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

Homogenous Ensemble Phonotactic Language Recognition Based on SVM Supervector Reconstruction

Currently, acoustic spoken language recognition (SLR) and phonotactic SLR systems are widely used language recognition systems. To achieve better performance, researchers combine multiple subsystems with the results often much better than a single SLR system. Phonotactic SLR subsystems may vary in the acoustic features vectors or include multiple language-specific phone recognizers and different acoustic models. These methods achieve good performance but usually compute at high computational cost. In this paper, a new diversification for phonotactic language recognition systems is proposed using vector space models by support vector machine (SVM) supervector reconstruction (SSR). In this architecture, the subsystems share the same feature extraction, decoding, and N-gram counting preprocessing steps, but model in a different vector space by using the SSR algorithm without significant additional computation. We term this a homogeneous ensemble phonotactic language recognition (HEPLR) system. The system integrates three different SVM supervector reconstruction algorithms, including relative SVM supervector reconstruction, functional SVM supervector reconstruction, and perturbing SVM supervector reconstruction. All of the algorithms are incorporated using a linear discriminant analysis-maximum mutual information (LDA-MMI) backend for improving language recognition evaluation (LRE) accuracy. Evaluated on the National Institute of Standards and Technology (NIST) LRE 2009 task, the proposed HEPLR system achieves better performance than a baseline phone recognition-vector space modeling (PR-VSM) system with minimal extra computational cost. The performance of the HEPLR system yields 1.39%, 3.63%, and 14.79% equal error rate (EER), representing 6.06%, 10.15%, and 10.53% relative improvements over the baseline system, respectively, for the 30-, 10-, and 3-s test conditions

Efficient Embedded Speech Recognition for Very Large Vocabulary Mandarin Car-Navigation Systems

Automatic speech recognition (ASR) for a very large vocabulary of isolated words is a difficult task on a resource-limited embedded device. This paper presents a novel fast decoding algorithm for a Mandarin speech recognition system which can simultaneously process hundreds of thousands of items and maintain high recognition accuracy. The proposed algorithm constructs a semi-tree search network based on Mandarin pronunciation rules, to avoid duplicate syllable matching and save redundant memory. Based on a two-stage fixed-width beam-search baseline system, the algorithm employs a variable beam-width pruning strategy and a frame-synchronous word-level pruning strategy to significantly reduce recognition time. This algorithm is aimed at an in-car navigation system in China and simulated on a standard PC workstation. The experimental results show that the proposed method reduces recognition time by nearly 6-fold and memory size nearly 2- fold compared to the baseline system, and causes less than 1% accuracy degradation for a 200,000 word recognition task

Topology optimization of freeform large-area metasurfaces

We demonstrate optimization of optical metasurfaces over $10^5$--$10^6$ degrees of freedom in two and three dimensions, 100--1000+ wavelengths ($\lambda$) in diameter, with 100+ parameters per $\lambda^2$. In particular, we show how topology optimization, with one degree of freedom per high-resolution "pixel," can be extended to large areas with the help of a locally periodic approximation that was previously only used for a few parameters per $\lambda^2$. In this way, we can computationally discover completely unexpected metasurface designs for challenging multi-frequency, multi-angle problems, including designs for fully coupled multi-layer structures with arbitrary per-layer patterns. Unlike typical metasurface designs based on subwavelength unit cells, our approach can discover both sub- and supra-wavelength patterns and can obtain both the near and far fields

Robust Environmental Mapping by Mobile Sensor Networks

Constructing a spatial map of environmental parameters is a crucial step to preventing hazardous chemical leakages, forest fires, or while estimating a spatially distributed physical quantities such as terrain elevation. Although prior methods can do such mapping tasks efficiently via dispatching a group of autonomous agents, they are unable to ensure satisfactory convergence to the underlying ground truth distribution in a decentralized manner when any of the agents fail. Since the types of agents utilized to perform such mapping are typically inexpensive and prone to failure, this results in poor overall mapping performance in real-world applications, which can in certain cases endanger human safety. This paper presents a Bayesian approach for robust spatial mapping of environmental parameters by deploying a group of mobile robots capable of ad-hoc communication equipped with short-range sensors in the presence of hardware failures. Our approach first utilizes a variant of the Voronoi diagram to partition the region to be mapped into disjoint regions that are each associated with at least one robot. These robots are then deployed in a decentralized manner to maximize the likelihood that at least one robot detects every target in their associated region despite a non-zero probability of failure. A suite of simulation results is presented to demonstrate the effectiveness and robustness of the proposed method when compared to existing techniques.Comment: accepted to icra 201

Latent Class Model with Application to Speaker Diarization

In this paper, we apply a latent class model (LCM) to the task of speaker diarization. LCM is similar to Patrick Kenny's variational Bayes (VB) method in that it uses soft information and avoids premature hard decisions in its iterations. In contrast to the VB method, which is based on a generative model, LCM provides a framework allowing both generative and discriminative models. The discriminative property is realized through the use of i-vector (Ivec), probabilistic linear discriminative analysis (PLDA), and a support vector machine (SVM) in this work. Systems denoted as LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid are introduced. In addition, three further improvements are applied to enhance its performance. 1) Adding neighbor windows to extract more speaker information for each short segment. 2) Using a hidden Markov model to avoid frequent speaker change points. 3) Using an agglomerative hierarchical cluster to do initialization and present hard and soft priors, in order to overcome the problem of initial sensitivity. Experiments on the National Institute of Standards and Technology Rich Transcription 2009 speaker diarization database, under the condition of a single distant microphone, show that the diarization error rate (DER) of the proposed methods has substantial relative improvements compared with mainstream systems. Compared to the VB method, the relative improvements of LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid systems are 23.5%, 27.1%, and 43.0%, respectively. Experiments on our collected database, CALLHOME97, CALLHOME00 and SRE08 short2-summed trial conditions also show that the proposed LCM-Ivec-Hybrid system has the best overall performance

To What Extent do Investors in a Financial Market Anchor Their Judgments? Evidence from the Hong Kong Horserace Betting Market

This paper explores the use of the anchoring and adjustment heuristic by decision makers in a financial market; in particular, the degree to which horserace bettors anchor their probability judgments on the advantage afforded by a horse‟s barrier-position. The results suggest that under certain conditions bettors anchor on barrier-position information revealed at previous race meetings, but not on the most recent race outcomes. In fact, bettors appear to use the most recent race outcomes appropriately when forming probability estimates; but only when the results are in line with their mental model of barrier-position advantage. Bettors with varying levels of expertise are shown to be subject to anchoring, although greater expertise is generally associated with less anchoring. The paper concludes that the manner and degree of anchoring in real world environ.

A study on factors affecting the degradation of magnesium and a magnesium-yttrium alloy for biomedical applications.

Controlling degradation of magnesium or its alloys in physiological saline solutions is essential for their potential applications in clinically viable implants. Rapid degradation of magnesium-based materials reduces the mechanical properties of implants prematurely and severely increases alkalinity of the local environment. Therefore, the objective of this study is to investigate the effects of three interactive factors on magnesium degradation, specifically, the addition of yttrium to form a magnesium-yttrium alloy versus pure magnesium, the metallic versus oxide surfaces, and the presence versus absence of physiological salt ions in the immersion solution. In the immersion solution of phosphate buffered saline (PBS), the magnesium-yttrium alloy with metallic surface degraded the slowest, followed by pure magnesium with metallic or oxide surfaces, and the magnesium-yttrium alloy with oxide surface degraded the fastest. However, in deionized (DI) water, the degradation rate showed a different trend. Specifically, pure magnesium with metallic or oxide surfaces degraded the slowest, followed by the magnesium-yttrium alloy with oxide surface, and the magnesium-yttrium alloy with metallic surface degraded the fastest. Interestingly, only magnesium-yttrium alloy with metallic surface degraded slower in PBS than in DI water, while all the other samples degraded faster in PBS than in DI water. Clearly, the results showed that the alloy composition, presence or absence of surface oxide layer, and presence or absence of physiological salt ions in the immersion solution all influenced the degradation rate and mode. Moreover, these three factors showed statistically significant interactions. This study revealed the complex interrelationships among these factors and their respective contributions to degradation for the first time. The results of this study not only improved our understanding of magnesium degradation in physiological environment, but also presented the key factors to consider in order to satisfy the degradation requirements for next-generation biodegradable implants and devices

RNN Language Model with Word Clustering and Class-based Output Layer

The recurrent neural network language model (RNNLM) has shown significant promise for statistical language modeling. In this work, a new class-based output layer method is introduced to further improve the RNNLM. In this method, word class information is incorporated into the output layer by utilizing the Brown clustering algorithm to estimate a class-based language model. Experimental results show that the new output layer with word clustering not only improves the convergence obviously but also reduces the perplexity and word error rate in large vocabulary continuous speech recognition

Small-angle x-ray-scattering study of phase separation and crystallization in the bulk amorphous Mg62Cu25Y10Li3 alloy

We report on a small-angle x-ray-scattering (SAXS) and differential scanning calorimetry study of phase separation and crystallization in rapidly quenched amorphous Mg62Cu25Y10Li3 alloy samples. Differential scanning calorimetry demonstrates the occurrence of crystallization and grain growth upon isothermal annealing of these samples at 135 °C. The SAXS studies show the presence of large inhomogeneities even in the rapidly quenched as-prepared Mg62Cu25Y10Li3 alloy that is attributed to phase separation in the undercooled liquid during the cooling process. After isothermal annealing at 135 °C for longer than 30 min the samples exhibit a strong SAXS intensity that monotonically increases with increasing annealing time. During heat treatment, crystallization and growth of a nanocrystalline bcc-Mg7Li3 phase occurs in the Y-poor and MgLi-rich domains. The initially rough boundaries of the nanocrystals become sharper with increasing annealing time. Anomalous small-angle x-ray-scattering investigations near the Cu K edge indicate that while Cu is distributed homogeneously in the as-prepared sample, a Cu composition gradient develops between the matrix and the bcc-Mg7Li3 nanocrystals in the annealed sample