Is Conventional Defense of Europe Feasible?

While conventional defense of Nato Europe has long preoccupied allied military planners, it received surprisingly little public attention so long as nuclear deterrence occupied center stage

COVID-19 amongst the Pandemic of Medical Student Mental Health

The medical community has been greatly impacted by the COVID-19 pandemic.  As medical students, we face unique challenges and uncertainty during this time.  While the world gears up to fight the battle with this physical illness, our battle with mental health should not be forgotten.  Medical students are disproportionately affected by mental health issues and psychological distress.  This experience piece aims to shed light on these challenges and provoke a discussion around mental health in medical students during these trying times

Relativistic Effects in Simulations of the Fragmentation Process with the Microscopic Framework

We simulate the fragmentation processes in the \CaCa collisions at the bombarding energy 1.05 GeV/u using the Lorentz covariant RQMD and the non-covariant QMD approaches, incorporated with the statistical decay model. By comparing the results of RQMD with those of QMD, we examine the relativistic effects and find that the multiplicity of the $\alpha$ particle after the statistical decay process is sensitive to the relativistic effects. It is shown that the Lorentz covariant approach is necessary to analyze the fragmentation process even at the energy around \Elab = 1 GeV/u as long as we are concerned with the final observables of the mass distribution, particularly, the light fragments around $A = 3 \sim 4$.Comment: 8pages, Latex is used, 3 Postscript figures are available by request from [email protected]

Professional Reading

Thinking About National Security: Defense and Foreign Policy in a Dangerous Worl

Biologically Inspired Spatial Representation

In this thesis I explore a biologically inspired method of encoding continuous space within a population of neurons. This method provides an extension to the Semantic Pointer Architecture (SPA) to encompass Semantic Pointers with real-valued spatial content in addition to symbol-like representations. I demonstrate how these Spatial Semantic Pointers (SSPs) can be used to generate cognitive maps containing objects at various locations. A series of operations are defined that can retrieve objects or locations from the encoded map as well as manipulate the contents of the memory. These capabilities are all implemented by a network of spiking neurons. I explore the topology of the SSP vector space and show how it preserves metric information while compressing all coordinates to unit length vectors. This allows a limitless spatial extent to be represented in a finite region. Neurons encoding space represented in this manner have firing fields similar to entorhinal grid cells. Beyond constructing biologically plausible models of spatial cognition, SSPs are applied to the domain of machine learning. I demonstrate how replacing traditional spatial encoding mechanisms with SSPs can improve performance on networks trained to compute a navigational policy. In addition, SSPs are also effective for training a network to localize within an environment based on sensor measurements as well as perform path integration. To demonstrate a practical, integrated system using SSPs, I combine a goal driven navigational policy with the localization network and cognitive map representation to produce an agent that can navigate to semantically defined goals. In addition to spatial tasks, the SSP encoding is applied to a more general class of machine learning problems involving arbitrary continuous signals. Results on a collection of 122 benchmark datasets across a variety of domains indicate that neural networks trained with SSP encoding outperform commonly used methods for the majority of the datasets. Overall, the experiments in this thesis demonstrate the importance of exploring new kinds of representations within neural networks and how they shape the kinds of functions that can be effectively computed. They provide an example of how insights regarding how the brain may encode information can inspire new ways of designing artificial neural networks

Biologically Inspired Adaptive Control of Quadcopter Flight

This thesis explores the application of a biologically inspired adaptive controller to quadcopter flight control. This begins with an introduction to modelling the dynamics of a quadcopter, followed by an overview of control theory and neural simulation in Nengo. The Virtual Robotics Experimentation Platform (V-REP) is used to simulate the quadcopter in a physical environment. Iterative design improvements leading to the final controller are discussed. The controller model is run on a series of benchmark tasks and its performance is compared to conventional controllers. The results show that the neural adaptive controller performs on par with conventional controllers on simple tasks but exceeds far beyond these controllers on tasks involving unexpected external forces in the environment

Blaine it on Politics: The (Non-) Effect of Anti-Aid Amendments on Private School Choice Programs in the U.S. States

James G. Blaine was a prominent American politician of the late 19th Century. Although Blaine was an unsuccessful Republican candidate for President in 1884, U.S. Secretary of State, Speaker of the House, and a Senator from Maine, his primary legacy was the enshrinement of anti-aid amendments in the constitutions of 39 U.S. states. These so-called Blaine Amendments were designed to prohibit government funds from supporting sectarian religious organizations such as schools and charities. In Blaine\u27s day, sectarian was widely understood to be a euphemism for Catholic . Nondenominationally Protestant organizations such as the public schools of the day were considered to be non-sectarian and entirely worthy of government support. The Blaine Amendments ensured that government-sponsored schools in the U.S. would be pervasively Protestant, at least until religion was banned from public schools in the 1960s, and that Catholic schools would have to make do without any substantial financial assistance from the government

Setting up a Reinforcement Learning Task with a Real-World Robot

Reinforcement learning is a promising approach to developing hard-to-engineer adaptive solutions for complex and diverse robotic tasks. However, learning with real-world robots is often unreliable and difficult, which resulted in their low adoption in reinforcement learning research. This difficulty is worsened by the lack of guidelines for setting up learning tasks with robots. In this work, we develop a learning task with a UR5 robotic arm to bring to light some key elements of a task setup and study their contributions to the challenges with robots. We find that learning performance can be highly sensitive to the setup, and thus oversights and omissions in setup details can make effective learning, reproducibility, and fair comparison hard. Our study suggests some mitigating steps to help future experimenters avoid difficulties and pitfalls. We show that highly reliable and repeatable experiments can be performed in our setup, indicating the possibility of reinforcement learning research extensively based on real-world robots.Comment: Submitted to 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS