Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Bids on Task Allocation for Multi-Robot Exploration

We propose a real time single item auction based task allocation method for the multi-robot exploration problem and investigate new bid evaluation strategies in this domain. In this problem, a different version of the well known NP-hard MTSP (Multiple Traveling Salesman Problem), each target must be visited by at least one robot in its open tour. Various objectives may be defined for this problem (e.g. minimization of total path length, time). In this article, we present an extensive analysis of our bid evaluation strategies for minimization of total path length objective. An integer programming (IP) approach may be used to allocate tasks to robots. However, IP approach may become impractical when the size of the mission is not small, the environment is dynamic or unknown, or the structure of the mission changes by online tasks. In real world domains, initial allocations assigned by computationally expensive methods are usually subject to change during run time. Our framework, capable of handling diverse contingencies, performs an incremental allocation method based on the up-to-date situations of the environment. Experimental results in simulations compared to both the results of the Prim Allocation method and the optima reveal efficiency of the bid evaluation heuristics combined with our framework.
How social cognition can inform social decision making

Social decision-making is often complex, requiring the decision-maker to make inferences of others' mental states in addition to engaging traditional decision-making processes like valuation and reward processing. A growing body of research in neuroeconomics has examined decision-making involving social and non-social stimuli to explore activity in brain regions such as the striatum and prefrontal cortex, largely ignoring the power of the social context. Perhaps more complex processes may influence decision-making in social vs. non-social contexts. Years of social psychology and social neuroscience research have documented a multitude of processes (e.g., mental state inferences, impression formation, spontaneous trait inferences) that occur upon viewing another person. These processes rely on a network of brain regions including medial prefrontal cortex (MPFC), superior temporal sulcus (STS), temporal parietal junction, and precuneus among others. Undoubtedly, these social cognition processes affect social decision-making since mental state inferences occur spontaneously and automatically. Few studies have looked at how these social inference processes affect decision-making in a social context despite the capability of these inferences to serve as predictions that can guide future decision-making. Here we review and integrate the person perception and decision-making literatures to understand how social cognition can inform the study of social decision-making in a way that is consistent with both literatures. We identify gaps in both literatures—while behavioral economics largely ignores social processes that spontaneously occur upon viewing another person, social psychology has largely failed to talk about the implications of social cognition processes in an economic decision-making context—and examine the benefits of integrating social psychological theory with behavioral economic theory.
On Successive Refinement of the Binary Symmetric Markov Source

We show that for every n>2 the standard nth-order approximation R<sub>n</sub>(D), to the rate-distortion function of the binary-symmetric Markov source (BSMS) is not successively refineable under the Hamming distortion measure in an open interval of the form D <sub>n</sub><D<1/2=D<sub>max</sub>
Covering link calculus and iterated Bing doubles

We give a new geometric obstruction to the iterated Bing double of a knot being a slice link: for n>1 the (n+1)-st iterated Bing double of a knot is rationally slice if and only if the n-th iterated Bing double of the knot is rationally slice. The main technique of the proof is a covering link construction simplifying a given link. We prove certain similar geometric obstructions for n <= 1 as well. Our results are sharp enough to conclude, when combined with algebraic invariants, that if the n-th iterated Bing double of a knot is slice for some n, then the knot is algebraically slice. Also our geometric arguments applied to the smooth case show that the Ozsvath-Szabo and Manolescu-Owens invariants give obstructions to iterated Bing doubles being slice. These results generalize recent results of Harvey, Teichner, Cimasoni, Cha and Cha-Livingston-Ruberman. As another application, we give explicit examples of algebraically slice knots with non-slice iterated Bing doubles by considering von Neumann rho-invariants and rational knot concordance. Refined versions of such examples are given, that take into account the Cochran-Orr-Teichner filtration.
The Neural Bases of Social Intention Understanding: The Role of Interaction Goals

Decoding others' intentions is a crucial aspect of social cognition. Neuroimaging studies suggest that inferring immediate goals engages the neural system for action understanding (i.e. mirror system), while the decoding of long-term intentions requires the system subserving the attribution of mental states (i.e. mentalizing). A controversial issue, stimulated by recent inconsistent results, concerns whether the two systems are concurrently vs. exclusively involved in intention understanding. This issue is particularly relevant in the case of social interactions, whose processing has been mostly, but not uncontroversially, associated with the mentalizing system. We tested the alternative hypothesis that the relative contribution of the two systems in intention understanding may also depend on the shared goal of interacting agents. To this purpose, 27 participants observed social interactions differing in their cooperative vs. affective shared goal during functional-Magnetic-Resonance-Imaging. The processing of both types of interactions activated the right temporo-parietal junction involved in mentalizing on action goals. Additionally, whole-brain and regions-of-interest analyses showed that the action understanding system (inferior prefrontal-parietal cortex) was more strongly activated by cooperative interactions, while the mentalizing-proper system (medial prefrontal cortex) was more strongly engaged by affective interactions. These differences were modulated by individual differences in empathizing. Both systems can thus be involved in understanding social intentions, with a relative weighting depending on the specific shared goal of the interaction.
Real-Time Animating and Rendering of Large Scale Grass Scenery on GPU

A method for animating and rendering large grass scenery in real-time is presented. The grass scenery consists of geometry-based grass blades and image-based quads covered with clumps of grass texture. Geometry-based grass blades are rendered for animating interactively for grass scenery close to camera. Imaged-based quads covered with clumps of grass texture are rendered for those far away from the camera. Seamless transition of the above two level of details (LOD) can be attained by special arrangement of grass density distribution and modulating the alpha channel of grass texture. The density of grass is defined with a density map to simulate natural grass distribution. GPU-based collision detect between virtual object and grass blades is used for interactive animating. Real-time animation of virtual objects striding in the grass scenery illustrates that the method can give people deep immersion and high fidelity for simulators and computer games.
Generic Object Detection with Dense Neural Patterns and Regionlets

This paper addresses the challenge of establishing a bridge between deep convolutional neural networks and conventional object detection frameworks for accurate and efficient generic object detection. We introduce Dense Neural Patterns , short for DNPs, which are dense local features derived from discriminatively trained deep convolutional neural networks. DNPs can be easily plugged into conventional detection frameworks in the same way as other dense local features(like HOG or LBP). The effectiveness of the proposed approach is demonstrated with the Regionlets object detection framework. It achieved 46.1% mean average precision on the PASCAL VOC 2007 dataset, and 44.1% on the PASCAL VOC 2010 dataset, which dramatically improves the original Regionlets approach without DNPs. It is the first approach efficiently applying dee p convolutional features for conventional object detection models.
Hairdressers' preparedness to be informal helpers for their clients

Hairdressers have the potential to be gatekeepers for their clients. They are placed in the role of informal helpers because clients often raise personal and emotional problems with them during appointments. In the current study, 136 hairdressers completed self-report surveys about the types of problems raised by clients, hairdresser responses, ability to select appropriate helping responses to disclosures, interest in training, and preparedness to help. Of the problem types discussed, 43% comprised moderate to severe problems. Greater years of hairdressing experience were related to a better ability to identify appropriate responses. The multiple regression model found that 44.8% of the variance in hairdresser preparedness was predicted by whether substance use and violence problems were raised, supportive responses, unhelpful responses, and supportive emotions. Results provide support for hairdressers as potential gatekeepers and identify areas of training that may lead to increased preparedness for this role.
Feature set extension for heart rate variability analysis by using non-linear, statistical and geometric measures

The goal of this paper is to evaluate the application of a combination of heart rate variability features on successful classification of known heart disorders. We propose an extension over our previous work, which employs 11 features, both from non-linear and linear analysis of heart rate variability. The features were extracted from electrocardiogram recordings and analyzed in Weka system for data mining using several well-known classification algorithms: C4.5 decision tree, Bayesian network, random forest, and RIPPER rules. Significance of each feature is analyzed and the algorithms' success rates are compared. The selected combination of features has a high classification potential.
Genetic characterization of Bhutanese native chickens based on an analysis of Red Junglefowl (Gallus gallus gallus and Gallus gallus spadecieus), domestic Southeast Asian and commercial chicken lines (Gallus gallus domesticus)

The genetic diversity of Bhutanese chickens needs to be understood in order to develop a suitable conservation strategy for these birds in Bhutan. In this, work, we used microsatellite markers to examine the genetic diversity of Bhutanese chickens. Four Bhutanese chicken varieties (Black plumage, Frizzle, Naked neck and Red Junglefowl-like, corresponding to Yuebjha Narp, Phulom, Khuilay and Seim, respectively), two subspecies of Red Junglefowl (Gallus gallus gallus and Gallus gallus spadecieus), two varieties of Thai native chickens (Pradhu Hang Dam and Chee; Gallus gallus domesticus) representing the Southeast Asian domestic chicken, and two commercial lines (Broiler and Single Comb White Leghorn) were genotyped with 18 microsatellites that included 16 loci recommended by the FAO/ISAG for investigations of genetic variability in chickens. All loci were polymorphic, with the number of alleles ranging from six (MCW0111) to 23 (MCW0183). Substantial genetic variation was observed in all populations, with the Bhutanese native chicken Yuebjha Narp (Black plumage chicken) showing the lowest genetic variability. Despite extensive intrapopulation variation, the genetic differentiation among 10 populations was moderate. A neighbor-joining tree revealed the genetic relationships involved while principal component analysis showed that Bhutanese native chickens should be given priority in conservation efforts because of their genetic distinctiveness. Chee chickens are especially valuable as a reservoir of predomestic diversity, as indicated by their greater genetic variation and their position in the phylogenetic tree.
Circadian rhythms in subjective activation, mood, and performance efficiency

Circadian rhythms in subjective alertness, mood, and performance can be classified as psychological rhythm, compared with physiological rhythm such as body temperature and hormonal change. While in normal condition entrained by 24hr zeitgeber, subjective alertness would reach its maximum value around midday, subjective alertness would parallel body temperature rhythm with its peak at evening in non-entrained, free-running state. With desynchronization technique, subjective alertness rhythm is thought to be controlled by both temperature and sleep-wake rhythm oscillator. Circadian performance rhythms depend on the kind of task tested. It shows parallelism with body temperature rhythm when subjects are tested with simple, repetitive task. But when tested with tasks requiring complex verbal reasoning or immediate memory, subjects would perform them best at early morning, with performance decreasing as time of day advances. The desynchronization technique shows that circadian performance rhythm of simple, repetitive task is dependent on temperature oscillator but circadian performance rhythm of complex verbal reasoning is influenced by both temperature and sleep-wake rhythm oscillator or another independent oscillator. It would be worthwhile to compare psychological rhythm with hormonal change such as cortisol and melatonin. And more simple and time-saving method than desynchronization technique may facilitate the study of the mechanism underlying psychological rhythm.
Effect of Carrot Intake in the Prevention of Gastric Cancer: A Meta-Analysis

Purpose Gastric cancer is the third leading cause of cancer-related mortality, with the incidence and mortality being higher in men than in women. Various studies have shown that eating carrots may play a major role in the prevention of gastric cancer. We conducted a meta-analysis to determine the relationship between carrot consumption and gastric cancer. Materials and Methods We searched multiple databases including PubMed, Cochrane Library, Scopus, ScienceDirect, and Persian databases like Scientific Information Database (SID) and IranMedx. The following search terms were used: stomach or gastric, neoplasm or cancer, carcinoma or tumor, and carrot. Statistical analyses were performed using Comprehensive Meta Analysis/2.0 software. Results We retrieved 81 articles by searching the databases. After considering the inclusion and exclusion criteria, 5 articles were included in this study. The odds ratio (OR) obtained by fixed effects model showed that a 26% reduction in the risk of gastric cancer has been associated with the consumption of carrots) OR=0.74; 95% confidence interval=0.68~0.81; P<0.0001). According to funnel graph, the results showed that the possibility of a publication bias does not exist in this study. Conclusions The findings of this study showed an inverse relationship between the consumption of carrots and the risk of gastric cancer.
Double scaling limits and twisted non-critical superstrings

We consider double-scaling limits of multicut solutions of certain one matrix models that are related to Calabi-Yau singularities of type A and the respective topological B model via the Dijkgraaf-Vafa correspondence. These double-scaling limits naturally lead to a bosonic string with c ≤ 1. We argue that this non-critical string is given by the topologically twisted non-critical superstring background which provides the dual description of the double-scaled little string theory at the Calabi-Yau singularity. The algorithms developed recently to solve a generic multicut matrix model by means of the loop equations allow to show that the scaling of the higher genus terms in the matrix model free energy matches the expected behaviour in the topological B-model. This result applies to a generic matrix model singularity and the relative double-scaling limit. We use these techniques to explicitly evaluate the free energy at genus one and genus two.
Warped AdS3 black holes in new massive gravity

We investigate stationary, rotationally symmetric solutions of a recently proposed three-dimensional theory of massive gravity. Along with BTZ black holes, we also obtain warped AdS3 black holes, and (for a critical value of the cosmological constant) AdS2 × S1 as solutions. The entropy, mass and angular momentum of these black holes are computed.
Prediction of Protein Domain with mRMR Feature Selection and Analysis

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28–40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine.
The Effects of Some Demografical Variables on Secondary School Students’ Images About Scientist / Bazı Demografik Özelliklerin Ortaokul Öğrencilerin Bilim İnsanı İmgeleri Üzerine Etkisi

The purpose of this study is to examine scientist images of secondary school students in terms of some demographic variables. The application was conducted in the spring semester of 2013-2014 academic year. 877 (sixth grade=420, seventh grade=457; male=438, female=439) secondary school students who study at four different secondary schools located in Duzce participated in this study. Draw A Scientist Test (DAST) was applied to determine the scientist images of students. After students completed their drawings, they were asked to explain their drawings about scientist. The data obtained from DAST were coded by using code list developed by Chambers (1983). The stereotypical levels of scientist images of students were determined by counting stereotype indicators in students’ drawings. Analyses were conducted in SPSS 18.0 packet program. One way ANOVA and independent t-test were used to test hypothesis. As a result, it is found that the students having parents with high in-come occupation have higher stereotypical scientist images than their counterparts having parents with low in-come occupation. Likewise it is found that the students having a father with high education level have higher stereotypical scientist images than their counterparts having a father with low education level. On the other hand it is found that there is not significant effect of mothers’ education levels on scientist images of students. So it can be asserted that mother and father’s occupation (in-come level) is the most important factor affecting the scientist images of students, the effects of mother and father education level vary depending on whether justice of in-come is correlated with individuals’ education level.
The effects of software processes on meeting targets and quality

Firms developing software face increasing pressures to improve product quality, productivity, time to market, and customer satisfaction. As projects increase in size and complexity, and competition grows, firms are reengineering their software processes. They are adopting more intensive procedures for requirements management, project planning, defect tracking, configuration management, design and code inspections, and so forth. To explore the potential effectiveness of these efforts, we conducted a survey of senior practitioners at the 1993 Software Engineering Process Group National Meeting. The survey asked participants about the processes followed on, and the outcome of a specific software project. Certain practices, notably project planning and cross-functional teams, were consistently associated with favorable outcomes. Based on the survey results, other practices may have little impact on project outcomes. <<ETX>>
Action Video Game Training for Healthy Adults: A Meta-Analytic Study

Action video game (AVG) has attracted increasing attention from both the public and from researchers. More and more studies found video game training improved a variety of cognitive functions. However, it remains controversial whether healthy adults can benefit from AVG training, and whether young and older adults benefit similarly from AVG training. In the present study, we aimed to quantitatively assess the AVG training effect on the cognitive ability of adults and to compare the training effects on young and older adults by conducting a meta-analysis on previous findings. We systematically searched video game training studies published between January 1986 and July 2015. Twenty studies were included in the present meta-analysis, for a total of 313 participants included in the training group and 323 participants in the control group. The results demonstrate that healthy adults achieve moderate benefit from AVG training in overall cognitive ability and moderate to small benefit in specific cognitive domains. In contrast, young adults gain more benefits from AVG training than older adults in both overall cognition and specific cognitive domains. Age, education, and some methodological factors, such as the session duration, session number, total training duration, and control group type, modulated the training effects. These meta-analytic findings provide evidence that AVG training may serve as an efficient way to improve the cognitive performance of healthy adults. We also discussed several directions for future AVG training studies.
Diffraction with CDF II at the Tevatron

Results on diffraction from the Fermilab Tevatron collider obtained by the CDF II Collaboration using data from pp collisions at s = 1.96 TeV are reviewed and compared with theoretical expectations. Implications for predictions of exclusive Higgs boson production rates at the Large Hadron Collider are discussed.
Maximum‐likelihood estimation for multivariate spatial linear coregionalization models

A multivariate spatial linear coregionalization model is considered that incorporates the Matérn class of covariograms. An EM algorithm is developed for maximum‐likelihood estimation that has a few desirable properties and is capable of handling high‐dimensional data. Most estimates in the EM algorithm are updated through closed form expressions and these estimates automatically satisfy necessary constraints. The model and algorithm are illustrated through a real example. Copyright © 2006 John Wiley & Sons, Ltd.