Do Racial Disparities Exist During Pretrial Decisionmaking? Evidence From North Carolina

Racial Disparities in the Criminal Justice System are well documented in that minority defendants are over-represented compared with white defendants. The present authors argue that it is crucial to study the pretrial stages because they are a pivotal point in the criminal justice process continuum and racial disparities may begin to take root at an early stage of the process. We find some evidence of racial disparities in pretrial decisionmaking. The type of bond assigned differs by race. Black defendants who were unable to post bond spent more days in jail, compared to white counterparts. However, race is not a significant predictor of bond amount in the regression analysis, indicating that racial disparities may not be as pronounced as some advocates believe in terms of bond amounts set by judges. We acknowledge that the findings are limited due to small sample size and cautions should be taken when generalizing the findings

Closed strings from decaying D-branes

We compute the emission of closed string radiation from homogeneous rolling tachyons. For an unstable decaying D$p$-brane the radiated energy is infinite to leading order for $p\leq 2$ and finite for $p>2$. The closed string state produced by a decaying brane is closely related to the state produced by D-instantons at a critical Euclidean distance from $t=0$. In the case of a D0 brane one can cutoff this divergence so that we get a finite energy final state which would be the state that the brane decays into.Comment: harvmac, 30 pages, 2 figures. v3: Improved discussion for non compact brane

An Exploration of “Non-Economic” Damages in Civil Jury Awards

Using three primary data sources plus three supplemental sources discussed in an appendix, this paper examines how well non-economic damages could be predicted by economic damages and at how the ratio of non-economic damages to economic damages changed as the magnitude of the economic damages awarded by juries increased. We found a mixture of consistent and inconsistent patterns across our various datasets. One fairly consistent pattern was the tendency for the ratio of non-economic to economic damages to decline as the amount of economic damages increased. Moreover, the variability of the ratio also tended to decline as the amount of economic damages increased. We found less consistency in our simple regression models where we predicted the log of noneconomic damages from the log of economic damages. In all of those models, the slopes of the fitted line were positive, but the slopes and the measures of fit (r2) varied from dataset to dataset, and among type of case within those datasets with multiple case types. Also, where we had the same type of case across datasets, we found variation in the fit and slope. With two of the datasets we were able to extend our regression models with regard to medical malpractice cases. Using the RAND jury study from 1995-99 we were able to separate out California’s medical malpractice cases which were governed by the MICRA cap on noneconomic damages from the cases coming from five other states included in the study. We found that MICRA dampened the relationship between economic and non-economic damages. Using the data we coded from on Cook County, Illinois jury verdicts, we were able to expand our regression model to include the NAIC severity index plus the gender and age of the plaintiff. We found no evidence that the two demographic variables systematically influenced the amount of non-economic damages, but the severity of injury did make a difference. Most importantly, we found that the severity of the injury conditioned the relationship between economic and non-economic damages

DDD17: End-To-End DAVIS Driving Dataset

Event cameras, such as dynamic vision sensors (DVS), and dynamic and active-pixel vision sensors (DAVIS) can supplement other autonomous driving sensors by providing a concurrent stream of standard active pixel sensor (APS) images and DVS temporal contrast events. The APS stream is a sequence of standard grayscale global-shutter image sensor frames. The DVS events represent brightness changes occurring at a particular moment, with a jitter of about a millisecond under most lighting conditions. They have a dynamic range of >120 dB and effective frame rates >1 kHz at data rates comparable to 30 fps (frames/second) image sensors. To overcome some of the limitations of current image acquisition technology, we investigate in this work the use of the combined DVS and APS streams in end-to-end driving applications. The dataset DDD17 accompanying this paper is the first open dataset of annotated DAVIS driving recordings. DDD17 has over 12 h of a 346x260 pixel DAVIS sensor recording highway and city driving in daytime, evening, night, dry and wet weather conditions, along with vehicle speed, GPS position, driver steering, throttle, and brake captured from the car's on-board diagnostics interface. As an example application, we performed a preliminary end-to-end learning study of using a convolutional neural network that is trained to predict the instantaneous steering angle from DVS and APS visual data.Comment: Presented at the ICML 2017 Workshop on Machine Learning for Autonomous Vehicle

Delta Networks for Optimized Recurrent Network Computation

Many neural networks exhibit stability in their activation patterns over time in response to inputs from sensors operating under real-world conditions. By capitalizing on this property of natural signals, we propose a Recurrent Neural Network (RNN) architecture called a delta network in which each neuron transmits its value only when the change in its activation exceeds a threshold. The execution of RNNs as delta networks is attractive because their states must be stored and fetched at every timestep, unlike in convolutional neural networks (CNNs). We show that a naive run-time delta network implementation offers modest improvements on the number of memory accesses and computes, but optimized training techniques confer higher accuracy at higher speedup. With these optimizations, we demonstrate a 9X reduction in cost with negligible loss of accuracy for the TIDIGITS audio digit recognition benchmark. Similarly, on the large Wall Street Journal speech recognition benchmark even existing networks can be greatly accelerated as delta networks, and a 5.7x improvement with negligible loss of accuracy can be obtained through training. Finally, on an end-to-end CNN trained for steering angle prediction in a driving dataset, the RNN cost can be reduced by a substantial 100X

IS YIELD RESPONSE SITE-SPECIFIC? REVISITING NITROGEN RECOMMENDATIONS ON CORN

Replaced with revised version of paper 08/19/02.Crop Production/Industries,