Miracle-dz/UniFAR-FTUs huggingface dataset:
An Unsupervised Approach for Constructing Word Similarity Network

To evaluate how much a pair of entities or documents are similar is a common problem for current applications. Most approaches for this problem are based on the co-occurrence. However, different terms or words may represent the same entity or similar semantic in the real world since a concept often has more than one way of expression. Existing works always focus on computing semantic relatedness of words. But relatedness cannot reflect the similarity most of the time, on the other hand, most of their corpus are from common data sources such as Wikipedia and are not useful for the specialized vocabulary. In this paper, we propose a novel unsupervised approach for evaluating the semantic similarity between words by mapping texts to vector space and computing prior information. In our approach, we construct a model that can identify the words representing the same entity in special context even though they don't belong to the same concept. At last, we construct a network of words in which paths between words can reflect the evolution process of concepts. Our experimental results show that that our approach gives an effective solution to discover the semantic relationship between words, especially for words in specialty domains.
Optimization Methods for Convolutional Sparse Coding

Sparse and convolutional constraints form a natural prior for many optimization problems that arise from physical processes. Detecting motifs in speech and musical passages, super-resolving images, compressing videos, and reconstructing harmonic motions can all leverage redundancies introduced by convolution. Solving problems involving sparse and convolutional constraints remains a difficult computational problem, however. In this paper we present an overview of convolutional sparse coding in a consistent framework. The objective involves iteratively optimizing a convolutional least-squares term for the basis functions, followed by an L1-regularized least squares term for the sparse coefficients. We discuss a range of optimization methods for solving the convolutional sparse coding objective, and the properties that make each method suitable for different applications. In particular, we concentrate on computational complexity, speed to {\epsilon} convergence, memory usage, and the effect of implied boundary conditions. We present a broad suite of examples covering different signal and application domains to illustrate the general applicability of convolutional sparse coding, and the efficacy of the available optimization methods.
Orbits: Gaze Interaction for Smart Watches using Smooth Pursuit Eye Movements

We introduce Orbits, a novel gaze interaction technique that enables hands-free input on smart watches. The technique relies on moving controls to leverage the smooth pursuit movements of the eyes and detect whether and at which control the user is looking at. In Orbits, controls include targets that move in a circular trajectory in the face of the watch, and can be selected by following the desired one for a small amount of time. We conducted two user studies to assess the technique's recognition and robustness, which demonstrated how Orbits is robust against false positives triggered by natural eye movements and how it presents a hands-free, high accuracy way of interacting with smart watches using off-the-shelf devices. Finally, we developed three example interfaces built with Orbits: a music player, a notifications face plate and a missed call menu. Despite relying on moving controls -- very unusual in current HCI interfaces -- these were generally well received by participants in a third and final study.
On a Pedestal: effects of intellectual‐giftedness and some implications for programme planning

Abstract The popular notion that gifted children are immune from adjustment problems because of their high intelligence has relegated this research area to a secondary concern. In this study, self‐esteem, social isolation, locus of control, alienation and anxiety as indices of psychosocial adjustment were explored among the gifted and nongifted adolescents in three secondary schools. The findings suggest that some gifted adolescents are more susceptible than the nongifted to various dysphoric and adjustment crises. The author contends that preventive guidance is necessary to assist gifted adolescents in developing appropriate coping skills. The conclusions are brought together in a discussion of some implications for gifted programme planning.
La religion dans l’espace public : une enquête préliminaire sur les perceptions de quelques leaders juifs et musulmans en milieu montréalais

Ce texte se veut une reflexion autour de la religion, de sa presence publique et de son traitement par les institutions de l’Etat, dans une perspective qui cherche a mettre en lumiere les opinions et representations des acteurs du milieu religieux. Nous avons choisi d’explorer la gestion etatique du religieux a travers une enquete de type qualitatif basee sur des entretiens semi-directifs aupres de quelques leaders musulmans et juifs en milieu montrealais. Nous presentons d’abord le contexte des politiques qui encadrent le traitement du religieux au Quebec. Nous discutons ensuite brievement de quelques faits saillants relatifs au paysage religieux quebecois, particulierement au sujet des groupes que nous avons choisis pour les entretiens. Ces deux sections ont pour objectif de contextualiser les donnees empiriques et resultats que nous presentons finalement dans la suivante.
Performance Aware Adaptation in Open Corpus E-Learning Systems

This paper presents a Performance Oriented Adaptation Agent (POAA) architecture that improves adaptive selection of Learning Objects stored in distributed knowledge repositories. This approach can enhance adaptation process in the Open corpus Adaptive Educational Hypermedia System (OAEHS) Server. The focus is on enhancing existing adaptive selection process by considering network performance factors at a session level. The solution involves taking into consideration not only the user's characteristics and preferences but also throughput conditions on relevant connection links. Introduction of performance oriented DER selection minimises download latency and in turn increases the user's quality of experience.
From Structural Equation Models to Next-Generation Sequencing: The Evolving Landscape of Modern Behavioral Genetics

The present chapter introduces structural equation modeling, as applied in human quantitative genetics. After introducing the basic method of exploiting familial relationships to infer the effects of unmeasured genetic and environmental factors, the chapter reviews the implementation of models from the structural equation modeling literature into genetically informative designs, and structural equation models developed specifically within genetics. The former include simplex and latent growth curve models; the latter include common and independent genetic factor models, genotype-environment interaction models, sex-limitation models, and direction of causation models. The chapter concludes with a discussion of the incorporation of measured genetic variables into structural equation modeling-based association analysis. Based on: Franić, S. , Dolan, C.V. , Borsboom, D. , & Boomsma, D.I. (2012). Structural Equation Modeling in Genetics. In R. H. Hoyle (Ed.), Handbook of Structural Equation Modeling (pp. 617-635). New York: Guilford Press.
Regularized estimation of image statistics by Score Matching

Score Matching is a recently-proposed criterion for training high-dimensional density models for which maximum likelihood training is intractable. It has been applied to learning natural image statistics but has so-far been limited to simple models due to the difficulty of differentiating the loss with respect to the model parameters. We show how this differentiation can be automated with an extended version of the double-backpropagation algorithm. In addition, we introduce a regularization term for the Score Matching loss that enables its use for a broader range of problem by suppressing instabilities that occur with finite training sample sizes and quantized input values. Results are reported for image denoising and super-resolution.
The SINA E3 Ligase OsDIS1 Negatively Regulates Drought Response in Rice1[C][W][OA]

Ubiquitin-regulated protein degradation is a critical regulatory mechanism that controls a wide range of biological processes in plants. Here, we report that OsDIS1 (for Oryza sativa drought-induced SINA protein 1), a C3HC4 RING finger E3 ligase, is involved in drought-stress signal transduction in rice (O. sativa). The expression of OsDIS1 was up-regulated by drought treatment. In vitro ubiquitination assays showed that OsDIS1 possessed E3 ubiquitin ligase activity and that the conserved region of the RING finger was required for the activity. Transient expression assays in Nicotiana benthamiana leaves and rice protoplasts indicated that OsDIS1 was localized predominantly in the nucleus. Overexpression of OsDIS1 reduced drought tolerance in transgenic rice plants, while RNA interference silencing of OsDIS1 enhanced drought tolerance. Microarray analysis revealed that a large number of drought-responsive genes were induced or suppressed in the OsDIS1 overexpression plants under normal and drought conditions. Yeast two-hybrid screening showed that OsDIS1 interacted with OsNek6 (for O. sativa NIMA-related kinase 6), a tubulin complex-related serine/threonine protein kinase. Coexpression assays in N. benthamiana leaves indicated that OsNek6 was degraded by OsDIS1 via the 26S proteasome-dependent pathway and that this degradation was abolished by the OsDIS1(H71Y) mutation, which is essential for its E3 ligase activity. Together, these results demonstrate that OsDIS1 plays a negative role in drought stress tolerance through transcriptional regulation of diverse stress-related genes and possibly through posttranslational regulation of OsNek6 in rice.
Eye-Gaze Tracking Analysis of Driver Behavior While Interacting With Navigation Systems in an Urban Area

With the advent of global positioning system technology, smart phones are used as portable navigation systems. Guidelines that ensure driving safety while using conventional on-board navigation systems have already been published but do not extend to portable navigation systems; therefore, this study focused on the analysis of the eye-gaze tracking of drivers interacting with portable navigation systems in an urban area. Combinations of different display sizes and positions of portable navigation systems were adopted by 20 participants in a driving simulator experiment. An expectation maximum algorithm was proposed to classify the measured eye-gaze points; furthermore, three measures of glance frequency, glance time, and total glance time as a percentage were calculated. The results indicated that the convenient display position with a small visual angle can provide a significantly shorter glance time but a significantly higher glance frequency; however, the small-size display will bring on significantly longer glance time that may result in the increasing of visual distraction for drivers. The small-size portable display received significantly lower scores for subjective evaluation of acceptability and fatigue; moreover, the small-size portable display on the conventional built-in position received significantly lower subjective evaluation scores than that of the big-size one on the upper side of the dashboard. In addition, it indicated an increased risk of rear-end collision that the proportion of time that the time-to-collision was less than 1 s was significantly shorter for the portable navigation than that of traditional on-board one.
APPLICATION OF WAVELETS IN DETECTION AND CLASSIFICATION OF MICROCALCIFICATIONS IN DIGITAL MAMMOGRAMS-SOME RECENT ADVANCES

Microcalcification clusters in mammograms are an important early sign of breast cancer. Varying densities of parenchymal tissue make visual detection of these masses very difficult. Computeraided diagnosis using wavelet transform is recently being used to improve the diagnostic accuracy and efficiency of screening mammography. Wavelet coeffcients describe the local geometry of an image in terms of scale and orientation apart from being flexible and robust with respect to image resolution and quality. This paper surveys some recent developments in the application of wavelets in enhancement, detection and classification of microcalcifications in mammograms. Possible areas of future research have also been discussed.
Filters With Linear-Phase Properties for Repetitive Feedback

This paper proposes some modifications aimed for the practical implementation of repetitive-based schemes used in harmonic distortion compensation schemes. Practical implementation of repetitive schemes requires both to limit the gain originally infinite and to restrict the bandwidth of the controller originally of infinite dimension. Roughly speaking, each delay line in the repetitive scheme must include an associated limiting gain and a low-pass filter (LPF). However, it has been observed that this practical modifications can produce a considerable phase shift at the frequencies of interest. As a consequence, the compensation of harmonics cannot be guaranteed anymore. One possibility to compensate such a phase shift deterioration consists in modifying the delay time associated to the delay line of the repetitive scheme. However, in the case of conventional LPFs, this phase shift is usually a nonlinear function of the frequency, which makes this task extremely difficult if not impossible. To overcome this issue, this paper proposes the use of low-pass finite impulse response filters, whose phase shift can be made linear by following appropriate design rules. The idea behind this approach is that an LPF with a linear phase produces a constant delay time, which is much easier to compensate in the delay line. Experimental results are presented to confirm the benefits of the proposed scheme.
No apparent cost of evolved immune response in Drosophila melanogaster

Maintenance and deployment of the immune system are costly and are hence predicted to trade‐off with other resource‐demanding traits, such as reproduction. We subjected this longstanding idea to test using laboratory experimental evolution approach. In the present study, replicate populations of Drosophila melanogaster were subjected to three selection regimes—I (Infection with Pseudomonas entomophila), S (Sham‐infection with MgSO4), and U (Unhandled Control). After 30 generations of selection flies from the I regime had evolved better survivorship upon infection with P. entomophila compared to flies from U and S regimes. However, contrary to expectations and previous reports, we did not find any evidence of trade‐offs between immunity and other life history related traits, such as longevity, fecundity, egg hatchability, or development time. After 45 generations of selection, the selection was relaxed for a set of populations. Even after 15 generations, the postinfection survivorship of populations under relaxed selection regime did not decline. We speculate that either there is a negligible cost to the evolved immune response or that trade‐offs occur on traits such as reproductive behavior or other immune mechanisms that we have not investigated in this study. Our research suggests that at least under certain conditions, life‐history trade‐offs might play little role in maintaining variation in immunity.
A Two-Year Participatory Intervention Project with Owners to Reduce Lameness and Limb Abnormalities in Working Horses in Jaipur, India

Background Participatory methods are increasingly used in international human development, but scientific evaluation of their efficacy versus a control group is rare. Working horses support families in impoverished communities. Lameness and limb abnormalities are highly prevalent in these animals and a cause for welfare concern. We aimed to stimulate and evaluate improvements in lameness and limb abnormalities in horses whose owners took part in a 2-year participatory intervention project to reduce lameness (PI) versus a control group (C) in Jaipur, India. Methodology/Principal Findings In total, 439 owners of 862 horses participated in the study. PI group owners from 21 communities were encouraged to meet regularly to discuss management and work practices influencing lameness and poor welfare and to track their own progress in improving these. Lameness examinations (41 parameters) were conducted at the start of the study (Baseline), and after 1 year and 2 years. Results were compared with control horses from a further 21 communities outside the intervention. Of the 149 horses assessed on all three occasions, PI horses showed significantly (P<0.05) greater improvement than C horses in 20 parameters, most notably overall lameness score, measures of sole pain and range of movement on limb flexion. Control horses showed slight but significantly greater improvements in four parameters, including frog quality in fore and hindlimbs. Conclusions/Significance This participatory intervention succeeded in improving lameness and some limb abnormalities in working horses, by encouraging changes in management and work practices which were feasible within owners’ socioeconomic and environmental constraints. Demonstration of the potentially sustainable improvements achieved here should encourage further development of participatory intervention approaches to benefit humans and animals in other contexts.
BMP‐9‐induced muscle heterotopic ossification requires changes to the skeletal muscle microenvironment

Heterotopic ossification (HO) is defined as the formation of bone inside soft tissue. Symptoms include joint stiffness, swelling, and pain. Apart from the inherited form, the common traumatic form generally occurs at sites of injury in damaged muscles and is often associated with brain injury. We investigated bone morphogenetic protein 9 (BMP‐9), which possesses a strong osteoinductive capacity, for its involvement in muscle HO physiopathology. We found that BMP‐9 had an osteoinductive influence on mouse muscle resident stromal cells by increasing their alkaline phosphatase activity and bone‐specific marker expression. Interestingly, BMP‐9 induced HO only in damaged muscle, whereas BMP‐2 promoted HO in skeletal muscle regardless of its state. The addition of the soluble form of the ALK1 protein (the BMP‐9 receptor) significantly inhibited the osteoinductive potential of BMP‐9 in cells and HO in damaged muscles. BMP‐9 thus should be considered a candidate for involvement in HO physiopathology, with its activity depending on the skeletal muscle microenvironment. © 2011 American Society for Bone and Mineral Research.
Mathematical Models and Classifiers for The Implementation of Face Detector

The problem of face detection is quite vast. Before the implementation of any face detector its mathematical model and classifiers have to be defined. The process of cascading is used in many detectors to enhance the speed and accuracy of the detector. In this paper we present the various combinations of mathematical models and classifiers for the implementation of robust face detector. I. Introduction Every method addressed here uses a learning algorithm on a training set to begin the detection process. The training stage is extensive for some methods, and relatively small for others. This common training gives us an advantage when we consider the problem for the first time. We can assume that we have available to us pool of data about a general face and we can infer certain information regarding faces in general.
Towards Twitter context summarization with user influence models

Twitter has become one of the most popular platforms for users to share information in real time. However, as an individual tweet is short and lacks sufficient contextual information, users cannot effectively understand or consume information on Twitter, which can either make users less engaged or even detached from using Twitter. In order to provide informative context to a Twitter user, we propose the task of Twitter context summarization, which generates a succinct summary from a large but noisy Twitter context tree. Traditional summarization techniques only consider text information, which is insufficient for Twitter context summarization task, since text information on Twitter is very sparse. Given that there are rich user interactions in Twitter, we thus study how to improve summarization methods by leveraging such signals. In particular, we study how user influence models, which project user interaction information onto a Twitter context tree, can help Twitter context summarization within a supervised learning framework. To evaluate our methods, we construct a data set by asking human editors to manually select the most informative tweets as a summary. Our experimental results based on this editorial data set show that Twitter context summarization is a promising research topic and pairwise user influence signals can significantly improve the task performance.
A duplication growth model of gene expression networks

MOTIVATION There has been considerable interest in developing computational techniques for inferring genetic regulatory networks from whole-genome expression profiles. When expression time series data sets are available, dynamic models can, in principle, be used to infer correlative relationships between gene expression levels, which may be causal. However, because of the range of detectable expression levels and the current quality of the data, the predictive nature of such inferred, quantitative models is questionable. Network models derived from simple rate laws offer an intermediate level analysis, going beyond simple statistical analysis, but falling short of a fully quantitative description. This work shows how such network models can be constructed and describes the global properties of the networks derived from such a model. These global properties are statistically robust and provide insights into the design of the underlying network. RESULTS Several whole-genome expression time series data sets from yeast microarray experiments were analyzed using a Markov-modeling method (Dewey and Galas, FUNC: Integr. Genomics, 1, 269-278, 2001) to infer an approximation to the underlying genetic network. We found that the global statistical properties of all the resulting networks are similar. The overall structure of these biological networks is distinctly different from that of other recently studied networks such as the Internet or social networks. These biological networks show hierarchical, hub-like structures that have some properties similar to a class of graphs known as small world graphs. Small world networks exhibit local cliquishness while exhibiting strong global connectivity. In addition to the small world properties, the biological networks show a power law or scale free distribution of connectivities. An inverse power law, N(k) approximately k(-3/2), for the number of vertices (genes) with k connections was observed for three different data sets from yeast. We propose network growth models based on gene duplication events. Simulations of these models yield networks with the same combination of global graphical properties that we inferred from the expression data.
DEMO — A Demo of Epistemic Modelling

This paper introduces and documents DEMO, a Dynamic Epistemic Modelling tool. DEMO allows modelling epistemic updates, graphical display of update results, graphical display of action models, formula evaluation in epistemic models, translation of dynamic epistemic formulas to PDL formulas. Also, DEMO implements the reduction of dynamic epistemic logic to PDL. The paper is an exemplar of tool building for epistemic update logic. It contains the essential code of an implementation of DEMO in Haskell, in Knuth’s ‘literate programming’ style.