Miracle-dz/UniFAR-FTUs huggingface dataset:
A resource‐based analysis of recruitment and selection practices of Indian software companies

Purpose – The purpose of this paper is to detail staffing practices of five software companies located in India.Design/methodology/approach – This qualitative research paper uses purposeful sampling to provide rich data on senior‐level staffing practices. The interviews conducted in India are tape‐recorded and notes are also taken diligently. The interviews are coded to identify similar and dissimilar themes.Findings – This research identifies internal recruitment, employer references, succession planning, interviews, personality tests, newspaper recruitment, professional search agencies, and bio‐data as the predominant senior‐level staffing practices.Practical implications – The paper identifies successful staffing practices adopted by domestic software companies. As multinational companies significantly increase their presence in India, global practitioners can implement successful staffing practices by having a thorough understanding of local staffing practices.Originality/value – This paper identifies.
Hiding Informationin in Java Class File

A novel steganographic scheme is proposed in this paper to embed secret message into Java executable files. It analyzes firstly the format of Java class file, and then reorder the indexes of constants to embed messages. Expensive experiments show that the stego file modified by the proposed scheme still have the same file size and the implementation performance as cover. It is concluded that our scheme has the stronger covertness. Moreover, its maximum embedding capacity even amounts to 1/58.
Activation of PKR Causes Amyloid ß-Peptide Accumulation via De-Repression of BACE1 Expression

BACE1 is a key enzyme involved in the production of amyloid ß-peptide (Aß) in Alzheimer's disease (AD) brains. Normally, its expression is constitutively inhibited due to the presence of the 5′untranslated region (5′UTR) in the BACE1 promoter. BACE1 expression is activated by phosphorylation of the eukaryotic initiation factor (eIF)2-alpha, which reverses the inhibitory effect exerted by BACE1 5′UTR. There are four kinases associated with different types of stress that could phosphorylate eIF2-alpha. Here we focus on the double-stranded (ds) RNA-activated protein kinase (PKR). PKR is activated during viral infection, including that of herpes simplex virus type 1 (HSV1), a virus suggested to be implicated in the development of AD, acting when present in brains of carriers of the type 4 allele of the apolipoprotein E gene. HSV1 is a dsDNA virus but it has genes on both strands of the genome, and from these genes complementary RNA molecules are transcribed. These could activate BACE1 expression by the PKR pathway. Here we demonstrate in HSV1-infected neuroblastoma cells, and in peripheral nervous tissue from HSV1-infected mice, that HSV1 activates PKR. Cloning BACE1 5′UTR upstream of a luciferase (luc) gene confirmed its inhibitory effect, which can be prevented by salubrinal, an inhibitor of the eIF2-alpha phosphatase PP1c. Treatment with the dsRNA analog poly (I∶C) mimicked the stimulatory effect exerted by salubrinal over BACE1 translation in the 5′UTR-luc construct and increased Aß production in HEK-APPsw cells. Summarizing, our data suggest that PKR activated in brain by HSV1 could play an important role in the development of AD.
How to Yield an Unbiased Estimate of the Speed of Adjustment to Target Capital Structure

Recent studies show that a standard partial adjustment model with the debt ratio as the dependent variable cannot distinguish between mechanical mean reversion and adjustment to target capital structure. I propose a new approach that uses the net increase of debt as the dependent variable and uses only ex-post information to estimate the target capital structure. Simulation experiments show that this approach is mainly unaffected by mechanical mean reversion and hence able to provide a meaningful test for the target adjustment hypothesis. I estimate a speed of adjustment to target capital structure of 28% per year.
A Possible fundamental explanation of electroweak unification

Electroweak unification is implied by the local structure theorem of distribution theory applied to the causal interval R=X-Z between two space-time points X and Z. Taking R as generating function, the potentials of the electromagnetic and weak interactions are obtained by applying the causal d'Alembertian operator on R, and both potentials can be given a proper physical interpretation provided all calculations are made in the context of nonlinear generalized functions.
Being Close and Being Social: Peer Ratings of Distinct Aspects of Young Adult Social Competence

The present study had three main objectives: (1) to develop and validate scales of young adult social competence in two domains, close relationships and social groups, using peer ratings of California Q-sort (Block, 1974; Kremen & Block, 2002) items; (2) to test the hypothesis that social competence is associated with young adult well-being and ego development; (3) to test the hypothesis that close relationship competence aligns more closely than social group competence with young adult functioning. Psychometric data on peer ratings of social competence are presented. For 133 young adults, peer ratings of social competence were correlated in expected directions with indices of functioning (e.g., self-worth, education, psychological distress, criminal behavior, and ego development). Associations were generally stronger for competence in close relationships than in social groups.
Optimal Design and Dimensioning of Hydrogen Transmission Distribution Pipeline Networks

This work considers the problem of the optimal design of an hydrogen transmission network. This design problem includes the topology determination and the dimensioning problem. We dene a solution method that simultaneously looks for the least cost topology of the network and for the optimal diameter of each pipe. These two problems were generally solved separately these last years. The application to the case of development of future hydrogen pipeline networks in France has been conducted on several urban areas.
A beamforming approach to permutation alignment for multichannel frequency-domain blind speech separation

In this work, we explore important connections between blind source separation (BSS) and ideal beamforming. We first compare the performance of a null-steering beamformer against that of a frequency-domain BSS method in a reverberant environment, drawing some interesting conclusions. We then examine the feasibility of using beamformer concepts to resolve permutation inconsistency across frequency, which degrades the performance of BSS methods in a reverberant environment. We also propose a permutation alignment scheme based on information gathered from the microphone array directivity patterns. This technique is novel in the sense that it works satisfactorily even when the directivity patterns exhibit grating lobes, where, in fact, better separation can be achieved in principle. We perform experiments that support the viability of the proposed method under different operating conditions and microphone spacings.
A note on multiprocessor speed scaling with precedence constraints

We consider the problem of scheduling a set of jobs, under precedence constraints, on a set of speed scalable parallel processors. The goal is to minimize the makespan of the schedule, i.e. the time at which the last job finishes its execution, without violating a given energy budget. This situation finds applications in computer devices whose lifetime depends on a limited battery efficiency. In order to handle the energy consumption we use the energy model introduced in [Yao et al., FOCS'95], which captures the intuitive idea that the higher is the processor's speed the higher is the energy consumption. We propose a (2-1/m)-approximation algorithm improving the best known poly-log(m)-approximation algorithm for the problem [Pruhs et al., TOCS 2008], where m is the number of the processors. We also extend the simple idea used for the above problem, in order to propose a generalized framework that finds applications to other scheduling problems in the speed scaling setting.
Retrospective Evaluation of Horses Diagnosed with Neuroborreliosis on Postmortem Examination: 16 Cases (2004–2015)

Background Equine neuroborreliosis (NB), Lyme disease, is difficult to diagnose and has limited description in the literature. Objective Provide a detailed description of clinical signs, diagnostic, and pathologic findings of horses with NB. Animals Sixteen horses with histologically confirmed NB. Methods Retrospective review of medical records at the University of Pennsylvania and via an ACVIM listserv query with inclusion criteria requiring possible exposure to Borrelia burgdorferi and histologic findings consistent with previous reports of NB without evidence of other disease. Results Sixteen horses were identified, 12 of which had additional evidence of NB. Clinical signs were variable including muscle atrophy or weight loss (12), cranial nerve deficits (11), ataxia (10), changes in behavior (9), dysphagia (7), fasciculations (6), neck stiffness (6), episodic respiratory distress (5), uveitis (5), fever (2), joint effusion (2), and cardiac arrhythmias (1). Serologic analysis was positive for B. burgdorferi infection in 6/13 cases tested. CSF abnormalities were present in 8/13 cases tested, including xanthochromia (4/13), increased total protein (5/13; median: 91 mg/dL, range: 25–219 mg/dL), and a neutrophilic (6/13) or lymphocytic (2/13) pleocytosis (median: 25 nucleated cells/μL, range: 0–922 nucleated cells/μL). PCR on CSF for B. burgdorferi was negative in the 7 cases that were tested. Conclusion and Clinical Importance Diagnosis of equine NB is challenging due to variable clinical presentation and lack of sensitive and specific diagnostic tests. Negative serology and normal CSF analysis do not exclude the diagnosis of NB.
Evolving Neural Network Structures Using Axonal Growth Mechanisms

In the eld of arti cial evolution creating methods to evolve neural networks is an important goal. But how to encode the structure and properties of the neural network in the genome is still a problem. If one overloads the genome with detailed information for a network the evolutionary time increases prohibitively. If the genome is too simple, only simple problems can be solved. As Nature has found an e cient and evolvable solution to this problem, it is worthwhile imitating the mechanisms on how biological neural nets are generated. In this paper I propose a model in which arti cial genes tune the ability of axons to nd, detect and connect to speci c targets. Initial simulation results of simple tasks are evolved and the genetic tuning of the developmental processes for arti cial evolution is discussed.
Biodistribution, kinetics, and biological fate of SPION microbubbles in the rat

Background In the present investigation, we studied the kinetics and biodistribution of a contrast agent consisting of poly(vinyl alcohol) (PVA) microbubbles containing superparamagnetic iron oxide (SPION) trapped between the PVA layers (SPION microbubbles). Methods The biological fate of SPION microbubbles was determined in Sprague-Dawley rats after intravenous administration. Biodistribution and elimination of the microbubbles were studied in rats using magnetic resonance imaging for a period of 6 weeks. The rats were sacrificed and perfusion-fixated at different time points. The magnetic resonance imaging results obtained were compared with histopathologic findings in different organs. Results SPION microbubbles could be detected in the liver using magnetic resonance imaging as early as 10 minutes post injection. The maximum signal was detected between 24 hours and one week post injection. Histopathology showed the presence of clustered SPION microbubbles predominantly in the lungs from the first time point investigated (10 minutes). The frequency of microbubbles declined in the pulmonary vasculature and increased in pulmonary, hepatic, and splenic macrophages over time, resulting in a relative shift from the lungs to the spleen and liver. Meanwhile, macrophages showed increasing signs of cytoplasmic iron accumulation, initially in the lungs, then followed by other organs. Conclusion The present investigation highlights the biological behavior of SPION microbubbles, including organ distribution over time and indications for biodegradation. The present results are essential for developing SPION microbubbles as a potential contrast agent and/or a drug delivery vehicle for specific organs. Such a vehicle will facilitate the use of multimodality imaging techniques, including ultrasound, magnetic resonance imaging, and single positron emission computed tomography, and hence improve diagnostics, therapy, and the ability to monitor the efficacy of treatment.
Timing-Driven Interconnect Synthesis

In this chapter we address performance-driven interconnect synthesis, which seeks to optimize circuit performance by minimizing signal delays to critical sinks. Timing-driven wiring geometries are in general quite different from optimal-area (i.e., Steiner) interconnect trees, especially as die sizes continue to grow while feature dimensions steadily shrink. The exposition below focuses on selected approaches to performance-driven routing, and details key historical research developments that helped usher in the era of high-performance interconnect synthesis. For extensive surveys on this subject, see [19, 50]. For a general overview of computer-aided design (CAD) of very large scale integrated (VLSI) circuits, see some of the classical textbooks [40, 80, 90, 93, 95].
Some comments on the role of econometrics in economic theory

The basic tension facing econometricians is that structural models are necessary for addressing monetary policy questions. But all models are, by their very nature, false. Econometric programs that focus on testing whether models are true will be ignored by practicing macroeconomists. The critical task facing econometricians is to develop diagnostic tools for assessing the usefulness of models for addressing particular questions. This article reviews two diagnostic strategies.
PING : A new approach for nuclear fuel cycle instrumentation

We introduce here a new concept of nuclear instrumentation system. We have developed a new and flexible system for nuclear measurement called PING. PING stands for New Generation Instrument Platform. This new measurement system could be able to do measurements in active neutron condition, especially time stamping, as well as photon gamma spectrometry. This development is done in order to optimize Nuclear Process Control systems. In fact numbers of used instrumentations are specific and most of them are no longer supplied by any firm. Then the main idea that conducts this project development is to concentrate all hardware requirements inside one device. Only the software embedded inside the FPGA is considered as the real measurement part of the device. Any hardware upgrading should not modify measurement performances. Thus which is durable is the software.
Salary Determination in the Presence of Fixed Revenues

The assumption that workers are paid their marginal product underlies the theory of competitive labor markets and is the basis for comparison with non-competitive markets. Many firms, however, generate revenue in fixed lump-sums that are unrelated to the efforts of current workers. For example, many professional sports receive substantial income from broadcast rights, which are negotiated at wide intervals. We develop a theory of compensation in the presence of “fixed revenue” and test our theory using data from the National Basketball Association. Our results indicate that TV revenue tends to equalize players’ salaries. Players’ performance and popularity tend to enhance players’ bargaining positions. Popularity with fans particularly helps players with greater bargaining power.
When crowds hold privileges: Bayesian unsupervised representation learning with oracle constraints

Representation learning systems typically rely on massive amounts of labeled data in order to be trained effectively. Recently, high-dimensional parametric models like convolutional neural networks have succeeded in building rich representations using either compressive, reconstructive or supervised criteria. However, the semantic structure inherent in observations is oftentimes lost in the process. Human perception excels at understanding semantics but cannot always be expressed in terms of labels. Human-in-the-loop systems like crowdsourcing are often employed to generate similarity constraints using an implicit similarity function encoded in human perception. We propose to combine generative unsupervised feature learning with learning from similarity orderings in order to learn models which take advantage of privileged information coming from the crowd. We use a fast variational algorithm to learn the model on standard datasets and demonstrate applicability to two image datasets, where classification is drastically improved. We show how triplet-samples of the crowd can supplement labels as a source of information to shape latent spaces with rich semantic information.
Error Correction for Differential Linear Network Coding in Slowly-Varying Networks

Differential linear network coding (DLNC) is a precoding scheme for information transmission over random linear networks. By using differential encoding and decoding, the conventional approach of lifting, required for inherent channel sounding, can be omitted and in turn higher transmission rates are supported. However, the scheme is sensitive to variations in the network topology. In this paper, we derive an extended DLNC channel model which includes slow network changes. Based on this, we propose and analyze a suitable channel coding scheme matched to the situation at hand using rank-metric convolutional codes.
Islamic finance in the United States : product development and regulatory adoption

Thesis (M.A.)--Georgetown University, 2009. ; Includes bibliographical references. Over the past decade the Islamic finance sector internationally has grown steadily and rapidly. However, Islamic banking has been much slower to catch on in the United States, due in large part to rigid regulatory standards and lack of legislative accommodations made specifically for the industry. Still, a handful of banks and finance companies have dedicated themselves to the legal struggle of developing products and contracts that will comply both with religious law and with US regulations designed with interest-based banking in mind. This paper examines how Islamic finance has developed in this country, and how the financial regulatory system has treated the new products being offered by Islamic finance providers. The main focus is on how financial organizations offering Islamic finance products (be they banks, finance companies, or non-profit organizations) developed the products they offer so as to adhere to religious law while still complying with existing financial regulation. What is revealed is a willingness on the part of government officials and regulators to accommodate certain Islamic finance forms in order to serve what is perceived to be an "under-banked" population, namely those Muslims who consider conventional borrowing to be religiously prohibited. However, as of yet, Islamic contract forms have been adapted to the current regulation rather than that regulation changed in any way to accommodate Islamic finance. As the industry continues to grow there will arise issues that will not be able to be addressed in a manner acceptable under both Islamic and current US law. In these cases further negotiation between finance providers and regulators will have to take place to find a place for Islamic banking under the law, or perhaps to seek legislative changes that would grant changes appropriate to Islamic finance products.
Studying Coordination and Mobility in Organizations

Mobile information and communication technologies (m-ICT) are increasingly prevalent in today’s organizations and in people’s personal lives more generally. A wide range of devices are used: mobile phones of varying capabilities, PDAs, Blackberries, laptops and tablet computers as well as application specific devices (e.g. the ruggedized devices used by UPS and FedEx delivery personnel). These mobile technologies hold out the hope of increased efficiency and effectiveness through improved coordination within groups and organizations and perhaps between organizations as well. In this paper we focus on the question of how mobile computing effects the coordination at the individual and team levels.
Context-aware prediction of user's first click

Location-based services has attracted attentions from both industry and academia. The development of position tracking technologies and the increasing popularity of smart phones has collected large amounts of contexts. An important issue is how to leverage the rich contexts to predict a user's need accurately. In this paper, we propose a novel approach to predict the product type that a user will first click in an e-commerce application, after they update their location manually. Our proposed approach models the problem as a multi-label classification. We introduce three sets of features including location feature, time feature and behavioral feature. We use the Periodica algorithm [10], which was designed to mine the periodic behaviors of moving objects, to generate a series of periodicity templates. The templates are further exploited as behavioral features. Finally, we design several experiments using a real world dataset collected by an e-commerce application called WuXianGouXiang, which is developed by Nokia Research Center, Beijing and was released in September 2011. We have obtained a dataset from the service logs and the dataset contains over 3000 registered shops and 20000 users. Our experimental results demonstrate that the three sets of features contribute significantly to the classification of different users and the best result is achieved when using all of them.
Structured Prediction by Conditional Risk Minimization

We propose a general approach for supervised learning with structured output spaces, such as combinatorial and polyhedral sets, that is based on minimizing estimated conditional risk functions. Given a loss function defined over pairs of output labels, we first estimate the conditional risk function by solving a (possibly infinite) collection of regularized least squares problems. A prediction is made by solving an inference problem that minimizes the estimated conditional risk function over the output space. We show that this approach enables, in some cases, efficient training and inference without explicitly introducing a convex surrogate for the original loss function, even when it is discontinuous. Empirical evaluations on real-world and synthetic data sets demonstrate the effectiveness of our method in adapting to a variety of loss functions.