LElKpUKQ9/HMk06\nnS6rt2+Q5TnVSoWZVp1KY6pAeUOfKIjY3dqm1+/RmmniDh2++Q1v5F//m19gamqK+0/ege/71CoV\nhsMh0hDE0iAOQ4QU2Oybgfu7NSUcEsgy56+bhToVUnbC6H2j949StGry6M8VQpLtxwfr/Vvexq+E\nTTczRwvD4djh4ro4pOj1RUWnS8rcORzOST6p1FsNoljwF5/6U0ynip9HTE/VsaIYw7FJHRtp5oQp\nJFFMvTXFMEwZBG1OnzpFGAzZXr+NKzOSPGfY7xElCY1KC1yTqudw+tQdbG5uEgU+D91/H3Ea0ybB\nXBqQ+312u9vEvYDZ2hThtElSsRAzNTrrG0xh8eVnn+fk2dN85i8+wwd+6Afx/QBpwBvf9CZ+97c/\nglupcs9993HplUuExEhLEvgBSIFxbIGKaWILQQOLtGKzttnmznP34GYhfSG4vraKZQucqSaeVSEJ\nI1oyw+jucufpU/yv//NP8aN/9+9w59kTWFFETUqyMMaUOZiSRAhyBHmWFagcyMVrc+C67Ol0yohn\nPuy4hMKy030zOpVRnt/jERz5gRWp36crXJ1S0Te3lflpZV3qekQPiFBhs7ps6gyBDp4OInH2w6HV\n81QfGYZxcG5rlmWaczdGiMN8v6KBdFr5tcrrng9cb0DZlFKf6SE/ZeSpT2wpLCAkDmMSkbO0uMDz\nz75Au7PDzFyDuaVpTMclTGOGuz5RGLFy4zrVWg1DSizbwhSS6UYFz/MwTZNOp8P2uo9p20hhUHUr\nTB8/gWEI+sMBg6DHxuomP/j9P8hnP/85mpUK880GoR/RqtToBUP8PEGaEjsrDqNNcwhFjhRgZOPJ\nhcppaBWyVUXnpdX/5T496A9tYQAOztosj4NCymUaRv8ZhSAW3ysjpzJNUjhNRzK08GAAACAASURB\nVEhNH9MyLaPXUbW3/NlRCjzLMn77N36LY40mmVMlFYJrly8yZRgIr4ZZdRCmoOv3qbYa1BpTtNt9\nTCTd4ZDN29eoWhKSEGG6pHFIHCf4QYzdaHDqjjM4pgFRwMMXHsSVBns31vEsk8bsDE1vCeeWTZrE\ndPba5JbLPefv53pnB1sabLx0mTvuPM4rl17FrVR445veSJCEZBIMy+ad3/EdfPbP/pyYnLnlY9y4\ndg0jzXAdm0ajgWzWuPuhs1x75TLTd9xBMD3FMIfhc8/xxoce4IXbt5nCYm+vR8+S9K0hMi4SrDUr\nHv1Oj//tH/8MH/3jj7GTDnjs7N3YdoVhZ4Bd9YiyjMzISWURcijT/Zjoib1dFH1Phr4Yly023eIa\nKdHJud91C04pUxXzrcugLmO6HIVhePBOfVNb+UAG/UfNN/VsfVOTUtijqBGdulULSWExFtx2SpJk\nh94TRVEB5LQoFikljmOPLRSWZR1E5pXn4WuV102B64Oor2zliauERT/MAMbzEqh799ptvFq16BBD\ncs/dd3HxhefZ7WzTG1j0ej0s28XyXKbcWU6ePHnw/MGgR7fbpd3ZwzAMwiijWpvm3MJpotQgCCLa\nu212Njfw/WKjxMzsNNMzTYRlsLm2xZve8M185kuf5T3f9V3IYUR/MMSwLaI8xnYs7DjDyCDNM+I0\nw7EsLC3SRhcsXRHri1t5QMtn/+kKXTcJ9SiUo4r+XkVXlWNlDeMwBaLqrWdOFEIcTIgyMp+0QKgN\nXboCUJs4XguFbG9scv3Vyzx07kHs+XliU3Lj6a8x36iRWjlbO5skqcBttrjnwuN0BwHrncs0qzUw\nimiJeNBnqmKSOgaEOVkGm702liNZzAIq/pApz2V2pomHwdUvb7O5cRvf83Dn5gtrKwq5srLC4ukz\nfOWlF7E8h87tDe5bXODa3h5fefar/POf/SCpAMMySfIEy3VYWF7mybe8mS9/8Uv4YYzRqpH7ARXD\nxbZMNru7nK4Y5FHE4MY6XqtJVPEwfJ+Lzz5HOPCxgpgZt4JZtwgMQTaIWJyZpZ+nLFQdOjs7fM+7\n3s2v/t5vsvrqdb7r7W9ncX6RJByQJTFGTnHuq8jJjX35Ojq99hgPXQ6tK1Nkk5SmHoE1Dg5GuXrU\naTi6hV1G47pMua57MIeUwtXBng4W1WYa9Zle3zIHLuU4I6BHipTbW6vVx2hEHZiq36rtvj8Ys3T1\nsF+d7pm0I1ovr+up9GWErU/gEYqTYw3UBaNsolmWVcTTZhlJnGBKyezsLFEUs3zsBEtLJ5DSpO/7\npH7O7ZXV4h1C4FVcTNNiemZmX6BywjBidW2NOM6wLId6vUK95iHygnrwgyF7O7sIE0zLYGttnTtP\nneGD/9f/zT/80R9DZjlWEmOakjiIyJIMS0oyKTGkIEtSwiwZoyYmOfj0duqcsFKQupDo/VtGKhND\nxUY04UHRBW1c4ecFP6shg0mW1Giijh9+O4k7VxNIR0u6Ga5CHI8qX/j0Z8iDkG6vjcgTrIpLlviY\nrgFhRGpIogymWguEucNGt8vi6bsx85Qrz36ZgR/S9CoIYqQwkBSbLFzPwWnU2NnaZOf6Lc5fuICd\nprzwtWe4Y2aaZL5JuLdHv9fD8RwGSUDrnjM4rRk6tzchiKk7LlnV5plnvsqP/fjf58y5s0RxVOTP\nyTKwBFme0pieJkpzatMz3Ls0y8bKbUR7gEgFcRTy6af+nHc++ha8JKN7a5VLQZepwKRmSL75ice5\n8fRz+GFEO+oSuCZObrBy6zaxCbEpkRn0+32+7c3vYK/f5iOf/CTf++7vpOm6mKHAyjNknpHkGYlI\ngSIl8VELp45q1XiVwddR41z4YRijN9Q9Sub01BFhGB4oVLXIKz2hv08HD2UHpW7dFdQEY7KrZFOf\nX6MY7Ri5v9eg4KlHSH+0GaeIN1eOR/29CnyWQxaLnccjkKLTiDq78FqyD38FFLiqYDnaBIqGqtAc\nfVDUfWqVPTCdbIEUGaZj4wgBUvLYI0/wiU/+MaZdRQqHeqVOo9HEm/MwzcLDmyQJ/nBIGIWkcYLr\nOdRqNRzHodvtkvbadLu7bG+vY0qTRqNJo9Fkbm6apWMLDP0+27s77O3tkLsW3/d9/wO//8lP8u5v\n/zYs04YgQOayOIjZLt7pyCKDXi5G6BVGlokeqqX3i8pSqCPrSear6if1WYEcxnOojDg7Dr6nC56a\nVKNS7K4s7tXNZLW92kA/S7HMVyohnUSNSFnsxizaq8ZVYlkqRG2yKXn9pUvUbI9Ov4OXJaxe2cLv\n9ahMZdSFSW45NOpNWvPHuHp7g53OkPvvO0F7c41UGJiuRzgMcT2HPC14eyjQVHWqhZnB4sISlQyu\nv/ACHjnS9wmyCK/i0Rn02OsHxFWbex56gBvXbhKFEdHAx7QkH/mTp/kH/8tPcf/DDxc5uin6yrQM\n4iQhTFNsx2N6YZ7d7R1OLx/HtkxuXrxEsDfEM02cisXu3jZB2qXT3uL2zgZxbYZ0tsV2Z5fW/Bx5\nZ4+GCYZjINKcqueSWxa5bRD6ITWnwmDoszy3hFnx+D/+5c/xE3//R5mxHUDgGRbEPgYgpCDNjlYe\nZUtOD4VTP+W88mq8kyQ7cDYWERoKZIj9+Gy1hdzcV2gKcYNlHa7TSGGOOziVjOvZOsvRUeW5UqZn\nhRAkSUyeRwf3mqZ5oGiLthY7PgsFrW+vH7cQ9IR0hdynE9893iZxaLEsl9eVQlGdVBYIfWJP4oL1\n1deyrAMlk6RFCE6exoAgN0xs22boB7Sm54nDnNWVTdZu72JVrILzNgws28YwJKZp4LgeGCbt3gDR\nH5IkCbZjMT1zjIpXA6Df6+MPh3S7bVQGI9syOXHiJMMwZNDpkRsWL1+7zvkz56gIA0MKpCWIDcji\nBCvKChRmjnu9lQe6XPS+0vtnkpNQR7zjpuN4fLj+TDXxypEk+rP19+rXRiaxQD/NXD99RX1nUqRC\n8f7DedQhP1Dcutmrl2Z1CsersNbbJdnZZri2TmTA7TBkWtjsVTwu3H0/cZoT+RHTrRYrt26xev0S\nZhLRmGoRi5TuoE+aD3ENizCOWTp1Eq/WYPXadf7a2x4j6PW4ePFlmtUKjl2lNdWgn0ZsJj5+EnHX\n/N1ceeEVBsOIWr3OznDIqzcu8/d/6id44MLDxGlanJ9TrA/7DS/kotcf0Bv4LC6fYHNtm0qthtts\nkPQCqrZHagguXnqFNz/wGJ3tHdLb6wzmMuyKST/0OTHbwqy62MQ8d/MKqZQkMiQTkjQX5AKC/pBa\nrUrQ6RMNBvz4j/w9/uzPn+Ktb/4mWp5HLkHmgrrlEMVxEfJ6BP+q7z7U5WzSrl997BUFoctPWb71\n76gEcCNr8HDEVlkp6/OhHOmi5HsEQiYf0KDXzfOqY5+N6KAi787oXrkv/3IMhOl0jP6uOC449HJd\ny33yV9qJqYqqZHly64OqKyj9R/cO25aDyAANrTXrNaZnpnnx4kucWD7NiRPHmW7OMkh8Ot02Ozs7\npN0Ux3GoVqvkCBqOQxJGBMGQKIrJkgF77Q5SGlQqFRzHwa641KemsG0b3/cZDIYEfoRjOfQDn/MP\nn+e3fuvfMfPe93N6dg7bMojThOL8ILAExIIiLldrr+6hLzuBVD+o+3TqRb+njLB1tFAWkuLekflb\nzmpYpkDK13TroTxmulO6/Hx9vEembTbGY+ql7OBWpR8lTM01MXpt/E6XuVod38zohxGdXh93tsXe\n7i57bZ/5Yyep1etcv3YJKw/J04gUcGtNEsOl3+uwNRgyPT+P6VbY2dhmqtqkNdVidzDEzaC7vkFi\nWdjbHl0Hhi5Mzy9x6fmLWFj0woj7H3+Eqys3+Hv/8Cc4/8TDhHG6f9weRdbKrPgdxzHN5hSXXrnC\nwPdpphnb27vIPcFdd93D7SCjvbJOfziAHJ69+BxveeSb2N3e5pWrl3Fn6ly/eYMNY5WHzt3D6elF\nNjfX2QgGxBKa1TrBMCCRAstxCJIIMzOZsapsXrrBO9/2LfzG7/57vu3bvxVncREPGHSHOJaFMI/e\n9VjenVuOKFLjrY+d+r+s/HXao2xJjuRzfLONrpTLzxFCjNEtOlApo2JdpnSLd/z94w718gKh3l8s\nMiM+uzxvyn1jmgZZdnifRFnZH7WIHozFa37637BMqmBZYJSy0jkpdV0pA93czzNJnqaQZiCK4Hos\ni3vuO8sffvJPePzxR9m4ucHmxgq5adJoNjlz5jSVSoUgCPF9n16vz3A4ZDgc4DgujUYDx2wggCAK\nCcOM3Z1tfN8vFLltIw2J57pUqzVs26IiJL1uj5/56X/CH/3HjzH31rdQyQXCEMVOv8AnBQzTPEDw\nqk/Kf5cHsGyJlBPhqz7Uw6JGi+Bog8w4kj6sHMsTRF9Iy5znUfka9DEuL0R6fYt2jEfTlE9NOQqJ\nbA59ZrKc3dUtamlKSgRRjuPYhB4szkyTxwF+e4eBYbB++WUGgzY1T2J4BoE/JBE20q7h1iWiMUXi\nuXTDmDTOsasOt1ZX2L55AxHF2Aj6QZ80CMGu02y0WL98nVnh0W/3mT91HGGZZAY89sSjtP0O0nSL\n9grI0ow0V+FpJsOhz82bN2k0mqRJztKJk6zcuEHYG2JVK+yGA2wDRJqxvrXG1toq9504yUp3m+e+\n+hWmlhY58fBDZKZEtvvM5BY9w6BvGsRRhJHmCCGJk6igB2t1TGGSpykb12/zHX/9b/LsM1+j8oTF\nlGMxN1VnMPSRR2ycOko+ynOzHKWip8uYpJR0xTjJylM/esK2MpDTfSb6YSiqfuq6EiWdZinL2Ui2\nx+VQHRyht03RfeUAAr3+Zd9WmsYH4YwKpesWr2EY2Lb9VzcXSpkDzw+E2kB3oKlVsdwhk5R9lhem\njCEFUkBKRpqGnDl3iuQTQ4K4y+KxJjJr0RnEhHHM+toKlm3j2A627TA/N4NpmAyGQ4IgoNPeQwgL\ny7KpVipUqx6VWhMpJH7g02m3Gfb6+G5CmpoIy8c2JdJP+PyffIbW9DQvXL3E+QfvR4Yhwo9whIGw\nBHGeUWzZHCECnbdT18tHyOmDWkYjEx2VqIgUdRL9eDpNHTHrSEPVqdz3Uo7yJE9CLapMonv0MEnd\nwVpOiqUL/WtFz9z7xGNceuZZPGHi2QZkEXG3S7fXx1qYxSSj296hbhuY0ZBwZ5VgsIdZd2g2m7jV\nCkEEaWpi15osnj5OY2GO21eukvYCKpU6r1y5zN7NW8xaJv6wT+xKasfn2Rv06F6+gbk3JJUxb3/n\nt3LiiQtUji/wZ5/8OMQpmVn4OSTFuZVIiSmU3yfjheeex3E8bNPBdVz6wZDTp07z/Be+yNKpZezp\nJuHuLmaa0qzVuPjqizx874Mszc9w+vgJ3vGOd5BWbTZfuMzLX3ieEydP0qi4dFIfQ9pY0sTPc1zL\nBlMy8IeYpoUQkqgzIOn7vPXhN/DFz3+Rex6+B1F18eoO1fBoBX7UDuBJcqjAVxlslRF6WXbVc/Q8\nQuUcQroi1/OJqPt1PaEjW5VkTRU9gKCMsMvAYZI1qtdZDzJQi03ZOT9Jf6k+LCv8v7IUSnkH1FHp\nHcskvhDFLr2ikYqv3c8fYrhIITEMMMgRIiEVOVXX5fHHL/DMl7/IXafuJAliGs1jTNVrVOarGIbJ\nYDhgOAxY39nZV5oWrVaL5YVFbK/OYDCk1++zvb1Df9DHsW3qjTr33fcAju3QbrfZ29ul7/foBQE1\ny8KRJnfedRe/+bH/gFfzuHD6LLZIi8N3pSRLs+JQiBIqdRxnRKdo5iCM704rm5cqxElHKkWM6qjP\nFac8zrkddirqaEinOvSoIeM1eNJJk1XfYaZzhVJK4jgsUUZFbvDCstqP2phQvHqNIAxpmg4iC4kB\nhEG9VsOcmSPwfUgzluYWuHX1CrnfY77pkROztXGL6bllLLuKIxzmlo7RWF6gEw9ZPr7M3Jl7uHX5\nMiu3V2DQx7VMhsMhTm2KXp6DNLDjjEW3ztu++W1sJjk319e4s16n6TQY7PWJmymmsW9WqwVrv429\nXp/d3V163SHTUzOEYZ/6XIv2zds8+OB5Pvvlz3HXfWdZjUKSTp/OoEfi52zv7eBUKvz1t/8NZmZn\nubq7ztr6Gg1pIoch0zMtupkgHsY07Aa5zEnIMAwTPIMEgWd7OIaFZxisv3qN/+47vpuPfuaPiOo2\nx+fmqIisHJykTcJi+3ieZqDFdSuAoEBHIRsKPXNI7nSFVcgFB4ejQH4ojXSZX9ZBThm5j4CH2gGq\nO/tH80d3upqmeThiJBvpHA7yqkCWFnUsqlDMMb1uqq1qLhT14qAfdPSug6QDNqFkGRxVXtedmKqj\nddSnh/MUiigau1dlPtMHrOg4gRAJiCKENQXyTCCFRe7D2598G7/6ax9m+vFZkjSlu5Ex6A1omz6W\nU+SBMAyJW62QJCmmNAmGEb32Gqa9hmUVJs2pE7Pk+Qx5njPo99hZv3ZQB8/Kma63iOOYIE4wqXD9\n5hrf8tZv4/ataxyfW2amUUGaGVmWYMpiI7NqzwFa2M+NbewfhYXIyfKMPJ98pqQqo88y1FYM07T3\nOeoigsQwDtMhutJWP2X+Xb8+cgYdjnzR61ZW4Lp5qOgf/ZnFGJv7z1PHyVnEWYwpJ4tqeOk2d80s\nIERCe9Cm5xvsxILm9HFIGqwPNnAtj1ev38b0u8xXc7Kkx24QEWIQkGLEbaJ+j51+G778FP2tm2QV\nm4FhIfGYsVzuWJplL+jAVAOnMoU1bNGcqmO4KYuRxZUXX+ZaLrh99RIrv/47nHaPYRt1hN0miSMs\nLPL9HC9ZWijy9vYOt26uMDd/jDCRuF6Vtc0us0snSTtt7l48RbbW4fjJk1y9dZ3BrR0MPD5/6xbv\n/+f/GLm4yI3NdW5euUbSj2hNT2NFKc7OgCUzZTNL2DNCNoYx09NTEA2JsxjDgH40wDMM/CCn0fB4\n8dkXOHfiHp575grRmRRjeZqmMTkPR5jHiExg5hKZQ5QGY3N6FIY4UpoKcClAMYZahUQgECJHMFLy\nSTYCAcoK1X1EuuxJ6YxZdKNoFCXf6kzOcaCiA0Q9YuVAXuX+nEK9E0b5T9ifVwczUM2Asfmpz41R\nHcb3aejzQ9Vfp6KOKq9rFEqZT1UncOgJXKIoOhiYJBkPgRspdXVtPMe0YRjkQmDaFvEgpVKpcPXq\nVZpTTe6++xEqbpU4SWh327S7bZI0Jk0TarUqrUYLyzDpdnvstXfodApKxTAMarUalYpLa6pFtVIh\nz3N836ff77PXbhe7qlyPmakmURKz12lzfOk4n/3s5/hb3/NuhkmMQBQ5qy15wIGlaUKWZ/tJcQp0\ng5BIBJChYyLVxqS0hTfZT5xlmiOnSNm8LXOMZVSkVn/duVJGFer7oJL3HF58x/n3w7sudYSiEnip\n+9RGjrW1NZaXl9nY2JgoR51BQn+nzfx8E0tIbENw4aEHsLwpbtzcoHaiQrjaZrB1m7kZi+3hHvEg\nJ40sMlMShDnVSoNmbQHjXIObX9rBq8zSH+4ys+ix1d0mri1xtd2n1mrhOhkvv/xFbHMRryaxjSFD\nr0nUk2ynJms7Q56/+BXe+He+D7+yxzACNzdIswxDCgwtvM71XGq1Krt7eywt1ZFS0vIq1A2TKBdc\nfeVl5msVZmSLc/PLrMeSGxev857v/D7uqs/Qaff53X/3W5yZP8aZpRNE7Q4r3T3m/YwTC3Nsbawg\nKh533XUnW7dWmPOqxCRklsC0TCxTItIMx/Hwk5TEMjhz+hQvX3yBY1OPkOSTMXg48DGkAdLCEBJj\nX9EXiLGwXvVoptF4G6T7cfYGIyfiSA9Atj/PlY4o88eTZEnfpKZTjXrRLVWlP8bffRhs5Hl+cOqU\nTg0VtNDhowv1UNdyBNikUqZNlXWrip5q5Kjyuinw8pZxKDoiDEPCMDz4HziklPWiKyT1/5hjTwjy\nwMerVbn7rru5fvMG9957Lxur10jSlByJ63nUKi6WbZOmGcPhkHZ7B0FOHEfUahUWFuaRUtDr9QjD\ngCRJWFtdpUheb2M7NkE4xPUq5AjCKCDeTQmCAMu2sU0Tz6nw55/9DI+cfwjDtDCkCVmOYUqkIbAw\nyfOMKAoP6ANpGOSySHylo5lJPJkQ6jBkFeYkxnhDRT0djQ5Gwjsp+X/ZxCsvBur+o7hrHZnD+AQV\notgYoe5TCP3YsWOEYcj8/MJEOYqjPm6twk6vR25KnKkmp+45y8r6NuefeBA/2ePm2gtMTy3QC3fp\nRykN18WQDlZzitqZU/Q6PbxIsHnrFTr+HlXHI82aBDsxZ2pLzDeWSOtNbrZ7JD2DE/ZDDFhjb6tN\nY3qaTdsmchKGwYDexm3uvfMYj775UfKajRlKzMzAkPvLbz5qv23ZtNsdpqfn8VyHNI0xHJO9qEcv\nbHPm8Ye59rWv0r3Vw2nVYKHBuTPfzJ3f/BDdrQ0uv3yJ6QiqnYCk0mEgQvbyAe2r6yx1djl+fJHr\nsc+tm5dpWB5x4BOLnDjOyfaRZBgEeK6LNA2CLMGsuDz60MP8zu9+lG9/57dO7HMjzyCDMN2nAFBA\nYD+HPwlCgPn/M/feQZZld53n55xz/bPps7JcV3V1VZvqaquWYGQYWTSIlgRCjERgJEwsTDATK2Ij\ndmEhxE6sNBGYQWIXEKZnVsMs0sghCYQsciAJqZ26ukttypusykrz/Lv+nP3jvpt5MysbEcRGiPtP\nvnzvPnPP/Z3f+Z3v7/v7/pRdwC150RYPIbBUIeeQa02aFYJZSlY440JMeFrFYD0f3l7a4E6nXbXX\nnQnJKs5dJUaU/8P2uooS8tv53VV44x8LUnYGMdVj51wo50s1+Hk+eLJ6fM8ceNXJVjnQsLWqZlmG\n53nbVtedjqK65SifL1/bXDEtxdrKdU6cOMGHPvJhXvCCFzA9E1APGqSZptPpE41HKKmwLYu5mRk8\nzyHNYnq9HhsbfQaDAVoXUfz8/DytRpM0i4nCiGsr1xiOBoXTkxD4Hq7rY1sug36/iMwHA2anZxmM\ne1xeWWP/gb2gEyxZqgSWN8xg25OkhywMWm/CRtsTPtUbvAU5FeNqWbISkXODoVfHaieTpYyIq864\nNM7yHlUX32r0sttEKs+pfk55lP8nSbotWSulxHVdhsMhUiqydLyrHUkzxq3VQUuu99aYm5ljY7RB\nZka4XkJ36FP3DYuWxfLYw5o9hkrBz3KsqTZT+w9xKjqH7TrcsuATpDb5RoJbs3jbT76Fs08+hmVJ\nXvFjb+SZlXUunV3G6qRM+/Dhj3+KTmrAn+XSpWew8hF72zP85rvejW416Pc0ddcGtuAmuZlvEHiu\nR61WY2Z6CqMzXMdHC41rO4wtxdTSIisX2ojBiLAzoDU/jTvbYmHfHs586u/ZOH2Bq8+d4Z5XvgpM\nRpYnJDJjGG7QjBycVXAaHgePHKIzGpFbDrk2ICRCCzzHoeHW0FmG47v4OgVbMh4MeP0bf5Q//pP3\n8bpdxtwRCiw1EcEC22xRT8uFdxyOtmG7UkqEFBidIYXCkhJhKbQQEzx5Cx/ehF7MlvZJaVPV3WDV\nxnYreCnzRTuL42C72FXVr+xsvlKsKduDkKLW4UbxOCFulMLeeU55PB+Lpwxed17f8x3fcy2UqiM2\nZnt3DsuySJOtXnTVFa0c6CpVTqrtK6EUAiUEUZIwNTVFZgwvfMEDPPboY+xfnMGxPTy3juvWadRq\nuK7PaByRpWN6vS7apLiuzZ7FBVzXR+ucKIzobKyxev0ajuPg+x7z83P4nk+cRERpRqpz+qvXydMc\n3w3wHY96rUaURoRJzONPPsXUwjxOnpFmKUqCmrBEyqa+enNswJAjKbTFq9FBFUOrRhdlsqU6zsAN\nuHVpICWdqTx2TphqFFFyvqvnV+Gw6lGdHDtZJjujqDJhVUx2gOJ6XNfFsh2Gkw4vO490OIIwJpeG\nKdfhxNFjrHY3CgchBdeuXkHpIT0zRNXqOKaObWlM2MX3XTztsuC1OTLb4HT/Al7us2dqjpv3L/LN\nr3waYaf40zOcW13h3NoqJ158K92Lp1m40ua33/mf+NaF5zi1vsxGb5WWNc2//ZE3ENSmiYXPYt0l\n7F7DeGrzfpY7KSZywklUtPJrNNt4foBOUkaDIcO1LtMH9nDk9jv4wgc/StNxiMx5XnjTLXzi//kA\nL2ruR/VDfMdiebSBK2tcXVmm01slTHpEKyPun7mLKQzXL5zDXZxjddjBtX0Cu0ae5EgEyhR5pWwc\nYXuKLElQxtDtDHn5a/4NPPT/3jDmwiiSVJMogxGFJIQQArKUsuuMwRTa8WnRB9K2bSxhIbTBSINB\noo1AG02apBhRUPY2ITpzo1PeaWM7o92qbVd3p7s5/N0KbqpB4lZEvr15evUoA81y7lnWdjruzqBm\n52s7zxNCbDbCKH/Xd3Pi39NCniqeWsWMyghsi+kARYRavncHi2KzQKKCNeki228wBL5fVD5J+JE3\nvpE/et/7eOmL7i+YJf2QwWCNZnMWiUc9qE1IGZpMJ/QHXUKd4Loxjm1jOxazs0USs9fr0u12ELoQ\nc/c8D7/mI22bwPEY9IboLCXNU6LIoDyb6elZglaDD37ow/zcT7wFlcabRi9EoT8spUAg2YIgd+dD\nV7Gz8kZvacrcqBW+G5VrNwil+v8/hglWf0s1wi7PLYoVCly0hHOM2S7YX0Y4UloVrrAs1OVkju24\n5FrTak3takdWmBANV9FK0FzcQz42ZLpGMDNHX3v4V75CHGasiRq5VpD3MVbMOI/xmi0u9DbY6F7m\nwHQDfywJc81auMbqk5eYqrvcsv8gcQfaqs3Df/8p/vB3f5dX3H0XL1+8HYIUKxC86MhxPv/pD/O6\nH34zt957H6kLab6Onbp4UhFLhZiIIqGL6lKdF+L9YTjm8qVLzC+keK5LomA97BMqOHn5Mnka4x25\nibWzl4nOLRM8/AT333EXqWPRicYMyfj28jmEhPHqGnkeE4uIoe9ydbjG//OupwAAIABJREFU4enD\n5EnKE088wYYEx/bx/QaWcmk2p/DdAG1yWs06o0GHPM9IETSm54ifR1I21QYjBcq2QUqU2MpNFTkn\nB8tSJGm82V6wYK7kKFPMzSQpNE4QE5kMoVBCIycMF53npKaMkMtIurDroiCtnBdbjRu2onWDEFs7\n8cI2q7a8PZFf/VvljRffuVVNvBXIFInYKvRSPOaGeVH1Z1XopVqJXv3+6qLwLzoC337hW4+rzqF8\nrVpyWt2i7HRA5SHMVgwqKbg7eZbhBT6WY3PvPffwzUceYWnPfqbas0zPBGAUnU6HwWBATtH6qdmq\nYdk2tbo/gRSKSZdMtl/NZoO56WmCWoDWmm63y+rqKqMwRBpJq9FgZrpwPJbtMAhHjOKYIKjhOi6n\nnnmWY4cOooTCCzzC8RB7U0SoVM6QIEDsWODKMXIcp+IcS4fOpmOsGlB17HeOJ2ydX26Dy+eq7ymf\n2/ne3SMjvS3K2cL39LZJVPzurSgpTVMMRW4ACr60el5NiAynFtCPU/qJ5pGnnqWXaeb2Z3QHQ+bC\nFQ4ELVydk0jNVW1zPgyIbJel1gyB6OLeNE124C6Wn/wbao7g4KEl5hfn6K91OX/mCnOWw+q3vsy7\n3vajpD/5On73//wtVlvXOfPYM4j5+5g3FsoRvOqNr+bCWhfpg0lHpDokF9PkZqLzQuHAjQGBIPB8\nhsMBnY0e165d48mTJ3HmpxgOh0zXGsiaTyoMSyeOszSzl97FKwyWu6xPrfPRM4+w/+A+5mpzDMkQ\ntqB/fQUdhUg7Z5DkfPHkP5AJw9GFg9weNHmms0bdrePVfKjVuLB6lUvXV6nX66RRTM22Obx3H1K5\nnD1zAW3t7kD8xhSjOCLLcnKTF5Wm5NiWjbBscgkGSYoAaVGyMzKd4yGL3aRlFXz0SZd2o3OSKC4K\ncaQsckphMpFjKHJRWbaVjC853yXHvPAX+SRg2LLz0vkXjnOLRljaWtXZVm26nEt5nm6z8/IzS/ut\nQpPlubvtNnc65Oo83gkFPV/UvtvxL6KlWhU3rf5fvF5VuCv4mDudThlxlvdCsmOrJSW2ZSERxGHE\nsaNH+R8f/jb3veD7uXr5Kq6T0Ki1mZpqMr8wSxTGxGlMrnOGgxFROKZWC7AsiyiKSJKYLMsZDvqo\nCVbreR5B4LOvUccgSJOU4WDIcDREKglxVLBLpGAUhzxwz/387Rc+x9HDR8h0zjhKkNLCsi3yLMNo\nDaYQ2geBzjW6UvK+SbXapTS5iHJ3Rg03ShWU41+lT5WfvTOnUF0Ins9x77yPeb4Fje2Gk1cXY8ey\nSbJkonlStPZK0xTpWEglGQ53x8CfxWIQZfRHGUt1zVTaI+tfJstPUYs6nJVNTLhBs3ed9TTk0Xya\nk6ODNN2ARv4Yt0yvcGUt4elzLu1ayvXrXRprkvWV8wg5YnpxGhHM8+g6cCVBDJ/j3/38K/D3nGa1\nf4K//VqTxx5Z51f+93dzobtCbvvoXCII0EqQezW0GSGFhPJeTLZWzz33HMLAiRMnEFJRr9Xpx0Ma\nh25m3/weZvbswZ9qUXMDWjh85cOf4Asf/wRpd0xrZob7XvNKpvbvASmJTcJzzz3N3/yPv8DLU/zA\nYUzK3598mMGlaxy/+VbunpmjG8fEvQ6HbjnE3S+8h74wGOVgcoOVGhrKQwiLkYFUP4+UqV9Ucw56\nXVwvIJNpgb9HhSJfnIQANBo13CCYBBcaaVuYXKM373tOnuUFjVBKHM/C8bzCEecaz/M2W4xVNVSq\nuZtqxFy1s23QaiUxWKWsVm1wp81v4ffb7bv4/kKQa9O/bNr07kVt1aT+TumL6lyAG6mM3+34nrJQ\ngG3Y005IoIjGksp2aSuSq+K25fm2XQiiK7GdX5knKa7nok0Rms9Oz3Dinnv52te/wQte8ABZkhHG\nI1avnaNeb+J5Hu32NM321GSbOyBJE4bDAWmaEgQBrZZHs16fYMKa4XDImTNXSDKN67g0Wy3a7Tau\n6xYVnf0e3f4GYRiiLAfbc/mBH3g5v/rr7+Q/ves/4kgBOiWKQmxV8GKVkEWPQgRaaIzeak5cGkop\nZF+94YWBZdvwv2I7uL0CrRzjavedKu2wNO6qklsV165ihzudcnGIbfe3PL8KoZS/LY5icvLJRDUF\nnz/PSbK02FE8j6l+a2yTaQ/HDwj7Ibc7mmMzhnZ0Fses8IlnF3lazHHi2MuIZURvbZklSyLGPU6d\nPktwq+TonIMTfpOOn3E2GzByFtnbnGE6jjmwNM9fntJ8jX389gdWeZG6yu++2CFuXmHKP4KvJa61\nyBf/4Vl+4Zd+hKunn8XOcjJVo5dGWK7GNhoz4bIrpRCqaGH26GOPYlkWF86fZ3Z+jtXVFfbdvA+/\n4RCZELIME6d04x7rWc4LH3w1l9aW6Scp/+s7f53Es9nodlma20M/HbNw+ACWNHzuL/6CeDgmNim2\nsnhm+TxaZxw8fJBjR49xaW2N73z9q+zt3kbr0CHsVpso0yjpkaNAg6OszeYOO4+xFowjjee2EIDl\n+DhSTCSBi5Zstq3Y2FjjmWcv0u/3mZ+fZ2q6RWB5ZHmGFAYpLRzXQ8lC9S/JUozJsYSF5QiSJCOY\nLABlw99qkrFsiLCTwlp1xuV55f9F27Ibg8Cdzn8n660arZfP3whn7t6kuRrklL+5mvOpBmQ7v++7\n8cCF+afG6v8/HkII/v6Ln9xWMl91ANXsdZZFmwNW3dpUb9oWPusXANdmNrsYANu2idMEqRRSKYSU\n5J7DQ3/2EEePHGHPwgLuJMkSR0lRTYYgCOpEaUYQeDjOFtfVGIPJi752xhQwRokNC1TR0SUMidIE\n27GRSuF5Hq7rkk8i5tFozMpGl/bcPNevXuHlL/1XmCzGxCFKiAmNcLJdo3DKupKVL8drZ4eeYjy3\nCmG2sz92V2GrGmYZZeyUqd1p8DvhnPKztvPEzaY2RXWR2G1HII2FsMpFIUWLQoN9HMbUa00Go4T7\nH/hXN9jSkdf+B8ZhQjoaY8UDZqwhR6cNh6ZSpuyI9ZljnL+muTRs0EnAyTsccBJqSvH0ygbtmYAX\nz4x5gPNcwCZkipA2OsvY42pq2IxrRzgbHGIjDLkjX+H1fo/mfavU21OM87187kyDb11r8qK77+OF\nezLa1iphPeC68DC2j2USpDFIJMKISeGKZGOjw9PfeYbFxT1kWaFWqGVMKlLCUYyIwJM+lwddNvKE\nNE042J5h2gtQriLXFr5TIxdQb9fxAou6K/nchz7A+ulnaTkuvXDEWBqwDK3A5baDN3N0/2HCfkiY\ngTc7T/vAIepLBzBujf4oRloWjmORRyl3njh2w5h/+fFTqNxgZwYlFNpxkarkV5fOSyOVQEwS8wLo\n93vkWcR4HLK4MIfvOVy5dAHHtvA9ByVB5xl6YtO+tbXo76ZtUrX53eocqs6+9BUFrdbedu7OYKU8\nr/iu7VBt8TlFArrqrwq73t2VVufoziDm+SLw6ly7875//byQyvc0ifl80VsV7I/j8AYnUm5FYDvl\nME0Lx2spVeBjUoGyEFLQqNWJkrhw3jonSVNe/LKX8jd/9Ve86Q1vwJnAI1PTTeq1FlmmCccx692r\nXLp0HsdxCIKAWq1Gq9kkqPl43hRRFNHtdtnY2MCyLJqNFp7r0Ww0MKpo+rDR6XDx4kpRpKQsWo0G\n87NztGYWWB0MWNvosLx8lYWZNoEfkKcJUhQTXQMm12Qm34SToEpz2i2bXTj8KqZdGnl5lJ+jVNGz\nb2vHUwjje55XwQG3JDFhaxHbjZu/tc00KLWDRlaJSoqs/XYNimgYISTUagFxFmNZipmZGbJMs7S0\nZ1c7mtKCpuUQOhb9xKVnZjnZ1zzZGVOvKVoXP48FRJ2ckXWMvruXdJjx4hOH2OPu4eTTlxk+26N5\nS5sXvPYY68+u8Nxjp8lai4xuPszljTPMXPogb1iEaSfl0NE76Qws4o1Z5rwV7NpZjh17MeneV/LU\nN69y2+0xS0tXWBtrlu58KVc3BnhOQJZmmNwUMIq0sJTF3NwcC/OLhGGI47hobZB6hJQGZTnQz6jh\nkdYDVj1D5iisUYw9jhiOBkSjjHOXVzhz7TIf+8uPouMxrmO4/eZ9zGuo5Qosj76XcSXZoBYN6D49\nYHB9jUW7yXR9htnGHONLKxi3TW3/NHnNwQ0CLpw7zc17D+w65tJxUalGxzFhFGJcge04GMqmvhDU\nAoQQpGlEkuQ0Gg2aLYc0G+PXM5TrgqW4+djtBJ7H8pULXL54ASk0c3Nz1OsB2Wi4rbCvhAurtlT6\ngpJ+Vw0A0zTdZE1Vg8DRhNG0m18pnXiZW1Jqa6eZpukk4t9qbFJdSCxre+PuKl5fpceW0ftuu9Zy\nTvxTE5nfNQJ/+9vfzl//9V8zPz/PyZMnAXjnO9/Jn/7pnzI3NwfAu971Ll772tcC8O53v5uHHnoI\npRTvfe97efWrX33jlwrB1770ic3Jbtv2phhNdeWB3TVTysGpVgmWA1IVkt9KXtzIPw60w0ho/uTD\nf4HbqHPrgcM0tUvN83EaNQZJgqUsapaLciwsx97E3DqdzqbztCyLWq2GlJIkSciyZPM3VG+a7/tk\nWUaSJERRVPxuoxiMhview9/93Rd56799E41agCUMeZrhuh55WjS1cF0PCsLNVuQwcdKWXfw2JttY\nnWVINAUqZyYUNrCtIvKQgNBFclcbyHKJMRmFIkGOELrg7BpDloGQNko5mFwj0wFCOhjLIbdsUi3I\ndYalDMokOCqBPEahMdrBQFFBawzCdrAtl0xLQCGlgzFFAU/q5Hi2xfLly5w7fYZrK2v0BjFRLjl3\n8RKNRov3//F7b7ClEw/+B3qjYUFFywwi1Vha0Aqa9De6xH5IlmtQDrbtkmcJKh+z0JAsNQymd4mD\n0w5TNcVy0oFxwr0HjtDojdHXN9C+zXU3YNyYxq/VuGna5WAb9rY2aCwMqdsKa9hCeNMs9yUOszQ8\neOTyCqveHRy995Uoa4SXaJRKGQRjIuEREEDWQ8gMV1sYDV3LQWqbunQwWqPzHGtSkl7SQzVFEY5S\nClsE2FKCHnDm9LP8lz/5cy6cucbMdJs7Txyi11/GdlyeeuocifQQgcv+dpN528KOxrQCj+N33U19\nboknzlzBas2D22D/oZs4fvcxkjTllqO33jDmjz7+DGma4Ps+g+GIWOcEfh0hBEmcIIQ1KYlXZHlK\nvV4wtJIsRUdFtyvf90nSlEynuG7R6b3erGNMTqfTZW1tlWY9J44jtMmp132kFCgM6BQLEDoDrVFS\nohMbISVZluJ4DmEU4rp2UdlpJrtlCsqsJ2zykg0kZPHYFIur1jlKgJyomRrkNt9SJk1LLfDqUTZp\nqEIy1Qi//LtTOrb0aTsVCcvjjnte9s+PwN/2trfxy7/8y/zUT/3U5nNCCN7xjnfwjne8Y9u5p06d\n4oMf/CCnTp3iypUrvPKVr+TZZ5/dtkptXez2SHJn0qx8XJZYl99bXdGq0pKls65yysuVzLa3nG95\nruvaCAFvffOP89vv+T3uPXoHlpZISxWyssMBSZwWXbqVxPOKogvHKZrNuq5LGIb0ej1GoxGe59Fq\ntZibm0UINp8fjUabi02tVmNmZoZ6vT4pdkgIgnlWri9z//0P8IlPfooff/ObyNE0603yLEMqPWk0\n4aAxZHlGnptJJzSJpSwQoOytTLxUEiUURhSMHEWRN8uzfJJJkAglJ4q7GiEjhNYwMUopLNI0R+cG\nS1lYskisaq3Bb9Ko+XTWV/GVg8oSLMdBI0mlQ6Ia4LkE9RZxOOnSY1KSJCZOQrI8RUhNpnNGvVXW\n1tcZj0f0Bi7ReMTjDz/MeDwmSQ2OV8Pymgh3nuVetNOECqPPbITxi6o/mRPGPWxH0pgSHL3tGI7y\nWF3f4PTZSyQxSOFhUPTGOWmcINM2q/0Rc1MuK8ktDPs5K2GDVx9t0ZDX6HfXSZM97PMF0/Ish3VM\ns+Pix4sIL6fvjiE2eLWcqJYQiDXUeMDe2u0sX6tz+fQTHDl6O9rVDKM+DTxMEqGZxcqmsKzzhFZK\nnu+nFgFqRKaLJLbR+WYxi5STSW40mAxjchITMRhFBJ7k0JGj/Phb38Jvvet3WF27zsULNguLLdbX\n13Etm9jAMIxoHjrEYH2Flq1ILUliCWYPLPHG7/t+3v3eP+Lhk89w66238bVvzXL33fdyyy5jbkyK\nUoI4HtNs+KRas7RnDysr13GkWzRFMYay404YjhgPIzCglINtW6RZSK1WY3VjgNYJ2miyPEJIQa3m\nEQT7mGm7KCUZh2Oee+4ZkiRmfmYKS9nkSUwWZ7QaDXrdHs1GQBRGZDpBaoGQkE2qkC1hI5yJxo4U\nmLyMfivMks1rkxMHDqARcmuXmOc5YRgCAs/zN31U6bSTZAvureapqsFc6duqvquKvZe/55+KgX9X\nB/6Sl7yE8+fP73ITb1wRPv7xj/OWt7wF27a56aabOHLkCN/85jd50YtedMO55dao3LKXK1v186uZ\n4/K56mq40+lXBW/KRGc1UoZK5V+WkxuDQvPKl76UZ575Di950YtZu3adLErZt3cvlmNjpERrw3A4\nZDgcsra2im07OI4zcepFB/syoXjhwgWgjLoDZmeDzeROFEVcunSZPC+ia8t2cGyYn1tgbX2V6dkF\nnj17gaNHDtMPI3Sa4E06Dg3HQ4RdGqHEVlX6VIHzl9suJVWhFKeLZKDGYLTBsu1tOYIixtAImRXM\nAAxSWAgUgV8jTQqOuiDHsQUoi462EGmK41nYJsa3NaNxl5VOxFpk8cxyjzPLPXrjjDAcbuGLTAzV\naIQslOuULLaolrLAqhONx4jmYRpTNmGUkBlFbIr35m64q30q6RF4LlEckcRD6o0GWdJhdl+LTA+w\nohQdjSCPEMZCC5s4hRDJ2PFRwmZq6iDhTJujfp/VUc7pCxeYve7x4vm9HHA7LK+GrFzdoOe3CDsd\nDtnrZI1ppB3i16ERxaBj3JYGexk1NaQ/OkFj7xv45qmPsjBzCquxj6B2M2q4yrTK6IsxlpQEWQNP\nJ0Qmwc0zEpETq8l2XDigzSRYSWFS6afKvIjU1FpBATMiufX4cf7Vy76fL3z2c4zCCFggiSVJkhGm\nEXajSW8w4KY9S/RXLlN3bLx2k69/+2Ge+OhHePTp03TimKdOn+LqlTr79x/cdcwPHNyPUhJLSbIk\nQQjJaDhgaX6aNEk3Of2DQR9L2DR8G8/3iuYnabaZWFxbX2Zqaor+oEd7qsV4HOLYLkkSMjM7Q783\nKGiEwuLWoyfI8pSLF85DnmArhes0GYYavzbHOOngeDbK+EXQNoGkjCmYa3bpFDMNqpCb1qYgCiAm\nzCdjikhnEoljKHZsE4ds2/aETlx8ZnXnD9s7UFVhxwJKSjcddjlvyyi7bCBehVrgRrGr3Y5/Ngb+\n+7//+7z//e/n/vvv53d+53dot9ssLy9vc9b79u3jypUru76/xLPK6LTEscujuo2oJjVLJ14mx6o4\nbBnJVwdhexJva3tjOQ4y09SUwwN33c1DT7yfZ849y80HDiHilDwKERKubqwy1Z4mCHyazQZaF53M\nh8Mh4/GQ4bAQnwqCAAClrAlzJSSKks1rabfb+L7PwsLi5rWOhyOE5TAajZmdW8T2fD7z+c/TaLfZ\nMz9Pq1Fn3OviORY6U1DJUCdJgjYGOVnBs03qoSGXCj2h8KHKQokiyjCiKHSYjHIxTloiJrAGUqEN\nhGmIbUksS6BNirAM0rJxtMc4HDLXbvHUY48gpeDwkdtoug4f/dCnWQ8tIlOjOb2E5a8WCWFjIbEw\nWpIlGqO3AgCJQGuI4ghlT2G0ZhSnjBOB7XgoxykaYOTPQ2nLImzlEWY5lrSRaA4fvoXz5y8gZUbN\nBKxudBglI4xyQXnUWjVybbBsB2Ur1qKI9WvrSOsae/c0OXDrDMtnr3De1JmeMhxeinikF/K17n7i\nwTQvnOqydzXCiTIOLU5xVAWkaZ8kbePWbZy9NiMcvvzkNa7EFvXx4+TjDifXr3HPieNYmUUj0IxM\nh4gWymSg1hiYACEdhEjJsxw9kXTNdSFQZrQp6gEQaAxRPMJ2HSzLI45zfMfibb/wM0zNNPmbT36W\n7186wuragFozIer3iYdj0t4A0SxogAvzi2gN/f6Q7zzzNPVaGztokSWaW2+9jcU9S7sOuRCGK1cu\n0qgFuI7D1StXWF1d4/bb78APaniey2AwZGlpHp3ryQJjSOMQ23VwnYAoiZmbu5lOt8uBfUuMw5Bm\nPQAhCGyPNImp+XVGo5BavUaaJGQ57F06ROD75GmCQnBl+QqDcULQcMkoGmYgFZZlI3OzGVnLyZgh\nCudc0JGLOaBNUR4vpUQYhTH5JvsLnW466i34RGwKblUdbJalm76m+lq1pqKcuzsJGFXUYWdh3j92\n/LMc+C/+4i/yG7/xGwD8+q//Or/yK7/Cn/3Zn+167k6cqDwKbZGJqM9Ed6PqjKs4UZXutlMbAbbz\nmXeubmX0XZ4jRNFgdJDFOEiSUYRl2bzhDa/nve/7I370wTeQbgw4sLiEEi5Hjt5Mb6P4rZ1OZzOh\nMj09vUlx0rqgEXY6HRzHpVar0263qdfrRFHEeDwmjmPW19c3sf49e/bg2DZJHDE3O8fl5avUp6d5\n9Wtfxwc+9BF+/u1vIxwPqLkWSZoU3VwyjRQFO0UpUEZMJGkn0UApYmUEbilGVdDIAUM8Dgu4RcnJ\nFq+IQFRem1T+gFCSzOQgIJWaOAkRQuMomyyMQAuWL1/lO8+Mufve7+fRJ5/mP//HPyRM4NDNt5PE\nOVNtl7VLZ/CnG1jKRkkHJSxAgld8T5JERFmEZSksW6GHBW6qhcCShppnF+3njMFVEunuLm0ajq4x\nNbVI05fkWtHthjz6zcdZXJxlOBpi1xSZcHHbPkJK0ixHWaAQ5HlEEuYoZaG14eTwKPHFDV522Ke9\nN6eXrLGMZD6PWKr3uZpv8M1em49e3kM9iVHrHrecG/AjSxm37DEImshxje76k7SnDF/9xp9z5IFD\nuGmMpZ8kz4/wWx8c8pY3P8hs8izGTujYGqMMyljkSmFnBrvcJOUaZSmyNENZVoGxasOETITr2FhK\nEI0zWo0ZorBHblLe8GM/ynBk8BqzaKuG34Y7lvaQDSIWplrk/SH75hbJo4zxIOTC2Yu4wifXigP7\nD/PmH/8J7rv7Xi4vX911zM+cOcNUu4ARCyjScM89dxX3F0MYhdh2AT3EcThhXmlm5ufoDoasr19n\nfn6eJA6Znp4ijmLa7aJJSn8wwHNctNE4uHgzAVEUFQleYWHbNuNxoR2vHJdbjt2B1obHT/4tnluU\noVtSkmSmgAWNQWLAFBG1lAJpqUJIjqJ2RDGRndUaIQuKpzClL9LbdvtbWkPONqdbPLcd+tiphbIz\nmt7pp6rOe2cA+nzHP8uBz8/Pbz7+uZ/7OX74h38YgL1793Lp0qXN1y5fvszevXt3/YwPfOgTm87v\nnruPc/eJO7YxHqpZ2ZLMX17obtuNcsDK6qyy2XF1QMsbEUURwiuwMZUXTm5pYYHXv+H1PPPMM7zu\nZa8k7g9J04SN5ct4KsC1PYQxoA1ZlpKnGdE4xPd9XNeh1WjSajSI4pTxaMwgjguMVUosKWlNzzA/\nO0ev1yMMQ9I4IYtjsiyjs7FBq9WiO6kCvfW2O3j62Wc5ccetxFmM47voNMdVdpHc0sUM31y0pAQN\ntrJBTooc0qQoINo0AkMt8EmzBJ3n5HmhL46W2MYvGC5WjlAU5dRKIyyL9U7CcBgyNT1P4DVRSczS\nvlv49le+wV//wQdozu1lz/GXIJGkoyGKHjUxojFnsY7cFOYSUheVe2iUYyMdiUgF0lNI26ItfcAw\nHo4xeV4U9MitoodSF37nEY+u08lHCGnj2D51V9Dau4/XvOo1fPGLX2LdOEiZksQRyoAtQeagsxRp\nBLZyMSkYFBuNgDPJmMala7zoiIPpZTx5WXD7zB3sUxv8gLuMqQ/4un0PT6VLOPZe8t5ZVltD9g7G\nSNFhnObYDYtnn/kSrfo0er1FnDbZu3+dE23Nk8NjvP+rF3jb6xdpJmdwTYcsldQti1xfJkw8hDtL\nvdEijmOM0ZCDQRR5i5KyBlhSk8cpngxIwoQ8M2ghyIXkF3753/PFL3yDbpyQ6TFZOmLB9YnHfepu\noS0zs7hIpz9mY2PEK17+Wu5+4Yvx61MgJGfPXiKM4l3HvNlsMTczh+87nHrqKfYu7SOOM7wgINWa\nQufFMBj3cG0HlCIOU/IMXMdjasoiy4qmwOPhuEjwT6QuGrU6tmWTZhlkBlsJ3EYNU/MZR/EmRJpl\nGcPxmOF4DAIOHb4TKWA4HNDd2EDnCe1GgzyLQReyzVrnSGGIUo1lFWhinmmMLppdCFE0WdmCUTRC\nbZeELZyw2BYQlr6orMQsg82yVmUnm6RazLPTYSul+NbDj/OtRx7/J7FQ/lkO/OrVq+zZU9C6Pvax\nj3HnnXcC8OCDD/LWt76Vd7zjHVy5coXnnnuOBx54YNfP+Pm3/8QmCb8q67iTVlg672qyoIzM0zTF\ncRwsy2JtbY25uTlGoxElHW59fR0pJY7jkOf5Jm5tWRbnz5xhtjnFjN/ED3ziLOfO229j9eo1vvXo\nwxzcs5dmq8Xc9Ay9jRGFQkRxY+tBbbO58Wg0ot/rkyTJpDFyg+bCQsH5znNGoyFRFHPp4kUajTq2\n7TA/N1dE72nC1WtXybOUURyRa4PfqFELajx58kmWFubYt7TAOInQcULTs4ijeJLYLYwkTVOUo7CU\ntbniF7z0ApoajUabieBi4kscx8YISLMM13ERqcAoGMURylb04xFPnz6HFh659hmP4NyVDYaDyzTs\nkG6Ucun6kMUDx8EJIMlIRj0c10XFiuvXLjLTrNGaDtCkZJnBtgK0tElyQRQbjHKR1Oh1Y5Tr4sV9\njM4xwtBoNck1JHkBlSFtbBHsakc//G9ey0ZnlfWNdbq9AZ2NIeTzqSzxAAAgAElEQVQeT596AnSO\nbQRhlOFJizxLMTonzbNiV5YbstSgbBdhwMvWSOyEk+EQ+0LIPQvTBE2byyt9HBL2+X3uDzRp+zK3\nhxdozxwl78xwPlJcW3bR44R7j3jMDhpcPHuOWdZpDfej5BTWeI1jTsiDhxb46/UX8p6PnOaX3nQT\n8+EKc5aPNmNC2eQLX3uYL335G9TrNU6cuJPbb7+dAwcOonVOmmXbdk/xcMzc7Cwb6yGgsVyPeqvN\nV/7u7/nLj/8eP/RDbyKTEqM8Ll07R+q7TDdbjBKLWq3O1598ikO33sFP/9K/54GX/GvOXVwhSQED\n3W6HO08c33XMm/UWKyurrFxb5tixoyChPTNFtz/ADwK0LvTxHS9gOBzSajZpugGdTh+35hMEDU4/\nd5pbbjnM6soalrDwPK9ohBLGaDsnqNVI9BjPlcRxShRFWMLC9Rx8z2EURkVkzqQiM5VI22ZmqsHB\nvYe5dOk8Fy6cod0IqNUdtEmK4MGSWLibO2Hf90mTpIAcKRaVIrs/IUxMIMpqr00w2yoxS18lxHaU\noOq3dvLWS3pjiYmXi4DWmvvuPcE9dx/f9HV//Gd//ry++LvSCN/ylrfw5S9/mbW1NRYWFvjN3/xN\nvvSlL/H4448jhODQoUO8733vY2Gh0Gt+17vexUMPPYRlWbznPe/hNa+5UVNYCMFXv/CxbatX9QKr\nq9HO50tHVDr/8ihX5hKKKV+3bXsz6i5pQHmeE5ucpl9DTppEZBIyS7CyusZnPvVpXvbilzDVajEa\nj3HsOjpnG3xTRv/VxqRVkn7JT3VdFyHEZkVmmcwAsJRCG43tuijLZhxHxGlKb9BDSsGTT57kVa98\neUHDEgLiQhsiSYq/WZ5hWTZZniGk3Pyu8todp+jGY9s2STJJoii52RgiN5osyRgNQvyGT5QlpMJQ\na08jrIC//MvPMhhAPLZIYoXj+hi7T5zl7N1/iNX1HrVGkzhOECYjcEAnQ5JwQBwO0OmYOEqp15tF\nhOzWsJwGWD4pDlEiyYRCC0UqxmRJTBpFuLZNFCUoxyfThY5NlkR85g9/+QZbeuAHf4406xPHA1zX\nYeXadTy3jhAWlnJJElk0U5hsbwuoSICwyDQYoZC2S5pqAm0ROxrLCplK17ln3udgG6acMY3RNWT3\nGsZxGc8sckvSp88CF8U+Tg3qfG05Jawl3NwccWuWs9fpobOzTAWL/Mgr7sKfyRjVQ4RzlP/+9RN8\n8upRmOoyHX+bg6qLbyvaU7fTu36Gq5efQkjBzOxssdvTOXEc0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FhGAe9973uI4pI0bcji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pNGwkyzBgeP0INMnickpWVJiWRafb5ehgnyLLiOOYe/dOKWso6wbb8Rnt7tM0FWkaE4YnLGYL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/iex9mNTcIw5Pe/+Ht89KMf1fS7wRIHd/bwPJ84zvEDn/FojB8GONh0goAkiamq\nkn6/g1BobxL0rMyV/pCDO9fJ06Rmutw/Ez336HuoSqOA1pOeLMtAr6oJ4GVZNAmUbS98V3SSZyHU\nQqNBK9hnedE0GU0Wvgj+1YnEq92MNEZzJvtu88gty2I0Gp1I1Mzvmc8wCah2Xn37411UYqKl8J6v\nJb5SkOeZnpVo65vQ7+ubnGU5gsUABNvWU6Mdx9PSaSGJkwTbFhpGqSqUaSaKOiurFGVVYVUKJaSm\nkJlpHVJ7GQvZNrNZZEp6I9COiNo/QU/htix5gl0ia66uwcAD36co9UOV1RtKhiJNag65FFQ1bq8l\n8QVJEp/oarePONZWnu2NyBj0mAfL8GC1/YDV4H9VUWLbDpYt9cBu22Z9fY2r165jeR5FWYKQLC0t\ns7d/yHsff5xf+ZVf48PPfTuPPHyOJJ4zVworDMnTgsPDo7ohp9jYWCUMfe7tHbC5dYaiVKyurlNa\nWp48GY/ZfmCbF2/e5MyZM9y8cYPTZ07rDc3SPY/rN3Y5+8BZth94kKtXr7G0tEQYhLW3NNy4eYNO\np8tgMGA+mzOfJ6ytrZPmBZfevML+/qGeJi60XapjCRzLQSUFTzz+BJPplMAPkELW0nXzLFkkcUJe\n6RI6yzN6vS5xHBHFMSsrS1oJCQyHA8raT8dyXJI4QVqWxlaB6XRKr9urGVN643Zch7KoCEItYKkq\n7dERRVEDD1bUhkYVVHmBLW0qVTGZjFlaHnJ8PML1PNZW13XPwnXRtq26UW+qR9u2Gx6647gsLy0j\nsLCkVXPvHVZXV7Asm/F4RF7kOJbNm29cZuv0aZ555v14vs94OqbX6zI+HuF5Ps9++7cTBgFvvvkm\nm1tbuJ7L0tISqysrnNrYoN/rk2U5j5w7x8XXX2dtbRVVVXTDDkmSIIRkPpuytDQgTmp2h9BmdYHn\nIaRAGud5BWVVMJtNyMqKJC+Qlo207s/GmM/mWNKh2/FJEh2gTaVsyAewGDJu27YWGJmkjdqnPNcN\nf9uxMeZaQugpRJohU9WMuQVsslB3GorvYgiDwdoNO84EZOp7bZK1dvL4duy6dzretQCulESo2u6y\nyjT+ZLm4vkNeaEWVbelMpCxLjROWGb7vkRUFtuVQJYn+CqIiyxSep7MzxzI+wprMbzsu0raxpdBS\nWVHLzSuF72sfkjRNEZbAYnFRixqKsS2roT5pP4MFVCIAZUlkPblaC47KesyI5nlnZY1xu7bOfOrf\nVULoxmJdlkkpWsb09zs0bt/2UtbnlLeCt9SS4vrhdOpyugKtBkWB1GY+n/n0p/hH/+jndQkoJMJy\niOIMafm8euENPvD+p1ldXcd3bWwBjm2xu3cX3/fp9TykVXJ79w6nNjbIs4Ikihn2hlRFgSNtqrzk\n1s2bbG1s4FoWgecT+loAUuSLDrsUkk5/iB/2WF61am6sYq40J7Y78HECzdm9fOMGm+troKAoFC+8\n+DJ+OGA826XT6er7LTVen6UJn/+hz+L7gZ4yVMN2urdh14rSHMdZeFH4odc8nwO315gfFXlO2cI8\nBRIptbdGluoA7zgelQIh6/JbWpSlHktnKKPSNtPbF77zlhAIy9asBW8Bi4XdUGf3XlgHC4nr6OdW\nKOgEPpYUQJ09Wha2o5uARZaRFwmB6+FYAhk4VEJw89YNzecOAgb9IUrBo489Xj9/NpOJFgpZCALf\n1yrbyYSg3+VTn/lerl69SpplpEnCA2fOsL+/z/FszPvf/36klAzXlpjNZlzfvYHbClwaMnBwbJcg\nDOjIjmaIBIvGopCQJSm9fp8r169T2R6ldCmokOr+Adx3PSxpkyQLK9uiWPjrv1UPkmUZrqOVxCYA\nW5ZFXuT1OlisrbIsmc3nzfg402szcIh+TjSkqcVCCxfSPM+b4N32RDJJYRuKabPvzAxd4B0ZaeZ4\n1wK4V1OJZtOoeaBRFQiF77vYti5H8kzvpEHo1SrGnKLUYp6iLEDlSGnX2XeG49h1Vq5HI5VlQZFl\nFFk9ncR2cF2PMisoVUWSZ03DwXdssnriulJaaFOiyEuFJQWWpcs7VWmudRCGmuGhSkqlS+O8qFDC\nQkid/buehorKugMVZ3WjRlpU1YJNYryUDXXyfsdbpbhCLCxpzaajNxWNrxZ5Tlrk2KW2519aWmIe\nRVRCkRY5p9bWeeIcuVkAACAASURBVPTcOfaOxozHUxynlvErQZLnXLp4Ccqcz376u8nTjPk8b/C6\n5eVldm/tsrqyVGOAiuXlIbYjka6NZQmKRFPTLMvi6tWrrK+vk+c5Dz74oJaY11BSFEV66HQW4/kO\n+wf7PHD2Ae7du4fnew0M1O/1WV9f59Lrl1hdX+fazZscjkZceP0iw+ESYagdD+M4RQgFquL555/n\n8PDwBBRmPhc4wctvw2ZtRWybew81xUxJLGsh6mqzEcy9av4rtL3BW5kKhkrWNMzqErvtzmcWets3\n2thNOK5fPzfdhv9fliA9i063o2XygyUyMo7GY7r9PmfOnGneO8tybGsx8i/L9NBvyxZkheZGT6dT\nXn/9dT70kY/we7/zO8RxzM7ODlEU8d73vpcPf/jDfPOb3+TmzZsEQcDa2hpSSs6eOUOe53S73YZS\np3sx86ZqLIqC27dvAwtWRloPIpnN57idkKpSdSb9doFMQ6SVOtkobOAQc79Y6CjM60wgzvOcTu1d\nk6ZpYwmtKbmLe2nIB+bZ0dOlVEMoqKqK8XjckB3az4R59jR5wWvutzk/0/iEBTPu7Xph7eNdzMBV\no7xsNwmzLD1RggQdPRIty3MQEHRCVJ356qaDqh/4iqB+SOJoBoI6WOsGShTFFEWFGYPkeR0QkjQt\nkJZEWg7zOK2NdXTAlYD2E7a0oZMSyKqeXi20wkshENJBKL2AqDPgsqooauhHN1U0dcmxPRRKT2ev\nNGTieT5FUeE4ZhG+/a7bVn2ZEqutuDSBp6y0k6KuCvRDMplM9KR5UUMH8zmf+6G/wD/9v/7veghF\njsDSrnBCMktiDkcj/vDLf8T22TOcO/cQJQXz2Yy7t++QZ1kTDPf29tjYOEVZFtiep020qor19XXC\nMGQ6nVIURTPdxIijwlBnmfPpnDxLEShOnVrj8GifU5vr3LlzB9/3WVpaZjwek6Qx65tbfPVPvsa9\ngwNGk6k2jvI94iRGc9+hzHO+77Of5vbt281nGozSbJptzHI+nzeLxpynOdpZnIGuNCyRNB4dxhAs\nCAJtY6rQFhBliRKLXot5H6PkbQvBdIaWt+6lxHHc5nf1hBtFEOjg4ne6zeu1Mjmj1+tQFiWdIOT1\n117n+eeeB8vi1MYp8qo8IS7xfQ9LLgKS49iUZY607GbTe+ONN3j44Yc52NvjO7/zO9nd3WV7e5v9\nfS2Z393dZWtrS1NOq4rRaNSomvMsZzqdMp1Oeeyxx5r+z4I/vRhIsrW1RRzHzcb+ta//CUpV5HmG\n7WiJ/f0O27YRjkWatgdBLHpZhshgGpC2bWu7BSH+DNvDmOqZ17fXlOnPzeuM3Fwz82wZP6Z2T8pc\nZ1MZm2fNZNnm39r8b8Ng+/871PidWeL/CQ/bdilLxTxKyPISIaV2UXMDfC/EdQMEFnmuaWLay8Ah\nTTOSJKUoS6q6IWdJievYTbnmOI622KwqkjhhPBrrMW2eR+AHLC8tN2rNTldLrn3fpxMGdXZUNQvc\nsiyc2k5T1RzsItezG5MkJU0zsiyv4Q+PTqdLGIaEYUAQhniBj+frP0opslzP4cwLPdLMthceKiA0\nfzm7vwdwlmVNx96UW7CgK7azctfzyMtCY321kZKiJPB90iRBlRWqqAg9n6eeeor9vX1A+yEXZUnY\n6yKkzetvXGb/eIQXdkmLCt/z2NzcRAjB+vo6Se0pM51OybIUqJjPpxRFzmg0amh4Kysr2nwqjplM\nJk0X3sw5LauiYXCgKm7dukGSxE1gmEwnuJ5LpxMySzMOxhNu3b7D8fGxHi6i9AYYRzM816IT+Dz7\nwW9rmtfGKM0chhHQrmg6nQ5BEDQ0P0PxMpmyEKKxVsjyDMu26HRCgsAnCHzCTqCZNGlClqdUVYll\na68ZY85mWFYmQzOf1+12mywwCHzCMKgXsfHGLxvGxmw2JU31RHqtaNZKX1UrFtNamzCdz+n2+lQK\nilYmX1VVQ1s7Hh3q87SsJmAfHR3x3ve+l1deeYWnn36aRx99FL/2Nze02D/90z/liSeeaILhH//x\nH58gDTi2hg62trZ49tlnuXDhAufPn+fatWvNM6vjgN2cU7+v/eNv376NHwRY1iKDNn+/35rIssVQ\nYRM0mzhQZ9lms6iqiuFwSK/Xa/xmTLA3xIeqqoiiiMlkAtBs+GEYnqAK+77fGIIdHx8338W8h/FB\nMpRJc35vVVi2q7I0TU/M5P1zC6H829/6baQUDPoDBoN+U37Zjo2qG5GWlFilJC8y8lzvwr7XRQhF\nXmRNcxF0GaNqK0kbw9TQfuB+4CMQJxaskIZqlTZm+I7jMPR62vqzVU5VpRECOHWXum4MlhWoUtOU\nlB7/Fs9n5IX2EnZsPSPQTPTxPAeUwnM1iwMFTuDoKfL1Tt/veycCTftom9abcttkBibzAL3JlFUJ\naPy/aajU2Vevo8ta6Uhypfj093wvL73wElVVUpQ5jmM3HHi/0+GNy9eI4pQnn3gPzz37FJdeebUe\nq6ezmDt37nDmzGn0+DINsezv77O6uto8jNPptLEl6Pf77O7uNpuoUoput8PB/kHtNWHz5JNPNtao\nnW6X2WzOeDxhOpvxlW+cJ8szprOoUZAWVUE6ibCl4GB/j//mp36KNI3I87KxdfV9v1HtmlK4rUcw\n+GZ78RsYw5xnQwGss6l22WsCRbsfIaXUY+3Ewmt+gZ8upr2bhe84dg3/LQYcGBpskiQN1AMwmUz4\nxCc+wX/4D1+pMdZSD3wIOyg0AycvtNJUaw6CE5WbwXWns0ktoHM5tblBFM2bAeZra2sNK8w0Ai9e\nvMjHP/5xZrMZGxsbenBFt0tVVc2QByrNqtrf3+f4+Jj3ve99HBwcgNAUw4MDne16rktRQxue57G0\nvMzB0TFRkuD5emTiO0EJupfhUBTZCTGcyZLNvW5TdRsIqvYWMpmw4ZAbRpgJxO173248mvsc1EZp\n5jXtCsv8fztgt7N8UxWYza9RWLd45u90vGsB/PVLlymKevSQqg3PswxpWZw+fZoPfvADnN46jS1d\nHMen0wmZTCZMpglCKqQUONLBti1NvK8kwlZYtosCVKmwXBtbyCYrsOusoMhzBBorzgsjw9fBPImL\n5kbZtvZpXiy+jCLXbAVNP9NsmKqqNOca8FyHJNOZQYXZRQvm07hpaBjIIy+0z4N5sEwjRL6NaMFk\ngCZDNFmkPrdF8CjKHGlJqrIkSSOk0IG+1+tRFoUu7ysFFfiOQyIEP/Pf/jT/4H/9h4R+SF4WulmG\nwgsC0jjh9Tcu0xsMuXbtEh/7yEfp9oZalm7ZZLm2OTUTVKqq4vbt2wyHK8zncQvmMVlGznC4TFnq\n+5jnJaHvMZlMeXBnhyiOcR2fopwTJ5n24Vha4uBozB9+6Y8g6HP12g0GS30812E60/4mQkASx/yl\nH/0RVlaWiGvbXLMgqqpqaJ0GlzUTYky5bTxxzGI0GZApt9u/O5vNmM1mJ7JqOJlRmderSi9Gz7VR\nSjMdzGsrx0JVJb63mJkqxcIzwzHBvd6wm0wayXd/6rv50pe+VAcxTZ2bTmeEYQfH8Tg6HLHz0LZu\nAL/xRtOD6PV6jUjNZL6XaivYOIbZeMrWqU1Gx8d6IMZcT53au3uPwWCAY9XnZNncuXOHZ555hsuX\nL/Pwww/r61TPm11eXiaOtSHUyuoKCrize5tut0sYdijzEsdWuAOPNMv5kz/5Bhcuvk6S5VQUiNpZ\n8+36Qu3+gMm2lVJNEmQy/bY1q6qKBkozQdhsogbSNdl1nCSEYdiYmB0dHTEcDptM3FR2eZ5zfHxc\nV9/hiY3BfIYJ6ELoST3mnJeWlprYYKjHRpX5H8PB37UA/tC5RzCT5ff393XQdLRnwa3dXfb298my\njI0VrRjb2tqi1+013FppSaQA23XxfS1vz8sEhaLb6dbDGMCxbXzPw6kVn5VSWI5DkccN9l2VFdE8\nJc8z9HxC/QCWed1cras3S1gIe6HeTFPdNKWCQpVY0moyTs/z9EBUIbHDbnMjy6LQk34sm8C3cbyg\nUXZ5nqaHvZ36yuCvbbvcNs/c/MyRbkNP1EFEb2DjPNdYvNTTVSSCoiwQno9nS777U9/Jr/3av2aw\nvNQ87FWlcb5Ot8crF17jzOYqX3/hPB9yA3q9LqPJHNcLAQvPc5lMpmRZzuM1s2E4HHL9+nU9k3Iy\nIUkShsMhVVVx8eJF3ve+9wFwNBrR6fVIkoIozlA4zGYxXtBjNJlz8eIlvvmtbwFw79Yu3V4fKj0g\nwLUdkniOIwWPPLzDY48+SlWWmqetVGtjXDjHaVHSQvFqMihjAWqyb7MhtbMhkykZ+MMsZKMgbusG\nTMZr/m7KcbNgzfuZsXrmc6Moau6zNmNyGqWuObIsxXYdut0Ok8kUy/LJMh3ssiynOxxy+eoVJtMJ\np05t8NhjjzUVgMlQR6NjQHB0dMTq6gpRNGM4HLC2tloLh/oNvGE2udOnTzfPm+M4PP/88/z+7/8+\nOzs7XLp0iZ2dHU3jqzebMAw5OjpiNBoxWBrS6enpQFmSNhBilmVY0mbn3MO88vprdLpd4mh0wlrj\nfofv+41FcxtGNAG7bedqDsP9N9qMdiOyqdBbkIwx2AOazLo9Ws0ke5ubm01QbsMg7c81z0Gn0zlh\nL51lWQPVGQM/8z3e6XjXAvizH/hA7eUhNYWn3hGPj4+5efMme3t3ieZTdndv4Loeo9ERCD381XF0\n2QXo0VWWpYcgu/X4Lq/eHXOdkbg19xMBYRCysrxMkc0py4LBcMBjjz7GxsYG0vEJ/EBT8OoGaZ7n\n5Elel9V2zRfXvPEiz0kzLZO2TOksHVCSsqoQUlIqKFMNaViWnp6uR5wpKCvSfN4EirKsUEq8rZ2s\neRhMJlgURWOpa0qvstScemWgHWlECRJB2zBf86WFgjxJSLOMDz33LEkS8Vtf/F38sIO0bEpVEXRC\n4ijBth3uHY4osPmdP/gyjz/2CMNBn62Ndc13zlJWljc4ONhj0F8iTpOW8nWhODMc3dXV1abE/LVf\n+zf85E/+JJevXufBnR2yvGBlfZMvfOELTOeR9jmZzMnzgqDTpSxyVD2FSIoKVZQsra/wEz/+4yRx\nhO9qKEoJ2dgjtMtRU1qbRpEJvgZCMYvbVGPmvM21jqKo2TjbbIdGedsKAib7dxynafpqkVrQ3NO2\niZopyw2332TKRqVnyv2yLJnNZuzsPMjVq9e1k6IwSs6ULCs4Ph7x2c9+lqOjwxPX4datW9i2Tb/f\naxrcQsDa2hpXrlxhZWWFXk9z2o3n/De+8Q12dnaaqkUIPcJwMpmwvb2NlJIPf/jDvPLKKwx6/Qb3\nj+OY4XDYzI6VUuL6DkWWM5vN6ff7FFXJcGnAV7/xdfKiYNDrMpseNnDR262JPE81I6yesGMqKHNt\nTQBs87OLvB5W3pK5mwy5be1qGqBmfRqYpw1bAs3nNrRYubCZNZtz2+jOPB9mkzGfYyqHPM8b6+E/\nt1L60LPwfT1XMnC0J3A3cDi1NuSpJx4lDHXJJC3J4eERL774Mnf3DhgdT0nzXDs2WZaelWlZSMsi\nqwqkG5IWJagKPem7oFQKt85cRpOI4/GcNI+QUlDeuM1Xv3keUePnqlL4rku30yXshDj1rEXjs+D7\nGs4JwoCgvpnad0HPIxRSq760OU7NUBCyLsO0UX4QBHiui+852EI0O67jOliWJI7n971mJtNr7+Rt\no51FmamaTF7zzWsGBXrSd1GWlEWGKkuEAum4uJakyjO+7zOf4d69PV44/zKW7eC4AaOR9t7Isxxh\nSW7e3mN9dZkv/9FXOX16i97HPoJtu/T6S+zdu4dj+2SpvgY3btxgc3OzyTKMH0lZliwvLzd+1LN5\nrIckZyXCcti/s88rFy5wd18rKF+/dEXL6r0AURm1Y0SZ5wgJg36Pv/7X/iuKPMexbZTSLnaqlQm3\nhVmwULaaxfRW/NHAVW3c2vxOEHgNRGKCgG1LhDC0PDOsAYLApywXG64JGG3+vgneekaixLIcbHvh\ncGk8OzzPabJwT7rkRcXDDz3MtWs3NFyktH92Uejznc4jvv71b/Dg9ln2pxOqSittt7e3m8adgeGk\nlNy+fZuzZ89QlosJMp7ncXBwwMc+9jEuXrzIfD7n4Ycf1jh6on3d5/M5QRBw8eJFVldXOTo4bH5/\naWmJ0WhEr9dj//iAwAsIPB/PcRkMhty6tUt/OOTW7V1efOkl/E6Ha9dvstx3CcNQe8v0em8fS8Kw\n8Tdp9yZs22749u1moGkqmoBqvru53+a1VU39NQlH2yfJvMZk7MbTqf1Z5plpwy3tSq7d9DVr2lQc\nURQ15/9Ox7vHA7fAVrrstB2LKs8RqkKgBw5PE50h42la26nTa3ihR5JeIRmnesJ8oXHuCkizlKIS\nuK5evGVRNAsqzQvStHYItG0saSE9SVEWFFWJHQS49c0viwIhLaZpxSSZ1je6aBarKbmEVFS5HnSs\nb5wiDAOkFM1mkGfakN7zXG1iX2lPcw3x2MiqZNjrsry8xPr6Glunt/TP5f077ibbbne0YaHYMrxU\nVVXYVp1tWxJRd/CzPEVgUZVlM4wZpVBlTpxmWI7DUZnzuc/9EGG3y+/+/h/SG1ioShDHCYPhkFxp\n4cjB8QTHEty+fZd/9+9+i363w/Kwj+84fOyjHwF0xrO3t8fW1lbDrTV+N1mW0e12GY1GFEXBA9sP\nkWYF12/e4lsvvYwSkjt37zIajylLWFpeJck0xU1VBUmWUJUFQinyLOdv/K2/ie95RNEM3/XJc+1F\nkdfKObP4gCaDMgsIaNgVJttuwx5m8bUFGe2gZ+5Xm8vbpnQWRdks8rZxmekNmOxbZ4sn2QftEt98\npgn8RaU1CltbW9rPPOwwj3Xm3et0ube3x+rKCi+efxnPdRgO+9i2zQMPPFDT6Bzmcx34hsMhh4eH\ntZBMNbxs40vd6XS4ePEivV6Pu3fvNiwog/cqpQ2ajo+PG4M5M+HdsFeSJGFpuESaJEwnU7qdDkVR\ncvbsWZI05ytf/SqDwYCkKLEcrXZuoIjq/upkkzWX5eJ+mvtkrl/7qKqKQi3EUs3P3hJY21CbCepR\nFDUB2yQipk/SdiVtwy/mPdqNbeNSau69Efy0m+P9vh628ec2Ay+zmCoTKBRSWbVDhFY25UWhHdM6\nHSb5nKqsWBr22drc4vTpM0xnCa9ffIO7e3tkhYYKHNcim2fkmcK2HZTQF9dzPTxb4+GWrDPQqqJQ\nAsfv4EuJmTEpKgnCASTSlgi0h7CUNkgFElzXr+W+JbajdBMUzUyZJTmureXNQujzyvOCeZQ15XCa\nK6ZzHYgdUXEbTVusVKkbjAI2NtaBj/6Za3Y0muB7PrYtqMocVcvqHdtpsF7bsZGAJXUWVpZVYxpU\nlRUIvfFJKZG2FploGpnUszTzChvJ5/7CD5KmGV/546/RHyyjkEymMywnYNDvM59PUUqyt3/A3r2K\n9dUVJuMp8XymYZDtbXbOnWEyi7m3f0RVaSOxg/191tbWKIqCeXTI/sExo/GM6Tzin/4f/ye249Dt\n9fnWCy+wtLyMlDbDpSHTerJ6kqaIMtGWA1XJ8rDHP/j7P8fR4QFRnNDpdIkjjbMnSUKZlQs2SJ0R\ntgOk53kLambDMBFYlpa+a4jOxnX0oiuLCtuyEdKuM+M2tCWb0r39GU0jSmltgvkdwxnWwUSfW6UW\nVskGH5ZSz6xUSuHUTChLSrJIN8ZPnznNgw9uc+PmLrZjE8URYeBTlWUTjPcPD3n4oR1mc910rcqS\n6WxGVfcpkjgmS1Me2NYNz9Ont8hS/dxevXqVra0ttre3ieOIpaUhQgjW1tbY399juLysG8Sex3Qy\nod/vMxwMmopDWha+79HpdkizjMAPmGVTZrMZQRiyt7/HPIq5desWUZLiBgFLgyGOTJnPZvT6fYIg\nvG8cMRi4Y2vthZHBg/HY11WQuZZ5sZg3CTQbdqOfMJBKC/9us0vMvdUWAQsBmPk9WGzk5t6ZCs5U\nBoZNlNfCICFFw0YyAdxYa/+5xcCla0xfHLK6fJ1HMUEQUgpBZVnM0wy7dtXzLQvKguWOx0o34PEH\nv4P5fE6cJsxmMWVZEpWK0fExo/GU/YMDDg+OSIsIx/X1mCbHJTflriuoVEaR134GlqgXFghb6HOy\nbNzQR5VlbXBjVFIKYdfOhLZAWosdvypyRKXl+iiBkK62tFQChaBCC4WUEChpERv2CCWl1Jna7vj+\nDZt//qtfhFocIoXmvnuuw2A4xPM9bQVgSSQVnizp9/v0+9o6dfPUJgqFW+ODhru9t79Pb9ihGwZ0\na9HTdDplerDPp7/zkzx67lF+4Z//Mkpq0ZVVzpjmsb42bogV9igV7I9j7h7N8PyQ4zziyt6rDC9d\nx/M8Xn5zr4GaqrLEEm82kEqWZURRxDSa6gZtEeFO56yfPYNlCXzPZzw+ohuGxNERWZoQODZVHvPs\ns8/yqU99isPDYw1R5AV5ocdfFccjyqKgE/ongqkJtmaBGZy7qqqaIaLqCkV7ddjCRihJmVWgJFJJ\nVKaoLEWpNJPJcL31wlXN3xWmGrOpSj3qS1pGLai9f4RQ5HlWCzgqwiCkqIVclQKFbrT3B8Om0jJ4\nvONYFGVGWab8Zz/8ef7hz/1vuCLAdgLiLKPf7XF3/4DHH3uENy9f5QPv/wC+F+I5Lnf3d+n3eoRL\nKwDcHc9YWdkgmufYts9sopW5e/t7nN7aIk0TUC62Zenxe5ZkNh0TBgFlXmBLTQlcGi7ppuXxESsr\nKziOw729PRzlaC/cuMTr+OSOS1pkxFnM2ul1/uBXfo0onrG8vMboeExgu5RFTuD5dPyAcw/t3HdN\nlHmFLR0tjy9LpO03Ga+m/BoDswolBbZrI4sKJbWlhBH0pFmmh0EopW0TqhKVZ4jq5LPTzpJNg7r9\nX/Na05Q08wPaUKdSClSJJY1/EcTRjCRN8bygCfzmXr/T8a4FcD1DMmtA/m63d6LLrne5CM9zcByX\n+TzCcV06nQ5Zli+G+Qpt1F5VsOa6nNlcRynwvQDH8ZjMZrz00nmuXrnGeDoFBX4QoqgoygpR1da2\nlVZpuo4LZU5gawvbIs2wbJ29CilxpE1S5vqhdRwcy6nNiGpjKYy82kz4kNjCRtWLuihLvTk4DlY7\ne8PCcWQjq7/vNasqLAWW7SKoiNOMNMsZzzXcZDs2tuNAVZAnOmOTliSJE3zPJYpiXM+tueADpJQc\nHR8iZO3TIiWbG2s8+uijbG5t0e31efK9j/O3//uf5n//+X9MVaWUQmI7FnmRcnCcNAwJ33dQyibJ\nEt3YlZKbu0caG1SLqdsmGymLQpsaFQVRHNPpdvF9n42VoZ4xmiegJLnIcKTFnd3bDIc9bMtmdLjP\nT/zET/Dcc88RRRHj0RFh6BN4Gn8u8xRJhbAEk8mogUzafFzTGCyKrFEEK6EVnLZlU6mSLM1r7FM2\nFVxRaOWu47iUpa2b6QqqsqZmAqUw1seyZibVlsnC+ETnyNZsTkNLTFM90EHKhYLPlO7t/sGijO+g\nVEma5QwGfT703PO88OJ5JpMJUlqMywm+77F76w6ubfEvf/mX+cm/8le4eu06a6vLuK5miWiIwyIM\nfCzbYTKesLKyymuvvcbp06fpdjWfO460ZW6n2yfNcuy6LyKVwHV9sqxke3uHw8NDkkQPv7h79x6b\nm5vcuXMHd81leXWF8XiMFwSoVBIlMb/wz36Rg4NjVtdWODjYo9cbUJQZttQVzfLyMjs79w/gCz8T\nUSuzFy6TQojG3KssS8gEWZHTqSmy0lpYvFqtBq+sA7ntu1D3Lswz09YCNHCqWFjKWpbVKFENe2XR\n36BR3wa+eyI7dxwHy7apKpp18lb2zP2Ody2Aa0620/LyQEu5hWxoRaaBlOe173bT8NN2oOYCuLVQ\nw7UthNDeI1WpqKqMYcflY89/gE9+9HmqUnFweMDB0RFpVtSNHkVVaQXiZDJp/hSVhncsaWlqoQAh\nLFRV4AqF8LUIoKIANCvFDT2NMddDW5UALEWFhlyktJC2jWXw9KrSU7cxQVzpkt117n/NXI8yLzTD\nRSmQlv4MoVDYZEqQZdqbxXF1KV8qRTAIiaMIy+8gbYdSKQ4nUx3MhANKECWaj/vaxSvc3L3NA9sP\nECcRx+MJwrYZ9n3u7u2TYDNPI3xf4/1ZnlBWCxtb17Eaa1ff7zVycyEEUTxH1cFcoZCORTcMGCwN\nUUpfi9FohO1Y2Jb2cpknkVYcBh7T0YQPftu38f2f+a9ZWV4hjWdI4PTmOtPpFFE7BfZCv2b0qEZY\nYhYO0ARFI6IwdD3qTTcz4gsgyWLIwLFd8iSrx895pOnJYbWWrf1gqO91VVZQ6Sxau0Pq6fMLWpkO\nwkEQNEyPqqrq503qa4ZezI5t0+10dIZXY7xlWaJK7WXdCUNmUcKHnv8Qr712CSFs0iQFT2DbLqPp\nnOXhECUc/p8v/Fs+8fGPYdl6IIRSFdMoqmE7xXw+wfMcrl+7ycb6Jr4XcO/eAa7jcXg8ZmVtndXV\ndeI4qnnLJUrZ2JaLJR2KvCJLC85snWU0GrG5cZoszljqrzA+mpB42hs+y3M6vSVevfgm8yinPxgy\nGk+oUAhR0ev3KFINNZw5c4Y0vf9kGlONCAlKLYajW5ZFVrNlDEQC4HseWU1JFKrF18/zBrOHekZu\ni5poftbGs01AB5pNeD6fNxxvY35VvuV9HMdpuOjmfUFTnl0vaPohBrJ5xzj6jv/6n/Bo0940PuoT\nBGHjrWzI84YraXYoXZL4jMeTuivvo1Ds7++z0uvWZamosUYtwQ2DANfTviBCdVld6lCUOjsyuJfx\nOzAXzvfDhjkwTeZM5zOuXLnK6xcvMo3mYEk6YRclBFUFZT2LsxIWjus1za6yzggcZIuyVONkRU5e\nZCBN40XfjrfzP6iEdj4s8lIb8lOrx6RAqIWfghA2eaUQUn+32TzBdnw6nk+SJnrkmmuDAoEkLyok\nFbM4AiTzbpR77QAAIABJREFUOOeFl15muDTgqaffx2A45Nwjj3Br9za//aWvcP78eexoztryCmnd\nWGu69LXq0LYtdLkgaj68qO0T6n6DFHUDJ2E6neL7Hv1+H9sSRDO9uaiqRFQV/1973xpj13Wd9+29\nz/PeO0+SM3zJpsOHZD3MoZ5OE1eWbVlpFNNGjTiSDEFAErTIvySAIwQt4AJF9IjjFnKbBilapawL\n1EqLRGJdWbESy7YcN5H1dkzXlmxSJIfkiOQMZ+bee1777N0fa699zvAh/0hEWuVdsKHhPO49d5+z\n116Pb31fng0xWF3Fv/mDP8DU1BSK4RKs4/QWsFg8fZqgaEwQZAEJSpcLvVZN6ew6JTv3iYkJotkV\nNNQB50CrqoRSAYrScVePEf+Irow/JIiPxPpmlk+1XfYbhAQ95UOOIjLi2FlaWvJICbrOZgqUr5Hp\nCsIwhC5LDIuCBosUwWap12Gxft06TE5M4MeHDiOKEhSoEAQaExMTWF7tY6I3hoWTi3j94GFcd/VV\nCAOJujbYsmUrkoSELbqdDlZX+ph0wz2l1hj0h9hy5Va8/uMfw1qL8YkJVI4Sta5rlHnlm8AASFnL\nAEmYYP7wPDZu3EgorqSDhVML6IyNAyLE6z86iK9+9RmknQTj42MwNdDtplCRwMmTxzG7fhYzM7N4\n97u3XTASbZBAdJhWFWXnjCKKW4gT3udt1MfZI/1thxkEAQaDgVf4In8T+xmCs9EkDFJoT1nye7Yh\nv3Ecoypz73PaXD2nF0k0e3JyEhs2bLjgVDabsG/h4vM8x6233uqHGz7+8Y/jwQcfxOLiIn7lV34F\nb7zxBrZt24Y//dM/9eruDz74IB599FEopfCFL3wBH/3oR899UyHwv/7nfwQgfRebOQNWV1eRpqn/\n0Hyy1XXtuLWJO7uqnAiBkj7CC02NIAyQ5wWIL7lRZef3BSgiCsLULzyjMbQmTpZAOdFT4Q4a+mPU\noBHbStc4fOQoDh85iv4ww3AwRF6UKKsKZVW7ryktC4IQ1jKDnfU1OWtBzceaHj7iGZZg3ov/9uif\nnLNu9/yzf44ooOjcGgtGNRtLUEFjKUIzEKg1OYQoIEkzy5ED/YFnbyNhg5CQQGUBYTTqugRsvWY8\nuaprRFEMrYgSd3VlFcvLZyhKDEkRPQxDBLJh8RNRM45e1zToBNPoDXKJACC+coDrjG6asdaQAGbW\nrcM/+YU7YLRGEsWArGENaRiGYQihqLwROErWddNTJCUWSEAJt2FKCAg3uWtIbo6RR/yMCIqiCb1E\nTrZJfTOPICjyDAIJ3U6HtoGjE3aPiiuDECNmoYcgYirKdrRmaKD08LWiKBG4e8sHC5edONjhhho7\nkcDxyQdBCKlCJ2EX4nOf+zxWhxnyvMLE5CTSlDg/wiCgqU5dYc/u67Bj+zaU+RDrpqcQRwF0XaHX\n7eLEiQVMjpHa0ZITgAjjAN87cADXXncttNaYn5/Hhg0bAAgkUYLTi4sYHxtz2p0CU5OTAARWV1c9\nDE8IAREpHD48j+MLb+Kv/uoZrNswg7STYnX1DNIkxNRUF4PBKnrdFJtmtuKGG25wkShw7Z4PnbMn\nvv/Ss3TQmWpNpG0M5bWcXbVpKBhxxgc+E6tFZ9Wq24gpDr7aOH0+YLmEwnMA/NptJAwfDhzoSNEM\njZFPonp9VTUT1fz+u2+6/YKR+FtG4EmS4JlnnvGpwM///M/jW9/6Fvbv34/bb78dv/M7v4OHH34Y\nDz30EB566CEcOHAAjz32GA4cOID5+Xl85CMfwQ9/+MPznp5p2vWLwR+SJa84vY2iCLASp08dRxRH\nCMIASlUYG+uhKDJIqaDCgNjfAAirIJRAGMeIZQpWXgkC6tr7JbAklCqVG2hxQztSEXqkrEqXAqXI\nc061Q1gI5H0NFUbYPLseWzdvQllVsJC+/qZdrbwqabT2xIkTePPkSZx88yTJS0URTW8CUAAggNqW\nkILUuIWklPl8JgWQ50OEngCI8N4AIN3mtE6F3lgB66LLMAwhw4gGdyRFpxwhGGuRlQUs9ejooVPE\nB62iLiCIkTFh2B1obH58YgoTE5OEdNA1qpJIxjI9BJyj1FkDw/PkYG6A6OyGUOCmWGkDkCp3t9eD\nsBZlWWH//i+jKnKESqFWjjHRrYlSypFgEc1B2kmga+2gewHiOMGmjbOIoxhT01PY/jPbkSQJls7Q\n8zY21nMRYg+VLmBqahTrmjjaBQBbA2EYocwL1LqGRQEhSNTDOJ52GVApjSI+FxwIYDyhmqiuKgQq\nQuQi7GyYo8wL2MgiDKhByEpCfLiwk+BpUo7UaYNrRDEpuZd1CQiJAAKf/vQ92LfviyhLTY3+LMf0\n9DRKx1E9OTGBv33+BRw/cRx33/XLKPIMttaIAoUjR45iamoKVhgIZRGEEp0uEaNpXdChzrTPKUEO\njamQJCHyYoB16ydx8uQpDIaEouj2Upw8eRLGEqwzDjr44euv4+VXXsXkuvWIkwRnziyh202RdkL0\n+wN0uwnGx8dxxdYrqHEsAsftc65xc7zUhY+CuWwBa6nX5J4xys4scjc2T1mSopJOQUITbbw3O9he\nt0tlDa0Bdz8M16jdM2yNgXHDQDysw30XHjLiCFwIAYGGR4dr4FrXCJwsHNe//94oFG6c8Ck0NTWF\n/fv34xvf+AYA4L777sMHP/hBPPTQQ3jiiSdw9913IwxDbNu2DTt27MBzzz2H97///ee8roAiSJtL\nubnpd/YUm66Mh4QFQYiVlRWKotxJaWBRlI6/2UrIjKLYJE1pQ2iajCP6WdpgAOlGCilpg0o4/LWD\n7ThYT1bkDtYFvyEtQtRVSSPorlbqoXgWiEKCFMYyQHfjerx784znYDHG4NSpUzh48CBOnFhAxsK/\nouebI/zf89nkWIo8o0abrQhqaUACAtpoUhVRJDRgKirdxIGEqSsI7mbXQOE4z6OIhpRSt+amooiS\nkTlKhnTAKUsbSQI1MnJwxlH6BoBMuCxBw0vcjS9M4Z2NdeUFJRr+CE9uX1NzNowjl06GPjswdY0K\nJKVmLCCtQGWlj3ikFDAAKr9uFtlKDmbYK0+fhgoCnFg4ibKsYDiLEwJTU1PYtJGYFfv9VXSiFOPj\n4+h0KAPsdlOMT4yh00nRTTuoKgMpI4JcgnHHQBSEqLVGPiwafcOayagEsox6O0lMiunG1NAV4Ys7\nnS6EkM0kqEur2tEdl2baUSRAOP5aG9e4C6BrjbIosX56EldeuRMvvfoqsV4Kp3ZlDNK0g+XVFURh\nhNcPvoH//Og+/MIdt2N6ahKnTp9Cd2zC9VpKDLMMVhgUbnJx48aNUEqim3axurqMLBsgCEIYbRCG\nClVFpGXr16/DwsICJqNphGGAyekpwvOXJb7853+GH/7wNaxbvwFCSvT7ywAMJibGUFc5xntjiJMA\n66en8Z73bPe9iwvhoQcDwkpHSeQRRZ4CwVpS3pLN88JOtT0H4HtpDurLRG7tzMEfCI4GgQ8Jjoyr\nqkLpAk+OntuNS57A9RO9mjQN2Ni5V7qZ82gPkF3IfqIDN8bg+uuvx49+9CP8xm/8Bq655hosLCxg\ndnYWADA7O4uFhQUAwLFjx9Y4661bt2J+fv68r8unEgCnbiK8GgYD3LmRCVD5QyiJrVu3gniCqZEz\nyIYeA63cwqowwOqgj1qToAA08zkLgvBZi6IifGrgHJuQgiS1nCgEl1VkEEDUhFIXVOClB0Q4ciZT\no65pc1Bjz7gTXkEFCmWRAXUFhRhG15jsdXDz9XP0oJQaUOTYwyhEJ+1gaYkGWx75t+eu2fZ3bcLS\n0hKywRBZRh3uqiQnGUQhkjhBbQ2MqRAoOpi4uaOUgDa1i7SJ6TEQhAsXsITaiBQpeUcCUioURQVr\ngDgiyTXCBSvIkBy7NdbXtOkeKWJldE47EKopl7SeJyqPNKUUay1C5pEBrTncZEAYRFSasMSnDgio\nKHGRlnNkkuYI6tpQvV0CxkrUNRDGxDC3mpVIwhhJQs4blqZyl5dfpyZ5RNJ8LIQcBgGVaYTB2Ng4\n0jTG+PgYJicnsGXzZmx512aknRShq/3WBgijhMozgib4LAQgqJQSqAC1tig0H2oGUSRQFGuHh3RV\nQbQyVsIyGy+fx0NJeZ5DOj1NU1DWglqjqgooJXHnL96BLBvi+RdfwvS6DQT565JUXJqmGGbE9Lg8\nyPA//uwJXH3VLmycncGVO3agLHMIKbG0tIQNG9ZT8GIs0rSDQIWoygqwAp2UGqvCCT0DGmEYI89L\nDIYFpqYD1BYQKsILL76Kw4cPY3m1jw0bNqCoiCmx1+sgSWLoknD9nXQc777iCux+3/ugayCOUy8W\nfD6LEzrUApfVrJl4RMMzA/e11hqx46/hQEMp5fodFdI0RZqmayZzm95SI2PI1Ajsm7rdLnotlBMj\nUNo0CMx2KISAFNZDBXm/xHGMSjeyapyFvZX9RAcupcTLL7+M5eVl3HHHHXjmmWfW/Pxsjomz7UI/\n+3d/9CgASn/n3nc15nZf48eEh8Ohf1hXc6IhXVpawvjkhHewVUU4y7GxcT/8UFU1ej0qzfR6XRhd\n48wSi7iS/BWMhRQSSkVQgXKisy5qtAZhGDtH4oY0TA1bsxoOaWqyCC4/MNYY5EWOYjiADEjSSskY\nKgypsxzGfkItVCHyaghrgTiNoa2GlAam1DiT9THWG0NZnn/NPnLrz0FJhTiMoHWNSmvEaYKDh97A\njw8ewuKZJaz2V5EPM1ijAZDOZRTHGA5y1BXV55KUHHJV5YgCBSVomEkICSEr4mmsa6ShgFQhrDUI\nlEU3TVDkA4DkLahcFQSAaxDryqCqa6LKFUASpf4ZsKYZzRbOgRpTk1KRBayuEUWKprncNCs/f9ZY\nwBDnjDEGuSb0SBw2GoOwgFQRDeFIAY5tpMvs4oBq1kVZ+4xAScfRHSTo9wcIkwgqigEL5LWGcqyQ\nJ8/0ofoDnDi9DK0PQQV/B6NzCFhs3rIFe+b2YOPsrJuAVYhCQi5RI1QgVApaasf+IF2z2nrYIa+J\ncWtBcYLLalwWEsimxqpLymQIdWEoY4QlzvskhtYFAlj88i//Uxhb48WXX0WSpFhdqdDt9VAUFknS\nRX84RKAUxsfG8I1vfRubZzdByABbNm+CADDMKwwGJYSgKDKJY2QZ1ZTTuIv+8tCVdlIYI2BNAGsk\n4jTBhg0bMX/8TQwGQzz19F8AlmQMp6fX49TpN9Ed76IThQhCC4ESYRRivEsDQDt37IIuDbKSp3cv\nTKvKAUFtGj1KLkHxgdjmgIkcfS1clM5RcuB+n2vYjFBqR9P8+sTBHvpAU2uN5eVlp+TVDHEZY3wJ\nk6Xpmp6LQewa034KOAggFfCd51/Gd1545R8WhTIxMYE777wTL7zwAmZnZ3HixAls3LgRx48fd9zQ\nwJYtW3DkyBH/N0ePHsWWLVvO+3r3ffpTIJJ6lyKWFfI8AzWRAj86rGQMrQ0RH5naf9g4JrKiTq/n\npgItkjhBVWaIwtCp4QBTU+Othp/xdc1AREANGGtd9EI9xigOISxN9IRBCCsNSuvGbi1xXBhDEKQ8\nH9BhIEEDKpIiVxsKQEiUhXYE7QY9x6SoZOAjwKLKIKXDJ0sgVArD1TPU7DuPibpAXVr0B32nlRih\nXwyxYXIcszfuAZRAbSx0VUKYElFICJ28qHD46FHMH1tAWVXIixKD/hBWANMT09i8bj2qmhotRgKA\ngBHAyuqKU0Mqsby6jOFwFagpQhSQgKXyhRUkSRvFCgGo429q+lxR2EBFAYpy4IYjhGtYBmFAA7AC\nnsuDv6YhK+ovClgoC3TChu+CJlEDQFjC6p+1z7OSGqEN0RBRBFtBA1mDQe42YgItBSrXXFSOR0UX\nJcKkAwiLstIIotRNZyaANTh27BSOH/tLp0hfk2hI7AQZXLrd6ySAJaqFiYlJ9HoddLtdzMzOgFRw\ntK97AljDoNdW7WmP9GutSds1iQFwNEl1/yila8+LDB/f+0uQUuKVV18FhMTymQrjk9Mw+RBKhai0\nxsrqAOvWz+Lk6SU8/vj/RhyHmJ1dh+0/8zPYsGETEVy9/jrWT01gMBgA1qKqLOKIZgCUJE6fxdVV\nqKDCsR/8CAcPvYFDh4+QGlTY8QLOgwFlAGkSodI5QgWU5RDTExuxZfMm3LDnRsAEsLVBmgae2vVC\nxmCHMInpwHQZYFVVPutjR2g01a0DR20hAb+2tW644I0xSONkDf0rf5+HzzgTapNUsTPnUjBH/HwA\ncAReltTzOhsjbuoatQGuu/YqvO+693os+3/6k/9+wc//lg781KlTCIIAk5M0XPH000/js5/9LPbu\n3Yt9+/bh/vvvx759+/CJT3wCALB3717cc889+O3f/m3Mz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CKjRVWzPaXKeal4TgOZuNMVKh8oy93V3Qmv76CKUUa6M1nh8dMlpfp5cXjCdj\ntnZ3aVuLtxaVZ+xeuUTQirt3P2Nja4fPvvicwhgePXqIA9752tfZXOvx5rvv84ff/DaZ6UcHv7nO\n2f4Br1+6jBEaF2C0sUk7rVFeMOhvMK7HPD854ObNy5i1LZwNeG2YBoe1AaVzJALpPdujEf1BjlY5\ngoB3Fmdr2oRQREqQKSmRRiNE4NnsiFy1VB7GYRMz8PjWRUnpYJNsZ++l86joGVI1NJnWSCHo5QW5\n0gTrmFSTyDtqtQBu1jXMqxlCKqwLCC2oXcWsPMP5jJAZYgweUD3F6fiYzZ03ODoqKQY9pmVJL1NI\nIXBJ76+1QatY7YuIfTRU0q+HxKMLrSP69J6mboDYm2Y6HYMQ+GCRQaCANlha5whCkBUGKUlONzpe\nJwR53uPu3fu8/eZNgitQSjKbzchMwfbODltbe9y994BBf42333qXn/iJn0RJzXe/+12GQ4OUsYLS\nOrC2WaLxlCf6KvPWLtbOkraAtl3sTwuKIeZGujL1QNHp/ZNOOrILSz48/XI8zkv6N8Vq+aXji0g6\nJiy7dd6h447iCF1UHjzCxRYCoTsHUf8tCCgpojpm5RghxCS97IqXVGrJEJb69uDj2rd0uZeYMI1P\ncjke5xQ1K45/1YkLqRdOu5Pa/jB7dSoUrfHOMRwMcM7R7xXkeY5rXdR81zVKa1rvuHr1KltbWwyH\nQ3ywFNl7vPXWG2xsbPL+7ds4WzE+O2Jvb4/9/X3m8znDXp+qqhLVEW9TJnmhEF11YvdQVKJuOq00\nKKmXIn2XUE/y9D4lO2JSNGlZE8r3siuJj4tNSon1kYO31pLlMaHpgc2dbcZnZ9jhgE8//ZQiz2lD\nw6XXLuFay6wu2bm0x+NHjymrijfefAul4nTPewUB2NrdpWpbTudzPrv7BTt7e/RGI3YuX0UKxddv\nv8vZyQnPjw54/vQhxwcH7F25Qts0tE3AhcAXTx8hyzlrTYmqZoy213h+esz0ZMrl0SZaSz782jfg\nY8NaL5CLjLYRhKJHU5Zk2mBcoHAOX5WgQGeGoPIYxViB0xCCo2lqgoghaOMtobVxs8x7VN7hB2vc\neTalmR9SeEvRNgx7BcG9nAu0KpY+ewFIhZdgdSAIh8dhjFoUZbRtG1GW1pg8i/rzEBdlJjQ3b9yi\nths82p8QvMMUhrPJmM0Cpk3LR5/dIe+9jsiLhORCLJpKTU5ig7BODeFoXUMgoGREs0apWBQVYisw\npTS7uzuyo8crAAAgAElEQVT87M/+De7c+ZymqanGY6RMtQjexerAhPAjcPALNU0zmzEYDnm2/zwm\nMQc91tfWuHrtGscnpxyfnHH92ut8cucOt29/wKXLrzGdTNjY2EhghYWUrWtApZRC6ajeqqv6pWOO\n8AsJHimRuXDoyRkrmdQhzp9zXLOqjHUGWkeViXMgup4/sWpTahXX6ZcceFjozBErraHCl5N9AnBd\nP5YUeccNR5xrRNb9HV8kFvMnofsdEcGHkjIm3b1LDlzERHbnoGXsHSPFisLJ+3PovDukThuY8B7P\nebQdUmS1SPCG87TQy+yVOXCTZXGStw1CQJEbBLE3B0GiJQTfYvI8FpBohfOWTCmGwyGbm5vYpuWd\nd95iPp9jjAEzYmNjI950QitZltE0CV2ElRArLMMbt5Kw6egW75ca09isRi4Qh9KSTEmsDWgjFlxl\nCAGbuPdoAusdxitG/T6tc2ysr6O0SqhNR822ybh5/Tp5nnN8dETbxIZXbdPSNA11VZNlObfefIv9\n/X0+v/MZV69do6oqTK9AGM3mziaXr73GpctX6PV7TI6PQGX8m9/9fX7y6++zNlyjcrEAYzqesLW5\nyXTWIJRkZ2+X7z75Nq/dusHW61f43kefMJnXvPPubeoA+8+fMLFTnj19RHZlyLwVZPmI1nkOjo64\nfPU6g6Kg7xwYhcxi2OmauDF6ZXCuiWXKSExmQHYZ/yivPLOeor/O2GU4J6iDwDUO1wJCUD2fvnQe\nlW1FY1tkZpITl9TeRVlca8G5hLTiIlU6i3MlfoKSAtu2qTHTJpOZw7sWIWp8cOQmw8sBT56XzBqJ\n0y3GwNHJDIFEG0NmCqyrmc3nqXVDTGTppMwR3uOtX3DbSkUZpvWOum34xje+QVEUPHnyBL+5jfeO\n2jscscVAlG8kpBeg9Z7GOfIs59qVK4yGfba3t8BZxmenfPfjjxlPpmysb/L8+Ii333mPrCioa0uv\nP0jAIqJKRNRPF5nBdsUpUhBw6Ozlm2bHLRNCqhZ0aQ0sUXDk1bsiGFJiV4CRtM7GjptK4v1yI4ib\nAAgVnb9K7iny2/G/VOKoFwx2OqURL+jDE98c13pY5EGFYFEB2c2L+H+XPhNprkR/oUXsX0RSJvmQ\nIgff9eNc+gmVNlaR1DNpkFIydakLDyE68i7qX+reI82n5FKO+qfZK3PgPrjUWXQpl1FKoLWgaerF\ng2pSJlrpOGht2tXauiHgmM2nUVsbWly7bAsp09+dEiUWlCx3Nr6isu8cL5YG1yLAdZ3a6Gi32LNB\nLDupxXqv+F0pZayQ1FF+5kPAGEVIDaU8Ub412N1baKqFEAx7/QVPX1c1UkquXL5MAIbDEcO1ERtb\nG4xGIx48fIjWGus9W0IwnpyhMgNESuPk9Akm63H33kO2tre58cZb7F67yXc+/gyC5Cc+/DqZyTk8\n3ifvFxzNxjx4+IC+lDgP+48fUfmWYBsefv4Rm0XOd55+yiAb8td/5t/le3fu0grBwfN9Lt16h2ef\nfcbRs0dcunqJeTNng3W2t7co8oKsP6AsZ/Q2thitjZA6ltOfHJ8ynk7ZdZpiMOS3/+QT7JmAXGFw\nvHPtBlvbe3xy/+GXHxaQpZUZrCckRCdlTls7XOtQSPqDPhsbG6l5UpxXDx7cZz6bQwgURcH45Iz9\n45raarwrMXnDbHbGzqW3cGXg6X6FKXZiQljOKHp9pIwtBuZtTfAOoaBs5jRNg8nMIok+yDKCc5gs\nQ0pNWVXUbYNzniLvRRlqnscNWRk8JJle7EfTlZF7HykVlMYCo/U1JtMx4+kZeS+jV+Q83n8ae91I\nwWB9jf1nB/zD/+g/ZlbVqMmUzY21CDKI3LDEpcZKDimXKBVsSmh+2VxbEylORVggxhiNLOlHsUja\ndXy7FAKpRezroyQKEztCduqQAF4KCA48uCTci1LOqCLxbZ1o0LgWpRA479HCrHDKIRaPSZk206WC\nJXZmXKUz0loWJGoGFjw8JAosOnmRJLAiBJROrSW7XSdAcA5EbC+w4iaA2JJLSIlWkXprIOnS/VIr\nH0c1nj/RNP5PceKvzIEvvWH6V3A433XpWyYMpOh29vM9lyHE/thLhdG50GP1vjsqo+O3hBCL0tnu\n97r/XqALlg5cKXPuYZxz8mnCL4Ik78Cn1qcr31epiY8QYlFeHDn5yLMrGaVTXVRmrWI4GEY54uZm\nTPRIyc7uDm/zNt473nr7LcqyQiZK4Ps/+Ji816epGh7Xj7j9wfvMTk9iEbFWND6wvrnB9vYWs7Jm\nfHJEYTK28gJfNQzzjCwElHVsbG5QSUGjFW/ceIePfn/CdP85Nq+ZHY15cPdzjJQE23J6+JxHmeaj\n7/4xG4OCjz4+4tH+I/7WX/33GPo2th9VPWrb4kvPrCoZDodsbG/RH62RDwb01YjD41M+/87HNOtX\nMZsZWWjZGwx5/bUbfPtPPnvpLPrwrXfYP3jOtGmY1TW66HN4dEq/GKCF4Xg6Ybixw87eNSaTCUII\nXr/xOru7lzk7OeLOZ58xPjlGCcHl3R1msxZdDAh6TL/3Gn2zi/TblOUpShoyM8Cplmoe56bSkqAC\nrasxucYohWwiH25UhjES6QFU7HWTK+o2Fl4pHamwyWTCr/3GrwMwPZujsgznY2SaSYlsLet5j6as\nyIZD5iJg1kbs7z9lrdcnBM+9L75ga2czOjkBvUE/VkkGywdf+4DnByfUVnB8fEwIsd1rwDIajXDO\nUrctKjU0szaCKx/al465SAlWUtK2dU0M9zGpI6ZO8l2Pd6CXLQQJWiJcp71PG5Xoeot0Fbix6Mf6\nWOyS5xm2cRFBJ4faUTZeRKo8UkxLOXDnABaR88JRn/cjMq27iICXvb+7X1mAtOSPFpHQgldPLYFT\nncMi0lhZ+6vJSiFigZwSEhFiS+PYFjuC2NyoBaj1K4nQr7JX5sAXzlaQerAvG8Ikr50QbZcBJuLb\nlWxzLJvtnKlfhFmrm0M3eCF0qYpo4YWfdxZecOzxM784rxQihngsd9jV70vBgrvrGMF4RWFxrI46\ndGnnXY5JbMAjU99qH2Jv4rKtKYpiEWKFELu69XWP4WhIVVcEoXj33bc5ODhkd2uTQksypTl4WsTE\n3qBHf2ONwWjEz+zuIELcNJpZxeeffM6l9Q3eu3GTDInuj8iyHqflHNUzuLMT2ukZvVwxvHKF+qzk\n4NlTtl+7zs5wiBUSb2uuXb3MoFdweHbET735M7RGc9jOyYVmUjYEPIUUtE3DZOY4shVN06KMYavn\nqYPHisCsOmM95BSZ4M7H3+b4bErd8Zkv2FZ/xN67u8zblqPplO99cgfXgMgzil6fvlDorM9kVlE1\nkc8cn014/PAe77z5BpPNI9xsSr/I8AZCWTPqD9HFCCEM5WzG8fER9+59hrOaYjAi67XUTaygG40K\n1jf6zOanEBoybRgMhmysbzHoDzg7qynnNf3+IPaa7vq+CIHSmoePH/Frv/prkRppGoZrI1rbooIB\nZ1Ftw4bJuX3tdfIs47OH9xlPx9S+InhBdXyCyTTTU8nRwTOUVgwGQ5xreXD/PkXe4/jwKNY9eCjr\nhsY2sUVyazk5fR6LaUyO8gapNL1ejs7UVzoPncrOIVGOwiwSrBG2RDVXpA+69rDpfR9WLjTVXbVs\nlz+K3HZEntEh6kRpplZTISBCTBJ2rWONMXR97mMUkNrIChYqm5D+F3NfAtd22B6Esx38Xq7lFFVD\nlCt3/Hxc5ilxK0W6fhkbdPlYZbr03V0PpsAqsIy0UvyZ87HzoDYGmX4W2YJUUOVZjNFX2avjwFfU\nITFwSLxP+iSkhJBIbUY7iqJzpH4xMN3D7RoepaonVjjslFDpjhtP+3KNa4fWz6F9HwAXNwsRJ2/H\nX0GcOotdXJDCQnHOsasXduXu87CySHwIy4SY9zgcVRsVLvV0itIqopngkU4steapt3GR56yNhhwe\nHrK3t8Vbb77J8eExTdPG5lXOIozi+pVLkRJyDiU1vbU1RptDfuKD92nahvXNTU7OxlxFcHp0yJOH\nd8l6mp/88CcJWvHozj2asub61T2O65qty5d4eO8+WRZfkLGxs8XutdeYTZvoeAOcjmcIApnJCN6T\nW0ebXraR5znjecnx4SkuD9z+2puYfs7R5x/x5rWbnB49x9mXh5JP9vdZ29yAPOdPvv8D7j55Tq+/\nztyOmU73eXLwiDfffCNKN3VsSlZNJ+xtbdDWJbdu3sC4ig9uv8f/+sv/Mz29xU5vC5FJ7t97ytbm\ndU5tRT0/Rul15qeSRw+e4aRib28TYxycTTk720crT12V2NbzjQ//LX7+7/wdjCrYf3bM84NDqqpi\nWlZMZnMyk1HWNb/zu/83Z9MphFioU9bziOylQPiGvdGIr1+/gRvP2B72Gb79FvLhF9w7PWLYG+Fq\nR6E1vX6BdZadrW3uP3jI1vYuTdWSScPnn33CzbffxyEwmcZ7F7tYajBoEJAXBfPaUtclLjj8vD0X\njZ6zDhAl1NzJAFcrI0MI57TgnT7eB4mMgXPS40uqql42bRKpdwigRUDiadsW3a3/RI0YIxcJ3bCQ\nJXYVoURAmHqPrL6dSEuN8y1dZU3nI0LwSW+/ROsx+lbn1mv379gLXy0cuRAC4bqWyMuGVKzo0Vff\nyhTpqdT4KxV5idBVh35ZXvhV9uq6Ea5MDiGSg/XpEQm52Lk78iJmieXC8abbB2IGeDV7K6VErtz/\natjU8V/xWOeR9up3V5G7EultGR2Nk2ibEJbfwccIwAuZmueEc865u99uI5Hdq8XkUs6IjzxujBQE\nDsiKfOX6Qkym+Fh62zQWrVTqy6AAz9bWJsNBj9xIjo4P0NpQ6ALVy2i8i53YVCA4i1IBhGP3xlWu\nv3YJ0bQEDXMVWLu2i5tV7G2tcXl7xI03r1DZmksbu4jasbm2zvreLnu5QWQZO2tryNYzr0qOZxN6\nm2tc2srjZCUmZL1tEaHbdBTT6YSgDArNzLcM14d88MF7tJmkrk55bXvAds+wPthlQvXSefTk6IDj\npiSYDDHoQ55z0tTMZhNm0zkKwfODQ2bTCaPhkPlkyu76iL/+U9/gyvabHDx9zLf+8A842n/C7OwR\nP/PTX+f0tObJo8c8uveYja/vgZ0jqehlG7S1JtRAHuj1CvCWuqwY9DSvvbbN7vYWRwcnBFdx+Owx\ng/46W1vbrK2ts3flCk/3n/P9H/yAz764y6effsazZ89ikt2LBZrUStDLFNs761wdriHahus72+w/\n22fz2mVy4RnkCuEchdKs9fpkRazazKRiczRicnpCrzeknM/417/8r/gnv/A2rRO01ZyiyGirKVVb\nQ4i9g6xtIUlnm6biPIN73jrZbORtBTqPc9Rau6ipiJFsdFrWWYyJb3jCpQAbQXAO51uUFGQyj1Gp\ns8nBKbSWsa2FEOktVulVZYsXKZAURpast+xFY60lyKUj7d6wg4hFUfEVgKmfCrGMvqM0RUfn+Bix\n+0TnLDTdziY0nl6d6Fg4bZVecPJiVN/l4GAJ3rrEaIw2liAwfscvKZwVmvll9uoqMe0KCsVFgn/h\n5EDrDgXnEU2voOJVmmMpw1ki6iizOo94l+MZHXDXnvRFmc6ybWkXkpHeJ5mmdHLALnTtJ88je7n6\nAMXyc5Wa0XdNedxKBrt7RlIo8OnhdlSR+zJ10FFOWmdxJ3fLlq7ethR5HsdQ6AVakEAuBMEG6km5\nGCcpHaI9Y97p3ps41r5pY5N7JRns7TG6fBkIaKXZvf4mTV0vxgMBDBKH1wzYZCc2+krIp0NAq7Kz\nLqIKSQVgvUNKza3XZ8zrGu+gnFdMZ1N6esbbbvzSeXQ6ndL3HgFsZRnDq7vMyjnrt64gtUZUmiAF\nj588xkvBsZG8du0aAPPa8+DJETOb8/mDUxq7gVNDWjHm6LTBhpxnz59zcloBmwSZM7MTGgJCl1hf\nIssAdcPkbMJuu8PRwwmnp3NcKDm9Pqd3dZvJLPD5/Sf87MY7bO1twb1DwvCQO/v3cb1AW7X01RDV\nFjgjsKFkIBSvD3pczhWybjmZHpHtrtEOc4Z727jPjyj6fbQ3XN7c4LWNNQgtR+MTNl/b4rtf3OOs\nHBNkTnNwyLd/8Mf85F/9kIP9J8xnh+S5xnpwOqNRUE3OGPUKMgO1rWi1gJdT4JxwRiYNoo09vp2K\nKq+6buj3BrR1g3eBqipRSpH3eoQQ3xnptEc6iUajpEovFgl4Uac+4SCcBOtoElevtY5No3KDVAYb\nYpVi01iUVvRHA5pmjAoRDGQii31YlKPyc7wEFTR15dFk2NBELj14Ek+BlqnRFQKZErAqCIzOQCqc\nj+/NRaQKYtcifIr808bS9ERKdsbXtzkbKRXfRCQvlYxpshAorIgOXASkl8TujfGNPbFFQESJS0Xb\ny+2VOfDudVywdMiLHVOslMF2jnslTHsx+bj6ey9+p/veqr2sAupFO/c7YrkPrpbnnk+GnA83X3bO\nl5070j3nv7usYhMru/KXNaHLkPX8n3iM5QbZ9Xd5EQWsnu+FAy90tS41mF81rfWicVP31pLu8+56\nM2NwUn7pXN1xX9yAjffxNWvasBFSPwsfqJuWLM8Wb2950W7durXoT93RVq2zVFXFrJzTTj3XXr/O\n5vZtfAgcHB+ysbmJkor7D77g9OyYjc0Rg/4AFzTPDg6o5g3G5FFpIGKFsDEmvndRRFy2ZkbIWjIY\n9jg5LQHDvQePqcsZ/bxPb9Dj6OAJ2ztrfHLvAb/0q7+F2VznvQ8+5Pn+Kd/8/T9helQzP5iy1R/R\njkuKbFnEMez12BqtkQMSyfPDp5SnY66MhnhrWR+OmM0r+v2cXq7RCowwrPf7HM6mfO32u3z+eJ/D\nsxnV/JTf+zf/J0eHj7m0sx0dos6wTYUU8Rn3exmEQN3YWLEY/EK19aKpEFUV1jmUzvA+vh9WKUHT\nVgQ8Usc+/iHE8ZOpSMbZNhYOOYUUCilBOAiptYFIL2yRCJDxrU9CRiRuXRv70jsX32sp4ztby6rF\n2opMxBeZRGoyYEOLw0YgJQRFlpOpHGfb9NaexHUr0N18dLFFAsQo3/rlO3ch5uE6cLaa4AwhvifX\n+6UcMb6HVcd2Cy52I+zWdPdCdiliS9qOQRCie9l0pF/+wiYxu+KaTs7TOYPu310YtpAArlAQLzqz\n1cqllylKVh1i97MXz79Ayp3Q/ksIf2kvbhQ/7FrOtRVdpVHEea3ni5nr1e8t6ZwvX0f32WpWfLWR\n/OoxV0t7VzeUJVd5/ryrY/Pi9a32i3nZWHQdHV8csxc3m8WGk8LqRes3YpFU18GwyIe8zLZ3tuK7\nOLWhaVta20a5l4/ISrZR5VA1dey82IPxeMK8bVEI1tZ7BF8xn51x+eo2bes4HZ9xcnbM4eFzqmqG\nyTRGBhSevZ0Ntrc24iL3gXZmqWYN9XzOoJdjPfTXBxTDgv3Jc57+wTO+OJgyqU7oD/t881t/zG//\n1v+DYZ1bN79OqDyTo+esDXuUszO0MmRGs9kfUJiMUNe0rWMwWqMwGf28YGu4zng8RfUEl7ZHrA0K\nFAEtoJ9lrPmCk5MT3rp6hQ+/tsXaxg5ZnrO+tUmvN2AyneN9rDjWzoFvaUuLRaOzPliPlssCpBdN\nO49QsUjKuhYlBGU5RwiJMbEniLXtIjqLFabLDodCgnct3ll0ov4WdRk4nND4oAmuTYm+aEblNE21\nyBXZpo495rXGZJpgPU1TIYVGKUEbapyM7+0MriGTjtY3IHzqLgo69c33IUZxCBAq9ZpBUbuIortK\nWOfjS2Kk1CnZGfNwIQRMEAgZOxLGVhwB6Vx8eXLi6YWMbyVSRifk3vm6lHuT3WaxbNfxw+yVNrNa\nILiVxd39rLOXo0u/cIhf9fMQwuL1RKuOpHM0qxHAi074q5Dvi05xdWPoHODLHNWL537RWb547Jc5\n39XrWT3+i/0WVhOwf5YoY5mQeVGmef5azucsxLlS69VNZPW6VqvjVq9ldSNb/Lx7ozgSraKW3WSK\nXBpsa78ylFxfX4+hZ+vQRpHlJiajSDJOZVE69jQRUnJ5sMelS7sopbBNSzUvqcuSqqxAB4zKGAxG\nTKdzdna2WN8YMpmMmc3n7Gxv8Natt5lOS+4/fYhtPPVkzru3brExWuNb3/wWbdOii3Vufe0D8rUe\nTw+ecfXNTa6+cYvf/b3f5e69Q2Zzx+HRCRsbBeu71wlG8nz/AaPNAnc2Y2+4Td9k2KZFAZbA2++9\ny6Pnh5RlRTmZUQhF3i/YHOYYLMKBkTlG5zjjaV1Ae8fZs6f8o7//97jzxRdRpz6b0zaAUhA0wrdo\naynyjMYZghrg2hrl3Fe+DSYHqrrFK09W9Ajtsv1yXcdmX+mdw7ExlIgFbd7G3uGxWVZsL+CdjQ4u\nvSokvZsHgUWSeo8nBUhZzZaFRiHmvbSSaOWZzUu0NEgREW9dW7xwmDxGAv8vc+8dY1uS3/d9qk66\nsXN4r1+eeTM7aWdmd2Y2cZdLchMpkivLf8iQLFsCJMiGAmQZBiQYsAzLVrAJC0q2JVAQTEqyTZES\nSTGI3KWWK23eHc5OTi/NvNTdr+PN94Sq8h91zu26p8/tNxQlDOuh3733hMr1rd/vV7+glMKoBJUl\nhLUIjbb2F2kGGYBB5uIQIcQkZKAW1lI3CAKrwaIKM/scPyaHbZpQ+vncNyhpuQFyat06qMrXOhop\nrDFfmiWT8wL33MESVt7sg+Q8ve8UeFlUANOL3aWIy9R68awLYuU8XYBy77lg424ixfWy/maRl/vb\nfa+oSxUlDRAVPrSdv/eSJpouDki67Zi1uRR1rMqvDLhlrRt3YyjyKe67YF/uH3cTgWpuZla7Umty\naRVHlZqwqkYdBZit7h+VT3aJlxtNqXyRa0zuZlajhWXrdV4/YwwEHvPzbYKlRQaDATIKSJOUpaVV\nHrr8EOPxiMC3KpLD8RApPGpRk/3DLucvL9PtDRh2xoSizs13NnnsmY/QHyZ0hgO+9PWXiFo1tCdY\nXV9ke/sGvU5GGC4hhM/C3AL97j4CQT1ocuHcRe7t3OH08jKLjRbSwHA8QgQ+MgrYOewgo5BRkjDs\nj6iHEWEE840avjYEwkNqK7Ndqoc0Gi2GWcrTT36QbNhjrhbiRw26211u3N4hyTSXzq5x/uIpxoN9\nPD/k+u199kYJUinWWwLq9co+97SX+4IXDAcD5vLgFp7v4QcBqcpIktQGzXbOoqQorKEBT05ENMqk\nVrlAMPkTwnp1NUqTaWU3Agm+J8nSjEzFOeUsGSVj/FqIURqlY6uvH3mkSk08RHo55RsG0lp7A0Us\n1+JMxioQ5Fynto6ntBSWWzE28LnK8s/c4tqKjiyWWZVDmVun5mbzucqlkMV6sYeUfuAjPZEfdR3F\n1kyyFG0EWue6+veBiffRkGea7S4inRTAWDSoLAM6CbjKIFb126UoTwKWcj1dYHevuRSxC/KzgHXW\nRlUur+q3C45FqnL4XgBelcil3F9VXELVGUKZEi+3w90AyxzSrLKnvhf3zYSbRgpJMs6sPvAMfVhr\ntesjDCR55JrCylb6HlrZPgvDiDAKSdJ0ImNsNpqkcYwnJPPz82jfim2SYUyj0WDRzJNmQ4Y9Ta0+\nh1YQhnWajSZRI8EISUpASoMHn9Ps91J++h/9LIPBGJMakpuHoDVv8Dae7zEeZsy3enQPBzz3zHOY\npk+WjpHMcXBwD3/OMB8KaqFPphTDLEEpz4oI5JhUa4bjlJWVFcbxEE9mmCQFYZ1vaW0IfJ8wCJir\nh6hQ8NQHHqOXjlhemCdWPkr1WD91DhlEnNtYYXWtTtzzubO5zb3dAwamTSgkq5fOEi3OV/Z5vdZG\nhxla2MAfF1dW6fV6HHY7pFmKMdbthecHuaaINcjxAj/3xy4xntVKEcIQCg+RU+BKCIzwQUpUFk8i\nHSldmLIbvMCbWIlalUKfJLcDMUKTqRidaetAzAOjPetpUWUEfq4Z5XloxORQ0crqM2spYo78JmXG\nBiKeKATkBjuF/D1RKULnYsQ4m0SwB1BaToJ6CymPDHQUIGQO6po0zaw4x/etuMY78q8jZsz7Ir2v\nAF4ANdjKl0UdVfLXsiihuOZSgO79KtCYJYeFI6p0VnLfKT9bdl7jchZuRGs3ryoqeZYoo+q6u8Ed\nOxytEMVUlXM/oL9f3SYULUyNpzsW5XE5VoeJ+4gjlU2Mdf3KCcpUYZTLabUpMpgYTWmdIZQ3MZhI\n0xEFV2uMIR1n1kxb5CbZwlL+9UYdMEitCaMmSwvtnCLSebRwCSpGGTBBjau3tvjy17/H61ffYTjO\naNaa9Pr7eJkmFB7+uIHvSdpCM9rfo+37PP/NL7O8sor0PZZWl+mnmrmVswy2b+DphGbkUw8DlCcY\npskkRFdmFCbTRLWIUPhII4jCiMD41L06GMny3CKthQUWz6xyuNch9jTG8+in8Pa16/SykDhT3L3Z\noP3pZ2gGAa+//jZbuyNMXZAMR1yNFHOnTlX2uR/W2d28w5s3b6KBO60Wa2trrK6vYYRgd/+A69eu\nMjc3ZzfC+QXSJCFJNY1aSJLmaq/SQwqNVBqMYhzHiCBEiYyo3iBTY+IkzilRTRRad61RFBFEvj2o\nzFLCwEdmGUIYG2UqCKwriixD+D5GGdaWlphvtOju75MNB3ieIM61WKzfFEPgeda0PTvyZyS0ttal\nwvq3GWUjgtyWwYZ1s5F8pJR4UTDRlrPYpUBCpjS1oJb7ZLJy7nE8yOerQeXReIhzJS1hA4r/vj7E\nLAPXLGq0LC91Qdq9Xz6orKLSXfApDgfcDaE4NK1KJ8mHZ9Xfpdwn5vyOSKeIFlTV5irKtQpwy/Wt\n2rjKbS7fq+JyZo2HW5+ymMl9ZlaqclUABWDbbwaDcOSvJ+YnCg2bnP02JtcwsJ9FNCW3TFe90xhj\nQ74JQTK2Pj4KWagUYExGpzuiVqsRBBHI0Drxb59FK8Nvfem3+M53XmD/oENy0EPFCVsjq2s9Tsas\nXt4QZZoAACAASURBVLzAeuM8t955l3HaJWxH9OIOC2fm6Aw7nD37AJ3RmNbyGqPRGB0EGM+QYPCM\nsQezqcJDYMMh5P5LjI23KIwkTlLqzQZgf6+srFFvNzEK0lQz0GNSAWF7gWef+TAimmcYJ8w17eaY\nacMwTqxIw2gevHSeRx97gJfferu6032fO7c3Cf0aRhj6vREPP7zM4UGPN956k/3DLqdPn+bM6fMc\n7B3w+itvMRqOqEUh+/v3CGoNPvmZz3Dl2nX2drdpBQEXz21wam2NQTzm9s4Oc4uChXadTqdDo9Fk\nPBxRy0MpCiHYP7ReGz3PQwa+lWVjfawMhj2yTONHNYaDEfEopVVrcv70BhfOnuHfffc7gKFWsyqC\nCMGgP8LzfYxnDQgLjs/LtbyyLENpRRQEqDTJ1RvDIyOhICATVoNF5NS79KxWlhdacZ7xQOnMBiX3\nfEf8eyQG9sMg9wJpTwTS9PfpIeYs8Ub5kK58H0pGMbPYco6OBKoAZpa/41lU66y6lMFh+rBvcmfq\n+rR2xnT5BXVYBY7l32555U2rCoCDoBju44BYls2/F/FOUZ8qEQy4OvVMZHkup3SUl0FrMflePsw5\nKSmVW7GRq4QJJvJGIQBdsYEXbZ30hgABkQ0vAUbmsloFwqMeNBHCY5QohFAcHA741veusnnrLq9+\n53dQ+138UcqzZ87RbjcYm5iRzBj7iqAd0Vg8w4effJivffu3eHf7Gs35kMP+PWpejRs3rvPgmQeo\nGUk7atKLmmzde4eNtSWQHlmc4AcB2hh8X1pvhjJAeoYkSWxQDXseiRAQegHX373KqY2zyEadaHmB\nQAc2ilAQknYO6eztI6VkfeEcB7u7jEZ9VtdWyUwXjWZ/+xav0mN5udoH+6/9xq9TazRZXFplc3OT\ntVNrrK2t8a9/4zdI0gxPemzd3cYXITtb9/Cl5OzaBjpTBL5Hdzji+o13GY5HGGM3zqtvvs3lCxf5\n/ksvc3d3lwsPgU7qLC0tEccxtXqbUazY3d2lVqsThhFJlrK0sMhoPGYuatLvH6JUShQGgEIpgxQB\nd+/eZKk9z7e+9R021ldoNuvs7e2DkIzjlCCsobTGCNDKinK0p/GExJOKOE7xPSsa8YMAncu/LV/o\n59GeBNLjKFi1J0FoDIV64dGaDqOALE4mhn7W1ZNVM8wy6+WwUEH0ZoQSLNLvCcCFEO8AXUABqTHm\nI0KIJeDngAvAO8AfNsYcVrx77NNlxavY+PuBq3uwmX85BnLFZ3FqXqVaV87/mGk9s52tz8rHFblM\nH4hWA1T5oHMWVVwlwilfL5JS1cBYtTGeBOAnbWDuofJ0PafrWM7fw6dwzwky1wt2yp3hPXJSZ8NE\n9l3SPM83DzPZHCeiq1IdZCH79PJI5iZnaX2PJFXUWnP8lb/y1zl//kGGco7Xvv8iK1GDh5+6RDAc\nEx92ePLiRZRQXL17k/1Rlywbs9AydO/t8MnHn+Ar+3foHnSYb8+hE4FUkv3NbZ576kPcvHaNs2c2\n6PbvkSljnUsJDx1nyDAgkBJpfALPRjeS0sp8hSfAU8ggQOmEO7fuctDrYOp1fuALX2BpfRUlDcKT\nnNnYwGjNaDRiHI8JF5qk7Tq+73Pm9FniQYzxBETS6nZWpJXVRbrdPp3tbc4uLdNqNdjcvIMQBqVS\nBNaIZ9DrMR4OWV1aYb45T6dzyHg0ZH//AK/VJMPQqNeYi0IWwoCN9TV6h4csLcxz9/Yt/DNnWV5a\n4/y5DQajMYPhkKVlj9t37rKxcQZBQKY9PvjUszywsszuziZb27e4t3uPyPMJG3O89sZVdnYPaTwz\nx6XzZ9nf3QYpSNOUWr2BkBlxHBPWImq1Or1eF50ppAe1Ws1GJ8KqEY7HMXGc2wTkroKjIMAYiOOY\nKAitOWGu3FCc7YRRbaJyG8dx7kvJILzcH4wo/KaQ6757KGVIsxirbTM7/V4pcAP8kDFm37n2l4Ev\nG2P+NyHEX8p//+Wql6vEA/8+qcinbCRSLNSqzWJqIVMtW6+iul0AOwn0q9pT3mDcDauqTe5nOd9Z\n5c4Sa7j3yu10ZfnlzfCkVG5/AeBljZ2qepTrYKPUixzErRy8OGw0wvWvXE6uSGZ68zEGpCicnE1q\nnYux0uN9io1nqHWu52uVghkOR7TmFsi05E//13+Gr3/ju3zz536Fs6dOc35tEa2H6Br80Bc/ixqM\nifD4A5/5ArtbO9x59xbD2yOMjBgOhjz6zOf57pVXePHGNUyjRTeJCaKIX//OV/j4xz/Cwd4WG6fP\nMOrs4wsfP6qTmFHupljTrEXYUG3aBn3A4IWSYTwkiYfUvRpnzq3z2BNPc/PePo12EzxJpjNMkiB1\nRiQMzVaIaoWMjUGGEadOnSKUHpEQHPQ6HKqUVtCo7PGoHnC2sc5CY57xaIzxBP1Bj4uXLrC7t0+/\nP6Req1GrRcy12tap2vY9Op1DtJ+xvLKIEIZGvU42HBA1a8y1W6TjMZfOn2Wr22WxvkC32+ell17h\n7LnzjJOY1dV11k+d4eCwz/dfeoXx2Hri7PbHNJ76IM1mjdXlVa5cu0KcKuptw6uvvg5GcOPGO9T9\nS8TxmEcefhylDf3BgHqtQZykHB526PX6NBp12nNtayykUjyTa5MYw/Lycn7oqanX6mRZRppkJGlK\nrVYjiYd5IHTr1VEUFIC0XhujWt0G3PZ80lADHlpnk/Xn+z6pShHSxtjVWlgu8IT0H0KEUl5ZXwQ+\nnX//GeCrzADwMohCBWWeE6ki/+c8aFkXYa8abSY1OekwbxZVX6b8p4FgGtRdirWKUq5qZ1Ubq35X\nabCcRBGX8yieLR+wniTfr7peJZt3yyr3Q1Wdq0RAbn2mDjn1EXBbIbVHPrxYaJ2RJBijc9/KR/6s\nrRzSGl9Qsla1HuvcOlgKKI56mNTDzyICFeATooWmVgsYp4rX3nqDv/v3/gE7e11+7IlP8eADD1Jv\nN7i1vcnK6VMMV1boyH0+ePEy33z1ZYKDDi2lefhcg+bCHAeDASEBj/hN9DDja4NN1HwIo5hFHfLi\ny68jSXnu0UdJBiOa9QbJ4IBxMkBEIamBmueDslo+Yb1OIH0gINGKtUuX2O92+dQXfoyVU6c5J33w\nfJB5FKDcaZLRhYsD6x1Sa4OuaVSmkAja3gJBHB/5Qy6lTz77LJ7nU49sVKhUZwyHI5RWXDq7ysH+\ngbXSlB5pssR4NKbb7SIjjztbY86un2d5bZUrV68SBj4H3Q5BFPLClbfRjTob83MsLS/nvlU0wvNZ\na6ySKcPm9ibD4ch60ySAGG68cZ27bz3P0tIyly4/SKx9dvY7jDcPQCukMOwf3mOvM8fNd2/w3GMf\nZM747B0MORiNuXrrLsM0ZWF5lblmRquuWF1e5tT6BbxWh/W1DTZvbjHsxqSjlIW5eR5/9DHOnt1A\nGc3vvPAiL734EtIMrc9/37cHnQiMFiwvn2Z+YQ2VSXbudVhcXEFGinE8wvMEO/fu2jOTcR8z6CKN\nwg8kBJAmM/wZ5Ok/BAX+W0IIBfxDY8xPA+vGmO38/jawXvWi57AZxcKu0gCpotKrAAOsU5rKSlZQ\nzS5AVclyy3mXr1VR81X3ivJPirAxi/s4iZo+KRW7+UkbUbmc8veTOICpw0chJs6NZrW/SK7YqQz0\n1rfYbC5sVt29XNyBN123InZkAd6uZaybn33efvoysk6QPIlQGkWK0dbf853NLf7+3/k/GA81Tz3y\nJGfPn6PT7TBKR6gkoV6rcefOXc6dPcfGxYv44zHe3h70etzr76FGI8JGg5V6g0dPf5Afahpe+tIv\n0xnHmMzH9yMCLdBCMooTWu221U3PMlrtJkYKMiEYjUZ4QhD5AWmmiRo+0guI45gHP/AIn3/kUQZJ\nwiBLrQVkHhrQqvIdEUw2YIpXbS2bk0qzNvGlxcUjQsEYIi+k1WxM+vjcmQ3SNJ0ai/F4TBwnjAZW\n3zuMQpr1kMFwyGG3S6vdRGnF9tYWg8GAxaVF1tdXWV8/lfvKTmhEDeLxmMO9HRZaDYL5EE/4jMdj\nPNkgU5Kd7S79bsa77+5Yl7TUGA16bN/ZY6G9SLu1ws7WNttbW7z6yssMtUDUm3jSZ9QfsLq8wtz8\nPI88+iiL7TbBch+hPG4pxRtvvcVCY57OQYeV5WWCMKBWr9FotFhbP80oPaDX61Fvt1mYXyBNrTdR\nFQb00oQ7t7a5ePEhbty4xa3Nm3z4mQ8TD0esnL6IEJqWTgkCASZje/suc+0m3V61D6Ai/V4B/AeM\nMZtCiFXgy0KIN92bxhgjjhx2T6Wf+ac/P5kwTz/5OE8/9XilhZ7rj6EMku5hn6W4quVFZbAQQhyX\nl3Ncvuted8ssf5YBrVxXOFKrKwNY1SHg7ybNovhdrZf7bQKz/K24bSj3tQuAZVHQSd/LG0C5DVX9\nVzUmRSqceJXrWSWecjmTKi5Kj61JuicM0ldAhhaCeq3F3/wbP4WnAj73A58E7TEY9RgN+9y7vsXq\n8grXX36FMxcusv3OO/zCa2+wGIU8uL7Kgw9fYhSc5bDfRWWKpLlAs7XAf/bkH+Kdrdv81ne/zdDT\nDGSCSD2CwFod+r6PUIIoqmHIrOFL7hPE833bbgTjTFGvS8J6g/nFRYTnUavX7AFoZij0iL38gPdo\nc9O5KElN9QUc6UDPSqdc9UJh/Vprra2/klzTqVDlK+Z7FEVW7W4OEII4SZi7/ADCsyHIDFaP//Kl\nc2RphhFYs3hPWu0cbRjFY9L+kPOnV/A9n163y9LiPE8/+QlqtYioVuflV15n++4e48EIgWA4GtJu\nthn2Rmzf3eGhBx9g9fQ6/STm4eGQzAvojWJSBPd29tnZvMO9OzeJ+x12d3e4+Og8C3NL7G13iHzP\nujmYX+R73/su3/jG1zh3/jy1WoPr16+zcHqRuZV1uoMB927eYTgcceHCRcZG0e3tY+pQWwpY1Yv0\nzYCwVUeEHr/4r38dYwyf/sEf5KUXv4/QGdt37zA/P89oNJw5DvB7BHBjzGb+uSOE+EXgI8C2EOKU\nMWZLCHEauFf17p/8E39kJpVXJCGs5db9KLsi3c/stAqQpu9Xq7kV1OysPN4L6LqLpgCxQn+9qo6z\nQLCqTVXXymKbk9Ksfi0Da9k6swyIVeNX1P+kNpQ3yPK1orxZqcpKtni3sPiF4/5iynWLsnmETDBy\nhBYjUpOiTECWJPyPf+V/4Td/+cvUdZ1GELG1/w7bm3do+AHjw13a7UW88RghPR5//BGuvfUWA0/w\nwrvXSBuSRx95mA8+9gSvv/kG17a22Dhs8Mc/9mme/+3fYlyPSMOQ5UabO3fvsDlX46nLF+htD0nT\nxPqvNh7jLMMPIoQXWtEFhjSJ8UyTP/cX/xtGSUqcpUjfByPwPAG5cTp5kBC3b8tzzO0LdQLHOBgN\npw7kC8o7DEPq9foUgeTOGa01yXCM0ookTqzpfZrkB3yWY2jXAmrzLYLcgCdVGXFs9bWNgfF4jCXr\nBDozVgNJDdAqYfPuHd65/grjcZe15UZ+sBig9ZDTp+bpdTZptx5k52CfN6+8xWicsLC8RlSvce7c\nRbbu3ePSgw/Q7/fwPMnh+gKJvodJE+bqdYYMeP673+bDT32I3Z0dBsM+aZby2ONP0m63afktvvSr\nX+bHf+InmN+Yp9FoWI7Jk2z2NpmrhfzOt7/K448/xsUL69y9e50rb1/hYx/9MPV6EykD5heWCMOI\nS5efoNlsIaTk137xX8yc+//eAC6EaACeMaYnhGgCnwf+J+BfAX8c+F/zz1+qer9q53fyBnK5rfSO\nAUlFXez1+4giiu/uNfev/Ows8YGbbzkf9145lU3LXQrFzdvdMI61saJtVanK0vS9bDSz3nFBumw0\nVa7fSX1dfr74K9j18jvF5yx2vjCQmtWGrMKbotsWt14+AUJotCetIyzpo02NwFvgrTdep+63SfY7\n9Lt3Gahdmj7MN0LS4Zhk0GHzzrvIZouX33qNZqvFvf17tGoh436Prdeu885LV/ihH/1hzHgEdzo0\nOn3+2p/48/zpf/y3SPw6wyCi3rCimMXIo+1r5lttRuM+Xi0iHgwxniTLQ3rVGzVqzTrNxUUOej2i\nRhO0si5JsdaPkwhW1kh8qh+MMVNz0u2nKkveItVqtYl8GpgiTNI0nXB/5fGWUuIH1mtiq1UnTa18\nt/Bu6fpesT7vbX5JkGDjjyra9Rqj0YgwDBmPxzQbTXq9Pkl2wMJCwGd+5DmUMvR6ffqDAXt7e4xH\nQ8bjIUG4xMK8j/HhwoMXSZOM4TBmZ2ePr/zmr3HqzBk2b10nM4oHLz9ArVHn0pnLDPsxh1mfS+fP\nceH0OdI0YTDo0et3WF9bs0GIleL221e5uLbBWy++SppkLCwsEgQ++we7LC7PM7/Q4vFLF1mfa+Lp\nDhuXN3jq4fP0e0Pm5hdRGq682mNhoU2vu8edd67/RzXkWQd+MR8kH/hnxpgvCSGeB/65EOJPkqsR\nVr3saozMEl3ANIXkAkQZDIGJE6NyqtIwqTq4K5flLvRZk7kMZMXELXe8C3xlCrkM/JWcyIz+mTXA\n99OqKedfvl+1eVW9X2WQVR4v97yjqh8LIHHB+72KllwAP4kbKbeliqL3PIPxDUZoUiNITYD0Wnzr\nm69iTJ3Ir7NzcJ2st4sXZdQCH5kleCiSdMj50w8zFD5b71zn4x94iLlGg3OnTvGt//eXWX7gQX7n\nV77Ey89/h49+4lmeXNzghddf5dmPfZw/8Nwn+JXrrzJSMa35ObZuvctoHLOxsYpWQ/67v/yXWNs4\nw63NTW68c5MbV65a7ZZ4iFev89Szz7Kwsspht4P0fOtn3RTnSspapEpvEtzX7UtrHThbq6oqFeNV\n9gVUNecnetH5c+NczFKvWdU63/fJkmSyHnyZr2tp322325P3oyjK5fmK4aiPlAskaUq9EaJ0g85h\nD+EJkiSlWauBykgbDR44f44oCvF9nzDySY1Pe34eDGwEIR+4/CBaP0e338WvhcRJzEG3w3y7zvrS\nMmIp4BvvfourV95lZXmNBx54AMQpanXrYrjbOaTVbNCq+ZxaP838/CK9/oj5hSV2d/dY7a3iB/DS\nSy9w5eqbJFmMl6UMByOiKKLWaNLpDmi3F/CCkNv9Q7q9AT/wA58iiiL+7xPGQvxuZa7/IZIQwvyb\nf/1zlROm/NuXwbEFXQbJyYKUxyntgsU7CciqQNt9torSdPOsAq1yqro/i3Kv6pNZm8h7Ea+UOYhy\nfavAzBVLlDfYsrjifptbmav5923Hk8995ti9F7/z5fcknilT9pVtzjyUTIkaPqMsJagt8rWvv8Kt\ndw5Yba9x8/WXObj5JjLrMhfZKDZR6NOPR7RWV1k6d5762in8WhNhBBvLq9x48y2e8Oe49dZbPPHM\nk7zy7lvc2rnF4xcv86GNB/i3X/oKanmBX3vzZV7v7dKeW2T3zibn1xd54sFzPPLQRX7iJ3+cBI2W\nHkJ4hNIn9DziLCZFI6UgU1bf3cvPBIpoUWAFDrmuzbG+vt98/cinfvzYve99/dfv+365HPLyVW4l\nizFWECKEFe0YY1UinLmlyV1JI3LjLPLDUX3kqS+X6wcitC4TjA1CnmUZSZzYDR5jvQFifZ+nvkGl\nmTWhT1Wu621VWIejIQqNF/gMRgOiQBB4EQKfLLE+xOfmWlx68BIIwd7+Ia+++gb9wZjMH9EbDFmY\nW8b3Q4zxaNRb9AZ94nhEe65Js1UnHo+oJ9ZQaP/wgIND60NmMIp599Yt2vPz9Pp9G/ih1uBf/vy/\nxBQ7cim9r6b0Lis+c/c305PEZc2qHDuV1fUKKqD4XWb/q1jHY1WYATguZVFl7FN+fhaF6NbVLa8M\nwO4772XhlDcG13hplhFSOY+qjcot/36+Y8r5Vv12r1XV4aT2FkEkXLl2OZX9xRSO08opE2N8z6PT\nHSFljX/1S7+KJxfwtc87V6+SjQcsr8yzv73HcCCp1QKG45h6q8nKqTWu3XmXp8+fQwnY3tzGz2Bn\ne4ffObzG+soyr777NhsXz3L+8Qf57gvP89yPfJo/+tRf4O/81N/m3MIyvUiyM4g5vXEWnQ1ZXT/N\n53/0x/DCAKEVRtjo5bHKrNMoYTASKzIRxaG8ACTGFUcUAQLyafRezkZmbbRFH5ZFieVUqRqMwIZP\nzN8ReTjB/D+L39YiEmMQvnVSZjcfENp6+xPC+hw3uXMrgyEr4pIJCLQBrJOrwkEUudooYch8q2Y9\nBmqNyTQqsw6rtNHMqzZJlpCpjGbDhibMUo3n1VCZXTf1eo1OZx8NjEdjlpcWaLcVCR3ObpxCyoAs\nM8RxSrtdRxhF7AlUqmnVWjSiJk0RsbOzw/zyBuceeJRhPKLb6xMLq0PuN9vUGnUOO50Tx+l9NaUv\ng1cVK2fUyXLvKZAzx0UVLqXuyuqqNC+EEFM+Ulxqu+rZWSb973VCu4BYXhBVC6iqjJNEQVXAWbSr\n7ELXzV+I42qWbt+dJA6pSuV+q0onyfreKzifBPIFcAthY0+W8wcwniFLFauLp3jhe6/SpEY9avLa\n22/SPzykGRqiRsDS6hpbtw/wpDVxv3DxEvv9Pj6S3e17PPTYU9zbvEe92aA+N8fe3l2GnZi1U6cY\nZgln2mv8qT/zZ9k6PODezl1+/I/9EWS9xn/+F/8stBcJ6zX6vUNeevlV/tyf/a/oDTrWuk9K62ND\nG6QGpMil2sfBtGzV/F5A1+lILGFc3Z/e5GA459D08bzc8o7GyDoOK8LoTew7pKDQVSui3ACYvB6Y\n3O22lPmhtPUKqLH6/wAeaU7IG5RKiurZOeFZ9VVjLzDopta+RkqrneNJpC/wDUivTlNYD4qeZyMy\nWW8N1lNloU+fpAlKZ2idMT/XIEkyUNayMk0V9VaTkRzjGc3iyhKdbpcwrCMy6PdH6IUmYa3FYeeQ\ng94mRsA4HbO0sobneyhjCMKQM2fP8/OzR+r9A/CyRgZMg2+VQyd3IbueDIt3Qz+sBEd/wlYen8Bl\nUKzSQqkSHZSv3w+g3OQCYdl8vYrKdetb/n0/4HPTLNez5ToXh1Bue8oiq/fa1qpnT6LAq66dpEPv\nRhyvyqPgPNw8y5vPpI6eTyto85Xf/G3efOkKpB7Nxj41NcCvQ5qMUGkNKVr4yx6HoyGPXb5MtzdC\npRltWUP3Y9q1OqtLy4xVRm1+jl1S0tGQ3o0eWzdvsXf7HmvrZ3j4scf4yvbXObW8QDOs8yf+6H/B\nP/v1LzEYjohqdYyBr33jGzz+1GM2fiUGI5SN6KI0RguK2Km2/Uf+Z6b7y5B7i6kUoZWTNmYS6Lcq\nTSj6wkCq4uypPJ8n6zZ/L0dXW7s8uLAxZgLIQO6C1YZ2m8xyLfPNxSAxTHx560I92AL/JHsh7AFo\nLkASQuKluVM5bR1UWSoejNY2zicSlWRgQPuaIIhQyuD7AZ5vXf1GQYCUAcKA79VIUwXjhCAIiOOx\nDcgcWde1wvOp+fMoZRiPUhubNuky15DUgjbjOMYLAqS/RK8/oNVuIf2AXq9nQ9OdkN5XAIdpACnL\nVuGIAp8YH1RQjkU+Vb62q+TbJ4HRSaKM4tr9qM9ZQPJenz1JK6csPpolCplFvVfVY1aby3UrU3Un\ntcFNJ4FFkcqHmO67J4lQyi6Iy8nlJsrqhuWxN0LyzvVbfOPffosLq+dZXJjn2tUrDIZdllcW8X3B\naJRQCxuESw1WGmcYZwkizWj4EUmmyfojku6A27duI6OQYTym29lDDwY0wgbJ2DC6vc/WO5v8qb/6\nP/ADn/0st7e3ePu1N3nqiSf50jdf4CAeIMaKC5ce5LDTJQgixunIBtLGUqtSWgMkivmYu8xFgDLK\naacVs0gh8cRxo51Kd8ZgnXrdZ6wmZdxnLciCcjbWWVR+42jcPEdTxs1X6MKDtlUnBtA2HLg5kgih\nhEFQJ1Xa+iIxZop30DJ305A3KcqDfQgp0cJuBJnW1nsgEl8IRBAhEYx0jBTexJeJUhnapGSZDYhs\nMo0UYzAS32uQKQVBgPLACyOUsS6K2/PzqAwWpE+mDDLtHfVb7lRrNI5ZbS3ZOLC1gIVwgayCu3HT\n+xhSrZgILnDnrJs4Csor84HPjEalR5ErJixePoE8KVAimnSKEEfPGG0mhyASMSnHPlcGBlc0Uyzw\nItvC3NvNQzCdhWvVmVMaFO8b5xmbr1LT4ZSK56Q8qkcZcGadA5SBbxZQTWo6AfWjNhZ1seXa7/bT\nrpgqn1KF5d6xJKctXE/aRIwpg7SZzAGqc69s0/HkQx59vKDcLBtsD6QsqEsQkppo843f+HlOz61T\nqzegIdG6S5MBMpbIxhyH45QFQs5/4AOszje58vy3aQcSEw/JhmP6e7tcf/55GuOE5LDLUhTQU4KF\nepuGF9Benyftj0k6e/zDv/HX+S//+/+WZhQx3N5kezxk7fQyi9kid25f5Ymnn+BDz3wQpRUhnvWN\nkbfB9ms+XjnlagrgEkd9bAwYZTDCoES1KuYsTm9W3wpD7oTJjpOpttWrpMCnRjiv+4RYY3quT767\nZVPIxAuPkgaZf5pCpGLyM7bJIgeDdfWqjSHJw6CZ4lhX2EDN+TEnSuflCEGQR9vxAm9SnhBRvl60\nRVAhEEiSNJ3ULkkyyOX5UlqXv8bYjVEIQYB1aeDlIiE/9Jivz2EMNB38uN95xfsK4EodRbkpJqRN\nRws+mwqHZm9pk+98UiLy97SR6Fyv1JVzC2FlbBgLCKoIh+T7CCkmLOd0PYqKFGIKF5Bt/QqOoChv\nUvMZYFnlhe+ozOP9o5wI1naj0xQRr8vGEVXA7P65ZU2PQbFpFuKS6XtHG5fDGXGcW2Ki3zCdXFg/\nyRcM2HBU5aVabJAnpVkbWJH0BLNsmKoi8osXeEjpgZFkmSLwA/75P/05djZ3Obdxkf5wyNUbb7IQ\n+XhKYOIhola3ZuytJhfXTnPtjVfYv3MH7SlCnVhvj9qzDpqCkCzLiPwQLT16nR6Ndpso9HnqfaY5\nhwAAIABJREFUmacQnZSbh/t89Rd+iY9/5rPUDvt4kaAdBXTVmI3T6/zsP/kZ/uAX/x92d+8RBiHG\nWABHGLRFPzuuTFOcx8REJxBxVYZYE1HiLMq6RCi45brj4uZXxV0VFIMoni1xf5N33KKPSsv/t+33\nPWttekx0VGCBMRhpw6RNZm2RvzbH56SwVLp0xE5grVStnN09E7J+vgVHfv7DwJ+0oRDzuesl01iP\nk3lA5DRNjwVBeS/pfT3EdHfbskx7kibAwaQzpJSTgxStFEZpUNo6gKEYWktpSQRSHOVtpDnyG+0M\nWjk8miumKRs7uGqMLkAW16om9awBqaKmy5S0NUY5rrJ3kuy9vBnNEkPMImBn1cUTx9syq22uPPN+\n2ipSklOUVRv6/dNMMYsBKyfWWIcrBrQ9eIvjmDCsk8aK2zffZefOLssbZ+jEIwIN/iBlmPVZXKiT\npSnJYZePPf1JGmtnuf3qK2y9/RreoG8DJwcetTBCa+iNu6ytX2T/3oCl5Tnm1pft3E0y9u5ucXUQ\n86Hzj3CmPkcmAnZef4MlP+IX/79f4HBxgZu9Qx566DzPPvMhxvGIZrNOluViIOFhhPXzIoWcmLzP\nAsnytfJzs9wg3G+8TgoC4oqt3DxniTmLe+7nrFQlGizKLvIvcMK9X5bJV82XKo6huF6OBVuFHVWi\nXbe+03Yg1nK1uB/m0YbKdblfet8AvHywdARUYsrwQ0o5Ae+iY4uYi5POzGVo8cSdY4AU0oY9Mtbx\nvR/4Ez++Nrr0ccu/oi4u6LplwpHVmFKKWq02NegwQ6boDHYVoBbvF06hXHn/0X1dECxTaRaYFi5o\n7rej2z4+vmiqxC9CCDD62CSeOdHMtLl6Oc9yPU6Sdc9K92MxPV9iUTx/TtivRsNcu03nsMf62ml+\n9Vd+g3ZzgfMPPUymDd/45V+jGaeEvsdhr0uzVidMFUn3kGh5hV7vHmnaJ82GJEbTrjVRKiX0ApJB\nj8AYxr0e49GIqF5j9dwFxnc22drZxIwTvnn3gLA1x5n1Z3n2Cz+M7Mf8ws//PKQpKhlz9/ZNfuZn\nf5qde3cJQquVYPlxkQupXY7wONFRNeeqUtlS9b1QgO5arXIHUQWy7r3y/WJtFfkUxlknuTS+X/sK\ng58yWBebS3m+HXG65th8nXAkpTaUN60jMeh0+1w116KtSh1tni7x59brvbh0fh9FKMflupWR5rWZ\nnHpP/UmZE1f5BDLge0cn7VpnE5AOghDIwyLlEaONduXp1QY1Rb2Kz/JEKazYXM2ZKkCdtdsXO7er\n2gd2QqdpOtNKrjzximQlDo6UsLRYqia65VyrZNLHKXgAydHEvR/Yzpr0Ve8pleXPlO/87iiyY28L\n2xcT5ttYPxq+7zMcxMy3F3nx+ZfYv3fAU49/lDGG/YMDlleWkbv7aD0gjRUDNUKNFFfefov6aMDC\nSotxssC+6qK1YqQzWvUmWWIQqSLuDqjJgOFej1pznquvvsGz5y/x7Cc+hsbgjxRhvcnOYo3Xe7uk\n3R6f/kNf5Mr2Fq//5q9Sq2mSeEQQeiRpjMz9nxiTcxCFKFEfH9fy2Ln9dGw8K+b1/UA8CIJK7an3\nOi5VADdr/ZVTua6z6g7VHk+L8socieurqMhXSjml4VRFhEzP62njt+LZMrEohHCiYx3Ve1Z4xJPS\n+wrgLngVyQVEAE/61hzYFLElcx8PUtpDCCFQWAbZN4rCYMkTkjAMc5DOD0tFzkgLkQe6PSrTndhl\n0CtMft0Yli7L406CMshW7ebF90Js5N6btDunBmZN6nIdi02tzMbdj6oVAmYYeU2VVd7Q3HbNUuHL\nHEqnnF91PapA/uRJfD/ZehFzsJxPHKc0621Uanjhey+ysrjKuzvbzM3Pc/udm0ilWF5dRsU+XuKx\ns7eLkD71VkirWSMQ0GrU2YpThFH4Gjwvw3ghB70u7TShtrBMrT3HwoU1/Myws3OI2N7j4cceZiFo\nEo9TzFKbyx94hNFhl91vvsHu1hZpMuYv/Pm/xKB3iBd6SM+zJueKfN7mQJArU5fnSVn8VT5/OYl6\nPYlLKlKWZZP5qYrwYiWAm7UxuHV1530ZPN9rKpfnrt+T1l1503HFokUdq7DJnffl5wvjqTKeTHy9\n5JKD4nohDnbX0f18n5TT+yoDL6uAuR1XPFMEEzWmoJjzs2KlrF6sBOkHJFkGQqK1IgxD0jjFN4U2\nS35YmgN4UbbgOHtXrmPxV7beKzrfrW9VKh90VgGvu/sbYyYe9Kqoh/JCcb3Cufrb7kFwEY8PjruO\ntZzPbHewRTpaYEX+xWIrjimqFp2NpFNok1jNoOMb1jQLOb35ldU9y0mIk9nMsjdjYwwID09CmmY8\n/+0X2Nvd44lHn+Qg8unf2yfrdGgEPj01ZnFuHvqC9bMNhhKaq6fY3dtjPpCcWllhpz7P4PCAXpzg\nBQ38KGDl3DnOPvYBRHuBu4eH+CureMt7DPe67HQ6bH7z25xZWOW5xz9M3WshDxJWo3keO/Mg/+JX\nfonPf+4zPPvMs3T7e4AlFgwCX0iE8dBGgTBWG4RqMC7PNyGOu5QoOJ7ikn2/ANfZQBI4gaLdgzc4\nvsEfH49qcUfVO/ebk+VUpn6rABiOPIO675WtuI2xYqIgCKbyKNaBC9zFBuZi71H/W2Kh6Fsvj61Z\n1M/Nt6ov7tfu9w3Ai4E/abcrnhNKoI1CiNxQQYDWtrFvX7/B5YcfIag3GA8OqUc1PD9A4KGShEat\nThLHVv1KCLyCHVLKHmbClOy9XH75upsKGVvx6VIQ5Y4vJsKsCexSBWXquxhsV/e7mECuu1R3YpX7\ntdgMC8B3KYgy11A1HpPFr6epqOJeVbsKXeJyv7j9c7QxTPv0fq/JzbuqDp7wcidnFqyEsLrURkg8\nJN9//gVMqunsH9K+fIadvR1WGnUykzHSmt1eh8gIMt/nwac/yJlLF3n5699jtL/DYX9Ic3GFODX4\naMJmm4XlJWLfYzQe01z0GSUZg3FC5nsMjcZTBoXmdmeP5c3bPHPuHEtRi348ZG59jR/53Gd5+oc/\nSlaE6VIpSimiWsNySoY8YrpC5m5Vq4CrTH3avplm8afGaopYMJxECJY5TleW646zm7f7vTxPXc+k\n7jhWEUaz5oZbZplSLlP2VRbZVcRSOS/Xe2hV3abPrKo3tfsldy3NcnnhpvfdkGfWQBcpyyxwF0FE\npRRIz7Mx6xB853vf4//8R/+YT/3gp/ncj3wakyqUkZBpamGNOEltp8h8oolC19yfiDBc8KzyqFYG\nNBdo3XpXAVMV5e3+LjYId7JVnXgniXXM40mPQoOmAGWRG3BYYDrurKlqsbl1KFtolhfDBHgBP9eL\nLYN41eSU3rSxkZtXVf2q+nhW3kWqWozT93Pd3QljIsBYz3Zvv3aFYX/Axvo5DvcOuLZ1HQ4HLId1\n/NDD9xscxockytBeWqW9fIreMGXj/EVWPvo0m3fvcvrRh3n1+e8jk4xs2CPwfITSjHf3WF07QzTO\nqGWGOAzpBT6hFOjQkPiCt/fvMP/uVeT1ZZYeuYh4aJ3z3UdYXFxGSkOaQa3eIAgD+oOxrb7tEMAg\n5FHAhvL4ueN4RFkflzO7/X10z66zWf1+EuHlluvWpVwvl8ssj3XVWnHLrEpl8HUJhTJR42p/HOdI\npvGoTJwV7a2yaq4ihtwy3st8djezKjwqp/dVhFKWW5U7AIqDCEBYxXdj7ILMtCaoNwiiGk8+9TR+\nFPGPf+afcubMBj/6mc+xONdGpakDgnmHYK27PI4s/zzPm1CnVVR4sRO6O6nWeuJTo2qAyru7S/mW\nWbDi0y2vDODWiCbXXcYBQ6YXSnGtAMmiHoUjK7c+7qQs6n2/xVlWmZo1maGQ0U7LKas4FftbTOKa\nGvsSiEKTfLYc3+VAqpLRGopysByEQKOV4rd/+6ssLy6zv7vH0sIyg6s3CQ0cej71Rh3jBzTrTQZa\nsHbpAeKxDXXVqtdpGkXmS05duMC1q+9wcPs2gdYMuh2iKESPBrR9j9OLc9SDEFGv0Q0lDIf0R0M6\nvmGofL7z2wfgCR5uB8j5BsqHU6c3OOjsoIR1tKR0ShhG9hBHmNywXKK1QZvj4FW1jqrEV+5GWh77\nKi5yMq4Ol+bOicr+r6D0CyKlTCGXlQruZ61dzrf8OQv0Z20E5TLKYO6+V5aju/UtlzWLKKxKbuSu\nKg2fY8+fePc/YkpzcC3AZRYLbGV3gCioDkmcpSwsLNAdjWi12ozSQ5ZWVlleWeHG9ev8rb/7d/mJ\nz3+Bjz3zLL70MSoDY6mwKploGWBsueLYdXcgpJQTh/TuxCuzUe4mUQXw7nMuR1Ck8uIoNppiAdTr\ndaev8skrBZ4np54vNpwqqqDMDRSgXN60bIWOL7hZi8podWxBuZuH2zbfC9CWvHRzOAY05VTeKI/V\nwdrgTaz0tDH4wuflV17h7u07PHTxIbyWT7/TZVkJxmTgCXo7uxgkLC0TbJwhmJvDJIJsqFi6eJr9\nuzd45fsvMu/VObt+mtH2PTyd0esd0ppbJ2j4xGZMPxvRu9sjjgfs7tylvt9BR4JxHdq0aA0VyStX\n0A+cR15Y5+Mf+SgvvvgiZ8+dttHO6xG9YZ8giPKzWGtVKlEgfYQ8zgVWUb0mP6eoAsYyoFgOtbK7\n7VjlIFMGTfdalR64C0yFF8kyten+uW1xtcFmzYMqyr14x73vyrvdze53u4GV5335vMvt5/JmMCvd\nj9Mop/cNwIOgOJm1B5THnVPZT19oUm1QKkNkCuEL/DAk6fQJAo+l8+u8u7fNqheQGMOHH36Upx76\nAK++9hpXrl3lx77wBRYX5vClRChF4PsYpZBZan2Na33kB8ErJmauO1wCc1f+5Ype3FR4Myzeq6Im\nyvExywNWJV4qfhdlu9S/++eyi9Lz8LycChdySjRTgGOxqIs2lr0xloHeKJNbtjKJBJ+3Nm+fW9/j\nVH6Zsp9MdnX8sNJyE9bPx6z5XK5vud+ksEENijHwvYjRSPOVf/MtTp26QL/TZ6FeZ39/k2atznjY\nQZgUz8/ItKDT3eHspbMMuoeMuyPOn11HJCP23r5GNBzzwr/9Kj/+4z/B3mIbNRCkPozHGZ3xDiZ6\ni26SEnfHREGApyTGC6n7AWkywpgxMYK93W2GuwfIxQaNxgLbt+9y6tIZZCYhyQjqEZmAUIGvvdy3\nh9WsKix23Y3vOLgW433Uv2XZ7/H5djLrXgal8ly9HyHjEiFlsCoICTdPV3tjVn3gOIfopvI6Ka5V\nle+2cRaglwG8StbtEiDuBlZF5VfV5/etCEWprAQc09Rs8aeSlMS37Hjk+SDtYvcTEJ7HmUvn+fK/\n+ypRnFJv1DnsdlGex/qpU7QXF/jpf/KzfOLjH+dHP/dZhp0uARLf85BG4UvITOGnTeT+JGxZRitr\n5ensiF6uzlVQD4WeNlQfglQNZAEkBZiWT6JdWXVhzCOlRClNlmZTeYHlZIq+OvKNfbSwXY4jUylZ\nluWgZ/C8gto/6u+i7DJ7bCeSmJThyqyn1SkdUDbHdWhnTcgsy6bKF8LV6T/ZKrCoQ5XoIIg80lHC\n8vISnc6ANFFsbx+gVAAyotWIGOzfI+7tkxpJvVYDHTPQCf0kYX79NHWjuXPtCr3+kGYIO1t3STfv\nsb4wRz8Z89orL9Bq1dja26V/2EelB/hBQC1qsLA0z9Z4hBeGRM0WWQbNhRbzPmTJkGE/JqtL6s0m\nKqwT+jX8VCOlRygDUDEmgBTrhdDXHgaByjWyPKcP3M/yHLT9f1xsVy2OgJPEVkU6icU/TtUfiU5m\nPV/mNqu4u7LqbhUX4BI6Vam8GVRRx+6G4yoNuOWV83c5WbfNVWH9ykScWzcXF3/filBcirt8yAC2\nIVmWIZUkA3zPy2WZGqUVSguUkiwvLZHFCaM0pu0vsr4xT9RosKEUg3jMD//QZ3n9tVf5G7/zU/yn\nP/mTPHz5MsNBn5rnE+eROIQnSVVqT/h965FMeB44VIQLzkkeAsoVdRRqhkW7ygMchiFw/IS8SFWD\nOcVOIibybze5vq2P+nGS6wRQi8lQr9cdKqgQk0xTRVrbCONFOgJVJtaiBVhWhbYrt6Gcj9u24neV\nV0F38zgp//Im4m6m3W6fRqPGzZt3aNTbRLUmL3z/qzzxwafYvnkHkSXc3ryLjyAA0JBmikZzHtnQ\nnDl3if3+kGFnwPryMru3bnHz2hWaKiPTKX6jxp3bt1laXKTX64JWJKMxUkr6nUNW1lfw2jXkXI1G\ntkgvSzAC1tdXGB4cIJRP2uvz0je/w4dO/wQ6U+zcusPNF17l8hMPM9KCIDWIwEP71rOeZyQmV/0U\npfa6/VcGt+pD3uMbqh2baqB18zlpQy5zrSeJBoq6F4RBGQDdTcidP+67RSo2ieKzqrwqnyNVG6BL\ndBSgXxWsuZqDObrnipyqrD3dOlRZqJ+U3tdDTJiW8Zbl4cYYfAKCvE3CntjgiwBhDJ6UhEgW5+a5\nuXWXx1bOs384QPZGjJKERqtJ4EU898zHMEbzz//lL/Lxj36UT//gp0jSBBmFCGMAjZTWFaXWdoMA\nAdIeGiqVIcWRv5YCNAuDhuIgrdhtC3ByzY2TJJk6oHEp3pP6p5hAWukJMJcnWfmwsrjuTm7brmyK\nCpLSw/OmfaUX77iHg1OiIDUtv3frWAYSl3pxx7RKrOKC+6TNJ7DDRer3+5P6lsVKAFG9TrfbY2l5\njSw1fP3ffYvdnT3qG3NcvHSRrRvXac0tMe4eoOIhsc7QGHrdAXOrawRhAz8esdCSRNqweecWYZLg\nSUEgJfUoItHKmsxHIVoZZL1GFIXEo7EV12lNFo/xPUmSxDYAsTCgEkb7h5iR4k6asfVvIj7x8U/y\no5/9PH/tf/6r/NQ/+b84vNenKTy8DIYBJMIQKm1tIDBgjm+e5bGsuleeZ9Op2qrYfcelmE+KYuXO\neaj2SV9+3q1XFTiW61UAf/HdFdOU14FbB3duuRyJ++cSZOV+rVo37txz532ZY3UJnzIHcD+xiZve\ndwAvy5fKjZHSAy/f8SQ2Sr0RCGlItMLLJJ/86Me5/uJrdPo9rF9en7lGRKPWIDOag8N9kizmU5/8\nNC+8+Dx3tjf54k/+QWphiNAZgbBGEjoZE+TBHxA+wvNByNwJfUkWbMzkULCgvpVSE4tNl0L1fX8i\nq3UpkVksnHsCfWStiQ37pI+iC7mp+F1QyGX2FY78uBTPFODvsovFJCu4jHIdpZimlMuT3i03U9Mi\nlOLQ100TUVnJarPI634sZHGIW5Y/FotrnI6pN5qMhjHJWPGdbz/P+fOX0UoTJwmb9+5Rb7WIfEk9\nrbN/cMAw0Yhak9byKbb3+/QGQ85vbCCTMSIeExmF0IJhv0c/GSKjAKMNZ9dPMdCHZJjcDaKif3iI\nTBRZZqxaZaYxCrbu3uNzX/gM7165BkoybkREa8vc7uxx4aEP8vADD9GoNag1Gsg0I5CS1DNkwh5S\nB7nqqMdsgCtzefcTiUw/N/vZYq65FpjlNFMz6QQKvKhzFXiX53M5H3eeuBxHFWVdntNV3HDx54o/\n3LaWtXfca+XDy6r+cQmUqvv3o7yL9L7qgbtilDKrVHxmRqFyB72uu0jP95BoakgubJzlW1/9Gp87\ndZpOt0s9jOz7aUo6HtMMQhpRyFiPefrpp9na3uJv/u9/m//ki1/kuQ8/xbCzh6czWvWINIkt8PpF\nJA+7YATiWF3LwFU+WS9YL1fmVh6YYiDhaEEUeqrTlCj5uer0gJeNIKrqCHZyFZOxsKRzxRhunu4E\nLC9SrfQUBVNlNTppv5lebC77WWZBi03PDb7gUiwnsepuHscoqNBDpZrQq3HlnXdZmF9iZWkZreDm\n9esMhwOEFMw1W/gZzIc+jBLqC0t4zTn27m4y325jjOLGtStInRJFPnGSoZXi8KBLKgxhGLK6uESj\n2aQXp8TjGL8eMej2qC2ust3rsrRxirlTp/H7Yxqex6kzF7lw/kF8GfLa7Vt84EPPcGvzLkko+eBz\nzyCiBqNxTK3dJuv1ERp0CGMkNS0QBlQFzpap2N8tiNu+u/+z7jwpJ9das2pt36/8k8QHVdxalQbZ\nLO6j6rCxStXPJbSmuNB8nlaJgN3+mNV/xdx2KXiX8HDbcL/0PlLgHraurvhkWnZcgJOPzGPp5Q01\nhsQY/ChEZIbTy6uYmo8wCaiELDFIIAxCVk6t0+n3kKEkiJbojQesraxw+dGn+bVf/RUOD/b4zKc+\nQSgMo2HfWrlpG7Xayw82rbN3gUJPgUMURXlrHEDNlN1scsrbpQaOg3L1qfZ4PJ7I04Ep9qtMlfoV\ncvGCglJK5Sw2WNNra7lXuAbxZM4tGFWpYlhssNMTVOP5wdSpetkXxmQi6+Pm8AXQFg6RiucnboId\nHxtlJ0BVqegXpdRENXWqLtpgMkEg4caVG5xZO824P2BpaYleZx9pFKPhmFAYAk8RC0l9aYnLTzzF\nIMlYzDSB0XS6hyTJCJMkBKFPEISMsiTfGBNqQUCqFSgIowjf9+n2e+zvaZ77yMfobt/l9GOPYmoN\netfuoOOMl15+nQtnzrAyv8Tjlz/AamuZ1WfPcOPdG3zgE89x/c23CKOAzYN95j0fmYIRkPiKWmYj\n7Bj/+EGay2HdD0yqxB82mMhsaHCtqKFaa8rVxnLHqTzHqqjUYs5U1f1+FGtRRtUhYRXl7eZ5Ehfj\nrrmyqLJ4tpC9z6qbm1IndoH7eT+Os5zeNwCH476wy5NMCBsE1UNYdTABwggkgkxYK81QeCRaEbQa\n7Nzbot1qk8YxUa3BcDig0+kQ1Wu0ghadww79YR+kIPVq/Minf4i333qDv//3/wF/7I/8YU6treAJ\nGA36RLUa43hswcYPchn09A5bNXGFEFbNjmnqM03TKQ0T9z2X4nBFL7NOud2Du7KBjhtyrninyF9p\ndSxfw7SbSxdkq8bEXYjFvWKxVlErLqC69S76owzaLrVefJ+1MIApTaByP/i+T6oT6vU6Uvtcf/sa\nly4+xGg4pLe/izQJq0ttzDhk1OvTNzEJknPnLtFYXKK3t8/Djz1C3RO89M2voY0irEfESUo9lOjU\n4NcivMTQWpgjVhn97oBWEFGLIuZ9jyCKkEKysXGWGFBhxOHw/2fuzYMsy+76zs85d39rvpd7bV3V\n1VW9V1f1pl0CWwIZiUUwgwwOzLAMWCwTJsYT4LGDiImZMbZngnHMGAwzDkAgIxAIkAwSIEC7hJaW\n1Gt1d+1b7svb737P/HHz5jvv5suWYDzRnIiMzLzvvnvP8ju/8/19z+/8fgFZprh2+w7dXo/HHzmP\na0ria9c4duFBEstktb/LAyfuZntng9Dx8qw8SUYWJ8QC0iTLU4GVUp8V/VuWy7KC0/u2XITYC1lx\nSCnvd0wLDnWYxVQGLtOokWmWQ7kd5XJY/PEyki2/T1/kCipP/265HdMWnGlK97C664p6mlVy2N+H\nlVc9oUMxoIVZcaCzTQlZLlDKEJiZQCqxlwdP4GSS1DSYO3aEtbU1Fs4tEEYRW51dwiCiWquRotjc\n3iUIQ6q1GrWax64/Ymu3y733nMGUZ/mlX/m/+cmfeA+uZbK8vEC/s0uzOUMU+PkRcm1Ff6VOTrMM\nlR48SakrmWmKWleWBZIsm3BF/5RRffHcw0zHcZ3F1JjjhblbRhPl03Ll+hTtL9D0NOWgt1sf67LX\nSZlSKzaBi3ceFo9Gpwv0TdMwDPPJZ2RkoeJzH/84rWaLF55+lhPHj/HylYtYhsKouLSrDertFrcG\nOyzOLXHPfQ+wNRjRG/kszM/SW79NHAXML8wx2O0SRCmpH4BhMgp9KjMzzC4u0dnYwnIcvEqdmUYT\ny7LY7uyydvEK8/fdw9ZuH8+wcBwbI0lJSBgRcWNrhSPeCRbaVdrtGWaGXQhjNm/eoVKvsjPoYVVt\nlCExhMSREmlCmhyM4ldWOtOUwGE+85Myc7jy0GXhMESsy6j+vWnz6DD0O81q+JtYEsWzykp7Wv8U\nddLBVbGnNa1e+sKgy3z53sOuTbMCvt73p5VXVYFPowR04ZBSkigQKrf6i+zRmcoQpoklJMLP3eCO\n3X0Xz33gLzlx8iRRmlKdmaHleEjDxDBM0iSjtedXLA3B0fkqi+0WW1s7hEnCo4+9hj/40J/w2IXz\nVOt1LMclDAPiKMC1vDz3nsbJlhVaUQyZx2wpFEnZ/ahAltOQcnnXvDzgZaSr96XuTlhQOuNUaXto\nXZoTdc+yLE/CyqTgFkq5PFbl8StKEWe9fF2fLEUdy0i/aGN5AurIbhrdVJSyJ0shN/vcvJkiYos/\n+9M/5bGHnmRupsWws4uRxvjDHmZoM9rapO5VSV2PhaUjuF6V7eu3qdUrRIHPzWtXyeIQ07LxKjWi\nKCPwuygzRZgGi0eWCZKYzJA4rsPm7i5JnLC8vEymFP2VNU6dOU3omDQrMyQLswTb22Sk+PGIyzde\n4tmLX+PsyhW+c36OumGTCbhz6yrtSpXmbIMokwyJEAKsMCUoKIbs66O/shLT0ep0hZj7jB+mLMub\n0WV6RAixv99SdgGd5laqK87DrIOylVUu03jt4ntlUDjNw2aap0kZWU97xzT6pVx0i6Vcr3L9vt7i\nWy5fV4ELIX4NeAewoZR6eO9aG/hd4C7gOvC9SqnO3mf/HPhhIAX+O6XUn097bhFPu0Bakx4XYwUu\npYGVKhKRkbC3eZBBIpI8QWgKwpTcffYMf3LzN+j7Pl6ljrRdjEoF07S5c2uVYX+AFIKaV2Gm0aRZ\nsxGWweL997HTGzG/dJTFI8f5xKc/jldxuOeuY5CEuLYkTnLyUbcasizb9+0GXZGA2FMg+uAUnxeJ\nGsobQOWDB4XwF26L0+iEct+NBUSR/5mhx78Iw3BisqVpnifU9ewDKF+PvKZPTP09Oj94GNIp5wPU\nPXf0xarwkCmjL91KmVbK/LvuUimEIEpGrKysUK9UsQwT0/O4fOkGUiYYKsE2baIwZNTHoIa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1y8chlzqcXWZgcjAykhSALe9oY38ofPPMdxWxDtdBmtbnH+sXv5zPZnqM+7nDtxD8P5Rf78S1/k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b9V2kEn/LVRlfCHm4HigqWxpN25ZTPfBB6aa2Shz9LGXRqAbLnFg+itBzKDyLY53z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fXVV/mVf/dLHB0d8a//1b9iZWWFH/6hH6Lb7dLr9Th//jz9bo9WENJtdxCVvm/nHnqExx57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\nYAMcO3qUM6dOc2xnh9lsJnn11Eoj1NYWR49sybXHk0vb1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9jY2KDIpBnD1Q6V97PxCF3m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\nwNe2trWt7QVq/w/Uvjt8hhUJzgAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x3f5b2d0>"
+        "<matplotlib.figure.Figure at 0x116bcb210>"
        ]
       }
      ],
@@ -797,19 +885,19 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "scores: [ 0.93699419 -0.65612102 -1.32907355]\n"
+        "scores: [ 0.86610985 -0.70051557 -1.34796357]\n"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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3uHXnNs/fvI1d1szOS8x0QuVbgm1YPHnMVpHTtseMsjF/+xf/Dn/53ofMhaCY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Qmmqk1kxWQ32OTm9QucvL2Fa1foDnyYrrFRE6iNFWTosrsZYnsW//CHfozHH/84RxcWeN1D59gY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UUnmRd4ZNdVWoOL4sQzctLicTojjj2edVuHSel3iujaDF0ARNpWh7bSuVa6RuECcpjmXw+//8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\n2G8So0PJ84UBUFSEwrElrR+5PVkYbZFrDRhL/fjKwliDq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w7PUhxMaYuqqRUnLj+nUCMByOGK6N2NjaYDQa8elnn6G1xnrPlhCMJ2eoPPKE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W6Ho01Mx7k+XnVkV+t3SCYqsljKn0N1IirrMAUn5rlqgjX9XHPiMIMFTBk9Smb176Zcuv4ryPPp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wenNsjbnpcteZYHjsvsSnvQ2N63I73aBJkm6Ge5gS0tZwG2//+gtcCoL8R7bNYSxS5SGPqx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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x5a3a290>"
+        "<matplotlib.figure.Figure at 0x114f15d10>"
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -842,4 +930,4 @@
    "metadata": {}
   }
  ]
-}
+}
\ No newline at end of file
diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
index 8e068805d62..0bfdb5caf68 100644
--- a/examples/filter_visualization.ipynb
+++ b/examples/filter_visualization.ipynb
@@ -4,7 +4,7 @@
   "example_name": "Filter visualization",
   "include_in_docs": true,
   "priority": 2,
-  "signature": "sha256:526501b358e0f60c489eaf5799e8603a75019cc65401533baa307b5603fdefa1"
+  "signature": "sha256:44536e4f82eb5748b6a3bb6fcfca01bc6c5815dad2641c994dab031f452b7606"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -62,14 +62,13 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "caffe.set_phase_test()\n",
       "caffe.set_mode_cpu()\n",
       "net = caffe.Classifier(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n",
       "                       caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
       "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n",
-      "net.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy'))  # ImageNet mean\n",
-      "net.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]\n",
-      "net.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB"
+      "net.transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1))  # ImageNet mean\n",
+      "net.transformer.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]\n",
+      "net.transformer.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB"
      ],
      "language": "python",
      "metadata": {},
@@ -214,7 +213,7 @@
      "collapsed": false,
      "input": [
       "# index four is the center crop\n",
-      "plt.imshow(net.deprocess('data', net.blobs['data'].data[4]))"
+      "plt.imshow(net.transformer.deprocess('data', net.blobs['data'].data[4]))"
      ],
      "language": "python",
      "metadata": {},
diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index 41384f9475b..c2dd62ec963 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -67,7 +67,7 @@ We will also lay out a protocol buffer for running the solver. Let's make a few
 * We will run in batches of 256, and run a total of 450,000 iterations (about 90 epochs).
 * For every 1,000 iterations, we test the learned net on the validation data.
 * We set the initial learning rate to 0.01, and decrease it every 100,000 iterations (about 20 epochs).
-* Information will be displayed every 20 epochs.
+* Information will be displayed every 20 iterations.
 * The network will be trained with momentum 0.9 and a weight decay of 0.0005.
 * For every 10,000 iterations, we will take a snapshot of the current status.
 
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index a4fb4a66065..2932687da6a 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -4,7 +4,7 @@
   "example_name": "Editing model parameters",
   "include_in_docs": true,
   "priority": 5,
-  "signature": "sha256:bf84bcbd78fe007310f86d71c5969ba4205b6e06f408029860eec94821844bee"
+  "signature": "sha256:811097f2151652d2b630c016a5f1de23bd824df3dfcfc72aa0aeb23b2d9686c0"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -28,7 +28,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "!diff imagenet/imagenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt"
+      "!diff imagenet/bvlc_caffenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt"
      ],
      "language": "python",
      "metadata": {},
@@ -146,7 +146,9 @@
       "import caffe\n",
       "\n",
       "# Load the original network and extract the fully-connected layers' parameters.\n",
-      "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
+      "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', \n",
+      "                '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel', \n",
+      "                caffe.TEST)\n",
       "params = ['fc6', 'fc7', 'fc8']\n",
       "# fc_params = {name: (weights, biases)}\n",
       "fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}\n",
@@ -181,7 +183,9 @@
      "collapsed": false,
      "input": [
       "# Load the fully-convolutional network to transplant the parameters.\n",
-      "net_full_conv = caffe.Net('imagenet/bvlc_caffenet_full_conv.prototxt', '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
+      "net_full_conv = caffe.Net('imagenet/bvlc_caffenet_full_conv.prototxt', \n",
+      "                          '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
+      "                          caffe.TEST)\n",
       "params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']\n",
       "# conv_params = {name: (weights, biases)}\n",
       "conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}\n",
@@ -279,19 +283,19 @@
       "import matplotlib.pyplot as plt\n",
       "%matplotlib inline\n",
       "\n",
-      "caffe.set_phase_test()\n",
-      "\n",
       "# load input and configure preprocessing\n",
       "im = caffe.io.load_image('images/cat.jpg')\n",
-      "net_full_conv.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy'))\n",
-      "net_full_conv.set_channel_swap('data', (2,1,0))\n",
-      "net_full_conv.set_raw_scale('data', 255.0)\n",
+      "transformer = caffe.io.Transformer({'data': net_full_conv.blobs['data'].data.shape})\n",
+      "transformer.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1))\n",
+      "transformer.set_transpose('data', (2,0,1))\n",
+      "transformer.set_channel_swap('data', (2,1,0))\n",
+      "transformer.set_raw_scale('data', 255.0)\n",
       "# make classification map by forward and print prediction indices at each location\n",
-      "out = net_full_conv.forward_all(data=np.asarray([net_full_conv.preprocess('data', im)]))\n",
+      "out = net_full_conv.forward_all(data=np.asarray([transformer.preprocess('data', im)]))\n",
       "print out['prob'][0].argmax(axis=0)\n",
       "# show net input and confidence map (probability of the top prediction at each location)\n",
       "plt.subplot(1, 2, 1)\n",
-      "plt.imshow(net_full_conv.deprocess('data', net_full_conv.blobs['data'].data[0]))\n",
+      "plt.imshow(transformer.deprocess('data', net_full_conv.blobs['data'].data[0]))\n",
       "plt.subplot(1, 2, 2)\n",
       "plt.imshow(out['prob'][0].max(axis=0))"
      ],
diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 5fc4ed3a3e1..890673cd7e6 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -104,8 +104,6 @@ class Caffe {
     return *singleton_;
   }
   enum Brew { CPU, GPU };
-  enum Phase { TRAIN, TEST };
-
 
   // This random number generator facade hides boost and CUDA rng
   // implementation from one another (for cross-platform compatibility).
@@ -137,16 +135,12 @@ class Caffe {
 
   // Returns the mode: running on CPU or GPU.
   inline static Brew mode() { return Get().mode_; }
-  // Returns the phase: TRAIN or TEST.
-  inline static Phase phase() { return Get().phase_; }
   // The setters for the variables
   // Sets the mode. It is recommended that you don't change the mode halfway
   // into the program since that may cause allocation of pinned memory being
   // freed in a non-pinned way, which may cause problems - I haven't verified
   // it personally but better to note it here in the header file.
   inline static void set_mode(Brew mode) { Get().mode_ = mode; }
-  // Sets the phase.
-  inline static void set_phase(Phase phase) { Get().phase_ = phase; }
   // Sets the random seed of both boost and curand
   static void set_random_seed(const unsigned int seed);
   // Sets the device. Since we have cublas and curand stuff, set device also
@@ -163,7 +157,6 @@ class Caffe {
   shared_ptr<RNG> random_generator_;
 
   Brew mode_;
-  Phase phase_;
   static shared_ptr<Caffe> singleton_;
 
  private:
diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index e0d5a8aca46..1f154408c27 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -14,6 +14,7 @@
 #include "caffe/filler.hpp"
 #include "caffe/internal_thread.hpp"
 #include "caffe/layer.hpp"
+#include "caffe/net.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/db.hpp"
 
@@ -47,8 +48,7 @@ class BaseDataLayer : public Layer<Dtype> {
 
  protected:
   TransformationParameter transform_param_;
-  DataTransformer<Dtype> data_transformer_;
-  Caffe::Phase phase_;
+  shared_ptr<DataTransformer<Dtype> > data_transformer_;
   bool output_labels_;
 };
 
diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index 60696c96dcc..880356601a4 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -16,7 +16,7 @@ namespace caffe {
 template <typename Dtype>
 class DataTransformer {
  public:
-  explicit DataTransformer(const TransformationParameter& param);
+  explicit DataTransformer(const TransformationParameter& param, Phase phase);
   virtual ~DataTransformer() {}
 
   /**
@@ -104,7 +104,7 @@ class DataTransformer {
 
 
   shared_ptr<Caffe::RNG> rng_;
-  Caffe::Phase phase_;
+  Phase phase_;
   Blob<Dtype> data_mean_;
   vector<Dtype> mean_values_;
 };
diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index c6461c19964..34e00d72c05 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -33,7 +33,8 @@ class Layer {
    */
   explicit Layer(const LayerParameter& param)
     : layer_param_(param) {
-      // The only thing we do is to copy blobs if there are any.
+      // Set phase and copy blobs (if there are any).
+      phase_ = param.phase();
       if (layer_param_.blobs_size() > 0) {
         blobs_.resize(layer_param_.blobs_size());
         for (int i = 0; i < layer_param_.blobs_size(); ++i) {
@@ -288,6 +289,8 @@ class Layer {
  protected:
   /** The protobuf that stores the layer parameters */
   LayerParameter layer_param_;
+  /** The phase: TRAIN or TEST */
+  Phase phase_;
   /** The vector that stores the learnable parameters as a set of blobs. */
   vector<shared_ptr<Blob<Dtype> > > blobs_;
   /** Vector indicating whether to compute the diff of each param blob. */
diff --git a/include/caffe/layer_factory.hpp b/include/caffe/layer_factory.hpp
index ede5d1fae3f..2fcd93869a0 100644
--- a/include/caffe/layer_factory.hpp
+++ b/include/caffe/layer_factory.hpp
@@ -53,7 +53,7 @@ class Layer;
 template <typename Dtype>
 class LayerRegistry {
  public:
-  typedef Layer<Dtype>* (*Creator)(const LayerParameter&);
+  typedef shared_ptr<Layer<Dtype> > (*Creator)(const LayerParameter&);
   typedef std::map<string, Creator> CreatorRegistry;
 
   static CreatorRegistry& Registry() {
@@ -70,7 +70,7 @@ class LayerRegistry {
   }
 
   // Get a layer using a LayerParameter.
-  static Layer<Dtype>* CreateLayer(const LayerParameter& param) {
+  static shared_ptr<Layer<Dtype> > CreateLayer(const LayerParameter& param) {
     LOG(INFO) << "Creating layer " << param.name();
     const string& type = param.type();
     CreatorRegistry& registry = Registry();
@@ -103,7 +103,7 @@ template <typename Dtype>
 class LayerRegisterer {
  public:
   LayerRegisterer(const string& type,
-                  Layer<Dtype>* (*creator)(const LayerParameter&)) {
+                  shared_ptr<Layer<Dtype> > (*creator)(const LayerParameter&)) {
     // LOG(INFO) << "Registering layer type: " << type;
     LayerRegistry<Dtype>::AddCreator(type, creator);
   }
@@ -116,8 +116,9 @@ class LayerRegisterer {
 
 #define REGISTER_LAYER_CLASS(type)                                             \
   template <typename Dtype>                                                    \
-  Layer<Dtype>* Creator_##type##Layer(const LayerParameter& param) {           \
-    return new type##Layer<Dtype>(param);                                      \
+  shared_ptr<Layer<Dtype> > Creator_##type##Layer(const LayerParameter& param) \
+  {                                                                            \
+    return shared_ptr<Layer<Dtype> >(new type##Layer<Dtype>(param));           \
   }                                                                            \
   REGISTER_LAYER_CREATOR(type, Creator_##type##Layer)
 
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index ea52f56e0f3..36413ccd176 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -169,8 +169,8 @@ class ContrastiveLossLayer : public LossLayer<Dtype> {
 
   /**
    * @brief Computes the Contrastive error gradient w.r.t. the inputs.
-   * 
-   * Computes the gradients with respect to the two input vectors (bottom[0] and 
+   *
+   * Computes the gradients with respect to the two input vectors (bottom[0] and
    * bottom[1]), but not the similarity label (bottom[2]).
    *
    * @param top output Blob vector (length 1), providing the error gradient with
@@ -189,7 +189,7 @@ class ContrastiveLossLayer : public LossLayer<Dtype> {
    *      the features @f$a@f$; Backward fills their diff with
    *      gradients if propagate_down[0]
    *   -# @f$ (N \times C \times 1 \times 1) @f$
-   *      the features @f$b@f$; Backward fills their diff with gradients if 
+   *      the features @f$b@f$; Backward fills their diff with gradients if
    *      propagate_down[1]
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 23da5dbbf2d..075afebc9b0 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -24,7 +24,7 @@ template <typename Dtype>
 class Net {
  public:
   explicit Net(const NetParameter& param);
-  explicit Net(const string& param_file);
+  explicit Net(const string& param_file, Phase phase);
   virtual ~Net() {}
 
   /// @brief Initialize a network with a NetParameter.
@@ -115,6 +115,8 @@ class Net {
   inline const vector<shared_ptr<Layer<Dtype> > >& layers() const {
     return layers_;
   }
+  /// @brief returns the phase: TRAIN or TEST
+  inline Phase phase() const { return phase_; }
   /**
    * @brief returns the bottom vecs for each layer -- usually you won't
    *        need this unless you do per-layer checks such as gradients.
@@ -207,6 +209,10 @@ class Net {
   /// @brief Get misc parameters, e.g. the LR multiplier and weight decay.
   void GetLearningRateAndWeightDecay();
 
+  /// @brief The network name
+  string name_;
+  /// @brief The phase: TRAIN or TEST
+  Phase phase_;
   /// @brief Individual layers in the net
   vector<shared_ptr<Layer<Dtype> > > layers_;
   vector<string> layer_names_;
@@ -239,7 +245,6 @@ class Net {
   vector<int> net_output_blob_indices_;
   vector<Blob<Dtype>*> net_input_blobs_;
   vector<Blob<Dtype>*> net_output_blobs_;
-  string name_;
   /// The parameters in the network.
   vector<shared_ptr<Blob<Dtype> > > params_;
   /// the learning rate multipliers
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 46c84171b9c..0c306fb41bf 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -8,6 +8,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/layer.hpp"
+#include "caffe/net.hpp"
 #include "caffe/proto/caffe.pb.h"
 
 #define HDF5_DATA_DATASET_NAME "data"
diff --git a/include/caffe/python_layer.hpp b/include/caffe/python_layer.hpp
new file mode 100644
index 00000000000..816ef453720
--- /dev/null
+++ b/include/caffe/python_layer.hpp
@@ -0,0 +1,68 @@
+#ifndef CAFFE_PYTHON_LAYER_HPP_
+#define CAFFE_PYTHON_LAYER_HPP_
+
+#include <boost/python.hpp>
+#include <vector>
+
+#include "caffe/layer.hpp"
+
+namespace bp = boost::python;
+
+namespace caffe {
+
+template <typename Dtype>
+class PythonLayer : public Layer<Dtype> {
+ public:
+  PythonLayer(PyObject* self, const LayerParameter& param)
+      : Layer<Dtype>(param), self_(self) { }
+
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+    try {
+      bp::call_method<bp::object>(self_, "setup", bottom, top);
+    } catch (bp::error_already_set) {
+      PyErr_Print();
+      throw;
+    }
+  }
+
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+    try {
+      bp::call_method<bp::object>(self_, "reshape", bottom, top);
+    } catch (bp::error_already_set) {
+      PyErr_Print();
+      throw;
+    }
+  }
+
+  virtual inline const char* type() const { return "Python"; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+    try {
+      bp::call_method<bp::object>(self_, "forward", bottom, top);
+    } catch (bp::error_already_set) {
+      PyErr_Print();
+      throw;
+    }
+  }
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+    try {
+      bp::call_method<bp::object>(self_, "backward", top, propagate_down,
+          bottom);
+    } catch (bp::error_already_set) {
+      PyErr_Print();
+      throw;
+    }
+  }
+
+ private:
+  PyObject* self_;
+};
+
+}  // namespace caffe
+
+#endif
diff --git a/include/caffe/test/test_caffe_main.hpp b/include/caffe/test/test_caffe_main.hpp
index 438acf2bf17..bd5f31e063f 100644
--- a/include/caffe/test/test_caffe_main.hpp
+++ b/include/caffe/test/test_caffe_main.hpp
@@ -15,7 +15,7 @@ using std::cout;
 using std::endl;
 
 #ifdef CMAKE_BUILD
-  #include <cmake_test_defines.hpp.gen.cmake>
+  #include "caffe_config.h"
 #else
   #define CUDA_TEST_DEVICE -1
   #define CMAKE_SOURCE_DIR "src/"
diff --git a/include/caffe/util/device_alternate.hpp b/include/caffe/util/device_alternate.hpp
index 5a45691bb17..6ea595dba2d 100644
--- a/include/caffe/util/device_alternate.hpp
+++ b/include/caffe/util/device_alternate.hpp
@@ -7,7 +7,7 @@
 
 // Stub out GPU calls as unavailable.
 
-#define NO_GPU LOG(FATAL) << "CPU-only Mode: cannot make GPU call."
+#define NO_GPU LOG(FATAL) << "Cannot use GPU in CPU-only Caffe: check mode."
 
 #define STUB_GPU(classname) \
 template <typename Dtype> \
diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index d3ecf5875fa..f43036fcebc 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -12,7 +12,7 @@
 
 namespace caffe {
 
-// Decaf gemm provides a simpler interface to the gemm functions, with the
+// Caffe gemm provides a simpler interface to the gemm functions, with the
 // limitation that the data has to be contiguous in memory.
 template <typename Dtype>
 void caffe_cpu_gemm(const CBLAS_TRANSPOSE TransA,
diff --git a/matlab/CMakeLists.txt b/matlab/CMakeLists.txt
index f6a03ee4625..791a4e70f43 100644
--- a/matlab/CMakeLists.txt
+++ b/matlab/CMakeLists.txt
@@ -1 +1,72 @@
-project( Matlab )
\ No newline at end of file
+# Builds Matlab (or Octave) interface. In case of Matlab caffe must be
+# compield as shared library. Octave can link static or shared caffe library
+# To install octave run: sudo apt-get install liboctave-dev
+
+if(NOT BUILD_matlab)
+  return()
+endif()
+
+if(HAVE_MATLAB AND Octave_compiler)
+  set(build_using ${Matlab_build_mex_using})
+elseif(HAVE_MATLAB AND NOT Octave_compiler)
+  set(build_using "Matlab")
+elseif(NOT HAVE_MATLAB AND Octave_compiler)
+  set(build_using "Octave")
+else()
+  return()
+endif()
+
+if(NOT BUILD_SHARED_LIBS AND build_using MATCHES Matlab)
+  message(FATAL_ERROR "Matlab MEX interface (with default mex options file) can only be built if caffe is compiled as shared library. Please enable 'BUILD_SHARED_LIBS' in CMake. Aternativelly you can switch to Octave compiler.")
+endif()
+
+# helper function to set proper mex file extention
+function(caffe_fetch_and_set_proper_mexext mexfile_variable)
+  execute_process(COMMAND ${Matlab_mexext} OUTPUT_STRIP_TRAILING_WHITESPACE RESULT_VARIABLE res OUTPUT_VARIABLE ext)
+  if(res MATCHES 0)
+    get_filename_component(folder  ${${mexfile_variable}} PATH)
+    get_filename_component(name_we ${${mexfile_variable}} NAME_WE)
+    set(${mexfile_variable} ${folder}/${name_we}.${ext} PARENT_SCOPE)
+  endif()
+endfunction()
+
+# global settings
+file(GLOB Matlab_srcs caffe/matcaffe.cpp)
+set(Matlab_caffe_mex ${PROJECT_SOURCE_DIR}/matlab/caffe/caffe.mex)
+
+caffe_get_current_cflags(cflags)
+caffe_parse_linker_libs(Caffe_LINKER_LIBS folders libflags macos_frameworks)
+set(folders $<TARGET_LINKER_FILE_DIR:caffe> ${folders})
+
+# prepare linker flag lists
+string(REPLACE ";" ";-L" link_folders "-L${folders}")
+string(REPLACE ";" ":"  rpath_folders   "${folders}")
+
+if(build_using MATCHES "Matlab")
+  set(libflags -lcaffe${CAffe_POSTFIX} ${libflags}) # Matlab R2014a complans for -Wl,--whole-archive
+
+  caffe_fetch_and_set_proper_mexext(Matlab_caffe_mex)
+  add_custom_command(OUTPUT ${Matlab_caffe_mex} COMMAND ${Matlab_mex}
+      ARGS -output ${Matlab_caffe_mex} ${Matlab_srcs} ${cflags} ${link_folders} ${libflags}
+      DEPENDS caffe COMMENT "Building Matlab interface: ${Matlab_caffe_mex}" VERBATIM)
+  add_custom_target(matlab ALL DEPENDS ${Matlab_caffe_mex} SOURCES ${Matlab_srcs})
+
+elseif(build_using MATCHES "Octave")
+
+  if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
+    set(libflags -Wl,-force_load,$<TARGET_LINKER_FILE:caffe> ${libflags})
+  elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
+    set(libflags -Wl,--whole-archive -lcaffe${CAffe_POSTFIX} -Wl,--no-whole-archive ${libflags})
+  endif()
+
+  add_custom_command(OUTPUT ${Matlab_caffe_mex} COMMAND ${Octave_compiler}
+      ARGS --mex -o ${Matlab_caffe_mex} ${Matlab_srcs} ${cflags} ${link_folders} ${libflags} -Wl,-rpath,${rpath_folders}
+      DEPENDS caffe COMMENT "Building Octave interface: ${Matlab_caffe_mex}" VERBATIM)
+
+  add_custom_target(octave ALL DEPENDS ${Matlab_caffe_mex} SOURCES ${Matlab_srcs})
+endif()
+
+# ---[ Install
+file(GLOB mfiles caffe/*.m)
+install(FILES ${mfiles} ${Matlab_caffe_mex} DESTINATION matlab)
+
diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp
index fd8397e7ff2..996d3d2149c 100644
--- a/matlab/caffe/matcaffe.cpp
+++ b/matlab/caffe/matcaffe.cpp
@@ -254,14 +254,6 @@ static void set_mode_gpu(MEX_ARGS) {
   Caffe::set_mode(Caffe::GPU);
 }
 
-static void set_phase_train(MEX_ARGS) {
-  Caffe::set_phase(Caffe::TRAIN);
-}
-
-static void set_phase_test(MEX_ARGS) {
-  Caffe::set_phase(Caffe::TEST);
-}
-
 static void set_device(MEX_ARGS) {
   if (nrhs != 1) {
     ostringstream error_msg;
@@ -278,7 +270,7 @@ static void get_init_key(MEX_ARGS) {
 }
 
 static void init(MEX_ARGS) {
-  if (nrhs != 2) {
+  if (nrhs != 3) {
     ostringstream error_msg;
     error_msg << "Expected 2 arguments, got " << nrhs;
     mex_error(error_msg.str());
@@ -286,12 +278,23 @@ static void init(MEX_ARGS) {
 
   char* param_file = mxArrayToString(prhs[0]);
   char* model_file = mxArrayToString(prhs[1]);
+  char* phase_name = mxArrayToString(prhs[2]);
+
+  Phase phase;
+  if (strcmp(phase_name, "train") == 0) {
+      phase = TRAIN;
+  } else if (strcmp(phase_name, "test") == 0) {
+      phase = TEST;
+  } else {
+    mex_error("Unknown phase.");
+  }
 
-  net_.reset(new Net<float>(string(param_file)));
+  net_.reset(new Net<float>(string(param_file), phase));
   net_->CopyTrainedLayersFrom(string(model_file));
 
   mxFree(param_file);
   mxFree(model_file);
+  mxFree(phase_name);
 
   init_key = random();  // NOLINT(caffe/random_fn)
 
@@ -377,8 +380,6 @@ static handler_registry handlers[] = {
   { "is_initialized",     is_initialized  },
   { "set_mode_cpu",       set_mode_cpu    },
   { "set_mode_gpu",       set_mode_gpu    },
-  { "set_phase_train",    set_phase_train },
-  { "set_phase_test",     set_phase_test  },
   { "set_device",         set_device      },
   { "get_weights",        get_weights     },
   { "get_init_key",       get_init_key    },
diff --git a/matlab/caffe/matcaffe_init.m b/matlab/caffe/matcaffe_init.m
index 7cc6935758e..5d0a0a70bde 100644
--- a/matlab/caffe/matcaffe_init.m
+++ b/matlab/caffe/matcaffe_init.m
@@ -25,7 +25,8 @@ function  matcaffe_init(use_gpu, model_def_file, model_file)
     % NOTE: you'll have to get network definition
     error('You need the network prototxt definition');
   end
-  caffe('init', model_def_file, model_file)
+  % load network in TEST phase
+  caffe('init', model_def_file, model_file, 'test')
 end
 fprintf('Done with init\n');
 
@@ -38,7 +39,3 @@ function  matcaffe_init(use_gpu, model_def_file, model_file)
   caffe('set_mode_cpu');
 end
 fprintf('Done with set_mode\n');
-
-% put into test mode
-caffe('set_phase_test');
-fprintf('Done with set_phase_test\n');
diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index 6470517d213..6afed4fa183 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -1,58 +1,30 @@
-project( Python )
-
-#    Python
-find_package(PythonLibs 2.7 REQUIRED)
-
-#   Numpy
-find_package(NumPy REQUIRED)
-
-#    Boost.Python
-find_package(Boost 1.46 COMPONENTS python REQUIRED)
-
-
-
-#In case you have both python2 and python3 installed the quickest way to
-#compile pycaffe with cmake is to replace the following hardcoded paths.
-#Althernativley the Find* scripts could be rewritten to support choice of
-#of python version.
-#if(${PYTHONLIBS_VERSION_STRING} MATCHES "^[3-9]+\\.[0-9]+(\\.[0-9]+.*)?$") 
-#    
-#    set( PYTHON_INCLUDE_DIRS "/usr/include/python2.7")
-#    set( PYTHON_LIBRARIES    "/usr/lib64/libpython2.7.so")
-#    set( NUMPY_INCLUDE_DIRS  "/usr/lib64/python2.7/site-packages/numpy/core/include/")
-#    set( PYTHON_LIBRARIES    "/usr/lib64/python2.7/site-packages/numpy/lib/")
-#    set(Boost_LIBRARIES      "/usr/lib64/libboost_python-2.7-mt.so")
-#
-#    message( "Warning: cmake found python3 by default, switching to hardcoded paths")
-#
-#    message( "PYTHON_INCLUDE_DIRS =/usr/include/python2.7")
-#    message( "PYTHON_LIBRARIES    =/usr/lib64/libpython2.7.so")
-#    message( "NUMPY_INCLUDE_DIRS  =/usr/lib64/python2.7/site-packages/numpy/core/include/")
-#    message( "PYTHON_LIBRARIES    =/usr/lib64/python2.7/site-packages/numpy/lib/")
-#    message( "Boost_LIBRARIES     =/usr/lib64/libboost_python-2.7-mt.so")
-#endif()
-
-
-include_directories(${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIRS}
-    ${Boost_INCLUDE_DIRS})
-
-file(GLOB_RECURSE Python_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
-
-add_library(pycaffe SHARED ${Python_SOURCES})
-
-add_dependencies(pycaffe protoPy)
-
-target_link_libraries(pycaffe ${CAFFE_STATIC_LINK} ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
-
-set_target_properties(pycaffe PROPERTIES PREFIX "")
-set_target_properties(pycaffe PROPERTIES OUTPUT_NAME "_caffe")
-
-###    Install    #############################################################
-
+if(NOT HAVE_PYTHON)
+  message(STATUS "Python interface is disabled or not all required dependecies found. Building without it...")
+  return()
+endif()
+
+include_directories(${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIR} ${Boost_INCLUDE_DIRS})
+file(GLOB_RECURSE python_srcs ${PROJECT_SOURCE_DIR}/python/*.cpp)
+
+add_library(pycaffe SHARED ${python_srcs})
+target_link_libraries(pycaffe ${Caffe_LINK} ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
+set_target_properties(pycaffe PROPERTIES PREFIX "" OUTPUT_NAME "_caffe")
+caffe_default_properties(pycaffe)
+
+if(UNIX OR APPLE)
+    set(__linkname "${PROJECT_SOURCE_DIR}/python/caffe/_caffe.so")
+    add_custom_command(TARGET pycaffe POST_BUILD
+                       COMMAND ln -sf $<TARGET_LINKER_FILE:pycaffe> "${__linkname}"
+                       COMMAND ${CMAKE_COMMAND} -E make_directory ${PROJECT_SOURCE_DIR}/python/caffe/proto
+                       COMMAND touch ${PROJECT_SOURCE_DIR}/python/caffe/proto/__init__.py
+                       COMMAND cp ${proto_gen_folder}/*.py ${PROJECT_SOURCE_DIR}/python/caffe/proto/
+                       COMMENT "Creating symlink ${__linkname} -> ${PROJECT_BINARY_DIR}/lib/_caffe${CAffe_POSTFIX}.so")
+endif()
+
+# ---[ Install
+file(GLOB files *.py requirements.txt)
+install(FILES  ${files} DESTINATION python)
 install(DIRECTORY caffe DESTINATION python)
-install(FILES requirements.txt DESTINATION python)
-
-#This installs a library named "libpycaffe.so"
 install(TARGETS pycaffe DESTINATION python/caffe)
 
 
diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index acdfc071032..37e8956da4f 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -1,6 +1,6 @@
 from .pycaffe import Net, SGDSolver
-from ._caffe import set_mode_cpu, set_mode_gpu, set_device, \
-    set_phase_train, set_phase_test
+from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver
+from .proto.caffe_pb2 import TRAIN, TEST
 from .classifier import Classifier
 from .detector import Detector
 import io
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index e576eebcf49..a5d0e64605e 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -1,19 +1,20 @@
-// pycaffe provides a wrapper of the caffe::Net class as well as some
-// caffe::Caffe functions so that one could easily call it from Python.
-// Note that for Python, we will simply use float as the data type.
-
 #include <Python.h>  // NOLINT(build/include_alpha)
 
+// Produce deprecation warnings (needs to come before arrayobject.h inclusion).
+#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
+
 #include <boost/make_shared.hpp>
+#include <boost/python.hpp>
 #include <boost/python/suite/indexing/vector_indexing_suite.hpp>
+#include <numpy/arrayobject.h>
 
 // these need to be included after boost on OS X
 #include <string>  // NOLINT(build/include_order)
 #include <vector>  // NOLINT(build/include_order)
 #include <fstream>  // NOLINT
 
-#include "_caffe.hpp"
 #include "caffe/caffe.hpp"
+#include "caffe/python_layer.hpp"
 
 // Temporary solution for numpy < 1.7 versions: old macro, no promises.
 // You're strongly advised to upgrade to >= 1.7.
@@ -22,12 +23,22 @@
 #define PyArray_SetBaseObject(arr, x) (PyArray_BASE(arr) = (x))
 #endif
 
+namespace bp = boost::python;
+
 namespace caffe {
 
-// for convenience, check that input files can be opened, and raise an
+// For Python, for now, we'll just always use float as the type.
+typedef float Dtype;
+const int NPY_DTYPE = NPY_FLOAT32;
+
+// Selecting mode.
+void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
+void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }
+
+// For convenience, check that input files can be opened, and raise an
 // exception that boost will send to Python if not (caffe could still crash
 // later if the input files are disturbed before they are actually used, but
-// this saves frustration in most cases)
+// this saves frustration in most cases).
 static void CheckFile(const string& filename) {
     std::ifstream f(filename.c_str());
     if (!f.good()) {
@@ -37,42 +48,7 @@ static void CheckFile(const string& filename) {
     f.close();
 }
 
-bp::object PyBlobWrap::get_data() {
-  npy_intp dims[] = {num(), channels(), height(), width()};
-
-  PyObject *obj = PyArray_SimpleNewFromData(4, dims, NPY_FLOAT32,
-                                            blob_->mutable_cpu_data());
-  PyArray_SetBaseObject(reinterpret_cast<PyArrayObject *>(obj), self_);
-  Py_INCREF(self_);
-  bp::handle<> h(obj);
-
-  return bp::object(h);
-}
-
-bp::object PyBlobWrap::get_diff() {
-  npy_intp dims[] = {num(), channels(), height(), width()};
-
-  PyObject *obj = PyArray_SimpleNewFromData(4, dims, NPY_FLOAT32,
-                                            blob_->mutable_cpu_diff());
-  PyArray_SetBaseObject(reinterpret_cast<PyArrayObject *>(obj), self_);
-  Py_INCREF(self_);
-  bp::handle<> h(obj);
-
-  return bp::object(h);
-}
-
-PyNet::PyNet(string param_file, string pretrained_param_file) {
-  Init(param_file);
-  CheckFile(pretrained_param_file);
-  net_->CopyTrainedLayersFrom(pretrained_param_file);
-}
-
-void PyNet::Init(string param_file) {
-  CheckFile(param_file);
-  net_.reset(new Net<float>(param_file));
-}
-
-void PyNet::check_contiguous_array(PyArrayObject* arr, string name,
+void CheckContiguousArray(PyArrayObject* arr, string name,
     int channels, int height, int width) {
   if (!(PyArray_FLAGS(arr) & NPY_ARRAY_C_CONTIGUOUS)) {
     throw std::runtime_error(name + " must be C contiguous");
@@ -94,10 +70,39 @@ void PyNet::check_contiguous_array(PyArrayObject* arr, string name,
   }
 }
 
-void PyNet::set_input_arrays(bp::object data_obj, bp::object labels_obj) {
+// Net constructor for passing phase as int
+shared_ptr<Net<Dtype> > Net_Init(
+    string param_file, int phase) {
+  CheckFile(param_file);
+
+  shared_ptr<Net<Dtype> > net(new Net<Dtype>(param_file,
+      static_cast<Phase>(phase)));
+  return net;
+}
+
+// Net construct-and-load convenience constructor
+shared_ptr<Net<Dtype> > Net_Init_Load(
+    string param_file, string pretrained_param_file, int phase) {
+  CheckFile(param_file);
+  CheckFile(pretrained_param_file);
+
+  shared_ptr<Net<Dtype> > net(new Net<Dtype>(param_file,
+      static_cast<Phase>(phase)));
+  net->CopyTrainedLayersFrom(pretrained_param_file);
+  return net;
+}
+
+void Net_Save(const Net<Dtype>& net, string filename) {
+  NetParameter net_param;
+  net.ToProto(&net_param, false);
+  WriteProtoToBinaryFile(net_param, filename.c_str());
+}
+
+void Net_SetInputArrays(Net<Dtype>* net, bp::object data_obj,
+    bp::object labels_obj) {
   // check that this network has an input MemoryDataLayer
-  shared_ptr<MemoryDataLayer<float> > md_layer =
-    boost::dynamic_pointer_cast<MemoryDataLayer<float> >(net_->layers()[0]);
+  shared_ptr<MemoryDataLayer<Dtype> > md_layer =
+    boost::dynamic_pointer_cast<MemoryDataLayer<Dtype> >(net->layers()[0]);
   if (!md_layer) {
     throw std::runtime_error("set_input_arrays may only be called if the"
         " first layer is a MemoryDataLayer");
@@ -108,9 +113,9 @@ void PyNet::set_input_arrays(bp::object data_obj, bp::object labels_obj) {
       reinterpret_cast<PyArrayObject*>(data_obj.ptr());
   PyArrayObject* labels_arr =
       reinterpret_cast<PyArrayObject*>(labels_obj.ptr());
-  check_contiguous_array(data_arr, "data array", md_layer->channels(),
+  CheckContiguousArray(data_arr, "data array", md_layer->channels(),
       md_layer->height(), md_layer->width());
-  check_contiguous_array(labels_arr, "labels array", 1, 1, 1);
+  CheckContiguousArray(labels_arr, "labels array", 1, 1, 1);
   if (PyArray_DIMS(data_arr)[0] != PyArray_DIMS(labels_arr)[0]) {
     throw std::runtime_error("data and labels must have the same first"
         " dimension");
@@ -120,99 +125,155 @@ void PyNet::set_input_arrays(bp::object data_obj, bp::object labels_obj) {
         " multiple of batch size");
   }
 
-  // hold references
-  input_data_ = data_obj;
-  input_labels_ = labels_obj;
-
-  md_layer->Reset(static_cast<float*>(PyArray_DATA(data_arr)),
-      static_cast<float*>(PyArray_DATA(labels_arr)),
+  md_layer->Reset(static_cast<Dtype*>(PyArray_DATA(data_arr)),
+      static_cast<Dtype*>(PyArray_DATA(labels_arr)),
       PyArray_DIMS(data_arr)[0]);
 }
 
-PySGDSolver::PySGDSolver(const string& param_file) {
-  // as in PyNet, (as a convenience, not a guarantee), create a Python
-  // exception if param_file can't be opened
-  CheckFile(param_file);
-  solver_.reset(new SGDSolver<float>(param_file));
-  // we need to explicitly store the net wrapper, rather than constructing
-  // it on the fly, so that it can hold references to Python objects
-  net_.reset(new PyNet(solver_->net()));
-  for (int i = 0; i < solver_->test_nets().size(); ++i) {
-    test_nets_.push_back(boost::make_shared<PyNet>(solver_->test_nets()[i]));
-  }
+Solver<Dtype>* GetSolverFromFile(const string& filename) {
+  SolverParameter param;
+  ReadProtoFromTextFileOrDie(filename, &param);
+  return GetSolver<Dtype>(param);
 }
 
-void PySGDSolver::SolveResume(const string& resume_file) {
-  CheckFile(resume_file);
-  return solver_->Solve(resume_file);
-}
+struct NdarrayConverterGenerator {
+  template <typename T> struct apply;
+};
 
-BOOST_PYTHON_MODULE(_caffe) {
-  // Caffe utility methods
-  bp::def("set_mode_cpu",          &set_mode_cpu);
-  bp::def("set_mode_gpu",          &set_mode_gpu);
-  bp::def("set_phase_train",       &set_phase_train);
-  bp::def("set_phase_test",        &set_phase_test);
-  bp::def("set_device",            &Caffe::SetDevice);
+template <>
+struct NdarrayConverterGenerator::apply<Dtype*> {
+  struct type {
+    PyObject* operator() (Dtype* data) const {
+      // Just store the data pointer, and add the shape information in postcall.
+      return PyArray_SimpleNewFromData(0, NULL, NPY_DTYPE, data);
+    }
+    const PyTypeObject* get_pytype() {
+      return &PyArray_Type;
+    }
+  };
+};
+
+struct NdarrayCallPolicies : public bp::default_call_policies {
+  typedef NdarrayConverterGenerator result_converter;
+  PyObject* postcall(PyObject* pyargs, PyObject* result) {
+    bp::object pyblob = bp::extract<bp::tuple>(pyargs)()[0];
+    shared_ptr<Blob<Dtype> > blob =
+      bp::extract<shared_ptr<Blob<Dtype> > >(pyblob);
+    // Free the temporary pointer-holding array, and construct a new one with
+    // the shape information from the blob.
+    void* data = PyArray_DATA(reinterpret_cast<PyArrayObject*>(result));
+    Py_DECREF(result);
+    npy_intp dims[] = {blob->num(), blob->channels(),
+                       blob->height(), blob->width()};
+    PyObject* arr_obj = PyArray_SimpleNewFromData(4, dims, NPY_FLOAT32, data);
+    // SetBaseObject steals a ref, so we need to INCREF.
+    Py_INCREF(pyblob.ptr());
+    PyArray_SetBaseObject(reinterpret_cast<PyArrayObject*>(arr_obj),
+        pyblob.ptr());
+    return arr_obj;
+  }
+};
+
+BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1);
 
+BOOST_PYTHON_MODULE(_caffe) {
   // below, we prepend an underscore to methods that will be replaced
   // in Python
-  bp::class_<PyNet, shared_ptr<PyNet> >(
-      "Net", bp::init<string, string>())
-      .def(bp::init<string>())
-      .def("copy_from",             &PyNet::CopyTrainedLayersFrom)
-      .def("share_with",            &PyNet::ShareTrainedLayersWith)
-      .def("_forward",              &PyNet::Forward)
-      .def("_backward",             &PyNet::Backward)
-      .def("reshape",               &PyNet::Reshape)
-      .add_property("_blobs",       &PyNet::blobs)
-      .add_property("layers",       &PyNet::layers)
-      .add_property("_blob_names",  &PyNet::blob_names)
-      .add_property("_layer_names", &PyNet::layer_names)
-      .add_property("inputs",       &PyNet::inputs)
-      .add_property("outputs",      &PyNet::outputs)
-      .add_property("mean",         &PyNet::mean_)
-      .add_property("input_scale",  &PyNet::input_scale_)
-      .add_property("raw_scale",    &PyNet::raw_scale_)
-      .add_property("channel_swap", &PyNet::channel_swap_)
-      .def("_set_input_arrays",     &PyNet::set_input_arrays)
-      .def("save",                  &PyNet::save);
-
-  bp::class_<PyBlob<float>, PyBlobWrap>(
-      "Blob", bp::no_init)
-      .add_property("num",      &PyBlob<float>::num)
-      .add_property("channels", &PyBlob<float>::channels)
-      .add_property("height",   &PyBlob<float>::height)
-      .add_property("width",    &PyBlob<float>::width)
-      .add_property("count",    &PyBlob<float>::count)
-      .def("reshape",           &PyBlob<float>::Reshape)
-      .add_property("data",     &PyBlobWrap::get_data)
-      .add_property("diff",     &PyBlobWrap::get_diff);
-
-  bp::class_<PyLayer>(
-      "Layer", bp::no_init)
-      .add_property("blobs", &PyLayer::blobs);
-
-  bp::class_<PySGDSolver, boost::noncopyable>(
-      "SGDSolver", bp::init<string>())
-      .add_property("net",       &PySGDSolver::net)
-      .add_property("test_nets", &PySGDSolver::test_nets)
-      .add_property("iter",      &PySGDSolver::iter)
-      .def("solve",              &PySGDSolver::Solve)
-      .def("solve",              &PySGDSolver::SolveResume)
-      .def("step",               &PySGDSolver::Step);
-
-  bp::class_<vector<shared_ptr<PyNet> > >("NetVec")
-      .def(bp::vector_indexing_suite<vector<shared_ptr<PyNet> >, true>());
-
-  bp::class_<vector<PyBlob<float> > >("BlobVec")
-      .def(bp::vector_indexing_suite<vector<PyBlob<float> >, true>());
-
-  bp::class_<vector<PyLayer> >("LayerVec")
-      .def(bp::vector_indexing_suite<vector<PyLayer>, true>());
-
+  // Caffe utility functions
+  bp::def("set_mode_cpu", &set_mode_cpu);
+  bp::def("set_mode_gpu", &set_mode_gpu);
+  bp::def("set_device", &Caffe::SetDevice);
+
+  bp::class_<Net<Dtype>, shared_ptr<Net<Dtype> >, boost::noncopyable >("Net",
+    bp::no_init)
+    .def("__init__", bp::make_constructor(&Net_Init))
+    .def("__init__", bp::make_constructor(&Net_Init_Load))
+    .def("_forward", &Net<Dtype>::ForwardFromTo)
+    .def("_backward", &Net<Dtype>::BackwardFromTo)
+    .def("reshape", &Net<Dtype>::Reshape)
+    // The cast is to select a particular overload.
+    .def("copy_from", static_cast<void (Net<Dtype>::*)(const string)>(
+        &Net<Dtype>::CopyTrainedLayersFrom))
+    .def("share_with", &Net<Dtype>::ShareTrainedLayersWith)
+    .add_property("_blobs", bp::make_function(&Net<Dtype>::blobs,
+        bp::return_internal_reference<>()))
+    .add_property("layers", bp::make_function(&Net<Dtype>::layers,
+        bp::return_internal_reference<>()))
+    .add_property("_blob_names", bp::make_function(&Net<Dtype>::blob_names,
+        bp::return_value_policy<bp::copy_const_reference>()))
+    .add_property("_layer_names", bp::make_function(&Net<Dtype>::layer_names,
+        bp::return_value_policy<bp::copy_const_reference>()))
+    .add_property("_inputs", bp::make_function(&Net<Dtype>::input_blob_indices,
+        bp::return_value_policy<bp::copy_const_reference>()))
+    .add_property("_outputs",
+        bp::make_function(&Net<Dtype>::output_blob_indices,
+        bp::return_value_policy<bp::copy_const_reference>()))
+    .def("_set_input_arrays", &Net_SetInputArrays,
+        bp::with_custodian_and_ward<1, 2, bp::with_custodian_and_ward<1, 3> >())
+    .def("save", &Net_Save);
+
+  bp::class_<Blob<Dtype>, shared_ptr<Blob<Dtype> >, boost::noncopyable>(
+    "Blob", bp::no_init)
+    .add_property("num",      &Blob<Dtype>::num)
+    .add_property("channels", &Blob<Dtype>::channels)
+    .add_property("height",   &Blob<Dtype>::height)
+    .add_property("width",    &Blob<Dtype>::width)
+    .add_property("count",    &Blob<Dtype>::count)
+    .def("reshape",           &Blob<Dtype>::Reshape)
+    .add_property("data",     bp::make_function(&Blob<Dtype>::mutable_cpu_data,
+          NdarrayCallPolicies()))
+    .add_property("diff",     bp::make_function(&Blob<Dtype>::mutable_cpu_diff,
+          NdarrayCallPolicies()));
+
+  bp::class_<Layer<Dtype>, shared_ptr<PythonLayer<Dtype> >,
+    boost::noncopyable>("Layer", bp::init<const LayerParameter&>())
+    .add_property("blobs", bp::make_function(&Layer<Dtype>::blobs,
+          bp::return_internal_reference<>()))
+    .def("setup", &Layer<Dtype>::LayerSetUp)
+    .def("reshape", &Layer<Dtype>::Reshape)
+    .add_property("type", bp::make_function(&Layer<Dtype>::type));
+  bp::register_ptr_to_python<shared_ptr<Layer<Dtype> > >();
+
+  bp::class_<LayerParameter>("LayerParameter", bp::no_init);
+
+  bp::class_<Solver<Dtype>, shared_ptr<Solver<Dtype> >, boost::noncopyable>(
+    "Solver", bp::no_init)
+    .add_property("net", &Solver<Dtype>::net)
+    .add_property("test_nets", bp::make_function(&Solver<Dtype>::test_nets,
+          bp::return_internal_reference<>()))
+    .add_property("iter", &Solver<Dtype>::iter)
+    .def("solve", static_cast<void (Solver<Dtype>::*)(const char*)>(
+          &Solver<Dtype>::Solve), SolveOverloads())
+    .def("step", &Solver<Dtype>::Step);
+
+  bp::class_<SGDSolver<Dtype>, bp::bases<Solver<Dtype> >,
+    shared_ptr<SGDSolver<Dtype> >, boost::noncopyable>(
+        "SGDSolver", bp::init<string>());
+  bp::class_<NesterovSolver<Dtype>, bp::bases<Solver<Dtype> >,
+    shared_ptr<NesterovSolver<Dtype> >, boost::noncopyable>(
+        "NesterovSolver", bp::init<string>());
+  bp::class_<AdaGradSolver<Dtype>, bp::bases<Solver<Dtype> >,
+    shared_ptr<AdaGradSolver<Dtype> >, boost::noncopyable>(
+        "AdaGradSolver", bp::init<string>());
+
+  bp::def("get_solver", &GetSolverFromFile,
+      bp::return_value_policy<bp::manage_new_object>());
+
+  // vector wrappers for all the vector types we use
+  bp::class_<vector<shared_ptr<Blob<Dtype> > > >("BlobVec")
+    .def(bp::vector_indexing_suite<vector<shared_ptr<Blob<Dtype> > >, true>());
+  bp::class_<vector<Blob<Dtype>*> >("RawBlobVec")
+    .def(bp::vector_indexing_suite<vector<Blob<Dtype>*>, true>());
+  bp::class_<vector<shared_ptr<Layer<Dtype> > > >("LayerVec")
+    .def(bp::vector_indexing_suite<vector<shared_ptr<Layer<Dtype> > >, true>());
   bp::class_<vector<string> >("StringVec")
-      .def(bp::vector_indexing_suite<vector<string> >());
+    .def(bp::vector_indexing_suite<vector<string> >());
+  bp::class_<vector<int> >("IntVec")
+    .def(bp::vector_indexing_suite<vector<int> >());
+  bp::class_<vector<shared_ptr<Net<Dtype> > > >("NetVec")
+    .def(bp::vector_indexing_suite<vector<shared_ptr<Net<Dtype> > >, true>());
+  bp::class_<vector<bool> >("BoolVec")
+    .def(bp::vector_indexing_suite<vector<bool> >());
 
   import_array();
 }
diff --git a/python/caffe/_caffe.hpp b/python/caffe/_caffe.hpp
deleted file mode 100644
index 78470c0fa1a..00000000000
--- a/python/caffe/_caffe.hpp
+++ /dev/null
@@ -1,198 +0,0 @@
-#ifndef PYTHON_CAFFE__CAFFE_HPP_
-#define PYTHON_CAFFE__CAFFE_HPP_
-
-#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
-
-#include <Python.h>  // NOLINT(build/include_alpha)
-
-#include <boost/python.hpp>
-#include <boost/shared_ptr.hpp>
-#include <numpy/arrayobject.h>
-
-// these need to be included after boost on OS X
-#include <string>  // NOLINT(build/include_order)
-#include <vector>  // NOLINT(build/include_order)
-
-#include "caffe/caffe.hpp"
-
-namespace bp = boost::python;
-using boost::shared_ptr;
-
-namespace caffe {
-
-// Selecting mode and phase.
-void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
-void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }
-void set_phase_train() { Caffe::set_phase(Caffe::TRAIN); }
-void set_phase_test() { Caffe::set_phase(Caffe::TEST); }
-
-// wrap shared_ptr<Blob> in a class that we construct in C++ and pass
-// to Python
-template <typename Dtype>
-class PyBlob {
- public:
-  explicit PyBlob(const shared_ptr<Blob<Dtype> > &blob)
-      : blob_(blob) {}
-
-  int num() const { return blob_->num(); }
-  int channels() const { return blob_->channels(); }
-  int height() const { return blob_->height(); }
-  int width() const { return blob_->width(); }
-  int count() const { return blob_->count(); }
-  void Reshape(const int n, const int c, const int h, const int w) {
-    return blob_->Reshape(n, c, h, w);
-  }
-
-  // this is here only to satisfy boost's vector_indexing_suite
-  bool operator == (const PyBlob &other) {
-      return this->blob_ == other.blob_;
-  }
-
- protected:
-  shared_ptr<Blob<Dtype> > blob_;
-};
-
-// We need another wrapper (used as boost::python's HeldType) that receives a
-// self PyObject * which we can use as ndarray.base, so that data/diff memory
-// is not freed while still being used in Python.
-class PyBlobWrap : public PyBlob<float> {
- public:
-  PyBlobWrap(PyObject *p, const PyBlob<float> &blob)
-      : PyBlob<float>(blob), self_(p) {}
-
-  bp::object get_data();
-  bp::object get_diff();
-
- private:
-  PyObject *self_;
-};
-
-class PyLayer {
- public:
-  explicit PyLayer(const shared_ptr<Layer<float> > &layer)
-    : layer_(layer) {}
-
-  vector<PyBlob<float> > blobs() {
-    return vector<PyBlob<float> >(layer_->blobs().begin(),
-        layer_->blobs().end());
-  }
-
-  // this is here only to satisfy boost's vector_indexing_suite
-  bool operator == (const PyLayer &other) {
-      return this->layer_ == other.layer_;
-  }
-
- protected:
-  shared_ptr<Layer<float> > layer_;
-};
-
-class PyNet {
- public:
-  // For cases where parameters will be determined later by the Python user,
-  // create a Net with unallocated parameters (which will not be zero-filled
-  // when accessed).
-  explicit PyNet(string param_file) { Init(param_file); }
-  PyNet(string param_file, string pretrained_param_file);
-  explicit PyNet(shared_ptr<Net<float> > net)
-      : net_(net) {}
-  virtual ~PyNet() {}
-
-  void Init(string param_file);
-
-
-  // Generate Python exceptions for badly shaped or discontiguous arrays.
-  inline void check_contiguous_array(PyArrayObject* arr, string name,
-      int channels, int height, int width);
-
-  void CopyTrainedLayersFrom(const string filename) {
-    net_->CopyTrainedLayersFrom(filename);
-  }
-  void ShareTrainedLayersWith(PyNet* other) {
-    net_->ShareTrainedLayersWith(other->net_.get());
-  }
-  void Forward(int start, int end) { net_->ForwardFromTo(start, end); }
-  void Backward(int start, int end) { net_->BackwardFromTo(start, end); }
-  void Reshape() { net_->Reshape(); }
-
-  void set_input_arrays(bp::object data_obj, bp::object labels_obj);
-
-  // Save the network weights to binary proto for net surgeries.
-  void save(string filename) {
-    NetParameter net_param;
-    net_->ToProto(&net_param, false);
-    WriteProtoToBinaryFile(net_param, filename.c_str());
-  }
-
-  vector<PyBlob<float> > blobs() {
-    return vector<PyBlob<float> >(net_->blobs().begin(), net_->blobs().end());
-  }
-
-  vector<PyLayer> layers() {
-    return vector<PyLayer>(net_->layers().begin(), net_->layers().end());
-  }
-
-  vector<string> blob_names() { return net_->blob_names(); }
-  vector<string> layer_names() { return net_->layer_names(); }
-
-  bp::list inputs() {
-    bp::list input_blob_names;
-    for (int i = 0; i < net_->input_blob_indices().size(); ++i) {
-      input_blob_names.append(
-          net_->blob_names()[net_->input_blob_indices()[i]]);
-    }
-    return input_blob_names;
-  }
-
-  bp::list outputs() {
-    bp::list output_blob_names;
-    for (int i = 0; i < net_->output_blob_indices().size(); ++i) {
-      output_blob_names.append(
-          net_->blob_names()[net_->output_blob_indices()[i]]);
-    }
-    return output_blob_names;
-  }
-
-  // Input preprocessing configuration attributes. These are public for
-  // direct access from Python.
-  bp::dict mean_;
-  bp::dict input_scale_;
-  bp::dict raw_scale_;
-  bp::dict channel_swap_;
-
-  // this is here only to satisfy boost's vector_indexing_suite
-  bool operator == (const PyNet &other) {
-      return this->net_ == other.net_;
-  }
-
- protected:
-  // The pointer to the internal caffe::Net instance.
-  shared_ptr<Net<float> > net_;
-  // if taking input from an ndarray, we need to hold references
-  bp::object input_data_;
-  bp::object input_labels_;
-};
-
-class PySGDSolver {
- public:
-  explicit PySGDSolver(const string& param_file);
-
-  shared_ptr<PyNet> net() { return net_; }
-  vector<shared_ptr<PyNet> > test_nets() { return test_nets_; }
-  int iter() { return solver_->iter(); }
-  void Solve() { return solver_->Solve(); }
-  void Step(int iters) { solver_->Step(iters); }
-  void SolveResume(const string& resume_file);
-
- protected:
-  shared_ptr<PyNet> net_;
-  vector<shared_ptr<PyNet> > test_nets_;
-  shared_ptr<SGDSolver<float> > solver_;
-};
-
-// Declare the module init function created by boost::python, so that we can
-// use this module from C++ when embedding Python.
-PyMODINIT_FUNC init_caffe(void);
-
-}  // namespace caffe
-
-#endif
diff --git a/python/caffe/classifier.py b/python/caffe/classifier.py
index f9a13a39865..94dd063a2c7 100644
--- a/python/caffe/classifier.py
+++ b/python/caffe/classifier.py
@@ -14,33 +14,32 @@ class Classifier(caffe.Net):
     by scaling, center cropping, or oversampling.
     """
     def __init__(self, model_file, pretrained_file, image_dims=None,
-                 gpu=False, mean=None, input_scale=None, raw_scale=None,
+                 mean=None, input_scale=None, raw_scale=None,
                  channel_swap=None):
         """
         Take
         image_dims: dimensions to scale input for cropping/sampling.
             Default is to scale to net input size for whole-image crop.
-        gpu, mean, input_scale, raw_scale, channel_swap: params for
+            mean, input_scale, raw_scale, channel_swap: params for
             preprocessing options.
         """
-        caffe.Net.__init__(self, model_file, pretrained_file)
-        caffe.set_phase_test()
-
-        if gpu:
-            caffe.set_mode_gpu()
-        else:
-            caffe.set_mode_cpu()
+        caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST)
 
+        # configure pre-processing
+        in_ = self.inputs[0]
+        self.transformer = caffe.io.Transformer(
+            {in_: self.blobs[in_].data.shape for in_ in self.inputs})
+        self.transformer.set_transpose(in_, (2,0,1))
         if mean is not None:
-            self.set_mean(self.inputs[0], mean)
+            self.transformer.set_mean(in_, mean)
         if input_scale is not None:
-            self.set_input_scale(self.inputs[0], input_scale)
+            self.transformer.set_input_scale(in_, input_scale)
         if raw_scale is not None:
-            self.set_raw_scale(self.inputs[0], raw_scale)
+            self.transformer.set_raw_scale(in_, raw_scale)
         if channel_swap is not None:
-            self.set_channel_swap(self.inputs[0], channel_swap)
+            self.transformer.set_channel_swap(in_, channel_swap)
 
-        self.crop_dims = np.array(self.blobs[self.inputs[0]].data.shape[2:])
+        self.crop_dims = np.array(self.blobs[in_].data.shape[2:])
         if not image_dims:
             image_dims = self.crop_dims
         self.image_dims = image_dims
@@ -82,7 +81,7 @@ def predict(self, inputs, oversample=True):
         caffe_in = np.zeros(np.array(input_.shape)[[0,3,1,2]],
                             dtype=np.float32)
         for ix, in_ in enumerate(input_):
-            caffe_in[ix] = self.preprocess(self.inputs[0], in_)
+            caffe_in[ix] = self.transformer.preprocess(self.inputs[0], in_)
         out = self.forward_all(**{self.inputs[0]: caffe_in})
         predictions = out[self.outputs[0]].squeeze(axis=(2,3))
 
diff --git a/python/caffe/detector.py b/python/caffe/detector.py
index b78abd12a89..4ea07fb7b36 100644
--- a/python/caffe/detector.py
+++ b/python/caffe/detector.py
@@ -29,28 +29,27 @@ def __init__(self, model_file, pretrained_file, gpu=False, mean=None,
                  context_pad=None):
         """
         Take
-        gpu, mean, input_scale, raw_scale, channel_swap: params for
+            mean, input_scale, raw_scale, channel_swap: params for
             preprocessing options.
         context_pad: amount of surrounding context to take s.t. a `context_pad`
             sized border of pixels in the network input image is context, as in
             R-CNN feature extraction.
         """
-        caffe.Net.__init__(self, model_file, pretrained_file)
-        caffe.set_phase_test()
-
-        if gpu:
-            caffe.set_mode_gpu()
-        else:
-            caffe.set_mode_cpu()
+        caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST)
 
+        # configure pre-processing
+        in_ = self.inputs[0]
+        self.transformer = caffe.io.Transformer(
+            {in_: self.blobs[in_].data.shape for in_ in self.inputs})
+        self.transformer.set_transpose(in_, (2,0,1))
         if mean is not None:
-            self.set_mean(self.inputs[0], mean)
+            self.transformer.set_mean(in_, mean)
         if input_scale is not None:
-            self.set_input_scale(self.inputs[0], input_scale)
+            self.transformer.set_input_scale(in_, input_scale)
         if raw_scale is not None:
-            self.set_raw_scale(self.inputs[0], raw_scale)
+            self.transformer.set_raw_scale(in_, raw_scale)
         if channel_swap is not None:
-            self.set_channel_swap(self.inputs[0], channel_swap)
+            self.transformer.set_channel_swap(in_, channel_swap)
 
         self.configure_crop(context_pad)
 
@@ -76,12 +75,13 @@ def detect_windows(self, images_windows):
                 window_inputs.append(self.crop(image, window))
 
         # Run through the net (warping windows to input dimensions).
+        in_ = self.inputs[0]
         caffe_in = np.zeros((len(window_inputs), window_inputs[0].shape[2])
-                            + self.blobs[self.inputs[0]].data.shape[2:],
+                            + self.blobs[in_].data.shape[2:],
                             dtype=np.float32)
         for ix, window_in in enumerate(window_inputs):
-            caffe_in[ix] = self.preprocess(self.inputs[0], window_in)
-        out = self.forward_all(**{self.inputs[0]: caffe_in})
+            caffe_in[ix] = self.transformer.preprocess(in_, window_in)
+        out = self.forward_all(**{in_: caffe_in})
         predictions = out[self.outputs[0]].squeeze(axis=(2,3))
 
         # Package predictions with images and windows.
@@ -170,7 +170,7 @@ def crop(self, im, window):
             # with mean padding
             context_crop = im[box[0]:box[2], box[1]:box[3]]
             context_crop = caffe.io.resize_image(context_crop, (crop_h, crop_w))
-            crop = self.crop_mean.copy()
+            crop = np.ones(self.crop_dims, dtype=np.float32) * self.crop_mean
             crop[pad_y:(pad_y + crop_h), pad_x:(pad_x + crop_w)] = context_crop
 
         return crop
@@ -178,20 +178,30 @@ def crop(self, im, window):
 
     def configure_crop(self, context_pad):
         """
-        Configure amount of context for cropping.
+        Configure crop dimensions and amount of context for cropping.
         If context is included, make the special input mean for context padding.
 
         Take
         context_pad: amount of context for cropping.
         """
+        # crop dimensions
+        in_ = self.inputs[0]
+        tpose = self.transformer.transpose[in_]
+        inv_tpose = [tpose[t] for t in tpose]
+        self.crop_dims = np.array(self.blobs[in_].data.shape[1:])[inv_tpose]
+        #.transpose(inv_tpose)
+        # context padding
         self.context_pad = context_pad
         if self.context_pad:
-            raw_scale = self.raw_scale.get(self.inputs[0])
-            channel_order = self.channel_swap.get(self.inputs[0])
+            in_ = self.inputs[0]
+            transpose = self.transformer.transpose.get(in_)
+            channel_order = self.transformer.channel_swap.get(in_)
+            raw_scale = self.transformer.raw_scale.get(in_)
             # Padding context crops needs the mean in unprocessed input space.
-            mean = self.mean.get(self.inputs[0])
+            mean = self.transformer.mean.get(in_)
             if mean is not None:
-                crop_mean = mean.copy().transpose((1,2,0))
+                inv_transpose = [transpose[t] for t in transpose]
+                crop_mean = mean.copy().transpose(inv_transpose)
                 if channel_order is not None:
                     channel_order_inverse = [channel_order.index(i)
                                             for i in range(crop_mean.shape[2])]
@@ -200,5 +210,4 @@ def configure_crop(self, context_pad):
                     crop_mean /= raw_scale
                 self.crop_mean = crop_mean
             else:
-                self.crop_mean = np.zeros(self.blobs[self.inputs[0]].data.shape,
-                                          dtype=np.float32)
+                self.crop_mean = np.zeros(self.crop_dims, dtype=np.float32)
diff --git a/python/caffe/draw.py b/python/caffe/draw.py
index d95c193cc93..6a4dbd47351 100644
--- a/python/caffe/draw.py
+++ b/python/caffe/draw.py
@@ -17,13 +17,6 @@
          'style': 'filled'}
 BLOB_STYLE = {'shape': 'octagon', 'fillcolor': '#E0E0E0',
         'style': 'filled'}
-def get_enum_name_by_value():
-  desc = caffe_pb2.LayerParameter.LayerType.DESCRIPTOR
-  d = {}
-  for k,v in desc.values_by_name.items():
-    d[v.number] = k
-  return d
-
 
 def get_pooling_types_dict():
     """Get dictionary mapping pooling type number to type name
@@ -39,11 +32,11 @@ def determine_edge_label_by_layertype(layer, layertype):
     """Define edge label based on layer type
     """
 
-    if layertype == 'DATA':
+    if layertype == 'Data':
         edge_label = 'Batch ' + str(layer.data_param.batch_size)
-    elif layertype == 'CONVOLUTION':
+    elif layertype == 'Convolution':
         edge_label = str(layer.convolution_param.num_output)
-    elif layertype == 'INNER_PRODUCT':
+    elif layertype == 'InnerProduct':
         edge_label = str(layer.inner_product_param.num_output)
     else:
         edge_label = '""'
@@ -64,7 +57,7 @@ def determine_node_label_by_layertype(layer, layertype, rankdir):
         # horizontal space is not; separate words with newlines
         separator = '\n'
 
-    if layertype == 'CONVOLUTION':
+    if layertype == 'Convolution':
         # Outer double quotes needed or else colon characters don't parse
         # properly
         node_label = '"%s%s(%s)%skernel size: %d%sstride: %d%spad: %d"' %\
@@ -77,7 +70,7 @@ def determine_node_label_by_layertype(layer, layertype, rankdir):
                       layer.convolution_param.stride,
                       separator,
                       layer.convolution_param.pad)
-    elif layertype == 'POOLING':
+    elif layertype == 'Pooling':
         pooling_types_dict = get_pooling_types_dict()
         node_label = '"%s%s(%s %s)%skernel size: %d%sstride: %d%spad: %d"' %\
                      (layer.name,
@@ -99,11 +92,11 @@ def choose_color_by_layertype(layertype):
     """Define colors for nodes based on the layer type
     """
     color = '#6495ED'  # Default
-    if layertype == 'CONVOLUTION':
+    if layertype == 'Convolution':
         color = '#FF5050'
-    elif layertype == 'POOLING':
+    elif layertype == 'Pooling':
         color = '#FF9900'
-    elif layertype == 'INNER_PRODUCT':
+    elif layertype == 'InnerProduct':
         color = '#CC33FF'
     return color
 
@@ -112,10 +105,9 @@ def get_pydot_graph(caffe_net, rankdir, label_edges=True):
   pydot_graph = pydot.Dot(caffe_net.name, graph_type='digraph', rankdir=rankdir)
   pydot_nodes = {}
   pydot_edges = []
-  d = get_enum_name_by_value()
-  for layer in caffe_net.layers:
+  for layer in caffe_net.layer:
     name = layer.name
-    layertype = d[layer.type]
+    layertype = layer.type
     node_label = determine_node_label_by_layertype(layer, layertype, rankdir)
     if (len(layer.bottom) == 1 and len(layer.top) == 1 and
         layer.bottom[0] == layer.top[0]):
diff --git a/python/caffe/io.py b/python/caffe/io.py
index a8354021b05..0ce9ecfeeed 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -5,6 +5,255 @@
 
 from caffe.proto import caffe_pb2
 
+## proto / datum / ndarray conversion
+
+def blobproto_to_array(blob, return_diff=False):
+    """Convert a blob proto to an array. In default, we will just return the data,
+    unless return_diff is True, in which case we will return the diff.
+    """
+    if return_diff:
+        return np.array(blob.diff).reshape(
+            blob.num, blob.channels, blob.height, blob.width)
+    else:
+        return np.array(blob.data).reshape(
+            blob.num, blob.channels, blob.height, blob.width)
+
+
+def array_to_blobproto(arr, diff=None):
+    """Converts a 4-dimensional array to blob proto. If diff is given, also
+    convert the diff. You need to make sure that arr and diff have the same
+    shape, and this function does not do sanity check.
+    """
+    if arr.ndim != 4:
+        raise ValueError('Incorrect array shape.')
+    blob = caffe_pb2.BlobProto()
+    blob.num, blob.channels, blob.height, blob.width = arr.shape;
+    blob.data.extend(arr.astype(float).flat)
+    if diff is not None:
+        blob.diff.extend(diff.astype(float).flat)
+    return blob
+
+
+def arraylist_to_blobprotovecor_str(arraylist):
+    """Converts a list of arrays to a serialized blobprotovec, which could be
+    then passed to a network for processing.
+    """
+    vec = caffe_pb2.BlobProtoVector()
+    vec.blobs.extend([array_to_blobproto(arr) for arr in arraylist])
+    return vec.SerializeToString()
+
+
+def blobprotovector_str_to_arraylist(str):
+    """Converts a serialized blobprotovec to a list of arrays.
+    """
+    vec = caffe_pb2.BlobProtoVector()
+    vec.ParseFromString(str)
+    return [blobproto_to_array(blob) for blob in vec.blobs]
+
+
+def array_to_datum(arr, label=0):
+    """Converts a 3-dimensional array to datum. If the array has dtype uint8,
+    the output data will be encoded as a string. Otherwise, the output data
+    will be stored in float format.
+    """
+    if arr.ndim != 3:
+        raise ValueError('Incorrect array shape.')
+    datum = caffe_pb2.Datum()
+    datum.channels, datum.height, datum.width = arr.shape
+    if arr.dtype == np.uint8:
+        datum.data = arr.tostring()
+    else:
+        datum.float_data.extend(arr.flat)
+    datum.label = label
+    return datum
+
+
+def datum_to_array(datum):
+    """Converts a datum to an array. Note that the label is not returned,
+    as one can easily get it by calling datum.label.
+    """
+    if len(datum.data):
+        return np.fromstring(datum.data, dtype = np.uint8).reshape(
+            datum.channels, datum.height, datum.width)
+    else:
+        return np.array(datum.float_data).astype(float).reshape(
+            datum.channels, datum.height, datum.width)
+
+
+## Pre-processing
+
+class Transformer:
+    """
+    Transform input for feeding into a Net.
+
+    Note: this is mostly for illustrative purposes and it is likely better
+    to define your own input preprocessing routine for your needs.
+
+    Take
+    net: a Net for which the input should be prepared
+    """
+    def __init__(self, inputs):
+        self.inputs = inputs
+        self.transpose = {}
+        self.channel_swap = {}
+        self.raw_scale = {}
+        self.mean = {}
+        self.input_scale = {}
+
+
+    def __check_input(self, in_):
+        if in_ not in self.inputs:
+            raise Exception('{} is not one of the net inputs: {}'.format(
+                in_, self.inputs))
+
+
+    def preprocess(self, in_, data):
+        """
+        Format input for Caffe:
+        - convert to single
+        - resize to input dimensions (preserving number of channels)
+        - transpose dimensions to K x H x W
+        - reorder channels (for instance color to BGR)
+        - scale raw input (e.g. from [0, 1] to [0, 255] for ImageNet models)
+        - subtract mean
+        - scale feature
+
+        Take
+        in_: name of input blob to preprocess for
+        data: (H' x W' x K) ndarray
+
+        Give
+        caffe_in: (K x H x W) ndarray for input to a Net
+        """
+        self.__check_input(in_)
+        caffe_in = data.astype(np.float32, copy=False)
+        transpose = self.transpose.get(in_)
+        channel_swap = self.channel_swap.get(in_)
+        raw_scale = self.raw_scale.get(in_)
+        mean = self.mean.get(in_)
+        input_scale = self.input_scale.get(in_)
+        in_dims = self.inputs[in_][2:]
+        if caffe_in.shape[:2] != in_dims:
+            caffe_in = resize_image(caffe_in, in_dims)
+        if transpose is not None:
+            caffe_in = caffe_in.transpose(transpose)
+        if channel_swap is not None:
+            caffe_in = caffe_in[channel_swap, :, :]
+        if raw_scale is not None:
+            caffe_in *= raw_scale
+        if mean is not None:
+            caffe_in -= mean
+        if input_scale is not None:
+            caffe_in *= input_scale
+        return caffe_in
+
+
+    def deprocess(self, in_, data):
+        """
+        Invert Caffe formatting; see preprocess().
+        """
+        self.__check_input(in_)
+        decaf_in = data.copy().squeeze()
+        transpose = self.transpose.get(in_)
+        channel_swap = self.channel_swap.get(in_)
+        raw_scale = self.raw_scale.get(in_)
+        mean = self.mean.get(in_)
+        input_scale = self.input_scale.get(in_)
+        if input_scale is not None:
+            decaf_in /= input_scale
+        if mean is not None:
+            decaf_in += mean
+        if raw_scale is not None:
+            decaf_in /= raw_scale
+        if channel_swap is not None:
+            decaf_in = decaf_in[channel_swap, :, :]
+        if transpose is not None:
+            decaf_in = decaf_in.transpose([transpose[t] for t in transpose])
+        return decaf_in
+
+
+    def set_transpose(self, in_, order):
+        """
+        Set the input channel order for e.g. RGB to BGR conversion
+        as needed for the reference ImageNet model.
+
+        Take
+        in_: which input to assign this channel order
+        order: the order to transpose the dimensions
+        """
+        self.__check_input(in_)
+        if len(order) != len(self.inputs[in_]) - 1:
+            raise Exception('Transpose order needs to have the same number of '
+                            'dimensions as the input.')
+        self.transpose[in_] = order
+
+
+    def set_channel_swap(self, in_, order):
+        """
+        Set the input channel order for e.g. RGB to BGR conversion
+        as needed for the reference ImageNet model.
+        N.B. this assumes the channels are the first dimension AFTER transpose.
+
+        Take
+        in_: which input to assign this channel order
+        order: the order to take the channels.
+            (2,1,0) maps RGB to BGR for example.
+        """
+        self.__check_input(in_)
+        if len(order) != self.inputs[in_][1]:
+            raise Exception('Channel swap needs to have the same number of '
+                            'dimensions as the input channels.')
+        self.channel_swap[in_] = order
+
+
+    def set_raw_scale(self, in_, scale):
+        """
+        Set the scale of raw features s.t. the input blob = input * scale.
+        While Python represents images in [0, 1], certain Caffe models
+        like CaffeNet and AlexNet represent images in [0, 255] so the raw_scale
+        of these models must be 255.
+
+        Take
+        in_: which input to assign this scale factor
+        scale: scale coefficient
+        """
+        self.__check_input(in_)
+        self.raw_scale[in_] = scale
+
+
+    def set_mean(self, in_, mean):
+        """
+        Set the mean to subtract for centering the data.
+
+        Take
+        in_: which input to assign this mean.
+        mean: mean ndarray (input dimensional or broadcastable)
+        """
+        self.__check_input(in_)
+        if mean.ndim == 1:
+            mean = mean[:, np.newaxis, np.newaxis]
+        mk, mh, mw = mean.shape
+        in_k, in_h, in_w = self.inputs[in_][1:]
+        #if mk != in_k or (mh, mw) != (in_h, in_w) and (mh, mw) != (1, 1):
+        #    raise Exception('Mean shape incompatible with input shape.')
+        self.mean[in_] = mean
+
+
+    def set_input_scale(self, in_, scale):
+        """
+        Set the scale of preprocessed inputs s.t. the blob = blob * scale.
+        N.B. input_scale is done AFTER mean subtraction and other preprocessing
+        while raw_scale is done BEFORE.
+
+        Take
+        in_: which input to assign this scale factor
+        scale: scale coefficient
+        """
+        self.__check_input(in_)
+        self.input_scale[in_] = scale
+
+
+## Image IO
 
 def load_image(filename, color=True):
     """
@@ -102,76 +351,3 @@ def oversample(images, crop_dims):
             ix += 1
         crops[ix-5:ix] = crops[ix-5:ix, :, ::-1, :]  # flip for mirrors
     return crops
-
-
-def blobproto_to_array(blob, return_diff=False):
-  """Convert a blob proto to an array. In default, we will just return the data,
-  unless return_diff is True, in which case we will return the diff.
-  """
-  if return_diff:
-    return np.array(blob.diff).reshape(
-        blob.num, blob.channels, blob.height, blob.width)
-  else:
-    return np.array(blob.data).reshape(
-        blob.num, blob.channels, blob.height, blob.width)
-
-
-def array_to_blobproto(arr, diff=None):
-  """Converts a 4-dimensional array to blob proto. If diff is given, also
-  convert the diff. You need to make sure that arr and diff have the same
-  shape, and this function does not do sanity check.
-  """
-  if arr.ndim != 4:
-    raise ValueError('Incorrect array shape.')
-  blob = caffe_pb2.BlobProto()
-  blob.num, blob.channels, blob.height, blob.width = arr.shape;
-  blob.data.extend(arr.astype(float).flat)
-  if diff is not None:
-    blob.diff.extend(diff.astype(float).flat)
-  return blob
-
-
-def arraylist_to_blobprotovecor_str(arraylist):
-  """Converts a list of arrays to a serialized blobprotovec, which could be
-  then passed to a network for processing.
-  """
-  vec = caffe_pb2.BlobProtoVector()
-  vec.blobs.extend([array_to_blobproto(arr) for arr in arraylist])
-  return vec.SerializeToString()
-
-
-def blobprotovector_str_to_arraylist(str):
-  """Converts a serialized blobprotovec to a list of arrays.
-  """
-  vec = caffe_pb2.BlobProtoVector()
-  vec.ParseFromString(str)
-  return [blobproto_to_array(blob) for blob in vec.blobs]
-
-
-def array_to_datum(arr, label=0):
-  """Converts a 3-dimensional array to datum. If the array has dtype uint8,
-  the output data will be encoded as a string. Otherwise, the output data
-  will be stored in float format.
-  """
-  if arr.ndim != 3:
-    raise ValueError('Incorrect array shape.')
-  datum = caffe_pb2.Datum()
-  datum.channels, datum.height, datum.width = arr.shape
-  if arr.dtype == np.uint8:
-    datum.data = arr.tostring()
-  else:
-    datum.float_data.extend(arr.flat)
-  datum.label = label
-  return datum
-
-
-def datum_to_array(datum):
-  """Converts a datum to an array. Note that the label is not returned,
-  as one can easily get it by calling datum.label.
-  """
-  if len(datum.data):
-    return np.fromstring(datum.data, dtype = np.uint8).reshape(
-        datum.channels, datum.height, datum.width)
-  else:
-    return np.array(datum.float_data).astype(float).reshape(
-        datum.channels, datum.height, datum.width)
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 31dc1f9b001..31c145d77a5 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -35,6 +35,17 @@ def _Net_params(self):
                         for name, lr in zip(self._layer_names, self.layers)
                         if len(lr.blobs) > 0])
 
+
+@property
+def _Net_inputs(self):
+    return [self.blobs.keys()[i] for i in self._inputs]
+
+
+@property
+def _Net_outputs(self):
+    return [self.blobs.keys()[i] for i in self._outputs]
+
+
 def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
     """
     Forward pass: prepare inputs and run the net forward.
@@ -202,138 +213,6 @@ def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs):
     return all_outs, all_diffs
 
 
-def _Net_set_mean(self, input_, mean, mode='elementwise'):
-    """
-    Set the mean to subtract for data centering.
-
-    Take
-    input_: which input to assign this mean.
-    mean: mean K x H x W ndarray (input dimensional or broadcastable)
-    mode: elementwise = use the whole mean (and check dimensions)
-          channel = channel constant (e.g. mean pixel instead of mean image)
-    """
-    if input_ not in self.inputs:
-        raise Exception('Input not in {}'.format(self.inputs))
-    in_shape = self.blobs[input_].data.shape
-    if mode == 'elementwise':
-        if mean.shape[1:] != in_shape[2:]:
-            # Resize mean (which requires H x W x K input).
-            mean = caffe.io.resize_image(mean.transpose((1,2,0)),
-                                         in_shape[2:]).transpose((2,0,1))
-        self.mean[input_] = mean
-    elif mode == 'channel':
-        self.mean[input_] = mean.mean(1).mean(1).reshape((in_shape[1], 1, 1))
-    else:
-        raise Exception('Mode not in {}'.format(['elementwise', 'channel']))
-
-
-def _Net_set_input_scale(self, input_, scale):
-    """
-    Set the scale of preprocessed inputs s.t. the blob = blob * scale.
-    N.B. input_scale is done AFTER mean subtraction and other preprocessing
-    while raw_scale is done BEFORE.
-
-    Take
-    input_: which input to assign this scale factor
-    scale: scale coefficient
-    """
-    if input_ not in self.inputs:
-        raise Exception('Input not in {}'.format(self.inputs))
-    self.input_scale[input_] = scale
-
-
-def _Net_set_raw_scale(self, input_, scale):
-    """
-    Set the scale of raw features s.t. the input blob = input * scale.
-    While Python represents images in [0, 1], certain Caffe models
-    like CaffeNet and AlexNet represent images in [0, 255] so the raw_scale
-    of these models must be 255.
-
-    Take
-    input_: which input to assign this scale factor
-    scale: scale coefficient
-    """
-    if input_ not in self.inputs:
-        raise Exception('Input not in {}'.format(self.inputs))
-    self.raw_scale[input_] = scale
-
-
-def _Net_set_channel_swap(self, input_, order):
-    """
-    Set the input channel order for e.g. RGB to BGR conversion
-    as needed for the reference ImageNet model.
-
-    Take
-    input_: which input to assign this channel order
-    order: the order to take the channels.
-           (2,1,0) maps RGB to BGR for example.
-    """
-    if input_ not in self.inputs:
-        raise Exception('Input not in {}'.format(self.inputs))
-    self.channel_swap[input_] = order
-
-
-def _Net_preprocess(self, input_name, input_):
-    """
-    Format input for Caffe:
-    - convert to single
-    - resize to input dimensions (preserving number of channels)
-    - reorder channels (for instance color to BGR)
-    - scale raw input (e.g. from [0, 1] to [0, 255] for ImageNet models)
-    - transpose dimensions to K x H x W
-    - subtract mean
-    - scale feature
-
-    Take
-    input_name: name of input blob to preprocess for
-    input_: (H' x W' x K) ndarray
-
-    Give
-    caffe_inputs: (K x H x W) ndarray
-    """
-    caffe_in = input_.astype(np.float32, copy=False)
-    mean = self.mean.get(input_name)
-    input_scale = self.input_scale.get(input_name)
-    raw_scale = self.raw_scale.get(input_name)
-    channel_order = self.channel_swap.get(input_name)
-    in_size = self.blobs[input_name].data.shape[2:]
-    if caffe_in.shape[:2] != in_size:
-        caffe_in = caffe.io.resize_image(caffe_in, in_size)
-    if channel_order is not None:
-        caffe_in = caffe_in[:, :, channel_order]
-    caffe_in = caffe_in.transpose((2, 0, 1))
-    if raw_scale is not None:
-        caffe_in *= raw_scale
-    if mean is not None:
-        caffe_in -= mean
-    if input_scale is not None:
-        caffe_in *= input_scale
-    return caffe_in
-
-
-def _Net_deprocess(self, input_name, input_):
-    """
-    Invert Caffe formatting; see Net.preprocess().
-    """
-    decaf_in = input_.copy().squeeze()
-    mean = self.mean.get(input_name)
-    input_scale = self.input_scale.get(input_name)
-    raw_scale = self.raw_scale.get(input_name)
-    channel_order = self.channel_swap.get(input_name)
-    if input_scale is not None:
-        decaf_in /= input_scale
-    if mean is not None:
-        decaf_in += mean
-    if raw_scale is not None:
-        decaf_in /= raw_scale
-    decaf_in = decaf_in.transpose((1,2,0))
-    if channel_order is not None:
-        channel_order_inverse = [channel_order.index(i)
-                                 for i in range(decaf_in.shape[2])]
-        decaf_in = decaf_in[:, :, channel_order_inverse]
-    return decaf_in
-
-
 def _Net_set_input_arrays(self, data, labels):
     """
     Set input arrays of the in-memory MemoryDataLayer.
@@ -376,7 +255,6 @@ def _Net_batch(self, blobs):
                                                  padding])
         yield padded_batch
 
-
 # Attach methods to Net.
 Net.blobs = _Net_blobs
 Net.params = _Net_params
@@ -384,11 +262,7 @@ def _Net_batch(self, blobs):
 Net.backward = _Net_backward
 Net.forward_all = _Net_forward_all
 Net.forward_backward_all = _Net_forward_backward_all
-Net.set_mean = _Net_set_mean
-Net.set_input_scale = _Net_set_input_scale
-Net.set_raw_scale = _Net_set_raw_scale
-Net.set_channel_swap = _Net_set_channel_swap
-Net.preprocess = _Net_preprocess
-Net.deprocess = _Net_deprocess
 Net.set_input_arrays = _Net_set_input_arrays
 Net._batch = _Net_batch
+Net.inputs = _Net_inputs
+Net.outputs = _Net_outputs
diff --git a/python/caffe/test/test_net.py b/python/caffe/test/test_net.py
index f0e9deef19c..62b407da8aa 100644
--- a/python/caffe/test/test_net.py
+++ b/python/caffe/test/test_net.py
@@ -11,21 +11,22 @@ def simple_net_file(num_output):
 
     f = tempfile.NamedTemporaryFile(delete=False)
     f.write("""name: 'testnet' force_backward: true
-    layers { type: DUMMY_DATA name: 'data' top: 'data' top: 'label'
+    layer { type: 'DummyData' name: 'data' top: 'data' top: 'label'
       dummy_data_param { num: 5 channels: 2 height: 3 width: 4
         num: 5 channels: 1 height: 1 width: 1
         data_filler { type: 'gaussian' std: 1 }
         data_filler { type: 'constant' } } }
-    layers { type: CONVOLUTION name: 'conv' bottom: 'data' top: 'conv'
+    layer { type: 'Convolution' name: 'conv' bottom: 'data' top: 'conv'
       convolution_param { num_output: 11 kernel_size: 2 pad: 3
         weight_filler { type: 'gaussian' std: 1 }
         bias_filler { type: 'constant' value: 2 } }
-        weight_decay: 1 weight_decay: 0 }
-    layers { type: INNER_PRODUCT name: 'ip' bottom: 'conv' top: 'ip'
+        param { decay_mult: 1 } param { decay_mult: 0 }
+        }
+    layer { type: 'InnerProduct' name: 'ip' bottom: 'conv' top: 'ip'
       inner_product_param { num_output: """ + str(num_output) + """
         weight_filler { type: 'gaussian' std: 2.5 }
         bias_filler { type: 'constant' value: -3 } } }
-    layers { type: SOFTMAX_LOSS name: 'loss' bottom: 'ip' bottom: 'label'
+    layer { type: 'SoftmaxWithLoss' name: 'loss' bottom: 'ip' bottom: 'label'
       top: 'loss' }""")
     f.close()
     return f.name
@@ -34,7 +35,7 @@ class TestNet(unittest.TestCase):
     def setUp(self):
         self.num_output = 13
         net_file = simple_net_file(self.num_output)
-        self.net = caffe.Net(net_file)
+        self.net = caffe.Net(net_file, caffe.TRAIN)
         # fill in valid labels
         self.net.blobs['label'].data[...] = \
                 np.random.randint(self.num_output,
@@ -68,7 +69,7 @@ def test_save_and_read(self):
         f.close()
         self.net.save(f.name)
         net_file = simple_net_file(self.num_output)
-        net2 = caffe.Net(net_file, f.name)
+        net2 = caffe.Net(net_file, f.name, caffe.TRAIN)
         os.remove(net_file)
         os.remove(f.name)
         for name in self.net.params:
diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py
new file mode 100644
index 00000000000..383c283959d
--- /dev/null
+++ b/python/caffe/test/test_python_layer.py
@@ -0,0 +1,62 @@
+import unittest
+import tempfile
+import os
+
+import caffe
+
+class SimpleLayer(caffe.Layer):
+    """A layer that just multiplies by ten"""
+
+    def setup(self, bottom, top):
+        pass
+
+    def reshape(self, bottom, top):
+        top[0].reshape(bottom[0].num, bottom[0].channels, bottom[0].height,
+                bottom[0].width)
+
+    def forward(self, bottom, top):
+        top[0].data[...] = 10 * bottom[0].data
+
+    def backward(self, top, propagate_down, bottom):
+        bottom[0].diff[...] = 10 * top[0].diff
+
+def python_net_file():
+    f = tempfile.NamedTemporaryFile(delete=False)
+    f.write("""name: 'pythonnet' force_backward: true
+    input: 'data' input_dim: 10 input_dim: 9 input_dim: 8 input_dim: 7
+    layer { type: 'Python' name: 'one' bottom: 'data' top: 'one'
+      python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }
+    layer { type: 'Python' name: 'two' bottom: 'one' top: 'two'
+      python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }
+    layer { type: 'Python' name: 'three' bottom: 'two' top: 'three'
+      python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }""")
+    f.close()
+    return f.name
+
+class TestPythonLayer(unittest.TestCase):
+    def setUp(self):
+        net_file = python_net_file()
+        self.net = caffe.Net(net_file, caffe.TRAIN)
+        os.remove(net_file)
+
+    def test_forward(self):
+        x = 8
+        self.net.blobs['data'].data[...] = x
+        self.net.forward()
+        for y in self.net.blobs['three'].data.flat:
+            self.assertEqual(y, 10**3 * x)
+
+    def test_backward(self):
+        x = 7
+        self.net.blobs['three'].diff[...] = x
+        self.net.backward()
+        for y in self.net.blobs['data'].diff.flat:
+            self.assertEqual(y, 10**3 * x)
+
+    def test_reshape(self):
+        s = 4
+        self.net.blobs['data'].reshape(s, s, s, s)
+        self.net.forward()
+        for blob in self.net.blobs.itervalues():
+            for d in blob.data.shape:
+                self.assertEqual(s, d)
diff --git a/python/caffe/test/test_solver.py b/python/caffe/test/test_solver.py
index 832b75c3219..d59f23d973a 100644
--- a/python/caffe/test/test_solver.py
+++ b/python/caffe/test/test_solver.py
@@ -17,6 +17,8 @@ def setUp(self):
         display: 100 max_iter: 100 snapshot_after_train: false""")
         f.close()
         self.solver = caffe.SGDSolver(f.name)
+        # also make sure get_solver runs
+        caffe.get_solver(f.name)
         caffe.set_mode_cpu()
         # fill in valid labels
         self.solver.net.blobs['label'].data[...] = \
diff --git a/python/detect.py b/python/detect.py
index b67b500aafd..cb0c2645761 100755
--- a/python/detect.py
+++ b/python/detect.py
@@ -102,6 +102,8 @@ def main(argv):
     mean, channel_swap = None, None
     if args.mean_file:
         mean = np.load(args.mean_file)
+        if mean.shape[1:] != (1, 1):
+            mean = mean.mean(1).mean(1)
     if args.channel_swap:
         channel_swap = [int(s) for s in args.channel_swap.split(',')]
 
diff --git a/scripts/travis/travis_build_and_test.sh b/scripts/travis/travis_build_and_test.sh
index 53c6c341101..8ff63f31fdd 100755
--- a/scripts/travis/travis_build_and_test.sh
+++ b/scripts/travis/travis_build_and_test.sh
@@ -7,7 +7,7 @@ MAKE="make --jobs=$NUM_THREADS --keep-going"
 if $WITH_CMAKE; then
   mkdir build
   cd build
-  cmake -DBUILD_PYTHON=ON -DBUILD_EXAMPLES=ON -DCMAKE_BUILD_TYPE=Release -DCPU_ONLY=ON ..
+  cmake -DBUILD_python=ON -DCMAKE_BUILD_TYPE=Release -DCPU_ONLY=ON ..
   $MAKE
   if ! $WITH_CUDA; then
     $MAKE runtest
diff --git a/scripts/travis/travis_setup_makefile_config.sh b/scripts/travis/travis_setup_makefile_config.sh
index e8d85f9be78..ba326262bf8 100755
--- a/scripts/travis/travis_setup_makefile_config.sh
+++ b/scripts/travis/travis_setup_makefile_config.sh
@@ -19,4 +19,5 @@ PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
 PYTHON_LIB := $(ANACONDA_HOME)/lib
 INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
 LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
+WITH_PYTHON_LAYER := 1
 EOF
diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
index dda072688f8..40e6c11f5b0 100644
--- a/src/caffe/CMakeLists.txt
+++ b/src/caffe/CMakeLists.txt
@@ -1,55 +1,36 @@
-project( CaffeSrc )
+# generate protobuf sources
+file(GLOB proto_files proto/*.proto)
+caffe_protobuf_generate_cpp_py(${proto_gen_folder} proto_srcs proto_hdrs proto_python ${proto_files})
 
+# include python files either to force generation
+add_library(proto STATIC ${proto_hdrs} ${proto_srcs} ${proto_python})
+set(Caffe_LINKER_LIBS proto ${Caffe_LINKER_LIBS}) # note, crucial to prepend!
+caffe_default_properties(proto)
 
-add_subdirectory(proto)
+# --[ Caffe library
 
-#    Recursively find source files
-##    test sources
-file(GLOB_RECURSE TEST_CPP_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/test_*.cpp)
+# creates 'test_srcs', 'srcs', 'test_cuda', 'cuda' lists
+caffe_pickup_caffe_sources(${PROJECT_SOURCE_DIR})
 
-##    all cpp sources
-file(GLOB_RECURSE CPP_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
+if(HAVE_CUDA)
+  caffe_cuda_compile(cuda_objs ${cuda})
+  list(APPEND srcs ${cuda_objs} ${cuda})
+endif()
 
-##    remove test sources from cpp sources
-list(REMOVE_ITEM CPP_SOURCES ${TEST_CPP_SOURCES})
+add_library(caffe ${srcs})
+target_link_libraries(caffe proto ${Caffe_LINKER_LIBS})
+caffe_default_properties(caffe)
 
-add_library(caffe ${CPP_SOURCES})
-# both depend on proto
-add_dependencies(caffe proto)
+# ---[ Tests
+ add_subdirectory(test)
+
+# ---[ Install
+install(DIRECTORY ${Caffe_INCLUDE_DIR}/caffe DESTINATION include)
+install(FILES ${proto_hdrs} DESTINATION include/caffe/proto)
+install(TARGETS caffe proto EXPORT CaffeTargets DESTINATION lib)
+
+file(WRITE ${PROJECT_BINARY_DIR}/__init__.py)
+list(APPEND proto_python ${PROJECT_BINARY_DIR}/__init__.py)
+install(PROGRAMS ${proto_python} DESTINATION python/caffe/proto)
 
-#    cuda sources
-if(NOT CPU_ONLY)
-    file(GLOB_RECURSE CU_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cu)
-    file(GLOB_RECURSE TEST_CU_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/test_*.cu)
-    list(REMOVE_ITEM CU_SOURCES ${TEST_CU_SOURCES})
-    cuda_add_library(caffe_cu ${CU_SOURCES})
-    add_dependencies(caffe_cu proto)
-    target_link_libraries(caffe caffe_cu
-        ${CUDA_CUBLAS_LIBRARIES}
-        ${CUDA_curand_LIBRARY}
-    )
-endif()
 
-target_link_libraries(caffe proto
-        ${BLAS_LIBRARIES}
-        ${Boost_LIBRARIES}
-        ${GFLAGS_LIBRARIES}
-        ${GLOG_LIBRARIES}
-        ${HDF5_LIBRARIES}
-        ${LEVELDB_LIBS}
-        ${LMDB_LIBRARIES}
-        ${OpenCV_LIBS}
-        ${CMAKE_THREAD_LIBS_INIT}
-)
-
-#set output directory
-set_target_properties(caffe PROPERTIES
-        ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib
-        LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib
-)
-
-add_subdirectory(test)
-
-###    Install    #################################################################################
-
-install(TARGETS caffe DESTINATION lib)
diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index 834d5694aad..af96cac40aa 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -42,7 +42,7 @@ void GlobalInit(int* pargc, char*** pargv) {
 #ifdef CPU_ONLY  // CPU-only Caffe.
 
 Caffe::Caffe()
-    : random_generator_(), mode_(Caffe::CPU), phase_(Caffe::TRAIN) { }
+    : random_generator_(), mode_(Caffe::CPU) { }
 
 Caffe::~Caffe() { }
 
@@ -86,7 +86,7 @@ void* Caffe::RNG::generator() {
 
 Caffe::Caffe()
     : cublas_handle_(NULL), curand_generator_(NULL), random_generator_(),
-    mode_(Caffe::CPU), phase_(Caffe::TRAIN) {
+    mode_(Caffe::CPU) {
   // Try to create a cublas handler, and report an error if failed (but we will
   // keep the program running as one might just want to run CPU code).
   if (cublasCreate(&cublas_handle_) != CUBLAS_STATUS_SUCCESS) {
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index c0ad72ee67d..b0b98e478c1 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -11,9 +11,9 @@
 namespace caffe {
 
 template<typename Dtype>
-DataTransformer<Dtype>::DataTransformer(const TransformationParameter& param)
-    : param_(param) {
-  phase_ = Caffe::phase();
+DataTransformer<Dtype>::DataTransformer(const TransformationParameter& param,
+    Phase phase)
+    : param_(param), phase_(phase) {
   // check if we want to use mean_file
   if (param_.has_mean_file()) {
     CHECK_EQ(param_.mean_value_size(), 0) <<
@@ -80,7 +80,7 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
     height = crop_size;
     width = crop_size;
     // We only do random crop when we do training.
-    if (phase_ == Caffe::TRAIN) {
+    if (phase_ == TRAIN) {
       h_off = Rand(datum_height - crop_size + 1);
       w_off = Rand(datum_width - crop_size + 1);
     } else {
@@ -247,7 +247,7 @@ void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
     CHECK_EQ(crop_size, height);
     CHECK_EQ(crop_size, width);
     // We only do random crop when we do training.
-    if (phase_ == Caffe::TRAIN) {
+    if (phase_ == TRAIN) {
       h_off = Rand(img_height - crop_size + 1);
       w_off = Rand(img_width - crop_size + 1);
     } else {
@@ -325,7 +325,7 @@ void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
     CHECK_EQ(crop_size, height);
     CHECK_EQ(crop_size, width);
     // We only do random crop when we do training.
-    if (phase_ == Caffe::TRAIN) {
+    if (phase_ == TRAIN) {
       h_off = Rand(input_height - crop_size + 1);
       w_off = Rand(input_width - crop_size + 1);
     } else {
@@ -398,7 +398,7 @@ void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
 template <typename Dtype>
 void DataTransformer<Dtype>::InitRand() {
   const bool needs_rand = param_.mirror() ||
-      (phase_ == Caffe::TRAIN && param_.crop_size());
+      (phase_ == TRAIN && param_.crop_size());
   if (needs_rand) {
     const unsigned int rng_seed = caffe_rng_rand();
     rng_.reset(new Caffe::RNG(rng_seed));
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index c3fd1f30ff2..d6a1cac5090 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -5,11 +5,15 @@
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/vision_layers.hpp"
 
+#ifdef WITH_PYTHON_LAYER
+#include "caffe/python_layer.hpp"
+#endif
+
 namespace caffe {
 
 // Get convolution layer according to engine.
 template <typename Dtype>
-Layer<Dtype>* GetConvolutionLayer(
+shared_ptr<Layer<Dtype> > GetConvolutionLayer(
     const LayerParameter& param) {
   ConvolutionParameter_Engine engine = param.convolution_param().engine();
   if (engine == ConvolutionParameter_Engine_DEFAULT) {
@@ -19,10 +23,10 @@ Layer<Dtype>* GetConvolutionLayer(
 #endif
   }
   if (engine == ConvolutionParameter_Engine_CAFFE) {
-    return new ConvolutionLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new ConvolutionLayer<Dtype>(param));
 #ifdef USE_CUDNN
   } else if (engine == ConvolutionParameter_Engine_CUDNN) {
-    return new CuDNNConvolutionLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new CuDNNConvolutionLayer<Dtype>(param));
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
@@ -33,7 +37,7 @@ REGISTER_LAYER_CREATOR(Convolution, GetConvolutionLayer);
 
 // Get pooling layer according to engine.
 template <typename Dtype>
-Layer<Dtype>* GetPoolingLayer(const LayerParameter& param) {
+shared_ptr<Layer<Dtype> > GetPoolingLayer(const LayerParameter& param) {
   PoolingParameter_Engine engine = param.pooling_param().engine();
   if (engine == PoolingParameter_Engine_DEFAULT) {
     engine = PoolingParameter_Engine_CAFFE;
@@ -42,7 +46,7 @@ Layer<Dtype>* GetPoolingLayer(const LayerParameter& param) {
 #endif
   }
   if (engine == PoolingParameter_Engine_CAFFE) {
-    return new PoolingLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new PoolingLayer<Dtype>(param));
 #ifdef USE_CUDNN
   } else if (engine == PoolingParameter_Engine_CUDNN) {
     PoolingParameter p_param = param.pooling_param();
@@ -50,9 +54,9 @@ Layer<Dtype>* GetPoolingLayer(const LayerParameter& param) {
         param.top_size() > 1) {
       LOG(INFO) << "CUDNN does not support padding or multiple tops. "
                 << "Using Caffe's own pooling layer.";
-      return new PoolingLayer<Dtype>(param);
+      return shared_ptr<Layer<Dtype> >(new PoolingLayer<Dtype>(param));
     }
-    return new CuDNNPoolingLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new CuDNNPoolingLayer<Dtype>(param));
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
@@ -63,7 +67,7 @@ REGISTER_LAYER_CREATOR(Pooling, GetPoolingLayer);
 
 // Get relu layer according to engine.
 template <typename Dtype>
-Layer<Dtype>* GetReLULayer(const LayerParameter& param) {
+shared_ptr<Layer<Dtype> > GetReLULayer(const LayerParameter& param) {
   ReLUParameter_Engine engine = param.relu_param().engine();
   if (engine == ReLUParameter_Engine_DEFAULT) {
     engine = ReLUParameter_Engine_CAFFE;
@@ -72,10 +76,10 @@ Layer<Dtype>* GetReLULayer(const LayerParameter& param) {
 #endif
   }
   if (engine == ReLUParameter_Engine_CAFFE) {
-    return new ReLULayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new ReLULayer<Dtype>(param));
 #ifdef USE_CUDNN
   } else if (engine == ReLUParameter_Engine_CUDNN) {
-    return new CuDNNReLULayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new CuDNNReLULayer<Dtype>(param));
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
@@ -86,7 +90,7 @@ REGISTER_LAYER_CREATOR(ReLU, GetReLULayer);
 
 // Get sigmoid layer according to engine.
 template <typename Dtype>
-Layer<Dtype>* GetSigmoidLayer(const LayerParameter& param) {
+shared_ptr<Layer<Dtype> > GetSigmoidLayer(const LayerParameter& param) {
   SigmoidParameter_Engine engine = param.sigmoid_param().engine();
   if (engine == SigmoidParameter_Engine_DEFAULT) {
     engine = SigmoidParameter_Engine_CAFFE;
@@ -95,10 +99,10 @@ Layer<Dtype>* GetSigmoidLayer(const LayerParameter& param) {
 #endif
   }
   if (engine == SigmoidParameter_Engine_CAFFE) {
-    return new SigmoidLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new SigmoidLayer<Dtype>(param));
 #ifdef USE_CUDNN
   } else if (engine == SigmoidParameter_Engine_CUDNN) {
-    return new CuDNNSigmoidLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new CuDNNSigmoidLayer<Dtype>(param));
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
@@ -109,7 +113,7 @@ REGISTER_LAYER_CREATOR(Sigmoid, GetSigmoidLayer);
 
 // Get softmax layer according to engine.
 template <typename Dtype>
-Layer<Dtype>* GetSoftmaxLayer(const LayerParameter& param) {
+shared_ptr<Layer<Dtype> > GetSoftmaxLayer(const LayerParameter& param) {
   SoftmaxParameter_Engine engine = param.softmax_param().engine();
   if (engine == SoftmaxParameter_Engine_DEFAULT) {
     engine = SoftmaxParameter_Engine_CAFFE;
@@ -118,10 +122,10 @@ Layer<Dtype>* GetSoftmaxLayer(const LayerParameter& param) {
 #endif
   }
   if (engine == SoftmaxParameter_Engine_CAFFE) {
-    return new SoftmaxLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new SoftmaxLayer<Dtype>(param));
 #ifdef USE_CUDNN
   } else if (engine == SoftmaxParameter_Engine_CUDNN) {
-    return new CuDNNSoftmaxLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new CuDNNSoftmaxLayer<Dtype>(param));
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
@@ -132,7 +136,7 @@ REGISTER_LAYER_CREATOR(Softmax, GetSoftmaxLayer);
 
 // Get tanh layer according to engine.
 template <typename Dtype>
-Layer<Dtype>* GetTanHLayer(const LayerParameter& param) {
+shared_ptr<Layer<Dtype> > GetTanHLayer(const LayerParameter& param) {
   TanHParameter_Engine engine = param.tanh_param().engine();
   if (engine == TanHParameter_Engine_DEFAULT) {
     engine = TanHParameter_Engine_CAFFE;
@@ -141,10 +145,10 @@ Layer<Dtype>* GetTanHLayer(const LayerParameter& param) {
 #endif
   }
   if (engine == TanHParameter_Engine_CAFFE) {
-    return new TanHLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new TanHLayer<Dtype>(param));
 #ifdef USE_CUDNN
   } else if (engine == TanHParameter_Engine_CUDNN) {
-    return new CuDNNTanHLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new CuDNNTanHLayer<Dtype>(param));
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
@@ -153,6 +157,23 @@ Layer<Dtype>* GetTanHLayer(const LayerParameter& param) {
 
 REGISTER_LAYER_CREATOR(TanH, GetTanHLayer);
 
+#ifdef WITH_PYTHON_LAYER
+template <typename Dtype>
+shared_ptr<Layer<Dtype> > GetPythonLayer(const LayerParameter& param) {
+  Py_Initialize();
+  try {
+    bp::object module = bp::import(param.python_param().module().c_str());
+    bp::object layer = module.attr(param.python_param().layer().c_str())(param);
+    return bp::extract<shared_ptr<PythonLayer<Dtype> > >(layer)();
+  } catch (bp::error_already_set) {
+    PyErr_Print();
+    throw;
+  }
+}
+
+REGISTER_LAYER_CREATOR(Python, GetPythonLayer);
+#endif
+
 // Layers that use their constructor as their default creator should be
 // registered in their corresponding cpp files. Do not register them here.
 }  // namespace caffe
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index eb0aaf82120..352200915d7 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -2,6 +2,7 @@
 #include <vector>
 
 #include "caffe/data_layers.hpp"
+#include "caffe/net.hpp"
 #include "caffe/util/io.hpp"
 
 namespace caffe {
@@ -9,8 +10,7 @@ namespace caffe {
 template <typename Dtype>
 BaseDataLayer<Dtype>::BaseDataLayer(const LayerParameter& param)
     : Layer<Dtype>(param),
-      transform_param_(param.transform_param()),
-      data_transformer_(transform_param_) {
+      transform_param_(param.transform_param()) {
 }
 
 template <typename Dtype>
@@ -23,7 +23,9 @@ void BaseDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   }
   // The subclasses should setup the size of bottom and top
   DataLayerSetUp(bottom, top);
-  data_transformer_.InitRand();
+  data_transformer_.reset(
+      new DataTransformer<Dtype>(transform_param_, this->phase_));
+  data_transformer_->InitRand();
 }
 
 template <typename Dtype>
@@ -45,8 +47,7 @@ void BasePrefetchingDataLayer<Dtype>::LayerSetUp(
 
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::CreatePrefetchThread() {
-  this->phase_ = Caffe::phase();
-  this->data_transformer_.InitRand();
+  this->data_transformer_->InitRand();
   CHECK(StartInternalThread()) << "Thread execution failed";
 }
 
@@ -61,6 +62,9 @@ void BasePrefetchingDataLayer<Dtype>::Forward_cpu(
   // First, join the thread
   JoinPrefetchThread();
   DLOG(INFO) << "Thread joined";
+  // Reshape to loaded data.
+  top[0]->Reshape(this->prefetch_data_.num(), this->prefetch_data_.channels(),
+      this->prefetch_data_.height(), this->prefetch_data_.width());
   // Copy the data
   caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
              top[0]->mutable_cpu_data());
diff --git a/src/caffe/layers/base_data_layer.cu b/src/caffe/layers/base_data_layer.cu
index 204a16d260a..775f6c47f7e 100644
--- a/src/caffe/layers/base_data_layer.cu
+++ b/src/caffe/layers/base_data_layer.cu
@@ -9,6 +9,9 @@ void BasePrefetchingDataLayer<Dtype>::Forward_gpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   // First, join the thread
   JoinPrefetchThread();
+  // Reshape to loaded data.
+  top[0]->Reshape(this->prefetch_data_.num(), this->prefetch_data_.channels(),
+      this->prefetch_data_.height(), this->prefetch_data_.width());
   // Copy the data
   caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
       top[0]->mutable_gpu_data());
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 7716406f672..8877caf89c8 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -51,7 +51,7 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   int crop_size = this->layer_param_.transform_param().crop_size();
   if (crop_size > 0) {
     top[0]->Reshape(this->layer_param_.data_param().batch_size(),
-                       datum.channels(), crop_size, crop_size);
+        datum.channels(), crop_size, crop_size);
     this->prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
         datum.channels(), crop_size, crop_size);
     this->transformed_data_.Reshape(1, datum.channels(), crop_size, crop_size);
@@ -85,13 +85,25 @@ void DataLayer<Dtype>::InternalThreadEntry() {
   CPUTimer timer;
   CHECK(this->prefetch_data_.count());
   CHECK(this->transformed_data_.count());
+
+  // Reshape on single input batches for inputs of varying dimension.
+  const int batch_size = this->layer_param_.data_param().batch_size();
+  const int crop_size = this->layer_param_.transform_param().crop_size();
+  if (batch_size == 1 && crop_size == 0) {
+    Datum datum;
+    datum.ParseFromString(cursor_->value());
+    this->prefetch_data_.Reshape(1, datum.channels(),
+        datum.height(), datum.width());
+    this->transformed_data_.Reshape(1, datum.channels(),
+        datum.height(), datum.width());
+  }
+
   Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
   Dtype* top_label = NULL;  // suppress warnings about uninitialized variables
 
   if (this->output_labels_) {
     top_label = this->prefetch_label_.mutable_cpu_data();
   }
-  const int batch_size = this->layer_param_.data_param().batch_size();
   bool force_color = this->layer_param_.data_param().force_encoded_color();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     timer.Start();
@@ -101,15 +113,17 @@ void DataLayer<Dtype>::InternalThreadEntry() {
 
     cv::Mat cv_img;
     if (datum.encoded()) {
-      if (force_color)
+      if (force_color) {
         cv_img = DecodeDatumToCVMat(datum, true);
-      else
+      } else {
         cv_img = DecodeDatumToCVMatNative(datum);
-      if (cv_img.channels() != this->transformed_data_.channels())
+      }
+      if (cv_img.channels() != this->transformed_data_.channels()) {
         LOG(WARNING) << "Your dataset contains encoded images with mixed "
         << "channel sizes. Consider adding a 'force_color' flag to the "
         << "model definition, or rebuild your dataset using "
         << "convert_imageset.";
+      }
     }
     read_time += timer.MicroSeconds();
     timer.Start();
@@ -118,9 +132,9 @@ void DataLayer<Dtype>::InternalThreadEntry() {
     int offset = this->prefetch_data_.offset(item_id);
     this->transformed_data_.set_cpu_data(top_data + offset);
     if (datum.encoded()) {
-      this->data_transformer_.Transform(cv_img, &(this->transformed_data_));
+      this->data_transformer_->Transform(cv_img, &(this->transformed_data_));
     } else {
-      this->data_transformer_.Transform(datum, &(this->transformed_data_));
+      this->data_transformer_->Transform(datum, &(this->transformed_data_));
     }
     if (this->output_labels_) {
       top_label[item_id] = datum.label();
diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index 5f81cc1c692..ec1256fd2fa 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -37,7 +37,7 @@ void DropoutLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   Dtype* top_data = top[0]->mutable_cpu_data();
   unsigned int* mask = rand_vec_.mutable_cpu_data();
   const int count = bottom[0]->count();
-  if (Caffe::phase() == Caffe::TRAIN) {
+  if (this->phase_ == TRAIN) {
     // Create random numbers
     caffe_rng_bernoulli(count, 1. - threshold_, mask);
     for (int i = 0; i < count; ++i) {
@@ -55,7 +55,7 @@ void DropoutLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   if (propagate_down[0]) {
     const Dtype* top_diff = top[0]->cpu_diff();
     Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
-    if (Caffe::phase() == Caffe::TRAIN) {
+    if (this->phase_ == TRAIN) {
       const unsigned int* mask = rand_vec_.cpu_data();
       const int count = bottom[0]->count();
       for (int i = 0; i < count; ++i) {
diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu
index df13d8ecb23..f9ea04f4acf 100644
--- a/src/caffe/layers/dropout_layer.cu
+++ b/src/caffe/layers/dropout_layer.cu
@@ -26,7 +26,7 @@ void DropoutLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
   const int count = bottom[0]->count();
-  if (Caffe::phase() == Caffe::TRAIN) {
+  if (this->phase_ == TRAIN) {
     unsigned int* mask =
         static_cast<unsigned int*>(rand_vec_.mutable_gpu_data());
     caffe_gpu_rng_uniform(count, mask);
@@ -56,7 +56,7 @@ void DropoutLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   if (propagate_down[0]) {
     const Dtype* top_diff = top[0]->gpu_diff();
     Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
-    if (Caffe::phase() == Caffe::TRAIN) {
+    if (this->phase_ == TRAIN) {
       const unsigned int* mask =
           static_cast<const unsigned int*>(rand_vec_.gpu_data());
       const int count = bottom[0]->count();
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index bd4b8a033c2..f9046e1b3a1 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -102,15 +102,27 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
   CPUTimer timer;
   CHECK(this->prefetch_data_.count());
   CHECK(this->transformed_data_.count());
-  Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
-  Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
   ImageDataParameter image_data_param = this->layer_param_.image_data_param();
   const int batch_size = image_data_param.batch_size();
   const int new_height = image_data_param.new_height();
   const int new_width = image_data_param.new_width();
+  const int crop_size = this->layer_param_.transform_param().crop_size();
   const bool is_color = image_data_param.is_color();
   string root_folder = image_data_param.root_folder();
 
+  // Reshape on single input batches for inputs of varying dimension.
+  if (batch_size == 1 && crop_size == 0 && new_height == 0 && new_width == 0) {
+    cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
+        0, 0, is_color);
+    this->prefetch_data_.Reshape(1, cv_img.channels(),
+        cv_img.rows, cv_img.cols);
+    this->transformed_data_.Reshape(1, cv_img.channels(),
+        cv_img.rows, cv_img.cols);
+  }
+
+  Dtype* prefetch_data = this->prefetch_data_.mutable_cpu_data();
+  Dtype* prefetch_label = this->prefetch_label_.mutable_cpu_data();
+
   // datum scales
   const int lines_size = lines_.size();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
@@ -124,11 +136,11 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
     timer.Start();
     // Apply transformations (mirror, crop...) to the image
     int offset = this->prefetch_data_.offset(item_id);
-    this->transformed_data_.set_cpu_data(top_data + offset);
-    this->data_transformer_.Transform(cv_img, &(this->transformed_data_));
+    this->transformed_data_.set_cpu_data(prefetch_data + offset);
+    this->data_transformer_->Transform(cv_img, &(this->transformed_data_));
     trans_time += timer.MicroSeconds();
 
-    top_label[item_id] = lines_[lines_id_].second;
+    prefetch_label[item_id] = lines_[lines_id_].second;
     // go to the next iter
     lines_id_++;
     if (lines_id_ >= lines_size) {
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 6f584de32d0..effdad90aff 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -40,7 +40,7 @@ void MemoryDataLayer<Dtype>::AddDatumVector(const vector<Datum>& datum_vector) {
   added_data_.Reshape(num, channels_, height_, width_);
   added_label_.Reshape(num, 1, 1, 1);
   // Apply data transformations (mirror, scale, crop...)
-  this->data_transformer_.Transform(datum_vector, &added_data_);
+  this->data_transformer_->Transform(datum_vector, &added_data_);
   // Copy Labels
   Dtype* top_label = added_label_.mutable_cpu_data();
   for (int item_id = 0; item_id < num; ++item_id) {
@@ -64,7 +64,7 @@ void MemoryDataLayer<Dtype>::AddMatVector(const vector<cv::Mat>& mat_vector,
   added_data_.Reshape(num, channels_, height_, width_);
   added_label_.Reshape(num, 1, 1, 1);
   // Apply data transformations (mirror, scale, crop...)
-  this->data_transformer_.Transform(mat_vector, &added_data_);
+  this->data_transformer_->Transform(mat_vector, &added_data_);
   // Copy Labels
   Dtype* top_label = added_label_.mutable_cpu_data();
   for (int item_id = 0; item_id < num; ++item_id) {
@@ -81,9 +81,11 @@ void MemoryDataLayer<Dtype>::Reset(Dtype* data, Dtype* labels, int n) {
   CHECK(data);
   CHECK(labels);
   CHECK_EQ(n % batch_size_, 0) << "n must be a multiple of batch size";
-  // Refuse transformation parameters since a memory array is totally generic.
-  CHECK(!this->layer_param_.has_transform_param()) <<
-      this->type() << " does not transform data.";
+  // Warn with transformation parameters since a memory array is meant to
+  // be generic and no transformations are done with Reset().
+  if (this->layer_param_.has_transform_param()) {
+    LOG(WARNING) << this->type() << " does not transform array data on Reset()";
+  }
   data_ = data;
   labels_ = labels;
   n_ = n;
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 0d3f2183e71..d1d48501af3 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -182,7 +182,7 @@ void PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
         kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data);
     break;
   case PoolingParameter_PoolMethod_STOCHASTIC:
-    if (Caffe::phase() == Caffe::TRAIN) {
+    if (this->phase_ == TRAIN) {
       // We need to create the random index as well.
       caffe_gpu_rng_uniform(count, Dtype(0), Dtype(1),
                             rand_idx_.mutable_gpu_data());
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index 8f6a4ef7959..0c9ba2c6626 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -15,7 +15,7 @@ void SoftmaxWithLossLayer<Dtype>::LayerSetUp(
   LossLayer<Dtype>::LayerSetUp(bottom, top);
   LayerParameter softmax_param(this->layer_param_);
   softmax_param.set_type("Softmax");
-  softmax_layer_.reset(LayerRegistry<Dtype>::CreateLayer(softmax_param));
+  softmax_layer_ = LayerRegistry<Dtype>::CreateLayer(softmax_param);
   softmax_bottom_vec_.clear();
   softmax_bottom_vec_.push_back(bottom[0]);
   softmax_top_vec_.clear();
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 73408c6e1f2..36e41560327 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -1,3 +1,4 @@
+#include <opencv2/highgui/highgui_c.h>
 #include <stdint.h>
 
 #include <algorithm>
@@ -22,10 +23,6 @@
 //   'source' field specifies the window_file
 //   'crop_size' indicates the desired warped size
 
-#if CV_VERSION_MAJOR == 3
-const int CV_LOAD_IMAGE_COLOR = cv::IMREAD_COLOR;
-#endif
-
 namespace caffe {
 
 template <typename Dtype>
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index b3e11943c1b..c359be9b575 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -24,14 +24,17 @@ Net<Dtype>::Net(const NetParameter& param) {
 }
 
 template <typename Dtype>
-Net<Dtype>::Net(const string& param_file) {
+Net<Dtype>::Net(const string& param_file, Phase phase) {
   NetParameter param;
   ReadNetParamsFromTextFileOrDie(param_file, &param);
+  param.mutable_state()->set_phase(phase);
   Init(param);
 }
 
 template <typename Dtype>
 void Net<Dtype>::Init(const NetParameter& in_param) {
+  // Set phase from the state.
+  phase_ = in_param.state().phase();
   // Filter layers based on their include/exclude rules and
   // the current NetState.
   NetParameter filtered_param;
@@ -62,9 +65,13 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   top_id_vecs_.resize(param.layer_size());
   bottom_need_backward_.resize(param.layer_size());
   for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) {
+    // Inherit phase from net if unset.
+    if (!param.layer(layer_id).has_phase()) {
+      param.mutable_layer(layer_id)->set_phase(phase_);
+    }
+    // Setup layer.
     const LayerParameter& layer_param = param.layer(layer_id);
-    layers_.push_back(shared_ptr<Layer<Dtype> >(
-          LayerRegistry<Dtype>::CreateLayer(layer_param)));
+    layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
     layer_names_.push_back(layer_param.name());
     LOG(INFO) << "Creating Layer " << layer_param.name();
     bool need_backward = false;
@@ -211,20 +218,6 @@ template <typename Dtype>
 void Net<Dtype>::FilterNet(const NetParameter& param,
     NetParameter* param_filtered) {
   NetState net_state(param.state());
-  // Let the phase of the net be the current global phase provided in the Caffe
-  // singleton, unless explicitly provided by the state.
-  if (!net_state.has_phase()) {
-    switch (Caffe::phase()) {
-      case Caffe::TRAIN:
-        net_state.set_phase(TRAIN);
-        break;
-      case Caffe::TEST:
-        net_state.set_phase(TEST);
-        break;
-      default:
-        LOG(FATAL) << "Unknown phase: " << Caffe::phase();
-    }
-  }
   param_filtered->CopyFrom(param);
   param_filtered->clear_layer();
   for (int i = 0; i < param.layer_size(); ++i) {
diff --git a/src/caffe/proto/CMakeLists.txt b/src/caffe/proto/CMakeLists.txt
deleted file mode 100644
index 12e7ce0a326..00000000000
--- a/src/caffe/proto/CMakeLists.txt
+++ /dev/null
@@ -1,46 +0,0 @@
-project( Proto )
-
-#	Google Protocol Buffers
-find_package( Protobuf REQUIRED )
-
-# As of Ubuntu 14.04 protoc is no longer a part of libprotobuf-dev package and should be installed
-# separately as in: sudo apt-get install protobuf-compiler
-if(PROTOBUF_PROTOC_EXECUTABLE)
-	message(STATUS "Found PROTOBUF Compiler: ${PROTOBUF_PROTOC_EXECUTABLE}")
-else()
-	message(FATAL_ERROR "Could not find PROTOBUF Compiler")
-endif()
-
-include_directories(${PROTOBUF_INCLUDE_DIR})
-file(GLOB ProtoFiles "${CMAKE_CURRENT_SOURCE_DIR}/*.proto")
-PROTOBUF_GENERATE_CPP(ProtoSources ProtoHeaders ${ProtoFiles})
-PROTOBUF_GENERATE_PYTHON(ProtoSourcesPy ${ProtoFiles})
-
-add_custom_target(protoPy DEPENDS ${ProtoSourcesPy})
-
-add_library(proto
-        ${ProtoSources}
-        ${ProtoHeaders}
-        )
-   
-
-target_link_libraries(proto ${PROTOBUF_LIBRARIES})
-
-# Create proto include directory
-file(MAKE_DIRECTORY ${CMAKE_SOURCE_DIR}/include/caffe/proto)
-
-# Copy proto headers to include/caffe/proto/
-foreach(header ${ProtoHeaders})
-
-    ADD_CUSTOM_COMMAND(TARGET proto
-        COMMAND cmake -E copy ${header}
-        ${Caffe_INCLUDE_DIRS}/caffe/proto/
-        DEPENDS ${header}
-)
-
-endforeach(header)
-
-file(WRITE __init__.py)
-install(PROGRAMS __init__.py       DESTINATION python/caffe/proto)
-install(PROGRAMS ${ProtoSourcesPy} DESTINATION python/caffe/proto)
-
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 8ba60753570..84b475ce3cd 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -246,13 +246,16 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 129 (last added: window_data_param)
+// LayerParameter next available layer-specific ID: 131 (last added: python_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
   repeated string bottom = 3; // the name of each bottom blob
   repeated string top = 4; // the name of each top blob
 
+  // The train / test phase for computation.
+  optional Phase phase = 10;
+
   // The amount of weight to assign each top blob in the objective.
   // Each layer assigns a default value, usually of either 0 or 1,
   // to each top blob.
@@ -307,6 +310,7 @@ message LayerParameter {
   optional MVNParameter mvn_param = 120;
   optional PoolingParameter pooling_param = 121;
   optional PowerParameter power_param = 122;
+  optional PythonParameter python_param = 130;
   optional ReLUParameter relu_param = 123;
   optional SigmoidParameter sigmoid_param = 124;
   optional SoftmaxParameter softmax_param = 125;
@@ -614,6 +618,12 @@ message PowerParameter {
   optional float shift = 3 [default = 0.0];
 }
 
+// Message that stores parameters used by PythonLayer
+message PythonParameter {
+  optional string module = 1;
+  optional string layer = 2;
+}
+
 // Message that stores parameters used by ReLULayer
 message ReLUParameter {
   // Allow non-zero slope for negative inputs to speed up optimization
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 9866d7cbc56..8ed8aec2fc8 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -219,7 +219,6 @@ void Solver<Dtype>::Step(int iters) {
 
 template <typename Dtype>
 void Solver<Dtype>::Solve(const char* resume_file) {
-  Caffe::set_phase(Caffe::TRAIN);
   LOG(INFO) << "Solving " << net_->name();
   LOG(INFO) << "Learning Rate Policy: " << param_.lr_policy();
 
@@ -266,8 +265,6 @@ template <typename Dtype>
 void Solver<Dtype>::Test(const int test_net_id) {
   LOG(INFO) << "Iteration " << iter_
             << ", Testing net (#" << test_net_id << ")";
-  // We need to set phase to test before running.
-  Caffe::set_phase(Caffe::TEST);
   CHECK_NOTNULL(test_nets_[test_net_id].get())->
       ShareTrainedLayersWith(net_.get());
   vector<Dtype> test_score;
@@ -318,7 +315,6 @@ void Solver<Dtype>::Test(const int test_net_id) {
     LOG(INFO) << "    Test net output #" << i << ": " << output_name << " = "
         << mean_score << loss_msg_stream.str();
   }
-  Caffe::set_phase(Caffe::TRAIN);
 }
 
 
diff --git a/src/caffe/test/CMakeLists.txt b/src/caffe/test/CMakeLists.txt
index ce0aa4c5148..35a803f2f41 100644
--- a/src/caffe/test/CMakeLists.txt
+++ b/src/caffe/test/CMakeLists.txt
@@ -1,105 +1,36 @@
-#
-#
-#    All test files' names must begin with a "test_" prefix
-#
-#
-project( Test )
-
-# Configuration
-set(TEST_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)    # test executables are going to be placed there
-set(TEST_EXT .testbin)           # test executable extension
-set(ALL_TEST test${TEST_EXT})    # name of an executable comprising of all tests
-set(RUN_TEST runtest)            # dummy target for running tests
-set(TEST_MAIN test_caffe_main.cpp)     # main test file (with main function)
-
-#    Generate config files
-add_definitions(-DCMAKE_BUILD)    # definition needed in order to include CMake's generated files
-set(IN_EXT .in)            # generator input file extension
-set(GEN_EXT .gen.cmake)    # generated output file extension
-set(TEST_DEFINES_FILE ${CMAKE_CURRENT_SOURCE_DIR}/cmake_test_defines.hpp)
-set(TEST_DATA_FILE ${CMAKE_CURRENT_SOURCE_DIR}/test_data/sample_data_list.txt)
-
-# Function prepares name of a test executable
-#    @output_name -  output variable's name
-#    @filename    -  test_*.cpp file path
-function(test_name output_name filename)
-    get_filename_component(name ${filename} NAME_WE)
-    set(${output_name} ${name}${TEST_EXT} PARENT_SCOPE)
-endfunction()
-
-set(IN_FILES             # generator input files
-    ${TEST_DEFINES_FILE}
-    ${TEST_DATA_FILE}
-)
-
-foreach(in_file ${IN_FILES})
-    configure_file(
-        ${in_file}${IN_EXT}
-        ${in_file}${GEN_EXT}
-    )
-endforeach()
-
-include_directories(
-        ${Caffe_SOURCE_DIR}
-        ${CMAKE_CURRENT_SOURCE_DIR}
-)
-
-# Remove main from test sources and prepare an Object lib with main
-file(GLOB TEST_MAIN ${TEST_MAIN})
-list(REMOVE_ITEM TEST_CPP_SOURCES ${TEST_MAIN})
-add_library(main_obj EXCLUDE_FROM_ALL OBJECT ${TEST_MAIN})
-
-#    Build each test separately from *.cpp files
-foreach(source ${TEST_CPP_SOURCES})
-    test_name(TEST_NAME ${source})
-
-    #
-    add_library(${TEST_NAME}.obj EXCLUDE_FROM_ALL OBJECT ${source})
-    set(TEST_OBJ_LIB $<TARGET_OBJECTS:${TEST_NAME}.obj>)
-
-    add_executable(${TEST_NAME} EXCLUDE_FROM_ALL ${TEST_OBJ_LIB} $<TARGET_OBJECTS:main_obj>)
-    target_link_libraries(${TEST_NAME} gtest ${CAFFE_STATIC_LINK})
-
-    #    output dir
-    set_target_properties(${TEST_NAME} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
-
-    # Targets and object libs
-    set(TEST_TARGETS ${TEST_TARGETS} ${TEST_NAME})
-    set(TEST_OBJ_LIBS ${TEST_OBJ_LIBS} ${TEST_OBJ_LIB})
-endforeach()
-
-#    Build each test separately from *.cu files
-foreach(source ${TEST_CU_SOURCES})
-    test_name(TEST_NAME ${source})
-
-    cuda_add_library(${TEST_NAME}.lib EXCLUDE_FROM_ALL ${source})
-
-    add_executable(${TEST_NAME} EXCLUDE_FROM_ALL $<TARGET_OBJECTS:main_obj>)
-    target_link_libraries(${TEST_NAME} ${TEST_NAME}.lib gtest ${CAFFE_STATIC_LINK})
-
-    #    output dir
-    set_target_properties(${TEST_NAME} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
-
-    # Targets and object libs
-    set(TEST_TARGETS ${TEST_TARGETS} ${TEST_NAME})
-    set(TEST_CU_LIBS ${TEST_CU_LIBS} ${TEST_NAME}.lib)
-endforeach()
-
-#    Build a compound test excluded from the ALL target
-add_executable(${ALL_TEST} EXCLUDE_FROM_ALL ${TEST_OBJ_LIBS} $<TARGET_OBJECTS:main_obj>)
-if(NOT CPU_ONLY)
-    target_link_libraries(${ALL_TEST} ${TEST_CU_LIBS})
+# The option allows to include in build only selected test files and exclude all others
+# Usage example:
+#  cmake -DBUILD_only_tests="common,net,blob,im2col_kernel"
+set(BUILD_only_tests "" CACHE STRING "Blank or comma-separated list of test files to build without 'test_' prefix and extention")
+caffe_leave_only_selected_tests(test_srcs ${BUILD_only_tests})
+caffe_leave_only_selected_tests(test_cuda ${BUILD_only_tests})
+
+# For 'make runtest' target we don't need to embed test data paths to
+# source files, because test target is executed in source directory
+# That's why the lines below are commented. TODO: remove them
+
+# definition needed to include CMake generated files
+#add_definitions(-DCMAKE_BUILD)
+
+# generates test_data/sample_data_list.txt.gen.cmake
+#caffe_configure_testdatafile(test_data/sample_data_list.txt)
+
+set(the_target test.testbin)
+set(test_args --gtest_shuffle)
+
+if(HAVE_CUDA)
+  caffe_cuda_compile(test_cuda_objs ${test_cuda})
+  list(APPEND test_srcs ${test_cuda_objs} ${test_cuda})
+else()
+  list(APPEND test_args --gtest_filter="-*GPU*")
 endif()
-target_link_libraries(${ALL_TEST} gtest ${CAFFE_STATIC_LINK})
-add_dependencies(${ALL_TEST} ${TEST_TARGETS})
 
-#    Output directory
-set_target_properties(${ALL_TEST} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${TEST_OUTPUT_DIRECTORY})
-
-#    Test command
-set(TEST_ARGS --gtest_shuffle)
-if(CPU_ONLY)
-    set(TEST_ARGS ${TEST_ARGS} --gtest_filter="-*GPU*")
-endif()
+# ---[ Adding test target
+add_executable(${the_target} EXCLUDE_FROM_ALL ${test_srcs})
+target_link_libraries(${the_target} gtest ${Caffe_LINK})
+caffe_default_properties(${the_target})
+caffe_set_runtime_directory(${the_target} "${PROJECT_BINARY_DIR}/test")
 
-add_custom_target(${RUN_TEST} COMMAND ${ALL_TEST} ${TEST_ARGS})
+# ---[ Adding runtest
+add_custom_target(runtest COMMAND ${the_target} ${test_args}
+                          WORKING_DIRECTORY ${PROJECT_SOURCE_DIR})
diff --git a/src/caffe/test/cmake_test_defines.hpp.in b/src/caffe/test/cmake_test_defines.hpp.in
deleted file mode 100644
index 870eaf5c26e..00000000000
--- a/src/caffe/test/cmake_test_defines.hpp.in
+++ /dev/null
@@ -1,4 +0,0 @@
-#define CUDA_TEST_DEVICE @CUDA_TEST_DEVICE@
-#define CMAKE_SOURCE_DIR "@CMAKE_SOURCE_DIR@/src/"
-#define EXAMPLES_SOURCE_DIR "@CMAKE_SOURCE_DIR@/examples/"
-#define CMAKE_EXT ".gen.cmake"
diff --git a/src/caffe/test/test_common.cpp b/src/caffe/test/test_common.cpp
index 0b3639c7706..b3a61b0fd25 100644
--- a/src/caffe/test/test_common.cpp
+++ b/src/caffe/test/test_common.cpp
@@ -29,13 +29,6 @@ TEST_F(CommonTest, TestBrewMode) {
   EXPECT_EQ(Caffe::mode(), Caffe::GPU);
 }
 
-TEST_F(CommonTest, TestPhase) {
-  Caffe::set_phase(Caffe::TRAIN);
-  EXPECT_EQ(Caffe::phase(), Caffe::TRAIN);
-  Caffe::set_phase(Caffe::TEST);
-  EXPECT_EQ(Caffe::phase(), Caffe::TEST);
-}
-
 TEST_F(CommonTest, TestRandSeedCPU) {
   SyncedMemory data_a(10 * sizeof(int));
   SyncedMemory data_b(10 * sizeof(int));
diff --git a/src/caffe/test/test_data/sample_data_list.txt.in b/src/caffe/test/test_data/sample_data_list.txt.in
deleted file mode 100644
index 9860ef583ab..00000000000
--- a/src/caffe/test/test_data/sample_data_list.txt.in
+++ /dev/null
@@ -1,2 +0,0 @@
-@CMAKE_SOURCE_DIR@/src/caffe/test/test_data/sample_data.h5
-@CMAKE_SOURCE_DIR@/src/caffe/test/test_data/sample_data_2_gzip.h5
\ No newline at end of file
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 8cc31b24167..afe2a40d227 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -69,6 +69,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
   void TestRead() {
     const Dtype scale = 3;
     LayerParameter param;
+    param.set_phase(TRAIN);
     DataParameter* data_param = param.mutable_data_param();
     data_param->set_batch_size(5);
     data_param->set_source(filename_->c_str());
@@ -103,9 +104,75 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     }
   }
 
-  void TestReadCrop() {
+  void TestReshape(DataParameter_DB backend) {
+    const int num_inputs = 5;
+    // Save data of varying shapes.
+    LOG(INFO) << "Using temporary dataset " << *filename_;
+    scoped_ptr<db::DB> db(db::GetDB(backend));
+    db->Open(*filename_, db::NEW);
+    scoped_ptr<db::Transaction> txn(db->NewTransaction());
+    for (int i = 0; i < num_inputs; ++i) {
+      Datum datum;
+      datum.set_label(i);
+      datum.set_channels(2);
+      datum.set_height(i % 2 + 1);
+      datum.set_width(i % 4 + 1);
+      std::string* data = datum.mutable_data();
+      const int data_size = datum.channels() * datum.height() * datum.width();
+      for (int j = 0; j < data_size; ++j) {
+        data->push_back(static_cast<uint8_t>(j));
+      }
+      stringstream ss;
+      ss << i;
+      string out;
+      CHECK(datum.SerializeToString(&out));
+      txn->Put(ss.str(), out);
+    }
+    txn->Commit();
+    db->Close();
+
+    // Load and check data of various shapes.
+    LayerParameter param;
+    param.set_phase(TEST);
+    DataParameter* data_param = param.mutable_data_param();
+    data_param->set_batch_size(1);
+    data_param->set_source(filename_->c_str());
+    data_param->set_backend(backend);
+
+    DataLayer<Dtype> layer(param);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
+    EXPECT_EQ(blob_top_data_->num(), 1);
+    EXPECT_EQ(blob_top_data_->channels(), 2);
+    EXPECT_EQ(blob_top_label_->num(), 1);
+    EXPECT_EQ(blob_top_label_->channels(), 1);
+    EXPECT_EQ(blob_top_label_->height(), 1);
+    EXPECT_EQ(blob_top_label_->width(), 1);
+
+    for (int iter = 0; iter < num_inputs; ++iter) {
+      layer.Forward(blob_bottom_vec_, blob_top_vec_);
+      EXPECT_EQ(blob_top_data_->height(), iter % 2 + 1);
+      EXPECT_EQ(blob_top_data_->width(), iter % 4 + 1);
+      EXPECT_EQ(iter, blob_top_label_->cpu_data()[0]);
+      const int channels = blob_top_data_->channels();
+      const int height = blob_top_data_->height();
+      const int width = blob_top_data_->width();
+      for (int c = 0; c < channels; ++c) {
+        for (int h = 0; h < height; ++h) {
+          for (int w = 0; w < width; ++w) {
+            const int idx = (c * height + h) * width + w;
+            EXPECT_EQ(idx, static_cast<int>(blob_top_data_->cpu_data()[idx]))
+                << "debug: iter " << iter << " c " << c
+                << " h " << h << " w " << w;
+          }
+        }
+      }
+    }
+  }
+
+  void TestReadCrop(Phase phase) {
     const Dtype scale = 3;
     LayerParameter param;
+    param.set_phase(phase);
     Caffe::set_random_seed(1701);
 
     DataParameter* data_param = param.mutable_data_param();
@@ -141,7 +208,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
           num_with_center_value +=
               (center_value == blob_top_data_->cpu_data()[i * 2 + j]);
           // At TEST time, check that we always get center value.
-          if (Caffe::phase() == Caffe::TEST) {
+          if (phase == caffe::TEST) {
             EXPECT_EQ(center_value, this->blob_top_data_->cpu_data()[i * 2 + j])
                 << "debug: iter " << iter << " i " << i << " j " << j;
           }
@@ -150,7 +217,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
       // At TRAIN time, check that we did not get the center crop all 10 times.
       // (This check fails with probability 1-1/12^10 in a correct
       // implementation, so we call set_random_seed.)
-      if (Caffe::phase() == Caffe::TRAIN) {
+      if (phase == caffe::TRAIN) {
         EXPECT_LT(num_with_center_value, 10);
       }
     }
@@ -158,6 +225,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
 
   void TestReadCropTrainSequenceSeeded() {
     LayerParameter param;
+    param.set_phase(TRAIN);
     DataParameter* data_param = param.mutable_data_param();
     data_param->set_batch_size(5);
     data_param->set_source(filename_->c_str());
@@ -212,6 +280,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
 
   void TestReadCropTrainSequenceUnseeded() {
     LayerParameter param;
+    param.set_phase(TRAIN);
     DataParameter* data_param = param.mutable_data_param();
     data_param->set_batch_size(5);
     data_param->set_source(filename_->c_str());
@@ -285,17 +354,19 @@ TYPED_TEST(DataLayerTest, TestReadLevelDB) {
   this->TestRead();
 }
 
+TYPED_TEST(DataLayerTest, TestReshapeLevelDB) {
+  this->TestReshape(DataParameter_DB_LEVELDB);
+}
+
 TYPED_TEST(DataLayerTest, TestReadCropTrainLevelDB) {
-  Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
-  this->TestReadCrop();
+  this->TestReadCrop(TRAIN);
 }
 
 // Test that the sequence of random crops is consistent when using
 // Caffe::set_random_seed.
 TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLevelDB) {
-  Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
   this->TestReadCropTrainSequenceSeeded();
@@ -304,17 +375,15 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLevelDB) {
 // Test that the sequence of random crops differs across iterations when
 // Caffe::set_random_seed isn't called (and seeds from srand are ignored).
 TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededLevelDB) {
-  Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
   this->TestReadCropTrainSequenceUnseeded();
 }
 
 TYPED_TEST(DataLayerTest, TestReadCropTestLevelDB) {
-  Caffe::set_phase(Caffe::TEST);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
-  this->TestReadCrop();
+  this->TestReadCrop(TEST);
 }
 
 TYPED_TEST(DataLayerTest, TestReadLMDB) {
@@ -323,17 +392,19 @@ TYPED_TEST(DataLayerTest, TestReadLMDB) {
   this->TestRead();
 }
 
+TYPED_TEST(DataLayerTest, TestReshapeLMDB) {
+  this->TestReshape(DataParameter_DB_LMDB);
+}
+
 TYPED_TEST(DataLayerTest, TestReadCropTrainLMDB) {
-  Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->Fill(unique_pixels, DataParameter_DB_LMDB);
-  this->TestReadCrop();
+  this->TestReadCrop(TRAIN);
 }
 
 // Test that the sequence of random crops is consistent when using
 // Caffe::set_random_seed.
 TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLMDB) {
-  Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->Fill(unique_pixels, DataParameter_DB_LMDB);
   this->TestReadCropTrainSequenceSeeded();
@@ -342,17 +413,15 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLMDB) {
 // Test that the sequence of random crops differs across iterations when
 // Caffe::set_random_seed isn't called (and seeds from srand are ignored).
 TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededLMDB) {
-  Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->Fill(unique_pixels, DataParameter_DB_LMDB);
   this->TestReadCropTrainSequenceUnseeded();
 }
 
 TYPED_TEST(DataLayerTest, TestReadCropTestLMDB) {
-  Caffe::set_phase(Caffe::TEST);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->Fill(unique_pixels, DataParameter_DB_LMDB);
-  this->TestReadCrop();
+  this->TestReadCrop(TEST);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_data_transformer.cpp b/src/caffe/test/test_data_transformer.cpp
index 28c7241050b..16570e20356 100644
--- a/src/caffe/test/test_data_transformer.cpp
+++ b/src/caffe/test/test_data_transformer.cpp
@@ -37,10 +37,10 @@ class DataTransformTest : public ::testing::Test {
       num_iter_(10) {}
 
   int NumSequenceMatches(const TransformationParameter transform_param,
-      const Datum& datum) {
+      const Datum& datum, Phase phase) {
     // Get crop sequence with Caffe seed 1701.
     DataTransformer<Dtype>* transformer =
-        new DataTransformer<Dtype>(transform_param);
+        new DataTransformer<Dtype>(transform_param, phase);
     const int crop_size = transform_param.crop_size();
     Caffe::set_random_seed(seed_);
     transformer->InitRand();
@@ -92,7 +92,7 @@ TYPED_TEST(DataTransformTest, TestEmptyTransform) {
   FillDatum(label, channels, height, width, unique_pixels, &datum);
   Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
   DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param);
+      new DataTransformer<TypeParam>(transform_param, TEST);
   transformer->InitRand();
   transformer->Transform(datum, blob);
   EXPECT_EQ(blob->num(), 1);
@@ -116,7 +116,7 @@ TYPED_TEST(DataTransformTest, TestEmptyTransformUniquePixels) {
   FillDatum(label, channels, height, width, unique_pixels, &datum);
   Blob<TypeParam>* blob = new Blob<TypeParam>(1, 3, 4, 5);
   DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param);
+      new DataTransformer<TypeParam>(transform_param, TEST);
   transformer->InitRand();
   transformer->Transform(datum, blob);
   EXPECT_EQ(blob->num(), 1);
@@ -141,7 +141,7 @@ TYPED_TEST(DataTransformTest, TestCropSize) {
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
   DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param);
+      new DataTransformer<TypeParam>(transform_param, TEST);
   transformer->InitRand();
   Blob<TypeParam>* blob =
       new Blob<TypeParam>(1, channels, crop_size, crop_size);
@@ -170,8 +170,7 @@ TYPED_TEST(DataTransformTest, TestCropTrain) {
   transform_param.set_crop_size(crop_size);
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
-  Caffe::set_phase(Caffe::TRAIN);
-  int num_matches = this->NumSequenceMatches(transform_param, datum);
+  int num_matches = this->NumSequenceMatches(transform_param, datum, TRAIN);
   EXPECT_LT(num_matches, size * this->num_iter_);
 }
 
@@ -188,8 +187,7 @@ TYPED_TEST(DataTransformTest, TestCropTest) {
   transform_param.set_crop_size(crop_size);
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
-  Caffe::set_phase(Caffe::TEST);
-  int num_matches = this->NumSequenceMatches(transform_param, datum);
+  int num_matches = this->NumSequenceMatches(transform_param, datum, TEST);
   EXPECT_EQ(num_matches, size * this->num_iter_);
 }
 
@@ -205,8 +203,7 @@ TYPED_TEST(DataTransformTest, TestMirrorTrain) {
   transform_param.set_mirror(true);
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
-  Caffe::set_phase(Caffe::TRAIN);
-  int num_matches = this->NumSequenceMatches(transform_param, datum);
+  int num_matches = this->NumSequenceMatches(transform_param, datum, TRAIN);
   EXPECT_LT(num_matches, size * this->num_iter_);
 }
 
@@ -222,8 +219,7 @@ TYPED_TEST(DataTransformTest, TestMirrorTest) {
   transform_param.set_mirror(true);
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
-  Caffe::set_phase(Caffe::TEST);
-  int num_matches = this->NumSequenceMatches(transform_param, datum);
+  int num_matches = this->NumSequenceMatches(transform_param, datum, TEST);
   EXPECT_LT(num_matches, size * this->num_iter_);
 }
 
@@ -239,12 +235,12 @@ TYPED_TEST(DataTransformTest, TestCropMirrorTrain) {
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
   transform_param.set_crop_size(crop_size);
-  Caffe::set_phase(Caffe::TRAIN);
-  int num_matches_crop = this->NumSequenceMatches(transform_param, datum);
+  int num_matches_crop = this->NumSequenceMatches(
+      transform_param, datum, TRAIN);
 
   transform_param.set_mirror(true);
   int num_matches_crop_mirror =
-      this->NumSequenceMatches(transform_param, datum);
+      this->NumSequenceMatches(transform_param, datum, TRAIN);
   // When doing crop and mirror we expect less num_matches than just crop
   EXPECT_LE(num_matches_crop_mirror, num_matches_crop);
 }
@@ -261,12 +257,11 @@ TYPED_TEST(DataTransformTest, TestCropMirrorTest) {
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
   transform_param.set_crop_size(crop_size);
-  Caffe::set_phase(Caffe::TEST);
-  int num_matches_crop = this->NumSequenceMatches(transform_param, datum);
+  int num_matches_crop = this->NumSequenceMatches(transform_param, datum, TEST);
 
   transform_param.set_mirror(true);
   int num_matches_crop_mirror =
-      this->NumSequenceMatches(transform_param, datum);
+      this->NumSequenceMatches(transform_param, datum, TEST);
   // When doing crop and mirror we expect less num_matches than just crop
   EXPECT_LT(num_matches_crop_mirror, num_matches_crop);
 }
@@ -286,7 +281,7 @@ TYPED_TEST(DataTransformTest, TestMeanValue) {
   FillDatum(label, channels, height, width, unique_pixels, &datum);
   Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
   DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param);
+      new DataTransformer<TypeParam>(transform_param, TEST);
   transformer->InitRand();
   transformer->Transform(datum, blob);
   for (int j = 0; j < blob->count(); ++j) {
@@ -309,7 +304,7 @@ TYPED_TEST(DataTransformTest, TestMeanValues) {
   FillDatum(label, channels, height, width, unique_pixels, &datum);
   Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
   DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param);
+      new DataTransformer<TypeParam>(transform_param, TEST);
   transformer->InitRand();
   transformer->Transform(datum, blob);
   for (int c = 0; c < channels; ++c) {
@@ -349,7 +344,7 @@ TYPED_TEST(DataTransformTest, TestMeanFile) {
   FillDatum(label, channels, height, width, unique_pixels, &datum);
   Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
   DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param);
+      new DataTransformer<TypeParam>(transform_param, TEST);
   transformer->InitRand();
   transformer->Transform(datum, blob);
   for (int j = 0; j < blob->count(); ++j) {
diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index 77523ef8c18..931a5ebf137 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -25,17 +25,24 @@ class ImageDataLayerTest : public MultiDeviceTest<TypeParam> {
         blob_top_data_(new Blob<Dtype>()),
         blob_top_label_(new Blob<Dtype>()) {}
   virtual void SetUp() {
-    MakeTempFilename(&filename_);
     blob_top_vec_.push_back(blob_top_data_);
     blob_top_vec_.push_back(blob_top_label_);
     Caffe::set_random_seed(seed_);
-    // Create a Vector of files with labels
+    // Create test input file.
+    MakeTempFilename(&filename_);
     std::ofstream outfile(filename_.c_str(), std::ofstream::out);
     LOG(INFO) << "Using temporary file " << filename_;
     for (int i = 0; i < 5; ++i) {
       outfile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << i;
     }
     outfile.close();
+    // Create test input file for images of distinct sizes.
+    MakeTempFilename(&filename_reshape_);
+    std::ofstream reshapefile(filename_reshape_.c_str(), std::ofstream::out);
+    LOG(INFO) << "Using temporary file " << filename_reshape_;
+    reshapefile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << 0;
+    reshapefile << EXAMPLES_SOURCE_DIR "images/fish-bike.jpg " << 1;
+    reshapefile.close();
   }
 
   virtual ~ImageDataLayerTest() {
@@ -45,6 +52,7 @@ class ImageDataLayerTest : public MultiDeviceTest<TypeParam> {
 
   int seed_;
   string filename_;
+  string filename_reshape_;
   Blob<Dtype>* const blob_top_data_;
   Blob<Dtype>* const blob_top_label_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
@@ -107,6 +115,33 @@ TYPED_TEST(ImageDataLayerTest, TestResize) {
   }
 }
 
+TYPED_TEST(ImageDataLayerTest, TestReshape) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter param;
+  ImageDataParameter* image_data_param = param.mutable_image_data_param();
+  image_data_param->set_batch_size(1);
+  image_data_param->set_source(this->filename_reshape_.c_str());
+  image_data_param->set_shuffle(false);
+  ImageDataLayer<Dtype> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_label_->num(), 1);
+  EXPECT_EQ(this->blob_top_label_->channels(), 1);
+  EXPECT_EQ(this->blob_top_label_->height(), 1);
+  EXPECT_EQ(this->blob_top_label_->width(), 1);
+  // cat.jpg
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_data_->num(), 1);
+  EXPECT_EQ(this->blob_top_data_->channels(), 3);
+  EXPECT_EQ(this->blob_top_data_->height(), 360);
+  EXPECT_EQ(this->blob_top_data_->width(), 480);
+  // fish-bike.jpg
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_data_->num(), 1);
+  EXPECT_EQ(this->blob_top_data_->channels(), 3);
+  EXPECT_EQ(this->blob_top_data_->height(), 323);
+  EXPECT_EQ(this->blob_top_data_->width(), 481);
+}
+
 TYPED_TEST(ImageDataLayerTest, TestShuffle) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter param;
diff --git a/src/caffe/test/test_layer_factory.cpp b/src/caffe/test/test_layer_factory.cpp
index 3ad635a792a..efb1b37ac42 100644
--- a/src/caffe/test/test_layer_factory.cpp
+++ b/src/caffe/test/test_layer_factory.cpp
@@ -24,8 +24,10 @@ TYPED_TEST(LayerFactoryTest, TestCreateLayer) {
   LayerParameter layer_param;
   for (typename LayerRegistry<Dtype>::CreatorRegistry::iterator iter =
        registry.begin(); iter != registry.end(); ++iter) {
+    // Special case: PythonLayer is checked by pytest
+    if (iter->first == "Python") { continue; }
     layer_param.set_type(iter->first);
-    layer.reset(LayerRegistry<Dtype>::CreateLayer(layer_param));
+    layer = LayerRegistry<Dtype>::CreateLayer(layer_param);
     EXPECT_EQ(iter->first, layer->type());
   }
 }
diff --git a/src/caffe/test/test_maxpool_dropout_layers.cpp b/src/caffe/test/test_maxpool_dropout_layers.cpp
index b1f4e4eac9a..611d9790863 100644
--- a/src/caffe/test/test_maxpool_dropout_layers.cpp
+++ b/src/caffe/test/test_maxpool_dropout_layers.cpp
@@ -88,8 +88,8 @@ TYPED_TEST(MaxPoolingDropoutTest, TestForward) {
 
 TYPED_TEST(MaxPoolingDropoutTest, TestBackward) {
   typedef typename TypeParam::Dtype Dtype;
-  Caffe::set_phase(Caffe::TRAIN);
   LayerParameter layer_param;
+  layer_param.set_phase(TRAIN);
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index bc0dae331b3..1680a3f28d5 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -1534,20 +1534,6 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       output_proto_test + " state: { phase: TEST } ";
   this->RunFilterNetTest(input_proto_train, output_proto_train_explicit);
   this->RunFilterNetTest(input_proto_test, output_proto_test_explicit);
-
-  // Also check that nets are filtered according to the Caffe singleton phase,
-  // if not explicitly specified in the input proto.
-  Caffe::set_phase(Caffe::TRAIN);
-  this->RunFilterNetTest(input_proto, output_proto_train);
-  Caffe::set_phase(Caffe::TEST);
-  this->RunFilterNetTest(input_proto, output_proto_test);
-
-  // Finally, check that the current Caffe singleton phase is ignored if the
-  // phase is explicitly specified in the input proto.
-  Caffe::set_phase(Caffe::TEST);
-  this->RunFilterNetTest(input_proto_train, output_proto_train_explicit);
-  Caffe::set_phase(Caffe::TRAIN);
-  this->RunFilterNetTest(input_proto_test, output_proto_test_explicit);
 }
 
 TEST_F(FilterNetTest, TestFilterOutByStage) {
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index b19a5abdcdf..ad10720116d 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -43,8 +43,8 @@ class NeuronLayerTest : public MultiDeviceTest<TypeParam> {
     if (dropout_ratio != 0.5) {
       layer_param.mutable_dropout_param()->set_dropout_ratio(dropout_ratio);
     }
-    Caffe::set_phase(Caffe::TRAIN);
     DropoutLayer<Dtype> layer(layer_param);
+    layer_param.set_phase(TRAIN);
     layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
     layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     // Now, check values
@@ -334,7 +334,7 @@ TYPED_TEST(NeuronLayerTest, TestDropoutThreeQuarters) {
 TYPED_TEST(NeuronLayerTest, TestDropoutTestPhase) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_phase(Caffe::TEST);
+  layer_param.set_phase(TEST);
   DropoutLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -351,7 +351,7 @@ TYPED_TEST(NeuronLayerTest, TestDropoutTestPhase) {
 TYPED_TEST(NeuronLayerTest, TestDropoutGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_phase(Caffe::TRAIN);
+  layer_param.set_phase(TRAIN);
   DropoutLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
@@ -361,7 +361,7 @@ TYPED_TEST(NeuronLayerTest, TestDropoutGradient) {
 TYPED_TEST(NeuronLayerTest, TestDropoutGradientTest) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_phase(Caffe::TEST);
+  layer_param.set_phase(TEST);
   DropoutLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
diff --git a/src/caffe/test/test_stochastic_pooling.cpp b/src/caffe/test/test_stochastic_pooling.cpp
index ad5151007c2..12962c65d85 100644
--- a/src/caffe/test/test_stochastic_pooling.cpp
+++ b/src/caffe/test/test_stochastic_pooling.cpp
@@ -62,8 +62,8 @@ TYPED_TEST(StochasticPoolingLayerTest, TestSetup) {
 
 TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPU) {
   Caffe::set_mode(Caffe::GPU);
-  Caffe::set_phase(Caffe::TRAIN);
   LayerParameter layer_param;
+  layer_param.set_phase(TRAIN);
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
@@ -106,8 +106,8 @@ TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPU) {
 
 TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPUTestPhase) {
   Caffe::set_mode(Caffe::GPU);
-  Caffe::set_phase(Caffe::TEST);
   LayerParameter layer_param;
+  layer_param.set_phase(TEST);
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
@@ -144,8 +144,8 @@ TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPUTestPhase) {
 
 TYPED_TEST(StochasticPoolingLayerTest, TestGradientGPU) {
   Caffe::set_mode(Caffe::GPU);
-  Caffe::set_phase(Caffe::TRAIN);
   LayerParameter layer_param;
+  layer_param.set_phase(TRAIN);
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index 7094055d4c8..eec627656ef 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -2901,7 +2901,7 @@ TEST_F(NetUpgradeTest, TestUpgradeV1LayerType) {
       continue;  // Empty string isn't actually a valid layer type.
     }
     layer_param.set_type(v2_layer_type);
-    layer.reset(LayerRegistry<float>::CreateLayer(layer_param));
+    layer = LayerRegistry<float>::CreateLayer(layer_param);
     EXPECT_EQ(v2_layer_type, layer->type());
   }
 }
diff --git a/src/gtest/CMakeLists.txt b/src/gtest/CMakeLists.txt
index 82a4120ca3f..ef7ff7ed14b 100644
--- a/src/gtest/CMakeLists.txt
+++ b/src/gtest/CMakeLists.txt
@@ -1,6 +1,5 @@
-project(gtest CXX C)
-cmake_minimum_required(VERSION 2.6.2)
+add_library(gtest STATIC EXCLUDE_FROM_ALL gtest.h gtest-all.cpp)
+caffe_default_properties(gtest)
 
-add_library(gtest gtest-all.cpp)
-add_library(gtest_main gtest_main.cc)
-target_link_libraries(gtest_main gtest)
\ No newline at end of file
+#add_library(gtest_main gtest_main.cc)
+#target_link_libraries(gtest_main gtest)
diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt
index 110f368b2c8..02fbd5cadd8 100644
--- a/tools/CMakeLists.txt
+++ b/tools/CMakeLists.txt
@@ -1,20 +1,29 @@
-project( Tools )
-
-#    Find all source files
-file(GLOB_RECURSE TOOLS_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
-
-
-#    Build each source file independently
-foreach(source ${TOOLS_SOURCES})
-    get_filename_component(name ${source} NAME_WE)
-    add_executable(${name}.bin ${source})
-    set_target_properties(${name}.bin PROPERTIES OUTPUT_NAME ${name})
-    target_link_libraries(${name}.bin ${CAFFE_STATIC_LINK})
-
-###    Install    #################################################################################
-
-    install(TARGETS ${name}.bin DESTINATION tools)
-
-
+# Collect source files
+file(GLOB_RECURSE srcs ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
+
+# Build each source file independently
+foreach(source ${srcs})
+  get_filename_component(name ${source} NAME_WE)
+
+  # caffe target already exits
+  if(name MATCHES "caffe")
+    set(name ${name}.bin)
+  endif()
+
+  # target
+  add_executable(${name} ${source})
+  target_link_libraries(${name} ${Caffe_LINK})
+  caffe_default_properties(${name})
+
+  # set back RUNTIME_OUTPUT_DIRECTORY
+  caffe_set_runtime_directory(${name} "${PROJECT_BINARY_DIR}/tools")
+  caffe_set_solution_folder(${name} tools)
+
+  # restore output name without suffix
+  if(name MATCHES "caffe.bin")
+    set_target_properties(${name} PROPERTIES OUTPUT_NAME caffe)
+  endif()
+
+  # Install
+  install(TARGETS ${name} DESTINATION bin)
 endforeach(source)
-
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index ad54bc3b9eb..f04e28a3674 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -139,8 +139,7 @@ int test() {
     Caffe::set_mode(Caffe::CPU);
   }
   // Instantiate the caffe net.
-  Caffe::set_phase(Caffe::TEST);
-  Net<float> caffe_net(FLAGS_model);
+  Net<float> caffe_net(FLAGS_model, caffe::TEST);
   caffe_net.CopyTrainedLayersFrom(FLAGS_weights);
   LOG(INFO) << "Running for " << FLAGS_iterations << " iterations.";
 
@@ -205,8 +204,7 @@ int time() {
     Caffe::set_mode(Caffe::CPU);
   }
   // Instantiate the caffe net.
-  Caffe::set_phase(Caffe::TRAIN);
-  Net<float> caffe_net(FLAGS_model);
+  Net<float> caffe_net(FLAGS_model, caffe::TRAIN);
 
   // Do a clean forward and backward pass, so that memory allocation are done
   // and future iterations will be more stable.
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index c17b88dd048..f86ff96ca82 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -64,7 +64,6 @@ int feature_extraction_pipeline(int argc, char** argv) {
     LOG(ERROR) << "Using CPU";
     Caffe::set_mode(Caffe::CPU);
   }
-  Caffe::set_phase(Caffe::TEST);
 
   arg_pos = 0;  // the name of the executable
   std::string pretrained_binary_proto(argv[++arg_pos]);
@@ -98,7 +97,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
    */
   std::string feature_extraction_proto(argv[++arg_pos]);
   shared_ptr<Net<Dtype> > feature_extraction_net(
-      new Net<Dtype>(feature_extraction_proto));
+      new Net<Dtype>(feature_extraction_proto, caffe::TEST));
   feature_extraction_net->CopyTrainedLayersFrom(pretrained_binary_proto);
 
   std::string extract_feature_blob_names(argv[++arg_pos]);