Miracle-dz/UniFAR-FTUs huggingface dataset:
Ontology and parser dependencies approach for spoken dialogue system

In this paper, we propose a mobile spoken dialogue system (MOBILE SDS) based on parser dependencies and ontology. The turn (semantic) analysis algorithm integrated in the spoken dialogue understanding (SLU) module uses, at each turn of the dialogue, the dependencies generated by Stanford parser and a domain ontology to analyze the sentence and extract user's intention and slots values (i.e. user dialogue acts, concepts and their values). The SLU maps the sentence into a structure. The dialogue manager receives this mapping structure from the SLU module and chooses the action to be taken by the system. The MOBILE SDS was developed as a remote system on a mobile phone. It uses Google server for recognition and Google text to speech for speech synthesis provided with Android system. Dialogue understanding, dialogue managing and text generating modules reside on a remote computer. Ten volunteers' users have tested our system and a score of 3.6 on Likert scale was obtained.
The harmonic and sideband structure of the kilohertz quasi-periodic oscillations in Sco X-1

ABSTRA C T We use data from the Rossi X-ray Timing Explorer to search for harmonics and sidebands of the two simultaneous kilohertz quasi-periodic oscillations (kHz QPOs) in Sco X-1. We do not detect any of these harmonics or sidebands, with 95 per cent confidence upper limits to their power between ,1 and ,10 per cent of the power of the upper kHz QPO. The oscillations produced at these frequencies may be attenuated in a scattering corona around the neutron star. We find that upper limits to the unattenuated power of some of the strongest theoretically predicted harmonics and sidebands are as low as ,2 per cent of the unattenuated power of the high-frequency QPO in Sco X-1.
Automatic scan prescription for brain MRI

Diagnostic brain MRI scans are usually performed by trained medical technologists who manually prescribe the position and orientation of a scanning volume. In this study, a fully automatic computer algorithm is described which compensates for variable patient positioning and acquires brain MRI scans in a predefined reference orientation. The method involves acquiring a rapid water‐only pilot scan, segmenting the brain surface, and matching it to a reference surface. The inverse matching transformation is then used to adapt a geometric description of the desired scanning volume, defined relative to the reference surface, to the current patient. Both pilot scan and processing are performed within 30 sec. The method was tested in 25 subjects, and consistently recovered orientation differences between the reference and each subject to within ±5°. Compared to manual prescription, automatic scan prescription promises many potential benefits, including reduced scan times, reproducible scan orientations along anatomically preferable orientations, and better reproducibility for longitudinal studies. Magn Reson Med 45:486–494, 2001. © 2001 Wiley‐Liss, Inc.
Psychopharmacological Interventions in Autism Spectrum Disorder

Learning Objectives: After participating in this educational activity, the physician should be better able to1. Prescribe the appropriate psychotropic medication to treat symptoms of ASD.2. Identify the side effects of the psychotropic medications used to treat ASD.Autism spectrum disorders (ASDs) are characterized by core deficits in social communication and language, and restrictive and repetitive behaviors that cause significant functional impairment and distress for affected individuals and their caregivers. The increasing prevalence of ASD, most recently estimated as 1 in 88 children, presents an ever-increasing burden on families, schools, medical systems, and society at large. Individuals with ASD commonly present for treatment of associated emotional and behavioral disturbances that include anxiety, symptoms of ADHD, compulsions and other repetitive behaviors, mood lability, irritability, aggression, and sleep disturbance. Psychotropic medications are widely utilized in alleviating these symptoms, though rigorous clinical trials in ASD are lacking for most areas of impairment. Strong evidence from randomized, placebo-controlled trials supports the use of atypical antipsychotics, particularly risperidone and aripiprazole, for managing severe irritability and aggression in ASD. Serotonin reuptake inhibitors are commonly used to treat anxiety and compulsions, though reports of efficacy in the literature are mixed, and behavioral side effects in children are common. Minimal evidence supports the utility of anticonvulsants and traditional mood stabilizers in managing mood lability and aggression. Stimulant and nonstimulant ADHD medications can be effective for reducing hyperactivity, inattention, and impulsivity, though to a lesser degree than in ADHD populations without ASD and with greater risk of adverse effects. Psychopharmacological interventions in development for core symptoms of autism include those that target the glutamatergic and GABAergic neurotransmitter systems and the neuropeptide oxytocin. Further research is needed to establish evidence-based interventions in ASD populations.
Novel Insights on Nutrient Management of Sarcopenia in Elderly

Sarcopenia is defined as a syndrome characterized by progressive and generalized loss of muscle mass and strength. The more rationale approach to delay the progression of sarcopenia is based on the combination of proper nutrition, possibly associated with the use of dietary supplements and a regular exercise program. We performed a narrative literature review to evaluate the till-now evidence regarding (1) the metabolic and nutritional correlates of sarcopenia; (2) the optimum diet therapy for the treatment of these abnormalities. This review included 67 eligible studies. In addition to the well recognized link between adequate intake of proteins/amino acids and sarcopenia, the recent literature underlines that in sarcopenic elderly subjects there is an unbalance in vitamin D synthesis and in omega-6/omega-3 PUFA ratio. Given the detrimental effect of these metabolic abnormalities, a change in the lifestyle must be the cornerstone in the treatment of sarcopenia. The optimum diet therapy for the sarcopenia treatment must aim at achieving specific metabolic goals, which must be reached through accession of the elderly to specific personalized dietary program aimed at achieving and/or maintaining muscle mass; increasing their intake of fish (4 times/week) or taking omega-3 PUFA supplements; taking vitamin D supplementation, if there are low serum levels.
Translation initiation factor IF2 of the myxobacterium Stigmatella aurantiaca: presence of a single species with an unusual N-terminal sequence

The structural gene for translation initiation factor IF2 (infB) was isolated from the myxobacterium Stigmatella aurantiaca on a 5.18-kb BamHI genomic restriction fragment. The infB gene (ca. 3.16 kb) encodes a 1,054-residue polypeptide with extensive homology within its G domain and C terminus with the equivalent regions of IF2s from Escherichia coli, Bacillus subtilis, Bacillus stearothermophilus, and Streptococcus faecium. The N-terminal region does not display any significant homology to other known proteins. The S. aurantiaca infB gene encodes a single protein which cross-reacted with antiserum to E. coli IF2 and was able to complement an E. coli infB mutant. The S. aurantiaca IF2 is distinguished from all other IF2s by a sequence of 160 residues near the N terminus that has an unusual composition, made up essentially of alanine, proline, valine, and glutamic acid. Within this sequence, the pattern PXXXAP is repeated nine times. Complete deletion of this sequence did not affect the factor's function in initiation of translation and even increased its capacity to complement the E. coli infB mutant.
Identification of a Comprehensive Spectrum of Genetic Factors for Hereditary Breast Cancer in a Chinese Population by Next-Generation Sequencing

The genetic etiology of hereditary breast cancer has not been fully elucidated. Although germline mutations of high-penetrance genes such as BRCA1/2 are implicated in development of hereditary breast cancers, at least half of all breast cancer families are not linked to these genes. To identify a comprehensive spectrum of genetic factors for hereditary breast cancer in a Chinese population, we performed an analysis of germline mutations in 2,165 coding exons of 152 genes associated with hereditary cancer using next-generation sequencing (NGS) in 99 breast cancer patients from families of cancer patients regardless of cancer types. Forty-two deleterious germline mutations were identified in 21 genes of 34 patients, including 18 (18.2%) BRCA1 or BRCA2 mutations, 3 (3%) TP53 mutations, 5 (5.1%) DNA mismatch repair gene mutations, 1 (1%) CDH1 mutation, 6 (6.1%) Fanconi anemia pathway gene mutations, and 9 (9.1%) mutations in other genes. Of seven patients who carried mutations in more than one gene, 4 were BRCA1/2 mutation carriers, and their average onset age was much younger than patients with only BRCA1/2 mutations. Almost all identified high-penetrance gene mutations in those families fulfill the typical phenotypes of hereditary cancer syndromes listed in the National Comprehensive Cancer Network (NCCN) guidelines, except two TP53 and three mismatch repair gene mutations. Furthermore, functional studies of MSH3 germline mutations confirmed the association between MSH3 mutation and tumorigenesis, and segregation analysis suggested antagonism between BRCA1 and MSH3. We also identified a lot of low-penetrance gene mutations. Although the clinical significance of those newly identified low-penetrance gene mutations has not been fully appreciated yet, these new findings do provide valuable epidemiological information for the future studies. Together, these findings highlight the importance of genetic testing based on NCCN guidelines and a multi-gene analysis using NGS may be a supplement to traditional genetic counseling.
Social media approaches to modeling wildfire smoke dispersion: spatiotemporal and social scientific investigations

ABSTRACT Wildfires have significant effects on human populations, economically, environmentally, and in terms of their general well-being. Smoke pollution, in particular, from either prescribed burns or uncontrolled wildfires, can have significant health impacts. Some estimates suggest that smoke dispersion from fire events may affect the health of one in three residents in the United States, leading to an increased incidence of respiratory illnesses such as asthma and pulmonary disease. Scarcity in the measurements of particulate matter responsible for these public health issues makes addressing the problem of smoke dispersion challenging, especially when fires occur in remote regions. Crowdsourced data have become an essential component in addressing other societal problems (e.g., disaster relief, traffic congestion) but its utility in monitoring air quality impacts of wildfire events is unexplored. In this study, we assessed if user-generated social media content can be used as a complementary source of data in measuring particulate pollution from wildfire smoke. We found that the frequency of daily tweets within a 40,000 km2 area was a significant predictor of PM2.5 levels, beyond daily and geographic variation. These results suggest that social media can be a valuable tool for the measurement of air quality impacts of wildfire events, particularly in the absence of data from physical monitoring stations. Also, an analysis of the semantic content in people’s tweets provided insight into the socio-psychological dimensions of fire and smoke and their impact on people residing in, working in, or otherwise engaging with affected areas.
Reproductive experience modified dendritic spines on cortical pyramidal neurons to enhance sensory perception and spatial learning in rats

Behavioral adaptations during motherhood are aimed at increasing reproductive success. Alterations of hormones during motherhood could trigger brain morphological changes to underlie behavioral alterations. Here we investigated whether motherhood changes a rat’s sensory perception and spatial memory in conjunction with cortical neuronal structural changes. Female rats of different statuses, including virgin, pregnant, lactating, and primiparous rats were studied. Behavioral test showed that the lactating rats were most sensitive to heat, while rats with motherhood and reproduction experience outperformed virgin rats in a water maze task. By intracellular dye injection and computer-assisted 3-dimensional reconstruction, the dendritic arbors and spines of the layer III and V pyramidal neurons of the somatosensory cortex and CA1 hippocampal pyramidal neurons were revealed for closer analysis. The results showed that motherhood and reproductive experience increased dendritic spines but not arbors or the lengths of the layer III and V pyramidal neurons of the somatosensory cortex and CA1 hippocampal pyramidal neurons. In addition, lactating rats had a higher incidence of spines than pregnant or primiparous rats. The increase of dendritic spines was coupled with increased expression of the glutamatergic postsynaptic marker protein (PSD-95), especially in lactating rats. On the basis of the present results, it is concluded that motherhood enhanced rat sensory perception and spatial memory and was accompanied by increases in dendritic spines on output neurons of the somatosensory cortex and CA1 hippocampus. The effect was sustained for at least 6 weeks after the weaning of the pups.
Software unit test coverage and adequacy

Objective measurement of test quality is one of the key issues in software testing. It has been a major research focus for the last two decades. Many test criteria have been proposed and studied for this purpose. Various kinds of rationales have been presented in support of one criterion or another. We survey the research work in this area. The notion of adequacy criteria is examined together with its role in software dynamic testing. A review of criteria classification is followed by a summary of the methods for comparison and assessment of criteria.
A New Method for Predicting the Subcellular Localization of Eukaryotic Proteins with Both Single and Multiple Sites: Euk-mPLoc 2.0

Information of subcellular locations of proteins is important for in-depth studies of cell biology. It is very useful for proteomics, system biology and drug development as well. However, most existing methods for predicting protein subcellular location can only cover 5 to 12 location sites. Also, they are limited to deal with single-location proteins and hence failed to work for multiplex proteins, which can simultaneously exist at, or move between, two or more location sites. Actually, multiplex proteins of this kind usually posses some important biological functions worthy of our special notice. A new predictor called “Euk-mPLoc 2.0” is developed by hybridizing the gene ontology information, functional domain information, and sequential evolutionary information through three different modes of pseudo amino acid composition. It can be used to identify eukaryotic proteins among the following 22 locations: (1) acrosome, (2) cell wall, (3) centriole, (4) chloroplast, (5) cyanelle, (6) cytoplasm, (7) cytoskeleton, (8) endoplasmic reticulum, (9) endosome, (10) extracell, (11) Golgi apparatus, (12) hydrogenosome, (13) lysosome, (14) melanosome, (15) microsome (16) mitochondria, (17) nucleus, (18) peroxisome, (19) plasma membrane, (20) plastid, (21) spindle pole body, and (22) vacuole. Compared with the existing methods for predicting eukaryotic protein subcellular localization, the new predictor is much more powerful and flexible, particularly in dealing with proteins with multiple locations and proteins without available accession numbers. For a newly-constructed stringent benchmark dataset which contains both single- and multiple-location proteins and in which none of proteins has pairwise sequence identity to any other in a same location, the overall jackknife success rate achieved by Euk-mPLoc 2.0 is more than 24% higher than those by any of the existing predictors. As a user-friendly web-server, Euk-mPLoc 2.0 is freely accessible at http://www.csbio. sjtu.edu. cn/bioinf/euk-multi-2/. For a query protein sequence of 400 amino acids, it will take about 15 seconds for the web-server to yield the predicted result; the longer the sequence is, the more time it may usually need. It is anticipated that the novel approach and the powerful predictor as presented in this paper will have a significant impact to Molecular Cell Biology, System Biology, Proteomics, Bioinformatics, and Drug Development.
Analysis and design of a novel variable-inductor-based LED driver for DC lighting grids

This paper presents a novel variable inductor (VI) based LED driver for DC grid lighting applications. The proposed driver requires only a series inductor and a transformer as major components to drive the LED lamp from a half-bridge inverter. By introducing a VI as the series inductor, the LED current can be controlled independently from any other parameter, which makes it possible to drive and regulate several LED branches from the same half-bridge output. Other advantages of the proposed converter include inherent open circuit and short circuit protections, ZVS for the bridge transistor and ZCS for the output rectifier diodes, simple dynamics, possibility of analog and PWM dimming, constant switching frequency operation and high efficiency. The converter is thoroughly analyzed and modelled for both steady-state and dynamic operation. As another novelty of this paper, the dynamic response of the VI has been studied and taken into account to obtain the complete transfer function of the VI-controlled system. In addition, some housekeeping issues that usually arise when dealing with VI, e.g. how to drive the VI bias winding, are solved in this work. Experimental results provided from a 50 W laboratory prototype demonstrate the correctness of the performed analysis and the good possibilities of the proposed converter.
Yield analysis of a novel scheme for defect-tolerant memories

The recent increases in the size of memory ICs have made designers realize that there exists a need for new defect-tolerance techniques, since the traditional methods are no longer effective. One such new technique, the Flexible Multi-Macro (FMM) technique has recently been suggested and implemented in a 1 Gb DRAM circuit. In this paper we present a yield analysis of the FMM design and compare its yield to that of the most common defect-tolerance technique of adding spare rows and columns to the memory array.
Vehicle stability enhancement by an energy optimal control approach

Vehicle stability enhancement based on an energy optimal control method is presented. The direct yaw moment control of a road vehicle is aimed by the intervention of individual braking forces. The optimal controller has been designed to minimize the given criteria function. This function has two essential elements: the control performance measure and the input power. The employment of yaw moment and yaw rate in the control performance measure enables the incorporation of the most important dynamics into the control law. By designing the input power based on the braking forces, the yaw moment input is automatically calculated by the controller. The effectiveness of the control approach has been shown by the application of the controller in the case of rapid lane change for two different initial velocity conditions.
Trading Accuracy for Power with an Underdesigned Multiplier Architecture

We propose a novel multiplier architecture with tunable error characteristics, that leverages a modified inaccurate 2x2 building block. Our inaccurate multipliers achieve an average power saving of 31.78% ? 45.4% over corresponding accurate multiplier designs, for an average error of 1.39%? 3.32%. Using image filtering and JPEG compression as sample applications we show that our architecture can achieve 2X - 8X better Signal-Noise-Ratio (SNR) for the same power savings when compared to recent voltage over-scaling based power-error tradeoff methods. We project the multiplier power savings to bigger designs highlighting the fact that the benefits are strongly design dependent. We compare this circuit-centric approach to power quality tradeoffs with a pure software adaptation approach for a JPEG example. We also enhance the design to allow for correct operation of the multiplier using a residual adder, for non error resilient applications.
Bayesian CCA via Group Sparsity

Bayesian treatments of Canonical Correlation Analysis (CCA) -type latent variable models have been recently proposed for coping with overfitting in small sample sizes, as well as for producing factorizations of the data sources into correlated and non-shared effects. However, all of the current implementations of Bayesian CCA and its extensions are computationally inefficient for high-dimensional data and, as shown in this paper, break down completely for high-dimensional sources with low sample count. Furthermore, they cannot reliably separate the correlated effects from non-shared ones. We propose a new Bayesian CCA variant that is computationally efficient and works for high-dimensional data, while also learning the factorization more accurately. The improvements are gained by introducing a group sparsity assumption and an improved variational approximation. The method is demonstrated to work well on multi-label prediction tasks and in analyzing brain correlates of naturalistic audio stimulation.
Simulation of EAP method selection and negotiation mechanism

Authentication is an important aspect in network security, especially in wireless network where unauthorized user can gain access to the network without any physical access. One of the authentication protocols that have been used in wireless networks is the extensible authentication protocol (EAP). This paper discusses the simulation development and simulation results of a mechanism to select and negotiate the suitable EAP method to be used in EAP authentication between two nodes.
Genomic selection for feed efficiency traits

Introduction Feed costs comprise the majority of variable expenses in beef cattle systems making feed efficiency an important economic consideration within the beef industry (Koch et al ., 1963; Dickerson et al ., 1974) . Aside from the direct economic impact of this trait complex at the individual producer level, the projections of global population growth provide extra pressure for efficient beef cattle production as producers try to combat the growing food demand with limited resources (Eggen, 2012) . Improved feed efficiency also has an environmental impact through a decreased carbon footprint as more efficient cattle have fewer days to finish, emitting less methane throughout their lifetime (Freetly, 2013) .
Analysis of discovery and access procedure for D2D communication in 5G cellular network

Device-to-device (D2D) communication, which is defined as a direct communication between two mobile users without traversing the Base Station (BS) or the core network to offload the increasing traffic to the user equipments, is one of the key technologies in the fifth generation (5G) of wireless communication systems. Discovery and communication are the basic two features to fulfill the need for the D2D communication. However, Most of existing D2D studies focused on the communication issues always assume that the discovery is completed. In this paper, we propose two strategies of device discovery and access scheme for the 5G cellular networks. Then the performance analysis based on two dimensional discrete time Markov process model is provided. In addition, we present numerical simulation on the Matlab platform. The simulation results demonstrate the viability of the proposed scheme.