Miracle-dz/UniFAR-FTUs huggingface dataset:
Linking Korean Words with an Ontology

The need for ontologies has increased in computer science or information science recently. Especially, NLP systems such as information retrieval, machine translation, etc. require ontologies whose concepts are connected to natural language words. There are a few Korean wordnets such as U-WIN, KorLex, CoreNet, etc. Most of them, however, stand alone without any link to an ontology. Hence, we need a Korean wordnet which is linked to a language-neutral ontology such as SUMO, OpenCyc, DOLCE, etc. In this paper, we will present a method of linking Korean word senses with the concepts of an ontology, which is part of an ongoing project. We use a Korean-English bilingual dictionary, Princeton WordNet (Fellbaum 1998), and the ontology SmartSUMO (Oberle et al. 2007). The current version of WordNet is mapped into SUMO, which constitutes a major part of SmartSUMO. We focus on mapping Korean word senses with corresponding English word senses by way of Princeton WordNet which is mapped into SUMO. This paper will show that we need to apply different algorithms of linking, depending on the information types that a bilingual dictionary contains.
MLL Becomes Functional through Intra-Molecular Interaction Not by Proteolytic Processing

The mixed lineage leukemia (MLL) protein is an epigenetic transcriptional regulator that controls proliferative expansion of immature hematopoietic progenitors, whose aberrant activation triggers leukemogenesis. A mature MLL protein is produced by formation of an intra-molecular complex and proteolytic cleavage. However the biological significance of these two post-transcriptional events remains unclear. To address their in vivo roles, mouse mutant alleles were created that exclusively express either a variant protein incapable of intra-molecular interaction (designated de) or an uncleavable mutant protein (designated uc). The de homozygous mice died during midgestation and manifested devastating failure in embryonic development and reduced numbers of hematopoietic progenitors, whereas uc homozygous mice displayed no apparent defects. Expression of MLL target genes was severely impaired in de homozygous fibroblasts but unaffected in uc homozygous fibroblasts. These results unequivocally demonstrate that intra-molecular complex formation is a crucial maturation step whereas proteolytic cleavage is dispensable for MLL-dependent gene activation and proliferation in vivo.
Immunomodulatory Effects of Etanercept in an Experimental Model of Spinal Cord Injury

Etanercept is a tumor necrosis factor antagonist with anti-inflammatory effects. The aim of our study was to evaluate for the first time the therapeutic efficacy of in vivo inhibition of tumor necrosis factor-α (TNF-α) in experimental model of spinal cord trauma, which was induced by the application of vascular clips (force of 24 g) to the dura via a four-level T5–T8 laminectomy. Spinal cord injury in mice resulted in severe trauma characterized by edema, neutrophil infiltration, and cytokine production that it is followed by recruitment of other inflammatory cells, such as production of a range of inflammation mediators, tissue damage, apoptosis, and disease. Treatment of the mice with etanercept significantly reduced the degree of 1) spinal cord inflammation and tissue injury (histological score); 2) neutrophil infiltration (myeloperoxidase evaluation); 3) inducible nitric-oxide synthase, nitrotyrosine, cyclooxygenase-2, and cytokines expression (TNF-α and interleukin-1β); and 4) apoptosis (terminal deoxynucleotidyl transferase dUTP nick-end labeling staining and Bax and Bcl-2 expression). In a separate set of experiment, we have also clearly demonstrated that TNF-α inhibitor significantly ameliorated the recovery of limb function (evaluated by motor recovery score). Taken together, our results clearly demonstrate that treatment with etanercept reduces the development of inflammation and tissue injury events associated with spinal cord trauma.
Reproducing “rhetrickery” in online fertility marketing: harnessing the “rhetoric of the possible”

ABSTRACT This article addresses the gap in research on visual and narrative persuasion in online fertility marketing contexts and reveals their reliance on rhetorical ruses embedded in the language of “choice” and “empowerment”. We assess four websites targeting women and men who have experienced infertility and expose their “digirhetrickery”, or use of deceptive rhetoric in digital space which exploits gendered stereotypes of the female body in ways that ultimately mislead their target markets about assisted reproductive technologies (ARTs) and the “liberatory” potential they offer. We advance digital rhetoric as an analytical method to the field of consumer research in order to engage in a reflexive analysis that reveals these underhand ideological operations. As “authorial voices” and narrative agents in digital advertising discourse are more cunningly subterranean, this study shows how the instrumentalization of “consumer empowerment” has become increasingly hyperbolic with particularly problematic consequences for infertile women consumers.
Identification of Neuregulin-2 as a novel stress granule component

Stress Granules (SGs) are microscopically visible, phase dense aggregates of translationally stalled messenger ribonucleoprotein (mRNP) complexes formed in response to distinct stress conditions. It is generally considered that SG formation is induced to protect cells from conditions of stress. The precise constituents of SGs and the mechanism through which SGs are dynamically regulated in response to stress are not completely understood. Hence, it is important to identify proteins which regulate SG assembly and disassembly. In the present study, we report Neuregulin-2 (NRG2) as a novel component of SGs; furthermore, depletion of NRG2 potently inhibits SG formation. We also demonstrate that NRG2 specifically localizes to SGs under various stress conditions. Knockdown of NRG2 has no effect on stress-induced polysome disassembly, suggesting that the component does not influence early step of SG formation. It was also observed that reduced expression of NRG2 led to marginal increase in cell survival under arsenite-induced stress. [BMB Reports 2016; 49(8): 449-454]
A new functional fault model for FPGA application-oriented testing

The objective of this paper is to propose a new fault model suitable for test pattern generation for an FPGA configured to implement a given application. The paper demonstrates that the faults affecting the bit cells of the look-up tables (LUTs) are not redundant, although they store constant values. We demonstrate that these faults cannot be neglected and that the fault model corresponding to modifying the content of each LUT memory cell must be considered in order to cover the full range of possible faults. In order to evaluate the fault coverage of the proposed fault model, a set of circuits mapped on a Xilinx Virtex 300 FPGA have been considered. Test sequences generated by a gate-level commercial ATPG and an academic RT-level one have been fault simulated on these benchmark circuits. The obtained figures show that a high percentage of faults affecting the LUT bit cells are undetected, thus suggesting that suitable ATPG algorithms adopting the new fault model are required.
Expressing the Modification of Diet in Renal Disease Study equation for estimating glomerular filtration rate with standardized serum creatinine values.

PURPOSE We sought to reexpress the 4-variable Modification of Diet in Renal Disease (MDRD) Study equation for estimation of glomerular filtration rate (GFR) using serum creatinine (S(cr)) standardized to reference methods. METHODS Serum specimens included creatinine reference materials prepared by the College of American Pathologists (CAP), traceable to primary reference material at the NIST, with assigned values traceable to isotope dilution mass spectrometry (IDMS), a calibration panel prepared by the Cleveland Clinic Research Laboratory (CCRL), and frozen samples from the MDRD Study. Split specimens were measured at the CCRL using the Roche enzymatic and Beckman CX3 kinetic alkaline picrate assays. RESULTS Roche enzymatic assay results on CAP samples were comparable to IDMS-assigned values. Beckman CX3 assay results in 2004-2005 were significantly higher than but highly correlated with simultaneous Roche enzymatic assay results (r(2) = 0.9994 on 40 CCRL samples) and showed minimal but significant upward drift from Beckman CX3 assay results during the MDRD Study in 1989-1991 (r(2) = 0.9987 in 253 samples). Combining these factors, standardized S(cr) = 0.95 x original MDRD Study S(cr). The reexpressed 4-variable MDRD Study equation for S(cr) (mg/dL) is GFR = 175 x standardized S(cr)(-1.154) x age(-0.203) x 1.212 (if black) x 0.742 (if female), and for S(cr) (micromol/L) is GFR = 30849 x standardized S(cr)(-1.154) x age(-0.203) x 1.212 (if black) x 0.742 (if female) [GFR in mL x min(-1) x (1.73 m(2))(-1)]. CONCLUSION When the calibration of S(cr) methods is traceable to the S(cr) reference system, GFR should be estimated using the MDRD Study equation that has been reexpressed for standardized S(cr).
Ballistic Rockets Tracking: Kalman versus αβγ Filters

This article presents the problem of tracking ballistic rockets through the propelled and ballistic stages using measured radar signal processing. We developed a dynamic model of a moving target. We have compared the performance of the estimation through quadratic mean error of the αβγ and Kalman filters. The results show that the Kalman filter has a better performance, it combines the statistical efficiency with a modest computational effort. This conclusion is valid when the target's ballistic coefficient is known a priori.
Lexical accent perception in highly-proficient L2 Japanese learners: The roles of language-specific experience and domain-general resources

This article reports empirical findings on the roles of domain-general resources and language-specific experience in the second language (L2) acquisition of Japanese lexical pitch accent. Sixty-one advanced-proficiency L2 Japanese learners from two first languages (L1s), Mandarin Chinese and Korean, identified and categorized Japanese nouns embedded in short sentences in two aurally-presented tasks. Mixed effects models showed that although the tonal-language Chinese group outperformed non-tonal Korean speakers, L2 lexical knowledge, but not overall proficiency or learning experience, predicted performance on both perception tasks regardless of L1, suggesting that long-term knowledge of L2 phonological structure facilitates perception of lexical-level prosody. Domain-general resources, however, played no predictive role in advanced learners’ accent perception. A decision-tree analysis then revealed further divergence in perception accuracy by accent pattern, L1, and task type. Taken together, the results establish a close connection between language learning experience and L2 speech perception at the advanced level, and highlight the complexity inherent in the learning of non-native prosodic categories.
Experiments with SAT-based Answer Set Programming

Answer Set Programming (ASP) emerged in the late 1990s as a new logic programming paradigm which has been successfully applied in various application domains. Propositional satisfiability (SAT) is one of the most studied problems in Computer Science. ASP and SAT are closely related: Recent works have studied their relation, and efficient SAT-based ASP solvers (like assat and Cmodels) exist. In this paper we report about (i) the extension of the basic procedures in Cmodels in order to incorporate the most popular SAT reasoning strategies, and (ii) an extensive comparative analysis involving also other state-of-the-art answer set solvers. The experimental analysis points out, besides the fact that Cmodels is highly competitive, that the reasoning strategies that work best on “small but hard” problems are ineffective on “big but easy” problems and vice-versa.
Modeling Effective Connectivity in High-Dimensional Cortical Source Signals

To study the effective connectivity among sources in a densely voxelated (high-dimensional) cortical surface, we develop the source-space factor VAR model. The first step in our procedure is to estimate cortical activity from multichannel electroencephalograms (EEG) using anatomically constrained brain imaging methods. Following parcellation of the cortical surface into disjoint regions of interest (ROIs), latent factors within each ROI are computed using principal component analysis. These factors are ROI-specific low-rank approximations (or representations) which allow for efficient estimation of connectivity in the high-dimensional cortical source space. The second step is to model effective connectivity between ROIs by fitting a VAR model jointly on all the latent processes. Measures of cortical connectivity, in particular partial directed coherence, are formulated using the VAR parameters. We illustrate the proposed model to investigate connectivity and interactions between cortical ROIs during rest.
Detecting emergent leader in a meeting environment using nonverbal visual features only

In this paper, we propose an effective method for emergent leader detection in meeting environments which is based on nonverbal visual features. Identifying emergent leader is an important issue for organizations. It is also a well-investigated topic in social psychology while a relatively new problem in social signal processing (SSP). The effectiveness of nonverbal features have been shown by many previous SSP studies. In general, the nonverbal video-based features were not more effective compared to audio-based features although, their fusion generally improved the overall performance. However, in absence of audio sensors, the accurate detection of social interactions is still crucial. Motivating from that, we propose novel, automatically extracted, nonverbal features to identify the emergent leadership. The extracted nonverbal features were based on automatically estimated visual focus of attention which is based on head pose. The evaluation of the proposed method and the defined features were realized using a new dataset which is firstly introduced in this paper including its design, collection and annotation. The effectiveness of the features and the method were also compared with many state of the art features and methods.
Reference-driven performance anomaly identification

Complex system software allows a variety of execution conditions on system configurations and workload properties. This paper explores a principled use of reference executions--those of similar execution conditions from the target--to help identify the symptoms and causes of performance anomalies. First, to identify anomaly symptoms, we construct change profiles that probabilistically characterize expected performance deviations between target and reference executions. By synthesizing several single-parameter change profiles, we can scalably identify anomalous reference-to-target changes in a complex system with multiple execution parameters. Second, to narrow the scope of anomaly root cause analysis, we filter anomaly-related low-level system metrics as those that manifest very differently between target and reference executions. Our anomaly identification approach requires little expert knowledge or detailed models on system internals and consequently it can be easily deployed. Using empirical case studies on the Linux I/O subsystem and a J2EE-based distributed online service, we demonstrate our approach's effectiveness in identifying performance anomalies over a wide range of execution conditions as well as multiple system software versions. In particular, we discovered five previously unknown performance anomaly causes in the Linux 2.6. 23 kernel. Additionally, our preliminary results suggest that online anomaly detection and system reconfiguration may help evade performance anomalies in complex online systems.
Local Feature Matching For Face Recognition

In this paper a novel technique for face recognition is proposed. Using the statistical Local Feature Analysis (LFA) method, a set of feature points is extracted for each face image at locations with highest deviations from the expectation. Each feature point is described by a sequence of local histograms captured from the Gabor responses at different frequencies and orientations around the feature point. Histogram intersection is used to compare the Gabor histogram sequences in order to find the matched feature points between two faces. Recognition is performed based on the average similarity between the best matched points, in the probe face and each of the gallery faces. Several experiments on the FERET set of faces show the superiority of the proposed technique over all considered state-of-the-art methods (Elastic Bunch Graph Matching, LDA+PCA, Bayesian Intra/extrapersonal Classifier, Boosted Haar Classifier), and validate the robustness of our method against facial expression variation and illumination variation.
Using trajectory data to improve bayesian optimization for reinforcement learning

Recently, Bayesian Optimization (BO) has been used to successfully optimize parametric policies in several challenging Reinforcement Learning (RL) applications. BO is attractive for this problem because it exploits Bayesian prior information about the expected return and exploits this knowledge to select new policies to execute. Effectively, the BO framework for policy search addresses the exploration-exploitation tradeoff. In this work, we show how to more effectively apply BO to RL by exploiting the sequential trajectory information generated by RL agents. Our contributions can be broken into two distinct, but mutually beneficial, parts. The first is a new Gaussian process (GP) kernel for measuring the similarity between policies using trajectory data generated from policy executions. This kernel can be used in order to improve posterior estimates of the expected return thereby improving the quality of exploration. The second contribution, is a new GP mean function which uses learned transition and reward functions to approximate the surface of the objective. We show that the model-based approach we develop can recover from model inaccuracies when good transition and reward models cannot be learned. We give empirical results in a standard set of RL benchmarks showing that both our model-based and model-free approaches can speed up learning compared to competing methods. Further, we show that our contributions can be combined to yield synergistic improvement in some domains.
Endurance Capacity, Not Body Size, Determines Physical Activity Levels: Role of Skeletal Muscle PEPCK

Some people remain lean despite pressure to gain weight. Lean people tend to have high daily activity levels, but the source of this increased activity is unknown. We found that leanness cannot be accounted for by increased weight-corrected food intake in two different types of lean rats. As previously reported in lean people, we found that lean rats had higher daily activity levels; lean rats also expended more energy. These lean rats were developed through artificial selection for high aerobic endurance capacity. To test whether our findings extended to a human population, we measured endurance capacity using a VO2max treadmill test and daily activity in a group of non-exercising individuals. Similar to lean rats selectively bred for endurance capacity, our study revealed that people with higher VO2max also spent more time active throughout the day. Hence, endurance capacity may be the trait that underlies both physical activity levels and leanness. We identified one potential mechanism for the lean, active phenotype in rats, namely high levels of skeletal muscle PEPCK. Therefore, the lean phenotype is characterized by high endurance capacity and high activity and may stem from altered skeletal muscle energetics.
Premonition of storage response class using Skyline ranked Ensemble method

Tertiary storage areas are integral parts of compute environment and are primarily used to store vast amount of data that is generated from any scientific/industry workload. Modelling the possible pattern of usage of storage area helps the administrators to take preventive actions and guide users on how to use the storage areas which are tending towards slower to unresponsive state. Treating the storage performance parameters as a time series data helps to predict the possible values for the next `n' intervals using forecasting models like ARIMA. These predicted performance parameters are used to classify if the entire storage area or a logical component is tending towards unresponsiveness. Classification is performed using the proposed Skyline ranked Ensemble model with two possible classes, i.e. high response state and low response state. Heavy load scenarios were simulated and close to 95% of the behaviour were explained using the proposed model.
High-Resolution Random Mesh Algorithms for Creating a Probabilistic 3D Surface Atlas of the Human Brain

Striking variations exist, across individuals, in the internal and external geometry of the brain. Such normal variations in the size, orientation, topology, and geometric complexity of cortical and subcortical structures have complicated the problem of quantifying deviations from normal anatomy and of developing standardized neuroanatomical atlases. This paper describes the design, implementation, and results of a technique for creating a three-dimensional (3D) probabilistic surface atlas of the human brain. We have developed, implemented, and tested a new 3D statistical method for assessing structural variations in a data-base of anatomic images. The algorithm enables the internal surface anatomy of new subjects to be analyzed at an extremely local level. The goal was to quantify subtle and distributed patterns of deviation from normal anatomy by automatically generating detailed probability maps of the anatomy of new subjects. Connected systems of parametric meshes were used to model the internal course of the following structures in both hemispheres: the parieto-occipital sulcus, the anterior and posterior rami of the calcarine sulcus, the cingulate and marginal sulci, and the supracallosal sulcus. These sulci penetrate sufficiently deeply into the brain to introduce an obvious topological decomposition of its volume architecture. A family of surface maps was constructed, encoding statistical properties of local anatomical variation within individual sulci. A probability space of random transformations, based on the theory of Gaussian random fields, was developed to reflect the observed variability in stereotaxic space of the connected system of anatomic surfaces. A complete system of probability density functions was computed, yielding confidence limits on surface variation. The ultimate goal of brain mapping is to provide a framework for integrating functional and anatomical data across many subjects and modalities. This task requires precise quantitative knowledge of the variations in geometry and location of intracerebral structures and critical functional interfaces. The surface mapping and probabilistic techniques presented here provide a basis for the generation of anatomical templates and expert diagnostic systems which retain quantitative information on intersubject variations in brain architecture.
THE VALIDITY OF THE TECHNOLOGY ACCEPTANCE MODEL IN COLLABORATION SYSTEMS SOFTWARE

This study applies the technology acceptance model (TAM) in order to understand consumer acceptance of the expanding market of collaboration systems. A panel of college students who were trained in the use of SkyDrive, Facebook Docs and Google Docs was conducted to determine satisfaction with the systems and adoption choice. It was determined that the Technology Acceptance Model is a strong predictor of product success in this market as the system with the greatest levels of perceived ease of use and usefulness, Google Docs, was the system that was most widely adopted. This is indicative of an efficient market with minimal network effects distorting the relationship between perceived value and sales.