diff --git a/Chemistry/src/DataModel/DataModel.csproj b/Chemistry/src/DataModel/DataModel.csproj
index 5f7d7dba3ea..39485766909 100644
--- a/Chemistry/src/DataModel/DataModel.csproj
+++ b/Chemistry/src/DataModel/DataModel.csproj
@@ -14,7 +14,7 @@
     <Copyright>© Microsoft Corporation. All rights reserved.</Copyright>
     <PackageId>Microsoft.Quantum.Chemistry</PackageId>
     <PackageReleaseNotes>See: https://docs.microsoft.com/en-us/quantum/relnotes/</PackageReleaseNotes>
-    <PackageLicenseUrl>https://github.com/Microsoft/QuantumLibraries/raw/master/LICENSE.txt</PackageLicenseUrl>
+    <PackageLicenseExpression>MIT</PackageLicenseExpression>
     <PackageProjectUrl>https://github.com/Microsoft/QuantumLibraries/tree/master/Chemistry</PackageProjectUrl>
     <PackageIconUrl>https://secure.gravatar.com/avatar/bd1f02955b2853ba0a3b1cdc2434e8ec.png</PackageIconUrl>
     <PackageTags>Quantum Q# Qsharp</PackageTags>
diff --git a/MachineLearning/src/Datasets/IrisDataset.qs b/MachineLearning/src/Datasets/IrisDataset.qs
index 0f7cc0101db..0fefe4bee3f 100644
--- a/MachineLearning/src/Datasets/IrisDataset.qs
+++ b/MachineLearning/src/Datasets/IrisDataset.qs
@@ -5,86 +5,87 @@ namespace Microsoft.Quantum.MachineLearning.Datasets {
     open Microsoft.Quantum.MachineLearning;
 
     operation IrisTrainingData() : LabeledSample[] {
-        return [LabeledSample(([0.581557, 0.562824, 0.447721, 0.380219], 1)),
-                LabeledSample(([0.570241, 0.544165, 0.503041, 0.354484],
-                1)), LabeledSample(([0.510784, 0.475476, 0.453884, 0.554087],
-                0)), LabeledSample(([0.492527, 0.473762, 0.471326, 0.557511],
-                0)), LabeledSample(([0.543273, 0.501972, 0.518341, 0.429186],
-                0)), LabeledSample(([0.520013, 0.485702, 0.440061, 0.547747],
-                0)), LabeledSample(([0.585261, 0.545431, 0.462126, 0.382641],
-                1)), LabeledSample(([0.541059, 0.479438, 0.568697, 0.392401],
-                0)), LabeledSample(([0.555604, 0.517196, 0.474722, 0.445479],
-                1)), LabeledSample(([0.592542, 0.537541, 0.468725, 0.374486],
-                1)), LabeledSample(([0.552254, 0.51027, 0.511855, 0.415505],
-                0)), LabeledSample(([0.530874, 0.465606, 0.503344, 0.498025],
-                0)), LabeledSample(([0.568502, 0.492452, 0.524331, 0.399215],
-                0)), LabeledSample(([0.511768, 0.53197, 0.46875, 0.485156],
-                0)), LabeledSample(([0.555756, 0.420141, 0.553663, 0.456152],
-                0)), LabeledSample(([0.584546, 0.562276, 0.439516, 0.385976],
-                1)), LabeledSample(([0.608485, 0.577022, 0.427781, 0.337336],
-                1)), LabeledSample(([0.546234, 0.59768, 0.46082, 0.36339],
-                1)), LabeledSample(([0.596632, 0.510739, 0.482188, 0.388162],
-                1)), LabeledSample(([0.512997, 0.525043, 0.460839, 0.49879],
-                0)), LabeledSample(([0.477408, 0.488846, 0.465015, 0.562914],
-                0)), LabeledSample(([0.553381, 0.457028, 0.546788, 0.431182],
-                0)), LabeledSample(([0.543981, 0.555533, 0.491698, 0.392047],
-                1)), LabeledSample(([0.532066, 0.497762, 0.5178, 0.448354],
-                1)), LabeledSample(([0.505981, 0.460209, 0.506897, 0.524639],
-                0)), LabeledSample(([0.44959, 0.489591, 0.490236, 0.563772],
-                0)), LabeledSample(([0.498647, 0.482584, 0.502011, 0.516187],
-                0)), LabeledSample(([0.552142, 0.553439, 0.474121, 0.405035],
-                1)), LabeledSample(([0.495714, 0.452003, 0.497858, 0.549635],
-                0)), LabeledSample(([0.523342, 0.480002, 0.484639, 0.510722],
-                0)), LabeledSample(([0.493365, 0.473391, 0.504036, 0.527673],
-                0)), LabeledSample(([0.552146, 0.542635, 0.505733, 0.380679],
-                1)), LabeledSample(([0.578287, 0.517882, 0.46856, 0.421704],
-                1)), LabeledSample(([0.588389, 0.569435, 0.47621, 0.320571],
-                1)), LabeledSample(([0.572852, 0.583312, 0.441711, 0.369431],
-                1)), LabeledSample(([0.540173, 0.571013, 0.440259, 0.43397],
-                1)), LabeledSample(([0.588118, 0.554021, 0.452409, 0.377498],
-                1)), LabeledSample(([0.499325, 0.454156, 0.500229, 0.542391],
-                0)), LabeledSample(([0.541172, 0.446455, 0.491748, 0.515746],
-                0)), LabeledSample(([0.501365, 0.513378, 0.488352, 0.496577],
-                0)), LabeledSample(([0.519525, 0.498491, 0.475854, 0.505137],
-                0)), LabeledSample(([0.549086, 0.561405, 0.474075, 0.398223],
-                1)), LabeledSample(([0.504199, 0.486123, 0.476877, 0.53109],
-                0)), LabeledSample(([0.530715, 0.466196, 0.504931, 0.496032],
-                0)), LabeledSample(([0.515663, 0.527232, 0.474253, 0.480835],
-                0)), LabeledSample(([0.498647, 0.482584, 0.502011, 0.516187],
-                0)), LabeledSample(([0.591455, 0.54028, 0.471969, 0.368136],
-                1)), LabeledSample(([0.459772, 0.46144, 0.462874, 0.601191],
-                0)), LabeledSample(([0.527031, 0.492257, 0.472236, 0.506867],
-                0)), LabeledSample(([0.534498, 0.534498, 0.495766, 0.427598],
-                0)), LabeledSample(([0.561849, 0.441966, 0.530269, 0.455857],
-                0)), LabeledSample(([0.483984, 0.503088, 0.458885, 0.549624],
-                0)), LabeledSample(([0.525126, 0.566848, 0.450923, 0.446761],
-                1)), LabeledSample(([0.576674, 0.501348, 0.480182, 0.430723],
-                1)), LabeledSample(([0.58787, 0.558697, 0.451917, 0.371534],
-                1)), LabeledSample(([0.584716, 0.552543, 0.446305, 0.391937],
-                1)), LabeledSample(([0.604866, 0.502993, 0.484769, 0.382275],
-                1)), LabeledSample(([0.576834, 0.538774, 0.469003, 0.39626],
-                1)), LabeledSample(([0.588747, 0.563029, 0.444888, 0.372089],
-                1)), LabeledSample(([0.575899, 0.560012, 0.4573, 0.38158],
-                1)), LabeledSample(([0.552402, 0.574207, 0.444699, 0.409123],
-                1)), LabeledSample(([0.589006, 0.546658, 0.46965, 0.365605],
-                1)), LabeledSample(([0.540387, 0.443462, 0.537296, 0.471843],
-                0)), LabeledSample(([0.570654, 0.548912, 0.458326, 0.403716],
-                1)), LabeledSample(([0.544644, 0.547271, 0.467682, 0.430268],
-                1)), LabeledSample(([0.525228, 0.503964, 0.508832, 0.459615],
-                0)), LabeledSample(([0.462827, 0.527655, 0.461528, 0.542553],
-                0)), LabeledSample(([0.50897, 0.522189, 0.507054, 0.459527],
-                0)), LabeledSample(([0.546369, 0.577899, 0.460934, 0.393768],
-                1)), LabeledSample(([0.615382, 0.467063, 0.492079, 0.401268],
-                1)), LabeledSample(([0.573572, 0.473185, 0.510765, 0.431544],
-                1)), LabeledSample(([0.510624, 0.60155, 0.43847, 0.430285],
-                1)), LabeledSample(([0.563956, 0.532924, 0.469591, 0.421223],
-                1)), LabeledSample(([0.581565, 0.592669, 0.391677, 0.396376],
-                1)), LabeledSample(([0.533848, 0.501219, 0.4732, 0.489762],
-                0)), LabeledSample(([0.530036, 0.577194, 0.452731, 0.425375],
-                1)), LabeledSample(([0.595573, 0.439349, 0.494919, 0.455325],
-                1)), LabeledSample(([0.584424, 0.557699, 0.438769, 0.393576],
-                1)), LabeledSample(([0.544759, 0.441244, 0.494108, 0.514196],
-                0)), LabeledSample(([0.552072, 0.545641, 0.487013, 0.400388], 1))
+        return [
+            LabeledSample(([0.581557, 0.562824, 0.447721, 0.380219], 1)),
+            LabeledSample(([0.570241, 0.544165, 0.503041, 0.354484], 1)),
+            LabeledSample(([0.510784, 0.475476, 0.453884, 0.554087], 0)),
+            LabeledSample(([0.492527, 0.473762, 0.471326, 0.557511], 0)),
+            LabeledSample(([0.543273, 0.501972, 0.518341, 0.429186], 0)),
+            LabeledSample(([0.520013, 0.485702, 0.440061, 0.547747], 0)),
+            LabeledSample(([0.585261, 0.545431, 0.462126, 0.382641], 1)),
+            LabeledSample(([0.541059, 0.479438, 0.568697, 0.392401], 0)),
+            LabeledSample(([0.555604, 0.517196, 0.474722, 0.445479], 1)),
+            LabeledSample(([0.592542, 0.537541, 0.468725, 0.374486], 1)),
+            LabeledSample(([0.552254, 0.51027, 0.511855, 0.415505], 0)),
+            LabeledSample(([0.530874, 0.465606, 0.503344, 0.498025], 0)),
+            LabeledSample(([0.568502, 0.492452, 0.524331, 0.399215], 0)),
+            LabeledSample(([0.511768, 0.53197, 0.46875, 0.485156], 0)),
+            LabeledSample(([0.555756, 0.420141, 0.553663, 0.456152], 0)),
+            LabeledSample(([0.584546, 0.562276, 0.439516, 0.385976], 1)),
+            LabeledSample(([0.608485, 0.577022, 0.427781, 0.337336], 1)),
+            LabeledSample(([0.546234, 0.59768, 0.46082, 0.36339], 1)),
+            LabeledSample(([0.596632, 0.510739, 0.482188, 0.388162], 1)),
+            LabeledSample(([0.512997, 0.525043, 0.460839, 0.49879], 0)),
+            LabeledSample(([0.477408, 0.488846, 0.465015, 0.562914], 0)),
+            LabeledSample(([0.553381, 0.457028, 0.546788, 0.431182], 0)),
+            LabeledSample(([0.543981, 0.555533, 0.491698, 0.392047], 1)),
+            LabeledSample(([0.532066, 0.497762, 0.5178, 0.448354], 1)),
+            LabeledSample(([0.505981, 0.460209, 0.506897, 0.524639], 0)),
+            LabeledSample(([0.44959, 0.489591, 0.490236, 0.563772], 0)),
+            LabeledSample(([0.498647, 0.482584, 0.502011, 0.516187], 0)),
+            LabeledSample(([0.552142, 0.553439, 0.474121, 0.405035], 1)),
+            LabeledSample(([0.495714, 0.452003, 0.497858, 0.549635], 0)),
+            LabeledSample(([0.523342, 0.480002, 0.484639, 0.510722], 0)),
+            LabeledSample(([0.493365, 0.473391, 0.504036, 0.527673], 0)),
+            LabeledSample(([0.552146, 0.542635, 0.505733, 0.380679], 1)),
+            LabeledSample(([0.578287, 0.517882, 0.46856, 0.421704], 1)),
+            LabeledSample(([0.588389, 0.569435, 0.47621, 0.320571], 1)),
+            LabeledSample(([0.572852, 0.583312, 0.441711, 0.369431], 1)),
+            LabeledSample(([0.540173, 0.571013, 0.440259, 0.43397], 1)),
+            LabeledSample(([0.588118, 0.554021, 0.452409, 0.377498], 1)),
+            LabeledSample(([0.499325, 0.454156, 0.500229, 0.542391], 0)),
+            LabeledSample(([0.541172, 0.446455, 0.491748, 0.515746], 0)),
+            LabeledSample(([0.501365, 0.513378, 0.488352, 0.496577], 0)),
+            LabeledSample(([0.519525, 0.498491, 0.475854, 0.505137], 0)),
+            LabeledSample(([0.549086, 0.561405, 0.474075, 0.398223], 1)),
+            LabeledSample(([0.504199, 0.486123, 0.476877, 0.53109], 0)),
+            LabeledSample(([0.530715, 0.466196, 0.504931, 0.496032], 0)),
+            LabeledSample(([0.515663, 0.527232, 0.474253, 0.480835], 0)),
+            LabeledSample(([0.498647, 0.482584, 0.502011, 0.516187], 0)),
+            LabeledSample(([0.591455, 0.54028, 0.471969, 0.368136], 1)),
+            LabeledSample(([0.459772, 0.46144, 0.462874, 0.601191], 0)),
+            LabeledSample(([0.527031, 0.492257, 0.472236, 0.506867], 0)),
+            LabeledSample(([0.534498, 0.534498, 0.495766, 0.427598], 0)),
+            LabeledSample(([0.561849, 0.441966, 0.530269, 0.455857], 0)),
+            LabeledSample(([0.483984, 0.503088, 0.458885, 0.549624], 0)),
+            LabeledSample(([0.525126, 0.566848, 0.450923, 0.446761], 1)),
+            LabeledSample(([0.576674, 0.501348, 0.480182, 0.430723], 1)),
+            LabeledSample(([0.58787, 0.558697, 0.451917, 0.371534], 1)),
+            LabeledSample(([0.584716, 0.552543, 0.446305, 0.391937], 1)),
+            LabeledSample(([0.604866, 0.502993, 0.484769, 0.382275], 1)),
+            LabeledSample(([0.576834, 0.538774, 0.469003, 0.39626], 1)),
+            LabeledSample(([0.588747, 0.563029, 0.444888, 0.372089], 1)),
+            LabeledSample(([0.575899, 0.560012, 0.4573, 0.38158], 1)),
+            LabeledSample(([0.552402, 0.574207, 0.444699, 0.409123], 1)),
+            LabeledSample(([0.589006, 0.546658, 0.46965, 0.365605], 1)),
+            LabeledSample(([0.540387, 0.443462, 0.537296, 0.471843], 0)),
+            LabeledSample(([0.570654, 0.548912, 0.458326, 0.403716], 1)),
+            LabeledSample(([0.544644, 0.547271, 0.467682, 0.430268], 1)),
+            LabeledSample(([0.525228, 0.503964, 0.508832, 0.459615], 0)),
+            LabeledSample(([0.462827, 0.527655, 0.461528, 0.542553], 0)),
+            LabeledSample(([0.50897, 0.522189, 0.507054, 0.459527], 0)),
+            LabeledSample(([0.546369, 0.577899, 0.460934, 0.393768], 1)),
+            LabeledSample(([0.615382, 0.467063, 0.492079, 0.401268], 1)),
+            LabeledSample(([0.573572, 0.473185, 0.510765, 0.431544], 1)),
+            LabeledSample(([0.510624, 0.60155, 0.43847, 0.430285], 1)),
+            LabeledSample(([0.563956, 0.532924, 0.469591, 0.421223], 1)),
+            LabeledSample(([0.581565, 0.592669, 0.391677, 0.396376], 1)),
+            LabeledSample(([0.533848, 0.501219, 0.4732, 0.489762], 0)),
+            LabeledSample(([0.530036, 0.577194, 0.452731, 0.425375], 1)),
+            LabeledSample(([0.595573, 0.439349, 0.494919, 0.455325], 1)),
+            LabeledSample(([0.584424, 0.557699, 0.438769, 0.393576], 1)),
+            LabeledSample(([0.544759, 0.441244, 0.494108, 0.514196], 0)),
+            LabeledSample(([0.552072, 0.545641, 0.487013, 0.400388], 1))
         ];
     }
 
diff --git a/MachineLearning/src/Datasets/Properties/NamespaceInfo.qs b/MachineLearning/src/Datasets/Properties/NamespaceInfo.qs
index b1559562869..1c4b5dc3258 100644
--- a/MachineLearning/src/Datasets/Properties/NamespaceInfo.qs
+++ b/MachineLearning/src/Datasets/Properties/NamespaceInfo.qs
@@ -2,5 +2,6 @@
 // Licensed under the MIT License.
 
 /// # Summary
-/// TODO
+/// This namespace provides example datasets for use
+/// with quantum machine learning classifiers.
 namespace Microsoft.Quantum.MachineLearning.Datasets { }
diff --git a/MachineLearning/src/MachineLearning.csproj b/MachineLearning/src/MachineLearning.csproj
index d9204dafa29..881c3d8b5e2 100644
--- a/MachineLearning/src/MachineLearning.csproj
+++ b/MachineLearning/src/MachineLearning.csproj
@@ -1,7 +1,6 @@
 <Project Sdk="Microsoft.Quantum.Sdk/0.10.2001.2831">
   <PropertyGroup>
     <TargetFramework>netstandard2.1</TargetFramework>
-    <PlatformTarget>x64</PlatformTarget>
     <AssemblyName>Microsoft.Quantum.MachineLearning</AssemblyName>
     <QsharpDocsGeneration>true</QsharpDocsGeneration>
     <!--
@@ -26,7 +25,7 @@
     <Copyright>© Microsoft Corporation. All rights reserved.</Copyright>
     <PackageId>Microsoft.Quantum.MachineLearning</PackageId>
     <PackageReleaseNotes>See: https://docs.microsoft.com/quantum/relnotes/</PackageReleaseNotes>
-    <PackageLicenseUrl>https://github.com/Microsoft/QuantumLibraries/raw/master/LICENSE.txt</PackageLicenseUrl>
+    <PackageLicenseExpression>MIT</PackageLicenseExpression>
     <PackageProjectUrl>https://github.com/Microsoft/QuantumLibraries/tree/master/MachineLearning</PackageProjectUrl>
     <PackageIconUrl>https://secure.gravatar.com/avatar/bd1f02955b2853ba0a3b1cdc2434e8ec.png</PackageIconUrl>
     <PackageTags>Quantum Q# Qsharp</PackageTags>
diff --git a/MachineLearning/src/Private.qs b/MachineLearning/src/Private.qs
index 83540965013..33a9ffa96b9 100644
--- a/MachineLearning/src/Private.qs
+++ b/MachineLearning/src/Private.qs
@@ -9,7 +9,7 @@ namespace Microsoft.Quantum.MachineLearning {
         return Length(v1) == Length(v2) and All(NearlyEqualD, Zip(v1, v2));
     }
 
-    operation _TailMeasurement(nQubits : Int) : (Qubit[] => Result) {
+    function _TailMeasurement(nQubits : Int) : (Qubit[] => Result) {
         let paulis = ConstantArray(nQubits, PauliI) w/ (nQubits - 1) <- PauliZ;
         return Measure(paulis, _);
     }
diff --git a/MachineLearning/src/Properties/NamespaceInfo.qs b/MachineLearning/src/Properties/NamespaceInfo.qs
index 3f9fc15eb88..f9f1fa73ac7 100644
--- a/MachineLearning/src/Properties/NamespaceInfo.qs
+++ b/MachineLearning/src/Properties/NamespaceInfo.qs
@@ -2,7 +2,8 @@
 // Licensed under the MIT License.
 
 /// # Summary
-/// TODO
+/// This namespace provides functions and operations used in
+/// quantum machine learning applications.
 ///
 /// # References
 /// - Maria Schuld, Alex Bocharov, Krysta Svore, Nathan Wiebe,
diff --git a/MachineLearning/src/Structure.qs b/MachineLearning/src/Structure.qs
index 80f31432225..a3a80c5abd1 100644
--- a/MachineLearning/src/Structure.qs
+++ b/MachineLearning/src/Structure.qs
@@ -56,8 +56,7 @@ namespace Microsoft.Quantum.MachineLearning {
                     // Uncontrolled rotation of target
                     R(input);
                 } else {
-                    //TODO: should one validate the control indices first?
-                    (Controlled R)(Subarray(gate::ControlIndices, qubits), input);
+                    Controlled R(Subarray(gate::ControlIndices, qubits), input);
                 }
             }
         }
diff --git a/MachineLearning/src/Training.qs b/MachineLearning/src/Training.qs
index a95e4b21ef1..a7cc57c9723 100644
--- a/MachineLearning/src/Training.qs
+++ b/MachineLearning/src/Training.qs
@@ -1,3 +1,6 @@
+// Copyright (c) Microsoft Corporation.
+// Licensed under the MIT License.
+
 namespace Microsoft.Quantum.MachineLearning {
     open Microsoft.Quantum.Math;
     open Microsoft.Quantum.Logical;
@@ -15,8 +18,7 @@ namespace Microsoft.Quantum.MachineLearning {
     /// # Summary
     /// Returns a bias value that leads to near-minimum misclassification score.
     function _UpdatedBias(labeledProbabilities: (Double, Int)[], bias: Double, tolerance: Double) : Double {
-        mutable min1 = 1.0;
-        mutable max0 = 0.0;
+        mutable (min1, max0) = (1.0, 0.0);
 
         // Find the range of classification probabilities for each class.
         for ((probability, label) in labeledProbabilities) {
@@ -154,7 +156,8 @@ namespace Microsoft.Quantum.MachineLearning {
         for (sample in miniBatch) {
             mutable err = IntAsDouble(sample::Label);
             if (err < 1.0) {
-                set err = -1.0; //class 0 misclassified to class 1; strive to reduce the probability
+                // Class 0 misclassified to class 1; strive to reduce the probability.
+                set err = -1.0;
             }
             let stateGenerator = ApproximateInputEncoder(effectiveTolerance, sample::Features)
                 // Force the number of qubits in case something else in the
@@ -241,7 +244,7 @@ namespace Microsoft.Quantum.MachineLearning {
             let (utility, updatedModel) = _RunSingleTrainingStep(
                 minibatch, options, bestSoFar
             );
-            if (utility > 0.0000001) {
+            if (utility > 1e-7) {
                 // There has been some good parameter update.
                 // Check if it actually improves things, and if so,
                 // commit it.
diff --git a/MachineLearning/src/Types.qs b/MachineLearning/src/Types.qs
index c86d223df5c..e62e70a152a 100644
--- a/MachineLearning/src/Types.qs
+++ b/MachineLearning/src/Types.qs
@@ -93,6 +93,10 @@ namespace Microsoft.Quantum.MachineLearning {
         Label: Int
     );
 
+    // NB: The newtype SamplingSchedule is intended to be used only as a
+    //     black-box, and thus does not provide a named item to access its
+    //     contents.
+
     /// # Summary
     /// A schedule for drawing batches from a set of samples.
     newtype SamplingSchedule = Range[];
@@ -151,7 +155,8 @@ namespace Microsoft.Quantum.MachineLearning {
     /// ## NMisclassifications
     /// The number of misclassifications observed during validation.
     newtype ValidationResults = (
-        NMisclassifications: Int
+        NMisclassifications: Int,
+        NValidationSamples: Int
     );
 
     /// # Summary
@@ -182,9 +187,12 @@ namespace Microsoft.Quantum.MachineLearning {
     /// by calling @"microsoft.quantum.machinelearning.defaulttrainingoptions"
     /// and then using the `w/` operator to override different defaults.
     ///
-    /// For example, to use 100,000 measurements:
+    /// For example, to use 100,000 measurements and at most 8 training
+    /// epochs:
     /// ```Q#
-    /// let options = DefaultTrainingOptions() w/ NMeasurements <- 100000;
+    /// let options = DefaultTrainingOptions()
+    ///               w/ NMeasurements <- 100000
+    ///               w/ MaxEpochs <- 8;
     /// ```
     ///
     /// # References
diff --git a/MachineLearning/src/Validation.qs b/MachineLearning/src/Validation.qs
index 83ebe87d17d..f9f15b39220 100644
--- a/MachineLearning/src/Validation.qs
+++ b/MachineLearning/src/Validation.qs
@@ -90,7 +90,8 @@ namespace Microsoft.Quantum.MachineLearning {
         let localPL = InferredLabels(model::Bias, probabilities);
         let nMisclassifications = NMisclassifications(localPL, labels);
         return ValidationResults(
-            nMisclassifications
+            nMisclassifications,
+            Length(localPL)
         );
     }
 
diff --git a/Numerics/src/Numerics.csproj b/Numerics/src/Numerics.csproj
index c26ff7edddf..ab4026b5ef1 100644
--- a/Numerics/src/Numerics.csproj
+++ b/Numerics/src/Numerics.csproj
@@ -14,7 +14,7 @@
     <Description>Microsoft's Quantum numerics libraries.</Description>
     <Copyright>© Microsoft Corporation. All rights reserved.</Copyright>
     <PackageReleaseNotes>See: https://docs.microsoft.com/en-us/quantum/relnotes/</PackageReleaseNotes>
-    <PackageLicenseUrl>https://github.com/Microsoft/QuantumLibraries/raw/master/LICENSE.txt</PackageLicenseUrl>
+    <PackageLicenseExpression>MIT</PackageLicenseExpression>
     <PackageProjectUrl>https://github.com/Microsoft/QuantumLibraries</PackageProjectUrl>
     <PackageIconUrl>https://secure.gravatar.com/avatar/bd1f02955b2853ba0a3b1cdc2434e8ec.png</PackageIconUrl>
     <PackageTags>Quantum Q# Qsharp</PackageTags>
diff --git a/Standard/src/Arithmetic/Reflections.qs b/Standard/src/Arithmetic/Reflections.qs
index 81e2af3afdc..07c05cc7075 100644
--- a/Standard/src/Arithmetic/Reflections.qs
+++ b/Standard/src/Arithmetic/Reflections.qs
@@ -34,7 +34,7 @@ namespace Microsoft.Quantum.Arithmetic {
                 Zip(Mapped(Not, IntAsBoolArray(index, Length(reg!))), reg!)
             );
         } apply {
-            (Controlled Z)(Most(reg!), Tail(reg!));
+            Controlled Z(Most(reg!), Tail(reg!));
         }
 	}
 
diff --git a/Standard/src/Canon/Utils/Multiplexer.qs b/Standard/src/Canon/Utils/Multiplexer.qs
index 0b4a2901e31..2373683d59f 100644
--- a/Standard/src/Canon/Utils/Multiplexer.qs
+++ b/Standard/src/Canon/Utils/Multiplexer.qs
@@ -153,8 +153,7 @@ namespace Microsoft.Quantum.Canon {
 
     function _AnyOutsideToleranceD(tolerance : Double, coefficients : Double[]) : Bool {
         // NB: We don't currently use Any / Mapped for this, as we want to be
-        //     able to short-circuit. That should be implied by immutable
-        //     semantics, but that's not yet the case.
+        //     able to short-circuit.
         for (coefficient in coefficients) {
             if (AbsD(coefficient) >= tolerance) {
                 return true;
diff --git a/Standard/src/Standard.csproj b/Standard/src/Standard.csproj
index 9c4d3cffb3a..fb04b33a3e9 100644
--- a/Standard/src/Standard.csproj
+++ b/Standard/src/Standard.csproj
@@ -13,7 +13,7 @@
     <Description>Microsoft's Quantum standard libraries.</Description>
     <Copyright>© Microsoft Corporation. All rights reserved.</Copyright>
     <PackageReleaseNotes>See: https://docs.microsoft.com/en-us/quantum/relnotes/</PackageReleaseNotes>
-    <PackageLicenseUrl>https://github.com/Microsoft/Quantum/raw/master/LICENSE.txt</PackageLicenseUrl>
+    <PackageLicenseExpression>MIT</PackageLicenseExpression>
     <PackageProjectUrl>https://github.com/Microsoft/Quantum</PackageProjectUrl>
     <PackageIconUrl>https://secure.gravatar.com/avatar/bd1f02955b2853ba0a3b1cdc2434e8ec.png</PackageIconUrl>
     <PackageTags>Quantum Q# Qsharp</PackageTags>