Miracle-dz/UniFAR-FTUs huggingface dataset:
Teaching Practice of Physical Education Teachers for Students with Special Needs: An Application of the Theory of Planned Behaviour

This study examines the teaching behavior of physical education (PE) teachers in teaching students with special needs and the factors that determine their teaching behaviour. An extended theory of planned behaviour (TPB) was utilised as the theoretical framework. Three secondary and two high school PE teachers participated in the study. Data collection consisted of systematic and non-participant observations of teaching behaviour of PE teachers, as well as semi-structured interviews on the factors that determine their behaviours. Results revealed that four of five participants interacted more verbally and less physically with students with special needs. Peer partners were used to enhance the learning of students with special needs. Only two teachers modified their instruction and equipment for students with special needs. Sometimes, teachers excluded this group of students from cooperative activities. Extended TPB components, including attitude, moral norm, affective beliefs, social norm, and perceived behaviour control, were identified to determine the intention of PE teachers to teach students with special needs and their inclusive teaching practice. The study has important implications for policy and practice of inclusive PE.
Enhancing program readability and comprehensibility with tools for program visualization

To make computer programs for comprehensible, the presentation of program source text, program documentation, and program execution needs to be enhanced. A number of techniques and tools developed to achieve these ends are described. One of these is a design for the effective presentation of source text in the C programming language using high-quality digital typography; a processor which implements the design is also given. Some experimental evidence is summarized to demonstrate that the resulting source text presentation is significantly more readable and comprehensible than the presentation conventionally used today. Brief descriptions are given of two other techniques, the development of a system of structured program documentation incorporating both text and graphics and the portrayal of program execution with colored computer animation. <<ETX>>
Fast Support Vector Machine Classification using linear SVMs

We propose a classification method based on a decision tree whose nodes consist of linear support vector machines (SVMs). Each node defines a decision hyperplane that classifies part of the feature space. For large classification problems (with many support vectors (SVs)) it has the advantage that the classification time does not depend on the number of SVs. Here, the classification of a new sample can be calculated by the dot product with the orthogonal vector of each hyperplane. The number of nodes in the tree has shown to be much smaller than the number of SVs in a non-linear SVM, thus, a significant speedup in classification time can be achieved. For non-linear separable problems, the trivial solution (zero vector) of a linear SVM is analyzed and a new formulation of the optimization problem is given to avoid it
The Gene Desert Mammary Carcinoma Susceptibility Locus Mcs1a Regulates Nr2f1 Modifying Mammary Epithelial Cell Differentiation and Proliferation

Genome-wide association studies have revealed that many low-penetrance breast cancer susceptibility loci are located in non-protein coding genomic regions; however, few have been characterized. In a comparative genetics approach to model such loci in a rat breast cancer model, we previously identified the mammary carcinoma susceptibility locus Mcs1a. We now localize Mcs1a to a critical interval (277 Kb) within a gene desert. Mcs1a reduces mammary carcinoma multiplicity by 50% and acts in a mammary cell-autonomous manner. We developed a megadeletion mouse model, which lacks 535 Kb of sequence containing the Mcs1a ortholog. Global gene expression analysis by RNA-seq revealed that in the mouse mammary gland, the orphan nuclear receptor gene Nr2f1/Coup-tf1 is regulated by Mcs1a. In resistant Mcs1a congenic rats, as compared with susceptible congenic control rats, we found Nr2f1 transcript levels to be elevated in mammary gland, epithelial cells, and carcinoma samples. Chromatin looping over ∼820 Kb of sequence from the Nr2f1 promoter to a strongly conserved element within the Mcs1a critical interval was identified. This element contains a 14 bp indel polymorphism that affects a human-rat-mouse conserved COUP-TF binding motif and is a functional Mcs1a candidate. In both the rat and mouse models, higher Nr2f1 transcript levels are associated with higher abundance of luminal mammary epithelial cells. In both the mouse mammary gland and a human breast cancer global gene expression data set, we found Nr2f1 transcript levels to be strongly anti-correlated to a gene cluster enriched in cell cycle-related genes. We queried 12 large publicly available human breast cancer gene expression studies and found that the median NR2F1 transcript level is consistently lower in ‘triple-negative’ (ER-PR-HER2-) breast cancers as compared with ‘receptor-positive’ breast cancers. Our data suggest that the non-protein coding locus Mcs1a regulates Nr2f1, which is a candidate modifier of differentiation, proliferation, and mammary cancer risk.
Contaminated speech training methods for robust DNN-HMM distant speech recognition

Despite the significant progress made in the last years, state-of-the-art speech recognition technologies provide a satisfactory performance only in the close-talking condition. Robustness of distant speech recognition in adverse acoustic conditions, on the other hand, remains a crucial open issue for future applications of human-machine interaction. To this end, several advances in speech enhancement, acoustic scene analysis as well as acoustic modeling, have recently contributed to improve the state-of-the-art in the field. One of the most effective approaches to derive a robust acoustic modeling is based on using contaminated speech, which proved helpful in reducing the acoustic mismatch between training and testing conditions. In this paper, we revise this classical approach in the context of modern DNN-HMM systems, and propose the adoption of three methods, namely, asymmetric context windowing, close-talk based supervision, and close-talk based pre-training. The experimental results, obtained using both real and simulated data, show a significant advantage in using these three methods, overall providing a 15% error rate reduction compared to the baseline systems. The same trend in performance is confirmed either using a high-quality training set of small size, and a large one.
Identification of ABCC2 as a binding protein of Cry1Ac on brush border membrane vesicles from Helicoverpa armigera by an improved pull‐down assay

Cry1Ac toxin‐binding proteins from Helicoverpa armigera brush border membrane vesicles were identified by an improved pull‐down method that involves coupling Cry1Ac to CNBr agarose combined with liquid chromatography–tandem mass spectrometry (LC‐MS/MS). According to the LC‐MS/MS results, Cry1Ac toxin could bind to six classes of aminopeptidase‐N, alkaline phosphatase, cadherin‐like protein, ATP‐binding cassette transporter subfamily C protein (ABCC2), actin, ATPase, polycalin, and some other proteins not previously characterized as Cry toxin‐binding molecules such as dipeptidyl peptidase or carboxyl/choline esterase and some serine proteases. This is the first report that suggests the direct binding of Cry1Ac toxin to ABCC2 in H. armigera.
Tactile Experience Is Evoked by Visual Image of Materials: Evidence from Onomatopoeia

Human beings get a lot of information from a picture based on what we see and our background knowledge. However, many computer vision researches are heavily dependent on the use of image features and have paid little attention to background knowledge we use in texture processing. The present study explores the degree to which onomatopoeia evoked by visual images is affected by the multimodal experience-based knowledge such as tactile experience. In Experiment 1 participants saw original complete images of Flickr Material Database (FMD) and answered onomatopoeia for expressing their textures and in Experiment 2 participants saw cut out images and answered onomatopoeia for expressing their textures. We obtained 17487 onomatopoeic words (1827 types) from experiment 1 and 30138 onomatopoeic words (2442 types) from experiment 2. We counted the number of types of onomatopoeia evoked by each image. Result showed that original image evoked significantly more variety of onomatopoeia than cut-off image. This result suggests that human texture evaluations based on the original complete images of FMD are affected more easily by experience-based knowledge about the material. Furthermore, we showed that image whose material category is relatively easy to recognize evokes significantly frequently tactile onomatopoeia than image whose material category is hard to recognize.
Switched adaptive strategies for contraction and incremental stability of Carathéodory systems using multiple norms

In this paper, we present switched adaptive strategies to incrementally stabilise a class of Carathéodory nonlinear systems. Using the concept of switched matrix measures based on the use of multiple norms, and the transaction coefficients between these norms, sufficient conditions are presented to prove incremental stability of a switched system. Furthermore, based on these sufficient conditions, two strategies are proposed to incrementally stabilise a switched system of interest: one based on monitoring the switching signal in a continuous way, the other on monitoring it only at selected discrete-time instants. We prove that in both situations, the switched system can be effectively stabilised. As a representative application, we illustrate the efficiency of these strategies by synchronizing a network system with blinking (switching) graph structures.
Pyrosequencing Analysis of Microbiota in Kaburazushi, a Traditional Medieval Sushi in Japan

The processing of archetypal Japanese sushi involves microbial fermentation. The traditional sushi kaburazushi, introduced in the middle ages, is made by fermenting salted yellow tail, salted turnip, and malted rice, and is distinguished from the ancient sushi narezushi, made from fish and boiled rice. In this study, we examined changes in the microbial population during kaburazushi fermentation by pyrosequencing the 16S ribosomal RNA genes (rDNA) of the organisms in the fermentation medium. Ribosomal Database Project Classifier analysis identified 31 genera, among which Lactobacillus drastically increased during fermentation (150-fold increment over 8 d), while the relative populations of the other gram-positive bacteria (Staphylococcus and Bacillus) decreased. Basic Local Alignment Search Tool analysis revealed the dominant species to be L. sakei. This organism constituted approximately 90% of Lactobacillus and 79% of total microbiota. The taxonomic diversity and species richness (assayed by Shannon-Weiner Index and Chao 1, respectively) were not significantly different between middle-ages kaburazushi and ancient narezushi. Both types were characterized by the preferential growth of Lactobacillales.
A compression-based morphable PCM architecture for improving resistance drift tolerance

Due to the growing demand for large memories, using emerging technologies such as Phase Change Memories (PCM) are inevitable. PCM with appropriate scalability, power consumption and multiple bits per cell storage capability is a probable candidate for substituting DRAM. Although storing multiple bits per cell seems to be a rational response to large memory demands, there is a significant problem to achieve this goal. Resistance drift problem is an important reliability concern that is coupled to a multi-level cell PCM (MLC PCM) memory system. In this paper, we propose a memory system architecture that, by exploiting the benefits of compression, converts resistance drift prone blocks to drift resilient blocks in order to protect the memory system from resistance drift. Evaluations on a full-system simulator, consisting of a quad-core ALPHA CMP and a banked PCM memory, show that our proposed approach provides up to 9.8× reduction in bit error rate, an average of 8% reduction in energy consumption, and 21% IPC improvement.
Automatic State Construction using Decision Tree for Reinforcement Learning Agents

Reinforcement Learning (RL) is a learning framework for modelling an agent and its interaction with its environment through actions, perceptions, and rewards. Intelligent agents should choose actions after every perception, such that their long-term reward is maximized. A well defined framework for this interaction is the partially observable Markov decision process model (POMDP). Unfortunately solving POMDPs is an intractable problem. To overcome the problem of partial observability, McCallum introduced the U-tree, a RL algorithm that uses selective attention and short-term memory to simultaneously address the intertwined problems of large perceptual state spaces and hidden states. A U-tree embodies the policy and the state representation of the environment of the agent. A U-tree combines the advantages of instance-based learning with robust statistical tests for separating noise from task structure. In this paper, we consider an alternative approach for the feature selection of past events for the construction of the state representation. We apply information theory and decision tree techniques to derive a variation of the U-tree. The relevance of the candidate features is assessed by ranking the information gain ratio with respect to the cumulative expected reward. Experiments carried on three different RL tasks demonstrate that our variant of the U-tree produces a more robust state representation and faster learning. This better performance can be explained by the fact that the information gain ratio exhibits a lower variance in return prediction than the Kolmogorov-Smirnov statistical test used in the original U-tree algorithm.
Body Dimensions of Elite Handball Players with Respect to Laterality, Playing Positions and Playing Standard

Body Dimensions of Elite Handball Players with Respect to Laterality, Playing Positions and Playing Standard The aim of the present study was to examine, using a large player database, between-playing positions and playing standard differences in body dimensions. Methods: We compared stature and body mass of 1295 male elite handball players from different playing positions, i.e., backs (left and right), center backs, goalkeepers, pivots, wings (left and right) and playing standards (European championship, Champions league matches and national leagues from Germany, Spain and France).
Confidence intervals for two sample means: Calculation, interpretation, and a few simple rules

Valued by statisticians, enforced by editors, and confused by many authors, standard errors (SEs) and confidence intervals (CIs) remain a controversial issue in the psychological literature. This is especially true for the proper use of CIs for within-subjects designs, even though several recent publications elaborated on possible solutions for this case. The present paper presents a short and straightforward introduction to the basic principles of CI construction, in an attempt to encourage students and researchers in cognitive psychology to use CIs in their reports and presentations. Focusing on a simple but prevalent case of statistical inference, the comparison of two sample means, we describe possible CIs for between- and within-subjects designs. In addition, we give hands-on examples of how to compute these CIs and discuss their relation to classical t-tests.
Point cloud compression for grid-pattern-based 3D scanning system

Recently it is relatively easy to produce digital point sampled 3D geometric models. In sight of the increasing capability of 3D scanning systems to produce models with millions of points, compression efficiency is of paramount importance. In this paper, we propose a novel competition-based predictive method for single-rate compression of 3D models represented as point cloud. In particular we aim at 3D scanning methods based on grid pattern. The proposed method takes advantage of the pattern characteristic made of vertical and horizontal lines, by assuming that the object surface is sampled in curve of points. We then designed and implemented a predictive coder driven by this curve-based point representation. Novel prediction techniques are specifically designed for a curve-based cloud of points, and been competing between them to achieve high quality 3D reconstruction. Experimental results demonstrate the effectiveness of the proposed method.
Efficient transaction processing in SAP HANA database: the end of a column store myth

The SAP HANA database is the core of SAP's new data management platform. The overall goal of the SAP HANA database is to provide a generic but powerful system for different query scenarios, both transactional and analytical, on the same data representation within a highly scalable execution environment. Within this paper, we highlight the main features that differentiate the SAP HANA database from classical relational database engines. Therefore, we outline the general architecture and design criteria of the SAP HANA in a first step. In a second step, we challenge the common belief that column store data structures are only superior in analytical workloads and not well suited for transactional workloads. We outline the concept of record life cycle management to use different storage formats for the different stages of a record. We not only discuss the general concept but also dive into some of the details of how to efficiently propagate records through their life cycle and moving database entries from write-optimized to read-optimized storage formats. In summary, the paper aims at illustrating how the SAP HANA database is able to efficiently work in analytical as well as transactional workload environments.
Can you moderate an unreadable message? 'Blind' content moderation via human computation

User-generated content (UGC) is fundamental to online social engagement, but eliciting and managing it come with many challenges. The special features of UGC moderation highlight many of the general challenges of human computation in general. They also emphasize how moderation and privacy interact: people have rights to both privacy and safety online, but it is difficult to provide one without violating the other: scanning a user's inbox for potentially malicious messages seems to imply access to all safe ones as well. Are privacy and safety opposed, or is it possible in some circumstance to guarantee the safety of anonymous content without access to that content. We demonstrate that such "blind content moderation" is possible in certain domains. Additionally, the methods we introduce offer safety guarantees, an expressive content space, and require no human moderation load: they are safe, expressive, and scalable Though it may seem preposterous to try moderating UGC without human- or machine-level access to it, human computation makes blind moderation possible. We establish this existence claim by defining two very different human computational methods, behavioral thresholding and reverse correlation . Each leverages the statistical and behavioral properties of so-called "inappropriate content" in different decision settings to moderate UGC without access to a message's meaning or intention. The first, behavioral thresholding, is shown to generalize the well-known ESP game.
Employment Trajectories and Later Employment Outcomes for Mothers in the British Household Panel Survey: An Analysis by Skill Level

Abstract This article explores the association between mothers’ involvement in paid employment when their children are young and their later employment prospects. Using 17 waves of the British Household Panel Survey (1997–2007), it examines the employment trajectories of 954 women for the decade after the birth of their youngest child, asking two main questions. Do mothers who enter or return to work tend to remain in employment? And do wages and job satisfaction further down the line (when the youngest child reaches ten years old) reflect the pathway taken? The article focuses in particular on differences between women with higher- and lower-level qualifications. Mothers are found to be following a variety of employment pathways, with instability relatively common: more than one in three move in and out of work over the period, and this movement is just as common among mothers with higher levels of qualifications as among those with only GCSE-level qualifications or none at all. A stable – and longer – work history is associated with increased wages later on, but the benefits are greater for women with higher levels of qualifications, as might be predicted by human capital theory. Women who were more highly qualified and who moved in and out of work over the decade had an hourly wage when their youngest child was ten which was 31 per cent lower than similar women with a stable work history; for women with few or no qualifications the corresponding figure was 10 per cent and statistically insignificant. For both groups, job satisfaction at the end of the decade was unrelated to the pathway taken.
The Profile of Long-term Parkinson's Disease Survivors with 20 Years of Disease Duration and Beyond.

BACKGROUND Parkinson's disease (PD) patients with 20 years or more survival (PD-20) are not well characterized. OBJECTIVE To evaluate PD-20 patient characteristics and identify areas for improvement of their health care. METHODS The international, multicenter National Parkinson's Foundation Quality Improvement Initiative (NPF-QII) study database was queried to identify PD-20 subjects. Demographic and clinical data were analyzed. RESULTS We identified 187 PD-20 subjects (55% men) representing 4% (187/4,619) of all NPF-QII participants. Subjects were mean age 69.5 years; mean age at PD onset was 44.0 years. The majority (75% ) had 20-25 years of PD duration, the longest duration being 49 years. They were median Hoehn and Yahr stage 3, and 75% had motor fluctuations. Half (54% ) reported exercising. The majority (89% ) were living at home and required a caregiver (88% ). They were mildly cognitively impaired for age (Montreal Cognitive Assessment estimate 22.6±3.7), with most deficits in verbal fluency and delayed recall. Quality of life (Parkinson's Disease Quality of Life Questionnaire index 36±15% ) was mild to moderately impaired, with most impairment in mobility and activities of daily living. Caregiver strain measured by the Multidimensional Caregiver Strain Index (27±16% ), recorded highest subscores in social constraint. PD-20 subjects aged <70 years versus ≥70 only differed significantly by worse cognition (P < 0.0001). CONCLUSIONS PD-20 subjects reflect an elite group of PD survivors with early-onset disease and relatively mild cognitive disability despite long disease duration. Interventions for caregivers, mobility, and activities of daily living are areas that could improve caregiver burden and patient quality of life.