Miracle-dz/UniFAR-FTUs huggingface dataset:
Blur Detection is Unaffected by Cognitive Load

Blur detection is affected by retinal eccentricity, but is it also affected by attentional resources? Research showing effects of selective attention on acuity and contrast sensitivity suggests that allocating attention should increase blur detection. However, research showing that blur affects selection of saccade targets suggests that blur detection may be pre-attentive. To investigate this question, we carried out experiments in which viewers detected blur in real-world scenes under varying levels of cognitive load manipulated by the N-back task. We used adaptive threshold estimation to measure blur detection thresholds at 0°, 3°, 6°, and 9° eccentricity. Participants carried out blur detection as a single task, a single task with to-be-ignored letters, or an N-back task with four levels of cognitive load (0, 1, 2, or 3-back). In Experiment 1, blur was presented gaze-contingently for occasional single eye fixations while participants viewed scenes in preparation for an easy picture recognition memory task, and the N-back stimuli were presented auditorily. The results for three participants showed a large effect of retinal eccentricity on blur thresholds, significant effects of N-back level on N-back performance, scene recognition memory, and gaze dispersion, but no effect of N-back level on blur thresholds. In Experiment 2, we replicated Experiment 1 but presented the images tachistoscopically for 200 ms (half with, half without blur), to determine whether gaze-contingent blur presentation in Experiment 1 had produced attentional capture by blur onset during a fixation, thus eliminating any effect of cognitive load on blur detection. The results with three new participants replicated those of Experiment 1, indicating that the use of gaze-contingent blur presentation could not explain the lack of effect of cognitive load on blur detection. Thus, apparently blur detection in real-world scene images is unaffected by attentional resources, as manipulated by the cognitive load produced by the N-back task.
Extreme Volatilities, Financial Crises and L-Moment Estimations of Tail-Indexes

Following Bali and Weinbaum (2005) and Maillet et al. (2010), we present several estimates of volatilities computed with high- and low-frequency data and complement their results using additional measures of risk and several alternative methods for Tail-index estimation. The aim here is to confirm previous results regarding the slope of the tail of various risk measure distributions, in order to define the high watermarks of market risks. We also produce synthetic general results concerning the method of estimation of the Tail-indexes related to expressions of the L-moments. Based on estimates of Tail-indexes, retrieved from the high frequency 30’ sampled CAC40 French stock Index series from the period 1997-2009, using Non-parametric Generalized Hill, Maximum Likelihood and various kinds of L-moment Methods for the estimation of both a Generalized Extreme Value density and a Generalized Pareto Distribution, we confirm that a heavy-tail density specification of the Log-volatility is not necessary.
Two quorum sensing systems control biofilm formation and virulence in members of the Burkholderia cepacia complex

The Burkholderia cepacia complex (Bcc) consists of 17 closely related species that are problematic opportunistic bacterial pathogens for cystic fibrosis patients and immunocompromised individuals. These bacteria are capable of utilizing two different chemical languages: N-acyl homoserine lactones (AHLs) and cis-2-unsaturated fatty acids. Here we summarize the current knowledge of the underlying molecular architectures of these communication systems, showing how they are interlinked and discussing how they regulate overlapping as well as specific sets of genes. A particular focus is laid on the role of these signaling systems in the formation of biofilms, which are believed to be highly important for chronic infections. We review genes that have been implicated in the sessile lifestyle of this group of bacteria. The new emerging role of the intracellular second messenger cyclic dimeric guanosine monophosphate (c-di-GMP) as a downstream regulator of the fatty acid signaling cascade and as a key factor in biofilm formation is also discussed.
Cognitive Ability and Human Behavior in Experimental Ultimatum Games

We investigate the relationship between decisions made in experimental ultimatum games and the cognitive ability of participants (students and adults aged 20s–80s). We find that the relationship between the offer amount and cognitive ability is quadratic and concave: a sender with lower cognitive ability offers about 25% of the endowment compared with 50% and 40% for senders with middle and high cognitive ability, respectively. However, we find no relationship between receiver behavior and cognitive ability. These findings suggest that cognitive ability plays an important role when a participant faces a more strategic role.
Journal of Economic Behavior & Organization Overconfidence, Experience, and Professionalism: an Experimental Study

This paper presents an online-experiment on overconfidence in the context of financial markets. Our subject pool consists of institutional investors, investment advisors and individual investors, all of them being registered users of a large online platform for market sentiment data. Due to their registration, several socioeconomic characteristics of participants can be controlled for in our analysis. It turns out that there are stable differences in overconfidence between the three investor groups. Moreover, investment experience and age have a significant impact on the degree of overconfidence which goes surprisingly in opposite direction. We argue that these results have important implications for studies analyzing the impact of experience on behavior in (financial) markets.
Automatic Cross-Lingual Similarization of Dependency Grammars for Tree-based Machine Translation

Structural isomorphism between languages beneﬁts the performance of cross-lingual applications. We propose an automatic al-gorithm for cross-lingual similarization of dependency grammars, which automatically learns grammars with high cross-lingual similarity. The algorithm similarizes the annotation styles of the dependency grammars for two languages in the level of classiﬁcation decisions, and gradually improves the cross-lingual similarity without losing linguistic knowledge resorting to iterative cross-lingual cooperative learning. The dependency grammars given by cross-lingual similariza-tion have much higher cross-lingual similarity while maintaining non-triviality. As applications, the cross-lingually similarized grammars signiﬁcantly improve the performance of dependency tree-based machine translation.
Discovery and Characterization of Silver Sulfide Nanoparticles in Final Sewage Sludge Products

B O J E O N G K I M , * , † C H E E S U N G P A R K , ‡ M I T S U H I R O M U R A Y A M A , ‡ , § A N D M I C H A E L F . H O C H E L L A , J R . † , § The Center for NanoBioEarth, Department of Geosciences, Virginia Tech, Blacksburg, Virginia 24061, Department of Materials Science and Engineering, Virginia Tech, Blacksburg, Virginia 24061, and Institute for Critical Technology and Applied Science, Virginia Tech, Blacksburg, Virginia 24061
From Canonical to Enhanced Extra Mixing in Low-Mass Red Giants: Tidally Locked Binaries

Stellar models that incorporate simple diffusion or shear-induced mixing are used to describe canonical extra mixing in low-mass red giants of low and solar metallicity. These models are able to simultaneously explain the observed Li and CN abundance changes along the upper red giant branch (RGB) in field low-metallicity stars and match photometry, rotation, and 12C/13C ratios for stars in the old open cluster M67. The shear mixing model requires that main-sequence (MS) progenitors of upper RGB stars possessed rapidly rotating radiative cores and that specific angular momentum was conserved in each of their mass shells during their evolution. We surmise that solar-type stars will not experience canonical extra mixing on the RGB because their more efficient MS spin-down resulted in solid-body rotation, as revealed by helioseismological data for the Sun. Thus, RGB stars in the old, high-metallicity cluster NGC 6791 should show no evidence for mixing in their 12C/13C ratios. We develop the idea that canonical extra mixing in a giant component of a binary system may be switched to its enhanced mode with much faster and somewhat deeper mixing as a result of the giant's tidal spin-up. This scenario can explain photometric and composition peculiarities of RS CVn binaries. The tidally enforced enhanced extra mixing might contribute to the star-to-star abundance variations of O, Na, and Al in globular clusters. This idea may be tested with observations of 12C/13C ratios and CN abundances in RS CVn binaries.
Combined FDTD/macromodel simulation of interconnected digital devices

Behavioral models of digital devices based on Radial Basis Functions (RBF) are incorporated into a Finite-Difference Time-Domain (FDTD) solver for full-wave analysis of interconnected drivers and receivers. This modeling strategy allows a very accurate and efficient full-wave solution of interconnection structures with possibly complex geometry including the nonlinear and dynamic effects of real-world digital devices, without the need of detailed transistor-level models. Examples of signal integrity and field coupling analysis are shown.
User evaluation of polymetric views using a large visualization wall

There are few visualization techniques for displaying complex software systems with large numbers of packages and classes. One visualization technique is the System Hotspots View, whose effectiveness has yet to be validated by any empirical studies. We have conducted a user evaluation to see whether participants of our modified System Hotspots View using a large visualization wall can accurately identify key measurements and comparisons in the underlying software system. The results of our user evaluation indicate that participants were able to effectively use our modified System Hotspots View to explore the example domain: version 1.6 of the Java API. Our observations also indicate that there are issues around interacting with the visualization wall.
Revisiting Technology Acceptance Model with Social Influence Factors

Despite many numbers of studies in Technology Acceptance Model (TAM), the social influence construct has not been recognized, nor handled clearly. This study includes the social influence construct in TAM and tests the robustness of the model for different information systems contexts: office automation (spreadsheet) and Internet usage. Study findings include that 1) social influence has a direct impact on the usage of spreadsheet, but not on Internet usage, and 2) social influence has a stronger indirect impact on IS use through PEU than through PU (for both Internet and spreadsheet). Keyword: Technology acceptance model, Social Influence, Structural Equation Modeling
Origin of the Scatter in the X-Ray Luminosity of Early-Type Galaxies Observed with ROSAT

Statistical properties of X-ray luminosity and temperature are studied for 52 early-type galaxies based on the ROSAT PSPC data. All of the X-ray luminous galaxies show largely extended emission with a radius of a few times 10re, while X-ray faint galaxies do not show such a component. This leads to a division of early-type galaxies into two categories: X-ray extended and X-ray compact galaxies. Except for a few galaxies in dense cluster environments, the luminosity and temperature of X-ray compact galaxies are well explained by a kinematical heating of the gas supplied by stellar mass loss. In contrast, X-ray extended galaxies indicate large scatter in the X-ray luminosity. We discuss the notion that X-ray extended galaxies are the central objects of large potential structures and that the presence or absence of this potential is the main origin of the large scatter in the X-ray luminosity.
A Realtime Clique Detection Algorithm: Time-Based Incremental Label Propagation

Most networks will evolve with time instead of keeping unchanged in reality. Therefore, we proposed the time-based incremental label propagation (TILP) algorithm to detect community structures in complex networks in realtime. TILP algorithm will only take the local changed vertexes into consideration. The computation time is greatly decreased, while vertex (edge) is changed. The algorithm will definitely converge for the original version of label propagation algorithm is converged. We also applied TILP to real-time community detection in a relationship network of online game.
A preliminary mechanistic test of the effects of light therapy in bulimia nervosa

ABSTRACT Negative affect is an important antecedent of binge eating episodes in bulimia nervosa (BN). A small number of studies have found that bright light therapy (BLT) decreases negative affect and binge eating frequency in BN. This study tested whether decreases in the frequency of binge eating in BN during BLT are accounted for by changes in negative affect. Participants (N = 9) with BN participated in a 6-week within-subjects trial of BLT, making daily ratings of the occurrence of eating disorder behaviours and negative affect, and resulting in 242 participant-days of data. They also completed an assessment battery on four occasions: baseline, pre-intervention, post-intervention, and 2-week follow-up. Use of the therapy light was associated with decreased likelihood of binge eating and lower negative affect; however, the likelihood of binge eating was not entirely accounted for by changes in negative affect. Future research should continue to investigate possible mechanisms through which BLT may influence binge eating in BN.
Improved Feature Selection Algorithm Based on Concentration and Dispersion

This paper analyzes the concentration and dispersion of the integrated feature selection algorithm (TFFS),and finds their shortcomings: it is difficult for concentration to measure the weigh of the low frequent terms; dispersion ignores the impact of term whose mutual information is negative. Propose a modified feature selection algorithm (TFFSL), which makes certain improvements on concentration and dispersion, and takes the length of terms as a measure of weight factors. The SVM classification experimental results show that: compared with TFFS algorithm, TFFSL algorithm has higher accuracy and more capacity of eliminating irrelevant terms.
Microarray Meta-Analysis Focused on the Response of Genes Involved in Redox Homeostasis to Diverse Abiotic Stresses in Rice

Plants are exposed to a wide range of abiotic stresses (AS), which often occur in combination. Because physiological investigations typically focus on one stress, our understanding of unspecific stress responses remains limited. The plant redox homeostasis, i.e., the production and removal of reactive oxygen species (ROS), may be involved in many environmental stress conditions. Therefore, this study intended to identify genes, which are activated in diverse AS, focusing on ROS-related pathways. We conducted a meta-analysis (MA) of microarray experiments, focusing on rice. Transcriptome data were mined from public databases and fellow researchers, which represented 36 different experiments and investigated diverse AS, including ozone stress, drought, heat, cold, salinity, and mineral deficiencies/toxicities. To overcome the inherent artifacts of different MA methods, data were processed using Fisher, rOP, REM, and product of rank (GeneSelector), and genes identified by most approaches were considered as shared differentially expressed genes (DEGs). Two MA strategies were adopted: first, datasets were separated into shoot, root, and seedling experiments, and these tissues were analyzed separately to identify shared DEGs. Second, shoot and seedling experiments were classed into oxidative stress (OS), i.e., ozone and hydrogen peroxide treatments directly producing ROS in plant tissue, and other AS, in which ROS production is indirect. In all tissues and stress conditions, genes a priori considered as ROS-related were overrepresented among the DEGs, as they represented 4% of all expressed genes but 7–10% of the DEGs. The combined MA approach was substantially more conservative than individual MA methods and identified 1001 shared DEGs in shoots, 837 shared DEGs in root, and 1172 shared DEGs in seedlings. Within the OS and AS groups, 990 and 1727 shared DEGs were identified, respectively. In total, 311 genes were shared between OS and AS, including many regulatory genes. Combined co-expression analysis identified among those a cluster of 42 genes, many involved in the photosynthetic apparatus and responsive to drought, iron deficiency, arsenic toxicity, and ozone. Our data demonstrate the importance of redox homeostasis in plant stress responses and the power of MA to identify candidate genes underlying unspecific signaling pathways.
Range Loss for Deep Face Recognition with Long-Tailed Training Data

Deep convolutional neural networks have achieved significant improvements on face recognition task due to their ability to learn highly discriminative features from tremendous amounts of face images. Many large scale face datasets exhibit long-tail distribution where a small number of entities (persons) have large number of face images while a large number of persons only have very few face samples (long tail). Most of the existing works alleviate this problem by simply cutting the tailed data and only keep identities with enough number of examples. Unlike these work, this paper investigated how long-tailed data impact the training of face CNNs and develop a novel loss function, called range loss, to effectively utilize the tailed data in training process. More specifically, range loss is designed to reduce overall intrapersonal variations while enlarge interpersonal differences simultaneously. Extensive experiments on two face recognition benchmarks, Labeled Faces in the Wild (LFW) [11] and YouTube Faces (YTF) [33], demonstrate the effectiveness of the proposed range loss in overcoming the long tail effect, and show the good generalization ability of the proposed methods.
Impact of Doxorubicin Treatment on the Physiological Functions of White Adipose Tissue

White adipose tissue (WAT) plays a fundamental role in maintaining energy balance and important endocrine functions. The loss of WAT modifies adipokine secretion and disrupts homeostasis, potentially leading to severe metabolic effects and a reduced quality of life. Doxorubicin is a chemotherapeutic agent used clinically because of its good effectiveness against various types of cancer. However, doxorubicin has deleterious effects in many healthy tissues, including WAT, liver, and skeletal and cardiac muscles. Our objective was to investigate the effects of doxorubicin on white adipocytes through in vivo and in vitro experiments. Doxorubicin reduced the uptake of glucose by retroperitoneal adipocytes and 3T3-L1 cells via the inhibition of AMP-activated protein kinase Thr172 phosphorylation and glucose transporter 4 content. Doxorubicin also reduced the serum level of adiponectin and, to a greater extent, the expression of genes encoding lipogenic (Fas and Acc) and adipogenic factors (Pparg, C/ebpa, and Srebp1c) in retroperitoneal adipose tissue. In addition, doxorubicin inhibited both lipogenesis and lipolysis and reduced the hormone-sensitive lipase and adipose tissue triacylglycerol lipase protein levels. Therefore, our results demonstrate the impact of doxorubicin on WAT. These results are important to understand some side effects observed in patients receiving chemotherapy and should encourage new adjuvant treatments that aim to inhibit these side effects.
Costly Transmission of Information . A comparison of signals and networks

Economists and sociologists disagree over markets’ potential to substitute for networks of private connections. In this paper we focus on the transmission of information in labor markets and study a model where workers differ in their (ex ante unobservable) productivity. Before entering the market, individuals can pay a cost and either establish a link to a currently employed worker ("network"), or take an exam that may certify them as high productivity ("signal"). High productivity individuals have a higher probability of connecting to a high productivity worker in the network case, and of achieving certification in the signaling case. The main difference between the two mechanisms is that networking, as opposed to signaling, reveals information only locally and thus ties the worker to one specific firm. The lower return from networking also makes it less informative. The final result is that under networking firms make positive profits (because of the informational rents), wages to high productivity workers are depressed (because information is less precise and because the rents are shared), and wages to low productivity workers are higher (because information is less precise). Our model then comes down in qualified support of the economists’ view: at equal cost, networking is not a very effective mechanism because it reveals the information only locally, creates positional rents and reduces the difference in rewards between high and low productivity workers. ∗Department of Economics, Columbia University, GREQAM, NBER and CEPR. ac186@columbia.edu †Earth Institute, Columbia University, nh85@columbia.edu
Factors Influencing Employees' Usage Behavior of KMS in e-business

The shift from a product-based to a knowledge-based economy has resulted in an increasing demand for organizations to implement knowledge management systems (KMS) at an accelerating pace. However, little attention has been paid to what influences the employees’ intention to use such. This study presented an integrative framework to explore factors affecting employees’ KMS usage behavior, particularly social and political ones. Results from a sample of 144 employees mostly from four international semiconductor manufacturing companies in the Hsin-Chu Science-based Industrial Park in Taiwan strongly support the integrative framework in predicting employees’ intention to use KMS. The analysis shows that subjective norm, perceived usefulness, perceived ease of use, and perceived power security all have significant positive influence on KMS usage. In addition, interaction effects of subjective norm, computer self-efficacy, and perceived power security on perceived ease of use and perceived usefulness were also found. These findings may help IT departments to develop KMS which are well accepted by employees, and provide IT managers more insight into methods of promoting this new technology.
The Influence of Organisational Culture across the Stages of Enterprise System Implementations

Enterprise systems (ES) are important business software which can be difficult to implement, while the failure to account for organisational culture can lead to projects that are expensive and late. However most of the existing research has focused on national level culture. As such little is known of how organisational culture influences the implementation of enterprise systems. This ongoing research aims to address this gap by identifying whether organisational culture affects ES implementations and whether the effect of culture varies across each implementation phase. To address the research aim, data will be collected from a case study of a university’s implementation of a student management system and analysed using Detert et al's framework on organisational culture. The preliminary results indicate that organisational culture impacts ES implementation projects and these impacts may differ for each phase of the implementation project.