Crime Trends: 1990-2016

This report examines crime trends at the national and city level during the last quarter century. It covers the years 1990 through 2016, as crime rates peaked in 1991. It analyzes data from the Federal Bureau of Investigation and from police departments from the nation's 30 largest cities. Data for 2016 are estimated, as full year data was not available at the time of publication.This report concludes that although there are some troubling increases in crimes in specific cities, there is no evidence of a national crime wave

Crime In 2016: A Preliminary Analysis

Earlier this year, the Brennan Center analyzed crime data from the 30 largest cities in 2015, finding that crime overall remained the same as in 2014. It also found that murder increased by 14 percent, with just three cities — Baltimore, Chicago, and Washington, D.C. — responsible for half that increase. All told, 2015's murder rate was still near historic lows. The authors concluded that reports of a national crime wave were premature and unfounded, and that "the average person in a large urban area is safer walking on the street today than he or she would have been at almost any time in the past 30 years."This report updates those findings. It collects midyear data from police departments to project overall crime, violent crime, and murder for all of 2016

Faraday spectroscopy of atoms confined in a dark optical trap

We demonstrate Faraday spectroscopy with high duty cycle and sampling rate using atoms confined to a blue-detuned optical trap. Our trap consists of a crossed pair of high-charge-number hollow laser beams, which forms a dark, box-like potential. We have used this to measure transient magnetic fields in a 500-micron-diameter spot over a 400 ms time window with nearly unit duty cycle at a 500 Hz sampling rate. We use these measurements to quantify and compensate time-varying magnetic fields to ~10 nT per time sample.Comment: 6 pages, 8 figures Accepted in Phys. Rev.

Echo State Networks: analysis, training and predictive control

The goal of this paper is to investigate the theoretical properties, the training algorithm, and the predictive control applications of Echo State Networks (ESNs), a particular kind of Recurrent Neural Networks. First, a condition guaranteeing incremetal global asymptotic stability is devised. Then, a modified training algorithm allowing for dimensionality reduction of ESNs is presented. Eventually, a model predictive controller is designed to solve the tracking problem, relying on ESNs as the model of the system. Numerical results concerning the predictive control of a nonlinear process for pH neutralization confirm the effectiveness of the proposed algorithms for the identification, dimensionality reduction, and the control design for ESNs.Comment: 6 pages,5 figures, submitted to European Control Conference (ECC