Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature Location in a Collection of Product Variants

Companies often develop and maintain a collection of product variants that share some common features but also support different, customer-specific features. To reengineering such legacy product variants for systematic reuse, one must identify features and their implementing code units (e.g. functions, files) in different product variants. Information retrieval (IR) techniques may be applied for that purpose. In this paper, we discuss problems that hinder direct application of IR techniques to a collection of product variants. To counter these problems, we present an approach to support effective feature location in product variants. The novelty of our approach is that we exploit commonalities and differences of product variants by software differencing and FCA techniques so that IR technique can achieve satisfactory results for feature location in product variants. We have implemented our approach and conducted evaluation with a collection of nine Linux kernel product variants. Our evaluation shows that our approach always significantly outperforms a direct application of IR technique in the subject product variants.
WU-Blast2 server at the European Bioinformatics Institute

Since 1995, the WU-BLAST programs (http://blast.wustl.edu) have provided a fast, flexible and reliable method for similarity searching of biological sequence databases. The software is in use at many locales and web sites. The European Bioinformatics Institute's WU-Blast2 (http://www.ebi.ac.uk/blast2/) server has been providing free access to these search services since 1997 and today supports many features that both enhance the usability and expand on the scope of the software.
Comparison of In Vivo and Ex Vivo MRI for the Detection of Structural Abnormalities in a Mouse Model of Tauopathy

With increasingly large numbers of mouse models of human disease dedicated to MRI studies, compromises between in vivo and ex vivo MRI must be fully understood in order to inform the choice of imaging methodology. We investigate the application of high resolution in vivo and ex vivo MRI, in combination with tensor-based morphometry (TBM), to uncover morphological differences in the rTg4510 mouse model of tauopathy. The rTg4510 mouse also offers a novel paradigm by which the overexpression of mutant tau can be regulated by the administration of doxycycline, providing us with a platform on which to investigate more subtle alterations in morphology with morphometry. Both in vivo and ex vivo MRI allowed the detection of widespread bilateral patterns of atrophy in the rTg4510 mouse brain relative to wild-type controls. Regions of volume loss aligned with neuronal loss and pathological tau accumulation demonstrated by immunohistochemistry. When we sought to investigate more subtle structural alterations in the rTg4510 mice relative to a subset of doxycycline-treated rTg4510 mice, ex vivo imaging enabled the detection of more regions of morphological brain changes. The disadvantages of ex vivo MRI may however mitigate this increase in sensitivity: we observed a 10% global shrinkage in brain volume of the post-mortem tissues due to formalin fixation, which was most notable in the cerebellum and olfactory bulbs. However, many central brain regions were not adversely affected by the fixation protocol, perhaps due to our “in-skull” preparation. The disparity between our TBM findings from in vivo and ex vivo MRI underlines the importance of appropriate study design, given the trade-off between these two imaging approaches. We support the utility of in vivo MRI for morphological phenotyping of mouse models of disease; however, for subtler phenotypes, ex vivo offers enhanced sensitivity to discrete morphological changes.
.1 past Work

Solid modeling is a useful tool for tasks such as representing the virtual environment for virtual reality systems; representing the real environment for robot programming; and modeling real objects for object recognition. Currently, most object models are constructed by human operators [2]. It would be much better to have a system that can automatically build models of real objects that it observes. If we can develop a reliable technique to generate accurate 3D object models by observing real objects from multiple views, we can reduce the effort and cost of model construction, and we can significantly broaden the application areas of solid modeling. Observation-based modeling systems usually work with a sequence of images of the object(s), where the sequence spans a smoothly varying change in the positions of the sensor and/or object(s). Most previous systems have attempted to apply inter-frame motion estimates to successive pairs of views in a sequential manner [12]. Whenever a new view is introduced, it is matched with the previous view, and the transformation between these two successive views has to be recovered before the object model is updated. This sequential method does not work well in practice because local motion estimates are subject to noise and missing data. Local mismatching errors accumulate and propagate along the sequence, yielding erroneous object models. Rather than sequentially integrating successive pairs of view, we should instead search for the statistically optimal object model that is most consistent with all the views. Although every single view provides only partial information of the object, it is likely that any part of the object will be observed a number of times along the sequence. Object modeling from this sequence of views can be formulated as an overdetermined minimization problem because significant redundancy exists among all the views. Much work has been done in object modeling from a sequence of range images [4]. Most work assumed that transformation between successive views is either known or can be recovered, so that all data can be transformed to a fixed coordinate system. For example, Bhanu [3] rotated the object through known angles. Ahuja and Veenstra [1] constructed an octree object model from orthogonal views. Soucy and Laurendeau [16] proposed to trian-gulate each view and merge multiple views via a Venn diagram when the transformation is known. Because building a Venn diagram is combinatorial, only four-view merging is pre
Beliefs about language learning: Exploring links to personality traits

This paper reports on an exploratory study that aimed to investigate a possible relationship between learners’ beliefs about language learning and their personality traits – a relationship suggested by a number of interdisciplinary studies, including those in cognitive and personality psychology. The motive for conducting this study was to seek to explain the previously reported stable nature of certain learner beliefs, particularly since negative beliefs can be detrimental to the learning process. Data from 262 ESL learners were collected using the Beliefs About Language Learning Inventory (Horwitz, 1987) and the NEO-Five Factory Inventory (Costa & McCrae, 1985), and analyzed using factor analysis and multivariate regression analysis. Overall, the results do not suggest a strong relationship between learner beliefs and personality traits in the population sampled.
An Open-Loop Flow Control Scheme Based on the Accurate Global Information of On-Chip Communication

3D stacked memory is being adopted as a promising solution to offer high bandwidth and low latency in memory access. Compared with the on-chip network design with conventional off chip memory, it gives a new problem of minimizing communication conflicts since multiple concurrent high bandwidth data transfers will flow through the on-chip network. In order to tackle this problem, we propose applying an open-loop flow control scheme based on the accurate global information (destination and status) of on-chip communication. The proposed open-loop flow control scheme exploits the information and selectively buffers and arbitrates data transfers to remove conflicts at destinations in a preventive manner. As an implementation of the presented scheme, we present on-chip buffers called Buf3D's that share the global information with each other to perform the selective buffering and arbitration of data transfers. Experiments with synthetic test cases and an industrial strength DTV design show that the proposed method improves aggregate memory bandwidth significantly (average 19.0 %~25.8 % in the synthetic cases and up to 18.4 % in the DTV case) with a small area overhead (15.2 % in the DTV case) of on-chip network.
Improving ISD Agility in Fast-Moving Software Organizations

Fast-moving software organizations must respond quickly to changing technological options and market trends while delivering high-quality services at competitive prices. Improving agility of information systems development (ISD) may reconcile these inherent tensions, but previous research of agility predominantly focused separately on managing either the individual project or the organization. Limited research has investigated the management that ties the agility of individual projects with the company agility characterizing fast-moving organizations. This paper reports an action research study on how to improve ISD agility in a fast-moving software organization. The study maps central problems in the ISD management to direct improvements of agility. Our following intervention addressed method improvements in defining types of ISD by customer relations and integrating the method with the task management tool used by the organization. The paper discusses how the action research contributes to our understanding of ISD agility in fast-moving software organizations with a framework for mapping and evaluating improvements of agility. The action research specifically points out that project managers need to attend to the company’s agility in relating to customers, that company agility links to project agility, and that this requires light method and tool support.
m-Power regularized least squares regression

Regularization is used to find a solution that both fits the data and is sufficiently smooth, and thereby is very effective for designing and refining learning algorithms. But the influence of its exponent remains poorly understood. In particular, it is unclear how the exponent of the reproducing kernel Hilbert space (RKHS) regularization term affects the accuracy and the efficiency of kernel-based learning algorithms. Here we consider regularized least squares regression (RLSR) with an RKHS regularization raised to the power of m, where m is a variable real exponent. We design an efficient algorithm for solving the associated minimization problem, we provide a theoretical analysis of its stability, and we compare it with respect to computational complexity and prediction accuracy to the classical kernel ridge regression algorithm where the regularization exponent m is fixed at 2. Our results show that the m-power RLSR problem can be solved efficiently, and support the suggestion that one can use a regularization term that grows significantly slower than the standard quadratic growth in the RKHS norm.
Expression of the AcrAB Components of the AcrAB-TolC Multidrug Efflux Pump of Yersinia enterocolitica Is Subject to Dual Regulation by OmpR

OmpR is a transcriptional regulator implicated in the control of various cellular processes and functions in Enterobacteriaceae. This study was undertaken to identify genes comprising the OmpR regulon in the human gastrointestinal pathogen Yersinia enterocolitica. Derivatives of an ompR-negative strain with random transposon insertions creating transcriptional fusions with the reporter gene lacZ were isolated. These were supplied with the wild-type ompR allele in trans and then screened for OmpR-dependent changes in β-galactosidase activity. Using this strategy, five insertions in genes/operons positively regulated by OmpR and two insertions in genes negatively regulated by this protein were identified. Genetic analysis of one of these fusion strains revealed that the gene acrR, encoding transcriptional repressor AcrR is negatively regulated by OmpR. Differential analysis of membrane proteins by SDS-PAGE followed by mass spectrometry identified the protein AcrB, a component of the AcrAB-TolC multidrug efflux pump, as being positively regulated by OmpR. Analysis of the activity of the acrR and acrAB promoters using gfp fusions confirmed their OmpR-dependent repression and activation, respectively. The identification of putative OmpR-binding sites and electrophoretic mobility shift assays confirmed that this regulator binds specifically to both promoter regions with different affinity. Examination of the activity of the acrR and acrAB promoters after the exposure of cells to different chemicals showed that bile salts can act as an OmpR-independent inducer. Taken together, our findings suggest that OmpR positively controls the expression of the AcrAB-TolC efflux pump involved in the adaptive response of Y. enterocolitica O:9 to different chemical stressors, thus conferring an advantage in particular ecological niches.
A robust protocol stack for multi-hop wireless body area networks with transmit power adaptation

Wireless Body Area Networks (WBANs) have characteristic properties that should be considered for designing a proper network architecture. Movement of on-body sensors, low quality and time-variant wireless links, and the demand for a reliable and fast data transmission at low energy cost are some challenging issues in WBANs. Using ultra low power wireless transceivers to reduce power consumption causes a limited transmission range. This implies that a multi-hop protocol is a promising design choice. This paper proposes a multi-hop protocol for human body health monitoring. The protocol is robust against frequent changes of the network topology due to posture changes, and variation of wireless link quality. A technique for adapting the transmit power of sensor nodes at run-time allows to optimize power consumption while ensuring a reliable outgoing link for every node in the network and avoiding network disconnection.
Localization and Quantification of Callose in the Streptophyte Green Algae Zygnema and Klebsormidium: Correlation with Desiccation Tolerance

Freshwater green algae started to colonize terrestrial habitats about 460 million years ago, giving rise to the evolution of land plants. Today, several streptophyte green algae occur in aero-terrestrial habitats with unpredictable fluctuations in water availability, serving as ideal models for investigating desiccation tolerance. We tested the hypothesis that callose, a β-d-1,3-glucan, is incorporated specifically in strained areas of the cell wall due to cellular water loss, implicating a contribution to desiccation tolerance. In the early diverging genus Klebsormidium, callose was drastically increased already after 30 min of desiccation stress. Localization studies demonstrated an increase in callose in the undulating cross cell walls during cellular water loss, allowing a regulated shrinkage and expansion after rehydration. This correlates with a high desiccation tolerance demonstrated by a full recovery of the photosynthetic yield visualized at the subcellular level by Imaging-PAM. Furthermore, abundant callose in terminal cell walls might facilitate cell detachment to release dispersal units. In contrast, in the late diverging Zygnema, the callose content did not change upon desiccation for up to 3.5 h and was primarily localized in the corners between individual cells and at terminal cells. While these callose deposits still imply reduction of mechanical damage, the photosynthetic yield did not recover fully in the investigated young cultures of Zygnema upon rehydration. The abundance and specific localization of callose correlates with the higher desiccation tolerance in Klebsormidium when compared with Zygnema.
Situated Software Development: Work Practice and Infrastructure Are Mutually Constitutive

Software developers' work is much more interesting and multifarious in practice than formal definitions of software development processes imply. Rational models of work are often representations of processes defined as they should be performed, rather than portrayals of what people actually do in practice. These models offer a simplified picture of the phenomena involved, and are frequently confused with how the work is carried out in reality, or they are advocated as the ideal way to accomplish the work. A longitudinal ethnographic study (45 days of fieldwork over 20 months) of a group of professional software developers revealed the importance of including their observed practice, and the "infrastructure" that supports and shapes this practice, in an authentic account of their work. Moreover, this research revealed that software development work practice and the infrastructure used to produce software are inextricably entwined and mutually constitutive over time.
How Much NFV Should a Service Provider Adopt?

Network function virtualization and software-defined networking have the potential to change provider revenue streams and offer new services. We measure the impact of NFV on large provider networks by accurately modeling a contemporary service provider. In our model, we consider actual equipment that is currently deployed and understand the impact of NFV on CapEx, OpEx, and service delivery. Apart from accurately modeling a contemporary provider network, we also inculcate robustness in the model to factor in for uncertainty of network traffic. We answer the key questions: what functions in a network can be virtualized and which functions need to continue as traditional hardware? We also harp upon the question as to what new services can be considered and in which circumstances? Our model considers various combinations of network architectures that are used in contemporary networks. The model is supported by extensive analysis and simulation that verify our results from cost, performance, and scalability (of services and the model itself) perspectives.
An exploration of consumers' online complaining behaviour on Facebook

This study explores online consumer complaining behaviour (CCB) on Facebook. The main objectives are: (1) to understand how Facebook is being used as a medium for consumer complaining, and (2) to understand why consumers complain on Facebook. An interpretative research position and multi-method qualitative design was adopted. Netnography, a form of online ethnography, is employed as the research design. First, participant-observational methods examined posts and discussions on Facebook’s official company pages, user-created pages/groups and user profiles. Then, online in-depth interviews were conducted with 37 consumers who were identified through online observations. These interviews were conducted in three forms: text-based, video-based, and email interviewing. The findings shed light on the nature (i.e. customs and manners) of online complaining on Facebook, consumers’ interactions with other consumers and companies, the role of Facebook in the complaint process, consumers’ objectives for complaining on Facebook, and outcomes of these. A key contribution of this study is the development of two models: ‘Integrated Model of CCB’ which aims to explain the organisation of complaining actions by integrating existing CCB theories and ‘Model of online CCB on Facebook’ which describes the range of online complaining actions on Facebook. On the basis of the findings of this study, research implications and recommendations for the management are suggested.
A dynamic conditional random field model for foreground and shadow segmentation

This paper proposes a dynamic conditional random field (DCRF) model for foreground object and moving shadow segmentation in indoor video scenes. Given an image sequence, temporal dependencies of consecutive segmentation fields and spatial dependencies within each segmentation field are unified by a dynamic probabilistic framework based on the conditional random field (CRF). An efficient approximate filtering algorithm is derived for the DCRF model to recursively estimate the segmentation field from the history of observed images. The foreground and shadow segmentation method integrates both intensity and gradient features. Moreover, models of background, shadow, and gradient information are updated adaptively for nonstationary background processes. Experimental results show that the proposed approach can accurately detect moving objects and their cast shadows even in monocular grayscale video sequences.
THE MEGASECOND CHANDRA X-RAY VISIONARY PROJECT OBSERVATION OF NGC 3115. II. PROPERTIES OF POINT SOURCES

We carried out an in-depth study of low-mass X-ray binaries (LMXBs) detected in the nearby lenticular galaxy NGC 3115 using the Megasecond Chandra X-ray Visionary Project observation (total exposure time 1.1 Ms). In total we found 136 candidate LMXBs in the field and 49 in globular clusters (GCs) above 2σ detection, with 0.3–8 keV luminosity LX ∼ 1036–1039 erg s−1. Other than 13 transient candidates, the sources overall have less long-term variability at higher luminosity, at least at L X ≳ 2 × 10 37 ? > erg s−1. In order to identify the nature and spectral state of our sources, we compared their collective spectral properties based on single-component models (a simple power law or a multicolor disk) with the spectral evolution seen in representative Galactic LMXBs. We found that in the LX versus photon index Γ PL ? > and LX versus disk temperature kTMCD plots, most of our sources fall on a narrow track in which the spectral shape hardens with increasing luminosity below L X ∼ 7 × 10 37 ? > erg s−1, but is relatively constant ( Γ PL ∼ 1.5 ? > or kT MCD ∼ 1.5 ? > keV) above this luminosity, which is similar to the spectral evolution of Galactic neutron star (NS) LMXBs in the soft state in the Chandra bandpass. Therefore, we identified the track as the NS LMXB soft-state track and suggested sources with L X ≲ 7 × 10 37 ? > erg s−1 as atolls in the soft state and those with L X ≳ 7 × 10 37 ? > erg s−1 as Z sources. Ten other sources (five are transients) displayed significantly softer spectra and are probably black hole X-ray binaries in the thermal state. One of them (persistent) is in a metal-poor GC.
Counterfactual Learning for Machine Translation: Degeneracies and Solutions

Counterfactual learning is a natural scenario to improve web-based machine translation services by offline learning from feedback logged during user interactions. In order to avoid the risk of showing inferior translations to users, in such scenarios mostly exploration-free deterministic logging policies are in place. We analyze possible degeneracies of inverse and reweighted propensity scoring estimators, in stochastic and deterministic settings, and relate them to recently proposed techniques for counterfactual learning under deterministic logging.
A method for predicting disease subtypes in presence of misclassification among training samples using gene expression: application to human breast cancer

MOTIVATION An accurate diagnostic and prediction will not be achieved unless the disease subtype status for every training sample used in the supervised learning step is accurately known. Such an assumption requires the existence of a perfect tool for disease diagnostic and classification, which is seldom available in the majority of the cases. Thus, the supervised learning step has to be conducted with a statistical model that contemplates and handles potential mislabeling in the input data. RESULTS A procedure for handling potential mislabeling among training samples in the prediction of disease subtypes using gene expression data was proposed. A real data-based simulation study about the estrogen receptor status (ER+/ER-) of breast cancer patients was conducted. The results demonstrated that when 1-4 training samples (N = 30) were artificially mislabeled, the proposed method was able not only in correcting the ER status of mislabeled training samples but also more importantly in predicting the ER status of validation samples as well as using 'true' training data.