Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Correlates of Multisensory Perceptual Learning

The brain's ability to bind incoming auditory and visual stimuli depends critically on the temporal structure of this information. Specifically, there exists a temporal window of audiovisual integration within which stimuli are highly likely to be perceived as part of the same environmental event. Several studies have described the temporal bounds of this window, but few have investigated its malleability. Recently, our laboratory has demonstrated that a perceptual training paradigm is capable of eliciting a 40% narrowing in the width of this window that is stable for at least 1 week after cessation of training. In the current study, we sought to reveal the neural substrates of these changes. Eleven human subjects completed an audiovisual simultaneity judgment training paradigm, immediately before and after which they performed the same task during an event-related 3T fMRI session. The posterior superior temporal sulcus (pSTS) and areas of auditory and visual cortex exhibited robust BOLD decreases following training, and resting state and effective connectivity analyses revealed significant increases in coupling among these cortices after training. These results provide the first evidence of the neural correlates underlying changes in multisensory temporal binding likely representing the substrate for a multisensory temporal binding window.
A Structured Approach to Hearing Aid Selection

Many different procedures have been suggested for use in hearing aid selection: the practicing audiologist must choose among them. A structured approach to the hearing aid selection process is most likely to result in an optimal choice for each patient. This paper describes one such approach to the process of hearing aid selection which attempts to customize the selection procedure to make maximum use of each individual patient's response capabilities. The approach is presented in generic form and one implementation is described in detail.
How Groups Co-ordinate their Concepts and Terminology: Implications for Medical Informatics

Abstract Conceptual and terminological systems are established and maintained by the communities who use them. This paper reports experiments which investigate the role of communication and interaction in the process. The experiments show that isolated pairs of communicators and virtual communities of interacting pairs naturally converge on their own conceptual and terminological systems when confronted with a common task. The results also indicate that the system converged on is optimal for that particular group engaged in that particular task. These findings are discussed in relation to the increasing use of tightly coordinated medical teams and its implications for getting them to adopt standardized medical terminologies.
Trial Watch

Adoptive cell transfer (ACT) represents a prominent form of immunotherapy against malignant diseases. ACT is conceptually distinct from dendritic cell-based approaches (which de facto constitute cellular vaccines) and allogeneic transplantation (which can be employed for the therapy of hematopoietic tumors) as it involves the isolation of autologous lymphocytes exhibiting antitumor activity, their expansion/activation ex vivo and their reintroduction into the patient. Re-infusion is most often performed in the context of lymphodepleting regimens (to minimize immunosuppression by host cells) and combined with immunostimulatory interventions, such as the administration of Toll-like receptor agonists. Autologous cells that are suitable for ACT protocols can be isolated from tumor-infiltrating lymphocytes or generated by engineering their circulating counterparts for the expression of transgenic tumor-specific T-cell receptors. Importantly, lymphocytes can be genetically modified prior to re-infusion for increasing their persistence in vivo, boosting antitumor responses and minimizing side effects. Moreover, recent data indicate that exhausted antitumor T lymphocytes may be rejuvenated in vitro by exposing them to specific cytokine cocktails, a strategy that might considerably improve the clinical success of ACT. Following up the Trial Watch that we published on this topic in the third issue of OncoImmunology (May 2012), here we summarize the latest developments in ACT-related research, covering both high-impact studies that have been published during the last 13 months and clinical trials that have been initiated in the same period to assess the antineoplastic profile of this form of cellular immunotherapy.
GRADE SERIES - SHARON STRAUS, RACHEL CHURCHILL AND SASHA SHEPPERD, GUEST EDITORS GRADE guidelines: 5. Rating the quality of evidencedpublication bias

AbstractIn the GRADE approach, randomized trials start as high-quality evidence and observational studies as low-quality evidence, but bothcan be rated down if a body of evidence is associated with a high risk of publication bias. Even when individual studies included in best-evidence summaries have a low risk of bias, publication bias can result in substantial overestimates of effect. Authors should suspect pub-lication bias when available evidence comes from a number of small studies, most of which have been commercially funded. A number ofapproaches based on examination of the pattern of data are available to help assess publication bias. The most popular of these is the funnelplot; all, however, have substantial limitations. Publication bias is likely frequent, and caution in the face of early results, particularly withsmall sample size and number of events, is warranted. 2011 Elsevier Inc. All rights reserved. Keywords: GRADE; Quality of evidence; Publication bias; Funnel plot; Conﬂict of interest; Pharmaceutical industry
Security Alert Management System for Internet Data Center Based on ISO/IEC 27001 Ontology

Internet Data Centers (IDC) emerge as a major network service platform to converge Internet related services and applications to one location, managing servers, networks, together with valuable and sensitive data of many enterprises. Therefore, an appropriate security approach is essential. Intrusion Detection Systems (IDS) are often deployed in IDC as a security measure to detect real-time intrusions and alert system administrators to take proper handling actions. However, a large number of low-level alerts lacking of classification make their management difficult. To tackle this problem, we propose a Security Alert Management System (SAMS) in which alerts generated by each IDS undergo alert aggregation. By incorporating ISO/IEC 27001 requirements into the ontology, our system classifies and aggregates alerts from multiple sources, providing a consolidated view of security incidents which are compliant with the ISO/IEC 27001 standard. We further facilitate effective handling of security alerts with different urgency classifications via an Alert Management System (AMS).
Conformational Optimization with Natural Degrees of Freedom: A Novel Stochastic Chain Closure Algorithm

The present article introduces a set of novel methods that facilitate the use of "natural moves" or arbitrary degrees of freedom that can give rise to collective rearrangements in the structure of biological macromolecules. While such "natural moves" may spoil the stereochemistry and even break the bonded chain at multiple locations, our new method restores the correct chain geometry by adjusting bond and torsion angles in an arbitrary defined molten zone. This is done by successive stages of partial closure that propagate the location of the chain break backwards along the chain. At the end of these stages, the size of the chain break is generally reduced so much that it can be repaired by adjusting the position of a single atom. Our chain closure method is efficient with a computational complexity of O(N(d)), where N(d) is the number of degrees of freedom used to repair the chain break. The new method facilitates the use of arbitrary degrees of freedom including the "natural" degrees of freedom inferred from analyzing experimental (X-ray crystallography and nuclear magnetic resonance [NMR]) structures of nucleic acids and proteins. In terms of its ability to generate large conformational moves and its effectiveness in locating low energy states, the new method is robust and computationally efficient.
Using SVM with Adaptively Asymmetric MisClassification Costs for Mine-Like Objects Detection

Real world data mining applications such as Mine Countermeasure Missions (MCM) involve learning from imbalanced data sets, which contain very few instances of the minority classes and many instances of the majority class. For instance, the number of naturally occurring clutter objects (such as rocks) that are detected typically far outweighs the relatively rare event of detecting a mine. In this paper we propose support vector machine with adaptive asymmetric misclassification costs (instances weighted) to solve the skewed vector spaces problem in mine countermeasure missions. Experimental results show that the given algorithm could be used for imbalanced sonar image data sets and makes an improvement in prediction performance.
Some System Identification Challenges and Approaches

Abstract The field of control-oriented system identification is mature. Nevertheless, it is still very active. This is because there are many important unsolved challenges. Of these, this paper considers a selection. This involves considering the estimation of general nonlinear model structures, together with accurate error bounds, using methods that scale well to models of high dimension. A particular strength of the system identification field is that it has always actively sought to understand, embrace and develop ideas from other fields, such as statistics, mathematics and econometrics. This paper proposes a continuation of this successful strategy by proposing and profiling the adoption of new ideas originating in statistics, signal processing and statistical mechanics.
Feature Selection for Arabic Topic Detection Using Named Entities

With the exponential growth of available Arabic electronic documents, Topic Detection has become a key task in classifying and processing large Arabic corpora. However, due to noisy information embedded in documents and the high dimension of associated feature sets, Topic Detection (TD) happens to be a challenging issue. Thus, adopting a proper features selection approach will positively influence the TD process performances and should consequently, receive great attention. In this paper, we propose a novel feature selection approach for Topic Detection in Arabic unvowelized documents using Named Entities (NEs). We propose to restrict the TD task to segments of documents that are bounded by Named Entity couples. Standard measures such as: Recall, Precision and F-measure were employed to evaluate the performances of the realized system on Wattan; an Arabic newspaper corpus. Experiments shows a significant dimension reduction of the feature sets and an improvement of topic detection accuracy. Keywords—Topic Detection;Named Entities;Feature Selection;Dimension Reduction.
The effect of rostering with a patient enrolment model on emergency department utilization.

OBJECTIVE To assess the effect of rostering with a patient enrolment model (PEM) in Ontario on emergency department utilization for non-emergent care. DATA SOURCES/STUDY SETTING Administrative data for fiscal years 2006/07 through 2010/11 from the Ontario Ministry of Health and Long-Term Care were used for the analysis. STUDY DESIGN Patient-level analysis with a difference-in-difference modelling approach was used to study the relationship. A control group was established using propensity score matching. PRINCIPAL FINDING Results suggest that rostering with a PEM is associated with a statistically significant reduction in emergency department (ED) (non-emergent) visits in Ontario. More specifically, enrolment with PEMs reduced ED visits by 3% during the study period, translating into cost savings of approximately $8 million for hospitals in Ontario. CONCLUSION This study shows that PEMs have achieved some degree of success in enhancing health system efficiency in Ontario through the reduction in the use of EDs for non-emergent care.
An Empirical Study of the Applicability of the Technology Acceptance Model to Application Development Outsourcing Decisions

This study empirically tests the applicability of the Technology Acceptance Model (TAM) to application development outsourcing decision making. Advances to TAM posit that perceived usefulness and ease of use mediate the effects of other variables on users’ attitudes towards a technology. The model tested in this study suggests that perceived usefulness and ease of use of outsourcing mediate the effects of the external environment, prior outsourcing relationships, and risks on outsourcing decision makers. A survey was developed and sent to 3000 IT decision makers. One hundred and sixty respondents provided data to confirm the applicability of TAM and the influences of these external variables. Support for the applicability of TAM was found. Three sub-dimensions of risk, Project Management, Relationship, and Employee Risk were identified. Project Management and Employee Risks along with Prior Relationships were found to significantly influence decision maker perceptions about application development outsourcing.
Emotional Responses to Artworks in Online Collections

Artworks have a strong emotional impact on the visitors of an exhibition. Many museums and associations opened their collections for access on the web and have studied the potential of social tagging. User data collected by tagging in art social platforms are a precious information source about emotional responses to artworks and a feedback about the way in which users perceives collections. In this paper, we present our new achievements on this topic within the ArsEmotica framework. Our focus is on eliciting sharable emotional meanings from online collection visitors tags, by interactively involving the users of the virtual communities in the process of capturing the latent emotions behind the tags. We rely on methods and tools from a set of disciplines ranging from Semantic and Social Web to NLP. Such disciplines provide the building blocks for creating a semantic social space, where artworks can be dynamically organized according to a new ontology of emotions inspired by the well-known Plutchik’s model of human emotions. The final aim is to involve users in the creation of such emotional space and, then, to offer them an emotion-driven access to the artworks.
Subclack: feature-oriented programming with behavioral feature interfaces

Feature-oriented programming extends object-oriented programming to support feature modularity. Feature modules typically cut across class boundaries to implement end-user-visible features. Customized program variants can be composed automatically given a selection of desired feature modules. We propose behavioral feature interfaces based on design by contract for precise localization of faulty feature modules. There are three different approaches for feature-module composition, which are considered to be equivalent in the literature. We discuss advantages and disadvantages for each approach with regard to behavioral feature interfaces. Based on our insights, we present Subclack as a new approach for feature-module composition combining the advantages of all existing approaches. In our examples, we use contracts defined in an feature-oriented extension of the Java Modeling Language, and discuss how they can be checked by means of runtime assertions.
A compact 2-D FDFD method for modeling microstrip structures with nonuniform grids and perfectly matched layer

Full-wave analysis of the microstrip structures is performed by using the compact two-dimensional (2-D) finite-difference frequency-domain (FDFD) method with nonuniform grids and perfectly matched layer (PML). The use of nonuniform grids can significantly reduce the computational matrix size. Less memory and CPU time are required as comparing with the original compact 2-D FDFD method. For the analysis of the microstrip structures with an absorbing boundary condition, the compact 2-D FDFD method with PML is presented. The performances of different PML thickness are studied. Numerical examples are presented to demonstrate the accuracy and efficiency of this method
Microfabricated platform for studying stem cell fates

Platforms that allow parallel, quantitative analysis of single cells will be integral to realizing the potential of postgenomic biology. In stem cell biology, the study of clonal stem cells in multiwell formats is currently both inefficient and time‐consuming. Thus, to investigate low‐frequency events of interest, large sample sizes must be interrogated. We report a simple, versatile, and efficient micropatterned arraying system conducive to the culture and dynamic monitoring of stem cell proliferation. This platform enables: 1) parallel, automated, long‐term (∼days to weeks), live‐cell microscopy of single cells in culture; 2) tracking of individual cell fates over time (proliferation, apoptosis); and 3) correlation of differentiated progeny with founder clones. To achieve these goals, we used microfabrication techniques to create an array of ∼10,000 microwells on a glass coverslip. The dimensions of the wells are tunable, ranging from 20 to >500 μm in diameter and 10–500 μm in height. The microarray can be coated with adhesive proteins and is integrated into a culture chamber that permits rapid (∼min), addressable monitoring of each well using a standard programmable microscope stage. All cells share the same media (including paracrine survival signals), as opposed to cells in multiwell formats. The incorporation of a coverslip as a substrate also renders the platform compatible with conventional, high‐magnification light and fluorescent microscopy. We validated this approach by analyzing the proliferation dynamics of a heterogeneous adult rat neural stem cell population. Using this platform, one can further interrogate the response of distinct stem cell subpopulations to microenvironmental cues (mitogens, cell–cell interactions, and cell–extracellular matrix interactions) that govern their behavior. In the future, the platform may also be adapted for the study of other cell types by tailoring the surface coatings, microwell dimensions, and culture environment, thereby enabling parallel investigation of many distinct cellular responses. © 2004 Wiley Periodicals, Inc.
Data Quality - The Key Success Factor for Data Driven Engineering

As the scale and diversity of data grows in the digital arena, the complexities of data driven engineering grow multifold with it. The last several years have brought forth several new technologies to service this need - semantic Web, grid systems, Web service composition to mention a few. However, a fundamental underpinning of the success of these technologies resides in the quality of data that they can provide. Often the failure of a technology is attributed to its functionality when the real problem lies in the quality of data it uses and subsequently produces. In this paper, we highlight a need to embrace data quality considerations in all aspects of data driven engineering.
GPU-based real-time virtual reality modeling and simulation of seashore

This paper is devoted to efficient algorithms for real-time rendering of seashore using programmable Graphics Processing Unit (GPU). The scene of seashore is a usual component of virtual environment in simulators or games and should be realistic and real-time. We realized the realtime seashore simulation in three steps: first the ocean wave generation, using a simple but high-efficiency model which can describe both shallow and deep ocean; second the optical effects imitation and third the interaction of ocean waves with the coast, including the coastline simulation designed mathematically and the breaking waves simulation designed by 3D Be´zier curved surface via metamorphosing and key-frame animation. Scenes under different atmospheric conditions are also presented in this paper.
MH-1, a computer-operated mechanical hand

MH-1 is a motorized and sensitized servomanipulator operated by the TX-O computer at the Massachusetts Institute of Technology. It serves as an experimental vehicle to explore the feasibility of direct relations between a digital computer and the physical world with which this computer is concerned. Usually, a human interpreter stands between the computer and the physical world. Instead, the TX-O computer in the MH-1 system is programmed to perform by itself some of the functions normally assigned to the human intermediary; namely, to perceive the world, to appreciate it, and to determine a reasonable course of action after a goal has been specified for the hand. The data processing tools used are, rather than numerical operations on quantitative signals, pattern recognition and simulation of higher cognitive processes such as awareness and understanding. This paper describes some of the experiments performed with MH-1 and the mechanisms upon which the capabilities of MH-1 are based.
Approximate Incremental Big-Data Harmonization

The needs of `big data analytics' increasingly require IT organizations to ingest, process, and extract business insights from ever larger volumes of data that arrive far more rapidly than before, as well as from new sources such as social media, mobile devices, and sensors. However, in order to extract insights from diverse information feeds from multiple, often unrelated sources, these first need to be correlated or harmonized to a common level of granularity. We formally define this commonly arising data harmonization problem. We show how to correlate disparate data sources using map-reduce, but in an approximate and/or incremental manner as often required in practice. We motivate our techniques through a real-life enterprise data-harmonization case study for which we describe our performance results on big-data technologies, namely, Map Reduce, Hadoop and PIG.
Cross Lingual Lexical Substitution Using Word Representation in Vector Space

Polysemous words acquire different senses and meanings from their contexts. Representing words in vector space as a function of their contexts captures some semantic and syntactic features for words and introduces new useful relations between them. In this paper, we exploit different vectorized representations for words to solve the problem of Cross Lingual Lexical Substitution. We compare our techniques with different systems using two measures: “best” and “out-of-ten” (oot), and show that our techniques outperform the state of the art in the “oot” measure while keeping a reasonable performance in the “best” measure.
Personal Data Lake with Data Gravity Pull

This paper presents Personal Data Lake, a unified storage facility for storing, analyzing and querying personal data. A data lake stores data regardless of format and thus provides an intuitive way to store personal data fragments of any type. Metadata management is a central part of the lake architecture. For structured/semi-structured data fragments, metadata may contain information about the schema of the data so that the data can be transformed into queryable data objects when required. For unstructured data, enabling gravity pull means allowing third-party plugins so that the unstructured data can be analyzed and queried.