Miracle-dz/UniFAR-FTUs huggingface dataset:
A Bayesian analysis of weighted stochastic block models with applications in brain functional connectomics

CHRISTOPHER BRYANT: A Bayesian Analysis of Weighted Stochastic Block Models with Applications in Brain Functional Connectomics (Under the direction of Joseph Ibrahim and Hongtu Zhu) The network paradigm has become a popular approach for modeling complex systems, with applications ranging from social sciences to genetics to neuroscience and beyond. Often the individual connections between network nodes are of less interest than network characteristics such as its community structure the tendency in many real-data networks for nodes to be naturally organized in groups with dense connections between nodes in the same (unobserved) group but sparse connections between nodes in different groups. Characterizing the structure of networks is of particular interest in the study of brain function, especially in the context of diseases and disorders such as Alzheimer’s disease and attention deficit hyperactivity disorder (ADHD), where disruption of functional brain networks has been observed. The stochastic block model (SBM) is a probabilistic formulation of the community detection problem that has been utilized to estimate latent communities in both binary and weighted networks, but as of yet not in brain networks. We build a flexible Bayesian hierarchical framework for the SBM to capture the community structure in weighted graphs, with a focus on the application in functional brain networks. First, we fit a version of the SBM to Gaussian-weighted networks via an efficient Gibbs sampling algorithm. We compare results from simulated networks to several existing estimation methods and then apply our approach to estimate the community structures in the functional resting brain networks of 185 subjects from the ADHD-200 sample. iii Next, we extend this probabilistic framework and our efficient estimation algorithm to capture the shared latent structure in groups of networks; we perform simulation studies and then apply this extended model to the same sample of brain networks from the ADHD-200 sample. Finally, we adapt this model to allow for more complex latent structures and incorporate a regression component to test for differences in the latent functional brain structure between study groups. After examining the ability of this approach to capture the latent structures in simulated networks, we apply this method once again to the same set of functional brain networks to assess the differences between ADHD subtypes and healthy control subjects in latent functional brain structure.
Analysis of health care data using different data mining techniques

Data mining is an interesting field of research whose major objective is to acquire knowledge from large amounts of data. With advances in heath care related research, there is a wealth of data available. However, there is a lack of effective analytical tools to discover hidden and meaningful patterns and trends in data, which is essential for any research.
Relationships between work–family dynamics and parenting experiences: a dyadic analysis of dual-earner couples

ABSTRACT Research on the work–family interface has not consistently addressed how the positive and negative features of work–family dynamics relate to the experience of parenting, and still fewer studies have explored these interrelationships using dyads. This investigation addressed this gap by examining the extent to which mothers’ and fathers’ reports of work–family conflict and enrichment inﬂuenced their own and their spouse’s parenting experiences. Each member of 346 dual-earner couples in Portugal completed measures of work–family conflict (WFC) and work–family enrichment (WFE), along with measures of parenting attitudes and competencies. Dyadic analysis revealed that WFC and WFE affected the quality of parenting attitudes and competencies differently in men and women. Also, a differential pattern of crossover effects between partners was found, particularly with respect to the effect of mothers’ work–family dynamics on fathers’ parenting experience. For women, while only one significant partner effect was observed (from fathers’ WFC to mothers’ increased relational frustration), four partner effects were found in men’s parenting experience (from mothers’ WFC to fathers’ increased relational frustration, reduced sense of competence and reduced attachment; and from mothers’ WFE to fathers’ increased involvement). Further implications of these findings for advancing understanding of the impact of work–family dynamics on intrafamily relationships are discussed.
Online non-negative dictionary learning via moment information for sparse Poisson coding

Online dictionary learning for sparse coding is an effective tool for data analysis. It incrementally learns a set of basis vectors with sparse linear combinations of these vectors when new samples appear. Previous work assumes that the samples embed Gaussian noises, which weaken the power of these methods in handling real applications with non-negative data (e.g., frequency data in word counts). Differently, in this paper, we concentrate on online learning for non-negative dictionary by using moment information for sparse Poisson coding. We exploit the non-negativity of Poisson models to learn a set of non-negative basis vectors and a non-negative sparse linear combination for the moment information of samples. Specifically, we first formulate the online learning problem via the maximum-a-posteriori (MAP) framework. We then propose a novel online algorithm which alternatively updates the sparse-coefficient vector and the basis vectors with non-negativity constraints when a new sample arrives. More importantly, we present sufficient convergence analyses to guarantee the performance of the proposed algorithm, which leads to convergence of a stable dictionary for characterizing the moment information of samples. We finally conduct a series of experiments on word-counts data and image data to show merits of the proposed online algorithm.
The Bayesian Fallacy: Distinguishing Four Kinds of Beliefs

This paper distinguishes among four kinds of beliefs: conviction, confidence, perception, conception. Conviction concerns self-ability:“I can build these stairs. ” Confidence also concerns the self—ut focuses on the assertion of will in the face of weakness of will. Perception is about the environment such as weather prediction. Conception is also about the environment—but usually couched with context. While convictions are noncognitive and nonevidential beliefs, the other beliefs are either cognitive, evidential, or both. This paper uses the terms “cognition” and “evidentiality” as axes to distinguish the four beliefs. While “cognitive beliefs” are about one’s environment, “noncognitive beliefs” are about one’s self. While the cognitive/noncognitive divide is unconventional, it generates a payoff in light of the evidentiality axis. While “evidential beliefs” are correctable via Bayes’s rule, “nonevidential beliefs” are not. However, when the nonevidential belief is about the environment, the evidence can at least make the belief more (or less) warranted—where “warrantability” is a weaker criterion than “correctability. ” And when the nonevidential belief is about the self, i.e., a conviction, the evidence cannot even make the belief more (or less) warranted. The evidence itself develops when one tries to test a conviction. This paper highlights that convictions are the basis of tenacity—crucial for entrepreneurship and economic growth. This paper further demonstrates how three major theories of action—standard rationality, normative theory, and procedural rationality—fail to distinguish the four kinds of beliefs. They, hence, commit, although in different ways, a set of confusions called here the “Bayesian fallacy. ”
Network performance QoS estimation

This paper deals with network performance simulation and quality of Triple-play services in IP networks. Based on our designed analytical model, the application has been created by means of which it is possible to predict qualitative parameters for QoS in IP networks, with regard to policies applied for packet processing by routers and with regard to the level of total network utilization. The contribution of this paper lies in designing a new model capable of predicting the quality of Triple-play services respecting queuing policy and transmission speed of interfaces on routers in packet networks.
Implementation of Privacy-preserving SimRank over Distributed Information Network

Information network analysis has drawn a lot attention in recent years. Among all the aspects of network analysis, similarity measure of nodes has been shown useful in many applications, such as clustering, link prediction and community identification, to name a few. As linkage data in a large network is inherently sparse, it is noted that collecting more data can improve the quality of similarity measure. This gives different parties a motivation to cooperate. In this paper, we address the problem of link-based similarity measure of nodes in an information network distributed over different parties. Concerning the data privacy, we propose a privacy-preserving SimRank protocol based on fully-homomorphic encryption to provide cryptographic protection for the links.
MORAL EMOTIONS AND SELF-MONITORING

This study offers a set of relationships between self-monitoring and two moral emotions: shame and guilt. After creating various combinations of relationships between the behavioral manifestations of shame and guilt and the sub-dimensions of self-monitoring, these relationships are tested with data obtained from lower level managers in a public bank in Turkey. Results show that there are positive relationships between these constructs. The implications of these findings for understanding moral emotions and self-monitoring were also considered, along with some potential applications.
Social dimensions of ICT diffusion in rural communities

In Lithuania, as in the most societies around the w orld, the Internet and other information communication technologies (ICT) are mainly used by young, educated, well paid and urban consumers. Elderly, low-educated, low-paid and rural residents are among those who use the Internet the least. In our post-modern network society they are at the risk of social exclusion. This paper is aimed at the analysis of ICT diffusio n in rural communities of Lithuania, exploring the main social patterns of diffusion and characteristics of rural Internet users. The study is based on focus group discussions and questionnaire-based survey of Lithu anian rural residents. The paper discusses types of change agents involved in the processes of ICT diff usion in rural communities and the main motives for using the Internet. It also explores the impact of ICT on ways of private communication and communication with relevant public authorities, dis cu ses both positive and negative attitudes to ICT use in everyday life activities.
An Orientation Model for Hierarchical Phrase-Based Translation

The hierarchical phrase-based (HPB) translation exploits the power of grammar to perform long distance reorderings, without specifying nonterminal orientations against adjacent blocks or considering the lexical information covered by nonterminals. In this paper, we borrow from phrase-based system the idea of orientation model to enhance the reordering ability of HPB translation. We distinguish three orientations (monotone, swap, discontinuous) of a nonterminal based on the alignment of grammar, and select the appropriate orientation of nonterminal using lexical information covered by it. By incorporating the orientation model, our approach significantly outperforms a standard HPB system up to 1.02 Bleu on large scale NIST Chinese-English translation task, and 0.51 Bleu on WMT German-English translation task.
Nonlinear image registration with bidirectional metric and reciprocal regularization

Nonlinear registration is an important technique to align two different images and widely applied in medical image analysis. In this paper, we develop a novel nonlinear registration framework based on the diffeomorphic demons, where a reciprocal regularizer is introduced to assume that the deformation between two images is an exact diffeomorphism. In detail, first, we adopt a bidirectional metric to improve the symmetry of the energy functional, whose variables are two reciprocal deformations. Secondly, we slack these two deformations into two independent variables and introduce a reciprocal regularizer to assure the deformations being the exact diffeomorphism. Then, we utilize an alternating iterative strategy to decouple the model into two minimizing subproblems, where a new closed form for the approximate velocity of deformation is calculated. Finally, we compare our proposed algorithm on two data sets of real brain MR images with two relative and conventional methods. The results validate that our proposed method improves accuracy and robustness of registration, as well as the gained bidirectional deformations are actually reciprocal.
Investigating sources of pre-service teachers' self-efficacy for preparing and implementing mathematical tasks

The goal of this study was to understand pre-service teachers' self-efficacy beliefs for preparing and implementing mathematical tasks and to explore which sources of self-efficacy they wighted and interpreted when judging their capabilities as a result of attending a mathematics teaching methods course designed with a focus on mathematics teaching through tasks. Nine pre-service teachers participated in this study and they were interviewed at the end of the methods course. Findings revealed that methods course provided opportunities for mostly vicarious experiences and physiological states sources, and participants were feeling highly efficacious to prepare mathematical tasks, but they had doubts about their capabilities to implement tasks effectively.
Livrable D6.1 of the PERSEE project : Perceptual Assessment : Definition of the scenarios

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Livrable D6.1 of the PERSEE project : Perceptual Assessment : Definition of the scenarios Junle Wang, Josselin Gautier, Emilie Bosc, Jing Li, Vincent Ricordel
Can a data center heat-flow model be scaled down?

Data centers require vast amounts of energy for keeping the servers cool at optimal operating temperatures. Recent research has focused on improving the cooling efficiency, and thereby lowering the energy consumption, through different rack arrangements and modifying the air-flow patterns. Thus far, this has been done using computational fluid dynamics (CFD) models as access to a real data centers is often restricted. The next step in this research is to build a physical model for testing purposes. The viability of building a scaled model of an actual data center is investigated using the scale modeling theory for airflow experiments. A full-scale prototype and a half-scale model are created using CFD software and simulated to see if similarity can be achieved in the scaled model for the temperature distribution as well as the airflow velocities. Our results show that the thermal similarity can be achieved within 5% error margin while the airflow similarity cannot be achieved with reasonable accuracy.
Maximum-likelihood expectation-maximization reconstruction of sinograms with arbitrary noise distribution using NEC-transformations

The maximum-likelihood (ML) expectation-maximization (EM) [ML-EM] algorithm is being widely used for image reconstruction in positron emission tomography. The algorithm is strictly valid if the data are Poisson distributed. However, it is also often applied to processed sinograms that do not meet this requirement. This may sometimes lead to suboptimal results: streak artifacts appear and the algorithm converges toward a lower likelihood value. As a remedy, the authors propose two simple pixel-by-pixel methods [noise equivalent counts (NEC)-scaling and NEC-shifting] in order to transform arbitrary sinogram noise into noise which is approximately Poisson distributed (the first and second moments of the distribution match those of the Poisson distribution). The convergence speed associated with both transformation methods is compared, and the NEC-scaling method is validated with both simulations and clinical data. These new methods extend the ML-EM algorithm to a general purpose nonnegative reconstruction algorithm.
Low power, high dynamic range, sample&hold circuit and analogue multiplexer for multi-channel recording of neuronal signals

In the paper we present the design and test results of an integrated circuit combining the sample&hold and the analogue multiplexer, which is foreseen to be implemented in a multi-channel Application Specific Integrated Circuit (ASIC) for recording signal from alive neuronal tissue. The design is optimised with respect to critical requirements for such circuits, i.e. short sampling time, low power dissipation, good linearity and high dynamic range. Presented design comprises sample&hold circuits with class AB operational amplifier, novel shift register, which allows minimising cross-coupling of the clock signal and control logic. The circuit has been designed in a 0.35 µm CMOS process and has been successfully implemented in a prototype multi-channel ASIC.
Corporate Diversification and the Cost of Capital

We examine whether organizational form matters for a firm's cost of capital. Contrary to conventional view, we argue that coinsurance among a firm's business units can reduce systematic risk through the avoidance of countercyclical deadweight costs. We find that diversified firms have on average a lower cost of capital than comparable portfolios of standalone firms. In addition, diversified firms with less correlated segment cash flows have a lower cost of capital, consistent with a coinsurance effect. Holding cash flows constant, our estimates imply an average value gain of approximately 5% when moving from the highest to the lowest cash flow correlation quintile.
Osteoporosis and its Association with Estrogen Receptor- alpha Gene Polymorphism in a population of Iranian Women Referring to Loghman Hospital

steoporosis is a common disease in which the bones become prone to fracture as a result of loss of bone mineral density (BMD). The estrogen receptor (ER) gene is a candidate gene for osteoporosis. This study assesses the relation between estrogen receptor- α gene polymorphism and osteoporosis in a population of Iranian women. Materials & Methods: In the present study, we investigated 200 pre- and/or postmenopausal Iranian women, aged 35-80 years, stratified for BMD into normal and patient groups. The genomic DNA of both groups was amplified by PCR using specific primers and products were digested by restriction enzymes PvuII or XbaI to identify the related genotypes. The genotypes of intron 1 PvuII or XbaI polymorphisms of the ER- α gene were detected and introduced so that the upper case and lower case letters of Pp (PvuII) and Xx (XbaI) signified the absence or presence of restriction sites in RFLP experiments. Results: Based on our results, no significant relationship was observed between BMD and intron 1 RFLPs of the estrogen receptor alpha gene. Three genotypes, Pp XX, pp XX and PP xx, were detected, all at a very low frequency in this population of Iranian women. Conclusion: To conclude no significant relationship was found between BMD and intron 1 RFLPs of the estrogen receptor alpha gene. Larger numbers of patients need to be investigated to ascertain and confirm whether ER- α genotypes are associated to the disease etiology and if any other factors are involved.
Five hundred and twenty-eight microsatellite markers for ecological genomic investigations using Daphnia

Until recently, an enormous effort was needed to apply genomic tools to ecological investigations, especially when striving to uncover the functional mechanisms of phenotypic plasticity and the genetic basis of evolutionary adaptation within natural populations. This present study aimed to develop a genomic resource for an organism ideally suited for functional ecology and evolutionary research. Over 760 unique DNA fragments containing microsatellite loci were isolated and characterized from Daphnia to provide more than 500 molecular markers for constructing a genetic map and for localizing chromosomal regions containing genes of ecological importance via quantitative trait locus analyses. Although primarily developed to genotype members of the Daphnia pulex species complex, a significant fraction of these markers is potentially valuable for population genetics and recombination mapping of distantly related species. Over 60% of markers tested in cross-specific amplifications are possibly conserved within the subgenus Daphnia , whereas 48 and 18% of tested primers are found to amplify subgenus Hyalodaphnia and subgenus Ctenodaphnia DNA, which represents ∼ 140 and 200 million years of evolutionary preservation.