Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimentally Manipulated Somatic Information and Somatization Tendencies and their Impact on Physical Symptom Reporting and Performance in a Physically Strenuous Task

This study attempts to determine whether the presentation of an experimentally manipulated somatic experience during a physically strenuous task can influence physical performance and symptom reporting. The study also compares the relative influence of experimentally manipulated somatic information (state somatization) with stable individual differences in the tendency to amplify physical symptoms (trait somatization) on performance and symptom reporting. 194 participants completed standardized measures of somatization tendencies, state anxiety, neuroticism and conscientiousness. Participants where then given a mock physical exam, with individuals randomly assigned to receive either favorable or unfavorable somatic information. All participants then had their body mass index assessed and completed a rigorous exercise task, with quantification of performance. Physiological measures of blood pressure and pulse were also assessed before and after the exercise task. The experimentally manipulated presentation of somatic information predicted both performance and physical symptoms, even after controlling for BMI, neuroticism, conscientiousness, and state anxiety. Moreover, expected performance uniquely and significantly predicted performance above and beyond condition, anxiety, BMI, neuroticism, and conscientiousness. Somatosensory amplification tendencies also predicted symptom endorsement, but not performance. Findings suggest that both state and trait expectations with respect to somatic experiences influence symptom reporting and to a lesser extent performance, even after controlling for variables known to strongly influence each of these outcomes. Results are consistent with the cognitive-perceptual and the cognitive-appraisal models of somatic interpretation.
Current perspective of venous thrombosis in the upper extremity

Summary. Venous thrombosis of the upper extremity is a rare disease. Therefore, not as much is known about risk factors, treatment and the risk of recurrence as for venous thrombosis of the leg. Only central venous catheters and strenuous exercise are commonly known risk factors for an upper extremity venous thrombosis. In this review an overview of the different risk factors, possible treatments and the complications for patients with a venous thrombosis of the upper extremity is given.
SHEF-NN: Translation Quality Estimation with Neural Networks

We describe our systems for Tasks 1 and 2 of the WMT15 Shared Task on Quality Estimation. Our submissions use (i) a continuous space language model to extract additional features for Task 1 (SHEFGP, SHEF-SVM), (ii) a continuous bagof-words model to produce word embeddings as features for Task 2 (SHEF-W2V) and (iii) a combination of features produced by QuEst++ and a feature produced with word embedding models (SHEFQuEst++). Our systems outperform the baseline as well as many other submissions. The results are especially encouraging for Task 2, where our best performing system (SHEF-W2V) only uses features learned in an unsupervised fashion.
Towards Gigayear Storage Using a Silicon-Nitride/Tungsten Based Medium

Current digital data storage systems are able to store huge amounts of data. Even though the data density of digital information storage has increased tremendously over the last few decades, the data longevity is limited to only a few decades. If we want to preserve anything about the human race which can outlast the human race itself, we require a data storage medium designed to last for 1 million to 1 billion years. In this paper a medium is investigated consisting of tungsten encapsulated by siliconnitride which, according to elevated temperature tests, will last for well over the suggested time.
Consequences of Misspecifying Levels of Variance in Cross-Classified Longitudinal Data Structures

The purpose of this study was to determine if modeling school and classroom effects was necessary in estimating passage reading growth across elementary grades. Longitudinal data from 8367 students in 2989 classrooms in 202 Reading First schools were used in this study and were obtained from the Progress Monitoring and Reporting Network maintained by the Florida Center for Reading Research. Oral reading fluency (ORF) was assessed four times per school year. Five growth models with varying levels of data (student, classroom, and school) were estimated in order to determine which structures were necessary to correctly partition variance and accurately estimate standard errors for growth parameters. Because the results illustrate that not modeling higher-level clustering inflated lower-level variance estimates and in some cases led to biased standard errors, the authors recommend the practice of including classroom cross-classification and school nesting when predicting longitudinal student outcomes.
Noncooperative Multimode Precoding with Limited Feedback in MIMO Interference Channels

This letter proposes an iterative multimode precoding scheme with limited feedback for nonreciprocal MIMO interference channels. Based on analysis of game theory, we model the iterative multimode precoding as a noncooperative game with a finite set of strategies. Numerical results are presented to verify the sum rate performance of the proposed scheme.
Hyperinsulinemia Down-Regulates TLR4 Expression in the Mammalian Heart

Toll-like receptors (TLR) are key regulators of innate immune and inflammatory responses and their activation is linked to impaired glucose metabolism during metabolic disease. Determination of whether TLR4 signaling can be activated in the heart by insulin may shed light on the pathogenesis of diabetic cardiomyopathy, a process that is often complicated by obesity and insulin resistance. The aim of the current study was to determine if supraphysiological insulin concentrations alter the expression of TLR4, markers of TLR4 signaling and glucose transporters (GLUTs) in the heart. Firstly, the effect of insulin on TLR4 protein expression was investigated in vitro in isolated rat cardiac myocytes. Secondly, protein expression of TLR4, the pro-inflammatory cytokines interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) suppressor of cytokine signaling 3 (SOCS3) and GLUTs (1, 4, 8, 12) were examined in the equine ventricular myocardium following a prolonged, euglycemic, hyperinsulinemic clamp. Down-regulation of TLR4 protein content in rat cardiac myocytes was observed after incubation with a supraphysiologic concentration of insulin as well as in the equine myocardium after prolonged insulin infusion. Further, cardiac TLR4 expression was negatively correlated with serum insulin concentration. Markers of cardiac TLR4 signaling and GLUT expression were not affected by hyperinsulinemia and concomitant TLR4 down-regulation. Since TLRs are major determinants of the inflammatory response, our findings suggest that insulin infusion exerts an anti-inflammatory effect in the hearts of non-obese individuals. Understanding the regulation of cardiac TLR4 signaling during metabolic dysfunction will facilitate improved management of cardiac sequela to metabolic syndrome and diabetes.
Cosmological General Relativity against high redshift observations

Several key relations are derived for Cosmological General Relativity which are used in standard observational cosmology. These include the luminosity distance, angular size, surface brightness and matter density. These relations are used to fit type Ia supernova (SNe Ia) data, giving consistent, well behaved fits over a broad range of redshift 0.1 < z < 2. The best fit to the data for the local density parameter is Ωm = 0.0401± 0.0199. Because Ωm is within the baryonic budget there is no need for any dark matter to account for the SNe Ia redshift luminosity data. From this local density it is determined that the redshift where the universe expansion transitions from deceleration to acceleration is zt = 1.095 +0.264 −0.155. Because the fitted data covers the range of the predicted transition redshift zt, there is no need for any dark energy to account for the expansion rate transition. We conclude that the expansion is now accelerating and that the transition from a closed to an open universe occurred about 8.54 Gyr ago.
Efficiently Solving Time-Dependent Joint Activities in Security Games

Despite recent successful real-world deployments of Stackelberg Security Games (SSGs), scale-up remains a fundamental challenge in this field. The latest techniques do not scale-up to domains where multiple defenders must coordinate time-dependent joint activities. To address this challenge, this paper presents two branch-and-price algorithms for solving SSGs, SMARTO and SMARTH , with three novel features: (i) a column-generation approach that uses an ordered network of nodes (determined by solving the traveling salesman problem) to generate individual defender strategies; (ii) exploitation of iterative reward shaping of multiple coordinating defender units to generate coordinated strategies; (iii) generation of tighter upper-bounds for pruning by solving security games that only abide by key scheduling constraints. We provide extensive experimental results and formal analyses.
Dynamics of Price Sensitivity and Market Structure in an Evolutionary Economic Matching Model

The relationship between equilibrium convergence to a uniform quality distribution and price is investigated in the Qmodel, a self-organizing, evolutionary computational matching model of a fixed-price post-secondary higher education system created by Ortmann and Slobodyan (2006). The Q-model is replicated with price equaling its Ortmann and Slobodyan (2006) value P*. Varying the fixed price between 0 and 2P* reveals thresholds at which the Q-model reaches different market clustering configurations. Results indicate structural market robustness to prices less than P* and high sensitivity to prices greater than P*.
Realistic Rician fading channel based optimal linear MIMO precoding evaluation

For multiple-input/multiple-output (MIMO)-based wireless communications systems with precoding, we evaluate the symbol-detection performance for Rician fading. We first consider, as in conventional evaluation approaches, fixed channel statistics, i.e., typical settings for the azimuth spread (AS), Rician K-factor, and deterministic channel matrix component Hd with set rank. Then, to predict performance more realistically, we adopt WINNER II models for AS and K (i.e., lognormally-distributed, correlated, scenario-dependent), and generate Hd based on randomly-selected transmit and receive antenna geometries. Based on our recent work, we consider that the channel matrix, i.e., the instantaneous channel state information (CSI), is available only as an inaccurate estimate at the transmit-side, for precoding. Conventional precoding simply replaces the true instantaneous CSI with its estimate in precoder matrix expressions. Our approach has been to rewrite the true instantaneous CSI in terms of the estimated instantaneous CSI and an error matrix whose statistics depend on the channel statistical CSI, and use this reformulation for an optimal precoding approach. For the numerical results, both instantaneous CSI and statistical CSI are estimated from pilot symbols, unlike in previous work, which generally assumes perfect statistical CSI. Our evaluation reveals the substantial dependence of precoding performance on the various assumptions about AS and K, and Hd. Furthermore, we confirm that the new precoding approach can significantly improve performance.
Institutions and Self-Governing Social Systems: Linking Reflexivity and Institutional Theories for Cybersecurity and Other Commons Governance Policies

vii List of Figures . xii List of Tables . xiii Chapter 1: Introduction and Overview of the Research . 1 Research Purpose. 1 The Problem in Context: Commons Governance. 3 The Case: Federal Cybersecurity Policy. 10 Research Strategy and Questions. 20 Summary. 22 Chapter 2: Review of the Literature . 24 Introduction. 24 Social Systems and Reflexivity Theory. 24 Institutional Theory. 35 The IAD Framework . 44 Linking Reflexivity and Institutional Theories . 47 The Research Case: Federal Cybersecurity Policy. 50
Visual Integration of Meteorological and Sensor Data for Identifying Suspicious Company Behavior

We present an approach developed in course of the VAST 2017 Mini-Challenge 2. To help the ornithologist Mitch to investigate the noxious gases emitted by the four companies south of the nature preserve, we employ a combination of interactive visualizations that allow for an exploration of the data. In this paper, we present our visual-interactive approach for analyzing suspicious patterns in the data. By taking the wind data into consideration, as well, our approach allows the retrieval of patterns in the chemical releases and identify key polluters.
Antiradical and Cytoprotective Activities of Several C-Geranyl-substituted Flavanones from Paulownia tomentosa Fruit

Antiradical and cytoprotective activities of several flavanones isolated from Paulownia tomentosa (Thunb.) Steud. (Scrophulariaceae) have been evaluated using different in vitro and in vivo methods. The capacity of flavanones to scavenge radicals was measured in vitro by means of DPPH and ABTS assays, the inhibition of hydroxyl radicals produced in Fenton reactions, FRAP, scavenging superoxide radicals using enzymatic and nonenzymatic assays and the inhibition of peroxynitrite-induced nitration of tyrosine. The in vivo testing involved measuring the cytoprotective effect of chosen flavanones against alloxan-induced diabetes in mice. The activity of tested compounds was expressed either as a Trolox® equivalent or was compared with rutin or morine as known antioxidant compounds. The highest activity in most tests was observed for diplacone and 3´-O-methyl-5´-hydroxydiplacone, and the structure vs. the antioxidant activity relationship of geranyl or prenyl-substituted flavonoids with different substitutions at the B and C ring was discussed.
A Succinct Reflection on Data Classification Methodologies

Classification is a data mining (machine learning) technique used to assign group membership to various data instances. Indeed there are many classification techniques available for a scientist wishing to discover a model for his/her data. This diversity can cause trouble as to which method should be applied to which data set to solve a particular domain concentrated problem. This review paper presents several major classification techniques like Decision Tree Induction, Bayesian Classification, Rule-based Classification, classification by Back Propagation, Support Vector Machines, Lazy Learners, Genetic Algorithms, Rough Set Approach, and Fuzzy Set Approach. The goal of this survey is to provide a comprehensive review of different data classification techniques.
Equilibration Rate for the Linear Inhomogeneous Relaxation-Time Boltzmann Equation for Charged Particles

Abstract We study the long-time behavior of a linear inhomogeneous Boltzmann equation. The collision operator is modeled by a simple relaxation towards the Maxwellian distribution with zero mean and fixed lattice temperature. Particles are moving under the action of an external potential that confines particles, i.e., there exists a unique stationary probability density. Convergence rate towards global equilibrium is explicitly measured based on the entropy dissipation method and apriori time independent estimates on the solutions. We are able to prove that this convergence is faster than any algebraic time function, but we cannot achieve exponential convergence.
A Reinforcement Learning-Based Mixed Job Scheduler Scheme for Cloud Computing under SLA Constraint

Job scheduling is a necessary prerequisite for performance optimization and resource management in the cloud computing system. Focusing on accurate scaled cloud computing environment and efficient job scheduling under Virtual Machine (VM) resource and Server Level Agreement (SLA) constraints, we introduce the architecture of cloud computing platform and optimization job scheduling scheme in this study. The system model is comprised of clearly defined separate constituent parts, including portal, job scheduler, and resources pool. By analyzing the execution process of user jobs, we designed a novel job scheduling scheme based on reinforcement learning to minimize the makespan and Average Waiting Time (AWT) under the VM resource and deadline constraints, and employ parallel multi-age parallel technologies to balance the exploration and exploitation in learning process and accelerate the convergence of Q-learning algorithm. Both statistical and numerical analysis results demonstrate the efficiency of the proposed job scheduling scheme.
Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation

We survey the research on interactive evolutionary computation (IEC). The IEC is an EC that optimizes systems based on subjective human evaluation. The definition and features of the IEC are first described and then followed by an overview of the IEC research. The overview primarily consists of application research and interface research. In this survey the IEC application fields include graphic arts and animation, 3D computer graphics lighting, music, editorial design, industrial design, facial image generation, speed processing and synthesis, hearing aid fitting, virtual reality, media database retrieval, data mining, image processing, control and robotics, food industry, geophysics, education, entertainment, social system, and so on. The interface research to reduce human fatigue is also included. Finally, we discuss the IEC from the point of the future research direction of computational intelligence. This paper features a survey of about 250 IEC research papers.
\the Protein Data Bank: a Computer-based Archival Le for Macromolecular Structures,"

do not seem to be sucient. Thus, multiple protein structure alignment should be studied further. In this paper, each protein structure is treated as a rigid body. That is, alignments are computed considering global positions only. Although such a treatment is adequate for comparing structures with strong similarities, it seems to be inadequate for comparing structures with weak similarities. Especially, in the case of classication of protein structures (or folding patterns) into the small number of families, more exible pattern matching methods should be employed. Holm et al. combined several algorithms for that purpose [6]. We also proposed another method [3]. But, these methods do not seem to be sucient. Thus, more exible pattern matching methods should be developed. [2] T. Akutsu, \Ecient and robust three-dimensional pattern matching algorithms using hashing and dynamic programming techniques," Proc. \Ecient detection of three-dimensional structural motifs in biological macromolecules by computer vision techniques," Proc.
Discourse strategies that co-construct relational identities in STEM peer tutoring

ABSTRACT This study is a discourse analysis of the transcribed talk recorded between tutors and students in a university peer tutoring program. We focus on STEM tutoring sessions, showing how tutors and students co-construct relational identities in and among the technical, highly task-oriented work of solving math and science problems. Using Grounded Practical Theory as a theoretical framework, we propose that understanding the co-creation of relational identities provides insight into an important component of STEM tutoring sessions. Through the process of Action Implicative Discourse Analysis, we describe three conversational practices with which tutors and students negotiate their relational identity—encouragement talk, sensemaking checks, and metadisciplinary talk. We discuss implications in terms of tutoring sessions’ situated ideals and participants engaging in a community of practice.
Modeling Chemotaxis Reveals the Role of Reversed Phosphotransfer and a Bi-Functional Kinase-Phosphatase

Understanding how multiple signals are integrated in living cells to produce a balanced response is a major challenge in biology. Two-component signal transduction pathways, such as bacterial chemotaxis, comprise histidine protein kinases (HPKs) and response regulators (RRs). These are used to sense and respond to changes in the environment. Rhodobacter sphaeroides has a complex chemosensory network with two signaling clusters, each containing a HPK, CheA. Here we demonstrate, using a mathematical model, how the outputs of the two signaling clusters may be integrated. We use our mathematical model supported by experimental data to predict that: (1) the main RR controlling flagellar rotation, CheY6, aided by its specific phosphatase, the bifunctional kinase CheA3, acts as a phosphate sink for the other RRs; and (2) a phosphorelay pathway involving CheB2 connects the cytoplasmic cluster kinase CheA3 with the polar localised kinase CheA2, and allows CheA3-P to phosphorylate non-cognate chemotaxis RRs. These two mechanisms enable the bifunctional kinase/phosphatase activity of CheA3 to integrate and tune the sensory output of each signaling cluster to produce a balanced response. The signal integration mechanisms identified here may be widely used by other bacteria, since like R. sphaeroides, over 50% of chemotactic bacteria have multiple cheA homologues and need to integrate signals from different sources.
A Location-Based User Movement Prediction Approach For Geolife Project

Recently obtaining knowledge from raw trajectory data has been an interest of many researches. Trajectory data set consists of thousands of records. To discover valuable knowledge from these records advanced data mining techniques must be applied. Models developed from these techniques will be useful for predication. In this paper data mining classification techniques are analyzed on trajectory dataset and Performance of these techniques is evaluated with recall, precision, kappa and accuracy. KeywordsClassification Algorithms, WEKA, recall, precision, kappa and accuracy.
Onset of the DNA Replication Checkpoint in the Early Drosophila Embryo

The Drosophila embryo is a promising model for isolating gene products that coordinate S phase and mitosis. We have reported before that increasing maternal Cyclin B dosage to up to six copies (six cycB) increases Cdk1–Cyclin B (CycB) levels and activity in the embryo, delays nuclear migration at cycle 10, and produces abnormal nuclei at cycle 14. Here we show that the level of CycB in the embryo inversely correlates with the ability to lengthen interphase as the embryo transits from preblastoderm to blastoderm stages and defines the onset of a checkpoint that regulates mitosis when DNA replication is blocked with aphidicolin. A screen for modifiers of the six cycB phenotypes identified 10 new suppressor deficiencies. In addition, heterozygote dRPA2 (a DNA replication gene) mutants suppressed only the abnormal nuclear phenotype at cycle 14. Reduction of dRPA2 also restored interphase duration and checkpoint efficacy to control levels. We propose that lowered dRPA2 levels activate Grp/Chk1 to counteract excess Cdk1–CycB activity and restore interphase duration and the ability to block mitosis in response to aphidicolin. Our results suggest an antagonistic interaction between DNA replication checkpoint activation and Cdk1–CycB activity during the transition from preblastoderm to blastoderm cycles.
An approach to assess maintainability change

Maintenance is a very expensive activity in the software systems' lifecycle. The ISO 25010 standard considers it as one of its main eight characteristics. In this paper, we propose an approach to assess the maintainability characteristic by its own subcharacteristics as defined by ISO 25010, using several object oriented metrics. A set of rules is defined to capture the influence of the metrics values over each subcharacteristic. The rules are then used in the maintainability change assessment process. A case study that uses two software versions is discussed in order to validate our approach.