Against the new Cartesian Circle

In two recent papers, Michael Della Rocca accuses Descartes of reasoning circularly in the Fourth Meditation. This alleged new circle is distinct from, and more vicious than, the traditional Cartesian Circle arising in the Third Meditation. We explain Della Rocca’s reasons for this accusation, showing that his argument is invalid

Development of a prototype plastic space erectable satellite Quarterly report, Sep. - Nov. 1965

Test program for cap section mesh fabrication in prototype space erectable satellite developmen

Blowing-Up the Four-Dimensional Z_3 Orientifold

We study the blowing-up of the four-dimensional Z_3 orientifold of Angelantonj, Bianchi, Pradisi, Sagnotti and Stanev (ABPSS) by giving nonzero vacuum expectation values (VEV's) to the twisted sector moduli blowing-up modes. The blowing-up procedure induces a Fayet-Iliopoulos (FI) term for the ``anomalous'' U(1), whose magnitude depends linearly on the VEV's of the blowing-up modes. To preserve the N=1 supersymmetry, non-Abelian matter fields are forced to acquire nonzero VEV's, thus breaking (some of) the non-Abelian gauge structure and decoupling some of the matter fields. We determine the form of the FI term, construct explicit examples of (non-Abelian) D and F flat directions, and determine the surviving gauge groups of the restabilized vacua. We also determine the mass spectra, for which the restabilization reduces the number of families.Comment: 19 pages, Late

Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

Robots that navigate among pedestrians use collision avoidance algorithms to enable safe and efficient operation. Recent works present deep reinforcement learning as a framework to model the complex interactions and cooperation. However, they are implemented using key assumptions about other agents' behavior that deviate from reality as the number of agents in the environment increases. This work extends our previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules. This work also introduces a strategy using LSTM that enables the algorithm to use observations of an arbitrary number of other agents, instead of previous methods that have a fixed observation size. The proposed algorithm outperforms our previous approach in simulation as the number of agents increases, and the algorithm is demonstrated on a fully autonomous robotic vehicle traveling at human walking speed, without the use of a 3D Lidar

Socially Aware Motion Planning with Deep Reinforcement Learning

For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e.g., passing on the right). However, while instinctive to humans, socially compliant navigation is still difficult to quantify due to the stochasticity in people's behaviors. Existing works are mostly focused on using feature-matching techniques to describe and imitate human paths, but often do not generalize well since the feature values can vary from person to person, and even run to run. This work notes that while it is challenging to directly specify the details of what to do (precise mechanisms of human navigation), it is straightforward to specify what not to do (violations of social norms). Specifically, using deep reinforcement learning, this work develops a time-efficient navigation policy that respects common social norms. The proposed method is shown to enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians.Comment: 8 page