Miracle-dz/UniFAR-FTUs huggingface dataset:
Teacher Perspectives on Behaviors Exhibited by Students at Risk for EBD and the Implications of These Behaviors for the Development of an EBD Screener in Middle and Junior High Schools

TEACHER PERSPECTIVES ON ADOLESCENT BEHAVIORS: IMPLICATIONS FOR DEVELOPING A SCHOOL-BASED SCREENING INSTRUMENT FOR EMOTIONAL AND BEHAVIORAL DISORDERS Brittany L. Schilling McKay Department of Education Education Specialist Universal screening is an emerging practice in the field of education to provide at-risk students with early intervention services. Currently there is not a universal screener specifically designed for the middle school population. Therefore, the purpose of this study was to obtain junior high and middle school teachers’ perspectives on behaviors exhibited by students at risk for emotional and behavioral disorders in order to develop preliminary test items. Several themes were identified from the teachers’ perspectives. Teacher perspectives noted that at-risk students displayed a variety of internalizing and externalizing behaviors. These issues included difficulty maintaining peer and teacher relationships, difficulty with hygiene and sleep, challenging home and school relationships, and noncompliant behaviors. From these themes, the researcher created an initial item pool of 24 items, which can be used for future development of a screening instrument.
What characterizes persons with poor mental health? A nationwide study in Denmark

Background: The aim of the study was to identify and characterize groups with poor mental health defined by the SF-12 Mental Component Summary (MCS-12) scale. Methods: The study is based on the Danish Health and Morbidity Survey 2005 and includes 10,082 participants (16 years or older). Data were analysed by means of logistic regression models. Results: Men and women with poor mental health are characterized by being single, having a long-term illness, not being able to rely on help from others in case of illness and by feeling that family and friends demand too much of them. Men with poor mental health were further characterized by being a heavy smoker, and having a BMI below 25. Women with poor mental health were further characterized by being 16–44 years old and sedentary in leisure time. Conclusions: The prevalence of poor mental health is higher among women than men, and different factors characterize men and women with poor mental health. The present findings support the notion that both socio-demographics and lifestyle factors are independently related with poor mental health. We suggest taking into account all these areas of life when planning activities to prevent poor mental health and when promoting mental health.
MUSCLE DAMAGE AFTER A TENNIS MATCH IN YOUNG PLAYERS

The present study investigated changes in indirect markers of muscle damage following a simulated tennis match play using nationally ranked young (17.6 ± 1.4 years) male tennis players. Ten young athletes played a 3-hour simulated match play on outdoor red clay courts following the International Tennis Federation rules. Muscle soreness, plasma creatine kinase activity (CK), serum myoglobin concentration (Mb), one repetition maximum (1RM) squat strength, and squat jump (SJ) and counter movement jump (CMJ) heights were assessed before, immediately after, and 24 and 48 h after the simulated match play. All parameters were also evaluated in a non-exercised group (control group). A small increase in the indirect markers of muscle damage (muscle soreness, CK and Mb) was detected at 24-48 hours post-match (p < 0.05). A marked acute decrement in neuromuscular performance (1RM squat strength: -35.2 ± 10.4%, SJ: -7.0 ± 6.0%, CMJ: -10.0 ± 6.3%) was observed immediately post-match (p < 0.05). At 24 h post-match, the 1RM strength and jump heights were not significantly different from the baseline values. However, several players showed a decrease of these measures at 24 h after the match play. The simulated tennis match play induced mild muscle damage in young players. Coaches could monitor changes in the indirect markers of muscle damage to assess athletes’ recovery status during training and competition.
On the Applicability of Neural Network and Machine LearningMethodologies to Natural Language

We examine the inductive inference of a complex grammar-speciically, we consider the task of training a model to classify natural language sentences as grammatical or ungrammatical, thereby exhibiting the same kind of discriminatory power provided by the Principles and Parameters linguistic framework, or Government-and-Binding theory. We investigate the following models: feed-forward neural networks, Fransconi-Gori-Soda and Back-Tsoi locally recurrent networks, Elman, Narendra & Parthasarathy, and Williams & Zipser recurrent networks, Euclidean and edit-distance nearest-neighbors, simulated anneal-ing, and decision trees. The feed-forward neural networks and non-neural network machine learning models are included primarily for comparison. We address the question: How can a neural network, with its distributed nature and gradient descent based iterative calculations, possess linguistic capability which is traditionally handled with symbolic computation and recursive processes? Initial simulations with all models were only partially successful by using a large temporal window as input. Models trained in this fashion did not learn the grammar to a signiicant degree. Attempts at training recurrent networks with small temporal input windows failed until we implemented several techniques aimed at improving the convergence of the gradient descent training algorithms. We discuss the theory and present an empirical study of a variety of models and learning algorithms which highlights behaviour not present when attempting to learn a simpler grammar. We give a brief introduction to formal grammars, grammatical inference, and natural language; for a thorough introduction we recommend Harrison 24] and Fu 15]. Brieey, a grammar G is a four tuple fN; T; P; Sg,
Computer Programming: A Formal Operational Task.

Concerned with a high failure rate in computer programming courses, two studies were undertaken to discover if two individual cognitive styles--"analytic" (formal thought) and "heuristic" (concrete or pre-operational thought)--were predictors of performance in a beginning computer programming course. To appropriately measure those skills, a Piagetian-based paper and pencil test of cognitive development called "How Is Your Logic? " (HIYL) was administered to a total of 116 undergraduates in three beginning computer programming courses at a small liberal arts college. In the first study of 87 students, 91% who received a course grade of "B+" or higher were formal thinkers, while no one who was classified as concrete operational received a grade higher than a "C+. :" The correlation between course grades and HIYL was statistically significant. In study two, the performance of 29 students on a preand posttest of HIYL was compared with their performance in the course to measure cognitive development during the course; scores on the two forms did not demonstrate measurable development over the 10 weeks of the course. Using a Piagetian framework to analyze the text used by all classes, three major components requiring formal thought were identified--hierarchical classifications of abstract concepts, control structures, and top-down design--and each is discussed. The criteria for classification, weighting components, correlations between HIYL and grades, and correlations between course and test grades are displayed in tabular format. (DJR) *********************************************************************** * Reproductions supplied by EDRS are the best that can be made * * from the original document. * *********************************************************************** U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) This document has been reproduced as received from the person or organization originating IL *10 C Minor changes have been made to improve reproduction Qualify. Points of view or opinions stated in this docu \ ment do not necessarily represent official OERI position or policy. NComputer Programming: A Formal Operational Task LLJ Gwen Bredendieck Fischer Psychology Department Hiram College Hiram, Ohio 44234
The SemSets model for ad-hoc semantic list search

The amount of semantic data on the web has been growing rapidly in recent years. One of the key challenges triggered by this growth is the ad-hoc querying, i.e., the ability to retrieve answers from semantic resources using natural language queries. This facilitates interaction with semantic resources for the users so they can benefit from the knowledge covered by semantic data without the complexities of semantic query languages. In this paper, we focus on semantic queries, where the aim is to retrieve objects belonging to a set of semantically related entities. An example of such an ad-hoc type query is "Apollo astronauts who walked on the Moon". In order to address the task, we propose the SemSets retrieval model that exploits and combines traditional document-based information retrieval, link structure of the semantic data and entity membership in semantic sets, in order to provide the answers. The novelty of the approach lies in the utilization of semantic sets, i.e., groups of semantically related entities. We propose two approaches to identify such semantic sets from the knowledge bases; the first one requires involvement of an expert user knowledgeable of the data set structure, the second one is fully automatic and provides results that are comparable with those delivered by the expert users. As demonstrated in the experimental evaluation, the proposed model has the state-of-the-art performance on the SemSearch2011 data set, which has been designed especially for the semantic list search evaluation.
On the Implementation of a Primal-Dual Interior Point Method

This paper gives an approach to implementing a second-order primal-dual interior point method. It uses a Taylor polynomial of second order to approximate a primal-dual trajectory. The computations for the second derivative are combined with the computations for the centering direction. Computations in this approach do not require that primal and dual solutions be feasible. Expressions are given to compute all the higher-order derivatives of the trajectory of interest. The implementation ensures that a suitable potential function is reduced by a constant amount at each iteration.There are several salient features of this approach. An adaptive heuristic for estimating the centering parameter is given. The approach used to compute the step length is also adaptive. A new practical approach to compute the starting point is given. This approach treats primal and dual problems symmetrically.Computational results on a subset of problems available from netlib are given. On mutually tested problems the results show.
Fast ℓ1-minimization algorithms and an application in robust face recognition: A review

We provide a comprehensive review of five representative ℓ1-minimization methods, i.e., gradient projection, homotopy, iterative shrinkage-thresholding, proximal gradient, and augmented Lagrange multiplier. The repository is intended to fill in a gap in the existing literature to systematically benchmark the performance of these algorithms using a consistent experimental setting. The experiment will be focused on the application of face recognition, where a sparse representation framework has recently been developed to recover human identities from facial images that may be affected by illumination change, occlusion, and facial disguise. The paper also provides useful guidelines to practitioners working in similar fields.
A graph theoretical approach to network encoding complexity

For an acyclic directed network with multiple pairs of sources and sinks and a group of edge-disjoint paths connecting each pair of source and sink, it is known that the number of mergings among different groups of edge-disjoint paths is closely related to network encoding complexity. Using this connection, we derive exact values of and bounds on two functions relevant to encoding complexity for such networks.
Exploring the Relationship Between Alignment and Strategy

Aligning business and IT strategy is a prominent area of concern. Organizations that successfully align their business strategy and their IT strategy, outperform their non-aligned peers (Chan et al., 1997). This paper explores the relationship between business strategy, IT strategy and alignment capability. We found that each business strategy can be supported by all IT strategies, but that certain combinations provide a better fit than others. Regarding business strategy and alignment capability we found no conclusive relationship. Regarding the relationship between IT strategy and alignment capability, however, a clear relationship appeared. We explored this relationship further in a dual case study of two organizations having distinctly different IT strategies. One organization sees IT as an enabler for the business processes with mainly an internal impact, whereas the other organization IT sees as a driver for business innovation that can create competitive advantage in the market place. Based upon an assessment of their alignment capabilities we found that the company with the ‘innovative’ IT strategy scored a distinctly higher alignment capability than the company with the ‘essential’ IT strategy. Although this conclusion may not be surprising, it provides further evidence for the statement that a more ‘progressive’ IT strategy pairs with a better alignment of business and IT.
Integration of Data Mining and Data Warehousing: A Practical Methodology

The ever growing repository of data in all fields poses new challenges to the modern analytical systems. Real-world datasets, with mixed numeric and nominal variables, are difficult to analyze and require effective visual exploration that conveys semantic relationships of data. Traditional data mining techniques such as clustering clusters only the numeric data. Little research has been carried out in tackling the problem of clustering high cardinality nominal variables to get better insight of underlying dataset. Several works in the literature proved the likelihood of integrating data mining with warehousing to discover knowledge from data. For the seamless integration, the mined data has to be modeled in form of a data warehouse schema. Schema generation process is complex manual task and requires domain and warehousing familiarity. Automated techniques are required to generate warehouse schema to overcome the existing dependencies. To fulfill the growing analytical needs and to overcome the existing limitations, we propose a novel methodology in this paper that permits efficient analysis of mixed numeric and nominal data, effective visual data exploration, automatic warehouse schema generation and integration of data mining and warehousing. The proposed methodology is evaluated by performing case study on real-world data set. Results show that multidimensional analysis can be performed in an easier and flexible way to discover meaningful knowledge from large datasets.
Type I and Type III Interferons Drive Redundant Amplification Loops to Induce a Transcriptional Signature in Influenza-Infected Airway Epithelia

Interferons (IFNs) are a group of cytokines with a well-established antiviral function. They can be induced by viral infection, are secreted and bind to specific receptors on the same or neighbouring cells to activate the expression of hundreds of IFN stimulated genes (ISGs) with antiviral function. Type I IFN has been known for more than half a century. However, more recently, type III IFN (IFNλ, IL-28/29) was shown to play a similar role and to be particularly important at epithelial surfaces. Here we show that airway epithelia, the primary target of influenza A virus, produce both IFN I and III upon infection, and that induction of both depends on the RIG-I/MAVS pathway. While IRF3 is generally regarded as the transcription factor required for initiation of IFN transcription and the so-called “priming loop”, we find that IRF3 deficiency has little impact on IFN expression. In contrast, lack of IRF7 reduced IFN production significantly, and only IRF3−/−IRF7−/− double deficiency completely abolished it. The transcriptional response to influenza infection was largely dependent on IFNs, as it was reduced to a few upregulated genes in epithelia lacking receptors for both type I and III IFN (IFNAR1−/−IL-28Rα−/−). Wild-type epithelia and epithelia deficient in either the type I IFN receptor or the type III IFN receptor exhibit similar transcriptional profiles in response to virus, indicating that none of the induced genes depends selectively on only one IFN system. In chimeric mice, the lack of both IFN I and III signalling in the stromal compartment alone significantly increased the susceptibility to influenza infection. In conclusion, virus infection of airway epithelia induces, via a RIG-I/MAVS/IRF7 dependent pathway, both type I and III IFNs which drive two completely overlapping and redundant amplification loops to upregulate ISGs and protect from influenza infection.
VGA-Classifier: design and applications

A method for pattern classification using genetic algorithms (GAs) has been recently described in Pal, Bandyopadhyay and Murthy (1998), where the class boundaries of a data set are approximated by a fixed number H of hyperplanes. As a consequence of fixing H a priori, the classifier suffered from the limitation of overfitting (or underfitting) the training data with an associated loss of its generalization capability. In this paper, we propose a scheme for evolving the value of H automatically using the concept of variable length strings/chromosomes. The crossover and mutation operators are newly defined in order to handle variable string lengths. The fitness function ensures primarily the minimization of the number of misclassified samples, and also the reduction of the number of hyperplanes. Based on an analogy between the classification principles of the genetic classifier and multilayer perceptron (with hard limiting neurons), a method for automatically determining the architecture and the connection weights of the latter is described.
Spread: A Measure of the Size of Metric Spaces

Motivated by Leinster-Cobbold measures of biodiversity, the notion of the spread of a finite metric space is introduced. This is related to Leinster’s magnitude of a metric space. Spread is generalized to infinite metric spaces equipped with a measure and is calculated for spheres and straight lines. For Riemannian manifolds the spread is related to the volume and total scalar curvature. A notion of scale-dependent dimension is introduced and seen for approximations to certain fractals to be numerically close to the Minkowski dimension of the original fractals.
Quelques questions de sélection de variables autour de l'estimateur Lasso

Le probleme general etudie dans cette these est celui de la regression lineaire en grande dimension. On s'interesse particulierement aux methodes d'estimation qui capturent la sparsite du parametre cible, meme dans le cas ou la dimension est superieure au nombre d'observations. Une methode populaire pour estimer le parametre inconnu de la regression dans ce contexte est l'estimateur des moindres carres penalises par la norme l1 des coefficients, connu sous le nom de LASSO. Les contributions de la these portent sur l'etude de variantes de l'estimateur LASSO pour prendre en compte soit des informations supplementaires sur les variables d'entree, soit des modes semi-supervises d'acquisition des donnees. Plus precisement, les questions abordees dans ce travail sont : i) l'estimation du parametre inconnu lorsque l'espace des variables explicatives a une structure bien determinee (presence de correlations, structure d'ordre sur les variables ou regroupements entre variables) ; ii) la construction d'estimateurs adaptes au cadre transductif, pour lequel les nouvelles observations non etiquetees sont prises en consideration. Ces adaptations sont en partie deduites par une modification de la penalite dans la definition de l'estimateur LASSO. Les procedures introduites sont essentiellement analysees d'un point de vue non-asymptotique ; nous prouvons notamment que les estimateurs construits verifient des Inegalites de Sparsite Oracles. Ces inegalites ont pour particularite de dependre du nombre de composantes non-nulles du parametre cible. Un controle sur la probabilite d'erreur d'estimation du support du parametre de regression est egalement etabli. Les performances pratiques des methodes etudiees sont par ailleurs illustrees a travers des resultats de simulation.
Heparin-binding domain of fibrin(ogen) binds growth factors and promotes tissue repair when incorporated within a synthetic matrix

By binding growth factors (GFs), the ECM tightly regulates their activity. We recently reported that the heparin-binding domain II of fibronectin acts as a promiscuous high-affinity GF-binding domain. Here we hypothesized that fibrin, the provisional ECM during tissue repair, also could be highly promiscuous in its GF-binding capacity. Using multiple affinity-based assays, we found that fibrin(ogen) and its heparin-binding domain bind several GFs from the PDGF/VEGF and FGF families and some GFs from the TGF-β and neurotrophin families. Overall, we identified 15 unique binding interactions. The GF binding ability of fibrinogen caused prolonged retention of many of the identified GFs within fibrin. Thus, based on the promiscuous and high-affinity interactions in fibrin, GF binding may be one of fibrin’s main physiological functions, and these interactions may potentially play an important and ubiquitous role during tissue repair. To prove this role in a gain-of-function model, we incorporated the heparin-binding domain of fibrin into a synthetic fibrin-mimetic matrix. In vivo, the multifunctional synthetic matrix could fully mimic the effect of fibrin in a diabetic mouse model of impaired wound healing, demonstrating the benefits of generating a hybrid biomaterial consisting of a synthetic polymeric scaffold and recombinant bioactive ECM domains. The reproduction of GF–ECM interactions with a fibrin-mimetic matrix could be clinically useful, and has the significant benefit of a more straightforward regulatory path associated with chemical synthesis rather than human sourcing.