Miracle-dz/UniFAR-FTUs huggingface dataset:
Lesion Mapping the Four-Factor Structure of Emotional Intelligence

Emotional intelligence (EI) refers to an individual’s ability to process and respond to emotions, including recognizing the expression of emotions in others, using emotions to enhance thought and decision making, and regulating emotions to drive effective behaviors. Despite their importance for goal-directed social behavior, little is known about the neural mechanisms underlying specific facets of EI. Here, we report findings from a study investigating the neural bases of these specific components for EI in a sample of 130 combat veterans with penetrating traumatic brain injury. We examined the neural mechanisms underlying experiential (perceiving and using emotional information) and strategic (understanding and managing emotions) facets of EI. Factor scores were submitted to voxel-based lesion symptom mapping to elucidate their neural substrates. The results indicate that two facets of EI (perceiving and managing emotions) engage common and distinctive neural systems, with shared dependence on the social knowledge network, and selective engagement of the orbitofrontal and parietal cortex for strategic aspects of emotional information processing. The observed pattern of findings suggests that sub-facets of experiential and strategic EI can be characterized as separable but related processes that depend upon a core network of brain structures within frontal, temporal and parietal cortex.
The Role of TLR2, TLR4 and CD14 Genetic Polymorphisms in Gastric Carcinogenesis: A Case-Control Study and Meta-Analysis

Background In addition to Helicobacter pylori infection, host genetic factors contribute to gastric cancer (GC). Recognition of H. pylori is known to involve Toll-like receptors (TLR), which subsequently leads to activation of NF-κB. Thus, the overall aim of this study was to estimate for the first time the pooled effect size of polymorphisms in TLR2, TLR4 and CD14 on GC development through a meta-analysis. Methods A case-control study comprising 284 ethnic Chinese individuals (70 non-cardia GC cases and 214 functional dyspepsia controls) was conducted for the genotyping of TLR2 -196 to -174del, CD14 -260 C/T and TLR4 rs11536889 using PCR, RT-PCR and mass spectrometry. Case-control studies of TLR2, TLR4 and CD14 polymorphisms and GC were searched up to June 2012. Pooled odds ratios and 95% confidence intervals were obtained by means of the random effects model. Results In our ethnic Chinese case-control study, the TLR4 rs11536889 C allele increased the risk of GC (OR: 1.89, 95%CI: 1.23–2.92) while the CD14 -260 T allele was protective (OR: 0.62, 95%CI: 0.42–0.91). TLR2 -196 to -174 increased the risk of GC only in H. pylori-infected individuals (OR: 3.10, 95%CI: 1.27–7.60). In the meta-analysis, TLR4 Asp299Gly showed borderline results in the general analysis (pooled OR: 1.58, 95%CI: 0.98–2.60), nevertheless, stratified analysis by ethnicity showed that the mutant allele was a definitive risk factor for GC in Western populations (pooled OR: 1.87, 95%CI: 1.31–2.65). There was a potential association between the TLR2 -196 to -174 deletion allele and GC in Japanese (pooled OR: 1.18, 95%CI: 0.96–1.45). TLR4 Thr399Ile did not provide significant results. Conclusions TLR4 rs11536889 and CD14 -260 C/T are associated with non-cardia GC in Chinese. Based on our meta-analysis, the TLR signalling pathway is involved in gastric carcinogenesis, TLR4 Asp299Gly and TLR2 -196 to -174del showing associations with GC in an ethnic-specific manner.
Examining the Lacunar Hypothesis With Diffusion and Perfusion Magnetic Resonance Imaging

Background— The clinical diagnosis of subcortical cerebral infarction is inaccurate for lesion location and pathogenesis. Clinically suspected small perforating artery occlusions may be embolic infarcts, with important implications for investigation and treatment. New MRI techniques may allow more accurate determination of the stroke mechanism soon after admission. Methods— In a prospective series of 106 patients evaluated with acute diffusion-weighted MRI (DWI) and perfusion-weighted MRI (PWI) within 24 hours of stroke, we enrolled 19 with a lacunar syndrome. On the basis of the topography, DWI and PWI findings, and outcome T2 MRI, we determined whether the mechanism of infarction was single perforating vessel occlusion or large artery embolism. Results— Thirteen patients had pure motor stroke, 2 had ataxic hemiparesis, and 4 had sensorimotor stroke. Six patients had lacunes on MRI, none with PWI lesions. Four patients had subcortical and distal cortical infarcts on DWI. Nine had solitary restricted striatocapsular infarcts. Seven of these 9 had PWI studies, 5 with PWI lesions. The presence of a PWI lesion reliably differentiated striatocapsular from lacunar infarction for solitary small subcortical infarcts (P =0.03). Conclusion— DWI and PWI altered the final diagnosis of infarct pathogenesis from small perforating artery occlusion to large artery embolism in 13 of 19 patients presenting with lacunar syndromes. Lacunes cannot be reliably diagnosed on clinical grounds.
Experiences with collaborative, distributed predictive human performance modeling

Although predictive human performance modeling has been researched for 30 years in HCI, to our knowledge modeling has been conducted as a solitary task of one modeler or, occasionally, two modelers working in tight face-to-face collaboration. In contrast, we used predictive human performance modeling in a collaborative, distributed mode and reflect on that experience. We discovered that our tool for modeling, CogTool, while sufficiently functional and expressive to perform the modeling task, did not support collaborative, distributed modeling as well as we would like. We suggest process improvements in model construction, the management of assumptions, consistency, and communication, and suggest design solutions for the future of CogTool or other modeling tools. We further speculate on the generalization of our experiences to other types of usability evaluation when conducted in a distributed, collaborative environment.
Online Forum Discussion: Making Sense of How Knowledge is Woven in a Tapestry of Social Interactions and Collaboration

This paper describes how students in a wholly online master course interacted and collaborated in constructing knowledge through forum discussions. This examination of roles and behaviours of forum participants is an attempt by the author, who was the facilitator, to understand how her students engaged in constructing knowledge, rather than to test existing theories on online discussions. For each thread in the forum, an overall analysis of its life, captivity, magnitude and period of interaction was first analysed. Two discussion threads with the highest value for life of thread, captivity and magnitude were then examined in detail. A visual representation of the network of communication among participants was created to reveal the strength of relationships and structure of social network. Then, in trying to get a richer snapshot of the postings, three different analysis tools namely, Johnson and Johnson’s categories of behaviours in collaborative learning situations, Fahy’s Transcript Analysis Tool as well as Gunawardena, Lowe and Anderson’s constructivist knowledge creation phases, were used. An interesting finding is that even with minimal facilitator intervention in the forum, discovery and exploration of dissonance occurred and this had led to the negotiation of meaning, testing and modification of proposed synthesis of knowledge, and finally, application of newly constructed knowledge. Other findings from the analyses of the two threads include (1) there was much collaborative behaviour among participants in terms of contributing to discussions and seeking input, (2) there were several instances where participants were observed to connect what they had discussed in other threads by referring to those postings, and (3) several participants displayed reflective thinking in revealing their beliefs, doubts, and their reasoning. (Author's abstract)
User-centric joint power and thermal management for smartphones

Excessive power consumption and high temperatures are some of the key factors limiting battery charge life and performance of current mobile devices or smartphones. Existing power management solutions do not account for user-experience, which can impact user satisfaction. In this paper we propose a joint power and thermal management solution, which takes a proactive approach in reducing energy consumption while providing expected user-experience. The proposed technique modulates the operating conditions based on users application preferences and exploits the “change blindness” effect to reduce display power consumption. Another important aspect of our implementation is that it does not require any restructuring of the underlying operating system. A novel thermal model of the entire smartphone was built with the purpose of monitoring and controlling the operating conditions to keep the device temperatures within safe operating ranges. Our ready-to-use management technique has been implemented on Google Nexus 5 and has been demonstrated to achieve a 46% application-specific savings on power consumption and up to 35% savings in power consumption at the device level. The mean temperature estimation error is 1.17°C.
A subtree-based factorization of dependency parsing

We propose a dependency parsing pipeline, in which the parsing of long-distance projections and localized dependencies are explicitly decomposed at the input level. A chosen baseline dependency parsing model performs only on ‘carved’ sequences at the second stage, which are transformed from coarse constituent parsing outputs at the first stage. When k-best constituent parsing outputs are kept, a third-stage is required to search for an optimal combination of the overlapped dependency subtrees. In this sense, our dependency model is subtree-factored. We explore alternative approaches for scoring subtrees, including feature-based models as well as continuous representations. The search for optimal subset to combine is formulated as an ILP problem. This framework especially benefits the models poor on long sentences, generally improving baselines by 0.75-1.28 (UAS) on English, achieving comparable performance with high-order models but faster. For Chinese, the most notable increase is as high as 3.63 (UAS) when the proposed framework is applied to first-order parsing models.
Treatment of Inverted Nipple with Subareolar Abscess: Usefulness of High-Resolution MRI for Preoperative Evaluation

Background. Inverted nipples with subareolar abscesses can recur due to insufficient resection. It is important to provide reliable curative treatment after determination of the extent of resection by preoperative imaging evaluation. Methods. Ten patients were treated for inverted nipples with subareolar abscess. Sonography and high-resolution MRI were used as preoperative imaging modalities. The endpoints of preoperative imaging evaluation were defined as the identification of the abscess site, isolated fistula site, and extent of inflammation. Results. In all patients, sonography confirmed the presence of abscesses but their locations could not be identified. Sonography could not confirm the presence of isolated fistula or inflammation. In contrast, high-resolution MRI not only confirmed the presence of abscesses but also revealed their positional relationships with the nipples. In addition, high-resolution MRI confirmed the presence of isolated fistulas and inflammation as well as revealed their positional relationships with the nipples. In all patients, no recurrence was observed, and satisfactory surgical results were obtained. Conclusion. High-resolution MRI is useful in determination of the extent of resection of subareolar abscess associated with inverted nipple.
Machine Learning for Domain-Adaptive Word Sense Disambiguation

This paper investigates the use of machine learning techniques for word sense disambiguation. The aim is to improve on the performance of general-purpose methods, by making the disambiguation method adaptable to new domains. Results are presented here for two different test cases: financial news from the Wall Street Journal, extracted from the SEMCOR corpus, and general-theme news from the same corpus. The two experiments show that the adaptive disambiguation method can achieve high recall and precision; more so in the restricted domain of financial news than in the general-theme case.
Towards an efficient exploitation of loop-level parallelism in Java

This paper analyzes the overheads incurred in the exploitat ion of loop-level parallelism using Java Threads and purposes some code t ransformat ions that-minimize them. Avoiding the intensive use of Java Threads and reducing the number of classes used to specify the parallelism in the applicat ion results in promising performance gains t ha t may encourage the use of Java for exploiting loop-level parallelism. On average, the execution t ime for our synthet ic benchmarks is reduced by 50% from the simplest t ransformat ion when 8 threads are used.
Composição corporal, somatótipo, actividade física e aptidão física, em crianças e adolescentes, em diferentes contextos de participação desportiva

xx Resume xxii 1 Introducao 2 1.1 Justificacao e pertinencia do estudo 2 1.1.1 O problema crescente do sedentarismo: implicacoes no estado de saude 2 1.1.2 O papel da actividade fisica 3 1.2 Objectivos e hipoteses 5 1.3 Estrutura do trabalho 6 2. Revisao da Literatura 8 2.1 Actividade fisica 8 2.1.1 Actividade fisica, composicao corporal e somatotipo 10
Gain scheduled observer state feedback controller for rational LPV systems

Abstract This paper addresses the design of gain scheduled observer-based controllers for rational linear parameter varying systems (LPV). Such systems are equivalently recast as affine descriptor LPV systems. Based on this new realization a descriptor observer-based controller is designed by means of some new sufficient conditions given as LMIs. The stability of the descriptor closed-loop system is proved. A state space rational controller, with an observer-based structure, is then derived. The stability of the obtained rational closed-loop is also proved. A numerical example is presented to illustrate the efficiency of the method.
Toward a Topological Theory of Relational Reinforcement Learning for Navigation Tasks

We examine application of relational learning methods to reinforcement learning in spatial navigation tasks. Specifically, we consider a goal-seeking agent with noisy control actions embedded in an environment with strong topological structure. While formally a Markov decision process (MDP), this task possesses special structure derived from the underlying topology that can be exploited to speed learning. We describe relational policies for such environments that are relocatable by virtue of being parameterized solely in terms of the relations (distance and direction) between the agent’s current state and the goal state. We demonstrate that this formulation yields significant learning improvements in completely homogeneous environments for which exact policy relocation is possible. We also examine the effects of non-homogeneities such as walls or obstacles and show that their effects can be neglected if they fall outside of a closed-form envelope surrounding the optimal path between the agent and the goal. To our knowledge, this is the first closed-form result for the structure of an envelope in an MDP. We demonstrate that relational reinforcement learning in an environment that obeys the envelope constraints also yields substantial learning performance improvements.
Functional annotation of regulatory pathways

MOTIVATION Standardized annotations of biomolecules in interaction networks (e.g. Gene Ontology) provide comprehensive understanding of the function of individual molecules. Extending such annotations to pathways is a critical component of functional characterization of cellular signaling at the systems level. RESULTS We propose a framework for projecting gene regulatory networks onto the space of functional attributes using multigraph models, with the objective of deriving statistically significant pathway annotations. We first demonstrate that annotations of pairwise interactions do not generalize to indirect relationships between processes. Motivated by this result, we formalize the problem of identifying statistically overrepresented pathways of functional attributes. We establish the hardness of this problem by demonstrating the non-monotonicity of common statistical significance measures. We propose a statistical model that emphasizes the modularity of a pathway, evaluating its significance based on the coupling of its building blocks. We complement the statistical model by an efficient algorithm and software, Narada, for computing significant pathways in large regulatory networks. Comprehensive results from our methods applied to the Escherichia coli transcription network demonstrate that our approach is effective in identifying known, as well as novel biological pathway annotations. AVAILABILITY Narada is implemented in Java and is available at http://www.cs. purdue.edu/homes/jpandey/narada/.
Query-dependent visual dictionary adaptation for image reranking

Although text-based image search engines are popular for ranking images of user's interest, the state-of-the-art ranking performance is still far from satisfactory. One major issue comes from the visual similarity metric used in the ranking operation, which depends solely on visual features. To tackle this issue, one feasible method is to incorporate semantic concepts, also known as image attributes, into image ranking. However, the optimal combination of visual features and image attributes remains unknown. In this paper, we propose a query-dependent image reranking approach by leveraging the higher level attribute detection among the top returned images to adapt the dictionary built over the visual features to a query-specific fashion. We start from offline learning transposition probabilities between visual codewords and attributes, then utilize the probabilities to online adapt the dictionary, and finally produce a query-dependent and semantics-induced metric for image ranking. Extensive evaluations on several benchmark image datasets demonstrate the effectiveness and efficiency of the proposed approach in comparison with state-of-the-arts.
Progress in Understanding Breakdown Characteristics of X-Band Choke-Mode Structures

As one of the higher-order-mode (HOM) damping structures, X-band choke-mode accelerating structures had been studied for several years. However, the breakdown characteristics of the X-band choke are still unknown. Five different single-cell choke-mode accelerating structures and one reference structure were designed, fabricated and high-gradient tested to study the related RF breakdown characteristics. The absence of field emission current flash was proposed to be the sign of breakdowns occurring inside the choke, this was verified by the post-mortem observation. Evaluation of the breakdown rate revealed that there is memory effect with pulse width and electric field. The breakdown rate in a single RF pulse did not have the 5th order pulse width and 30th order electric field dependency predicted by the empirical formula.
SAT-Based versus CSP-Based Constraint Weighting for Satisfiability

Recent research has focused on bridging the gap between the satisfiability (SAT) and constraint satisfaction problem (CSP) formalisms. One approach has been to develop a many-valued SAT formula (MV-SAT) as an intermediate paradigm between SAT and CSP, and then to translate existing highly efficient SAT solvers to the MV-SAT domain. Experimental results have shown this approach can achieve significant improvements in performance compared with the traditional SAT and CSP approaches. In this paper, we follow a different route, developing SAT solvers that can automatically recognise CSP structure hidden in SAT encodings. This allows us to look more closely at how constraint weighting can be implemented in the SAT and CSP domains. Our experimental results show that a SAT-based approach to handle weights, together with CSP-based approach to variable instantiation, is superior to other combinations of SAT and CSP-based approaches. A further experiment on the round robin scheduling problem indicates that this many-valued constraint weighting approach outperforms other state-of-the-art solvers.
Empirical study of various weight initialization techniques for FFANN

The weight initialization implementation is related to the convergence of any learning/training algorithm. In this paper we have analyzed Gradient Descent with Momentum Back propagation algorithm using eight different approximation functions and Nguyen & Widrow weight initialization method. We found that the best performance results are obtained using fifth approximation problem. The best performance output is also shown graphically in this paper.
Summer 5-26-2017 IT Enabled Organizational Agility and Firm Performance : Evidence from Chinese Firms

In this study, we examine the role of information technology (IT) in improving organizational agility and firm performance from the perspectives of the resource-picking and capability-building mechanisms of rent creation, and the hierarchy of dynamic capabilities. We divide IT capabilities into IT exploration capability, which corresponds to the resource-picking phase, and IT exploitation capability, which corresponds to the capability-building phase. Based on the concept of a hierarchy of dynamic capabilities, we establish the theoretical links between lower order capabilities (IT exploitation), higher order capabilities (organizational agility), and performance. Using the partial least squares (PLS) structural equation modeling approach, we empirically test the proposed relationships using data from 289 manufacturers in the Pearl River Delta region of Guangdong, China. Our results suggest that 1) IT exploration capability (resource picking) affects IT exploitation capability (capability building); 2) IT exploitation capability has positive effects on customer, operational, and partner agilities (higher order of capabilities); and 3) the IT enabled organizational agilities positively affect firm performance. We discuss the theoretical and practical implications of our findings and the opportunities for future
Overexpression of CaAPX Induces Orchestrated Reactive Oxygen Scavenging and Enhances Cold and Heat Tolerances in Tobacco

Ascorbate peroxidase (APX) acts indispensably in synthesizing L-ascorbate (AsA) which is pivotal to plant stress tolerance by detoxifying reactive oxygen species (ROS). Enhanced activity of APX has been shown to be a key step for genetic engineering of improving plant tolerance. However it needs a deeper understanding on the maintenance of cellular ROS homeostasis in response to stress. In this study, we identified and characterized an APX (CaAPX) gene from Camellia azalea. Quantitative real-time PCR (qRT-PCR) analysis showed that CaAPX was expressed in all tissues and peaked in immature green fruits; the expression levels were significantly upregulated upon cold and hot stresses. Transgenic plants displayed marked enhancements of tolerance under both cold and heat treatments, and plant growth was correlated with CaAPX expression levels. Furthermore, we monitored the activities of several ROS-scavenging enzymes including Cu/Zn-SOD, CAT, DHAR, and MDHAR, and we showed that stress tolerance was synchronized with elevated activities of ROS-scavenging. Moreover, gene expression analysis of ROS-scavenging enzymes revealed a role of CaAPX to orchestrate ROS signaling in response to temperature stresses. Overall, this study presents a comprehensive characterization of cellular response related to CaAPX expression and provides insights to breed crops with high temperature tolerances.