Miracle-dz/UniFAR-FTUs huggingface dataset:
Relevance Singular Vector Machine for Low-Rank Matrix Reconstruction

We develop Bayesian learning methods for low-rank matrix reconstruction and completion from linear measurements. For under-determined systems, the developed methods reconstruct low-rank matrices when neither the rank nor the noise power is known a priori. We derive relations between the proposed Bayesian models and low-rank promoting penalty functions. The relations justify the use of Kronecker structured covariance matrices in a Gaussian-based prior. In the methods, we use expectation maximization to learn the model parameters. The performance of the methods is evaluated through extensive numerical simulations on synthetic and real data.
Evaluation of fuzzy rule bases under delayed reinforcement

This article concerns the problem and solution of judging fuzzy rule bases according to environmental reinforcements. We propose an on-line, incremental credit assignment algorithm, which takes environmental reinforcement as input and assigns credit to individual rules. The proposed approach adopts a simple updating policy based on recency-weighted average, and demands only small amount of memory. We also contribute to the problem of delayed reinforcement. In the case of delayed reinforcement, the state preference function is constructed iteratively during the exploration phase.
Variables and Reversibility in Object Oriented Regular Expressions

Regular expressions are used to parse textual data to match patterns and extract variables. They have been implemented in a vast number of programming languages with a significant quantity of research devoted to improving their operational efficiency. However, little research has been done in extending regular expressions so as to increase their usefulness in object-oriented languages. In doing so, it is possible to parse textual or binary data to construct multilayered objects by simply defining the representation of each of those objects individually. Furthermore, it then becomes possible to reverse the regular expression process using the same representations, converting objects into textual or binary data. This paper describes the functionality required in regular expressions to add these two novel contributions. Example syntax and case studies are also presented.
Satisfaction, organizational commitment and future action in charity sport event volunteers

Charity sport events (CSEs) are a key revenue source for nonprofit organizations (NPOs) with large numbers of volunteers needed for success and cost-effectiveness. This study explored determinants of CSE volunteers' satisfaction, organizational commitment and intended future actions (CSE, other NPO activity/event volunteering, donating money). Relay for Life volunteers (N=290) from one Australian state completed a cross-sectional survey. Significant pathways were found from socializing/enjoyment (β=0.17), fighting cancer (β=0.29), financial support (β=0.21) motives and social norm (β=0.23) to satisfaction; 52% variance was explained. Age (β=-0.09), survivorship (β=0.09), region (β=0.07), fundraising goal (β=0.08), advocacy (β=0.15), financial support (β=0.25), social/enjoyment (β=0.23) motives, social norm (β=0.23) and satisfaction (β=0.21) were linked with commitment; 63% variance was explained. Paths between satisfaction, commitment and intended future actions (CSE, NPO activity/event volunteering) were significant (βs=0.17-0.43). Future targets to increase CSE volunteer satisfaction and commitment involve similar (social/enjoyment) and diverse (action-oriented) motives, with satisfaction and commitment key contributors to future actions supporting NPOs.
Treatment for Substance Use Disorder: Opportunities and Challenges under the Affordable Care Act

Addiction is a chronic brain disease with consequences that remain problematic years after discontinuation of use. Despite this, treatment models focus on acute interventions and are carved out from the main health care system. The Patient Protection and Affordable Care Act (2010) brings the opportunity to change the way substance use disorder (SUD) is treated in the United States. The treatment of SUD must adapt to a chronic care model offered in an integrated care system that screens for at-risk patients and includes services needed to prevent relapses. The partnering of the health care system with substance abuse treatment programs could dramatically expand the benefits of prevention and treatment of SUD. Expanding roles of health information technology and nonphysician workforces, such as social workers, are essential to the success of a chronic care model.
MEMBRANE AND PROTOPLASM RESISTANCE IN THE SQUID GIANT AXON

The direct current longitudinal resistance of the squid giant axon was measured as a function of the electrode separation. Large sea water electrodes were used and the inter-electrode length was immersed in oil. The slope of the resistance vs. separation curve is large for a small electrode separation, but becomes smaller and finally constant as the separation is increased. An analysis of the resistance vs. length curves gives the following results. The nerve membrane has a resistance of about 1000 ohm cm.2 The protoplasm has a specific resistance of about 1.4 times that of sea water. The resistance of the connective tissue sheath outside the fiber corresponds to a layer of sea water about 20µ in thickness. The characteristic length for the axon is about 2.3 mm. in oil and 6.0 mm. in sea water.
TweoLocator: a non-intrusive geographical locator system for Twitter

In the last decade, the Internet has seen the rise of social networking as the number one online activity worldwide. To estimate the geographical location of users of social networks at a particular moment, we propose an approach to geo-tag Twitter users based only on the content of their posts. These data can later be used for local sentiment analysis, emergency detection, finding a missing person, and other novel location-based purposes. Our approach carries out a semantic analysis of tweet content to infer where in the globe a particular user is located at a given time. Based on our experimental results, conducted through Amazon Mechanical Turk, the proposed framework was evaluated by 93 evaluators who assessed 654 twitter user profiles and 2,165 tweets from 17 countries. Our system inferred some geographical information for 81% of evaluated profiles. Results show 79% accuracy in identifying the user's country and 66% accuracy in identifying the user's current location. This high accuracy shows that our proposed method is feasible and effective.
The recent spread of a vertically transmitted virus through populations of Drosophila melanogaster

The sigma virus is a vertically transmitted pathogen that commonly infects natural populations of Drosophila melanogaster. This virus is the only known host‐specific pathogen of D. melanogaster, and so offers a unique opportunity to study the genetics of Drosophila–viral interactions in a natural system. To elucidate the population genetic processes that operate in sigma virus populations, we collected D. melanogaster from 10 populations across three continents. We found that the sigma virus had a prevalence of 0–15% in these populations. Compared to other RNA viruses, we found that levels of viral genetic diversity are very low across Europe and North America. Based on laboratory measurements of the viral substitution rate, we estimate that most European and North American viral isolates shared a common ancestor approximately 200 years ago. We suggest two explanations for this: the first is that D. melanogaster has recently acquired the sigma virus; the second is that a single viral type has recently swept through D. melanogaster populations. Furthermore, in contrast to Drosophila populations, we find that the sigma viral populations are highly structured. This is surprising for a vertically transmitted pathogen that has a similar migration rate to its host. We suggest that the low structure in the viral populations can be explained by the smaller effective population size of the virus.
Preliminary Study on Basic Needs Theory of Chinese Athletes

Objectives: Grounded in Basic Needs Theory,the present study aimed to test a theoretically-based model of coach autonomy support,basic psychological needs and burnout,satisfaction of training.Method: Combining the methods of qualitative and quantitative.Through interviewed 13 national team's athletes,then eight hundred and forty participants completed a multi-section questionnaire,the targeted variables and the data was deal with SPSS17.0 and Amos18.Results: Structural Equation Modeling(SEM) analysis revealed that coach support predicted participants' basic need satisfaction for autonomy,competence and relatedness.In turn,basic need satisfaction positively predicted satisfaction of training,negatively predicted burnout.Conclusions: Basic Needs Theory was adopted by Chinese athletes.Competence and relatedness partially mediated the path from autonomy support to satisfaction of training and burnout.
Virtual range scan for avoiding 3D obstacles using 2D tools

This paper presents a new method for seamless fusion of arbitrary range sensors for mobile robot obstacle avoidance. This method, named Virtual Range Scan (VRS), is able to deal with arbitrary sensor configurations (2D and 3D) and it is independent of the underlying obstacle avoidance strategy. This makes it a very flexible approach that can reuse existing 2D obstacle avoidance algorithms for 3D obstacle avoidance. Additionally the VRS is able to keep obstacles in memory in order to incorporate them even if they are no longer visible in the current real sensor data. To combine this memory effect with the presence of dynamic objects in the robot surroundings, a motion tracker is used. We demonstrate the ability of the VRS to realize 3D obstacle avoidance using two different sensor configurations for taking 3D range data and classical two dimensional obstacle avoidance methods.
Optimal agendas for multi-issue negotiation

There are two ways of handling bilateral multi-issue negotiations -- one is to negotiate all the issues together, and the other is to negotiate them one by one. The order in which issues are negotiated in issue-by-issue negotiation is specified by the agenda, which can be defined in two ways. One way is to decide it exogenously, i.e., before negotiation begins. The other way is to let the players decide which issue they will negotiate next, during the process of negotiation, i.e., the agenda is determined endogenously. Against this background, this paper studies the effect of combining the exogenous and endogenous agendas on the players' utilities. More specifically, we determine whether, decomposing a set of N issues into k stages (for 1 ≤ k ≤ N), determining the issues to be negotiated at each stage exogenously, and negotiating each stage sequentially using an endogenous agenda can improve an agent's utility relative to the utility it gets if the agenda for all the N issues is defined endogenously. For each agent, we find the expected utility for each value of k between 1 and N. The value of k that gives an agent maximum utility is its optimal number of stages.Our study shows that, in some negotiation scenarios, the optimal value of k is identical for the two players, and is greater than one. In other words, in some negotiation scenarios, both the agents can improve their utilities by using the k-stage negotiation relative to the single stage negotiation. However, since the players have incomplete information about the negotiation parameters, they cannot identify such scenarios. We therefore present an extended alternating offers protocol, that allows the agents to identify such scenarios through a mediator, thereby resulting in improved utility to both the agents.
Cardiac Radionuclide Imaging in Rodents: A Review of Methods, Results, and Factors at Play

The interest around small-animal cardiac radionuclide imaging is growing as rodent models can be manipulated to allow the simulation of human diseases. In addition to new radiopharmaceuticals testing, often researchers apply well-established probes to animal models, to follow the evolution of the target disease. This reverse translation of standard radiopharmaceuticals to rodent models is complicated by technical shortcomings and by obvious differences between human and rodent cardiac physiology. In addition, radionuclide studies involving small animals are affected by several extrinsic variables, such as the choice of anesthetic. In this paper, we review the major cardiac features that can be studied with classical single-photon and positron-emitting radiopharmaceuticals, namely, cardiac function, perfusion and metabolism, as well as the results and pitfalls of small-animal radionuclide imaging techniques. In addition, we provide a concise guide to the understanding of the most frequently used anesthetics such as ketamine/xylazine, isoflurane, and pentobarbital. We address in particular their mechanisms of action and the potential effects on radionuclide imaging. Indeed, cardiac function, perfusion, and metabolism can all be significantly affected by varying anesthetics and animal handling conditions.
Corpus Callosum Shape and Neuropsychological Deficits in Adult Males with Heavy Fetal Alcohol Exposure

Persons with brain damage consequent to prenatal alcohol exposure have typically been diagnosed with either fetal alcohol syndrome (FAS) or fetal alcohol effects (FAE), depending on facial features. There is great variability of behavioral deficits within these groups. We sought to combine neuroanatomical measures with neurocognitive and neuromotor measures in criteria of greater sensitivity over the variety of consequences of alcohol exposure. To this end, midline curves of the corpus callosum were carefully digitized in three dimensions from T1-weighted MR scans of 15 adult males diagnosed with FAS, 15 with FAE, and 15 who were unexposed and clinically normal. From 5 h of neuropsychological testing we extracted 260 scores and ratings pertaining to attention, memory, executive function, fine and gross motor performance, and intelligence. Callosal midline shape was analyzed by new morphometric methods, and the relation of shape to behavior by partial least squares. The FAS and FAE subgroups have strikingly more variability of callosal shape than our normal subjects. With the excess shape variation are associated two different profiles of behavioral deficit unrelated to full-scale IQ or to the FAS/FAE distinction within the exposed subgroup. A relatively thick callosum is associated with a pattern of deficit in executive function; one that is relatively thin, with a deficit in motor function. The two combine in a very promising bipolar discrimination of the exposed from the unexposed in this sample. Thus there is considerable information in callosal form for prognosis of neuropsychological deficits in this frequently encountered birth defect.
A simple and reliable algorithm for computing boundaries of power flow solutions due to system uncertainties

Power flow studies are typically used to determine the steady state or operating conditions of power systems for specified sets of load and generation values, and is one of the most intensely used tools in power engineering. When the input conditions are uncertain, numerous scenarios need to be analyzed to cover the required range of uncertainty, and hence reliable solution algorithms that incorporate the effect of data uncertainty into the power flow analysis are needed. To address this problem, this paper proposes a new solution methodology based on the use of optimization techniques and worst-case scenario analysis. The application of these techniques to the power flow problem with uncertainties is explained in detail, and several numerical results are presented and discussed, demonstrating the effectiveness of the proposed methodology
Computational modeling of human fetal normal sinus rhythm and arrhythmia

Non-invasive measures of the fetal ECG provide quantitative information on pacemaking rate, propagation times and velocities during normal sinus rhythm. One dimensional partial differential equation models of propagation that reproduce fetal normal sinus rhythm in hearts from 16-40 weeks gestational age were constructed Lengths were obtained from magnetic resonance imaging data, intercellular coupling informed by ECG timings, and cell excitation described by modified models for adult human cardiac cells. Spatially uniform progressive reduction of the L-type calcium conductance slowed the rate, and led to atrioventricular conduction blocks. Self terminating and persistent re-entry with a period of 240 - 300ms could be produced within a 1-D ring ventricular tissue model. The preliminary 1-D family of models quantitatively reproduces the observed timings of fECG intervals from 16-40 WGA, and predicts the characteristics of development of fetal A V block, and self terminating ventricular tachycardia.
Morphy -- German Morphology, Part-of-Speech Tagging and Applications

In this paper we present a freely available software package that consists of a morphology tool and a part-of-speech tagger for German. The software is userfriendly, runs on any PC and can be downloaded as a complete package (including the lexicon) from the World Wide Web. One application area is searching for linguistic phenomena in annotated corpora. The corpus query tool Tatoe which is able to import Morphy's tagging output is presented as an example.
TWO-STEP ESTIMATION FOR A GENERALIZED LINEAR MIXED MODEL WITH AUXILIARY COVARIATES

Generalized linear mixed models (GLMM) are useful in a variety of ap- plications. With surrogate covariate data, existing methods of inference for GLMM are usually computationally intensive. We propose a two-step inference procedure for GLMM with missing covariate data. It is shown that the proposed estimator is consistent and asymptotically normal with covariance matrix that can be easily estimated. Simulation studies show that the proposed method outperforms those ignoring random effects or only using the validation data. We illustrate the pro- posed method with a data set from an environmental epidemiology study on the maternal serum DDE level in relationship to male birth defects. Generalized linear mixed models (GLMM) are popular statistical models for analyzing data collected from different clusters or from longitudinal studies. They provide a flexible likelihood framework under which population character- istics can be modeled as fixed effects and individual variations can be modeled as random effects. The traditional likelihood approach to GLMM usually involves high dimensional integrations which are computationally intensive. Alternative methods for inference have been proposed. One such method is based on esti- mation of the random effects via maximization of the joint density of the obser- vations and random effects (McGilchrist (1994)). The idea can be traced back to the best linear unbiased prediction (BLUP) approach of Henderson (1950) for the linear mixed model, which is reviewed in Robinson (1991). Other approaches similar in principle include the penalized quasi-likelihood approach of Breslow and Clayton (1993), the approximate Bayes method of the Stiratelli, Laird and Ware (1984), the algorithms proposed in Schall (1991), the pseudo likelihood approach in Wolfinger and Connell (1993), the hierarchical likelihood of Lee and Nelder (1996), and the simulated moments method of Jiang (1998).
Piecewise linear source separation

We propose a new framework, called piecewise linear separation, for blind source separation of possibly degenerate mixtures, including the extreme case of a single mixture of several sources. Its basic principle is to: 1/ decompose the observations into "components" using some sparse decomposition/nonlinear approximation technique; 2/ perform separation on each component using a "local" separation matrix. It covers many recently proposed techniques for degenerate BSS, as well as several new algorithms that we propose. We discuss two particular methods of multichannel decompositions based on the Best Basis and Matching Pursuit algorithms, as well as several methods to compute the local separation matrices (assuming the mixing matrix is known). Numerical experiments are used to compare the performance of various combinations of the decomposition and local separation methods. On the dataset used for the experiments, it seems that BB with either cosine packets of wavelet packets (Beylkin, Vaidyanathan, Battle3 or Battle 5 filter) are the best choices in terms of overall performance because they introduce a relatively low level of artefacts in the estimation of the sources; MP introduces slightly more artefacts, but can improve the rejection of the unwanted sources.
Theory of Financial Risk and Derivative Pricing: From Statistical Physics to Risk Management

Foreword Preface 1. Probability theory: basic notions 2. Maximum and addition of random variables 3. Continuous time limit, Ito calculus and path integrals 4. Analysis of empirical data 5. Financial products and financial markets 6. Statistics of real prices: basic results 7. Non-linear correlations and volatility fluctuations 8. Skewness and price-volatility correlations 9. Cross-correlations 10. Risk measures 11. Extreme correlations and variety 12. Optimal portfolios 13. Futures and options: fundamental concepts 14. Options: hedging and residual risk 15. Options: the role of drift and correlations 16. Options: the Black and Scholes model 17. Options: some more specific problems 18. Options: minimum variance Monte-Carlo 19. The yield curve 20. Simple mechanisms for anomalous price statistics Index of most important symbols Index.
Peer-to-Peer Applications on Mobile Devices: A Case Study with Compact .NET on Smartphone 2003

The traditional approach to information systems, accessed by users by means of powerful devices (such as desktops and laptops) with known features, will not be anymore significant in the future years. Indeed, the current trend suggests that it will be possible to offer continuous access to all information sources, from all locations and through various kinds of devices, mainly small and mobile (e.g., palmtops and PDAs, cellular phones). Therefore, the need emerges for the design of applications for smart devices, which are highly flexible, capable of exploiting in an optimal way the resources. This experience paper analyzes the opportunity to design, develop and deploy interactive applications running on smart cellular phones (commonly referred to as smartphones), based on a peer-to-peer communication model and GPRS technology. A case study is presented to verify whether current development tools and technologies for small devices require a radical different approach with respect to more traditional application development. As a development platform, Window Mobile for Smartphone 2003 with Compact . NET has been used, which is currently available, at least in Europe, only on prototype devices.