Miracle-dz/UniFAR-FTUs huggingface dataset:
Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion

Many different methods have been proposed to construct nonparametric estimates of a smooth regression function, including local polynomial, (convolution) kernel and smoothing spline estimators. Each of these estimators uses a smoothing parameter to control the amount of smoothing performed on a given data set. In this paper an improved version of a criterion based on the Akaike information criterion (AIC), termed AICC, is derived and examined as a way to choose the smoothing parameter. Unlike plug‐in methods, AICC can be used to choose smoothing parameters for any linear smoother, including local quadratic and smoothing spline estimators. The use of AICC avoids the large variability and tendency to undersmooth (compared with the actual minimizer of average squared error) seen when other ‘classical’ approaches (such as generalized cross‐validation (GCV) or the AIC) are used to choose the smoothing parameter. Monte Carlo simulations demonstrate that the AICC‐based smoothing parameter is competitive with a plug‐in method (assuming that one exists) when the plug‐in method works well but also performs well when the plug‐in approach fails or is unavailable.
Approximating a data stream for querying and estimation: algorithms and performance evaluation

Obtaining fast and good-quality approximations to data distributions is a problem of central interest to database management. A variety of popular database applications, including approximate querying, similarity searching and data mining in most application domains, rely on such good-quality approximations. Histogram-based approximation is a very popular method in database theory and practice to succinctly represent a data distribution in a space-efficient manner. In this paper, we place the problem of histogram construction into perspective and we generalize it by raising the requirement of a finite data set and/or known data set size. We consider the case of an infinite data set in which data arrive continuously, forming an infinite data stream. In this context, we present single-pass algorithms that are capable of constructing histograms of provable good quality. We present algorithms for the fixed-window variant of the basic histogram construction problem, supporting incremental maintenance of the histograms. The proposed algorithms trade accuracy for speed and allow for a graceful tradeoff between the two, based on application requirements. In the case of approximate queries on infinite data streams, we present a detailed experimental evaluation comparing our algorithms with other applicable techniques using real data sets, demonstrating the superiority of our proposal.
Non-variational computation of the eigenstates of Dirac operators with radially symmetric potentials

We discuss a novel strategy for computing the eigenvalues and eigenfunctions of the relativistic Dirac operator with a radially symmetric potential. The virtues of this strategy lie on the fact that it avoids completely the phenomenon of spectral pollution and it always provides two-side estimates for the eigenvalues with explicit error bounds on both eigenvalues and eigenfunctions. We also discuss convergence rates of the method as well as illustrate our results with various numerical experiments.
Assessment of distractions inferred by in-vehicle information systems on a naturalistic simulator

Driving inattention is a major factor to highway crashes. The National Highway Traffic Safety Administration (NHTSA) estimates that approximately 25% of police-reported crashes involve some form of driving inattention. Increasing use of in-vehicle information systems (IVISs) such as cell phones or GPS navigation systems has exacerbated the problem by introducing additional sources of distraction. Enabling drivers to benefit from IVIS without diminishing safety is an important challenge. In this paper, an automatic distraction monitoring system based on gaze focalization for the assessment of IVISs induced distraction is presented. Driver's gaze focalization is estimated using a non-intrusive vision-based approach. This system has been tested in a naturalistic simulator with more than 15 hours of driving in different scenarios and conditions and 12 different professional drivers. The purpose of this work is, on the one hand, to assess the detection capacity of the monitoring system and, in the other hand, to study drivers reactions to different IVISs. Gathering this information the optimal IVISs location and the way the indications should be delivered to the drivers can be studied to reduce the interference with their driving.
2D Face Recognition Based on PCA & Comparison of Manhattan Distance, Euclidean Distance & Chebychev Distance

This paper is about human face recognition in image files. Face recognition involves matching a given image with the database of images and identifying the image that it resembles the most. Here, face recognition is done using: (a) Eigen faces and (b) Applying Principal Component Analysis (PCA) on image. The aim is to successfully demonstrate the human face recognition using Principal component analysis & comparison of Manhattan distance, Euclidean distance & Chebychev distance for face matching.
A Genome-Wide Association Study in Chronic Obstructive Pulmonary Disease (COPD): Identification of Two Major Susceptibility Loci

There is considerable variability in the susceptibility of smokers to develop chronic obstructive pulmonary disease (COPD). The only known genetic risk factor is severe deficiency of α1-antitrypsin, which is present in 1–2% of individuals with COPD. We conducted a genome-wide association study (GWAS) in a homogenous case-control cohort from Bergen, Norway (823 COPD cases and 810 smoking controls) and evaluated the top 100 single nucleotide polymorphisms (SNPs) in the family-based International COPD Genetics Network (ICGN; 1891 Caucasian individuals from 606 pedigrees) study. The polymorphisms that showed replication were further evaluated in 389 subjects from the US National Emphysema Treatment Trial (NETT) and 472 controls from the Normative Aging Study (NAS) and then in a fourth cohort of 949 individuals from 127 extended pedigrees from the Boston Early-Onset COPD population. Logistic regression models with adjustments of covariates were used to analyze the case-control populations. Family-based association analyses were conducted for a diagnosis of COPD and lung function in the family populations. Two SNPs at the α-nicotinic acetylcholine receptor (CHRNA 3/5) locus were identified in the genome-wide association study. They showed unambiguous replication in the ICGN family-based analysis and in the NETT case-control analysis with combined p-values of 1.48×10−10, (rs8034191) and 5.74×10−10 (rs1051730). Furthermore, these SNPs were significantly associated with lung function in both the ICGN and Boston Early-Onset COPD populations. The C allele of the rs8034191 SNP was estimated to have a population attributable risk for COPD of 12.2%. The association of hedgehog interacting protein (HHIP) locus on chromosome 4 was also consistently replicated, but did not reach genome-wide significance levels. Genome-wide significant association of the HHIP locus with lung function was identified in the Framingham Heart study (Wilk et al., companion article in this issue of PLoS Genetics; doi:10.1371/journal.pgen.1000429). The CHRNA 3/5 and the HHIP loci make a significant contribution to the risk of COPD. CHRNA3/5 is the same locus that has been implicated in the risk of lung cancer.
A Study of the Performance of Wireless Sensor Networks Operating with Smart Antennas

Wireless sensor networks (WSNs) have attracted a great deal of research interest during the last few years, with potential applications making them ideal for the development of the envisioned world of ubiquitous and pervasive computing. Energy and computational efficiency constraints are the main key issues when dealing with this type of network. The main research effort has been channeled towards routing and distributed processing, in order to achieve better quality of service (QoS) provisions, lower interference, and a lower power-consumption rate while data dissemination is carried out. The embedment of smart antennas on wireless-sensor nodes is proposed herein as an alternative and novel approach at the physical layer, with the potential for relieving traditional challenges faced by current wireless-sensor-network architectures. Studying the behavior of wireless sensor networks consisting of different types of antennas (omnidirectional or adaptive directional) yielded unexpectedly favorable results that improved the operation of networking systems of this type. In the test cases presented herein, the incorporation of smart antennas resulted in approximate improvements in the quality of service by 20%, the efficiency by 50%, the percentage of active nodes by 20%, and the energy consumption by 50%, depending on the simulation setup.
Clinical Utility of Mindfulness Training in the Treatment of Fatigue After Stroke, Traumatic Brain Injury and Multiple Sclerosis: A Systematic Literature Review and Meta-analysis

Background: Fatigue is a common symptom following neurological illnesses and injuries, and is rated as one of the most debilitating sequela in conditions such as stroke, traumatic brain injury (TBI), and multiple sclerosis (MS). Yet effective treatments are lacking, suggesting a pressing need for a better understanding of its etiology and mechanisms that may alleviate the symptoms. Recently mindfulness-based interventions have demonstrated promising results for fatigue symptom relief. Objective: Investigate the efficacy of mindfulness-based interventions for fatigue across neurological conditions and acquired brain injuries. Materials and Methods: Systematic literature searches were conducted in PubMed, Medline, Web of Science, and PsycINFO. We included randomized controlled trials applying mindfulness-based interventions in patients with neurological conditions or acquired brain injuries. Four studies (N = 257) were retained for meta-analysis. The studies included patients diagnosed with MS, TBI, and stroke. Results: The estimated effect size for the total sample was -0.37 (95% CI: -0.58, -0.17). Conclusion: The results indicate that mindfulness-based interventions may relieve fatigue in neurological conditions such as stroke, TBI, and MS. However, the effect size is moderate, and further research is needed in order to determine the effect and improve our understanding of how mindfulness-based interventions affect fatigue symptom perception in patients with neurological conditions.
Internal Fat and Cardiometabolic Risk Factors Following a Meal-Replacement Regimen vs. Comprehensive Lifestyle Changes in Obese Subjects

The aim of the present study was to investigate the effect of a meal-replacement regimen vs. comprehensive lifestyle changes in overweight or obese subjects on intra-abdominal fat stores (Magnetic Resonance Imaging (MRI) measurements) and cardiometabolic risk factors. Forty-two obese men (n = 18) and women (n = 24) (age 49 ± 8 years; weight 96.3 ± 12.1 kg; BMI 32.7 ± 2.3 kg/m2) were selected for this randomized parallel-group design investigation. Subjects in the lifestyle group (LS-G; n = 22) received dietary counselling sessions and instructions how to increase physical activity. In the meal replacement group (MR-G; n = 20) meals were replaced by a low-calorie drink high in soy protein. After six months, subjects in the LS-G lost 8.88 ± 6.24 kg and subjects in the MR-G lost 7.1 ± 2.33 kg; p < 0.01 for changes within groups; no significant differences were found between the groups. Lean body mass remained constant in both intervention groups. MRI analyses showed that internal fat was significantly reduced in both groups to a comparable amount; the higher fat loss in the LS-G in the abdominal area was due to a higher reduction in subcutaneous fat. Both interventions significantly reduced components of the cardiometabolic risk profile and leptin levels. The decrease in the adipokines fetuin A and resistin was more pronounced in the MR-G. In conclusion, both interventions significantly reduced body weight, total fat mass and internal abdominal fat while preserving lean body mass. The reduction in the adipokines fetuin A and resistin was more pronounced in the meal replacement group suggesting an additional effect of soy protein components.
Packet Scheduling Based General Self-organized Tree Routing Protocol for Wireless Sensor Network

In recent years, wireless sensor networks have attained more attraction owing to their potential ability in providing solutions in various areas. They have different restraint, such as computational power, storage capacity, energy supply and etc also the most important issue is energy constraint. The above challenges hold back the efficient use of WSNs to support different applications, such as the resource limitations of sensor devices and definite energy. The main reason for wasted energy consumption in WSN is packet collision. So packet scheduling algorithm is introduced to overcome this problem. Several packet scheduling algorithms can also influence and delay the data transmitting in the real-time wireless sensor networks. In this research, a novel cluster tree based routing protocol is proposed. The network is divided into several clusters using fuzzy possibility c means algorithm. The cluster tree is formed using shortest path algorithm. Hybrid time slot allocation is proposed for data transmission in order to overcome the delay. Hybrid time slot allocation is based on the combination of TDMA and CSMA/CA.
bluffer ’ s guide to auctions

There is a veritable menagerie of auctions—single dimensional, multidimensional, single sided, double sided, first price, second price, English, Dutch, Japanese, sealed bid—and these have been extensively discussed and analysed in the economics literature. the main purpose of this paper is to survey this literature from a computer science perspective, primarily from the viewpoint of computer scientists who are interested in auction theory. However, we also look at some attempts from the computer science literature to identify the important properties of auctions from the perspective of implementing auction systems.
Implementation of a Shoulder and Elbow musculoskeletal model in musculoskeletal modelling and simulation software (MSMS)

Musculoskeletal Models are useful for designing rehabilitation technology (RT). MSMS software has unique features which let simulate RT along with different controllers. Unfortunately there are technical differences across platforms which yield to different results. Therefore benchmarking of simulations has been proposed. Here we present the implementation of a Shoulder and Elbow musculosketal (SE) model based on the MoBL ARMS model. SE model consists of 5 rigid segments linked with 5 degrees of freedom driven by 12 muscles. Muscle fascicle length (FL) and active fiber force (AFF) were calculated during simulation of 2 single joint shoulder and elbow movements. RMS errors were calculated between results. Differences between models were smallest for muscle FL (RMSE≤0.0037) and AFF (RMSE≤22.0155). RMS errors were biggest for FL of deltoid posterior muscle (RMSE=0.1165) and for AFF of supraspinatus muscle (RMSE=164.97) during shoulder abduction. But both models showed similar outputs.
The Annotation of Event Schema in Chinese

We present a strategy for revealing event schema in Chinese based on the manual annotation of texts. The overall event information is divided into three levels and events are chosen as the elementary units in annotation. Event-level annotation content and the obtaining of events patterns are explored in detail. The discourse-level annotation, annotation of relations between events and annotation of the functional attributes provide a simple way to represent event schema.
Color and texture features for person recognition

The need for automatic visual surveillance is increasing and the research on person recognition systems is more and more supported. As many biometric recognition methods, e.g. face recognition, are based on quite high camera resolutions which are not available in many situations, we examine features as well as classifier techniques for full body recognition. We present our experiments with color and texture features in the application of full body person recognition. On a database of 53 individuals we tested approved features for object recognition as well as MPEG7 color and texture descriptors on a person recognition task. For comparison, we used an RBF network classifier as well as a nearest-neighbor classifier. Our experiments showed that color as well as texture information is important for a person recognition system. Additionally, a combination of these two kind of features results in a performance improvement.
Acquired tolerance to experimental autoimmune encephalomyelitis by intrathymic injection of myelin basic protein or its major encephalitogenic peptide

Experimental autoimmune encephalomyelitis (EAE) is an inflammatory disease of the central nervous system that can be induced in a number of species by immunization with myelin basic protein (MBP) in adjuvant, and serves as an experimental model for the study of multiple sclerosis. The role of the thymus in acquired tolerance in autoimmune models has not been thoroughly investigated. In this study, we examined the effects of intrathymic injection of MBP or its major encephalitogenic peptide on the course of EAE in Lewis rats. A single intrathymic injection of MBP 48 h pre- but not postimmunization protects animals from actively induced EAE. An intact MBP-primed thymus was required up to 10 d postimmunization, as thymectomy on days 1, 2, and 7 postimmunization abrogated the protective effect, whereas thymectomy on day 10 did not. The proliferative response of primed lymphocytes was significantly reduced in animals that were intrathymically injected with MBP. Protection against clinical EAE was induced by thymic injection of the major encephalitogenic region (residues 71-90) but not a nonencephalitogenic (21-40) MBP epitope. Immunohistologic examination of the brain from rats intrathymically injected with encephalitogenic peptide showed markedly reduced cellular infiltrate and virtual absence of activation and inflammatory cytokines as compared with rats intrathymically injected with the nonencephalitogenic peptide. These results indicate that the thymus may play an active role in acquired systemic immunologic tolerance in T cell-mediated experimental autoimmune diseases. This effect may be mediated by a process of clonal inactivation of autoreactive T cell clones circulating through the thymus.
EXPERIMENTAL AND NUMERICAL APPROACH TO VALIDATE PRESSURE LOSS PREDICTABILITY OF A COMMERCIAL CODE

Experimental and numerical works to validate a commercial CFD code for predicting the pressure loss of a PWR grid spacer were presented. The experimental data was obtained for full size spacer mockups with different inclination of mixing-vanes. The pressure loss in the complex configuration of spacers arises from a several hydrodynamic effects included in the flow. Only the experimental result therefore was not enough to provide detailed data for validating the turbulence model used in the CFD code. To this end this study used a large eddy simulation (LES) to look at the hydrodynamic effects. The result of the LES indicated that the flow field around the spacer included a large-scale unsteadiness and an undeveloped turbulent flow. Turbulence models based on a developed turbulent flow were theoretically inapplicable to these flows. The commercial code with the standard high Reynolds number k-ε model with the law of the wall however successfully reproduced the trend of the measurement. This suggests that a large-scale unsteadiness and an undeveloped turbulent flow are not dominant for the pressure loss. It is noted that commercial codes should be applied to the flows where dominant physics is clarified.
Modeling and delay analysis for urban vehicular delay-tolerant networks

As a significant application of Delay Tolerant Networks (DTN), Urban Vehicular Delay-tolerant-Networks (UVDN) has been paid much attention to mobile communication. Communication delay analysis is necessary for design of routing and optimization of networks protocol, however, the combinative geographic information of vehicular trajectories and urban map is complex, which caused existing delay analysis models for DTN could not be completely applied to UVDN delay analysis. In this paper, firstly we suggested a new map segmentation approach named MCMS (Major-road Centred Map Segmentation) to partition a map into adjacent regions. Then, based on these regions, we proposed a delay analysis model for geographic communication of UVDN and derived analytical expressions of the cumulative distribution function (CDF) of the delivery delay. Finally, through extensive simulations under real vehicular trajectories, the delay models exhibits good performance in UVDN delay analysis and prediction.
Reverse-correlation analysis of navigation dynamics in Drosophila larva using optogenetics

Neural circuits for behavior transform sensory inputs into motor outputs in patterns with strategic value. Determining how neurons along a sensorimotor circuit contribute to this transformation is central to understanding behavior. To do this, a quantitative framework to describe behavioral dynamics is needed. Here, we built a high-throughput optogenetic system for Drosophila larva to quantify the sensorimotor transformations underlying navigational behavior. We express CsChrimson, a red-shifted variant of Channelrhodopsin, in specific chemosensory neurons, and expose large numbers of freely moving animals to random optogenetic activation patterns. We quantify their behavioral responses and use reverse correlation analysis to uncover the linear and static nonlinear components of navigation dynamics as functions of optogenetic activation patterns of specific sensory neurons. We find that linear-nonlinear (LN) models accurately predict navigational decision-making for different optogenetic activation waveforms. We use our method to establish the valence and dynamics of navigation driven by optogenetic activation of different combinations of bitter sensing gustatory neurons. Our method captures the dynamics of optogenetically-induced behavior in compact, quantitative transformations that can be used to characterize circuits for sensorimotor processing and their contribution to navigational decision-making.
Black-box hamiltonian simulation and unitary implementation

We present general methods for simulating black-box Hamiltonians using quantum walks. These techniques have two main applications: simulating sparse Hamiltonians and implementing black-box unitary operations. In particular, we give the best known simulation of sparse Hamiltonians with constant precision. Our method has complexity linear in both the sparseness D (the maximum number of nonzero elements in a column) and the evolution time t, whereas previous methods had complexity scaling as D4 and were superlinear in t. We also consider the task of implementing an arbitrary unitary operation given a black-box description of its matrix elements. Whereas standard methods for performing an explicitly specified N × N unitary operation use O(N2) elementary gates, we show that a black-box unitary can be performed with bounded error using O(N2/3(log logN)4/3) queries to its matrix elements. In fact, except for pathological cases, it appears that most unitaries can be performed with only O(√N) queries, which is optimal.