Miracle-dz/UniFAR-FTUs huggingface dataset:
Non positive SVM

Recent developments with indefinite SVM [11, 17, 5] have effectively demonstrated SVM classification with a non-positive kernel. However the question of efficiency still applies. In this paper, an efficient direct solver for SVM with non-positive kernel is proposed. The chosen approach is related to existing work on learning with kernel in Krein space. In this framework, it is shown that solving a learning problem is actually a problem of stabilization of the cost function instead of a minimization. We propose to restate SVM with non-positive kernels as a stabilization by using a new formulation of the KKT conditions. This new formulation provides a practical active set algorithm to solve the indefinite SVM problem. We also demonstrate empirically that the proposed algorithm outperforms other existing solvers.
A reconfigurable visual-programming library for real-time closed-loop cellular electrophysiology

Most of the software platforms for cellular electrophysiology are limited in terms of flexibility, hardware support, ease of use, or re-configuration and adaptation for non-expert users. Moreover, advanced experimental protocols requiring real-time closed-loop operation to investigate excitability, plasticity, dynamics, are largely inaccessible to users without moderate to substantial computer proficiency. Here we present an approach based on MATLAB/Simulink, exploiting the benefits of LEGO-like visual programming and configuration, combined to a small, but easily extendible library of functional software components. We provide and validate several examples, implementing conventional and more sophisticated experimental protocols such as dynamic-clamp or the combined use of intracellular and extracellular methods, involving closed-loop real-time control. The functionality of each of these examples is demonstrated with relevant experiments. These can be used as a starting point to create and support a larger variety of electrophysiological tools and methods, hopefully extending the range of default techniques and protocols currently employed in experimental labs across the world.
Research on Power-Aware Scheduling for High-Performance Computing System

This paper analyzes workload characteristics and power usage of parallel high-performance computing system. According to the conclusions of the analysis, we propose two kinds of power-aware scheduling policies to reduce the power consumption of system by power down idle nodes reasoningly. Detailed experiments using data from the Parallel Workloads Archive indicates that both policies can achieve considerable overall system power savings while meeting the user-defined switch frequency limit for each node.
Bioinformatic Analyses of Unique (Orphan) Core Genes of the Genus Acidithiobacillus: Functional Inferences and Use As Molecular Probes for Genomic and Metagenomic/Transcriptomic Interrogation

Using phylogenomic and gene compositional analyses, five highly conserved gene families have been detected in the core genome of the phylogenetically coherent genus Acidithiobacillus of the class Acidithiobacillia. These core gene families are absent in the closest extant genus Thermithiobacillus tepidarius that subtends the Acidithiobacillus genus and roots the deepest in this class. The predicted proteins encoded by these core gene families are not detected by a BLAST search in the NCBI non-redundant database of more than 90 million proteins using a relaxed cut-off of 1.0e−5. None of the five families has a clear functional prediction. However, bioinformatic scrutiny, using pI prediction, motif/domain searches, cellular location predictions, genomic context analyses, and chromosome topology studies together with previously published transcriptomic and proteomic data, suggests that some may have functions associated with membrane remodeling during cell division perhaps in response to pH stress. Despite the high level of amino acid sequence conservation within each family, there is sufficient nucleotide variation of the respective genes to permit the use of the DNA sequences to distinguish different species of Acidithiobacillus, making them useful additions to the armamentarium of tools for phylogenetic analysis. Since the protein families are unique to the Acidithiobacillus genus, they can also be leveraged as probes to detect the genus in environmental metagenomes and metatranscriptomes, including industrial biomining operations, and acid mine drainage (AMD).
Gene Expression Signatures for Tumor Progression, Tumor Subtype, and Tumor Thickness in Laser-Microdissected Melanoma Tissues

Purpose: To better understand the molecular mechanisms of malignant melanoma progression and metastasis, gene expression profiling was done of primary melanomas and melanoma metastases. Experimental Design: Tumor cell–specific gene expression in 19 primary melanomas and 22 melanoma metastases was analyzed using oligonucleotide microarrays after laser-capture microdissection of melanoma cells. Statistical analysis was done by random permutation analysis and support vector machines. Microarray data were further validated by immunohistochemistry and immunoblotting. Results: Overall, 308 genes were identified that showed significant differential expression between primary melanomas and melanoma metastases (false discovery rate ≤ 0.05). Significantly overrepresented gene ontology categories in the list of 308 genes were cell cycle regulation, mitosis, cell communication, and cell adhesion. Overall, 47 genes showed up-regulation in metastases. These included Cdc6, Cdk1, septin 6, mitosin, kinesin family member 2C, osteopontin, and fibronectin. Down-regulated genes included E-cadherin, fibroblast growth factor binding protein, and desmocollin 1 and desmocollin 3, stratifin/14-3-3σ, and the chemokine CCL27. Using support vector machine analysis of gene expression data, a performance of >85% correct classifications for primary melanomas and metastases was reached. Further analysis showed that subtypes of primary melanomas displayed characteristic gene expression patterns, as do thin tumors (≤1.0 mm Breslow thickness) compared with intermediate and thick tumors (>2.0 mm Breslow thickness). Conclusions: Taken together, this large-scale gene expression study of malignant melanoma identified molecular signatures related to metastasis, melanoma subtypes, and tumor thickness. These findings not only provide deeper insights into the pathogenesis of melanoma progression but may also guide future research on innovative treatments.
Guaranteeing Coevolutionary Objective Measures

The task of understanding the dynamics of coevolutionary algorithms or comparing performance between such algorithms is complicated by the fact the internal fitness measures are subjective. Though several techniques have been proposed to use external or objective measures to help in analysis, there are clearly properties of fitness payoff, like intransitivity, for which these techniques are ineffective. We feel that a principled approach to this problem is to first establish the theoretical bounds to guarantee objective measures in one CEA model; from there one can later examine the effects of deviating from the assumptions made by these bounds. To this end, we present a model of competitive fitness assessment with a single population and non-parametric selection (such as tournament selection), and show minimum conditions and examples under which an objective measure exists, and when the dynamics of the coevolutionary algorithm are identical to those of a traditional EA.
Affective Visual Stimuli: Characterization of the Picture Sequences Impacts by Means of Nonlinear Approaches

Introduction: The main objective of the present study was to investigate the effect of preceding pictorial stimulus on the emotional autonomic responses of the subsequent one. Methods: To this effect, physiological signals, including Electrocardiogram (ECG), Pulse Rate (PR), and Galvanic Skin Response (GSR) were collected. As these signals have random and chaotic nature, nonlinear dynamics of these physiological signals were evaluated with the methods of nonlinear system theory. Considering the hypothesis that emotional responses are usually associated with previous experiences of a subject, the subjective ratings of 4 emotional states were also evaluated. Four nonlinear characteristics (including Detrended Fluctuation Analysis (DFA), based parameters, Lyapunov exponent, and approximate entropy) were implemented. Nine standard features (including mean, standard deviation, minimum, maximum, median, mode, the second, third, and fourth moment) were also extracted. Results: To evaluate the ability of features in discriminating different types of emotions, some classification approaches were appraised, of them, Probabilistic Neural Network (PNN) led to the best classification rate of 100%. The results show that considering the emotional sequences, GSR is the best candidate for the representation of the physiological changes. Discussion: Lower discrimination was attained when the sequence occurred in the diagonal line of valence-arousal coordinates (for instance, positive valence and positive arousal versus negative valence and negative arousal). By employing self-assessment ranks, no obvious improvement was achieved.
THE EFFECTIVENESS OF INQUIRY BASED LEARNING FOR NATURAL SCIENCE LEARNING IN ELEMENTARY SCHOOL

This paper primarily emphasized on describing the use of inquiry in natural science learning at primary school. This study also exposed the validity of inquiry strategy in natural science learning to improve primary schools students’ comprehension. Further, this present study employed a library research sourced from reputable journal articles in relation to the issue. Assisted inquiry strategy with 5E model became the one investigated. The model covered five phases namely, engaging, exploring, explaining, elaborating, and evaluating. Empirically, the inquiry strategy in natural science learning had proven improving the quality of learning and the learning achievement, particularly in conceptual learning
The PathLinker app: Connect the dots in protein interaction networks

PathLinker is a graph-theoretic algorithm for reconstructing the interactions in a signaling pathway of interest. It efficiently computes multiple short paths within a background protein interaction network from the receptors to transcription factors (TFs) in a pathway. We originally developed PathLinker to complement manual curation of signaling pathways, which is slow and painstaking. The method can be used in general to connect any set of sources to any set of targets in an interaction network. The app presented here makes the PathLinker functionality available to Cytoscape users. We present an example where we used PathLinker to compute and analyze the network of interactions connecting proteins that are perturbed by the drug lovastatin.
Application of Soft Computing in the Control of Hydroelectric Generating Unit

Hydroelectric generating unit system of water plant is a non-linear and complicated system. Conventional controller cannot get good controlling performance in control. In this study, an advanced soft computing technique based on fuzzy, neural network and genetic algorithm is used in the control of hydroelectric generating unit system of hydropower plant. Fuzzy reasoning system is used as controller and genetic algorithm is employed to optimize the parameters and rules of fuzzy controller in the design of controller or real-time control process. Dynamic identification model of control system based on RBF neural networks is designed to appraise the controlling performance of fuzzy controller. In the design, improved genetic algorithm is adopted based on general genetic algorithm and the character of control system. The improved genetic algorithm quickens optimizing speed and makes fuzzy controller acquire knowledge effectively. Simulation results show that the designed soft computing optimization control system can well control hydroelectric generating unit and its control performance is superior to conventional controller.
Variability and Stability in Radiation-Hydrodynamic Accretion Flows

In this paper we examine time-dependent and three-dimensional perturbations of spherical accretion flow onto a neutron star close to its Eddington limit. Our treatment assumes a Schwarzschild geometry for the spacetime outside the neutron star and is fully general relativistic. At all the accretion rates studied the response of the accretion flow to perturbations includes weakly damped oscillatory modes. At sufficiently high luminosities—but still well below the Eddington limit—the flows become unstable to aspherical perturbations. These unstable radiation hydrodynamic modes resemble the onset of convection and allow accretion to occur preferentially through more rapidly descending columns of gas, while the radiation produced escapes through neighboring columns in which the gas descends more slowly.
Genetic Variants of IDE-KIF11-HHEX at 10q23.33 Associated with Type 2 Diabetes Risk: A Fine-Mapping Study in Chinese Population

Background Genome-wide association studies (GWAS) in populations of European ancestry have mapped a type 2 diabetes susceptibility region to chromosome 10q23.33 containing IDE, KIF11 and HHEX genes (IDE-KIF11-HHEX), which has also been replicated in Chinese populations. However, the functional relevance for genetic variants at this locus is still unclear. It is critical to systematically assess the relationship of genetic variants in this region with the risk of type 2 diabetes. Methodology/Principal Findings A fine-mapping study was conducted by genotyping fourteen tagging single-nucleotide polymorphisms (SNPs) in a 290-kb linkage disequilibrium (LD) region using a two-stage case-control study of type 2 diabetes in a Chinese Han population. Suggestive associations (P<0.05) observed from 1,200 cases and 1,200 controls in the first stage were further replicated in 1,725 cases and 2,081 controls in the second stage. Seven tagging SNPs were consistently associated with type 2 diabetes in both stages (P<0.05), with combined odds ratios (ORs) ranging from 1.14 to 1.33 in the combined analysis. The most significant locus was rs7923837 [OR = 1.33, 95% confidence interval (CI): 1.21–1.47] at the 3′-flanking region of HHEX gene. SNP rs1111875 was found to be another partially independent locus (OR = 1.23, 95% CI: 1.13–1.35) in this region that was associated with type 2 diabetes risk. A cumulative effect of rs7923837 and rs1111875 was observed with individuals carrying 1, 2, and 3 or 4 risk alleles having a 1.27, 1.44, and 1.73-fold increased risk, respectively, for type 2 diabetes (P for trend = 4.1E-10). Conclusions/Significance Our results confirm that genetic variants of the IDE-KIF11-HHEX region at 10q23.33 contribute to type 2 diabetes susceptibility and suggest that rs7923837 may represent the strongest signal related to type 2 diabetes risk in the Chinese Han population.
Applying Fuzzy FP-Growth to Mine Fuzzy Association Rules

In data mining, the association rules are used to find for the associations between the different items of the transactions database. As the data collected and stored, rules of value can be found through association rules, which can be applied to help managers execute marketing strategies and establish sound market frameworks. This paper aims to use Fuzzy Frequent Pattern growth (FFP-growth) to derive from fuzzy association rules. At first, we apply fuzzy partition methods and decide a membership function of quantitative value for each transaction item. Next, we implement FFP-growth to deal with the process of data mining. In addition, in order to understand the impact of Apriori algorithm and FFP-growth algorithm on the execution time and the number of generated association rules, the experiment will be performed by using different sizes of databases and thresholds. Lastly, the experiment results show FFPgrowth algorithm is more efficient than other existing methods. Keywords—Data mining, association rule, fuzzy frequent pattern growth.
Efficient class incremental learning for multi-label classification of evolving data streams

Multi-label stream classification has not been fully explored for the unique properties of large data volumes, realtime, label dependencies, etc. Some methods try to take into account label dependencies, but they only focus on the existing frequent label combinations, leading to worse performance for multi-label classification. To deal with these problems, this paper proposes an algorithm which dynamically recognizes some new frequent label combinations and updates the trained classifier by class incremental learning strategy. Experimental results over both real-world and synthetic datasets demonstrate its better predictive performance.
Digital ink compression via functional approximation

Representing digital ink traces as points in a function space has proven useful for online recognition. Ink trace coordinates or their integral invariants are written as parametric functions and approximated by truncated orthogonal series. This representation captures the shape of the ink traces with a small number of coefficients in a form quite compact and independent of device resolution, and various geometric techniques may be employed for recognition. The simplicity and high performance of this method lead us to ask whether the same idea can be applied to another important aspect in online handwriting – the compression of digital ink strokes. We have investigated Chebyshev, Legendre and Legendre-Sobolev orthogonal polynomial bases as well as Fourier series and have found that Chebyshev representation is the most suitable apparatus for compressing digital curves. We obtain compression rates of 30* to 50* and have the added benefit that the Legendre- Sobolev form, used for recognition, may be obtained by a single linear transformation.
Revealing side-channel issues of complex circuits by enhanced leakage models

In the light of implementation attacks a better understanding of complex circuits of security sensitive applications is an important issue. Appropriate evaluation tools and metrics are required to understand the origin of implementation flaws within the design process. The selected leakage model has significant influence on the reliability of evaluation results concerning the side-channel resistance of a cryptographic implementation. In this contribution we introduce methods, which determine the accuracy of the leakage characterization and allow to quantify the signal-to-noise ratio. This allows a quantitative assessment of the side-channel resistance of an implementation without launching an attack. We validate the conclusions drawn from our new methods by real attacks and obtain similar results. Compared to the commonly used Hamming Distance model in our experiments enhanced leakage models increased the attack efficiency by up to 500%.
Texture analysis of breast thermogram for differentiation of malignant and benign breast

In this paper, we developed a new local texture feature extraction technique, called block variance (BV), for texture analysis in the thermal breast image. Then, present a method based on the different features extracted from the texture image obtained using BV to differentiate the malignant breast thermograms from the benign breast thermograms. Variance is an established measure of contrast in the image. Block variance (BV) uses the local variation of intensities to identify the contrast-texture in the gray-scale thermal breast image. Asymmetric temperature distribution between right and left breast in thermal breast image is an indicator of the presence of abnormality. Thus, we investigate the potential of our proposed features in asymmetry measure. For our experiment purpose, we used a set of forty malignant and sixty benign thermal breast images of DMR database. A feed-forward neural network (FANN) with gradient decent training rule has been employed to evaluate the classification performance. The effectiveness of our proposed features is compared against a feature set derived by Acharya et al. [16] in terms of classification accuracy, sensitivity, and specificity. From the experimental results, it is shown that the proposed features perform better compared to Acharya et al. features in differentiating malignant breast thermograms from benign breast thermograms.
Cache-oblivious ray reordering

We present a cache-oblivious ray reordering method for ray tracing. Many global illumination methods such as path tracing and photon mapping use ray tracing and generate lots of rays to simulate various realistic visual effects. However, these rays tend to be very incoherent and show lower cache utilizations during ray tracing of models. In order to address this problem and improve the ray coherence, we propose a novel Hit Point Heuristic (HPH) to compute a coherent ordering of rays. The HPH uses the hit points between rays and the scene as a ray reordering measure. We reorder rays by using a space-filling curve based on their hit points. Since a hit point of a ray is available only after performing the ray intersection test with the scene, we compute an approximate hit point for the ray by performing an intersection test between the ray and simplified representations of the original models. Our method is a highly modular approach, since our reordering method is decoupled from other components of common ray tracing systems. We apply our method to photon mapping and path tracing and achieve more than an order of magnitude performance improvement for massive models that cannot fit into main memory, compared to rendering without reordering rays. Also, our method shows a performance improvement even for ray tracing small models that can fit into main memory. This performance improvement for small and massive models is caused by reducing cache misses occurring between different memory levels including the L1/L2 caches, main memory, and disk. This result demonstrates the cache-oblivious nature of our method, which works for various kinds of cache parameters. Because of the cache-obliviousness and the high modularity, our method can be widely applied to many existing ray tracing systems and show performance improvements with various models and machines that have different cache parameters.
Time-dependent dual effects of high levels of unconjugated bilirubin on the human blood-brain barrier lining

In neonatal jaundice, high levels of unconjugated bilirubin (UCB) may induce neurological dysfunction (BIND). Recently, it was observed that UCB induces alterations on brain microvasculature, which may facilitate its entrance into the brain, but little is known about the steps involved. To evaluate if UCB damages the integrity of human brain microvascular endothelial cells (HBMECs), we used 50 or 100 μM UCB plus human serum albumin, to mimic the neuropathological conditions where levels of UCB free species correspond to moderate and severe neonatal jaundice, respectively. Our results point to a biphasic response of HBMEC to UCB depending on time of exposure. The early response includes increased number of caveolae and caveolin-1 expression, as well as upregulation of vascular endothelial growth factor (VEGF) and its receptor 2 (VEGFR-2) with no alterations of the paracellular permeability. In contrast, effects by sustained hyperbilirubinemia are the reduction in zonula occludens (ZO)-1 and β-catenin levels and thus of tight junctions (TJ) strands and cell-to-cell contacts. In addition, reduction of the transendothelial electrical resistance (TEER) and increased paracellular permeability are observed, revealing loss of the barrier properties. The 72 h of HBMEC exposure to UCB triggers a cell response to the stressful stimulus evidenced by increased autophagy. In this later condition, the UCB intracellular content and the detachment of both viable and non-viable cells are increased. These findings contribute to understand why the duration of hyperbilirubinemia is considered one of the risk factors of BIND. Indeed, facilitated brain entrance of the free UCB species will favor its parenchymal accumulation and neurological dysfunction.