Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of moderate sleep deprivation and low-dose alcohol on driving simulator performance and perception in young men.

STUDY OBJECTIVE To determine the combined effects of sleep restriction and low-dose alcohol on driving simulator performance, EEG, and subjective levels of sleepiness and performance in the mid-afternoon. DESIGN Repeated measures with 4 experimental conditions. Normal sleep without alcohol, sleep restriction alone (4 hours) and sleep restriction in combination with 2 different low blood alcohol concentrations (0.025 g/dL and 0.035 g/dL). SETTING Sleep Laboratory, Adelaide Institute for Sleep Health. PARTICIPANTS Twenty-one healthy young men, aged 18-30 years, mean (+/-SD) = 22.5(+/-3.7) years, BMI = 25(+/-6.7) kg/m2; all had normal sleep patterns and were free of sleep disorders. MEASUREMENTS Participants completed a 70-minute simulated driving session, commencing at 14:00. Driving parameters included steering deviation, braking reaction time, and number of collisions. Alpha and theta EEG activity and subjective driving performance and sleepiness were also measured throughout the driving task. RESULTS All measures were significantly affected by time. Steering deviation increased significantly when sleep restriction was combined with the higher dose alcohol. This combination also resulted in a significant increase in alpha/theta EEG activity throughout the drive, as well as greater subjective sleepiness and negative driving performance ratings compared to control or sleep restriction alone. DISCUSSION These data indicate that combining low-dose alcohol with moderate sleep restriction results in significant decrements to subjective alertness and performance as well as to some driving performance and EEG parameters. This highlights the potential risks of driving after consumption of low and legal doses of alcohol when also sleep restricted.
Gene selection for cancer clustering analysis based on expression data

Although the genomics data are accumulated in an exponential growth, the molecular complexity of cancer is still hard to understand. The most remarkable characteristics of the genomic data are severely high-dimensional features with a small number of samples, such as gene expression data. The traditional data mining method has a limited ability to process these asymmetry datasets. In order to select the key genes from the high-dimensional gene expression data for cancer clustering analysis, gene selection based on proportional hazard model is applied in this paper. The proportional hazard model is a statistical approach used for survival risk analysis. The significant genes are selected for clustering analysis based on gene expression dataset. We demonstrate the effectiveness of this method on breast cancer and lung cancer. The experiments show a better cancer clustering result of separating of samples into distinct subclasses.
BYBLOS Speech Recognition Benchmark Results

This paper presents speech recognition test results from the BBN BYBLOS system on the Feb 91 DARPA benchmarks in both the Resource Management (RM) and the Air Travel Information System (ATIS) domains. In the RM test, we report on speaker-independent (SI) recognition performance for the standard training condition using 109 speakers and for our recently proposed SI model made from only 12 training speakers. Surprisingly, the 12-speaker model performs as well as the one made from 109 speakers. Also within the RM domain, we demonstrate that state-of-the-art SI models perform poorly for speakers with strong dialects. But we show that this degradation can be overcome by using speaker adaptation from multiple-reference speakers. For the ATIS benchmarks, we ran a new system configuration which first produced a rank-ordered list of the N-best word-sequence hypotheses. The list of hypotheses was then reordered using more detailed acoustic and language models. In the ATIS benchmarks, we report SI recognition results on two conditions. The first is a baseline condition using only training data available from NIST on CD-ROM and a word-based statistical bi-gram grammar developed at MIT/Lincoln. In the second condition, we added training data from speakers collected at BBN and used a 4-gram class grammar. These changes reduced the word error rate by 25%.
Noncommutative Structures of ( super )-Spacetime

The recent developments in superstring theory prompted the study of non-commutative structures in superspace. Considering bosonic and fermionic strings in a constant antisymmetric tensor background yields a non-vanishing commutator between the bosonic coordinates of the spacetime. Likewise, the presence of constant Ramond-Ramond (RR) background leads to a non-vanishing anti-commutator for the Grassmann coordinates of the superspace. The non-vanishing commutation relation between bosonic coordinates can also be derived using a particle moving in a magnetic background, we use N=2 pure spinor superparticles and D0-branes to show how the non-commutative structures emerge in superspace. It is argued how a D0-brane in a background of RR fields reproduces the results obtained in string theory. Based on a lecture given at 43 International School of Subnuclear Physics, Erice, Sicily, Italy, Aug. 2005.
Hidden Markov Models for Modeling and Recognizing Gesture Under Variation

Conventional application of hidden Markov models to the task of recognizing human gesture may suffer from multiple sources of systematic variation in the sensor outputs. We present two frameworks based on hidden Markov models which are designed to model and recognize gestures that vary in systematic ways. In the first, the systematic variation is assumed to be communicative in nature, and the input gesture is assumed to belong to gesture family. The variation across the family is modeled explicity by the parametric hidden Markov model (PHMM). In the second framework, variation in the signal is overcome by relying on online learning rather than conventional offline, batch learning.
That's not fair! Examining individual differences in perceptions of fairness

Within organizations, fairness is an important concept and has received considerable research attention. Some research, interestingly, suggests that individuals differ in their perceptions of equity. Building on this research, four empirical studies were conducted to develop a measure of a new construct (i.e., equity orientation) and examine its predictive validity. In Study 1 (N = 836) and Study 2 (N = 600), the Equity Orientation Scale (EOS) was created and its relations with two popular personality models – the HEXACO and the Dark Tetrad – were examined across selfand peer-ratings. In Study 3 (N = 433) and Study 4 (N = 490), the EOS was measured in a team setting and predicted individuals’ task and contextual performance. Further, Study 4 examined social loafing as a mediating mechanism between the EOS and the performance-related behaviours with full and partial mediations being found. Conclusions and future research directions are discussed.
Incorporation of thermal shadows into real-time infrared three-dimensional image generation

Abstract. In the infrared spectrum, two contributions to shadows exist: one part is reflective shadows resulting from occlusion of instantly reflected infrared rays, and the other part is thermal (IR) shadows occurring through occlusion of irradiance in the past. The realization of thermal shadows requires a thermal balance calculation in four-dimensions (three-dimensional geometry in one-dimensional time), which is computationally expensive, and therefore mostly used for nonreal-time simulations. We present an approximation of thermal shadows resulting from the occlusion of direct rays from IR emitters. Our approach uses programmable graphics cards to achieve real-time frame rates in scenes with dynamic geometry.
An evolutionary approach to integrated inventory and routing management in a real world case

In this paper we present an evolutionary approach to the joint management of inventory and routing in a retail chain. With this purpose, an ad-hoc evolutionary algorithm was designed which includes a non-standard individual representation and two mutation operators specific to this particular problem. Also, this work considers the inclusion of time-window constraints in the problem. We compare this approach to that of previous work, in which two levels were used: one for obtaining the delivery patterns that minimise the inventory costs and another to find the optimal routes in order to minimise transportation costs. The analysis performed allows us to conclude that the one-level approach obtains better results than the previously used two-level methodology in a shorter time.
Application of vehicular communications for improving the efficiency of traffic in urban areas

This paper studies the impacts of vehicular communications on efficiency of traffic in urban areas. We consider a Green Light Optimized Speed Advisory application implementation in a typical reference area and present the results of its performance analysis using an integrated cooperative intelligent transportation systems simulation platform. In addition, we study route alternation using vehicle-to-infrastructure and vehicle-to-vehicle communications. Our interest was to monitor the impacts of these applications on fuel and traffic efficiency by introducing metrics for average fuel consumption, average stop time behind a traffic light and average trip time, respectively. For gathering the results, we implemented two traffic scenarios defining routes through an urban area including traffic lights. The simulations are varied for different penetration rates of application-equipped vehicles, driver's compliance to the advised speed and traffic density. Our results indicate that Green Light Optimized Speed Advisory systems could improve fuel consumption, reduce traffic congestion in junctions and the total trip time. Copyright © 2011 John Wiley & Sons, Ltd.
Reliability-oriented broadcast electrode-addressing for pin-constrained digital microfluidic biochips

Designs for pin-constrained digital microfluidic biochips (PDMFBs) are receiving much attention because they simplify chip fabrication and packaging, and reduce product cost. To reduce the pin count, broadcast addressing, by minimally grouping electrode sets with non-conflict signal merging, has emerged as a promising solution. Nevertheless, naive signal merging has the potential to cause excessive electrode actuations, which has been reported to have direct and adverse effect on chip reliability. According to recent studies, reliability is an important attribute for PDMFBs particularly developed for medical applications as it directly affects the final medical decision making. However, no research findings have been reported on the reliability problem in pin-constrained designs. To make PDMFBs more feasible for practical applications, we propose in this paper the first matching-based reliability-oriented broadcast-addressing algorithm for PDMFBs. We identify the factors that affect reliability and incorporate into the design-technique attributes that enhance reliability. Experimental results demonstrate the effectiveness of the proposed algorithm.
Exploiting analysis history to support collaborative data analysis

Coordination is critical in distributed collaborative analysis of multidimensional data. Collaborating analysts need to understand what each person has done and what avenues of analysis remain uninvestigated in order to effectively coordinate their efforts. Although visualization history has the potential to communicate such information, common history representations typically show sequential lists of past work, making it difficult to understand the analytic coverage of the data dimension space (i.e. which data dimensions have been investigated and in what combinations). This makes it difficult for collaborating analysts to plan their next steps, particularly when the number of dimensions is large and team members are distributed. We introduce the notion of representing past analysis history from a dimension coverage perspective to enable analysts to see which data dimensions have been explored in which combinations. Through two user studies, we investigated whether 1) a dimension oriented view improves understanding of past coverage information, and 2) the addition of dimension coverage information aids coordination. Our findings demonstrate that a representation of dimension coverage reduces the time required to identify and investigate unexplored regions and increases the accuracy of this understanding. In addition, it results in a larger overall coverage of the dimension space, one element of effective team coordination.
Automatic lung tumor segmentation on PET images based on random walks and tumor growth model

The segmentation of tumor on PET images is an important step for treatment planning process during the radiotherapy. In this paper, we present an automatic segmentation method on PET images based on the random walks (RW) algorithm. We propose an extension of the random walks framework to integrate a tumor evolution information, which is the predicted tumor region resulting from a model for lung tumor growth and response to radiotherapy. The region of interest (ROI) and labeled seeds are automatically generated. Our approach is compared to the well-known 40% thresholding method, an adaptive thresholding method, a statistical method (FLAB), and a traditional RW algorithm. The good performance of our method has been confirmed on 7 lung tumor patients who are treated with radiotherapy.
Release of U 18 snoRNA from its host intron requires interaction of Nop 1 p with the Rnt 1 p endonuclease

An external stem, essential for the release of small nucleolar RNAs (snoRNAs) from their pre-mRNAs, ̄anks the majority of yeast intron-encoded snoRNAs. Even if this stem is not a canonical Rnt1p substrate, several experiments have indicated that the Rnt1p endonuclease is required for snoRNA processing. To identify the factors necessary for processing of intronencoded snoRNAs, we have raised in vitro extracts able to reproduce such activity. We found that snoRNP factors are associated with the snoRNAcoding region throughout all the processing steps, and that mutants unable to assemble snoRNPs have a processing-de®cient phenotype. Speci®c depletion of Nop1p completely prevents U18 snoRNA synthesis, but does not affect processing of a dicistronic snoRNA-coding unit that has a canonical Rnt1p site. Correct cleavage of intron-encoded U18 and snR38 snoRNAs can be reproduced in vitro by incubating together puri®ed Nop1p and Rnt1p. Pull-down experiments showed that the two proteins interact physically. These data indicate that cleavage of U18, snR38 and possibly other intron-encoded snoRNAs is a regulated process, since the stem is cleaved by the Rnt1p endonuclease only when snoRNP assembly has occurred.
Optimal Scaling of Digital Transcriptomes

Deep sequencing of transcriptomes has become an indispensable tool for biology, enabling expression levels for thousands of genes to be compared across multiple samples. Since transcript counts scale with sequencing depth, counts from different samples must be normalized to a common scale prior to comparison. We analyzed fifteen existing and novel algorithms for normalizing transcript counts, and evaluated the effectiveness of the resulting normalizations. For this purpose we defined two novel and mutually independent metrics: (1) the number of “uniform” genes (genes whose normalized expression levels have a sufficiently low coefficient of variation), and (2) low Spearman correlation between normalized expression profiles of gene pairs. We also define four novel algorithms, one of which explicitly maximizes the number of uniform genes, and compared the performance of all fifteen algorithms. The two most commonly used methods (scaling to a fixed total value, or equalizing the expression of certain ‘housekeeping’ genes) yielded particularly poor results, surpassed even by normalization based on randomly selected gene sets. Conversely, seven of the algorithms approached what appears to be optimal normalization. Three of these algorithms rely on the identification of “ubiquitous” genes: genes expressed in all the samples studied, but never at very high or very low levels. We demonstrate that these include a “core” of genes expressed in many tissues in a mutually consistent pattern, which is suitable for use as an internal normalization guide. The new methods yield robustly normalized expression values, which is a prerequisite for the identification of differentially expressed and tissue-specific genes as potential biomarkers.
True2Form

True2Form is a sketch-based modeling system that reconstructs 3D curves from typical design sketches. Our approach to infer 3D form from 2D drawings is a novel mathematical framework of insights derived from perception and design literature. We note that designers favor viewpoints that maximally reveal 3D shape information, and strategically sketch descriptive curves that convey intrinsic shape properties, such as curvature, symmetry, or parallelism. Studies indicate that viewers apply these properties selectively to envision a globally consistent 3D shape. We mimic this selective regularization algorithmically, by progressively detecting and enforcing applicable properties, accounting for their global impact on an evolving 3D curve network. Balancing regularity enforcement against sketch fidelity at each step allows us to correct for inaccuracy inherent in free-hand sketching. We perceptually validate our approach by showing agreement between our algorithm and viewers in selecting applicable regularities. We further evaluate our solution by: reconstructing a range of 3D models from diversely sourced sketches; comparisons to prior art; and visual comparison to both ground-truth and 3D reconstructions by designers.
HIT_LTRC at TREC 2011 Microblog Track

This paper describes our entry into the TREC 2011 Microblog track. We submitted two runs in this year’s track, both in real-time fashion and without any external resources. The runs were generated through a three-step procedure, including scoring, threshold selection and re-ranking. The evaluation results of our submitted runs significantly outperform the disjunctive baseline run. We conducted additional runs to show our score decay and threshold selection strategies to be exceptionally effective.
An Image-Text Approach for Extracting Experimental Evidence of Protein-Protein Interactions in the Biomedical Literature

Proteins are complex biological polymers that mediate virtually all cellular functions. Typically these functions are modulated by protein-protein interactions (PPI). Tremendous efforts have been made by life scientists to detect PPIs through different experimental approaches and document the results through publications. On the informatics front, however, there lacks an effective means for retrieving PPI information from published literatures. In this work we present a novel framework for identifying experimental methods employed for analyzing PPI from biomedical articles. Different from state-of-the-art approaches based only on text, we explore using the combination of attributes from figures, figure captions, and text within figures for identifying PPI experimental methods. Our work is motivated by the observation that biomedical figures often constitute direct evidence of experimental results and therefore provide complementary information to texts. We start with automatically extracting unimodal panels (subfigures) and their associated subcaptions and then classifying the subfigure into different types using a proposed hierarchical image taxonomy. Next, we combine the subfigure types with text-based features to form a hybrid feature descriptor and use it for PPI method classification. We further construct a dataset starting from a set of 2,256 documents provided by the molecular interaction database MINT. Here we show that our new approach outperforms the text-only solution for associating figures with PPI methods.
Implications and factors underlying hostility toward women

The effects of hostility toward women, acceptance of interpersonal violence, and exposure to pornography on intimacy and personal happiness were examined among 92 female and 45 male university students. Questionnaires were given to participants in class and completed at home. The major hypothesis that intimacy and personal happiness would be inversely related to hostility toward women was confirmed. Additionally, acceptance of interpersonal violence was positively related to pornography exposure and to hostility toward women among women. Acceptance of interpersonal violence was inversely related to intellectual intimacy, personal happiness and sexual happiness among women. Women scored significantly higher on hostility toward women and lower on exposure to pornography and acceptance of interpersonal violence than men. Finally a regression analysis was performed with hostility toward women as the dependent variable. Controlling for gender, lack of sexual happiness and acceptance of interpersonal violence contributed unique variance to the equation. To eradicate hostility toward women, we must focus on attitudes condoning sexual violence among women, as well as among men.
The obfuscation efficiency measuring schemes

Obfuscation is primarily used as a tool for protecting the written code against reverse engineering. But there also exist other schemas for the obfuscation process, which primarily focus on hiding the true purpose of the written code by transforming its blocks, which causes the inability of comparison the obfuscated code sequence to code stored in database by static or hybrid analysis. When trying to overcome this type of analysis weakness, man should be well educated in the obfuscation schemas.
A novel fingerprint image enhancement algorithm using phase preserving

Phase information of an image plays a very important role during the process of perceiving image by human eyes. So phase preserving has been proposed as a principle of image enhancement. In the paper, importance of phase information of fingerprint image is shown by an experiment firstly, and then a novel fingerprint image enhancement algorithm based on phase preserving principle is submitted. Two kinds of low quality fingerprint images are selected to testify the new algorithm, and the results show that the algorithm can improve the quality of low quality fingerprint image evidently.
Development and Validation of the Interpersonal Affect Improvement Strategies Questionnaire

Abstract. This research outlines the development and validation of a new self-report measure to assess the tendency to use different interpersonal affect improvement strategies within the normal adult population (Interpersonal Affect Improvement Strategies Questionnaire, IAISQ). The scale is based on the interpersonal affect classification (IAC; Niven, Totterdell, & Holman, 2009 ) and accurately distinguishes between the two primary means suggested in that model: positive engagement and acceptance. Through four studies we tested the factor structure, reliability, content, criterion, and predictive validity of the scale.