Miracle-dz/UniFAR-FTUs huggingface dataset:
Socio-Affective Agents as Models of Human Behaviour in the Networked Prisoner's Dilemma

Affect Control Theory (ACT) is a powerful and general sociological model of human affective interaction. ACT provides an empirically derived mathematical model of culturally shared sentiments as heuristic guides for human decision making. BayesACT, a variant on classical ACT, combines affective reasoning with cognitive (denotative or logical) reasoning as is traditionally found in AI. Bayes\-ACT allows for the creation of agents that are both emotionally guided and goal-directed. In this work, we simulate BayesACT agents in the Iterated Networked Prisoner's Dilemma (INPD), and we show four out of five known properties of human play in INPD are replicated by these socio-affective agents. In particular, we show how the observed human behaviours of network structure invariance, anti-correlation of cooperation and reward, and player type stratification are all clearly emergent properties of the networked BayesACT agents. We further show that decision hyteresis (Moody Conditional Cooperation) is replicated by BayesACT agents in over $2/3$ of the cases we have considered. In contrast, previously used imitation-based agents are only able to replicate one of the five properties. We discuss the implications of these findings in the development of human-agent societies.
Massive gluons and quarks and the equation of state obtained from SU(3) lattice QCD

We analyze recent results of SU(3) lattice QCD calculations with a phenomenological parametrization for the quark-gluon plasma equation of state based on a quasiparticle picture with massive quarks and gluons. At high temperature we obtain a good fit to the lattice data using perturbative thermal quark and gluon masses from an improved HTL scheme. At temperatures close to the confinement phase transition the fitted masses increase above the perturbative value, and a nonzero (but small) bag constant is required to fit the lattice data.
A generic cycling hypoxia-derived prognostic gene signature: application to breast cancer profiling

Background Temporal and local fluctuations in O2 in tumors require adaptive mechanisms to support cancer cell survival and proliferation. The transcriptome associated with cycling hypoxia (CycHyp) could thus represent a prognostic biomarker of cancer progression. Methods We exposed 20 tumor cell lines to repeated periods of hypoxia/reoxygenation to determine a transcriptomic CycHyp signature and used clinical data sets from 2,150 breast cancer patients to estimate a prognostic Cox proportional hazard model to assess its prognostic performance. Results The CycHyp prognostic potential was validated in patients independently of the receptor status of the tumors. The discriminating capacity of the CycHyp signature was further increased in the ER+ HER2- patient populations including those with a node negative status under treatment (HR=3.16) or not (HR=5.54). The CycHyp prognostic signature outperformed a signature derived from continuous hypoxia and major prognostic metagenes (P<0.001). The CycHyp signature could also identify ER+HER2 node-negative breast cancer patients at high risk based on clinicopathologic criteria but who could have been spared from chemotherapy and inversely those patients classified at low risk based but who presented a negative outcome. Conclusions The CycHyp signature is prognostic of breast cancer and offers a unique decision making tool to complement anatomopathologic evaluation.
SPOKEN DOCUMENT RETRIEVAL AND SUMMARIZATION

National Taiwan University, TaipeiE-mail: berlin@csie.ntnu. edu.tw, whm@iis.sinica. edu.tw, lslee@gate.sinica. edu.twHuge, continually increasing quantities of multimedia content including speechinformation are ﬁlling up our computers, networks and lives. It is obvious thatspeech is one of the most important sources of information for multimedia con-tent, as it is the speech of the content that tells us of the subjects, topics andconcepts. As a result, the associated spoken documents of the multimedia con-tent will be key for content retrieval and browsing. Substantial eﬀorts along withvery encouraging results for spoken document transcription, retrieval, and sum-marization have been reported. This chapter presents a concise yet comprehensiveoverview of information retrieval and automatic summarization technologies thathave been developed in recent years for eﬃcient spoken document retrieval andbrowsing applications. An example prototype system for voice retrieval of Chinesebroadcast news collected in Taiwan will be introduced as well.
Learning spatial prior with automatically labeled landmarks

We propose a method of automatically labeling landmarks on target images, which are used for training a constellation model to recognize general object class. First, we randomly sample local features (parts) and generate hierarchical representations of images in a similar way to the ¿standard model¿ of visual cortex. Second, we pick out a unique location of each part among those local maxima in S2 layers by a matching procedure. Third, we model the spatial relations among parts as a sparse GMRF (Gaussian Markov random fields) graph, and learn the links by a lasso-based approach. Object localization in new images proceeds by maximizing the posterior of an object observed at a particular configuration. Our model is a thoroughly automatic scheme to perform ¿feature binding¿. Experimental results on the CalTech101 database demonstrate that the proposed algorithm locates the components more precisely and outperforms the ¿standard model¿ in object detection.
Impact and Process Evaluation of Integrated Community and Clinic-Based HIV-1 Control: A Cluster-Randomised Trial in Eastern Zimbabwe

Background HIV-1 control in sub-Saharan Africa requires cost-effective and sustainable programmes that promote behaviour change and reduce cofactor sexually transmitted infections (STIs) at the population and individual levels. Methods and Findings We measured the feasibility of community-based peer education, free condom distribution, income-generating projects, and clinic-based STI treatment and counselling services and evaluated their impact on the incidence of HIV-1 measured over a 3-y period in a cluster-randomised controlled trial in eastern Zimbabwe. Analysis of primary outcomes was on an intention-to-treat basis. The income-generating projects proved impossible to implement in the prevailing economic climate. Despite greater programme activity and knowledge in the intervention communities, the incidence rate ratio of HIV-1 was 1.27 (95% confidence interval [CI] 0.92–1.75) compared to the control communities. No evidence was found for reduced incidence of self-reported STI symptoms or high-risk sexual behaviour in the intervention communities. Males who attended programme meetings had lower HIV-1 incidence (incidence rate ratio 0.48, 95% CI 0.24–0.98), and fewer men who attended programme meetings reported unprotected sex with casual partners (odds ratio 0.45, 95% CI 0.28–0.75). More male STI patients in the intervention communities reported cessation of symptoms (odds ratio 2.49, 95% CI 1.21–5.12). Conclusions Integrated peer education, condom distribution, and syndromic STI management did not reduce population-level HIV-1 incidence in a declining epidemic, despite reducing HIV-1 incidence in the immediate male target group. Our results highlight the need to assess the community-level impact of interventions that are effective amongst targeted population sub-groups.
A Strategy to Detect the Moving Vehicle Shadows Based on Gray-Scale Information

In machine vision and the vehicle recognition system¿ removal of moving vehicle shadows is a significant topic. In this paper, we propose a novel method to detect shadows in traffic video sequences. Firstly, a set of moving regions are segmented from the video sequence using a background subtraction technique. Secondly, the fast normalized cross-correlation (FNCC) is adopted to detect shadows in moving regions from grayscale video sequences. By utilizing three sum-table schemes, the FNCC algorithm dramatically reduces the computational complexity compared to the traditional normalized cross correlation (NCC) algorithm. And our experimental results demonstrate that the proposed shadows removal method is accurate and efficient.
A novel method of FECG extraction combined self-correlation analysis with ICA

The extraction of fetal electrocardiogram (FECG) signal has an important value in clinical application. The FECG is extracted from the maternal abdominal signal which is collected by several electrodes placed on the maternal abdomen. The traditional independent component analysis (ICA) model does not consider the temporal correlation in the process of separating maternal abdomenal signal. In this paper, a new method for extracting FECG is proposed, which combines self-correlation analysis with independent component analysis. Firstly, the self-correlation analysis is used for intercepting signals, which can decrease the temporal correlation. Then FastICA is applied to obtain the model parameters of ICA model. Finally, bring the mixed-signal into this model to extract the fetal ECG. The experiments are conducted by clinical signals. The results indicate that the method proposed in this paper could extract FECG well. This method is superior to traditional FastICA.
Eﬃcient and Secure Identity-Based Signatures and Signcryption from Bilinear Maps

In this work we describe new identity-based signature and combined signature/encryption (signcryption) schemes built upon bilinear maps. These schemes turn out to be more eﬃcient than all others proposed so far. We prove their security in a proper security model under recently studied computational assumptions and in the random oracle model. We then give implementation results to illustrate the signiﬁcance of our improvements w.r. t. previous proposals.
Bacterial Concrete: New Era For Construction Industry

Micro-cracks are the main cause to structural failure. One way to circumvent costly manual maintenance and repair is to incorporate an autonomous self -healing mechanism in concrete. One such an alternative repair mechanism is currently being studied, i.e. a novel technique based on the application of bio- mineralization of bacteria in concrete. The applicability of specifically calcite mineral precipitating bacteria for concrete repair and plugging of pores and cracks in concrete has been recently investigated and studies on the possibility of using specific bacteria as a sustainable and concrete -embedded self-healing agent was studied and results from ongoing studies are discussed. Synthetic polymers such as epoxy treatment etc. are currently being used for repair of concrete are harmful to the environment, hence the use of a biological repair technique in concrete is focused. Recently, it is found that microbial mineral precipitation resulting from metabolic activities of favourable microorganisms in concrete improved the overall behaviour of concrete. Hence in this paper define the bacterial concrete, its classification and types of bacteria, chemical process to fix the crack by bacteria, advantages and dis-advantages and possibilities of application of MICP (Microorganism used for Calcium Carbonate Precipitation in Concrete) in construction area by literature review are discussed
Anger Management - Evaluation of a Cognitive-Behavioral Training Program for Table Tennis Players

Abstract Based on a systematic review of the literature on anger and anger management in sport, there is evidence that anger might be dysfunctional, especially in sports requiring selective attention and fine-tuned motor skills. The research literature suggests that cognitive-behavioral intervention programs can be fruitful in helping athletes to understand and control dysfunctional anger. Therefore, the main goal of the present study was to evaluate the effectiveness of a brief training program for table tennis players in cognitive-behavioral anger management that aimed at changing their noneffective anger reactions. The sample comprised 18 young competitive table tennis players (age range from 16 to 22 years) divided randomly into a treatment (n = 10) and a control group (n = 8). A trained group leader instructed the treatment group. Six sessions were held over a period of two months. Cognitive-relaxation coping skills associated with social skills of subjects from the treatment group were compared to no-treatment controls. Psychological measurements (i.e., self-reports on anger) were applied before, during and after treatment as well as in a follow-up session. The one-year follow-up session revealed that, in contrast to the control group, the treatment group showed a significant reduction in outwardly negative anger expression as well as anger reactions specific to table tennis. Despite limitations inherent in the research design, the training program was deemed effective.
Community Detection in Complex Network via BGLL Algorithm

A large number of networks in nature, society and technology are defined by a mesoscopic level of organization, in which groups of nodes form tightly connected units, called communities, that are sparsely inter-linked to each other . Identifying this community structure is one of the most important problems in understanding of functions and structures of real world complex systems, which is still a challenging task. Various methods proposed so far are not efficient and accurate for large networks which comprise of millions of nodes because of their high computational cost. In this manuscript we will provide the implementation and behavioral analysis of BGLL algorithm for determining the structure of complex networks. This method is a variant of hierarchical agglomerative clustering approach, which finds the communities which are nested within one another. This method emphasizes on the idea of building the communities by combining the initial partition into super networks by repeatedly optimizing the modularity. In this work we will implement the BGLL Algorithm on various large networks which exhibit the community structure . We will also determine the optimal modularity at every pass and determine the hierarchical structure of large complex systems that comprise of millions of nodes. We will also provide a brief comparison of BGLL algorithm with some methods. General Terms Graph Theory, Data Mining.
Towards a theory of software protection and simulation by oblivious RAMs

Software protection is one of the most important issues concerning computer practice. There exist many heuristics and ad-hoc methods for protection, but the problem as a whole has not received the theoretical treatment it deserves. In this paper, we make the first steps towards a theoretic treatment of software protection: First, we distill and formulate the key problem of learning about a program from its execution. Second, assuming the existence of one-way permutations, we present an efficient way of executing programs such that it is infeasible to learn anything about the program by monitoring its executions. How can one efficiently execute programs without allowing an adversary, monitoring the execution, to learn anything about the program? Traditional cryptographic techniques can be applied to keep the contents of the memory unknown throughout the execution, but are not applicable to the problem of hiding the access pattern. The problem of hiding the access pattern efficiently corresponds to efficient simulation of Random Access Machines (RAM) on an oblivious RAM. We define an oblivious RAM to be a (probabilistic) RAM for which (the distribution of) the memory access pattern is independent of the input. We present an (on-line) simulation of t steps of an arbitrary RAM with m memory cells, by less than t·m&egr; steps of an oblivious RAM with 2m memory cells, where &egr;>0 is an arbitrary constant.
Asymmetric quadratic landscape approximation model

This work presents an asymmetric quadratic approximation model and an $\epsilon$-archiving algorithm. The model allows to construct, under local convexity assumptions, descriptors for local optima points in continuous functions. A descriptor can be used to extract confidence radius information. The $\epsilon$-archiving algorithm is designed to maintain and update a set of such asymmetric descriptors, spaced at some given threshold distance. An in-depth analysis is conducted on the stability and performance of the asymmetric model, comparing the results with the ones obtained by a quadratic polynomial approximation. A series of different applications are possible in areas such as dynamic and robust optimization.
A Resource Framework for Quantum Shannon Theory

Quantum Shannon theory is loosely defined as a collection of coding theorems, such as classical and quantum source compression, noisy channel coding theorems, entanglement distillation, etc., which characterize asymptotic properties of quantum and classical channels and states. In this paper, we advocate a unified approach to an important class of problems in quantum Shannon theory, consisting of those that are bipartite, unidirectional, and memoryless.
The Brief Scale of Resilience in Disabled and Veteran Athletes; Psychometric Properties of the Persian Version

Aims Resilience is a person’s positive adaptation capability responding life problematic situations. And due its importance, making proper instruments to measure it is noticed by the researchers. The aim of this study was to investigate the psychometric properties of a Persian version of brief resilience scale in veteran athletes and athletes with physical disabilities. Instrument & Methods In this study, 168 athletes including veterans and persons with physical disabilities (100 male and 68 female athletes) were studied in Tehran in 2015. The subjects, randomly selected in different skill levels and sports, were with injuries in one of the body joints (waist, thighs, knees, etc.). A Persian version of brief Connor-Davidson resilience scale was studied. To investigate structure validity of Persian version of brief resilience scale, SEM-based confirmatory factor analysis was used. Cronbach’s alpha and intra-class correlation coefficient were used to determine internal consistency and temporal reliability, respectively. Findings The fitness indices showed structural validity and favorable fitness of Persian version of brief resilience scale. In addition, there were acceptable internal consistency (alpha=0.76) and temporal reliability (intra-class correlation coefficient=0.76). Conclusion Validity and reliability of Persian version of brief resilience scale are acceptable in the veteran athletes and athletes with physical disabilities. A B S T R A C T A R T I C L E I N F O
A Neurobiologically Plausible Vector Symbolic Architecture

Vector Symbolic Architectures (VSA) are approaches to representing symbols and structured combinations of symbols as high-dimensional vectors. They have applications in machine learning and for understanding information processing in neurobiology. VSAs are typically described in an abstract mathematical form in terms of vectors and operations on vectors. In this work, we show that a machine learning approach known as hierarchical temporal memory, which is based on the anatomy and function of mammalian neocortex, is inherently capable of supporting important VSA functionality. This follows because the approach learns sequences of semantics-preserving sparse distributed representations.
Nonlinear Estimation of Lifetime Intergenerational Economic Mobility and the Role of Education

Previous studies of intergenerational income mobility have typically focused at on estimating persistence across generations at the mean of the distribution of sons' earnings. Here, we use the relatively new unconditional quantile regression (UQR) technique to consider how the association between parental income in childhood and sons' adult earnings vary across the distribution of sons' earnings. We find a J-shaped relationship between parental income and sons' earnings, with parental income a particularly strong predictor of labour market success for those at the bottom, and to a greater extent, the top of the earnings distribution. We explore the potential role of early skills, education and early labour market attachment in this process. Worryingly, we find that education is not as meritocratic as we might hope, with the role of parental income dominating that of education at the top of the distribution of earnings. Early unemployment experience has long-lasting impacts on sorting those at the bottom, alongside parental income.
AAV-mediated gene transfer of human pigment epithelium-derived factor inhibits Lewis lung carcinoma growth in mice

Pigment epithelium-derived factor (PEDF) is the most potent inhibitor of angiogenesis in the mammalian eye, and mechanisms through which PEDF exerts its antitumour activity have recently been defined. The aim of our research was to evaluate the ability of adeno-associated virus (AAV) vector-mediated transfer of human PEDF to inhibit Lewis lung carcinoma (LCC) cell growth. Intratumoural injection of AAV-PEDF caused significant reduction of the tumour volume and prolonged the survival time of mice bearing LLC cells, which were associated with decreased microvessel density and increased apoptosis in the tumours. AAV vectors represent a very promising tool for cancer gene therapy. No noticeable toxicity concerning AAV was detected as inferred from monitoring changes in animal body weight as well as basic organ structure and histological morphology, and by analyzing mouse liver and kidney function. Our findings indicate that AAV-mediated PEDF gene expression may offer an active approach to inhibit LLC growth and that treatment with AAV-PEDF may provide a promising therapeutic strategy in lung cancer treatment.
A connected path approach for staff detection on a music score

The preservation of many music works produced in the past entails their digitalization and consequent accessibility in an easy-to-manage digital format. Carrying this task manually is very time consuming and error prone. While optical music recognition systems usually perform well on printed scores, the processing of handwritten musical scores by computers remain far from ideal. One of the fundamental stages to carry out this task is the staff line detection. In this paper a new method for the automatic detection of music staff lines based on a connected path approach is presented. Lines affected by curvature, discontinuities, and inclination are robustly detected. Experimental results show that the proposed technique consistently outperforms well-established algorithms.
Analysis of possible anomalies in the QSO distribution of the Flesch & Hardcastle catalogue

Aims. A recent catalogue by Flesch & Hardcastle presents two major anomalies in the spatial distribution of QSO candidates: $i)$ an apparent excess of such objects near bright galaxies, and $ii)$ an excess of very bright QSO candidates compared to random background expectations in several regions of the sky. Because anyone of these anomalies would be relevant in a cosmological context, we carried out an extensive analysis of the probabilities quoted in that catalogue. Methods. We determine the nature and redshift of a subsample of 30 sources in that catalogue by analysing their optical spectra (another 11 candidates were identified from existing public databases). These have allowed us to statistically check the reliability of the probabilities QSO status quoted by Flesch & Hardcastle for their candidates. Results. Only 12 of the 41 candidates turned out QSOs (7 of which have been identified here for the first time). Conclusions. The probabilities of the QSOs' being the candidates given by Flesch & Hardcastle are overestimated for m B ≤ 17 and for objects projected near ( ≤ 1 arcmin) bright galaxies. This is the cause of the anomalies mentioned above.