Miracle-dz/UniFAR-FTUs huggingface dataset:
SecureCyber: Risk-Based Optimization through Common Vulnerability Systems Scoring Over Energy Smart Grid

This paper introduces the idea of CVSS-host scores which utilize CVSS parameters to provide impact scoring for Smart Grid Environment. This scoring mechanism presents a novel view of system risk by framing an-upper bounds on the criticality of potential vulnerabilities in that system. Once this scoring system has been established, the CVSS vectors can then be utilized to perform more sophisticated calculations to investigate optimal costs and benefits for future security enhancements.
Tumor protein D52 (isoform 3) contributes to prostate cancer cell growth via targeting nuclear factor-κB transactivation in LNCaP cells

Our previous study showed that TPD52 overexpression could increase migration and proliferation of LNCaP cells contributing to the development of prostate cancer. However, mechanism of TPD52 in prostate cancer initiation and progression remains elusive. In this study, we investigated the possible underlying mechanism of TPD52 in prostate cancer progression. In LNCaP cells, TPD52 expression was altered by transfecting with either EGFP-TPD52 or specific short hairpin RNA. Overexpression of TPD52 protected LNCaP cells from apoptosis through elevated anti-apoptotic proteins XIAP, Bcl-2, and Cyclin D1, whereas Bax was downregulated. Mechanistically, we found that TPD52 confers transactivation of nuclear factor-κB, thereby enhancing its target gene expression in LNCaP cells. TPD52 promotes LNCaP cell invasion probably via increased matrix metalloproteinase 9 expression and its activity while tissue inhibitor of metalloproteinase expression is significantly downregulated. Notably, TPD52 might be involved in cell adhesion, promoting tumor metastasis by inducing loss of E-cadherin, expression of vimentin and vascular cell adhesion molecule, and additionally activation of focal adhesion kinase. Furthermore, TPD52 directly interacts with nuclear factor-κB p65 (RelA) and promotes accumulation of phosphorylated nuclear factor-κB (p65)S536 that is directly linked with nuclear factor-κB transactivation. Indeed, depletion of TPD52 or inhibition of nuclear factor-κB in TPD52-positive cells inhibited secretion of tumor-related cytokines and contributes to the activation of STAT3, nuclear factor-κB, and Akt. Interestingly, in TPD52 overexpressing LNCaP cells, nuclear factor-κB inhibition prevented the autocrine/paracrine activation of STAT3. TPD52 activates STAT3 through ascertaining a cross talk between the nuclear factor-κB and the STAT3 signaling systems. Collectively, these results reveal mechanism by which TPD52 is associated with prostate cancer progression and highlight the approach for therapeutic targeting of TPD52 in prostate cancer.
Shading for Fourier volume rendering

The work presented here describes two methods to incorporate viable illumination models into Fourier Volume Rendering (FVR). The lack of adequate illumination has been one of the impediments for the wide spread acceptance of FVR. Our first method adapts the Gamma Corrected Hemispherical Shading (GCHS) proposed by Scoggins et al. (2001) for FVR. We achieve interactive rendering for constant diffusive light sources. Our second method operates on data transformed by spherical harmonic functions. This latter approach allows for illumination under varying light directions. It should be noted that we only consider diffuse lighting in this paper. We demonstrate and compare the effect of these two new models on the rendered image and document speed and accuracy improvements.
Estimating query representativeness for query-performance prediction

The query-performance prediction (QPP) task is estimating retrieval effectiveness with no relevance judgments. We present a novel probabilistic framework for QPP that gives rise to an important aspect that was not addressed in previous work; namely, the extent to which the query effectively represents the information need for retrieval. Accordingly, we devise a few query-representativeness measures that utilize relevance language models. Experiments show that integrating the most effective measures with state-of-the-art predictors in our framework often yields prediction quality that significantly transcends that of using the predictors alone.
Novel Features of the Light Chain of Microtubule-associated Protein MAP1B: Microtubule Stabilization, Self Interaction, Actin Filament Binding, and Regulation by the Heavy Chain

Previous studies on the role of microtubule-associated protein 1B (MAP1B) in adapting microtubules for nerve cell-specific functions have examined the activity of the entire MAP1B protein complex consisting of heavy and light chains and revealed moderate effects on microtubule stability. Here we have analyzed the effects of the MAP1B light chain in the absence or presence of the heavy chain by immunofluorescence microscopy of transiently transfected cells. Distinct from all other MAPs, the MAP1B light chain–induced formation of stable but apparently flexible microtubules resistant to the effects of nocodazole and taxol. Light chain activity was inhibited by the heavy chain. In addition, the light chain was found to harbor an actin filament binding domain in its COOH terminus. By coimmunoprecipitation experiments using epitope-tagged fragments of MAP1B we showed that light chains can dimerize or oligomerize. Furthermore, we localized the domains for heavy chain–light chain interaction to regions containing sequences homologous to MAP1A. Our findings assign several crucial activities to the MAP1B light chain and suggest a new model for the mechanism of action of MAP1B in which the heavy chain might act as the regulatory subunit of the MAP1B complex to control light chain activity.
Évolution des stratégies d'élaboration du projet musical et de sa réalisation dans l'apprentissage du violon

L'etude de l'interpretation a souvent conduit a opposer des aspects moteurs et cognitifs ou encore un niveau de planification et d'execution. Ces descriptions confortees par l'opposition usuelle entre musique et technique, conduisent souvent a sur-valoriser le role de la planification et surtout a dissocier ces deux aspects de l'interpretation. En analogie a la definition de l'action proposee par K. Newell (1986), nous suggerons au contraire que l'interpretation emerge d'un ensemble de contraintes : celles de la partition, de l'instrument et des competences des violonistes. Les processus d'apprentissage s'apparentent donc davantage a une succession de reorganisations sous l'effet de ces differentes contraintes qu'a l'amelioration continue des memes strategies. Les experiences presentees ici jouent chacune sur l'un des poles de cette relation tripartite. Les deux premieres ont permis de mettre en valeur des changements de strategies de lecture en fonction des competences des musiciens et des caracteristiques de la partition. Les deux suivantes, grâce a la mise en relation d'une analyse du mouvement et de leur resultat sonore, montrent que la realisation du projet musical depend etroitement de la maitrise des caracteristiques physiques des instruments. A la difference des modeles seriels frequemment utilises, la definition de l'interpretation que nous proposons permet ainsi d'envisager l'existence de processus ascendants : les interactions du violoniste avec son instrument conduisent non seulement a l'enrichissement de son projet musical, mais aussi plus largement a l'elaboration de concepts essentiels pour la maitrise de la lecture musicale et pour les violonistes, celle de l'intonation.
Modeling biomass collection and woods processing life-cycle analysis.

Abstract A deterministic spreadsheet model developed in an earlier Consortium for Research on Renewable Industrial Materials (CORRIM) project that calculates cost, fuel, and chemical outputs of forest management and harvesting activities was modified to include logic for systems used to recover forest residue. Two illustrative biomass recovery systems with variations were modeled. A system to recover residues after whole-tree harvesting operations was applied to a representative forest stand in the Inland West. Whole-tree chipping in an early thinning was applied to a representative forest stand in the Southeast United States. Emission factors and life-cycle outputs were developed for the systems through the SimaPro v7.3 model using one if its environmental impact methodologies called TRACI2. Most environmental outputs, including global warming potential, had a direct relationship to fuel consumption of the recovery systems. These outputs were subsequently used as inputs to life-cycle analysis in biofuel .
Antigenic drift in influenza A viruses. I. Selection and characterization of antigenic variants of A/PR/8/34 [HON1] influenza virus with monoclonal antibodies

Antigenic variants of A/PR/8/34 [HON1] influenza virus were selected after a single passage of the parent virus in embryonated chicken eggs in the presence of monoclonal antibodies to this virus. The monoclonal antibodies were produced by a hybridoma and were specific for an antigenic determinant on the HA molecule of the parent virus. Seven antigenic variants were analyzed with 95 monoclonal anti-HA antibodies prepared in vitro in the splenic fragment culture system. Three subgroups of antigenic variants were distinguished. The antigenic changes were primarily recognized by monoclonal antibodies to the strain- specific determinants of the parental hemagglutinin (HA) molecule. Monoclonal antibodies to HA determinants shared (in an identical or cross-reactive form) by parental virus and more than three heterologous viruses of the HON1 and H1N1 subtypes were unable to recognize the antigenic change on the variants. Similarly, heterogeneous antibody preparations could not differentiate between parental and variant viruses. The results are compatible with the idea that the HA of PR8 has available a large repertoire of antigenic modifications that may result from single amino acid substitutions, and that antigenic changes can occur in the strain- specific determinants on the HA molecule in the absence of concomitant changes in the cross-reactive HA determinants. The findings suggest that antigenic drift, in order to be epidemiologically significant, probably requires a series of amino acid substitutions in, or close to, the antigenic area on the HA molecule.
Poststroke depression correlates with cognitive impairment and neurological deficits.

BACKGROUND AND PURPOSE The prevalence of poststroke depression is known to be high, but the knowledge of its neuropsychological correlates is limited. This 12-month prospective study was designed to evaluate the natural history of poststroke depression and to study its neuropsychological, clinical, and functional associates. METHODS We studied a series of 106 consecutive patients (46 women and 60 men, mean age 65.8 years) with acute first-ever ischemic stroke. The patients underwent a neurological, psychiatric, and neuropsychological examination at 3 and 12 months after the stroke. The psychiatric diagnosis of depression was based on DSM-III-R-criteria. RESULTS Depression was diagnosed in 53% of the patients at 3 months and in 42% of the patients at 12 months after the stroke. The prevalence of major depression was 9% at 3 months and 16% at 12 months. There was an association between poststroke depression and cognitive impairment; the domains most likely to be defective in stroke-related depression were memory (P=0.022), nonverbal problem solving (P=0.039), and attention and psychomotor speed (P=0.020). The presence of dysphasia increased the risk of major depression. The depressive patients were more dependent in ADL and had more severe impairment and handicap than the nondepressive patients. CONCLUSIONS More than half of the patients suffer from depression after stroke, and the frequency of major depression seems to increase during the first year. In addition to dysphasia, poststroke depression is correlated with other cognitive deficits. We emphasize the importance of psychiatric evaluation of stroke patients.
A CGRA-Based Approach for Accelerating Convolutional Neural Networks

Convolutional neural network (CNN) is an emerging approach for achieving high recognition accuracy in various machine learning applications. To accelerate CNN computations, various GPU-based or application-specific hardware approaches have been recently proposed. However, since they require large computing hardware and absolute energy amount, they are not suitable for embedded applications. In this paper, we propose a novel approach to accelerate CNN computations using a CGRA (Coarse Grained Reconfigurable Architecture) for low-power embedded systems. We first present a new CGRA with distributed scratchpad memory blocks for efficient temporal blocking to reduce memory bandwidth pressure. We then show the architecture of our CNN accelerator using the CGRA with some dedicated software implementation. We evaluated our approach by comparing some existing platforms, such as high-end and mobile GPUs, and general multicore CPUs. The evaluation result shows that our proposal achieves 1.93x higher performance per memory bandwidth and 2.92x higher area performance, respectively.
Machine Learning and Content-Based Multimedia Retrieval

This paper presents an overview of popular retrieval tech- niques based on machine learning for content based multimedia retrieval. Furthermore, we also propose to highlight current gaps and required im- provement in this context. We first introduce common retrieval problems, and the usual models and assumptions made on multimedia data. Thanks to these assumptions, techniques based on machine learning can be used in many application cases. In this scope, we present popular methods for indexing multimedia data, like the ones based on the training of visual dic- tionaries. Then, we present supervised techniques that use labeled data to train and design retrieval components. We show how this last topic could benefit from many improvement from the machine learning community. Finally, this paper presents interesting perspective and new paradigms for multimedia retrieval based on machine learning.
Practice of Epidemiology Adjusting for Covariates in Studies of Diagnostic, Screening, or Prognostic Markers: an Old Concept in a New Setting

The concept of covariate adjustment is well established in therapeutic and etiologic studies. However, it has received little attention in the growing area of medical research devoted to the development of markers for disease diagnosis, screening, or prognosis, where classification accuracy, rather than association, is of primary interest. In this paper, the authors demonstrate the need for covariate adjustment in studies of classification accuracy, discuss methods for adjusting for covariates, and distinguish covariate adjustment from several other related, but fundamentally different, uses for covariates. They draw analogies and contrasts throughout with studies of association. A popular topic in medical research today is the development of markers to classify subjects as diseased or disease free, as high or low risk, or in terms of treatment response or another future event. These markers may be the results of, for example, genetic or proteomic evaluations, imaging techniques, bacterial culture, or risk factor information. Oftentimes, factors other than disease affect marker observations. For example, levels of prostate-specific antigen (PSA), a biomarker widely used to screen men for prostate cancer, tend to increase with age. Many markers are also affected by aspects of the test procedure, test setting, or test operator; attributes of the specimen collection or storage method (e.g., storage time); or ''center effects'' in multicen-ter studies. Although adjustment for covariates is commonplace in therapeutic and etiologic studies, the issue of covariate effects is not well appreciated in the classification setting. In this paper, we demonstrate the need for covariate adjustment and describe statistical methods that can be used to accomplish it. We also distinguish covariate adjustment from several other related, but fundamentally different, uses for covariates, including matching, prediction, and incre-mental value. Finally, we provide practical recommendations for determining when and how to adjust for covariates, and we include links to software that can be used to implement these techniques. The classification accuracy of a continuous marker, Y, is its ability to distinguish between two groups defined by a binary outcome. We refer to the groups as ''cases'' and ''con-trols. '' Classification accuracy is most commonly quantified by using the receiver operating characteristic (ROC) curve, a plot of the true positive fraction (sensitivity) versus the false positive fraction (1 – specificity) for the set of rules that classify an individual as ''test positive'' if his or her marker value is above a threshold, c, for all possible thresholds. The ROC curve characterizes the separation …
Answerfinder: Question Answering by Combining Lexical, Syntactic and Semantic Information

We present a question answering system that combines information at the lexical, syntactic, and semantic levels, in the process to find and rank the candidate answer sentences. The candidate exact answers are extracted from the candidate answer sentences by means of a combination of information-extraction techniques (named entity recognition) and patterns based on logical forms. The system participated in the question answering track of TREC 2004.
Active Diagnosis for Agents with Belief Management

Autonomous agents performing tasks in dynamic environments have to deal with the challenge of unexpected changes in the world. Such changes may lead to an Agent’s belief that is in contradiction with reality. Such changes can be a result of an action which has an unexpected outcome or another agent in the world which changes the environment in an unpredictable way. In order to deal with unexpected changes in the world a diagnosis on the history of performed actions providing an explanation for the change can be used. Such a diagnosis may result in multiple equally likely explanations not suitable for a safe continuation by the agent. Thus we propose an active diagnosis approach which rules out diagnosis hypotheses by executing additional sensing actions and provides the agent a more secured knowledge for future decisions.
Phylogenetic Assessment of the Mitochondrial DNA Displacement Loop Haplotype in Japanese Patients with Leber’s Hereditary Optic Neuropathy Harboring the Mitochondrial DNA G11778A Mutation

To investigate the anthropological background and the association of mitochondrial DNA (mtDNA) haplotype with the disease phenotype, the nucleotide sequence in the hypervariable segment of the displacement loop (D-loop) region of mtDNA was determined in Japanese patients with Leber’s hereditary optic neuropathy (LHON) harboring the G11778A mutation. Genetic polymorphism of mtDNA was examined in 36 unrelated Japanese LHON patients who presented with bilateral optic nerve disease and had the mtDNA G11778A mutation. DNA was extracted from the peripheral blood after having obtained informed consent. The nucleotide sequence of the D-loop region (np 16,002–16,490) was directly determined. The intergenic deletion of the COII/tRNALys gene of mtDNA was also examined. From the data set of nucleotide alignments, the phylogeny of the mtDNA sequence and phenotypic diversity within the examined population were evaluated. One-base polymorphism was present at 37 different sites. The estimated value of nucleotide diversity was 0.69%. D-loop sequences were classified into 13 monophyletic clusters (CA to CM). There was not any definite ancestral haplotype of the D-loop sequence in the examined LHON population. Thus, the mutational event of G11778A appears to be independent of the evolutionary course in the D-loop haplotype. Patients with a CD plus CH cluster had a significantly older age at onset (p = 0.006), and had a family history being significantly lower as compared with patients with other clusters (p = 0.05). The mtDNA D-loop haplotype characterized by the presence of T16362C or C16290T, lacking G16129A and G16390A, may be a risk for older age at onset and other unusual clinical features in Japanese LHON patients with the G11778A mutation.
Evolving predictors for chaotic time series

Neural networks are a popular representation for inducing single-step predictors for chaotic times series. For complex time series it is often the case that a large number of hidden units must be used to reliably acquire appropriate predictors. This paper describes an evolutionary method that evolves a class of dynamic systems with a form similar to neural networks but requiring fewer computational units. Results for experiments on two popular chaotic times series are described and the current method's performance is shown to compare favorably with using larger neural networks.
An interactive space that learns to influence human behavior

A key question in the design of intelligent environments is how a space can influence the actions of its users, and how such behavior can be learned. We present the results of experiments conducted as part of the Ada project, an interactive entertainment exhibit deployed at the Swiss national exhibition Expo.02. We used a learning model called distributed adaptive control (DAC) that is based on the animal learning paradigms of classical and operant conditioning. DAC has been developed using mobile robots in foraging tasks. Here, it was applied to the learning of effective cues for guiding visitors in a given direction. Our results show that, by using this learning mechanism, Ada was able to influence the behavior of visitors by learning to deploy particular types of cues. Many visitors could be induced to move toward a region of the space that they normally avoided visiting-an effect that can be seen as a spatial classification of visitors into interactive and noninteractive categories. In our analysis, we also introduce a measure of human activity that combines different types of data to capture key aspects of human behavior in interactive spaces.
Pattern and Waves for a Model in Population Dynamics with Nonlocal Consumption of Resources

We study a reaction-diffusion equation with an integral term describing nonlocal consumption of resources in population dynamics. We show that a homogeneous equilibrium can lose its stability resulting in appearance of stationary spatial structures. They can be related to the emergence of biological species due to the intra-specific competition and random mutations. Various types of travelling waves are observed.
Designs of Training Signals for Spatially Correlated Multi-User MIMO-OFDM

Optimal training design and channel estimation for spatially correlated multi-user multi-input multi-output with orthogonal frequency-division multiplexing (MIMO-OFDM) systems is still an open research topic of great interest. This paper applies tractable semi-definite programming to obtain the optimal training signal for the general case of spatial channel correlations for multi-user MIMO-OFDM. The optimal solution in closed-form expression is also derived for a special case of transmit correlations. Analytical and simulation results demonstrate the excellent performance of the proposed designs and their performance advantage over the equi-powered training designs.
Feature Selection for high-dimensional data with RapidMiner

The number of recorded feature has grown exponentially over the last years. In the bioinformatics domain datasets with hundreds of thousands of features are no more unusual. Extracting knowledge from such huge heaps of data demands for new methods. Traditional methods that are applicable for low dimensional data like wrapper feature selection can no longer handle the growing number of features. We present an extension to RapidMiner containing feature selection and classification algorithms suited for the high-dimensional setting overcoming the limitations of traditional feature selection approaches.