Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactive Decision Making and Behavior of Experienced and Inexperienced Basketball Coaches during Practice

This study compared 10 experienced high school and 10 inexperienced junior high and middle school basketball coaches as they executed a 30-minute practice session on the “give and go” play in basketball. The coaches were given 30 minutes to plan a practice session. Following planning, coaches implemented their practice plan. Analysis of coaches’ behavior and interactive decision making indicated that experienced coaches exhibited significantly more technical instruction, whereas inexperienced coaches exhibited significantly higher frequencies of silent observation. With regard to interactive decision making, results indicated that both experienced and inexperienced coaches implemented practice in ways consistent with their plans. Experienced coaches, however, were significantly more reluctant to change their plans when problems were perceived. Despite these differences, experienced and inexperienced coaches exhibited greater similarities than differences. Limitations of research based on the behavioral an.
Introduction to the special issue: Direct observation in pediatric psychology research.

For every original research study that has been published in the Journal of Pediatric Psychology, the researchers began with framing one or more research questions and then determined how best to answer those questions. It is the second part of this process that is the focus of this special issue. Once a research question has been framed and specified adequately, the next critical step in designing and conducting a valuable research study is to decide what to measure and how to measure it. The papers that appear in this special issue have something in common: all of them addressed these questions, at least in part, by selecting, adapting, or developing assessment methods that used some variant of direct behavioral observation to quantify variables that were of primary interest in their studies. As you read on, I think you will be impressed with the breadth, creativity, and elegance of the papers that were selected for this special issue. The papers represent an impressive array of contributions that span many of the medical diagnoses that are commonly studied by pediatric psychologists, including the interface of psychological variables with child and family adaptation to cancer, asthma, diabetes, cystic fibrosis, obesity, and neonatal critical care. Tremendous gratitude is due to the editor, Dr Grayson Holmbeck, for supporting the solicitation of this special issue, to the many authors and their colleagues who submitted such high-quality papers for consideration, and to the many dedicated and conscientious reviewers who helped to make this issue truly special. Why Direct Observation?
Technological Transitions And System Innovations: A Co-evolutionary And Socio-technical Analysis

This important book addresses how long term and large scale shifts from one socio-technical system to another come about, using insights from evolutionary economics, sociology of technology and innovation studies. These major changes involve not just technological changes, but also changes in markets, regulation, culture, industrial networks and infrastructure. The book develops a multi-level perspective, arguing that transitions take place through the alignment of multiple processes at three levels: niche, regime and landscape. This perspective is illustrated by detailed historical case studies: the transition from sailing ships to steamships, the transition from horse-and-carriage to automobiles and the transition from propeller-piston engine aircraft to turbojets. This book will be of great interest to researchers in innovation studies, evolutionary economics, sociology of technology and environmental studies. It will also be useful for policy makers involved in long-term sustainability and systems transitions issues.
Module Network Inference from a Cancer Gene Expression Data Set Identifies MicroRNA Regulated Modules

Background MicroRNAs (miRNAs) are small RNAs that recognize and regulate mRNA target genes. Multiple lines of evidence indicate that they are key regulators of numerous critical functions in development and disease, including cancer. However, defining the place and function of miRNAs in complex regulatory networks is not straightforward. Systems approaches, like the inference of a module network from expression data, can help to achieve this goal. Methodology/Principal Findings During the last decade, much progress has been made in the development of robust and powerful module network inference algorithms. In this study, we analyze and assess experimentally a module network inferred from both miRNA and mRNA expression data, using our recently developed module network inference algorithm based on probabilistic optimization techniques. We show that several miRNAs are predicted as statistically significant regulators for various modules of tightly co-expressed genes. A detailed analysis of three of those modules demonstrates that the specific assignment of miRNAs is functionally coherent and supported by literature. We further designed a set of experiments to test the assignment of miR-200a as the top regulator of a small module of nine genes. The results strongly suggest that miR-200a is regulating the module genes via the transcription factor ZEB1. Interestingly, this module is most likely involved in epithelial homeostasis and its dysregulation might contribute to the malignant process in cancer cells. Conclusions/Significance Our results show that a robust module network analysis of expression data can provide novel insights of miRNA function in important cellular processes. Such a computational approach, starting from expression data alone, can be helpful in the process of identifying the function of miRNAs by suggesting modules of co-expressed genes in which they play a regulatory role. As shown in this study, those modules can then be tested experimentally to further investigate and refine the function of the miRNA in the regulatory network.
Elastic Matching and Retrieval of IVUS Images Using Contextual Information

We present a content-based image retrieval system of medical images of bodies with high elasticity, i.e. high inter and intra subject variability in shape. The system is based on a rich feature space which is able to describe all the relevant aspects of an image, including local, global and contextual information. For including relative spatial relations between the structures (i.e. contextual information) we do not need to obtain very accurate segmentations of the image, in contrast to the majority of methods employed for this kind of description. We also obtain invariance to spatial deformations of the same type of object along different instances. This is achieved by applying an efficient registration of the image before their comparison. The incorporation of all these components represent an innovative and powerful way of comparing and retrieving medical images. Validated results are reported on a database of 168 IVUS images, showing the appropriacy of our approach for images of such a high complexity.
Inosine Reduces Ischemic Brain Injury in Rats

Background and Purpose— Purinergic nucleoside inosine elicits protection and regeneration during various injuries. The purpose of this study was to examine the protective effects of inosine against cerebral ischemia. Methods— Adult Sprague–Dawley rats were anesthetized. Inosine, hypoxathine, or vehicle was administered intracerebroventricularly before transient right middle cerebral artery occlusion (MCAo). Animals were placed in behavioral chambers 2 days to 2 weeks after MCAo and then euthanized for tri-phenyl-tetrazolium chloride staining. Glutamate release was measured by microdialysis/high-performance liquid chromatography, and single-unit action potentials were recorded from neurons in the parietal cortex. Results— Stroke animals receiving inosine pretreatment demonstrated a higher level of locomotor activity and less cerebral infarction. Intracerebroventricular administration of the same dose of hypoxanthine did not confer protection. Coadministration of selective A3 receptor antagonist 3-ethyl-5-benzyl-2-methyl-4-phenylethynyl-6-phenyl-1, 4-(±)-dihydropyridine-3,5-dicarboxylate (MRS1191) significantly reduced inosine-mediated protection. Inosine did not alter basal glutamate release, nor did it reduce ischemia-evoked glutamate overflow from cerebral cortex. However, inosine antagonized glutamate-induced electrophysiological excitation in cerebral cortical neurons. Conclusions— Inosine inhibits glutamate postsynaptic responses and reduces cerebral infarction. Its protective effect against ischemia/reperfusion-related insults may involve activation of adenosine A3 receptors.
Description of Kingella potus sp. nov., an Organism Isolated from a Wound Caused by an Animal Bite

ABSTRACT We report the isolation and characterization of a hitherto unknown gram-negative, rod-shaped Neisseria-like organism from an infected wound resulting from a bite from a kinkajou. Based on both phenotypic and phylogenetic evidence, it is proposed that the unknown organism be classified as a new species, Kingella potus sp. nov.
Mutational analysis of the ribosome assembly GTPase RbgA provides insight into ribosome interaction and ribosome-stimulated GTPase activation

Ribosome biogenesis GTPase A protein (RbgA) is an essential GTPase required for the biogenesis of the 50S subunit in Bacillus subtilis. Homologs of RbgA are widely distributed in bacteria and eukaryotes and are implicated in ribosome assembly in the mitochondria, chloroplast and cytoplasm. Cells depleted of RbgA accumulate an immature large subunit that is missing key ribosomal proteins. RbgA, unlike many members of the Ras superfamily of GTPases, lacks a defined catalytic residue for carrying out guanosine triphosphate (GTP) hydrolysis. To probe RbgA function in ribosome assembly, we used a combined bioinformatics, genetic and biochemical approach. We identified a RNA-binding domain within the C-terminus of RbgA that is structurally similar to AmiR–NasR Transcription Anti-termination Regulator (ANTAR) domains, which are known to bind structured RNA. Mutation of key residues in the ANTAR domain altered RbgA association with the ribosome. We identified a putative catalytic residue within a highly conserved region of RbgA, His9, which is contained within a similar PGH motif found in elongation factor Tu (EF-Tu) that is required for GTP hydrolysis on interaction with the ribosome. Finally, our results support a model in which the GTPase activity of RbgA directly participates in the maturation of the large subunit rather than solely promoting dissociation of RbgA from the 50S subunit.
Beyond Inventory Management: The Bullwhip Effect and the Great Moderation

We resurrect the question if improved business practices contributed to increased macroeconomic stability since the 1980s – the Great Moderation. While previous studies on the issue are limited to examining inventory management, we analyse the role of better supply chain management on enhancing firms’ ability to coordinate their production. By investigating ordering and backordering behaviour in the durables manufacturing sector, we find that the improved business practices have significantly dampened order volatility to the sector (the ‘bullwhip effect’), by around 40-50%. Using the stylised fact that the durables manufacturing sector is responsible for half of the overall Great Moderation, we determine that the contribution of better business practices is quantitatively significant, at 20-25% of the overall Great Moderation. JEL Classifications: E32, L60
A Framework for Software Project Metrics

Software Engineering is a discipline which is making exceptional progress in the development of new paradigms, methods, tools and techniques for the production of information systems. A weakness is that the academic development of the theory underlying these advances is often “out of synchronisation” with current usage. The use of metrics is a key factor in the establishment of a scientific basis for Software Engineering and the reduction of the gap between academics and practitioners. Systematic measurement is necessary for the formal modelling and evaluation of the new techniques and their optimal use within industry. A number of approaches (e.g. the Software Factory, CMM, Bootstrap, SPICE, GQM, Balanced Scorecard), have been advocated for the systematic design and introduction of software metrics for the purposes of process improvement and capability assessment in an organisation. This paper defines a taxonomy of software metrics which is derived from the needs of users, developers and management. The properties of the classifications are discussed. Rules are then derived which generate a new set of metrics from an existing class. The rules complement standard approaches by focusing attention on user driven aspects of an organisation's measurement programme. They do this by directly linking product characteristics with organisational improvements. The method also has promise as an approach for validating process models and metrics. Examples of the theoretical and pragmatic use of the taxonomy are provided. The paper concludes with a discussion of useful applications.
Optimal Bank Capital with Costly Recapitalization

We study optimal bank capital choice as a dynamic trade-off between the opportunity cost of equity, the loss of franchise value following a regulatory minimum capital violation, and the cost of recapitalization. We introduce a recapitalization delay that results in a strictly positive probability of capital adequacy violation. We calibrate the model to bank accounting return data and evaluate the model's ability to explain observed bank capital ratios. Differences in return volatility explain a significant fraction of the cross-sectional variation in bank capital ratios. Differences in the level of capital market imperfections across banks constitute a secondary explanation.
Training Neural Networks Without Gradients: A Scalable ADMM Approach

With the growing importance of large network models and enormous training datasets, GPUs have become increasingly necessary to train neural networks. This is largely because conventional optimization algorithms rely on stochastic gradient methods that don't scale well to large numbers of cores in a cluster setting. Furthermore, the convergence of all gradient methods, including batch methods, suffers from common problems like saturation effects, poor conditioning, and saddle points. This paper explores an unconventional training method that uses alternating direction methods and Bregman iteration to train networks without gradient descent steps. The proposed method reduces the network training problem to a sequence of minimization substeps that can each be solved globally in closed form. The proposed method is advantageous because it avoids many of the caveats that make gradient methods slow on highly nonconvex problems. The method exhibits strong scaling in the distributed setting, yielding linear speedups even when split over thousands of cores.
Bombing Versus Negotiating: How Preferences for Combating Terrorism Are Affected by Perceived Terrorist Rationality

This research demonstrates that the degree of bias or rationality that people impute to terrorists influences the types of strategies that they advocate for combating terrorism. In 2 experiments, participants read depictions of terrorists that described their decision making as either rooted in rationality and objectivity or as distorted by irrationality and bias. When terrorists were depicted as biased and irrational (vs. as objective and rational), participants were more likely to advocate military action against terrorism and less likely to advocate diplomacy. This effect was mediated by perceptions of terrorists' capacity for reason rather than by affective responses toward terrorists. This research also shows that perceptions of terrorists as rational and objective versus irrational and biased are not simply fixed in individuals' minds but can be influenced by contextual manipulations, including putative news reports.
Interactive Virtual Relighting and Remodeling of Real Scenes

Lighting design is often tedious due to the required physical manipulation of real light sources and objects. As an alternative, we present an interactive system to virtually modify the lighting and geometry of scenes with both real and synthetic objects, including mixed real/virtual lighting and shadows. In our method, real scene geometry is first approximately reconstructed from photographs. Additional images are taken from a single viewpoint with a real light in different positions to estimate reflectance. A filtering process is used to compensate for inaccuracies, and per image reflectances are averaged to generate an approximate reflectance image for the given viewpoint, removing shadows in the process. This estimate is used to initialise a global illumination hierarchical radiosity system, representing real-world secondary illumination; the system is optimized for interactive updates. Direct illumination from lights is calculated separately using ray-casting and a table for efficient reuse of data where appropriate. Our system allows interactive modification of light emission and object positions, all with mixed real/virtual illumination effects. Real objects can also be virtually removed using texture-filling algorithms for reflectance estimation.
The effect of improper conformal coating on SnPb and Pb-free BGA solder joints during thermal cycling: Experiments and modeling

Electronic components, such as ball grid array (BGA), chip scale packages (CSP) and bottom terminated components (BTC) used in harsh use environments often require the use of conformal coatings to meet reliability requirements. In certain coating application methods, the conformal coating materials can flow underneath the component and cause solder joint failure during thermal expansion and contraction of the electronic assembly. In this study, BGA components were coated with an acrylic conformal coating material using two application methods and subjected to two different thermal cycling profiles to assess the integrity of SnPb and Pb-free BGA components. To better understand the observed failure modes, Finite Element Analysis (FEA) was performed on the conformally coated BGA packages. Material characterization was performed using Dynamic Mechanical Analysis (DMA) and Thermal Mechanical Analysis (TMA) to capture the temperature dependent properties of the conformal coating to better correlate simulation and experimental results. Failure modes were found to greatly depend on the conformal coating material properties around the glass transition temperature (Tg) rather than temperature cycle range. Significant differences in the failure mode were found between the Pb-free and SnPb BGA components with acrylic conformal coating materials and temperatures profiles.
Making sense of Microposts (#Microposts2015) named entity recognition & linking challenge

Microposts are small fragments of social media content and a popular medium for sharing facts, opinions and emotions. Collectively, they comprise a wealth of data that is increasing exponentially, and which therefore presents new challenges for the Information Extraction community, among others. This paper describes the Making Sense of Microposts (#Microposts2015) Workshop’s Named Entity rEcognition and Linking (NEEL) Challenge, held as part of the 2015 World Wide Web conference (WWW’15). The challenge task comprised automatic recognition and linking of entities appearing in different event streams of English Microposts on Twitter. Participants were set the task of investigating novel strategies for extracting entities in a tweet stream, typing these based on a set of pre-defined classes, and linking to DBpedia or NIL referents. They were also asked to implement a web service to run their systems, to minimize human involvement in the evaluation and allow measuring of processing times. The challenge attracted a lot of interest: 29 research groups expressed an intent to participate, out of which 21 signed the agreement required to be given a copy of the training and development datasets. Seven teams participated in the final evaluation of the challenge task, out of which six completed all requirements, including submission of an abstract describing their approach. The submissions covered sequential and joint linguistic methods, end-to-end and hybrid end-to-end, and linguistic approaches for tackling the challenge task. We describe the evaluation process and discuss the performance of the different approaches to the #Microposts2015 NEEL Challenge. We also release, with this paper, the #Microposts2015 NEEL Challenge Gold Standard, comprising the set of manually annotated tweets.
Reliable Approximation of Long Relaxation Timescales in Molecular Dynamics

Many interesting rare events in molecular systems, like ligand association, protein folding or conformational changes, occur on timescales that often are not accessible by direct numerical simulation. Therefore, rare event approximation approaches like interface sampling, Markov state model building, or advanced reaction coordinate-based free energy estimation have attracted huge attention recently. In this article we analyze the reliability of such approaches. How precise is an estimate of long relaxation timescales of molecular systems resulting from various forms of rare event approximation methods? Our results give a theoretical answer to this question by relating it with the transfer operator approach to molecular dynamics. By doing so we also allow for understanding deep connections between the different approaches.
Naturally Occurring Molecules as Alternative Therapeutic Agents against Cariogenic Biofilms

The authors declare no conflict of interest. Dental caries occurs as a result of persistent low-pH environment within biofilms containing elevated amounts of extracellular polysaccharides (EPS). The synthesis of EPS by Streptococcus mutans is a critical factor in promoting formation of cariogenic biofilm through enhancing bacterial adherence, protecting organisms from inimical influences, and affecting diffusion properties and inorganic composition of the biofilm’s matrix. The development of novel chemotherapeutic approaches, other than microbiocides, that affect bacterial virulence traits is an attractive route to prevent oral infectious diseases. Natural products may be major sources of innovative anti-caries agents. We have identified such agents that disrupt specific virulence attributes of S. mutans such as insoluble-glucan synthesis. Furthermore, these compounds enhance the protective effects of fluoride. This paper provides an overview of the postulated mechanisms through which naturally occurring molecules, alone or in combination with fluoride, modulate the cariogenicity of S. mutans within biofilms, which could be used as alternative or adjunctive anti-caries chemotherapy.
Bark resolution from speech data

This paper discusses the relevance of non-uniform frequency resolution used by current speech analysis methods like Mel frequency analysis and perceptual linear predictive (PLP) analysis. It is shown that linear discriminant analysis of short-time Fourier spectrum of speech yields spectral basis functions which provide comparatively lower resolution to the high frequency region of spectrum. This is consistent with critical-band resolution and is shown to be caused by the spectral properties of vowel sounds. Further, we show that this non-uniform resolution can be traced to the physiology of speech production mechanism. In ASR experiments, features extracted by the discriminant functions are shown to outperform the conventional features derived by cosine basis functions.