Does concealed handgun carry make campus safer? A panel data analysis of crime on college and university campuses

The purpose of this report is to recommend and test an empirical strategy for assessing the impact that concealed carry policies have on crime at college and university campuses. I use panel data obtained from the Department of Education for all crimes reported on four-year, undergraduate, federal financial aid-receiving institutions between 2001 and 2014 to model the impact of campus carry legislation. Differences in legislation across states, time, and school types allow for estimation of a triple difference regression model. Results of OLS estimations show that campus carry has no significant observable association with rates of aggravated assault, sexual assault, robbery, burglary, and motor theft committed on campus at the 95% confidence interval. These results are robust to a number of different assumptions, including time lag and negative binomial modeling approaches. However, true effects may be difficult to determine precisely as model estimations present large standard errors. Notably, my analysis does not attempt to control for variables that may also influence campus crime rates, such as local economic conditions, gun ownership rates, or rates of concealed carrying on campus. This analysis is therefore only a starting point for further research and the results contained here should be considered preliminary. At most, my analysis may throw partisan narratives surrounding campus carry into some measure of doubt. In particular, results fail to demonstrate a measurable deterrent effect theorized by campus carry advocates, or a criminal enabling effect theorized by opponents of the policy. Regardless of crime changes, I suggest that policymakers considering this controversial measure should also weigh how concealed carrying policies may influence a variety of other variables, including student suicides – a full understanding of which requires considerable caution and further research.Public Polic

MEDIUM VOLTAGE DC SOLID STATE CIRCUIT BREAKER BENCH TEST

Next generation fleets will rely on medium-voltage direct-current (MVDC) electric power distribution systems utilizing high power density and high-efficiency components. One key gap to make such MVDC systems feasible is a super-fast, high-efficiency, and high power density protection device. The U.S. Navy has previously developed a 1 kV, 1 kA solid state circuit breaker (SSCB). A new 2 kV, 1.2 kA SSCB has been designed by NPS with collaborating partners that has quadrupled power density. This innovative insulated gate bipolar transistor (IGBT)-based SSCB consists of anti-series IGBT modules, a parallel resistor-capacitor (RC) branch, and an electronically triggered metal-oxide varistor (MOV) branch. The novel electronically controlled MOV is comprised of a MOV in series with a silicon controlled rectifier (SCR) passively triggered during the IGBT turn-off process, improving the trade-off between the leakage current and clamping voltage. The use of a lower IGBT gate voltage allows the elimination of current limiting inductors, increasing the SSCB power density. This thesis focuses on the switching and thermal tests necessary to validate the implemented concepts, and the data will be used for down-selecting technical directions, improving the SSCB performance. The results show that the SSCB is sufficient to interrupt most faults while containing peak current and voltage within design parameters and the efficiency target can be met with comfortable thermal margins.Lieutenant, United States NavyApproved for public release. Distribution is unlimited

Hotels-50K: A Global Hotel Recognition Dataset

Recognizing a hotel from an image of a hotel room is important for human trafficking investigations. Images directly link victims to places and can help verify where victims have been trafficked, and where their traffickers might move them or others in the future. Recognizing the hotel from images is challenging because of low image quality, uncommon camera perspectives, large occlusions (often the victim), and the similarity of objects (e.g., furniture, art, bedding) across different hotel rooms. To support efforts towards this hotel recognition task, we have curated a dataset of over 1 million annotated hotel room images from 50,000 hotels. These images include professionally captured photographs from travel websites and crowd-sourced images from a mobile application, which are more similar to the types of images analyzed in real-world investigations. We present a baseline approach based on a standard network architecture and a collection of data-augmentation approaches tuned to this problem domain

A Simulation Approach to Dynamic Portfolio Choice with an Application to Learning About Return Predictability

We present a simulation-based method for solving discrete-time portfolio choice problems involving non-standard preferences, a large number of assets with arbitrary return distribution, and, most importantly, a large number of state variables with potentially path-dependent or non-stationary dynamics. The method is flexible enough to accommodate intermediate consumption, portfolio constraints, parameter and model uncertainty, and learning. We first establish the properties of the method for the portfolio choice between a stock index and cash when the stock returns are either iid or predictable by the dividend yield. We then explore the problem of an investor who takes into account the predictability of returns but is uncertain about the parameters of the data generating process. The investor chooses the portfolio anticipating that future data realizations will contain useful information to learn about the true parameter values.

Top-down neural attention by excitation backprop

We aim to model the top-down attention of a Convolutional Neural Network (CNN) classifier for generating task-specific attention maps. Inspired by a top-down human visual attention model, we propose a new backpropagation scheme, called Excitation Backprop, to pass along top-down signals downwards in the network hierarchy via a probabilistic Winner-Take-All process. Furthermore, we introduce the concept of contrastive attention to make the top-down attention maps more discriminative. In experiments, we demonstrate the accuracy and generalizability of our method in weakly supervised localization tasks on the MS COCO, PASCAL VOC07 and ImageNet datasets. The usefulness of our method is further validated in the text-to-region association task. On the Flickr30k Entities dataset, we achieve promising performance in phrase localization by leveraging the top-down attention of a CNN model that has been trained on weakly labeled web images.https://arxiv.org/abs/1608.00507Accepted manuscrip

Know Your Oil: Creating A Global Oil-Climate Index

Oil is changing. Conventional oil resources are dwindling as tight oil, oil sands, heavy oils, and others emerge. Technological advances mean that these unconventional hydrocarbon deposits in once-unreachable areas are now viable resources. Meanwhile, scientific evidence is mounting that climate change is occurring, but the climate impacts of these new oils are not well understood. The Carnegie Endowment's Energy and Climate Program, Stanford University, and the University of Calgary have developed a first-of-itskind Oil-Climate Index (OCI) to compare these resources.The Oil-Climate Index (OCI) is a metric that takes into account the total life-cycle Greenhouse Gas (GHG) emissions of individual oils -- from upstream extraction to midstream refining to downstream end use. It offers a powerful, yet user-friendly, tool that allows investors, policymakers, industry, the public, and other stakeholders to compare crudes and assess their climate consequences both before development decisions are made as well as once operations are in progress. The Oil-Climate Index will also inform oil and climate policy making