LLukas22/scidocs huggingface dataset:
Functional connectivity between core and shoulder muscles increases during isometric endurance contractions in judo competitors

The aim of this study was to assess the surface electromyogram (SEMG) changes within and between muscles of the torso and shoulder region during static endurance contraction in elite judokas. We hypothesized an increased functional connectivity of muscles from the shoulder and torso regions during sustained isometric contraction. Twelve healthy, right-handed judo competitors participated in the study. The SEMG signals from the dominant trapezius (upper, middle and lower part), deltoideus anterior, serratus anterior, and pectoralis major muscles were recorded during isometric endurance contraction consisting of bilateral arm abduction at 90°. The normalized mutual information (NMI) was computed between muscle pairs as an index indicating functional connectivity. The NMIs increased significantly during endurance test for 10 of the 15 muscle pairs (P < 0.001). We concluded that the increases in NMIs highlighted functional changes in the interplay between core and shoulder muscles during an endurance contraction in elite judokas.
QR code recognition based on image processing

To solve the QR code recognition problem caused by ordinary camera collection, the recognition algorithm based on image processing is put forward in this paper. The whole process including image binarization, image tilt correction, image orientation, image geometric correction and image normalization allows images collected on different illumination conditions, different acquisition angles to be quickly identified. Based on other recognition algorithm, some improvements are presented in the image tilt correction, image orientation, image normalization and so on to speed up the image processing and to achieve more simply. Experiments show that the improved method can enhance the recognition speed of two-dimensional code and accuracy.
Charge scheduling of electric vehicles in highways

Today, charging stations (CSs) for electric vehicles (EVs) are much less popular than gas stations. Therefore, searching and selecting CSs is an important issue for the drivers of EVs. This paper investigates the EV charging problem. We propose two types of CSselection algorithms. The first type only utilizes local information of an EV. The second type utilizes the global information obtained through interactions between the EVs and a Global CS-selection (GCS) server through the mobile telecommunications network. Our study indicates that by using the global information (specifically the workload status of each CS), the EVs can be effectively charged with short waiting times at the CSs. © 2011 Elsevier Ltd. All rights reserved.
Keeping the Faith: Origins of Confidence in Charitable Organizations and its Consequences for Philanthropy

Origins and consequences of charitable confidence are investigated with the Giving in the Netherlands Panel Survey 2002-2004 (n=1,246). Charitable confidence is higher among the higher educated, children of volunteers, younger age groups, those with more faith in people, those who are aware of standards of excellence for fundraising organizations, and among persons with altruistic and joy-of-giving motives for philanthropy. In a regression analysis, the relationship of confidence with philanthropy is found to be moderately strong. The relationship is strongest for donations to organizations that deal with social problems that are difficult to solve, like poverty, illness, and violation of human rights. Beliefs about program spending and irritation about fundraising campaigns confidence partly explain why confidence matters for philanthropy, especially for those with altruistic motives for giving.
MRPR: A MapReduce solution for prototype reduction in big data classification

In the era of big data, analyzing and extracting knowledge from large-scale data sets is a very interesting and challenging task. The application of standard data mining tools in such data sets is not straightforward. Hence, a new class of scalable mining method that embraces the huge storage and processing capacity of cloud platforms is required. In this work, we propose a novel distributed partitioning methodology for prototype reduction techniques in nearest neighbor classification. These methods aim at representing original training data sets as a reduced number of instances. Their main purposes are to speed up the classification process and reduce the storage requirements and sensitivity to noise of the nearest neighbor rule. However, the standard prototype reduction methods cannot cope with very large data sets. To overcome this limitation, we develop a MapReduce-based framework to distribute the functioning of these algorithms through a cluster of computing elements, proposing several algorithmic strategies to integrate multiple partial solutions (reduced sets of prototypes) into a single one. The proposed model enables prototype reduction algorithms to be applied over big data classification problems without significant accuracy loss. We test the speeding up capabilities of our model with data sets up to 5.7 millions of Email addresses: triguero@decsai.ugr.es (Isaac Triguero), dperalta@decsai.ugr.es (Daniel Peralta), jaume.bacardit@newcastle.ac.uk (Jaume Bacardit), sglopez@ujaen.es (Salvador Garćıa), herrera@decsai.ugr.es (Francisco Herrera) Preprint submitted to Neurocomputing March 3, 2014 instances. The results show that this model is a suitable tool to enhance the performance of the nearest neighbor classifier with big data.
Childhood psychiatric disorder and unintentional injury: findings from a national cohort study.

OBJECTIVE
We set out to examine the relationship between unintentional injury and common child psychiatric disorders, including both internalizing and externalizing diagnoses.


METHODS
The 1999 British Child and Adolescent Mental Health Survey provided a nationally representative sample of over 10,000 children aged 5-15 years. Measures included assessment of diagnoses of psychiatric disorder from the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, unintentional injury, and a range of potentially related psychosocial factors.


RESULTS
Children with psychiatric disorders had higher rates of unintentional injury. After accounting for psychosocial risk factors and comorbid psychopathology, oppositional defiant disorder (ODD) was independently associated with burns and poisoning. Attention deficit hyperactivity disorder (ADHD) was related to fractures, and depression and anxiety also showed independent links to some injury types.


CONCLUSIONS
ODD and ADHD, rather than conduct disorder, appear to be the externalizing disorders associated with unintentional injury. We discuss possible models of the relationships between internalizing disorders and unintentional injury.
The corpus approach: a common way forward for Contrastive Linguistics and Translation Studies

In this introductory chapter, Granger traces the development of Contrastive Linguistics and Translation Studies over the last decades to the present day, focusing on the role of the computer corpus in giving new impetus to each field and bringing them closer together. She discusses the different types of monolingual and multilingual corpora being used in CL and TS research, proposing at the same time a common corpus terminology. She then relates the contribution of the different corpus types to the major research interests of each discipline, highlighting the complementarity of the research and calling for increased cross-fertilization and resource-pooling. She then examines the practical issues of corpus exploitation, with a review of corpus analysis tools of particular value for CL and TS and finally, the contribution of corpus-based CL and TS research in the teaching of foreign languages and translation. 0. Contrastive Linguistics and Translation Studies, two converging disciplines Although the disciplines of Contrastive Linguistics (CL) and Translation Studies (TS) cover partly common ground, it is only recently, with the emergence of corpora, that they have started to converge. This rapprochement is apparent from recent publications and conferences that have brought together specialists from the two fields, bearing witness to the vitality of multilingual studies in general. The history of Contrastive Linguistics has been characterized by a pattern of success-decline-success. CL was originally a purely applied enterprise, aiming to produce more efficient foreign language teaching methods and tools. Based on the general assumption that difference equals difficulty, CL, which in those days was called Contrastive Analysis (CA), consisted in charting areas of similarity and difference between languages and basing the teaching syllabus on the contrastive findings. Advances in the understanding of Second Language Acquisition (SLA) mechanisms led to a questioning of the very basis of CA. Interlingual factors were found to be less prevalent than other factors, among which intralingual mechanisms such as the overgeneralization of target rules and external factors such as the influence of teaching methods or personal factors like motivation. This led to the decline of CA, but not to its death. At first, it gave rise to some drastic pedagogical decisions, which in some cases culminated in a total ban of the mother tongue in FL teaching. But research (see Odlin 1989, Selinker 1992, James 1998) re-established transfer as a major – if not the major – factor in SLA, which in turn led to a progressive – albeit limited – return of contrastive
Behçet's syndrome: disease manifestations, management, and advances in treatment

The acne lesions characteristic of Behçet's syndrome are not sterile and are commonly observed in combination with arthritis. The two main nodular skin lesions—superficial thrombophlebitis and erythema nodosum—are equally frequent, and rather difficult to distinguish. Superficial thrombophlebitis is usually observed in combination with thrombosis in large veins, and thrombosis of the large veins usually clusters with dural sinus thrombi, which make up approximately 20% of all central nervous system (CNS) lesions of Behçet's syndrome. The remaining CNS lesions are parenchymal, mainly located in the brainstem, and associated with a graver prognosis than dural sinus thrombi. The presence of clinical clusters indicates that there are at least two pathogenetic pathways in Behçet's syndrome: a reactive arthritis pathway and a thrombophilia pathway. Research into the pathogenesis of Behçet's syndrome has shown that the most consistent genetic marker of Behçet's syndrome is HLA-B51; however, the genetic association of this true-to-form 'complex' disorder with HLA-B51 is only 20%, and a whole-genome study showed associations with 16 different loci. The severity of Behçet's syndrome and the mortality associated with it tend to decrease with time, and there is no associated increase in incidence of atherosclerosis. Although treatment of skin–mucosa manifestations, eye disease and pulmonary artery aneurysms has improved significantly in the past decades, the treatment of CNS lesions and thrombophilia are still problematic.
Estimating the Effects of Private School Vouchers in Multidistrict Economies

This paper estimates a general equilibrium model of school quality and household residential and school choice for economies with multiple public school districts and private (religious and non-sectarian) schools. The estimates, obtained through full-solution methods, are used to simulate two large-scale private school voucher programs in the Chicago metropolitan area: universal vouchers and vouchers restricted to non-sectarian schools. In the simulations, both programs increase private school enrollment and affect household residential choice. However, under non-sectarian vouchers private school enrollment expands less than under universal vouchers and religious school enrollment declines for large vouchers. Fewer households benefit from non-sectarian vouchers. (JEL I22, H73, H42, C51). * This paper is based on my PhD dissertation at the University of Wisconsin-Madison. I wish to thank my advisors Derek Neal, Steve Durlauf and Phil Haile for their guidance and encouragement. Comments from the editor and the referees substantially improved this paper. I am also grateful to Peter Arcidiacono, Angela Dills, Dennis Epple, Arthur Goldberger, Ken Judd, John Kennan, Yuichi Kitamura, Grigory Kosenok, George Levi-Gayle, Bob Miller, Tom Nechyba, Uday Rajan, Richard Romano, Ananth Seshadri, Holger Sieg, Fallaw Sowell, Steve Spear, and M. Kathleen Thomas for many helpful ideas and suggestions. For their help with and insights on the technical aspects of this project, I am thankful to Javier Pena, R. Ravi, and Jeff Reminga. I benefited from comments and suggestions by seminar participants at Brown, Carnegie Mellon, Clemson, U. of Colorado-Boulder, Columbia Business School, Duke, U. of Georgia, U. of Oklahoma, Rutgers, U. of Illinois at Urbana-Champaign, U. of Virginia, and U. of Wisconsin, and participants at the 2002 CIRANO conference on education, Camp Resources X, the Stanford Conference on Empirical Boundaries for IO, Public Economics and Environmental Economics, the LSE conference on Economics of Cities, the 2003 Public Economic Theory conference, the 2003 Stanford Institute for Theoretical Economics, the 2003 European Meeting of the Econometric Society, the 2004 Southern Economic Association meetings, and the 2004 conference on Education and Economic Development at the Federal Reserve Bank of Cleveland. I owe special thanks to the Condor Teams at the UW-Madison Computer Science Department and the Pittsburgh Supercomputing Center, whose help has been essential to carry out this project. Financial support from the UW Department of Economics to purchase data, and from the UW Robock Award in Empirical Economics are gratefully acknowledged. Surendra Bagde provided excellent research assistance. All errors are mine.
Training Bit Fully Convolutional Network for Fast Semantic Segmentation

Fully convolutional neural networks give accurate, per-pixel prediction for input images and have applications like semantic segmentation. However, a typical FCN usually requires lots of floating point computation and large run-time memory, which effectively limits its usability. We propose a method to train Bit Fully Convolution Network (BFCN), a fully convolutional neural network that has low bit-width weights and activations. Because most of its computation-intensive convolutions are accomplished between low bit-width numbers, a BFCN can be accelerated by an efficient bit-convolution implementation. On CPU, the dot product operation between two bit vectors can be reduced to bitwise operations and popcounts, which can offer much higher throughput than 32-bit multiplications and additions. To validate the effectiveness of BFCN, we conduct experiments on the PASCAL VOC 2012 semantic segmentation task and Cityscapes. Our BFCN with 1-bit weights and 2-bit activations, which runs 7.8x faster on CPU or requires less than 1% resources on FPGA, can achieve comparable performance as the 32-bit counterpart. Introduction Deep convolutional neural networks (DCNN), with its recent progress, has considerably changed the landscape of computer vision (Krizhevsky, Sutskever, and Hinton 2012) and many other fields. To achieve close to state-of-the-art performance, a DCNN usually has a lot of parameters and high computational complexity, which may easily overwhelm resource capability of embedded devices. Substantial research efforts have been invested in speeding up DCNNs on both general-purpose (Vanhoucke, Senior, and Mao 2011; Gong et al. 2014; Han et al. 2015) and specialized computer hardware (Farabet et al. 2009; Farabet et al. 2011; Pham et al. 2012; Chen et al. 2014b; Chen et al. 2014c; Zhang et al. 2015a). Recent progress in using low bit-width networks has considerably reduced parameter storage size and computation burden by using 1-bit weight and low bit-width activations. In particular, in BNN (Kim and Smaragdis 2016) and XNOR-net (Rastegari et al. 2016), during the forward pass the most computationally expensive convolutions can Copyright c © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. network VOC12 Cityscapes speedup 32-bit FCN 69.8% 62.1% 1x 2-bit BFCN 67.0% 60.3% 4.1x 1-2 BFCN 62.8% 57.4% 7.8x Table 1: Summary results of our BFCNs. Performance measure in mean IoU. be done by combining xnor and popcount operations, thanks to the following equivalence when x and y are bit vectors:
A Generalized Mean Field Algorithm for Variational Inference in Exponential Families

We present a class of generalized mean field (GMF) algorithms for approximate inference in exponential family graphical models which is analogous to the generalized belief prop­ agation (GBP) or cluster variational meth­ ods. While those methods are based on over­ lapping clusters, our approach is based on nonoverlapping clusters. Unlike the cluster variational methods, the approach is proved to converge to a globally consistent set of marginals and a lower bound on the likeli­ hood, while providing much of the flexibility associated with cluster variational methods. We present experiments that analyze the ef­ fect of different choices of clustering on infer­ ence quality, and compare GMF with belief propagation on several canonical models. 1 Introduction The variational approach to probabilistic inference in­ volves converting the inference problem into an opti­ mization problem, by approximating the feasible set or the function to be optimized (or both), and solv­ ing the relaxed optimization problem. Thus, given a probability distribution p(x[O) which factors according to a graph, the variational methods yield approxima­ tions to marginal probabilities via the solution to an optimization problem that generally exploits some of the graphical structure. The earliest variational in­ ference methods were based on the use of a family of tractable distributions q(x['y), where"! are a set of free "variational parameters." In this case a simple appeal to Jensen's inequality produces a relaxed optimization problem that determines how to set the variational pa­ rameters (Jordan et al., 1999). We will refer to such methods as "mean field methods," a terminology that reflects the classical setting in which q(x['y) is taken to be a completely factorized distribution. In general, the derivation via Jensen's inequality shows that this class of algorithms yields a lower bound on the likelihood. More recently, Yedidia et al. (2001) realized that Pearl's belief propagation (BP) algorithm-when ap­ plied to general loopy graphs-is also a variational algorithm. The inference problem is transformed to an optimization functional-the "Bethe free energy"­ that imposes local consistency on the approximate marginals. The resulting marginals do not, however, need to be globally consistent, so that the Jensen in­ equality argument no longer applies (and thus the ap­ proximation does not yield a lower bound to the like­ lihood and may not converge). An advantage of this approach is the simplicity of the algorithm. Moreover, Yedidia et al. showed how to derive generalized be­ lief propagation (GBP) algorithms, in which the …
Changes in employment and household income during the 24 months following a cancer diagnosis

The purpose of the study was to describe the changes in employment and household income following a cancer diagnosis. Participants were 68 recent cancer survivors (6–24 months since diagnosis) aged 18 years or older who were employed full- or part-time prior to receiving a cancer diagnosis. Data were both quantitative and qualitative. Participants completed a mailed questionnaire about various issues faced by cancer survivors, including 36 questions about work, finances, and income. In addition, space was provided for participants to write optional narrative details about work experiences. Before cancer, all 68 participants were employed, 45 full-time and 23 part-time. After completion of primary cancer treatment, 49 (72%) were employed (29 full-time and 20 part-time). A decrease in household income after cancer diagnosis was reported by 37% of participants. Of those who were the main income earners in their households prior to cancer, 26% were no longer the main earners after cancer. The qualitative data about work difficulties fell broadly into two areas: physical difficulties and attitudes of employers. Though limited by a comparatively small sample size, this study is the first to focus on employment in the very early survivorship period, using both questionnaire and qualitative data. The findings show that many individuals continue to work during and after treatment, though reduction of work hours or quitting work is not uncommon. The complex factors associated with work decisions are not easily assessed with questionnaires, and in-depth qualitative studies of recent cancer survivors are warranted.
THE DETERMINANTS OF THE TUNISIAN BANKING INDUSTRY PROFITABILITY : PANEL EVIDENCE

This paper investigates the impact of bank’s characteristics, financial structure and macroeconomic indicators on bank’s net interest margins and profitability in the Tunisian banking industry for the 1980-2000 period. First, individual bank characteristics explain a substantial part of the within-country variation in bank interest margins and net profitability. High net interest margin and profitability tend to be associated with banks that hold a relatively high amount of capital, and with large overheads. Other important internal determinants of bank’s interest margins bank loans which have a positive and significant impact. The size has mostly negative and significant coefficients on the net interest margins. This latter result may simply reflect scale inefficiencies. Second, the paper finds that the macro-economic indicators such inflation and growth rates have no impact on bank’s interest margins and profitability. Third, turning to financial structure and its impact on bank’s interest margin and profitability, we find that concentration is less beneficial to the Tunisian commercial banks than competition. Stock market development has a positive effect on bank profitability. This reflects the complementarities between bank and stock market growth. We have found that the disintermediation of the Tunisian financial system is favourable to the banking sector profitability.