diff --git a/ci/install_osx.sh b/ci/install_osx.sh
index ed41f0b9c43ef..cf4164a45af48 100755
--- a/ci/install_osx.sh
+++ b/ci/install_osx.sh
@@ -1,24 +1,20 @@
 brew update > /dev/null
 
-PACKAGES='
-git
-cmake
-assimp
-dartsim/dart/fcl
-bullet --with-double-precision
-ode --with-libccd --with-double-precision
-flann
-boost
-eigen
-tinyxml
-tinyxml2
-dartsim/dart/libccd
-nlopt
-dartsim/dart/ipopt
-ros/deps/urdfdom
-ros/deps/urdfdom_headers
-ros/deps/console_bridge
-open-scene-graph
-'
-
-brew install $PACKAGES | grep -v '%$'
+brew install git | grep -v '%$'
+brew install cmake | grep -v '%$'
+brew install assimp | grep -v '%$'
+brew install dartsim/dart/fcl | grep -v '%$'
+brew install bullet --with-double-precision | grep -v '%$'
+brew install ode --with-libccd --with-double-precision | grep -v '%$'
+brew install flann | grep -v '%$'
+brew install boost | grep -v '%$'
+brew install eigen | grep -v '%$'
+brew install tinyxml | grep -v '%$'
+brew install tinyxml2 | grep -v '%$'
+brew install dartsim/dart/libccd | grep -v '%$'
+brew install nlopt | grep -v '%$'
+brew install dartsim/dart/ipopt | grep -v '%$'
+brew install ros/deps/urdfdom | grep -v '%$'
+brew install ros/deps/urdfdom_headers | grep -v '%$'
+brew install ros/deps/console_bridge | grep -v '%$'
+brew install open-scene-graph | grep -v '%$'
diff --git a/paper/paper.md b/paper/paper.md
index f79d2e6e7640d..57fda71ac5a90 100644
--- a/paper/paper.md
+++ b/paper/paper.md
@@ -52,9 +52,9 @@ bibliography: paper.bib
 
 # Summary
 
-DART (Dynamic Animation and Robotics Toolkit) is a collaborative, cross-platform, open source library created by the [Graphics Lab](http://www.cc.gatech.edu/~karenliu/Home.html) and [Humanoid Robotics Lab](http://www.golems.org/) at [Georgia Institute of Technology](http://www.gatech.edu/) with ongoing contributions from the [Personal Robotics Lab](http://personalrobotics.cs.washington.edu/) at [University of Washington](http://www.washington.edu/) and [Open Source Robotics Foundation](https://www.osrfoundation.org/). The library provides data structures and algorithms for kinematic and dynamic applications in robotics and computer animation. DART is distinguished by its accuracy and stability due to its use of generalized coordinates to represent articulated rigid body systems in the geometric notations [@park1995lie] and Featherstone’s Articulated Body Algorithm [@featherstone2014rigid] using a Lie group formulation to compute forward dynamics [@ploen1999coordinate] and hybrid dynamics [@sohl2001recursive]. For developers, in contrast to many popular physics engines which view the simulator as a black box, DART gives full access to internal kinematic and dynamic quantities, such as the mass matrix, Coriolis and centrifugal forces, transformation matrices and their derivatives. DART also provides an efficient computation of Jacobian matrices for arbitrary body points and coordinate frames. The frame semantics of DART allows users to define arbitrary reference frames (both inertial and non-inertial) and use those frames to specify or request data. For air-tight code safety, forward kinematics and dynamics values are updated automatically through lazy evaluation, making DART suitable for real-time controllers. In addition, DART gives provides flexibility to extend the API for embedding user-provided classes into DART data structures. Contacts and collisions are handled using an implicit time-stepping, velocity-based LCP (linear complementarity problem) to guarantee non-penetration, directional friction, and approximated Coulomb friction cone conditions [@stewart1996implicit]. DART has applications in robotics and computer animation because it features a multibody dynamic simulator and various kinematic tools for control and motion planning.
+DART (Dynamic Animation and Robotics Toolkit) is a collaborative, cross-platform, open source library created by the [Graphics Lab](http://www.cc.gatech.edu/~karenliu/Home.html) and [Humanoid Robotics Lab](http://www.golems.org/) at [Georgia Institute of Technology](http://www.gatech.edu/) with ongoing contributions from the [Personal Robotics Lab](http://personalrobotics.cs.washington.edu/) at [University of Washington](http://www.washington.edu/) and [Open Source Robotics Foundation](https://www.osrfoundation.org/). The library provides data structures and algorithms for kinematic and dynamic applications in robotics and computer animation. DART is distinguished by its accuracy and stability due to its use of generalized coordinates to represent articulated rigid body systems in the geometric notations [@park1995lie] and Featherstone’s Articulated Body Algorithm [@featherstone2014rigid] using a Lie group formulation to compute forward dynamics [@ploen1999coordinate] and hybrid dynamics [@sohl2001recursive]. For developers, in contrast to many popular physics engines which view the simulator as a black box, DART gives full access to internal kinematic and dynamic quantities, such as the mass matrix, Coriolis and centrifugal forces, transformation matrices and their derivatives. DART also provides an efficient computation of Jacobian matrices for arbitrary body points and coordinate frames. The frame semantics of DART allows users to define arbitrary reference frames (both inertial and non-inertial) and use those frames to specify or request data. For air-tight code safety, forward kinematics and dynamics values are updated automatically through lazy evaluation, making DART suitable for real-time controllers. In addition, DART provides flexibility to extend the API for embedding user-provided classes into DART data structures. Contacts and collisions are handled using an implicit time-stepping, velocity-based LCP (linear complementarity problem) to guarantee non-penetration, directional friction, and approximated Coulomb friction cone conditions [@stewart1996implicit]. DART has applications in robotics and computer animation because it features a multibody dynamic simulator and various kinematic tools for control and motion planning.
 
-# Research Publications Utilized DART
+# Research publications utilizing DART
 
 1. Data-Driven Approach to Simulating Realistic Human Joint Constraints, Yifeng Jiang, and C. Karen Liu [[arXiv](https://arxiv.org/abs/1709.08685)]
 1. Multi-task Learning with Gradient Guided Policy Specialization, Wenhao Yu, Greg Turk, and C. Karen Liu [[arXiv](https://arxiv.org/abs/1709.07979)]