Miracle-dz/UniFAR-FTUs huggingface dataset:
Chronic Stress Among Latino Day Laborers

Latino day laborers endure many hardships as they struggle to adjust as an immigrant community in the United States. This study sought to identify the extent of chronic stress reported by day laborers and the factors associated with stress. A total of 725 Latino day laborers were interviewed. The most reported sources of stress were having immigration-related problems, not having enough money to cover basic needs, having no savings, and having work hours change for the worse. Higher chronic stress was associated with homelessness (p < .001) and HIV-related risk behaviors in the previous 12 months (p < .05). In addition, chronic stress was found to be higher among respondents reporting incomes of US$5,000 to US$10,000 (p = .007) and still higher among respondents reporting incomes greater than US$10,000 (p < .001) compared with those in the lowest income level. Lower chronic stress was associated with having a partner (p < .05) or being single (p = .001) compared with being married. Addressing the stress experienced by day laborers is necessary to prevent potential negative health and mental health consequences among this population.
Affective States and Performance Outcomes – The Findings of Preliminary Research Involving Pentathletes

Abstract Introduction. The aim of this article is to discuss the relationship between affective states experienced by athletes and the outcome of their performance. The article presents the findings of a pilot study which made it possible to determine the relationship between the emotional states, mood, and level of stress of a group of pentathletes and the outcomes they achieved in a sports competition. Material and methods. The study involved 12 senior modern pentathletes, including 7 male and 5 female athletes. The following standard psychology questionnaires were used in the study: the 10-item Perceived Stress Scale (PSS-10), the Positive and Negative Affect Schedule (PANAS), and the Profile of Mood State (POMS). Performance was assessed based on the number of points achieved by the pentathletes in particular events in the pentathlon, which are held according to the rules set by the International Modern Pentathlon Union (UIPM). Results. The findings of the study confirmed that there was a correlation between the athletes’ mood and emotions and the outcome of their performance. The level of stress strongly negatively correlated with both the outcome they expected to achieve and the one they actually achieved for the combined event (running and shooting). For this event a relationship was also found between the athletes’ affective states and their outcomes: in running and shooting there was a positive and statistically significant correlation between the level of positive emotions and anger and the results achieved. However, friendliness, one of the other affective state variables that were measured, correlated negatively with the outcomes of the athletes’ performance. Conclusions. In the group of pentathletes who participated in the study, a high level of anger was associated with better outcomes, and a high level of friendliness had an adverse effect on the results achieved. The findings of the current study confirm that there is a relationship between affective states and performance outcomes, but the findings do not correspond with Morgan’s iceberg profile.
Paper Special Section on Parallel and Distributed Computing and Networking Blocked United Algorithm for the All-pairs Shortest Paths Problem on Hybrid Cpu-gpu Systems

SUMMARY This paper presents a blocked united algorithm for the all-pairs shortest paths (APSP) problem. This algorithm simultaneously computes both the shortest-path distance matrix and the shortest-path construction matrix for a graph. It is designed for a high-speed APSP solution on hybrid CPU-GPU systems. In our implementation, two most compute intensive parts of the algorithm are performed on the GPU. The first part is to solve the APSP sub-problem for a block of sub-matrices, and the other part is a matrix-matrix " multiplication " for the APSP problem. Moreover, the amount of data communication between CPU (host) memory and GPU memory is reduced by reusing blocks once sent to the GPU. When a problem size (the number of vertices in a graph) is large enough compared to a block size, our implementation of the blocked algorithm requires CPU GPU exchanging of three blocks during a block computation on the GPU. We measured the performance of the algorithm implementation on two different CPU-GPU systems. A system containing an Intel Sandy Bridge CPU (Core i7 2600K) and an AMD Cayman GPU (Radeon HD 6970) achieves the performance up to 1.1 TFlop/s in a single precision.
Built-in self testing of sequential circuits using precomputed test sets

We present a new approach to built-in self-test of sequential circuits using precomputed test sets. Our approach is especially suited to circuits containing a large number of flip-flops but few primary inputs. Such circuits are often encountered as embedded cores and filters for digital signal processing, and are inherently difficult to test. We show that statistical encoding of test sets can be combined with low-cost pattern decoding for deterministic BIST. This approach exploits recent advances in sequential circuit ATPG and unlike other BIST schemes, does not require access to gate-level models of the circuit under test. Experimental results show that the proposed method provides higher fault coverage than pseudorandom testing with shorter test application time.
Massive MIMO for Distributed Detection With Transceiver Impairments

This paper investigates an issue of massive MIMO-based distributed detection that considers transceiver hardware impairments at both a massive-antenna fusion center (FC) and multiple single-antenna sensors. First, we derive closed-form expressions of the probability of detection and the probability of false alarm, and show that hardware impairments create finite ceilings on the achievable detection performance. Then, we formulate a constrained optimization problem as sum of linear ratios programming to maximize the detection probability. By exploiting the inherent problem structures, we further develop an algorithm based on the alternating direction method of multipliers. Extensive simulations demonstrate that the nonideal hardware has a fundamental impact on the distributed detection performance. More specifically, in the limit of an infinite number of antennas and infinite sensor reporting power budget, the effects of FC impairment and FC receiver noise vanish, while the sensor impairment dominates the achievable distributed detection performance.
Anatomy of a hash-based long read sequence mapping algorithm for next generation DNA sequencing

MOTIVATION Recently, a number of programs have been proposed for mapping short reads to a reference genome. Many of them are heavily optimized for short-read mapping and hence are very efficient for shorter queries, but that makes them inefficient or not applicable for reads longer than 200 bp. However, many sequencers are already generating longer reads and more are expected to follow. For long read sequence mapping, there are limited options; BLAT, SSAHA2, FANGS and BWA-SW are among the popular ones. However, resequencing and personalized medicine need much faster software to map these long sequencing reads to a reference genome to identify SNPs or rare transcripts. RESULTS We present AGILE (AliGnIng Long rEads), a hash table based high-throughput sequence mapping algorithm for longer 454 reads that uses diagonal multiple seed-match criteria, customized q-gram filtering and a dynamic incremental search approach among other heuristics to optimize every step of the mapping process. In our experiments, we observe that AGILE is more accurate than BLAT, and comparable to BWA-SW and SSAHA2. For practical error rates (< 5%) and read lengths (200-1000 bp), AGILE is significantly faster than BLAT, SSAHA2 and BWA-SW. Even for the other cases, AGILE is comparable to BWA-SW and several times faster than BLAT and SSAHA2. AVAILABILITY http://www.ece. northwestern.edu/~smi539/agile.html.
Linear-Time Algorithms in Memory Hierarchies

Recent years have seen marked dissatisfaction with the computational realism of the classic machine models, such as Turing machines or the standard integer RAM (see [13, 12]). Many algorithms that theoretically run in linear time on the RAM scale non-linearly when it comes time to implement them. Cook [5,6] proposed replacing the usual unit-cost RAM measure by the log-cost criterion, by which an operation that reads an integer i stored at address a is charged log i + log a time units. Aggarwal, Alpern, Chandra, and Snir [1] went further by introducing a parameter μ : N → N called a memory access cost function, such that the above operation costs 1 + μ(a) time units. Note that this still treats register contents i as having unit size. Besides μ(a) = log a, they studied the parameters μd(a) = a , which model the asymptotic latency of memory laid out on a d-dimensional grid. To allow for pipelining, Aggarwal, Chandra, and Snir [2] defined the Block Transfer (BT) model with a special instruction
Consumer impulse buying of food at festivals and events: understanding the role of sensory cues

Impulse buying has gained interest from both researchers and practitioners because of its contribution to sales and profits. The past decade has seen a dramatic increase in the number of mobile food vending in the United States. Open-air selling by vendors may encourage consumers to buy food on impulse. Food sold on streets involves sensory cues that strongly induce impulse buying. Unlike normal buying behavior, impulse buying is greatly affected by emotion, but it may also be explained by cognition. Although impulse buying of food is a prevalent phenomenon, little academic research has been conducted regarding food consumption impulse. Further, there remains a lack of clear understanding of the link between emotions and impulse buying. The purpose of this study was to explore and empirically test consumers’ impulse buying behavior of food from street vendors and to identify determinants such as sensory cues, arousal and pleasure as emotional responses, perceived risk as a cognitive response, and the urge to buy impulsively. In particular, Study 1 proposed a theoretical model identifying the effects of sensory cues on arousal, pleasure, and perceived risk and, in turn, the urge to buy impulsively and impulse buying behavior. Study 2 proposed food neophobia and perceived human crowding as a possible moderator that may function in the relationship between emotions and impulse buying. Data were collected from 361 consumers who were 18 years or older and had purchased food from mobile vendors at a participating festival or event in the United States. The proposed relationships were tested using structural equation modeling and hierarchical multiple regression analysis. In terms of direct effects, Study 1 found that sensory cues were positively related to arousal, pleasure, and the urge to buy impulsively and were negatively related to perceived risk; arousal and pleasure were positively associated with the urge to buy impulsively; perceived risk was negatively associated with the urge to buy impulsively; and the urge to buy impulsively was positively linked with impulse buying behavior. Further, arousal, pleasure, and perceived risk partially mediated the relationship between sensory cues and the urge to buy impulsively. Study 2 concluded that perceived human crowding moderates the effect of arousal and pleasure on the urge to buy impulsively and, in turn, impulse buying behavior. Food neophobia had no moderating effect. The study findings add to the understanding of consumer impulse buying in the context of street food. In addition to its contribution to the literature, practical applications that mobile food businesses could use to attract and retain customers are provided. The study concludes with general discussions of limitations and areas for future research. CONSUMER IMPULSE BUYING OF FOOD AT FESTIVALS AND EVENTS: UNDERSTANDING THE ROLE OF SENSORY CUES
Pre-exposure to the First Dental Treatment

The present study attempts to verify the relative efficacy of two forms of pre-exposure which are identical in content and parallel in mode of exposure (in vivo or videotape). These two forms of pre-exposure were given to 38 children, aged four to six years old, before their first dental visit. The results showed that all groups manifested relatively little negative behavior while seeing the dentist. The theoretical and methodological implications of this study are discussed.
The Cytoplasmic Domain of the Large Myelin-Associated Glycoprotein Isoform Is Needed for Proper CNS But Not Peripheral Nervous System Myelination

The myelin-associated glycoprotein (MAG) is a member of the immunoglobulin gene superfamily and is thought to play a critical role in the interaction of myelinating glial cells with the axon. Myelin from mutant mice incapable of expressing MAG displays various subtle abnormalities in the CNS and degenerates with age in the peripheral nervous system (PNS). Two distinct isoforms, large MAG (L-MAG) and small MAG (S-MAG), are produced through the alternative splicing of the primary MAG transcript. The cytoplasmic domain of L-MAG contains a unique phosphorylation site and has been shown to associate with the fyn tyrosine kinase. Moreover, L-MAG is expressed abundantly early in the myelination process, possibly indicating an important role in the initial stages of myelination. We have adapted the gene-targeting approach in embryonic stem cells to generate mutant mice that express a truncated form of the L-MAG isoform, eliminating the unique portion of its cytoplasmic domain, but that continue to express S-MAG. Similar to the total MAG knockouts, these animals do not express an overt clinical phenotype. CNS myelin of the L-MAG mutant mice displays most of the pathological abnormalities reported for the total MAG knockouts. In contrast to the null MAG mutants, however, PNS axons and myelin of older L-MAG mutant animals do not degenerate, indicating that S-MAG is sufficient to maintain PNS integrity. These observations demonstrate a differential role of the L-MAG isoform in CNS and PNS myelin.
Information Extraction from Single and Multiple Sentences

Some Information Extraction (IE) systems are limited to extracting events expressed in a single sentence. It is not clear what effect this has on the difficulty of the extraction task. This paper addresses the problem by comparing a corpus which has been annotated using two separate schemes: one which lists all events described in the text and another listing only those expressed within a single sentence. It was found that only 40.6% of the events in the first annotation scheme were fully contained in the second.
Estimation of the number of virtual connections in a rate-based flow control problem

In this paper we deal with the rate-based flow control problem which we formulate as a tracking problem where the goal is to track a reference queue size chosen by the network administrator. We consider the single bottleneck link topology and use a non-linear model for the queueing dynamics. A known algorithm is then extended to improve performance. Integral action is added to remove any steady state errors and the control signal is then normalized by the estimated number of flows. The main contribution of this work is the estimation algorithm that we propose, which is based on on-line parameter identification techniques and is shown analytically to converge to the correct number of flows exponentially fast. The algorithm does not require maintenance of per flow states and can thus form the basis for the development of an end-to-end congestion control protocol. The performance of the proposed algorithm is evaluated through simulations carried out on the Ns simulator. Good estimation of the number of sessions and good tracking of the reference queue size are observed. 1
Cluster-Weighted Probabilistic Networks for Audio-Synthesis

A cluster-weighted framework is presented that allows powerful and transparent non-linear models to be built by integration of simple local models. Audio synthesis algorithms such as sampling, linear predictive coding and spectral synthesis become globally nonlinear synthesis models that integrate control and sound generation within a single framework. These networks are added additional levels of probabilistic abstraction, e.g. hierarchical structures or Hidden-Markov structures. A system has been implemented that computes the sound of a violin in real time, given the gesture input of a violinist.
Anger experience and expression across the anxiety disorders

The purpose of this study was to explore possible differences in the experience and expression of anger across four anxiety disorder groups and non‐clinical controls. Anger was assessed by two measures, the Reaction Inventory and the Aggression Questionnaire, in 112 individuals who met DSM‐IV criteria for panic disorder (PD) with or without agoraphobia (n=40), obsessive‐compulsive disorder (OCD; n=30), social phobia, (SOC; n=28), and specific phobia (SPC; n=14) as well as non‐clinical controls (n=49). Patients with PD, OCD, and SOC reported a significantly greater propensity to experience anger than controls, whereas patients with SPC exhibited no differences in anger experience in comparison to controls. In addition, patients with PD reported significantly greater levels of anger aggression compared to both controls and patients with OCD, and patients with SOC reported significantly lower levels of verbal aggression than controls. Most, but not all, of these differences disappeared when symptoms of depression were controlled in the analyses. The implications of these findings and future directions for research are discussed 0:1–7, 2007. © 2007 Wiley‐Liss, Inc.
PCube: Improving Power Efficiency in Data Center Networks

To alleviate the growing concern of energy waste in networked devices, we present PCube, a server-centric data center structure that conserves energy by varying bandwidth availability based on traffic demand. PCube not only supports a low-power mode for existing data centers offering full bisection bandwidth without hardware modification or re-wiring, but also provides an alternative for new data centers at a lower construction cost. The bandwidth demand could be dynamic and scalable in the optimal way when considering conserving the energy waste. To further reduce the cost, we take advantage of traffic locality, and propose Hybrid PCube, in order to offer different bandwidth to different servers. We use samples of traffic from a real-world production data center with full bisection bandwidth across thousands of servers, and evaluate our proposed algorithm using these collected samples. Our experimental results have shown convincing evidence that the proposed algorithm is able to substantially reduce energy costs incurred by networked devices in data centers.
A text block context informations based multiple Web contents extraction

In Web environment, in order to provide appropriate Web services to users' needs it becomes important to quickly and accurately extract from Web documents contents such as main-content, menu-list, article-list, comments and so on. In this paper, we propose an efficient method that extracts various contents from Web documents. In the method, text blocks are separated from the document and context information is extracted and used to classify content type of each text block. Context information consists of documenting patterns and structural features of a Web document. For documenting patterns, we use in/out link information, which is expanded from word/link density proposed by a previous work. For structural features, distances between text blocks and parent tags of the target text block are used. We experimented with our method using a published data set and a data set that we collected. The experiment results show that our method performs about 17% points better in accuracy for multiple contents extraction and about 14% points better in F-measure for main-content extraction compared to the existing methods.
A Deep Learning-Based Method for Similar Patient Question Retrieval in Chinese

The online patient question and answering (Q&A) system, either as a website or a mobile application, attracts an increasing number of users in China. Patients will post their questions and the registered doctors then provide the corresponding answers. A large amount of questions with answers from doctors are accumulated. Instead of awaiting the response from a doctor, the newly posted question could be quickly answered by finding a semantically equivalent question from the Q&A achive. In this study, we investigated a novel deep learning based method to retrieve the similar patient question in Chinese. An unsupervised learning algorithm using deep neural network is performed on the corpus to generate the word embedding. The word embedding was then used as the input to a supervised learning algorithm using a designed deep neural network, i.e. the supervised neural attention model (SNA), to predict the similarity between two questions. The experimental results showed that our SNA method achieved P@1 = 77% and P@5 = 84%, which outperformed all other compared methods.
Generating OLAP queries from natural language specification

Automatic translation of natural language (NL) questions to Structured Query Language (SQL) queries is a challenging task. It is a common knowledge that writing Online Analytical Processing (OLAP) queries for data warehouses is difficult, particularly, for the novice users. In this paper, we present a natural language processing based approach to automatically generate OLAP queries those can be used to communicate with the a data warehouse. In the presented approach, user provides queries in English and our approach process English queries and generate OLAP queries. In our approach, we incorporate OMG's recent standard Semantic of Business Vocabulary and Business Rules (SBVR) to simplify the translation process of English to OLAP. SBVR is used in detailed semantic analysis of English queries. The presented approach is also implemented in Java as a prototype tool. To test the performance of the tool, an experimental study is also conducted. Results of the experimental study imply that our approach is capable in communicating with a data warehouse.
An Intelligent Saliency Segmentation Technique and Classification of Low Contrast Skin Lesion Dermoscopic Images Based on Histogram Decision

Skin cancers primarily malignant melanoma is mortal and tough to recognize in the final stages. To minimize the increasing death rate it is a most essential goal to recognize the skin cancer at its first stage. Skin lesion classification is becoming challenging more and more due to low contrast images. In this research, we propose an intelligent method by implementing the histogram decision to separate the low contrast images into a large amount of dataset. This decision is helpful in the pre-processing stage for the enhancements just in low contrast image either applied into all dataset by avoiding the time complexity. The saliency-based method is applied for lesion segmentation and achieved 95.8 % accuracy. Feature selection is performed by the entropy method after the extraction of deep color and PHOG features. In this research, the SVM classifier is applied on three benchmark datasets ISIB 2016, ISIB 2017 and PH2. Through our proposed fusion feature vector, the best classification results in achieved are 99.5% accuracy on the dataset ISIB2017.