Miracle-dz/UniFAR-FTUs huggingface dataset:
Relational Graphical Models for Collaborative Filtering and Recommendation of Computational Workflow Components

This paper describes preliminary work on using graphical models to represent relational data in computational science portals such as myGrid. The objective is to provide a integrative collaborative filtering (CF) capability to users of data, metadata, source code, and experimental documentation in some domain of interest. Recent systems such as ResearchIndex / CiteSeer provide collaborative recommendation through citation indexing, and systems such as SourceForge and the Open Bioinformatics project provide similar tools such as contentbased indexing of software. Our current research aims at learning probabilistic relational models (PRMs) from data in order to support intellignet retrieval of data, source code, and experimental records. We present a system design and a précis of a test bed under development that applies PRM structure learning and inference to CF in repositories of bioinformatics data and software.
Methodology for the Calibration of VISSIM in Mixed Traffic

Mixed traffic, characterized by diverse vehicles, changing composition, lack of lane discipline, etc. is best modeled by micro simulation. However, the majority of the leading micro simulation packages and their calibration methodologies have been developed considering less complex homogeneous traffic. Hence, a methodology for calibrating a micro simulation model for mixed traffic is proposed. Driver behavior in mixed traffic is observed and adjustments were made to represent in the simulation. Calibration parameters were identified using multi parameter sensitivity analysis, and the optimum values for these parameters were obtained by minimizing the error between the simulated and field delay using a genetic algorithm. Multiple criteria were included in the optimization formulation by constraint insertion. The proposed methodology is illustrated using VISSIM, a widely used micro simulation software. Signalized intersections with different traffic characteristics from Mumbai are taken as case study.
A Tutorial on Pharmacodynamic Scripting Facility in Simcyp

The Simcyp Simulator provides a framework for mechanistic Physiologically‐Based Pharmacokinetic/Pharmacodynamic modeling of potentially interacting drugs. It also provides a scripting facility, using the Lua language, for developing customized pharmacodynamic and toxicity models driven by drug concentrations at the site of action. We present an overview of the scripting facility including the scripting language, the editor, and how scripts are embedded within the Simulator. Examples incorporating differential equations and including inter‐individual variability on parameters are presented.
Evaluation of Zona Pellucida Function for Sperm Penetration During In Vitro Fertilization in Pigs

Abstract In porcine oocytes, the function of the zona pellucida (ZP) with regard to sperm penetration or prevention of polyspermy is not well understood. In the present study, we investigated the effects of the ZP on sperm penetration during in vitro fertilization (IVF). We collected in vitro-matured oocytes with a first polar body (ZP+ oocytes). Some of them were freed from the ZP (ZP− oocytes) by two treatments (pronase and mechanical pipetting), and the effects of these treatments on sperm penetration parameters (sperm penetration rate and numbers of penetrated sperm per oocyte) were evaluated. There was no evident difference in the parameters between the two groups. Secondly, we compared the sperm penetration parameters of ZP+ and ZP− oocytes using frozen-thawed epididymal spermatozoa from four boars. Sperm penetration into ZP+ oocytes was found to be accelerated relative to ZP− oocytes. Thirdly, we evaluated the sperm penetration of ZP+ and ZP− oocytes at 1−10 h after IVF (3 h gamete co-incubation). The proportions of oocytes penetrated by sperm increased significantly with time in both groups; however, the number of penetrated sperm per oocyte did not increase in ZP− oocytes. Finally, we performed IVF using ZP− oocytes divided into control (3 h) and prolonged gamete co-incubation (5 h) groups. Greater numbers of sperm penetrated in the 5 h group than in the control group. These results suggest that the ZP and oolemma are not competent factors for prevention of polyspermy in our present porcine IVF system. However, it appears that ZP removal is one of the possibilities for reducing polyspermic penetration in vitro in pigs.
Class-Specific Kernel Fusion of Multiple Descriptors for Face Verification Using Multiscale Binarised Statistical Image Features

This paper addresses face verification in unconstrained settings. For this purpose, first, a nonlinear binary class-specific kernel discriminant analysis classifier (CS-KDA) based on spectral regression kernel discriminant analysis is proposed. By virtue of the two-class formulation, the proposed CS-KDA approach offers a number of desirable properties such as specificity of the transformation for each subject, computational efficiency, simplicity of training, isolation of the enrolment of each client from others and increased speed in probe testing. Using the proposed CS-KDA approach, a regional discriminative face image representation based on a multiscale variant of the binarized statistical image features is proposed next. The proposed component-based representation when coupled with the dense pixel-wise alignments provided by a symmetric MRF matching model reduces the sensitivity to misalignments and pose variations, gauging the similarity more effectively. Finally, the discriminative representation is combined with two other effective image descriptors, namely the multiscale local binary patterns and the multiscale local phase quantization histograms via a kernel fusion approach to further enhance system accuracy. The experimental evaluation of the proposed methodology on challenging databases demonstrates its advantage over other methods.
Paper : Where the Linked Dependence Assumption Fails and How to Move Beyond

The linked dependence assumption, a widely used simpli cation in probabilistic retrieval, is brie y reviewed and its validity is investigated. We show that the linked dependence assumption is violated particularly if the query features are good discriminators between relevant and non-relevant documents. We then propose the manual concept generation method, which takes into account the dependence between di erent query concepts. For a limited set of routing queries, experiments show a signi cant improvement when compared with retrieval methods which do not take into account feature dependencies. We consider this method to be the rst successful step which progresses beyond the linked dependence assumption.
Large-signal characterization of an 870 MHz inverse class-F cross-coupled push-pull PA using active mixed-mode load-pull

An inverse class-F cross-coupled push-pull PA in a 0.5mum SiGe technology is presented. It is shown that inverse class-F in combination with CBC compensation is preferred over class-F operation in terms of gain roll-off and efficiency as function of collector supply voltage. The active mixed-mode load-pull system is used to set up the proper output differential and common-mode impedances at the fundamental (870MHz) and harmonics to experimentally verify inverse class F operation with a measured PAE of 67% at 20dBm output power
A Study on Evaluation Analysis for Products Using Topic Model

Recently, E-commerce (EC) market has been expanding by diffusion of Web. Accordingly, a lot of reviews are written by users every day. Reviews contain a lot of useful information about items. Thus, there are strong needs for an automatic analysis method of reviews. For a user, it is useful for decisions whether to purchase the items or not by knowing the details of items. For a company, it is useful for marketing of the items by analyzing reviews. This paper studies the factor analysis of the evaluation of hotel putting the importance on the comparison among hotels by using user-reviews. An analysis method using a topic model and a multiple regression analysis is proposed. In the proposed method, each topic proportion is the explanatory variable and evaluation rating of review is the objective variable. This paper applies the proposed method to real user-reviews and investigates the effectiveness of this method. It also shows the difference of evaluation factor among the locality of hotels.
Enhancing User Engagement in Immersive Games through Multisensory Cues

In this article, we report on a user study investigating the effects of multisensory cues on triggering the emotional response in immersive games. Yet, isolating the effect of a specific sensory cue on the emotional state is a difficult feat. The performed experiment is a first of a series that aims at producing usable guidelines that can be applied to reproducing similar emotional responses, as well as the methods to measure the effects. As such, we are interested in methodologies to both design effective stimuli, and assess the quality and effect thereof. We start with identifying main challenges and the followed methodology. Thereafter, we closely analyze the study results to address some of the challenges, and identify where the potential is for improving the induced stimuli (cause) and effect, as well as the analytical methods used to pinpoint the extent of the effect.
The CaV2.3 Ca2+ channel subunit contributes to R‐Type Ca2+ currents in murine hippocampal and neocortical neurones

Different subtypes of voltage‐dependent Ca2+ currents in native neurones are essential in coupling action potential firing to Ca2+ influx. For most of these currents, the underlying Ca2+ channel subunits have been identified on the basis of pharmacological and biophysical similarities. In contrast, the molecular basis of R‐type Ca2+ currents remains controversial. We have therefore examined the contribution of the CaV2.3 (α1E) subunits to R‐type currents in different types of central neurones using wild‐type mice and mice in which the CaV2.3 subunit gene was deleted. In hippocampal CA1 pyramidal cells and dentate granule neurones, as well as neocortical neurones of wild‐type mice, Ca2+ current components resistant to the combined application of ω‐conotoxin GVIA and MVIIC, ω‐agatoxin IVa and nifedipine (ICa,R) were detected that were composed of a large R‐type and a smaller T‐type component. In CaV2.3‐deficient mice, ICa,R was considerably reduced in CA1 neurones (79 %) and cortical neurones (87 %), with less reduction occurring in dentate granule neurones (47 %). Analysis of tail currents revealed that the reduction of ICa,R is due to a selective reduction of the rapidly deactivating R‐type current component in CA1 and cortical neurones. In all cell types, ICa,R was highly sensitive to Ni2+ (100 μM: 71–86 % block). A selective antagonist of cloned CaV2.3 channels, the spider toxin SNX‐482, partially inhibited ICa,R at concentrations up to 300 nm in dentate granule cells and cortical neurones (50 and 57 % block, EC50 30 and 47 nm, respectively). ICa,R in CA1 neurones was significantly less sensitive to SNX‐482 (27 % block, 300 nm SNX‐482). Taken together, our results show clearly that CaV2.3 subunits underlie a significant fraction of ICa,R in different types of central neurones. They also indicate that CaV2.3 subunits may give rise to Ca2+ currents with differing pharmacological properties in native neurones.
On the evolution of cooperation under social pressure in multiplex networks

In this work, we aim to contribute to the understanding of human prosocial behavior by studying the influence that a particular form of social pressure, "being watched," has on the evolution of cooperative behavior. We study how cooperation emerges in multiplex complex topologies by analyzing a particular bidirectionally coupled dynamics on top of a two-layer multiplex network (duplex). The coupled dynamics appears between the prisoner's dilemma game in a network and a threshold cascade model in the other. The threshold model is intended to abstract the behavior of a network of vigilant nodes that impose the pressure of being observed altering hence the temptation to defect of the dilemma. Cooperation or defection in the game also affects the state of a node of being vigilant. We analyze these processes on different duplex networks structures and assess the influence of the topology, average degree and correlated multiplexity, on the outcome of cooperation. Interestingly, we find that the social pressure of vigilance may impact cooperation positively or negatively, depending on the duplex structure, specifically the degree correlations between layers is determinant. Our results give further quantitative insights in the promotion of cooperation under social pressure.
Modeling of EXG (ECG, EMG and EEG) non-idealities using MATLAB

EXG [Electrocardiography (ECG), Electromyography (EMG) and Electroencephalogram (EEG)] signals are of very low amplitude and frequency with immense diagnostic value. These signals acquire noise while travelling through electrodes, printed circuit board (PCB), integrated circuits (ICs), etc. and thus their detection and interpretation is a challenge in biomedical engineering. This paper presents MATLAB Simulink environment for an integrated EXG electronic measuring system that allows designers to analyze the time/frequency domain behavior of sensing electrodes, programmable instrumentation amplifiers (PIA), programmable band pass filter (BPF) and second order sigma delta analog to digital converter (SD-ADC). The Simulink model provides non-idealities such as sensing electrodes noise, programmable gain amplifier parameters (thermal noise, flicker noise, resistor mismatch, settling time (slew rate), dc gain, bandwidth, and voltage dynamic ranges), band pass filter parameters (noise and bandwidth), and sigma delta ADC parameters (clock jitter, capacitor mismatch, noise, settling time (slew rate), dc gain, bandwidth, and voltage dynamic ranges). A detailed simulation and implementation (Using CMOS, 180nm technology) results of the complete system is presented for both the ideal and actual performance parameters.
Proposal of Delay Tolerant Network with Cognitive Wireless Network for Disaster Information Network System

If a large scale of disaster occurs such as the East Japan Great Earthquake, there would be some possibility of the informational isolation from others because of the disconnection of communication network or high congestion. In facts, the East Japan Great Earthquake isolated many Japanese coastal resident areas, and the lack of disaster information is considered to affect the speed of rescue, evacuation, and sending life materials. DTN (Delay Tolerant Network) is supposed to be one of the effective methods to transmit significant data even under poor network conditions. However, when DTN is applied to local areas such as the Japanese Northern East coastal cities which were severely damaged by the earthquake, DTN might not work effectively. That is because there are some considerable problems such as fewer roads, cars, and pedestrians than in urban areas. Moreover, the scale of area is likely wider than that of urban areas because it includes many non-residential areas such as rice fields, gardens, and woods. Therefore, it is necessary to consider additional effective functions when DTN is applied for a Disaster Information Network System in local areas. In this paper, DTN with CWN (Cognitive Wireless Network) is proposed for a Disaster Information Network System for the usage of local areas. Then, we consider the actual usage of DTN in local areas, and the simulation of DTN with different wireless network interfaces as held by the GIS data of Taro, Miyako City, Iwate in Japan, a town severely damaged by the East Japan Great Earthquake. The results and future studies are discussed for the DTN usage of Disaster Information Network System in local areas.
Missing Value Prediction Using Co-clustering and RBF for Collaborative Filtering

The sparsity of user-item rating matrix will reduce the performance of collaborative filtering algorithm in news recommendation system. In order to overcome the problem, we predict the values of user-item rating matrix combining two approaches: co-clustering and Radial Basis Function network (RBF). Co-clustering algorithm simultaneous cluster the rows and columns of the user-item rating matrix. It can cluster the matrix into several small matrix with high similarity. We take advantage of the similarity of a cluster and then predict the values using RBF network. This method can complement the sparse user-item rating matrix and improve the accuracy of news recommendation system by collaborative filtering algorithm. Experimental results on Xiamen University campus news data set demonstrate the efficiency and effectiveness of the proposed missing value prediction method.
All you need to know about action research

PART ONE: WHAT DO I NEED TO KNOW What Is Action Research? Who Does Action Research? The Underpinning Assumptions of Action Research Where Did Action Research Come from? PART TWO: WHY DO I NEED TO KNOW? Why Do Action Research? Learning To Improve Practice Contributing to New Theory Evaluating Your Research PART THREE: HOW DO I FIND OUT? Feasibility Planning What Do You Need To Think About First? Action Planning How Do You Develop an Action Plan? Doing Your Action Research Carrying Out Your Action Plan Examples of Action Research Projects PART FOUR: HOW DO I GENERATE EVIDENCE TO SUPPORT MY CLAIM TO KNOWLEDGE? Monitoring Practice and Looking for Data Gathering, Sorting and Storing Data Turning the Data into Evidence PART FIVE: HOW DO I TEST AND CRITIQUE MY KNOWLEDGE? Testing the Validity of Your Claims to Knowledge Establishing the Legitimacy of Your Claim to Knowledge Engaging with the Politics of Knowledge PART SIX: HOW DO I REPRESENT AND DISSEMINATE MY KNOWLEDGE? Telling Your Research Story Writing a Workplace Report Writing a Report for Higher Degree Accreditation Publishing and Disseminating Your Research PART SEVEN: HOW DO I SHOW THE SIGNIFICANCE OF MY KNOWLEDGE? Explaining the Significance of Your Research Developing New Epistemologies for Organizational Cultures of Enquiry The Amazing Potential Global Influence of Your Action Research
Pairwise Sequence Alignment

The goal of pairwise sequence alignment is to establish a correspondence between the elements in a pair of sequences that share a common property, such as common ancestry or a common structural or functional role. In computational biology, the sequences under consideration are typically nucleic acid or amino acid polymers. We will consider three variants of the pairwise sequence alignment problem: global alignment, semi-global alignment, and local alignment.
From Semi-supervised to Transfer Counting of Crowds

Regression-based techniques have shown promising results for people counting in crowded scenes. However, most existing techniques require expensive and laborious data annotation for model training. In this study, we propose to address this problem from three perspectives: (1) Instead of exhaustively annotating every single frame, the most informative frames are selected for annotation automatically and actively. (2) Rather than learning from only labelled data, the abundant unlabelled data are exploited. (3) Labelled data from other scenes are employed to further alleviate the burden for data annotation. All three ideas are implemented in a unified active and semi-supervised regression framework with ability to perform transfer learning, by exploiting the underlying geometric structure of crowd patterns via manifold analysis. Extensive experiments validate the effectiveness of our approach.
GPGPU Implementation of Nearest Neighbor Search with Product Quantization

A nearest neighbor search with product quantization is a prominent method that achieves a high-precision search with less memory consumption than an exhaustive way. However, in order to accomplish a large size search with a large reference data, the search method have to be accelerated by using parallel systems such as multicore processors and GPGPU (General Purpose computing on GPU) systems. The distance calculation between a query and a reference data is an independent operation that is easily parallelized, but the reduction computation of distances after that is not completely parallel, so this leads to performace degradation. Therefore, in order to maximize a speedup, the adequate parameter selection is required in terms of parallelism. In this paper, the baseline of parallelization of the nearest neighbor search with product quantization is described, and the validity of our approach (Optimistic Search), which utilizes a small number of candidates of nearest neighbors, is discussed with experiments. We also show the effectiveness of pseudo matrix transposition for the sake of the efficient search. In addition, the method for autotuning is proposed and its effectiveness is empirically confirmed.
Developing Crowdsourced Ontology Engineering Tasks: An iterative process

It is increasingly evident that the realization of the Semantic Web will require not only computation, but also human contribution. Crowdsourcing is becoming a popular method to inject this human element. Researchers have shown how crowdsourcing can contribute to managing semantic data. One particular area that requires significant human curation is ontology engineering. Verifying large and complex ontologies is a challenging and expensive task. Recently, we have demonstrated that online, crowdsourced workers can assist with ontology verification. Specifically, in our work we sought to answer the following driving questions: (1) Is crowdsourcing ontology verification feasible? (2) What is the optimal formulation of the verification task? (3) How does this crowdsourcing method perform in an application? In this work, we summarize the experiments we developed to answer these questions and the results of each experiment. Through iterative task design, we found that workers could reach an accuracy of 88% when verifying SNOMED CT. We then discuss the practical knowledge we have gained from these experiments. This work shows the potential that crowdsourcing has to offer other ontology engineering tasks and provides a template one might follow when developing such methods.
A novel variant of androgen receptor is associated with idiopathic azoospermia

A variety of genetic variants can lead to abnormal human spermatogenesis. The androgen receptor (AR) is an important steroid hormone receptor that is critical for male sexual differentiation and the maintenance of normal spermatogenesis. In the present study, each exon of AR in 776 patients diagnosed with idiopathic azoospermia (IA) and 709 proven fertile men were sequenced using use panel re-sequencing methods to examine whether AR is involved in the pathogenesis of IA. Two synonymous variants and seven missense variants were detected. Of the missense variants, a luciferase assay demonstrated that the R630W variant reduced the transcriptional regulatory function of AR. This novel variant (p. R630W) of AR is the first to be identified in association with IA, thereby highlighting the importance of AR during spermatogenesis.