Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of Geospatial Product Virtualization in Grid Environment

This paper presents the work performed for the implementation of the geospatial product virtualization in the Grid environment. A virtual geospatial product is a product that does not exist in a geospatial data system but the system knows how to create such a product when a user requests the product. We use a two-step approach to the implementation. The first step is to represent a virtual geospatial product by a geospatial processing model and the second step is to convert the model into an executable Grid service workflow and execute the workflow in Grid environment. A geospatial processing model tells conceptually how a type of geospatial products can be produced from raw geospatial data. The conversion from a geospatial processing model to an executable Grid workflow will incorporate the user specifications on the requested product into the workflow. This paper discusses the conceptual model for implementing product virtualization in the Grid environment, the overall architecture of the Grid virtual geospatial product system, service components developed and Globus components extended for the system, and operational considerations of the system.
Control of neuronal differentiation by sumoylation of BRAF35, a subunit of the LSD1-CoREST histone demethylase complex

The LSD1–CoREST histone demethylase complex is required to repress neuronal genes in nonneuronal tissues. Here we show that sumoylation of Braf35, one of the subunits of the complex, is required to maintain full repression of neuron-specific genes and for occupancy of the LSD1–CoREST complex at its gene targets. Interestingly, expression of Braf35 was sufficient to prevent neuronal differentiation induced by bHLH neurogenic transcription factors in P19 cells and in neuronal progenitors of the chicken embryo neural tube. Sumoylation of Braf35 is required for this antineurogenic activity. We also show that iBraf, a paralogue of Braf35, forms heterodimers with Braf35. Braf35–iBraf heterodimerization impairs Braf35 interaction with the LSD1–CoREST complex and inhibits Braf35 sumoylation. Consistent with these results, iBraf prevents the antineurogenic activity of Braf35 in vivo. Our data uncover a mechanism of regulation of the LSD1–CoREST complex and provide a molecular explanation for the antagonism between Braf35 and iBraf in neuronal differentiation.
Multidimensional Analysis of Roll Call Data via Bayesian Simulation: Identification, Estimation, Inference, and Model Checking

Vote-specific parameters are often by-products of roll call analysis, the primary goal being the measurement of legislators' ideal points. But these vote-specific parameters are more important in higher-dimensional settings: prior restrictions on vote parameters help identify the model, and researchers often have prior beliefs about the nature of the dimensions underlying the proposal space. Bayesian methods provide a straightforward and rigorous way for incorporating these prior beliefs into roll call analysis. I demonstrate this by exploiting the close connections among roll call analysis, item-response models, and “full-information” factor analysis. Vote-specific discrimination parameters are equivalent to factor loadings, and as in factor analysis, they (1) enable researchers to discern the substantive content of the recovered dimensions, (2) can be used for assessing dimensionality and model checking, and (3) are an obvious vehicle for introducing and testing researchers' prior beliefs about the dimensions. Bayesian simulation facilitates these uses of discrimination parameters, by simplifying estimation and inference for the massive number of parameters generated by roll call analysis.
A Methodology for Developing Evidence about Meaning in Occupation: Exploring the Meaning of Working

Using research-based evidence to support practice is becoming a standard of care. Although occupational therapists believe that occupation can be used as a therapeutic agent to promote health and well-being in their clients, there is only limited research to support this. One reason for this is the difficulty in quantitatively constructing “occupation” for research. This article describes one method that can be used to quantitatively measure meaning, an important indicator of occupation. This study explicates the “meaning” of working as examined through three interconnected constructs of work meaning (work centrality, societal norms about working, and valued work outcomes) and provides an exemplar study in which these three constructs were used to examine the meaning of working in a group of telecommunication workers. The Meaning of Working Survey was used to gather information from 170 telecommunication workers. Data from the survey were analyzed using both descriptive methods and cluster analysis. By evaluating these three constructs, it was possible to identify trends and patterns of the meaning of working for this group of workers. They did not view work as a central life role and viewed working as a constraint or a reciprocal arrangement. They valued benefits and wages over intrinsic outcomes. The cluster analysis revealed that meaning was generally related to external outcomes. The discussion focuses on how this methodology for measuring meaning could be used to develop research on the meaning of occupation and its effect on health outcomes. This study provides occupational therapy researchers with one quantitative methodology for examining the meaning of working and an exemplar on how that methodology has been used in research on meaning and occupation.
The careers of research scientists

Purpose – This study seeks to identify some of the factors that explain biomedical research scientists' career commitment and that prompt them to consider leaving science. It also aims to examine whether these factors differ between men and women, and between those with different family responsibilities.Design/methodology/approach – A longitudinal questionnaire survey was used to identify the demographic and work‐related variables that predicted three dimensions of career commitment and intention to leave science one year later.Findings – The factors explaining career commitment and intention to remain in the profession were similar for men and women, for those with various types of family responsibilities, and for those on permanent and temporary contracts. Career planning was predicted by continuance organizational commitment and the opportunity for autonomy in the workplace, and career resilience by equitable treatment at work. Career identity, career resilience and salary were predictors of intention .
An improved power sharing strategy for hybrid energy storage system

For a hybrid energy storage system consisting of several types of energy storage devices such as super-capacitor and secondary batteries, a strategy of power sharing among them based on the state of charge is proposed in this paper. It is adaptable to different power requirements and can be easily designed no matter how many types of storage devices are adopted. The power sharing result will match device's power performance, capacity and initial condition automatically while the state of charge of each device is always under control. Simulation and experimental results are presented to verify this strategy.
Software protection and simulation on oblivious RAMs

Software protection is one of the most important issues concerning computer practice. There exist many heuristics and ad-hoc methods for protection, but the problem as a whole has not received the theoretical treatment it deserves. In this paper, we provide theoretical treatment of software protection. We reduce the problem of software protection to the problem of efficient simulation on oblivious RAM. A machine is oblivious if thhe sequence in which it accesses memory locations is equivalent for any two inputs with the same running time. For example, an oblivious Turing Machine is one for which the movement of the heads on the tapes is identical for each computation. (Thus, the movement is independent of the actual input.) What is the slowdown in the running time of a machine, if it is required to be oblivious? In 1979, Pippenger and Fischer showed how a two-tape oblivious Turing Machine can simulate, on-line, a one-tape Turing Machine, with a logarithmic slowdown in the running time. We show an analogous result for the random-access machine (RAM) model of computation. In particular, we show how to do an on-line simulation of an arbitrary RAM by a probabilistic oblivious RAM with a polylogaithmic slowdown in the running time. On the other hand, we show that a logarithmic slowdown is a lower bound.
Initial Evaluation of Pure and “ Latinized ” Centroidal Voronoi Tessellation for Non-Uniform Statistical Sampling *

A recently developed Centroidal Voronoi Tessellation (CVT) sampling method is investigated here to assess its suitability for use in statistical sampling applications. CVT efficiently generates a highly uniform distribution of sample points over arbitrarily shaped M-Dimensional parameter spaces. On several 2-D test problems CVT has recently been found to provide exceedingly effective and efficient point distributions for response surface generation. Additionally, for statistical function integration and estimation of response statistics associated with uniformly distributed random-variable inputs (uncorrelated), CVT has been found in initial investigations to provide superior points sets when compared against Latin-Hypercube and Simple-Random Monte Carlo methods and Halton and Hammersley quasi-Monte-Carlo sequence methods. In this paper, the performance of all these sampling methods and a new variant (“Latinized” CVT) are further compared for non-uniform input distributions. Specifically, given uncorrelated normal inputs in a 2-D test problem, statistical sampling efficiencies are compared for resolving various statistics of response: mean, variance, and exceedence probabilities.
On de-cluttering and de-noising ultrasound images with Time-domain Compressive Beamforming

Time-domain Compressive Beamforming (t-CBF) uses a beamforming matrix in conjunction with regularization techniques to improve the image quality in single-insonification ultrasound acquisitions. It was originally introduced as a way to reconstruct sparse images of point scatterers in a homogeneous medium. Later on, it was adapted to tissue imaging using the wavelet transform to sparsify the reconstruction.
Multivariate prediction of motor diagnosis in Huntington's disease: 12 years of PREDICT‐HD

It is well known in Huntington's disease that cytosine‐adenine‐guanine expansion and age at study entry are predictive of the timing of motor diagnosis. The goal of this study was to assess whether additional motor, imaging, cognitive, functional, psychiatric, and demographic variables measured at study entry increased the ability to predict the risk of motor diagnosis over 12 years.
Subspace Identification of 2-D CRSD Roesser Models With Deterministic-Stochastic Inputs: A State Computation Approach

In this brief, we present a subspace system identification framework for 2-D separable-in-denominator systems with deterministic-stochastic inputs in the Roesser form. The advantage of the proposed framework is that it is based on the computation of state matrices, as opposed to current algorithms that compute the system parameter matrices from Markov parameters and the observability matrix. As such, it does not require solving specialized Toeplitz or Hankel systems of equations while computing the system parameter matrices. In addition, the problem is broken down into two simple oblique projection computations—one in the horizontal direction and one in the vertical direction. Within this framework, Numerical algorithms for Subspace State Space System IDentification (N4SID), Past-Output Multivariable Output-Error State-sPace (PO-MOESP), and Canonical Variate Analysis (CVA) type algorithms are obtained. Simulation results show that the algorithms are accurate and provide new alternatives for modeling and identifying 2-D causal, recursive, and separable-in-denominator Roesser models.
Implementation of Job Scheduling Algorithms in a Cloud Computing Environment for B2C Electronic Commerce architecture using GI/G/1 queuing model

Traditional B2C Electronic Commerce architecture instigated using n-Tier Client/Server architecture has continually been sluggish, intricate, and costly. With the advent of Cloud computing environment, the amenities vital for the Electronic Commerce architecture are accomplished by proficient organization, which comprises servers, storage supervision, network skills, and virtualization technologies. The research recommends the Job Scheduling algorithm and its enactment on B2C Electronic Commerce architecture. The algorithm is implemented using GI/G/1 Queuing model, to assess the performance of the proposed algorithm using four queuing parameters, specifically, Average numbers of requests in an B2C E-Commerce architecture, Average numbers of requests in the queue, Average waiting time of requests in an B2C ECommerce architecture, Average waiting time of requests in queue. Finally, the allied simulations and numeral results are provided, to relate the performance of the proposed algorithm.
Neural plasticity in response to attention training in anxiety

Background Behavioral studies show that attention training can alter threat bias, influence vulnerability to stress and reduce clinical anxiety symptoms. The aim of this study was to examine which cognitive functions of attention processing are modulated by attention training, and how a priori anxiety interacts with the attention training procedure. Specifically, we expected modulation in the P1/N1 event-related potential (ERP) complex if early spatial attention was to be affected by training and modulation in later ERP components (P2, N2, P3) had training affected top-down attentional processes. Method Thirty anxious and 30 non-anxious adults performed a modified probe detection task. Electroencephalograms (EEGs) were recorded throughout for later ERP analyses. Half the participants in each anxiety group were randomly assigned to undergo a training procedure designed to divert their attention away from threat and the other half received placebo training. Results Anxious participants who were trained to avoid threat showed a linear reduction in response time (RT) to targets replacing neutral faces with the progression of training. This change in RT was not observed among non-anxious participants or among anxious participants who were exposed to placebo training. Following training, the anxious participants who were trained to avoid threat showed a reduction in P2 and P3 mean amplitudes and an enhancement in N2 mean amplitude. Conclusions Attention training affects anxious participants whereas non-anxious participants seem not to respond to it. The ERP data suggest that attention training modulates top-down processes of attention control rather than processes of early attention orienting.
Dynamic image sequence analysis using fuzzy measures

In this paper, we present an image understanding system using fuzzy sets and fuzzy measures. This system is based on a symbolic object-oriented image interpretation system. We apply a simple, powerful three-dimensional (3-D) recursive filter to tracking moving objects in a dynamic image sequence. This filter has a time-varying 3-D frequency-planar passband that is adapted in a feedback system to automatically track moving objects. However, as objects in the image sequence are not well-defined and are engaged in dynamic activities, their shapes and trajectories in most cases can be described only vaguely. In order to handle these uncertainties, we use fuzzy measures to capture subtle variations and manage the uncertainties involved. This enables us to develop an image understanding system that produces a very natural output. We demonstrate the effectiveness of our system with complex real traffic scenes.
Explanation and Implication for Configuration Problems

In this work we explore the problem of generating explanations for configuration problems using on the constraint satisfaction (CSP) framework. In addition, we are concerned with deriving implications from user choices in order to guide selection of later choices. We show that the consistency methods used in connection with constraint processing can be used to generate inferences that support both functions. In this work we use the -queens problem as a testbed. The system we have developed is interactive and allows the user to make selections and perform arc consistency on the current problem, as well as retracting selections, a typical arrangement with current configurator systems. At the same time it generates explanations for value deletions and current choices as well as implications in terms of the amount of future domain reduction that will follow certain choices and whether these choices will lead to solutions or non-solutions. Explanations take the form of trees which show the basis in terms of previous choices for current choices and deletions. Together, these methods suggest ways in which the process of solving combinatorial problems can be made more perspicuous and more interactive.
Robust Estimation of Albedo for Illumination-invariant Matching and Shape Recovery

In this paper, we propose a non-stationary stochastic filtering framework for the task of albedo estimation from a single image. There are several approaches in literature for albedo estimation, but few include the errors in estimates of surface normals and light source directions to improve the albedo estimate. The proposed approach effectively utilizes the error statistics of surface normals and illumination direction for robust estimation of albedo. The albedo estimate obtained is further used to generate albedo-free normalized images for recovering the shape of an object. Illustrations and experiments are provided to show the efficacy of the approach and its application to illumination-invariant matching and shape recovery.
Delineation of the Role of Toll-like Receptor Signaling during Peritonitis by a Gradually Growing Pathogenic Escherichia coli

In a mouse model of Escherichia coli sepsis characterized by a primary peritoneal infection with 104 E. coli and a gradually growing bacterial load, we here show that the early cytokine response and antibacterial defense are dominated by TLR4 via a cooperative action of MyD88 and Trif. Although MyD88−/− mice succumbed earlier than WT mice in this E. coli peritonitis model, Trif−/− mice displayed a small but significant survival advantage. Despite a large early deficit in antimicrobial defense, TLR4−/− mice showed an unaltered survival with normal neutrophil attraction to the peritoneal cavity and normal or even elevated late cytokine release. TLR2 compensated for the lack of TLR4 because TLR2−/−/TLR4−/− mice did show decreased neutrophil attraction and increased mortality compared with WT mice. Nearly normal early peritoneal TNFα production and lack of early counterregulating systemic levels of the chemoattractant KC were associated with normal peritoneal neutrophil attraction in TLR4−/− mice. Late stage increased TNF, IL-1β, IFN-β, and typical IFN-γ production in TLR4−/− mice prompted us to evaluate expression of the negative feedback regulator SOCS-1. Lack of early hepatic SOCS-1 expression in TLR4−/− mice explained the late innate production of IFN-γ by the liver in TLR4−/− mice in this low dose E. coli peritonitis model. In contrast, early TLR4-induced IFN-γ production is described as a hallmark in high dose E. coli peritonitis models. The present study displays how the kinetics of pro- and anti-inflammatory mechanisms are regulated by TLRs during peritonitis by a gradually growing E. coli load and how these kinetics may affect outcome.
Spatiotemporal System Identification With Continuous Spatial Maps and Sparse Estimation

We present a framework for the identification of spatiotemporal linear dynamical systems. We use a state-space model representation that has the following attributes: 1) the number of spatial observation locations are decoupled from the model order; 2) the model allows for spatial heterogeneity; 3) the model representation is continuous over space; and 4) the model parameters can be identified in a simple and sparse estimation procedure. The model identification procedure we propose has four steps: 1) decomposition of the continuous spatial field using a finite set of basis functions where spatial frequency analysis is used to determine basis function width and spacing, such that the main spatial frequency contents of the underlying field can be captured; 2) initialization of states in closed form; 3) initialization of state-transition and input matrix model parameters using sparse regression-the least absolute shrinkage and selection operator method; and 4) joint state and parameter estimation using an iterative Kalman-filter/sparse-regression algorithm. To investigate the performance of the proposed algorithm we use data generated by the Kuramoto model of spatiotemporal cortical dynamics. The identification algorithm performs successfully, predicting the spatiotemporal field with high accuracy, whilst the sparse regression leads to a compact model.
The Design of a Highly Parallel Computer Organization

The design of a computer organization is presented for general-purpose computing with a very high tolerance of failure while taking advantage of future large-scale integration techniques. A high degree of parallel computations may be executed. The organization distributes processing logic along with memory to form "cells. " The architecture and operation of the cells are developed. Design aspects of the cells are presented along with software considerations of the organization.
Identification and light-dependent translocation of a cone-specific antigen, cone arrestin, recognized by monoclonal antibody 7G6.

PURPOSE To elucidate the antigen recognized by monoclonal antibody (mAb) 7G6, a widely used cone-specific marker. METHODS 7G6 immunocytochemistry was performed on sections of human, primate, and bovine retina. The antigen was immunoprecipitated from human retinal lysates and purified with protein G. Edman degradation and liquid chromatography of tryptic peptides combined with tandem mass spectrometry (LC-MS/MS) identified the antigen. RESULTS Sequencing of peptides derived from the immunoprecipitated 7G6 antigen identified it as cone arrestin. The identity was confirmed by Western blot analysis with recombinant human cone arrestin and competition with the antibody in immunocytochemistry. Subcellular localization of cone arrestin in dark-adapted and bleached bovine retinas showed that cone arrestin accumulated in cone outer segments of light-adapted retina but was more concentrated in the inner segments of dark-adapted retina. By expression of truncated human cone arrestin mutants systematically deleting areas divergent from bovine and primate cone arrestins, the epitope of 7G6 was identified as a divergent loop exposed at the surface within the N-domain of cone arrestin. CONCLUSIONS Several independent methods established that the 7G6 antigen is cone arrestin. The 7G6 epitope is contained in a divergent loop, the sequence of which is conserved in bovine and primates but not other vertebrate species consistent with the specificity of the antibody. The light-dependent translocation of cone arrestin suggests a role in light-dark adaptation of cones. Because of the location of its gene on the X-chromosome, cone arrestin is a candidate gene for X-linked cone dystrophies.