Miracle-dz/UniFAR-FTUs huggingface dataset:
SHINE+: A General Framework for Domain-Specific Entity Linking with Heterogeneous Information Networks

Heterogeneous information networks that consist of multi-type, interconnected objects are becoming increasingly popular, such as social media networks and bibliographic networks. The task of linking named entity mentions detected from unstructured Web text with their corresponding entities in a heterogeneous information network is of practical importance for the problem of information network population. This task is challenging due to name ambiguity and limited knowledge existing in the network. Most existing entity linking methods focus on linking entities with Wikipedia and cannot be applied to our task. In this paper, we present SHINE+, a general framework for linking named entitieS in Web free text with a Heterogeneous I nformation NEtwork. We propose a probabilistic linking model, which unifies an entity popularity model with an entity object model. As the entity knowledge contained in the information network is insufficient, we propose a knowledge population algorithm to iteratively enrich the network entity knowledge by leveraging the context information of mentions mapped by the linking model with high confidence, which subsequently boosts the linking performance. Experimental results over two real heterogeneous information networks (i.e., DBLP and IMDb) demonstrate the effectiveness and efficiency of our proposed framework in comparison with the baselines.
The “Reading the Mind in the Eyes” Test: Complete Absence of Typical Sex Difference in ~400 Men and Women with Autism

The “Reading the Mind in the Eyes” test (Eyes test) is an advanced test of theory of mind. Typical sex difference has been reported (i.e., female advantage). Individuals with autism show more difficulty than do typically developing individuals, yet it remains unclear how this is modulated by sex, as females with autism have been under-represented. Here in a large, non-male-biased sample we test for the effects of sex, diagnosis, and their interaction. The Eyes test (revised version) was administered online to 395 adults with autism (178 males, 217 females) and 320 control adults (152 males, 168 females). Two-way ANOVA showed a significant sex-by-diagnosis interaction in total correct score (F(1,711) = 5.090, p = 0.024, ηp 2 = 0.007) arising from a significant sex difference between control males and females (p < 0.001, Cohen’s d = 0.47), and an absence of a sex difference between males and females with autism (p = 0.907, d = 0.01); significant case-control differences were observed across sexes, with effect sizes of d = 0.35 in males and d = 0.69 in females. Group-difference patterns fit with the extreme-male-brain (EMB) theory predictions. Eyes test-Empathy Quotient and Eyes test-Autism Spectrum Quotient correlations were significant only in females with autism (r = 0.35, r = -0.32, respectively), but not in the other 3 groups. Support vector machine (SVM) classification based on response pattern across all 36 items classified autism diagnosis with a relatively higher accuracy for females (72.2%) than males (65.8%). Nevertheless, an SVM model trained within one sex generalized equally well when applied to the other sex. Performance on the Eyes test is a sex-independent phenotypic characteristic of adults with autism, reflecting sex-common social difficulties, and provides support for the EMB theory predictions for both males and females. Performance of females with autism differed from same-sex controls more than did that of males with autism. Females with autism also showed stronger coherence between self-reported dispositional traits and Eyes test performance than all other groups.
A new treatment for premature ejaculation: Case series for a desensitizing band

Premature ejaculation (PE) is the commonest sexual dysfunction. The current treatments present difficulties in terms of accessibility, cost-benefit issues, acceptability and side-effects. A new desensitizing ring has been evaluated in a case series of six patients. The mean improvement of the latency period was 315% Further studies with control groups are warranted.
The roles of IL‐17A in inflammatory immune responses and host defense against pathogens

Summary: T‐helper 17 (Th17) cells are a newly discovered CD4+ helper T‐cell subset that produces interleukin‐17A (IL‐17A) and IL‐17F. IL‐17A plays important roles in allergic responses such as delayed‐type hypersensitivity, contact hypersensitivity, and allergic airway inflammation. IL‐17A promotes inflammation by inducing various proinflammatory cytokines and chemokines, recruiting neutrophils, enhancing antibody production, and activating T cells. IL‐17A expression is also augmented in autoimmune diseases such as multiple sclerosis and rheumatoid arthritis. Using mouse models of these diseases, we found that IL‐17A plays a central role in their development. IL‐6 is required for the development of Th17 cells and tumor necrosis factor functions downstream of IL‐17A during the effector phase. IL‐1 is important both for developing Th17 cells and eliciting inflammation. Th17 cells, like Th1 and Th2 cells, are involved in host defense against infections, but the contribution of these Th subsets to defense mechanisms differs among pathogens. The roles of IL‐17F remain largely unknown. In this review, we introduce how IL‐17A/IL‐17F are involved in inflammatory immune responses and host defense mechanisms and discuss their relationship with other cytokines in the development of inflammatory and infectious diseases.
Identification of the Alternative Splicing of the UL49 Locus of Human Cytomegalovirus

The UL49 ORF of human cytomegalovirus (HCMV) is essential for viral replication; conserved among all herpes viruses; however, the function is unclear. Once the UL49 ORF was precisely deleted from the start to stop codon, the mutant did not yield infectious progeny. In this study, we find out many alternatively processed ESTs in UL49 locus in HCMV-infected cells, in which there are two novel transcription termination sites in UL49 locus. Most of these ESTs are rare transcripts that contain directed repeat sequences in the intron splicing regions. There is a typical GU-AG intron splicing site in UL49Y transcripts. The 1847 bp UL49Y cDNA spans an ORF from 335 to 1618 and encodes a putative protein of 427 amino acids with a predicted molecular mass of 47.1 kDa. All the new EST sequences and UL49Y cDNA sequence have been deposited in the GenBank database (GenBank Accession nos. GW314860-GW314900 and GU376796). This study provides us with very important clues for revealing the importance of the UL49 locus alternative splicing.
Convolution Kernels on Discrete Structures UCSC CRL

We introduce a new method of constructing kernels on sets whose elements are discrete structures like strings trees and graphs The method can be applied iteratively to build a kernel on a in nite set from kernels involving generators of the set The family of kernels generated generalizes the family of radial basis kernels It can also be used to de ne kernels in the form of joint Gibbs probability distributions Kernels can be built from hidden Markov random elds generalized regular expressions pair HMMs or ANOVA de compositions Uses of the method lead to open problems involving the theory of in nitely divisible positive de nite functions Fundamentals of this theory and the theory of reproducing kernel Hilbert spaces are reviewed and applied in establishing the validity of the method
Visual Analysis of North Atlantic Hurricane Trends Using Parallel Coordinates and Statistical Techniques

Abstract : The integration of automated statistical analysis capabilities with a highly interactive, multivariate visualization interface is presented in this paper. Innovative visual interaction techniques such as dynamic axis scaling, conjunctive parallel coordinates, statistical indicators, and aerial perspective shading are exploited to enhance the utility of classical parallel coordinate plots. Moreover, the system facilitates statistical processes such as stepwise regression and correlation analysis to assist in the identification and quantification of the most significant predictors for a particular dependent variable. These capabilities are combined into a unique visualization system that is demonstrated via a North Atlantic hurricane climate study using a systematic workflow. This research corroborates the notion that enhanced parallel coordinates coupled with statistical analysis can be used for more effective knowledge discovery and confirmation in complex, real-world data sets.
The relevance of network micro-structure for neural dynamics

The activity of cortical neurons is determined by the input they receive from presynaptic neurons. Many previous studies have investigated how specific aspects of the statistics of the input affect the spike trains of single neurons and neurons in recurrent networks. However, typically very simple random network models are considered in such studies. Here we use a recently developed algorithm to construct networks based on a quasi-fractal probability measure which are much more variable than commonly used network models, and which therefore promise to sample the space of recurrent networks in a more exhaustive fashion than previously possible. We use the generated graphs as the underlying network topology in simulations of networks of integrate-and-fire neurons in an asynchronous and irregular state. Based on an extensive dataset of networks and neuronal simulations we assess statistical relations between features of the network structure and the spiking activity. Our results highlight the strong influence that some details of the network structure have on the activity dynamics of both single neurons and populations, even if some global network parameters are kept fixed. We observe specific and consistent relations between activity characteristics like spike-train irregularity or correlations and network properties, for example the distributions of the numbers of in- and outgoing connections or clustering. Exploiting these relations, we demonstrate that it is possible to estimate structural characteristics of the network from activity data. We also assess higher order correlations of spiking activity in the various networks considered here, and find that their occurrence strongly depends on the network structure. These results provide directions for further theoretical studies on recurrent networks, as well as new ways to interpret spike train recordings from neural circuits.
The State of Social Psychology: Issues, Themes, and Controversies

Foreword - Kenneth J Gergen Preface - Mark A Leary PART ONE: CONCEPTUAL AND METHODOLOGICAL ISSUES Social Psychology and How to Revitalize It - Bibb Latane The Evolution and Impact of Topics in the Mainstream of Social Psychological Research - Steven Prentice-Dunn Trends in Social Psychological Research - Debra Moehle McCallum and Richard McCallum A Figure-Ground Phenomenon Self-Disclosure - Valerian J Derlega Inside or Outside the Mainstream of Social Psychological Research? The Interactive Perspective - Cecilia H Solano Getting the Social Back into Social Psychology Myopic Social Psychology - David J Senn An Overemphasis on Individualistic Explanations of Social Behavior A Contextualist Approach to the Tension Between Intrapsychic and Impression Management Models of Behavior - Danny Axsom Social Psychology's Three Little Pigs - Donelson R Forsyth PART TWO: SOCIAL PSYCHOLOGY AND OTHER FIELDS Opening a Dialogue Between Social and Developmental Psychology - Tedra A Walden Social and Industrial/Organizational Psychologists - Garnett S Stokes Friends or Foes? Social-Clinical-Counseling Integration Indigestion - James E Maddux How do you Spell `Relief'? PART THREE: THE ROLES OF THE SOCIAL PSYCHOLOGIST Roles and Respectability in Social Psychology - Elaine C Nocks Teachers Who Research Versus Researchers Who Teach - Ann Weber A System for Balancing Departmental Teaching and Research - Mark R Leary and John E Williams The State of Social Psychology - Clyde Hendrick An Opinion
Internet Use and Collegiate Academic Performance Decrements: Early Findings

Recent research at colleges and universities has suggested that some college students’ academic performance might be impaired by heavier use of the Internet. This study reviews the relevant literature and presents data from a survey of 572 students at a large public university. Heavier recreational Internet use was shown to be correlated highly with impaired academic performance. Loneliness, staying up late, tiredness, and missing class were also intercorrelated with self-reports of Internet-caused impairment. Self-reported Internet dependency and impaired academic performance were both associated with greater use of all Internet applications, but particularly with much greater use of synchronous communication applications such as chat rooms and MUDs, as opposed to asynchronous applications such as email and Usenet newsgroups.
Dynamic Support Culture of Murine Skeletal Muscle-Derived Stem Cells Improves Their Cardiogenic Potential In Vitro

Ischemic heart disease is the main cause of death in western countries and its burden is increasing worldwide. It typically involves irreversible degeneration and loss of myocardial tissue leading to poor prognosis and fatal outcome. Autologous cells with the potential to regenerate damaged heart tissue would be an ideal source for cell therapeutic approaches. Here, we compared different methods of conditional culture for increasing the yield and cardiogenic potential of murine skeletal muscle-derived stem cells. A subpopulation of nonadherent cells was isolated from skeletal muscle by preplating and applying cell culture conditions differing in support of cluster formation. In contrast to static culture conditions, dynamic culture with or without previous hanging drop preculture led to significantly increased cluster diameters and the expression of cardiac specific markers on the protein and mRNA level. Whole-cell patch-clamp studies revealed similarities to pacemaker action potentials and responsiveness to cardiac specific pharmacological stimuli. This data indicates that skeletal muscle-derived stem cells are capable of adopting enhanced cardiac muscle cell-like properties by applying specific culture conditions. Choosing this route for the establishment of a sustainable, autologous source of cells for cardiac therapies holds the potential of being clinically more acceptable than transgenic manipulation of cells.
A Bayesian pick-the-winner design in a randomized phase II clinical trial.

PURPOSE Many phase II clinical trials evaluate unique experimental drugs/combinations through multi-arm design to expedite the screening process (early termination of ineffective drugs) and to identify the most effective drug (pick the winner) to warrant a phase III trial. Various statistical approaches have been developed for the pick-the-winner design but have been criticized for lack of objective comparison among the drug agents. METHODS We developed a Bayesian pick-the-winner design by integrating a Bayesian posterior probability with Simon two-stage design in a randomized two-arm clinical trial. The Bayesian posterior probability, as the rule to pick the winner, is defined as probability of the response rate in one arm higher than in the other arm. The posterior probability aims to determine the winner when both arms pass the second stage of the Simon two-stage design. RESULTS When both arms are competitive (i.e., both passing the second stage), the Bayesian posterior probability performs better to correctly identify the winner compared with the Fisher exact test in the simulation study. In comparison to a standard two-arm randomized design, the Bayesian pick-the-winner design has a higher power to determine a clear winner. In application to two studies, the approach is able to perform statistical comparison of two treatment arms and provides a winner probability (Bayesian posterior probability) to statistically justify the winning arm. CONCLUSION We developed an integrated design that utilizes Bayesian posterior probability, Simon two-stage design, and randomization into a unique setting. It gives objective comparisons between the arms to determine the winner.
An Immune Inspired-based Optimization Algorithm: Application to the Traveling Salesman Problem

The clonal selection is a mechanism used by the natural immune system to select cells that recognize the antigens to proliferate. The proliferated cells are subject to an affinity maturation process, which improves their affinity to the selective antigens. The concept of clonal selection is a vitally important one to the success of the human immune system, and it provides an excellent example of the principles of selection at work. The Positive and negative selection is another interesting mechanism in the immune system that work together to both retain cells that recognize the self peptides, while also removing cells that recognize any self peptides. In this paper, a cloning-based algorithm inspired by the clonal and the positive/negative selection mechanism of the natural immune system is presented. This algorithm is inherently parallel and the cloning strategy employs greedy criteria which lends to an adaptive approach. The well known TSP is used to illustrate the approach with experimental comparison with Ant approach. Simulations demonstrate that this approach generates good solutions to traveling salesman problem and greatly improve the convergence speed compared to the Ant-based optimization approach.
Some useful statistical methods for model validation.

Although formal hypothesis tests provide a convenient framework for displaying the statistical results of empirical comparisons, standard tests should not be used without consideration of underlying measurement error structure. As part of the validation process, predictions of individual blood lead concentrations from models with site-specific input parameters are often compared with blood lead concentrations measured in field studies that also report lead concentrations in environmental media (soil, dust, water, paint) as surrogates for exposure. Measurements of these environmental media are subject to several sources of variability, including temporal and spatial sampling, sample preparation and chemical analysis, and data entry or recording. Adjustments for measurement error must be made before statistical tests can be used to empirically compare environmental data with model predictions. This report illustrates the effect of measurement error correction using a real dataset of child blood lead concentrations for an undisclosed midwestern community. We illustrate both the apparent failure of some standard regression tests and the success of adjustment of such tests for measurement error using the SIMEX (simulation-extrapolation) procedure. This procedure adds simulated measurement error to model predictions and then subtracts the total measurement error, analogous to the method of standard additions used by analytical chemists. ImagesFigure 1Figure 3
Line percolation

We study a new geometric bootstrap percolation model, line percolation, on the d‐dimensional integer grid [n]d . In line percolation with infection parameter r, infection spreads from a subset A⊂[n]d of initially infected lattice points as follows: if there exists an axis‐parallel line L with r or more infected lattice points on it, then every lattice point of [n]d on L gets infected, and we repeat this until the infection can no longer spread. The elements of the set A are usually chosen independently, with some density p, and the main question is to determine pc(n,r,d) , the density at which percolation (infection of the entire grid) becomes likely. In this paper, we determine pc(n,r,2) up to a multiplicative factor of 1+o(1) and pc(n,r,3) up to a multiplicative constant as n→∞ for every fixed r∈ℕ . We also determine the size of the minimal percolating sets in all dimensions and for all values of the infection parameter.
Enhanced query processing for NoSQL crowdsourcing systems

In this paper, we provide a novel approach for effectively and efficiently support query processing tasks in novel NoSQL crowdsourcing systems. The idea of our method is to exploit the social knowledge available from reviews about products of any kind, freely provided by customers through specialized web sites. We thus define a NoSQL database system for large collections of product reviews, where queries can be expressed in terms of natural language sentences whose answers are modeled as lists of products ranked based on the relevance of reviews w.r. t. the natural language sentences. The best ranked products in the result list can be seen as the best hints for the user based on crowd opinions (the reviews). By exploiting the well-known IMDb dataset, which comprises more than 2 million reviews for more than 100,000 movies, we experimentally shows that our prototype obtains good performance in terms of execution time, demonstrating that our approach is feasible.
Using web-based peer assessment in fostering deep learning in computer programming

Active learning is considered by many academics as an important and effective learning strategy. Students can improve the quality of their work by developing their higher cognitive skills through reflection on their own ideas and practice of analytic and evaluative skills. Peer assessment is one of the successful approaches which can be used to enhance this deep learning. In this paper we discuss a novel web-based peer assessment system to support computer programming courses. We discuss the educational rational for the system, and the deep learning theory, report on its deployment on large programming modules. The preliminary results indicate that the system has successfully helped students to develop their higher cognitive skills in learning computer programming.
Tracking Actual Usage: the Attention Metadata Approach

The information overload in learning and teaching scenarios is a main hindering factor for efficient and effective learning. New methods are needed to help teachers and students in dealing with the vast amount of available information and learning material. Our approach aims to utilize contextualized attention metadata to capture behavioural information of users in learning contexts that can be used to deal with the information overload in user centric ways. We introduce a schema and framework for capturing and managing such contextualized attention metadata in this paper. Schema and framework are designed to enable collecting and merging observations about the attention users give to content and their contexts. The contextualized attention metadata schema enables the correlation of the observations, thus reflects the relationships that exists between the user, her context and the content she works with. We illustrate with a simple demo application how contextualized attention metadata can be collected from several tools, the merging of the data streams into a repository and finally the correlation of the data.
Three-Dimensional Single Gyroid Photonic Crystals with a Mid-Infrared Bandgap

A gyroid structure is a distinct morphology that is triply periodic and consists of minimal isosurfaces containing no straight lines. We have designed and synthesized amorphous silicon (a-Si) mid-infrared gyroid photonic crystals that exhibit a complete bandgap in infrared spectroscopy measurements. Photonic crystals were synthesized by deposition of a-Si/Al2O3 coatings onto a sacrificial polymer scaffold defined by two-photon lithography. We observed a 100% reflectance at 7.5 μm for single gyroids with a unit cell size of 4.5 μm, in agreement with the photonic bandgap position predicted from full-wave electromagnetic simulations, whereas the observed reflection peak shifted to 8 μm for a 5.5 μm unit cell size. This approach represents a simulation-fabrication-characterization platform to realize three-dimensional gyroid photonic crystals with well-defined dimensions in real space and tailored properties in momentum space.