Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards highly scalable pregel-based graph processing platform with x10

Many practical computing problems concern large graph. Standard problems include web graph analysis and social networks analysis like Facebook, Twitter. The scale of these graph poses challenge to their efficient processing. To efficiently process large-scale graph, we create X-Pregel, a graph processing system based on Google's Computing Pregel model [1], by using the state-of-the-art PGAS programming language X10. We do not purely implement Google Pregel by using X10 language, but we also introduce two new features that do not exists in the original model to optimize the performance: (1) an optimization to reduce the number of messages which is exchanged among workers, (2) a dynamic re-partitioning scheme that effectively reassign vertices to different workers during the computation. Our performance evaluation demonstrates that our optimization method of sending messages achieves up to 200% speed up on Pagerank by reducing the network I/O to 10 times in comparison with the default method of sending messages when processing SCALE20 Kronecker graph [2](vertices = 1,048,576, edges = 33,554,432). It also demonstrates that our system processes large graph faster than prior implementation of Pregel such as GPS [3](stands for graph processing system) and Giraph [4].
Unbalanced points and vertices problem

Starting from the points and vertices problem introduced in (R. Klasing and et al., 2005), given a graph G = (V, E) with |V| = n and a positive number isin, we consider the following problem. Place (1 - isin)n points on the vertices V of G independently and uniformly at random. Once the points are placed, relocate them by movements along the edges E of G using a function from the points to the vertices that minimizes the maximum distance between the random place of the points and their target vertices. The aim is to obtain in the final setting at most one point for each vertex. We look for an upper bound on this maximum relocation distance that holds with high probability over the initial placements of the points. We study several topologies for the graph G like paths, trees, d-dimensional grids, hypercubes and general graphs
YscU cleavage and the assembly of Yersinia type III secretion machine complexes

YscU, a component of the Yersinia type III secretion machine, promotes auto‐cleavage at asparagine 263 (N263). Mutants with an alanine substitution at yscU codon 263 displayed secretion defects for some substrates (LcrV, YopB and YopD); however, transport of effector proteins into host cells (YopE, YopH, YopM) continued to occur. Two yscU mutations were isolated that, unlike N263A, completely abolished type III secretion; YscUG127D promoted auto‐cleavage at N263, whereas YscUG270N did not. When fused to glutathione S‐transferase (Gst), the YscU C‐terminal cytoplasmic domain promoted auto‐cleavage and Gst‐YscUC also exerted a dominant‐negative phenotype by blocking type III secretion. Gst–YscUC/N263A caused a similar blockade and Gst–YscUC/G270N reduced secretion. Gst–YscUC and Gst–YscUC/N263A bound YscL, the regulator of the ATPase YscN, whereas Gst–YscUC/G270N did not. When isolated from Yersinia, Gst–YscUC and Gst–YscUC/N263A associated with YscK–YscL–YscQ; however, Gst–YscUC/G270N interacted predominantly with the machine component YscO, but not with YscK–YscL–YscQ. A model is proposed whereby YscU auto‐cleavage promotes interaction with YscL and recruitment of ATPase complexes that initiate type III secretion.
Bayesian variable selection for proportional hazards models

The authors consider the problem of Bayesian variable selection for proportional hazards regression models with right censored data. They propose a semi‐parametric approach in which a nonparametric prior is specified for the baseline hazard rate and a fully parametric prior is specified for the regression coefficients. For the baseline hazard, they use a discrete gamma process prior, and for the regression coefficients and the model space, they propose a semi‐automatic parametric informative prior specification that focuses on the observables rather than the parameters. To implement the methodology, they propose a Markov chain Monte Carlo method to compute the posterior model probabilities. Examples using simulated and real data are given to demonstrate the methodology.
A new finer resolution land-use mapping method using time series of NDVI from HJ-1/CCD data

HJ-1/CCD data has both high spatial resolution and high temporal frequency. By using NDVI time series and SVM classifier with HJ-1/CCD data source, this paper proposed a new resolution for land use classification of Dahuofang Reservoir, Liaoning Province, China. The validation result demonstrates that this resolution could obtain finer result than others.
Molecularly Imprinted Polymer for explosive detection

In this work, we research on explosive detection by using materials specific Molecularly Imprinting Polymer (MIP). Polyacrylamide was imprinted by 2,4-dinitrotoluene (DNT) and 2,4,6-trinitrotoluene (TNT) with ethylene glycol dimethacrylate (EGDMA) as a cross-linker. The result showed that DNT and TNT MIPs can recognize the corresponding template on a quartz crystal microbalance (QCM) successfully.
Tools for the Study of the Usual Data Sources found in Libre Software Projects

Due to the open nature of Free/Libre/Open Source software projects, researchers have gained access to a rich set of development-related information. Although this information is publicly available on the Internet, obtaining and analyzing it in a convenient way is not an easy task and many considerations have to be taken into account. In this paper we present the most important data sources that can be found in libre software projects and that are studied by the research community: source code, source code management systems, mailing lists and bug tracking systems. We will give advice for the problems that can be found when retrieving and preparing the data sources for a posterior analysis, as well as provide information about the tools that support these tasks.
Design of a Particle Swarm Optimization-based Classification System in a WBAN Environment

Many medical application services in a WBAN(Wireless Body Area Network) require a close diagnosis from a doctor after monitoring or storing data from medical sensors. In a WBAN environment, the amount of data is huge because the data is created in real time. When we analyze a large amount of data, there are some drawbacks in terms of time performance and space. Thus, a study of analysis methods for detecting new information from vital signals is needed. In this paper, we design a blood pressure classification system in the WBAN environment to aim at predicting cardiac disorder that is one of the highest mortality risks in Korea these recent years. To analyze stored blood pressure data in a database, we need to create classification rules of existing data and figure out the best rule based on accuracy. And then, this rule is applied to real data that needs to be classified. Accordingly, we suggest a design of a Particle Swarm Optimization (PSO) Classification System in the Wireless Body Area Network environment to analyze a large amount of blood pressure data efficiently. 
REJOINDER: Mixture Distribution State-Trait Models: Current Limitations and Future Directions

We greatly appreciate the six commentaries that highlight some areas of further applications and some limitations of the mixture distribution state–trait model and open the door to future developments in the measurement of variability and change. The commentaries can be grouped into two large classes : commentaries on more or less theoretical or substantive issues and commentaries that deal with more or less statistical issues. When it comes to classes as defined in the focus article, the “more or less” should be dropped, because they are exhaustive and disjunctive. However, the two classes of commentaries are interrelated, as the theory should guide the statistical analysis, and as some commentaries belong in both classes. The theoretical issues are related to (a) the role of situations in explaining stability versus variability over time, (b) the advantages of integrating personality measures into the models, (c) whether it is reasonable to assume a typological model for explaining interindividual differences in the stability versus variability of mood in contrast to the more predominant dimensional approaches. The more statistical issues deal with (a) the categorization of response categories and the opMEASUREMENT: INTERDISCIPLINARY RESEARCH AND PERSPECTIVES, 1(3), 232–240 Copyright © 2003, Lawrence Erlbaum Associates, Inc.
Differential effects of cognitive reserve and brain reserve on cognition in Alzheimer disease

Objective To examine cross-sectional effects of cognitive reserve (CR) and brain reserve (BR) on cognition across the spectrum of Alzheimer disease (AD). Methods We included 663 AD biomarker–positive participants with dementia (probable AD, n = 462) or in the predementia stages (preclinical/prodromal AD, n = 201). Education was used as a proxy of CR and intracranial volume as a proxy of BR. Cognition was assessed across 5 domains (memory, attention, language, visuospatial, and executive functions). We performed multiple linear regression models to examine effects of CR and BR on cognitive domain Z scores, adjusted for cerebral atrophy. Furthermore, we assessed differences in effects according to disease stage and across degrees of total reserve using a 4-level variable (high CR/high BR, high CR/low BR, low CR/high BR, and low CR/low BR). Results We found positive, independent effects of both CR and BR across multiple cognitive domains. Stratification for disease stage showed that effects of CR on attention and executive functioning were greater in predementia than in dementia (β = 0.39 vs β = 0.21 [Welch t = 2.40, p < 0.01] and β = 0.46 vs β = 0.26 [t = 2.83, p < 0.01]). Furthermore, we found a linear trend for better cognitive performance in all domains in the high CR/high BR group, followed by high CR/low BR, low CR/high BR, and then low CR/low BR (p for trend <0.05). Conclusions CR and BR both independently mitigate cognitive symptoms in AD. The positive effect of CR is most strongly expressed in the predementia stages and the additive effects of high CR and BR are most beneficial.
Segmenting destinations: in the eyes of the stakeholders

Purpose – This paper aims to recommend a two‐step approach to destination segmentation that incorporates the views both of multiple stakeholders and of tourists.Design/methodology/approach – Step one applies a case study approach incorporating semi‐structured interviews with 13 destination stakeholders. Step two involves segmenting tourists to the destination based on a questionnaire survey developed from the semi‐structured interviews. The study compares and contrasts the result with the current DMO approach.Findings – The two‐step approach produces three segments for the destination under study using four segmentation bases and ten variables. The DMO approach also utilizes all four segmentation bases but produces six segments with five different variables. The DMO approach captures fewer tourists visiting the destination.Research limitations/implications – This study considers one regional Australian destination. Future research is recommended in a range of alternative destinations to further understand.
Semantic TagPrint - Tagging and Indexing Content for Semantic Search and Content Management

Existing search and content management technology is facing a challenge of locating desired content with the exponentially growing volume of documents. An approach for mitigating this issue is to make use of user-generated tags. However, the improvements are limited because tags are (1) free from context and form, (2) user generated, (3) used for purposes other than description, and (4) often ambiguous. Since tagging is a voluntary action, some documents are not tagged at all. Furthermore, the interpretation of the tags associated with tagged documents also remains a challenge. To overcome these challenges, semantic web resources and technologies can be utilized to automatically generate semantic tags. Semantic tags not only reflect document content more accurately, they also enable better search results. Ontology coverage, ontology mapping and weighting significant ontological entities within a context are key challenges in semantic tagging systems. To address these challenges, this paper presents a semantic tagging system - Semantic TagPrint - to map a text document to semantic tags defined as entities in an ontology. Semantic TagPrint uses a linear time lexical chaining Word Sense Disambiguation (WSD) algorithm for real time concept mapping. In addition, it utilizes statistical metrics and ontological features of the ontology for weighting and recommending the semantic tags. A comparative evaluation shows that our mapping algorithm is fairly accurate and our tag recommendation algorithm performs better than other systems and algorithms.
Cancer Stratification by Molecular Imaging

The lack of specificity of traditional cytotoxic drugs has triggered the development of anticancer agents that selectively address specific molecular targets. An intrinsic property of these specialized drugs is their limited applicability for specific patient subgroups. Consequently, the generation of information about tumor characteristics is the key to exploit the potential of these drugs. Currently, cancer stratification relies on three approaches: Gene expression analysis and cancer proteomics, immunohistochemistry and molecular imaging. In order to enable the precise localization of functionally expressed targets, molecular imaging combines highly selective biomarkers and intense signal sources. Thus, cancer stratification and localization are performed simultaneously. Many cancer types are characterized by altered receptor expression, such as somatostatin receptors, folate receptors or Her2 (human epidermal growth factor receptor 2). Similar correlations are also known for a multitude of transporters, such as glucose transporters, amino acid transporters or hNIS (human sodium iodide symporter), as well as cell specific proteins, such as the prostate specific membrane antigen, integrins, and CD20. This review provides a comprehensive description of the methods, targets and agents used in molecular imaging, to outline their application for cancer stratification. Emphasis is placed on radiotracers which are used to identify altered expression patterns of cancer associated markers.
Improving semi-supervised deep neural network for keyword search in low resource languages

In this work, we investigate how to improve semi-supervised DNN for low resource languages where the initial systems may have high error rate. We propose using semi-supervised MLP features for DNN training, and we also explore using conﬁdence to improve semi-supervised cross entropy and sequence training. The work conducted in this paper was evaluated under the IARPA Babel program for the keyword spotting tasks. We focus on the limited condition where there are around 10 hours of supervised data for training
Opportunity and Risk in Transnational Education—Issues in Planning for International Campus Development: An Australian Perspective

This article, that is based upon the experience of Monash University, identifies a range of opportunities and risks involved in transnational education and addresses a number of key planning issues in establishing an international campus. These include the guiding principles adopted by the university, country selection factors, market research, considerations in developing an appropriate delivery model, and the assurance of the academic quality and educational integrity of the programmes offered. The authors refer to literature in the field concerning corporate risk and identify factors relevant to the higher education sector. The article stresses the need for an appropriate integration of strategic vision, academic priorities, and business considerations to ensure that opportunities offered by transnational education are successfully realized.
A state-of-the-art statistical machine translation system based on Moses

This paper describes a statistical machine translation system based on freely available programs such as Moses. Several new features were added, in particular a two-pass decoding strategy using n-best lists and a continuous space language model that aims at taking better advantage of the limited training data. We also investigated lexical disambiguation methods in the translation model based on POS information. The task considered in this work is the translation of the European Parliament Plenary Sessions between English and Spanish, in the framework of the T C-STAR project. The described systems performed very well in the 2007 T C-S AR evaluation.
Recourse to Stored Exemplars is not Necessarily Explicit: A Comment on Knowlton, Ramus, and Squire (1992)

In a recent Psychological Science article, Knowlton, Ramus, and Squire (1992) compared artificial grammar learning in normal and amnesic subjects. Amnesics performed as well as control subjects on grammaticality judgments, but were impaired on both item recognition and classification when explicit comparison to prior exemplars was emphasized. Both of these impairments were attributed to deficiencies in explicit retrieval. Based on these effects, Knowlton et al. concluded that "classification based on rules can occur without recourse to stored exemplars" (p. 177). This conclusion requires that explicit retrieval of items be equivalent to the use of exemplars. However, many contemporary models of classification and recognition memory have a mechanism that retrieves a "chorus of instances" similar to the probe item (e.g., Hintzman, 1986; Kahneman & Miller, 1986; Nosofsky, 1986). This retrieval-time averaging of instance-based knowledge has been provided as an explanation of "implicit abstraction" in some classification tasks (Hintzman, 1986) and for implicit, vague feelings of familiarity in recognition memory tasks (Atkinson & Juola, 1974; Jacoby, 1991; Mandler, 1980). Thus, although the demonstration of comparable classification learning with normal and explicit-memory-impaired subjects is interesting, it has little bearing on exemplar theories of memory and classification becl:luse none posit explicit retrieval as the primary basis of this performance ,(see Whittlesea & Dorken, 1993). Furthermore, because 'of retrieval-time averaging and nonlinear similarity funcitions, even the single-factor models have resources to explain the divergences between the two tasks reported by Knowlton et al. (see Hintzman, 1986, and discussion in Vokey & Brooks, 1992). The similarity distribution of 'the test instances has to be controlled and evaluated more tightly than in the study by Knowlton et al. to rule out such models. , A second problem is that the multiple response bases of both recognition memory and grammaticality judgments (see Higham & Brooks, 1993; Vokey & Brooks, 1992, in addition to references in Knowlton et al.) are not
Learning and memory impairment in PTSD: relationship to depression

Memory function was studied in combat veterans with posttraumatic stress disorder (PTSD), combat veterans without PTSD, and noncombat veterans. The Vocabulary and Digit Span subtests of the WAIS and Logical Memory (LMS) and Verbal Paired Associates (VPAS) subtests of the Wechsler Memory Scale III were administered. Combat veterans with PTSD showed impaired memory on the LMS and VPAS compared to combat veterans without PTSD or noncombat veterans. Veterans with PTSD also showed lower WAIS Vocabulary subtest scores—but not digit span subtest scores—than combat veterans without PTSD or noncombat veterans. Medication status, co‐morbid diagnosis, and age all failed to account for these memory differences, but when self‐assessed depression—as measured by the Zung Self‐Rating Depression Scale—or anxiety—as measured by the Spielberger State‐Trait Anxiety Scale—was statistically removed, group differences on these memory measures were no longer significant. However, using a stepwise regression procedure, in which both anxiety and depression were employed to predict the LMS and VPAS scores, only the Zung scale reliably predicated performance. The present results, showing that PTSD is associated with general learning and memory impairments, is an important finding, but the specific effects of depression as a mediator of these deficits should be further studied. Depression and Anxiety 0:1–9, 2007. Published 2007 Wiley‐Liss, Inc.
Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea

A method for the automatic processing of the electrocardiogram (ECG) for the detection of obstructive apnoea is presented. The method screens nighttime single-lead ECG recordings for the presence of major sleep apnoea and provides a minute-by-minute analysis of disordered breathing. A large independently validated database of 70 ECG recordings acquired from normal subjects and subjects with obstructive and mixed sleep apnoea, each of approximately eight hours in duration, was used throughout the study. Thirty-five of these recordings were used for training and 35 retained for independent testing. A wide variety of features based on heartbeat intervals and an ECG-derived respiratory signal were considered. Classifiers based on linear and quadratic discriminants were compared. Feature selection and regularization of classifier parameters were used to optimize classifier performance. Results show that the normal recordings could be separated from the apnoea recordings with a 100% success rate and a minute-by-minute classification accuracy of over 90% is achievable.
High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials

The development of reliable methods for the estimation of crown architecture parameters is a key issue for the quantitative evaluation of tree crop adaptation to environment conditions and/or growing system. In the present work, we developed and tested the performance of a method based on low-cost unmanned aerial vehicle (UAV) imagery for the estimation of olive crown parameters (tree height and crown diameter) in the framework of olive tree breeding programs, both on discontinuous and continuous canopy cropping systems. The workflow involved the image acquisition with consumer-grade cameras on board a UAV and orthomosaic and digital surface model generation using structure-from-motion image reconstruction (without ground point information). Finally, geographical information system analyses and object-based classification were used for the calculation of tree parameters. Results showed a high agreement between remote sensing estimation and field measurements of crown parameters. This was observed both at the individual tree/hedgerow level (relative RMSE from 6% to 20%, depending on the particular case) and also when average values for different genotypes were considered for phenotyping purposes (relative RMSE from 3% to 16%), pointing out the interest and applicability of these data and techniques in the selection scheme of breeding programs.
Review Essay : Future Directions in Historical Household Studies

The historical study of household forms in the European past became a major field of scholarly activity in the 1970s in good part due to the impetus provided by Peter Laslett and his colleagues of the Cambridge Group for the History of Population and Social Structure. The rallying point for these scholars was the 1972 volume, Household and Family in Past Time (HFPT), which staked out a typological approach to historic household study and outlined a revisionist view of the prevalence of simple family households in the European past. Since that publication, scores of scholars throughout much of Europe have churned out typologically oriented studies, producing