Miracle-dz/UniFAR-FTUs huggingface dataset:
See what you hear – How the brain forms representations across the senses

Abstarct Our senses are constantly bombarded with a myriad of signals. To make sense of this cacophony, the brain needs to integrate signals emanating from a common source, but segregate signals originating from the different sources. Thus, multisensory perception relies critically on inferring the world’s causal structure (i. e. one common vs. multiple independent sources). Behavioural research has shown that the brain arbitrates between sensory integration and segregation consistent with the principles of Bayesian Causal Inference. At the neural level, recent functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) studies have shown that the brain accomplishes Bayesian Causal Inference by dynamically encoding multiple perceptual estimates across the sensory processing hierarchies. Only at the top of the hierarchy in anterior parietal cortices did the brain form perceptual estimates that take into account the observer’s uncertainty about the world’s causal structure consistent with Bayesian Causal Inference.
Enhanced Biogeography-Based Optimization using modified clear duplicate operator

Enhanced Biogeography-Based Optimization (EBBO) technique is an improved version of BBO. BBO mainly uses the idea of probabilistically sharing features (Migration operator) among solutions based on the fitness values. The exploitation ability of BBO is good in comparison to many optimization techniques due to efficient sharing of information among solutions. However, migration operator creates similar habitats. To prevent the harmful over-similarity among the solutions, clear duplicate operator with random mutation is utilized which leads to slow down the algorithm. In this paper, the performance of BBO is enhanced by integrating the modified clear duplicate operator to prevent the similar solutions. It also maintains the diversity of the newly generated solution. To validate the performance of EBBO, experiments have been conducted on unimodal and multimodal benchmark functions. EBBO gives excellent performance when compared with BBO and other versions of BBO. Statistical two-tailed t-test is also carried out on EBBO and BBO and results are analyzed.
Nutrition support for critically ill patients in China: role of the pharmacist.

BACKGROUND AND OBJECTIVES The participation of a nutrition support pharmacist (NSP) in a multidisciplinary team (MDT) for patients receiving nutrition support therapy (NST) may lead to more favourable outcomes and fewer complications and adverse events. However, few studies have demonstrated the role of NSPs in MDTs in China. To investigate pharmacy interventions and physician acceptance of these interventions for patients receiving NST in an intensive care unit (ICU). METHODS AND STUDY DESIGN A prospective study over a 12-month period was conducted in an ICU at an academic hospital in China. Interventions were documented and divided into the following categories: indication of NST, parenteral nutrition (PN) prescription and delivery, enteral nutrition (EN) route and formulation, fluids and electrolytes, laboratory test monitoring, nutritional supplements, and other medication-related problems. Data regarding the intervention categories, timing, acceptance rates, and methods of communication to discuss pharmacy interventions were collected. RESULTS In total, 247 interventions for 120 patients were identified. The overall acceptance rate of interventions was 85.0% (210/247), and more than half of the interventions (143, 57.9%) were performed during daily follow-up. The most common intervention categories were PN prescription and delivery (81/247, 32.8%), EN route and formula (33/247, 13.4%), indication of NST (33/247, 13.4%), and nutritional supplements (30/247, 12.1%). The most accepted intervention category was PN prescription and delivery (79/81, 97.5%), and the most common method of communication was oral communication during MDT rounds (201/247, 81.4%). CONCLUSIONS This study demonstrated the unique perspectives offered and importance of having pharmacists as members of MDTs.
The Effect of Maternal Relaxation Training on Reactivity of Non-Stress Test, Basal Fetal Heart Rate, and Number of Fetal Heart Accelerations: A Randomized Controlled Trial

Background: Relaxation-training, as an anxiety-reducer intervention, plays an important role in fetal health. The present study aimed to analyze the effect of maternal relaxation on stress test (NST), basal fetal heart rate, and number of fetal heart accelerations. Methods: In this randomized controlled trial, 84 pregnant women were randomly divided into two groups of teaching relaxation and control groups in 2012. In the intervention group, 60-90 minute classes were held every week lasting for 4 weeks. Besides, home practice charts were given to the mothers and researchers controlled the home practices by phone calls every week. The control group received routine prenatal care. In the 4th week, NST was performed in the intervention group 30 minutes before and after the 4th session. In the control group, NST was done in the 4th week. The quantitative variables in the two groups were compared through ANOVA and Chi-square test. Results: The results of paired t-test showed that relaxation could improve the NST results (P=0.01). Mean and standard deviation of basal fetal heart rate was 138.95±8.18 before the intervention and 133.07±6.9 after the intervention. Paired t-test also showed that relaxation reduced the basal fetal heart rate (P=0.001). Mean and standard deviation of the number of fetal heart accelerations was 1.5±0.8 before the intervention and 2.2±0.9 after it. The results of paired t-test also showed that relaxation increased the number of fetal heart accelerations (P=0.001). Conclusions: Relaxation could improve the NST results, reduce the basal fetal heart rate, and increase the number of fetal heart accelerations. Therefore, relaxation is recommended during pregnancy. Trial Registration Number: IRCT2012072810418N1
A graph-coloring secondary resource allocation for D2D communications in LTE networks

The Long Term Evolution (LTE) system, and especially its current release, LTE-Advanced, is considered as the next step in mobile broadband networks. Two of the main challenges faced by the LTE system are: i) to offer ubiquitous and highly reliable wireless services, and ii) to efficiently utilize the assigned spectrum portion. A promising approach towards this direction is to allow the establishment of direct device-to-device (D2D) communications in the assigned spectrum. In this paper, we study how the traffic load between users located in the same cell (intra-cell communications) can be served by D2D transmissions utilizing uplink spatial spectrum opportunities. Aiming at decongesting the base station (or eNB) and letting more resources for the inter-cell traffic (e.g., web services) extra functionality is added to system users (or UEs) for providing interference information to eNB. The collected information is represented by an enriched node contention graph (eNCG) utilized by graph-coloring algorithms to provide an interference-free secondary allocation. Results show that spatial spectrum opportunities can sufficiently serve the intra-cell traffic, while increased data rates can be offered to the inter-cell traffic.
The Autonomous Agent Architecture

In this paper we give a summary of the Autonomous Agent Architecture (AAA architecture for short) for intelligent agents. The AAA architecture is the result of several years of research on the design and implementation of intelligent agents that reason about, and act in, changing environments. In the AAA architecture, the knowledge about the domain where the agent is situated is encoded in Answer Set Prolog and is shared by all of the reasoning components. The agent’s reasoning components are also formalized in Answer Set Prolog. Typical AAA agents are capable of observing the environment, interpreting the observations, recovering from unexpected observations, and planning to achieve their goals.
An Order Optimal Policy for Exploiting Idle Spectrum in Cognitive Radio Networks

In this paper, a spectrum sensing policy employing recency-based exploration is proposed for cognitive radio networks. We formulate the problem of finding a spectrum sensing policy for multiband dynamic spectrum access as a stochastic restless multiarmed bandit problem with stationary unknown reward distributions. In cognitive radio networks, the multiarmed bandit problem arises when deciding where in the radio spectrum to look for idle frequencies that could be efficiently exploited for data transmission. We consider two models for the dynamics of the frequency bands: 1) the independent model where the state of the band evolves randomly independently from the past and 2) the Gilbert-Elliot model, where the states evolve according to a two-state Markov chain. It is shown that, in these conditions, the proposed sensing policy attains asymptotically logarithmic weak regret. The policy proposed in this paper is an index policy, in which the index of a frequency band comprises a sample mean term and a recency-based exploration bonus term. The sample mean promotes spectrum exploitation, whereas the exploration bonus encourages further exploration for idle bands providing high data rates. The proposed recency-based approach readily allows constructing the exploration bonus such that it will grow the time interval between consecutive sensing time instants of a suboptimal band exponentially, which then leads to logarithmically increasing weak regret. Simulation results confirming logarithmic weak regret are presented, and it is found that the proposed policy provides often improved performance at low complexity over other state-of-the-art policies in the literature.
Making Consensus Practical

This paper presents the abstraction of lazy consensus and argues for its use as an effective component for building distributed agreement protocols in practical asynchronous systems where processes and links can crash and recover. Lazy consensus looks like consensus, is equivalent to consensus, but is not consensus. The specification of lazy consensus has an on-demand and a re-entrant flavors that makes its use very efficient, especially in terms of forced logs, which are known to be major sources of overhead in distributed systems. We illustrate the use of lazy consensus as a building block to develop efficient atomic broadcast and atomic commitment protocols: two central abstractions in our DACE middleware environment.
Corporate Citizenship: Toward an Extended Theoretical Conceptualization

We critically examine the content of contemporary understandings of corporate citizenship and locate them within the extant body of research dealing with business-society relations. Our main purpose is to realize a theoretically informed definition of corporate citizenship that is descriptively robust and conceptually distinct from existing concepts in the literature. Specifically, our extended perspective exposes the element of “citizenship” and conceptualizes corporate citizenship as the administration of a bundle of individual citizenship rights—social, civil, and political—conventionally granted and protected by governments.
Universal models for Lorenz maps

Lorenz maps are dynamical systems on the interval related to the Lorenz flow. Monotone Lorenz families will be introduced and it will be shown that all possible topological dynamical behavior of Lorenz maps is realized within every monotone Lorenz family. Moreover, the structure of the parameter space for such families is described.
A 0.1-/spl mu/m double-deck-shaped gate HJFET with reduced gate-fringing-capacitance for ultra-high-speed ICs

This paper describes a novel double-deck-shaped (DDS) gate technology for 0.1-/spl mu/m heterojunction-FETs (HJFETs) that have half the external gate fringing capacitance (C/sub f//sup ext/) of conventional T-shaped gate HJFETs. By introducing a T-shaped SiO/sub 2/-opening technique based on two-step dry-etching with W-film masks, we have fabricated 0.1-/spl mu/m DDS gate-openings adapted to the reduction in C/sub f//sup ext/ and to the voidless-filling of gate-metals. Moreover, by using WSi-collimated sputtering and electroless Au-plating, 0.1-/spl mu/m DDS WSi/Ti/Pt/Au gate HJFETs with high uniformity and reproducibility are made. Fabricated n-Al/sub 0.2/Ga/sub 0.8/As-In/sub 0.15/Ga/sub 0.75/As HJFETs exhibit an excellent V/sub th/ standard-deviation (/spl sigma/V/sub th/) of 39 mV. Also, the HJFET covered with a SiO/sub 2/ film shows a very high millimeter-wave performance with f/sub T/ of 120 GHz and f/sub max/ of 165 GHz, due to the low C/sub f//sup ext/. In addition, a high f/sub T/ of 151 GHz and f/sub max/ of 186 GHz are obtained without a SiO/sub 2/ film.
A Sentiment Analysis System of Spanish Tweets and Its Application in Colombia 2014 Presidential Election

What people say on social media has turned into a rich source of information to understand social behavior. Sentiment analysis of Twitter data has been widely used to capture trends in public opinion regarding important events such as political elections. However, current research in social media analysis in political domains faces two major problems, namely: sentiment analysis methods implemented are often too simple, and most of the works assume that all users and their tweets are trustworthy. This research is aimed at dealing with these problems to achieve more reliable public opinion measurements. First, a dataset of 513K tweets referring to Colombia 2014 presidential election was collected. To distinguish spammer accounts from non-spammer ones, a supervised learning technique was implemented on a labeled collection of users. Next, a sentiment analysis system was developed by following a supervised classification approach. Lastly, the system was applied in the Colombian election to investigate the potential of social media for voting intention inference. Experimental results show that inference methods based on Twitter data are not consistent, despite obtaining the lowest mean absolute error and correctly ranking the highest-polling candidates in the first round election with the proposed inference method.
Galectin-2 expression is dependent on the rs7291467 polymorphism and acts as an inhibitor of arteriogenesis.

AIMS In patients with obstructive coronary artery disease (CAD), the growth of collateral arteries, i.e. arteriogenesis, can preserve myocardial tissue perfusion and function. Monocytes modulate this process, supplying locally the necessary growth factors and degrading enzymes. Knowledge on factors involved in human arteriogenesis is scarce. Thus, the aim of the present study is to identify targets in monocytes that are critical for arteriogenesis in patients with CAD. METHODS AND RESULTS A total of 50 patients with a chronic total coronary occlusion were dichotomized according to their collateral flow index. From each patient, RNA was isolated from unstimulated peripheral blood monocytes, monocytes stimulated by lipopolysaccharide (LPS) or interleukin (IL)-4, and from macrophages. Increased mRNA expression of galectin-2 was found in three out of four monocytic cell types of patients with a low capacity of the collateral circulation (P= 0.03 for unstimulated monocytes; P= 0.02 for LPS-stimulated monocytes; P= 0.20 for IL-4-stimulated monocytes; P= 0.02 for macrophages). Additionally, galectin-2 mRNA expression was significantly associated with the rs7291467 polymorphism in LGALS2 encoding galectin-2 in all four monocytic cell types. Patient with the rs7291467 CC genotype displayed highest galectin-2 expression, and also tended to have a lower arteriogenic response. To evaluate the effect of galectin-2 on arteriogenesis in vivo, we used a murine hindlimb model. Treatment with galectin-2 markedly impaired the perfusion restoration at Day 7. CONCLUSION Collectively, these results identify galectin-2 as a novel inhibitor of arteriogenesis. Modulation of galectin-2 may constitute a new therapeutic strategy for the stimulation of arteriogenesis in patients with CAD.
Association between Attention and Heart Rate Fluctuations in Pathological Worriers

Recent data suggests that several psychopathological conditions are associated with alterations in the variability of behavioral and physiological responses. Pathological worry, defined as the cognitive representation of a potential threat, has been associated with reduced variability of heart beat oscillations (i.e., decreased heart rate variability; HRV) and lapses of attention indexed by reaction times (RTs). Clinical populations with attention deficit show RTs oscillation around 0.05 and 0.01 Hz when performing a sustained attention task. We tested the hypothesis that people who are prone to worry do it in a predictable oscillating pattern revealed through recurrent lapses in attention and concomitant oscillating HRV. Sixty healthy young adults (50% women) were recruited: 30 exceeded the clinical cut-off on the Penn State Worry Questionnaire (PSWQ; High-Worry, HW); the remaining 30 constituted the Low-Worry (LW) group. After a diagnostic assessment, participants performed two 15-min sustained attention tasks, interspersed by a standardized worry-induction procedure. RTs, HRV and moods were assessed. The analyses of the frequency spectrum showed that the HW group presents a significant higher and constant peak of RTs oscillation around 0.01 Hz (period 100 s) after the induction of worry, in comparison with their baseline and with the LW group that was not responsive to the induction procedure. Physiologically, the induction significantly reduced high-frequency HRV and such reduction was associated with levels of self-reported worry. Results are coherent with the oscillatory nature of the default mode network (DMN) and further confirm an association between cognitive rigidity and autonomic nervous system inflexibility.
An evolutionary perspective on intergroup dating bias

ABSTRACTAcross a diversity of contexts, men tend to exhibit greater intergroup bias than do women. However, in the domain of dating, this trend is reversed, such that women more strongly prefer to date men of their own racial group. Researchers employing an evolutionary perspective suggest that this sex difference can be explained by an evolutionary history in which men and women faced distinct adaptive challenges in their interactions with outgroup men. For women, outgroup men posed a recurrent threat of sexual coercion. Given the importance of reproductive choice in female mating strategies, this may have exerted selection pressure for psychological mechanisms that promote avoidance of outgroup men. Here we pre-registered a two-study design to examine whether women’s intergroup dating bias, manipulated via nationality to control for racial stereotypes, could be altered by manipulating the formidability of outgroup dating targets, thereby ameliorating or exacerbating their perceived threat. The design di.
Nonparametric Regression for Spherical Data

We develop nonparametric smoothing for regression when both the predictor and the response variables are defined on a sphere of whatever dimension. A local polynomial fitting approach is pursued, which retains all the advantages in terms of rate optimality, interpretability, and ease of implementation widely observed in the standard setting. Our estimates have a multi-output nature, meaning that each coordinate is separately estimated, within a scheme of a regression with a linear response. The main properties include linearity and rotational equivariance. This research has been motivated by the fact that very few models describe this kind of regression. Such current methods are surely not widely employable since they have a parametric nature, and also require the same dimensionality for prediction and response spaces, along with nonrandom design. Our approach does not suffer these limitations. Real-data case studies and simulation experiments are used to illustrate the effectiveness of the method.
Modeling Citizen Satisfaction with Mandatory Adoption of an E-Government Technology

While technology adoption is a major stream of research in information systems, few studies have examined the antecedents and consequences of mandatory adoption of technologies. To address this gap, we develop and test a model of mandatory citizen adoption of an e-government technology. Based on a framework that outlines the key stages associated with the launch of technology products, we identify various external factors as antecedents of four key technology adoption variables from the unified theory of acceptance and use of technology (UTAUT), i.e., performance expectancy, effort expectancy, social influence, and facilitating conditions, which ultimately impact citizen satisfaction. The four stages of technology launch and the salient antecedents in each stage are: market preparation stage - awareness; targeting stage - compatibility and self-efficacy; positioning stage - flexibility and avoidance of personal interaction; and execution stage - trust, convenience, and assistance. We test our model in a two-stage survey of 1,179 Hong Kong citizens, before and after they were issued a mandatory smart card to access e-government services. We find that the various factors tied to the different stages in launching the technology predict key technology adoption variables that, in turn, predict citizen satisfaction with e-government technology. We discuss the theoretical and practical implications for governments implementing technologies whose use by citizens is mandated.
Rule Discovery from Time Series

We consider the problem of finding rules relating patterns in a time series to other patterns in that series, or patterns in one series to patterns in another series. A simple example is a rule such as "a period of low telephone call activity is usually followed by a sharp rise in call volume". Examples of rules relating two or more time series are "if the Microsoft stock price goes up and Intel falls, then IBM goes up the next day," and "if Microsoft goes up strongly for one day, then declines strongly on the next day, and on the same days Intel stays about level, then IBM stays about level. " Our emphasis is in the discovery of local patterns in multivariate time series, in contrast to traditional time series analysis which largely focuses on global models. Thus, we search for rules whose conditions refer to patterns in time series. However, we do not want to define beforehand which patterns are to be used; rather, we want the patterns to be formed from the data in the context of rule discovery. We describe adaptive methods for finding rules of the above type from time-series data. The methods are based on discretizing the sequence by methods resembling vector quantization. We first form subsequences by sliding a window through the time series, and then cluster these subsequences by using a suitable measure of time-series similarity. The discretized version of the time series is obtained by taking the cluster identifiers corresponding to the subsequence. Once the time-series is discretized, we use simple rule finding methods to obtain rules from the sequence. We present empirical results on the behavior of the method.