The Growth in Applications for Social Security Disability Insurance: A Spillover Effect from Workers’ Compensation

We investigate the determinants of application for Social Security Disability Insurance (DI) benefits in approxi­mately 45 jurisdictions between 1981 and 1999. We reproduce findings of previous studies of the determinants of DI application then test the additional influence of changes to workers’ compensation program benefits and rules on DI application rates. Our findings indicate that the programs are interrelated: When workers’ compensation benefits declined and eligibility rules tightened in the 1990s, the DI application rate increased

Customizing kernel functions for SVM-based hyperspectral image classification

Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available algorithms. However, few efforts have been made to extend SVMs to cover the specific requirements of hyperspectral image classification, for example, by building tailor-made kernels. Observation of real-life spectral imagery from the AVIRIS hyperspectral sensor shows that the useful information for classification is not equally distributed across bands, which provides potential to enhance the SVM's performance through exploring different kernel functions. Spectrally weighted kernels are, therefore, proposed, and a set of particular weights is chosen by either optimizing an estimate of generalization error or evaluating each band's utility level. To assess the effectiveness of the proposed method, experiments are carried out on the publicly available 92AV3C dataset collected from the 220-dimensional AVIRIS hyperspectral sensor. Results indicate that the method is generally effective in improving performance: spectral weighting based on learning weights by gradient descent is found to be slightly better than an alternative method based on estimating ";relevance"; between band information and ground trut

Knowledge Distillation for Small-footprint Highway Networks

Deep learning has significantly advanced state-of-the-art of speech recognition in the past few years. However, compared to conventional Gaussian mixture acoustic models, neural network models are usually much larger, and are therefore not very deployable in embedded devices. Previously, we investigated a compact highway deep neural network (HDNN) for acoustic modelling, which is a type of depth-gated feedforward neural network. We have shown that HDNN-based acoustic models can achieve comparable recognition accuracy with much smaller number of model parameters compared to plain deep neural network (DNN) acoustic models. In this paper, we push the boundary further by leveraging on the knowledge distillation technique that is also known as {\it teacher-student} training, i.e., we train the compact HDNN model with the supervision of a high accuracy cumbersome model. Furthermore, we also investigate sequence training and adaptation in the context of teacher-student training. Our experiments were performed on the AMI meeting speech recognition corpus. With this technique, we significantly improved the recognition accuracy of the HDNN acoustic model with less than 0.8 million parameters, and narrowed the gap between this model and the plain DNN with 30 million parameters.Comment: 5 pages, 2 figures, accepted to icassp 201

A New Set of Potential Energy Surfaces for HCO: Influence of Renner-Teller Coupling on the Bound and Resonance Vibrational States

It is commonly understood that the Renner-Teller effect can strongly influence the spectroscopy of molecules through coupling of electronic states. Here we investigate the vibrational bound states and low-lying resonances of the formyl radical treating the Renner-Teller coupled X2A\u27 and Ã2A states using the MultiConfiguration Time Dependent Hartree (MCTDH) method. The calculations were performed using the improved relaxation method for the bound states and a recently published extension to compute resonances. A new set of accurate global potential energy surfaces were computed at the explicitly correlated multireference configuration interaction (MRCI-F12) level and yielded remarkably close agreement with experiment in this application and thus enable future studies including photodissociation and collisional dynamics. The results show the necessity of including the large contribution from a Davidson correction in the electronic structure calculations in order to appreciate the relatively small effect of the Renner-Teller coupling on the states considered here

Associations of Muscle Mass and Strength with All-Cause Mortality among US Older Adults

INTRODUCTION: Recent studies suggested that muscle mass and muscle strength may independently or synergistically affect aging-related health outcomes in older adults; however, prospective data on mortality in the general population are sparse. METHODS: We aimed to prospectively examine individual and joint associations of low muscle mass and low muscle strength with all-cause mortality in a nationally representative sample. This study included 4449 participants age 50 yr and older from the National Health and Nutrition Examination Survey 1999 to 2002 with public use 2011 linked mortality files. Weighted multivariable logistic regression models were adjusted for age, sex, race, body mass index (BMI), smoking, alcohol use, education, leisure time physical activity, sedentary time, and comorbid diseases. RESULTS: Overall, the prevalence of low muscle mass was 23.1% defined by appendicular lean mass (ALM) and 17.0% defined by ALM/BMI, and the prevalence of low muscle strength was 19.4%. In the joint analyses, all-cause mortality was significantly higher among individuals with low muscle strength, whether they had low muscle mass (odds ratio [OR], 2.03; 95% confidence interval [CI], 1.27-3.24 for ALM; OR, 2.53; 95% CI, 1.64-3.88 for ALM/BMI) or not (OR, 2.66; 95% CI, 1.53-4.62 for ALM; OR, 2.17; 95% CI, 1.29-3.64 for ALM/BMI). In addition, the significant associations between low muscle strength and all-cause mortality persisted across different levels of metabolic syndrome, sedentary time, and LTPA. CONCLUSIONS: Low muscle strength was independently associated with elevated risk of all-cause mortality, regardless of muscle mass, metabolic syndrome, sedentary time, or LTPA among US older adults, indicating the importance of muscle strength in predicting aging-related health outcomes in older adults

Are Socially Responsible Exchange-Traded Funds Paying Off in Performance?

This study examines the Socially Responsible (SR) exchange-traded funds (ETFs) by comparing their risk-adjusted performance with a matched group of conventional ETFs in the U.S. equity market. In contrast to prior studies that focus on actively managed mutual funds, we find that the risk-adjusted returns of SR ETFs are significantly lower than those of conventional ETFs during the 2005–2020 period. Such underperformance is only observed in non-crisis periods but not in economic crisis periods (i.e., the 2020 pandemic recession and 2008 financial turmoil). We attribute the observed underperformance of SR ETFs during the non-crisis periods to their limited diversification of unsystematic risks resulting from various negative or positive screens employed in the funds. We also find that net fund flows of the SR ETFs are less sensitive to past negative performance than are conventional fund flows. Collectively, our findings suggest that, instead of seeking wealth maximization, socially conscious investors may choose SR ETFs to gain non-economic utility

Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors

High resolution Magnetic Resonance (MR) images are desired for accurate diagnostics. In practice, image resolution is restricted by factors like hardware and processing constraints. Recently, deep learning methods have been shown to produce compelling state-of-the-art results for image enhancement/super-resolution. Paying particular attention to desired hi-resolution MR image structure, we propose a new regularized network that exploits image priors, namely a low-rank structure and a sharpness prior to enhance deep MR image super-resolution (SR). Our contributions are then incorporating these priors in an analytically tractable fashion \color{black} as well as towards a novel prior guided network architecture that accomplishes the super-resolution task. This is particularly challenging for the low rank prior since the rank is not a differentiable function of the image matrix(and hence the network parameters), an issue we address by pursuing differentiable approximations of the rank. Sharpness is emphasized by the variance of the Laplacian which we show can be implemented by a fixed feedback layer at the output of the network. As a key extension, we modify the fixed feedback (Laplacian) layer by learning a new set of training data driven filters that are optimized for enhanced sharpness. Experiments performed on publicly available MR brain image databases and comparisons against existing state-of-the-art methods show that the proposed prior guided network offers significant practical gains in terms of improved SNR/image quality measures. Because our priors are on output images, the proposed method is versatile and can be combined with a wide variety of existing network architectures to further enhance their performance.Comment: Accepted to IEEE transactions on Image Processin

Understanding the structure-emulsification relationship of gum ghatti – a review of recent advances

This paper is based on a series of physical and chemical investigations to understand the structure-function properties of gum ghatti. After elucidating the detailed molecular structure of two gum ghatti fractions, the structure of its component glycoprotein was investigated whereby, the protein sequence and hydrophobicity were identified, followed by the conformational analysis of the gum and its fractions. Many techniques were used for the elucidation of the fine structures, which included methylation analysis-GC-MS, Maldi-TOF MS and 2D NMR spectroscopy, homonuclear ¹H/¹H correlations spectroscopy (COSY, TOCSY), heteronuclear ¹³C/¹H multiple-quantum coherence spectroscopy (HMQC) and heteronuclear multiple bond correlation (HMBC). Conformational properties were studied using a modelling system (Insight II) to relate the hydrophobicity of the protein moieties with the complex structures of the carbohydrates. These studies now provide an explanation for the excellent emulsification properties of gum ghatti in oil-in-water emulsions, which enable its application in the food, cosmetic and/or pharmaceutical industries

KIC 9406652: An Unusual Cataclysmic Variable in the Kepler Field of View

KIC 9406652 is a remarkable variable star in the Kepler field of view that shows both very rapid oscillations and long term outbursts in its light curve. We present an analysis of the light curve over quarters 1 to 15 and new spectroscopy that indicates that the object is a cataclysmic variable with an orbital period of 6.108 hours. However, an even stronger signal appears in the light curve periodogram for a shorter period of 5.753 hours, and we argue that this corresponds to the modulation of flux from the hot spot region in a tilted, precessing disk surrounding the white dwarf star. We present a preliminary orbital solution from radial velocity measurements of features from the accretion disk and the photosphere of the companion. We use a Doppler tomography algorithm to reconstruct the disk and companion spectra, and we also consider how these components contribute to the object's spectral energy distribution from ultraviolet to infrared wavelengths. This target offers us a remarkable opportunity to investigate disk processes during the high mass transfer stage of evolution in cataclysmic variables.Comment: 31 pages, 13 figures, accepted for Ap

Strand bias in complementary single-nucleotide polymorphisms of transcribed human sequences: evidence for functional effects of synonymous polymorphisms

BACKGROUND: Complementary single-nucleotide polymorphisms (SNPs) may not be distributed equally between two DNA strands if the strands are functionally distinct, such as in transcribed genes. In introns, an excess of A↔G over the complementary C↔T substitutions had previously been found and attributed to transcription-coupled repair (TCR), demonstrating the valuable functional clues that can be obtained by studying such asymmetry. Here we studied asymmetry of human synonymous SNPs (sSNPs) in the fourfold degenerate (FFD) sites as compared to intronic SNPs (iSNPs). RESULTS: The identities of the ancestral bases and the direction of mutations were inferred from human-chimpanzee genomic alignment. After correction for background nucleotide composition, excess of A→G over the complementary T→C polymorphisms, which was observed previously and can be explained by TCR, was confirmed in FFD SNPs and iSNPs. However, when SNPs were separately examined according to whether they mapped to a CpG dinucleotide or not, an excess of C→T over G→A polymorphisms was found in non-CpG site FFD SNPs but was absent from iSNPs and CpG site FFD SNPs. CONCLUSION: The genome-wide discrepancy of human FFD SNPs provides novel evidence for widespread selective pressure due to functional effects of sSNPs. The similar asymmetry pattern of FFD SNPs and iSNPs that map to a CpG can be explained by transcription-coupled mechanisms, including TCR and transcription-coupled mutation. Because of the hypermutability of CpG sites, more CpG site FFD SNPs are relatively younger and have confronted less selection effect than non-CpG FFD SNPs, which can explain the asymmetric discrepancy of CpG site FFD SNPs vs. non-CpG site FFD SNPs