Miracle-dz/UniFAR-FTUs huggingface dataset:
An efficient functional coverage test for HDL descriptions at RTL

Until now, simulation has been the primary approach for the functional verification of register transfer level (RTL) circuit descriptions written in a hardware description language (HDL). A finite state machine (FSM) coverage test can find all the bugs in a FSM design. However, this is impractical for large designs because of the state explosion problem. In this paper, we modify the higher-level FSM models used in other applications to replace the FSM model in the FSM coverage test. The state transition graphs (STGs) can be significantly reduced in this model, so that the complexity of the test becomes acceptable even for large designs. This model can be easily extracted from the original HDL code automatically, with little computation overhead. Experimental results show that it is indeed a promising functional test for FSMs.
Searching Optimal Parallel Plans: A Filtering and Decomposition Approach

In the domain of planning, searching for optimal plans gives rise to many works. Most of the existing planners search for optimal parallel plans, that is plans which are optimal in the number of steps. FDP is an exception: it searches for optimal sequential plans, that is plans which are optimal in the number of actions. FDP implements a depth-first iterative-deepening search, using a CSP-like structure, decomposition rules, and filtering techniques similar to consistency-enforcing techniques. We propose a new planner based on FDP, which generates optimal parallel plans. This planner has been compared on a set of selected problems with the well-known optimal parallel planner SATPLAN, and with the planner FDP, so as to analyse the effects of the optimization criteria in the performances.
Electromagnetic interaction with exotic uniaxial media

The interface between an isotropic medium and a general uniaxial anisotropic medium is considered in different particular cases. A new boundary condition for the electromagnetic field is studied. It requires the cancellation of both the electric and the magnetic fields along a generic direction. The realization of this boundary condition is proposed by means of an exotic uniaxial medium: the properties of such medium are investigated. Possible applications of such media are analyzed.
Emotion Classification in a Serious Game for Training Communication Skills

We describe the natural language processing component of a new serious gaming project, deLearyous, which aims at developing an environment in which users can improve their communication skills by interacting with a virtual character in (Dutch) written natural language. The virtual characters’ possible dialogue paths are defined by Leary’s Rose, a framework for interpersonal communication. In order to apply this framework, input sentences must be classified into one of four possible “emotion” classes. We tried to carry out this emotion classification task using several machine learning algorithms. More specifically, classification performance was measured using TiMBL –a memory-based learner–, a Naive Bayes classifier, Support Vector Machines and Conditional Random Fields. Training was done on a relatively small dataset of manually tagged sentences. A large number of different features was extracted from the dataset, and a good feature subset was selected using a combination of a genetic algorithm and various filter
Generation of Induced Pluripotent Stem Cells from CD34+ Cells across Blood Drawn from Multiple Donors with Non-Integrating Episomal Vectors

The methodology to create induced pluripotent stem cells (iPSCs) affords the opportunity to generate cells specific to the individual providing the host tissue. However, existing methods of reprogramming as well as the types of source tissue have significant limitations that preclude the ability to generate iPSCs in a scalable manner from a readily available tissue source. We present the first study whereby iPSCs are derived in parallel from multiple donors using episomal, non-integrating, oriP/EBNA1-based plasmids from freshly drawn blood. Specifically, successful reprogramming was demonstrated from a single vial of blood or less using cells expressing the early lineage marker CD34 as well as from unpurified peripheral blood mononuclear cells. From these experiments, we also show that proliferation and cell identity play a role in the number of iPSCs per input cell number. Resulting iPSCs were further characterized and deemed free of transfected DNA, integrated transgene DNA, and lack detectable gene rearrangements such as those within the immunoglobulin heavy chain and T cell receptor loci of more differentiated cell types. Furthermore, additional improvements were made to incorporate completely defined media and matrices in an effort to facilitate a scalable transition for the production of clinic-grade iPSCs.
Editorial - Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research

We address key questions related to the explosion of interest in the emerging fields of big data, analytics, and data science. We discuss the novelty of the fields and whether the underlying questions are fundamentally different, the strengths that the information systems IS community brings to this discourse, interesting research questions for IS scholars, the role of predictive and explanatory modeling, and how research in this emerging area should be evaluated for contribution and significance.
Identifying the human attention to different colors and intensities using P300

In this paper the characteristics of the attentional mechanisms in the human brain are analyzed and the P300 component is stimulated using the three basic colors (RGB) and yellow. To define the effect of the color intensity on the human attention, all these colors are tested for two levels of intensity (High-Low). Healthy youth are involved in this study with normal or corrected to normal vision and ensemble averaging is implemented to extract the ERPs from the EEG background noise. The experimental results show a significant effect of the color intensity on the human attention since low responses are captured when the low intensity is presented and P300 responses vary within different color bands. Hence, using proper color combination to design the roads signs may enhance the driver attention and insure a high level of awareness.
Effective Diagnosis of Heart Disease through Bagging Approach

The diagnosis of heart disease is important issue, prompting many researchers to work on development of intelligent medical decision support systems to improve the ability of physicians. In this paper, we propose the use of a bagging algorithm to indentify the warning signs of heart disease in patients and also to compare the effectiveness of the bagging algorithm with the decision tree algorithm that has been used by many researchers.
A Novel Image Component Transmission Approach to Improve Image Quality and Energy Efficiency in Wireless Sensor Networks

Due to resource constraints, digital image transmissions are a significant challenge for image sensor based Wireless Sensor Networks (WSN). In this paper, we propose a novel robust and energy-efficient scheme, called Image Component Transmission (ICT) in WSN by providing various levels of reliability during image transmissions. Different components in the embedded wavelet image bit stream are considered and essential components of the compressed image such as the information for the positions of significant wavelet coefficients are transmitted with higher quality assurance. Relatively less important components such as the information for the values of pixels are transmitted with lower overhead. Simulation results show the proposed scheme achieves higher energy efficiency in WSN, with image transmission quality enhanced considerably by up to 6dB in terms of the Peak Signal to Noise Ratio (PSNR).
Implementation of hybrid cascaded multilevel inverter for generating different voltage levels

This paper explicates about engendering higher voltage levels using hybrid cascaded multilevel inverters for elimination of triplen harmonics. As multilevel inverters are unanimously endorsed for the reduction of harmonics and also for the total harmonic reduction for generating premium output waveforms with discrete frequency and minimized switching losses. With such characteristic advantages it is mostly used in empowering high power application systems. PSO optimization is used to generate different angles for their corresponding voltage levels. The proposed method is efficient producing higher voltage levels using the same prototype together with the capability of improving the speed of the convergence. The controller involved in enhancing the control strategy is FPGA (Field Programmable Gate Array) that is preferred due to its higher frequency ranges and harmonic profile. The advantage of using the proposed method is that the number of bridges connected can be minimized. This method is based on the appraisal of various angles generated for obtaining distinctive waveforms. The simulation results are carried out to authenticate the reduction of harmonics with the proposed method.
The Association between Routinization and Cognitive Resources in Later Life

The aim of this study was to investigate the association between routinization of daily life activities and cognitive resources during aging. Routinization could increase excessively during aging and become maladaptative in reducing individual resources. Fifty-two young participants (M = 20.8 years) and 62 older participants (M = 66.9 years) underwent a routinization scale and cognitive tasks of working memory, speed of processing, and attention. Results revealed that older adults presented a decrease on the three cognitive measures but no change on the routinization score. While no association was observed between routinization and cognitive measures for the young adults, a high routinization was associated with lower cognitive flexibility in the older adults. These findings are interpreted in the light of theories about the positive impact of variety in daily life environment on cognitive functions.
Gaussian belief with dynamic data and in dynamic network

In this paper we analyze Belief Propagation over a Gaussian model in a dynamic environment. Recently, this has been proposed as a method to average local measurement values by a distributed protocol (Consensus Propagation, Moallemi C. C. and Van Roy B., IEEE Trans. Inf. Theory, 52 (2006) 4753) where the average is available for read-out at every single node. In the case that the underlying network is constant but the values to be averaged fluctuate (“dynamic data”), convergence and accuracy are determined by the spectral properties of an associated Ruelle-Perron-Frobenius operator. For Gaussian models on Erdős-Rényi graphs, numerical computation points to a spectral gap remaining in the large-size limit, implying exceptionally good scalability. In a model where the underlying network also fluctuates (“dynamic network”), averaging is more effective than in the dynamic data case. Altogether, this implies very good performance of these methods in very large systems, and opens a new field of statistical physics of large (and dynamic) information systems.
Cyclophosphamide Resistant L 1210 Cells Characterization of Cytosolic Aldehyde Dehydrogenase from Updated

The cytosolic aldehyde dehydrogenase (ALDH) isozyme from cyclophosphamide (f PA) resistant 1.1210 cells (L1210/CPA) was purified to apparent homogeneity using ternary enzyme complex-dye ligand chromatography. The purified isozyme migrates as a single band at M, 51,000 in sodium dodecyl sulfate polyacrylamide gel electrophoresis and as a single charge species at isoelectric point = 5.8 in isoelectric focusing. Micromolar Ã„â€ž, values were estimated with both propionaldehyde (A',,,= 5 /IM) and 4-hydroxy cyclophosphamide (4-OH (l'A) (A',â€ž = 4 /IM) as substrates, indicating that this isozyme is capable of oxidizing the acti vated cyclophosphamide intermediate 4-hydroxy CPA/aldophosphamide to carboxyphosphamide. This isozyme is also potently inhibited by disulfiram (A",= 6 MM)and 4-(diethylamino)benzaldehyde (A",= 0.04 /IM). Both of these inhibitors are capable of sensitizing L1210/CPA cells to activated CPA in clonogenic survival assays. Thus, the increased levels of only the cytosolic ALDH isoform in L1210/CPA cells appear to be the single phenotypic difference necessary for conferring resistance to CPA. Monospecific antibodies to the L1210/CPA isozyme have been used in Western blot analysis to detect nanogram levels of ALDH in cell and tissue extracts. These antibodies cross-react with the cytosolic iso zyme in P388/CPA cells, mouse liver, mouse small intestine, and the 1C1C7 hepatoma cell line, whereas no ALDH is detected in sensitive LI 210 or P388 cells. Also, these antibodies show little cross-reactivity with the mitochondria! isozyme from mouse liver or 1(1(7 cells. From immunological and inhibitor characterization, the soluble ALDH isozyme in L1210/CPA cells appears identical to the normal mouse tissue isozyme.
Genetic variation in GABRA2 moderates peer influence on externalizing behavior in adolescents

Genetic predisposition and environmental influences are both important factors in the development of problematic behavior leading to substance use in adolescence. Involvement with delinquent peers also strongly predicts adolescent externalizing behavior. Several lines of evidence support a role of GABRA2 on externalizing behavior related to disinhibition. However, whether this genetic association is influenced by the environment such as peer behavior remains unknown.
A NOVEL SQUARE ROOT CUBATURE KALMAN FILTER USING MODEL-ENHANCED GAUSSIAN PROCESS STATE-SPACE MODELS

A novel nonlinear filter called model-enhanced Gaussian process square root cubature Kalman filter (MEGP-SRCKF) is proposed to estimate the state of nonlinear dynamic systems where their state-space models are unknown or insufficiently accurate. The algorithm integrates Gaussian process regression (GPR) into square root cubature Kalman filter (SRCKF). Given the training data, GPR model is used to learn and represent the residual of system after factoring the contributions of the parametric model. The combination of GPR and parametric models enhances the performance of either model alone. It improves the accuracy of the transition and measurement models. The resulting MEGP-SRCKF algorithm has several advantages over standard extended Kalman filter (EKF), nonaugmented unscented Kalman filter (UKF), augmented UKF, and SRCKF. Two cases are used to test these filters and the superiority of the proposed filter is demonstrated, where the MEGP-SRCKF can obtain the better results whether an accurate parametric state-space model is obtained.
Iranian Mental Health Survey: Design and Field Proced

Iranian Mental Health Survey (IranMHS) was conducted to assess the twelve-month prevalence and severity of psychiatric disorders in the Iranian adult population and to determine the pattern of health care utilization and cost of services. IranMHS is a cross-sectional national household survey with face-to-face interviews as the main data collection method. The study was carried out between January and June 2011. A three-stage probability sampling was applied for the selection of a representative sample from the non-institutionalized population aged 15 to 64. The primary instrument utilized for assessing the prevalence of mental disorders was the Persian version of Composite International Diagnosis Interview, version 2.1. The instruments for assessing the service and cost of mental illness were developed by the research team. The response rate was 86.2%, and a total of 7886 individuals participated in the study. Sampling weights were the joint product of inverse probability of unit selection, non-response weights and post-stratification weights. This paper presents an overview of the study design, fieldwork organization and procedures, weightings and analysis. The strengths and limitations of the study are also discussed.
A combination of transductive and inductive learning for handling non-stationarities in motor imagery classification

A major issue for bringing brain-computer interface (BCI) based on electroencephalogram (EEG) recordings outside of laboratories is the non-stationarities of EEG signals. Varying statistical properties of the signals during inter- or intra-session transfers can lead to deteriorated BCI performances over time. These variations may cause the input data distribution to shift when transitioning from the training phase (calibration session) to the testing/operating phase resulting in a covariate shift. We propose to handle this issue using a novel hybrid learning method based on two classifiers, wherein the first classifier allows including new information in the training dataset, and the second classifier performs an overall classification. The proposed method is motivated by the smoothness assumption, i.e., the points that are closest to each other are more likely to share the same label, and may be added online to enrich the training dataset. The method is evaluated on two real-world datasets corresponding to motor imagery detection (BCI competition 2008 dataset 2A and 2B). The results support the conclusion that an improvement in the classification accuracy over traditional inductive learning and semi-supervised learning methods can be obtained.
SDAR: a secure distributed anonymous routing protocol for wireless and mobile ad hoc networks

Providing security and privacy in mobile ad hoc networks has been a major issue over the last few years. Most research work has so far focused on providing security for routing and data content, but nothing has been done in regard to providing privacy and anonymity over these networks. We propose a novel distributed routing protocol which guarantees security, anonymity and high reliability of the established route in a hostile environment, such as an ad hoc wireless network, by encrypting the routing packet header and abstaining from using unreliable intermediate nodes. The major objective of our protocol is to allow trustworthy intermediate nodes to participate in the path construction protocol without jeopardizing the anonymity of the communicating nodes. We describe our protocol, SDAR (secure distributed anonymous routing), and provide its proof of correctness.
Comparisons of established risk prediction models for cardiovascular disease: systematic review

Objective To evaluate the evidence on comparisons of established cardiovascular risk prediction models and to collect comparative information on their relative prognostic performance. Design Systematic review of comparative predictive model studies. Data sources Medline and screening of citations and references. Study selection Studies examining the relative prognostic performance of at least two major risk models for cardiovascular disease in general populations. Data extraction Information on study design, assessed risk models, and outcomes. We examined the relative performance of the models (discrimination, calibration, and reclassification) and the potential for outcome selection and optimism biases favouring newly introduced models and models developed by the authors. Results 20 articles including 56 pairwise comparisons of eight models (two variants of the Framingham risk score, the assessing cardiovascular risk to Scottish Intercollegiate Guidelines Network to assign preventative treatment (ASSIGN) score, systematic coronary risk evaluation (SCORE) score, Prospective Cardiovascular Münster (PROCAM) score, QRESEARCH cardiovascular risk (QRISK1 and QRISK2) algorithms, Reynolds risk score) were eligible. Only 10 of 56 comparisons exceeded a 5% relative difference based on the area under the receiver operating characteristic curve. Use of other discrimination, calibration, and reclassification statistics was less consistent. In 32 comparisons, an outcome was used that had been used in the original development of only one of the compared models, and in 25 of these comparisons (78%) the outcome-congruent model had a better area under the receiver operating characteristic curve. Moreover, authors always reported better area under the receiver operating characteristic curves for models that they themselves developed (in five articles on newly introduced models and in three articles on subsequent evaluations). Conclusions Several risk prediction models for cardiovascular disease are available and their head to head comparisons would benefit from standardised reporting and formal, consistent statistical comparisons. Outcome selection and optimism biases apparently affect this literature.
Cost Optimization of Cloud-Based Data Integration System

Cloud computing provides virtualized, dynamically-scalable computing power. At the same time, reduction of cost is also considered as an important advantage of cloud computing. Data integration can notably benefit from cloud computing because integrating data is usually an expensive task. However, existing optimization techniques pay less attention on the fact that different execution plans of the same data integration application generate different usage costs while cloud computing provides good enough performance, so this paper introduces the cost optimization of cloud-based data integration system. The data integration system's data service layer facilitates accessing and composing information from a range of enterprise data sources through data service composition. In addition, two task scheduling algorithms for parallel part and non-parallel part are proposed to minimize the usage cost required to complete the execution of composite data service when computational capability provided by cloud computing is charged. Both of the two can obtain optimal plans in polynomial time. Experiments with the system indicate that our algorithms can lead to significant cost saving over more straightforward techniques.
Eye contact takes two – autistic and social anxiety traits predict gaze behavior in dyadic interaction

Research on social impairments in psychopathology has relied heavily on the face processing literature. However, although many sub-systems of facial information processing are described, recent evidence suggests that generalizability of these findings to social settings may be limited. The main argument is that in social interaction, the content of faces is more dynamic and dependent on the interplay between interaction partners, than the content of a non-responsive face (e.g. pictures or videos) as portrayed in a typical experiment. The question beckons whether gaze atypicalities to non-responsive faces in certain disorders generalize to faces in interaction. In the present study, a dual eye-tracking setup capable of recording gaze with high resolution was used to investigate how gaze behavior in interaction is related to traits of Autism Spectrum Disorder (ASD), and Social Anxiety Disorder (SAD). As clinical ASD and SAD groups have exhibited deficiencies in reciprocal social behavior, traits of these two conditions were assessed in a general population. We report that gaze behavior in interaction of individuals scoring high on ASD and SAD traits corroborates hypotheses posed in typical face-processing research using non-responsive stimuli. Moreover, our findings on the relation between paired gaze states (when and how often pairs look at each other’s eyes simultaneously or alternately) and ASD and SAD traits bear resemblance to prevailing models in the ASD literature (the ‘gaze aversion’ model) and SAD literature (the ‘vigilant-avoidance’ model). Pair-based analyses of gaze may reveal behavioral patterns crucial to our understanding of ASD and SAD, and more general to our understanding of eye movements as social signals in interaction.