Miracle-dz/UniFAR-FTUs huggingface dataset:
Thick de Sitter Brane Worlds, Dynamic Black Holes and Localization of Gravity

The embedding of a thick de Sitter 3-brane into a five-dimensional bulk spacetime is considered, by assuming that in the bulk a scalar field with potential that is a power of cosine of the scalar field, is present. A class of analytical solutions has been found in a closed form, which can represent a thick de Sitter 3-brane interpolating two dynamical black holes with R × S4 topology. The gravitational field is localized in a small region near the center of the 3-brane. The analysis of graviton fluctuations shows that the spectrum of the perturbations consists of a zeros mode and continuous modes, and no mass gap exists. The zero mode is responssible for the four-dimensional gravity localized on the 3-brane, while the continuous modes have only small corrections to the 4D gravity. Typeset using REVTEX E-mail: wang@dft.if. uerj.br 1
A Novice Guide towards Human Motion Analysis and Understanding

Human motion analysis and understanding has been, and is still, the focus of attention of many disciplines which is considered an obvious indicator of the wide and massive importance of the subject. The purpose of this article is to shed some light on this very important subject, so it can be a good insight for a novice computer vision researcher in this field by providing him/her with a wealth of knowledge about the subject covering many directions. There are two main contributions of this article. The first one investigates various aspects of some disciplines (e.g., arts, philosophy, psychology, and neuroscience) that are interested in the subject and review some of their contributions stressing on those that can be useful for computer vision researchers. Moreover, many examples are illustrated to indicate the benefits of integrating concepts and results among different disciplines. The second contribution is concerned with the subject from the computer vision aspect where we discuss the following issues. First, we explore many demanding and promising applications to reveal the wide and massive importance of the field. Second, we list various types of sensors that may be used for acquiring various data. Third, we review different taxonomies used for classifying motions. Fourth, we review various processes involved in motion analysis. Fifth, we exhibit how different surveys are structured. Sixth, we examine many of the most cited and recent reviews in the field that have been published during the past two decades to reveal various approaches used for implementing different stages of the problem and refer to various algorithms and their suitability for different situations. Moreover, we provide a long list of public datasets and discuss briefly some examples of these datasets. Finally, we provide a general discussion of the subject from the aspect of computer vision.
Causal Bayesian Networks for Robust and Efficient Fusion of Information Obtained from Sensors and Humans

This paper discusses techniques for fusion in contemporary situation assessment applications. Such applications often require reasoning about phenomena that cannot be observed directly, but information about their effects (i.e. symptoms) can be accessed through the existing sensory and communication infrastructure. Reasoning about hidden phenomena requires interpretation of relevant observations. Observations can be of heterogeneous types and can originate from humans as well as various sensory systems. Interpretation in such settings can be very challenging, as there might exist complex dependences between different phenomena. In addition, we are often confronted with significant modeling and observation uncertainties. Particularly challenging is the fact that a large portion of such information often originates from humans. Consequently, it can be very difficult to obtain perception models that precisely describe the distributions of hidden phenomena and human reports. In this paper we show that Bayesian networks (BNs) are suitable for the development of fusion systems in such settings, because they can efficiently describe the monitoring domains. Moreover, BNs support construction of efficient and robust distributed fusion systems.
Candidate cancer-targeting agents identified by expression-profiling arrays

Background One particularly promising component of personalized medicine in cancer treatment is targeted therapy, which aims to maximize therapeutic efficacy while minimizing toxicity. However, the number of approved targeted agents remains limited. Expression microarray data for different types of cancer are resources to identify genes that were upregulated. The genes are candidate targets for cancer-targeting agents for future anticancer research and targeted treatments. Methods and findings The gene expression profiles of 48 types of cancer from 2,141 microarrays reported in the Gene Expression Omnibus were analyzed. These data were organized into 78 experimental groups, on which we performed comprehensive analyses using two-tailed Student’s t-tests with significance set at P < 0.01 to identify genes that were upregulated compared with normal cells in each cancer type. The resulting list of significantly upregulated genes was cross-referenced with three categories of protein inhibitor targets, categorized by inhibitor type (‘Targets of US Food and Drug Administration (FDA)-approved anticancer drugs’, ‘Targets of FDA-approved nonantineoplastic drugs’, or ‘Targets of non-FDA-approved chemical agents’). Of the 78 experimental groups studied, 57 (73%) represent cancers that are currently treated with FDA-approved targeted treatment agents. However, the target genes for the indicated therapies are upregulated in only 33 of these groups (57%). Nevertheless, the mRNA expression of the genes targeted by FDA-approved treatment agents is increased in every experimental group, including all of the cancers without FDA-approved targeted treatments. Moreover, many targets of protein inhibitors that have been approved by the FDA as therapies for nonneoplastic diseases, such as 3-hydroxy-3-methylglutaryl-CoA reductase and cyclooxygenase-2 and the targets of many non-FDA-approved chemical agents, such as cyclin-dependent kinase 1 and DNA-dependent protein kinase, are also overexpressed in many types of cancer. Conclusion This research demonstrates a clinical correlation between bioinformatics data and currently approved treatments and suggests novel uses for known protein inhibitors in future antineoplastic research and targeted therapies.
Perceived competence and credit access of SMEs: can trust change the rules of the game?

Banks play an essential role in financing firms and especially small and medium en-terprises (SMEs). The process used by banks to decide whether and how much to lend is complex and banks rely on different lending techniques. Relationship lending, by leveraging a variety of private information gathered through contact with the firm, its owner, and the local community, has a peculiar role and can benefit SMEs by providing them with easier access to credit. No previous research focuses specifically on the perceived competence of the entrepreneur or the owner/manager of the firm as well as on competence as a substitute of trust. The present paper tries to fill this gap. The research is based on a panel of 535 entrepreneurial SMEs which operate in the widely studied and economically successful North East of Italy. The data were collected by administering a survey to bank managers of local community banks and of two national banks. The regressions show that competence is positively related to overall credit gained and negatively related to interest rate. In addition, in low trusted SMEs, credit gained is positively related to competence while the interest rate is negatively linked to competence. In highly trusted firms, these relationships are not significant. Our findings support the point that competence is an important factor irrespective of the quality of the firm and that it is a substitute for trust in low-trusted SMEs. The findings have two major implications: banks should develop tools that are capable to catch the competence of the entrepreneurs irrespective of the performance of the firm depicted in the firms' data; the entrepreneurs need to effectively communicate their competences to the relevant stakeholders such as the bank managers. Thus, perceived competence can play an important role during economic downturns when the performance of the firm is affected by hostile eco-nomic environment.
Social disclosure among Korean domestic adoptive parents: the role of self-esteem

ABSTRACT This study examines the relationship among involuntary childlessness, social support, and social disclosure of Korean domestic adoptive parents. Data were collected from a sample of 90 such parents who had already disclosed the adoption to their child. Path analysis revealed that involuntary childlessness does not directly affect social disclosure of adoption, but it does affect the self-esteem of adoptive parents, and parental self-esteem was significantly and positively related to social disclosure. Social support reinforces the self-esteem of adoptive parents, thus contributing to the social disclosure of adoption. This research highlights the importance of pre- and post-adoption counselling that address the unique experiences of adoptive families in South Korea.
Outage rate regions for the MISO IFC

We consider the two-user multiple-input single-output (MISO) interference channel (IFC) and assume that the receivers treat the interference as additive Gaussian noise. We study the rates that can be achieved in a slow-fading scenario, allowing an outage probability. We introduce three definitions for the outage region of the IFC. The definitions differ on whether the rates are declared in outage jointly or individually and whether there is perfect or statistical information about the channels. Even for the broadcast and the multiple-access channels, which are special cases of the IFC, there exist several definitions of the outage rate regions. We provide interpretations of the definitions and compare the corresponding regions via numerical simulations. Also, we discuss methods for finding the regions. This includes a characterization of the beamforming strategies, which are optimal in the sense that achieve rate pairs on the Pareto boundary of the outage rate region.
Arrested coarsening of granular roll waves

We study a system in which granular matter, flowing down an inclined chute with periodic boundary conditions, organizes itself in a train of roll waves of varying size. Since large waves travel faster than small ones, the waves merge, and their number gradually diminishes. This coarsening process, however, does not generally proceed to the ultimate one-wave state: Numerical simulations of the dynamical equations (being the granular analogue of the shallow water equations) reveal that the process is arrested at some intermediate stage. This is confirmed by a theoretical analysis, in which we show that the roll waves cannot grow beyond a certain limiting size (which is fully determined by the system parameters), meaning that on long chutes the material necessarily remains distributed over more waves. We determine the average lifetime τN of the successive N-wave states, from the initial state with typically N = 50 waves (depending on the length of the periodic domain) down to the final state consisting of on.
Uptake of airborne organic pollutants in pine needles

The fate and distribution of semivolatile organic pollutants (SOC) and has received much attention during the last decade. The aim of this thesis is to address key issues concerning the fate of some selected pollutants in the terrestrial environment. Specifically, several aspects of the uptake of airborne organic pollutants in pines has been studied. The geographical distribution of some organochlorines in pines was investigated in a transect from Central to Northernmost Europe and also in the former Eastern Europe. Most compounds with a recent active use in Europe showed higher concentrations in the south than in the north. However, the industrial by-product hexachlorobenzene had elevated levels close to industrial centra, and α-hexachlorocyclohexane (α-HCH) had similar concentrations throughout. To properly interpret such data the variability within a forest stand was investigated for selected compounds. The relative standard deviation was found to be better than 25 % for all analytes. The accumulation during the life span of the needles and the seasonal uptake pattern was investigated. A significant uptake during the warm season was found for the most volatile of the analytes, while little happened during winter. This is contrary to what would be expected from physicochemical considerations. It was therefore concluded that there must be a biological factor that alters the properties of the needles so that the accumulation is highest in summer. The least volatile analytes had a transient concentration peak during winter, and analytes with intermediate volatility showed an intermediate behaviour. To test if the terpene concentration in the needles could be the biological factor that explains the uptake pattern of the most volatile compounds, the mono- and sesquiterpene concentrations were measured. It was found that although the total terpene concentration in the needles did not covary with the uptake of α- and γ-HCH, the concentration of terpenes in the soluble cuticular lipids showed peak concentrations during summer, at the same time as the highest uptake of the volatile analytes is expected.
Role of Digoxin-Like Immunoreactive Substance in the Pathogenesis of Transient Tachypnea of Newborn

Background. Transient tachypnea of newborn (TTN) is usually observed in term or near-term infants. It constitutes an important part of the respiratory distress cases observed in the neonatal intensive care unit (NICU). Aim. This paper examines the effects of digoxin-like immunoreactive substance (DLIS) on fluid and ion balance, hemodynamic and echocardiographic parameters of neonates with TTN. Methods. Plasma DLIS, Na+, K+, urea, creatinine, serum and urine osmolarity, urine FeNa+, 24-hour urine output, echocardiographic investigation and mean blood pressure, and clinical parameters of disease severity were recorded in TTN group and compared with control on the 1st and 7th days of their lives. Results. Plasma DLIS levels were statistically higher in TTN group (0.66 ± 0.37 ng/mL) compared to control group (0.24 ± 0.20 ng/mL) both on the 1st day (P < 0.01) and the 7th day (P < 0.05). For TTN group, significant correlation was found between plasma DLIS levels and maximum respiratory rate, duration of tachypnea, and length of hospitalization on the 1st day. Plasma DLIS levels were correlated negatively with serum osmolarity levels. Plasma DLIS levels were positively correlated with urine output, urinary FeNa+ levels, cardiac output, left ventricles end diastolic diameters, and right ventricles end diastolic diameters. Conclusions. Increased DLIS levels were correlated with disease severity in cases with TTN. This increase may be a primary or secondary event in the disease progress. It may help reduce the fluid overload due to already disturbed cardiac functions in patients by increasing urine output and natriuresis; however it may also contribute to disease pathogenesis, by inhibiting alveolar Na+-K+-ATPase which further decreases fetal alveolar fluid resorption.
Training data optimization for language model adaptation

Language model (LM) adaptation is a necessary step when the LM is applied to speech recognition. The task of LM adaptation is to use out-domain data to improve in-domain model’s performance since the available in-domain (task-specific) data set is usually not large enough for LM training. LM adaptation faces two problems. One is the poor quality of the out-domain training data. The other is the mismatch between the n-gram distribution in out-domain data set and that in in-domain data set. This paper presents two methods, filtering and distribution adaptation, to solve them respectively. First, a bootstrapping method is presented to filter suitable portion from two large variable quality out-domain data sets for our task. Then a new algorithm is proposed to adjust the n-gram distribution of the two data sets to that of a task–specific but small data set. We consider preventing over-fitting problem in adaptation. All resulting models are evaluated on the realistic application of email dictation. Experiments show that each method achieves better performance, and the combined method achieves a perplexity reduction of 24% to 80%.
And/Or Trees Revisited

We consider Boolean functions over $n$ variables. Any such function can be represented (and computed) by a complete binary tree with and or or in the internal nodes and a literal in the external nodes, and many different trees can represent the same function, so that a fundamental question is related to the so-called complexity of a Boolean function: $L(f):=$ minimal size of a tree computing $f$. The existence of a limiting probability distribution $P(\cdot)$ on the set of and/or trees was shown by Lefmann and Savický [8]. We give here an alternative proof, which leads to effective computation in simple cases. We also consider the relationship between the probability $P(f)$ and the complexity $L(f)$ of a Boolean function $f$. A detailed analysis of the functions enumerating some sub-families of trees, and of their radius of convergence, allows us to improve on the upper bound of $P(f)$, established by Lefmann and Savický.
An Effective-Traffic Based Dual-Threshold Queue Control Scheme in Shared Memory Switch

This paper investigates the queue control or buffer management problem in shared memory switches. Traditional methods such as dynamic threshold and the newly proposed such as decay function threshold take decision largely based on the current total queue length. The dual threshold method proposed by this paper considers the traffic scenario as the main factor to decide a per-port threshold. Each port maintains a common traffic intensity, which is accounted for by the traffic rate and the allotted buffer. The traffic intensity takes shape from the classical effective bandwidth theory, yet the formula devised by the theory is too complex to implement. This paper reduces the formula to a simple form that is eligible in the runtime environment with little loss of accuracy. Keeping the common traffic intensity by dynamically adjust the per-port threshold can maintain the system equilibrium and reach a global optimality. Simulation shows the proposed scheme outperforms the tradition dynamic thresholds significantly.
Reducing Hospitalizations From Long-Term Care Settings

Hospital spending represents approximately one third of total national health spending, and the majority of hospital spending is by public payers. Elderly individuals with long-term care needs are at particular risk for hospitalization. While some hospitalizations are unavoidable, many are not, and there may be benefits to reducing hospitalizations in terms of health and cost. This article reviews the evidence from 55 peer-reviewed articles on interventions that potentially reduce hospitalizations from formal long-term care settings. The interventions showing the strongest potential are those that increase skilled staffing, especially through physician assistants and nurse practitioners; improve the hospital-to-home transition; substitute home health care for selected hospital admissions; and align reimbursement policies such that providers do not have a financial incentive to hospitalize. Much of the evidence is weak and could benefit from improved research design and methodology.
Continuous-Time Limit of Stochastic Gradient Descent Revisited

Stochastic Gradient Descent (SGD) is an important algorithm in machine learning. With constant learning rates, it is a stochastic process that reaches a stationary distribution. We revisit an analysis of SGD in terms of stochastic differential equations in the limit of small constant gradient steps. This limit, which we feel is not appreciated in the machine learning community, allows us to approximate SGD in terms of a multivariate Ornstein-Uhlenbeck process, and hence to compute stationary distributions in closed form. This formalism has interesting new implications for machine learning. We consider the case where the objective has the interpretation of a log-posterior. Traditional theory suggests choosing the learning rate such that the stationary distribution approximates a point mass at the optimum, but this can lead to wasted effort and overfitting. When the goal is instead to approximate the posterior as well as possible, we can derive criteria for optimal minibatch sizes, learning rates, and preconditioning matrices.
Conexões políticas em estruturas de propriedade: o Governo como acionista

Com o crescente interesse da literatura economica sobre o ativismo do governo percebe-se que, de maneira mais intensiva, o governo passa a influenciar a atividade produtiva e economica no Brasil. Sob a integracao de duas correntes teoricas, a teoria da estrategia politica corporativa e a teoria de agencia, este estudo procura explicar os efeitos das conexoes politicas por meio da estrutura de propriedade no desempenho das empresas. O objetivo principal deste trabalho e analisar a estrutura de propriedade estatal direta ou indireta das empresas brasileiras nao financeiras, listadas na BM&FBOVESPA de 1999 a 2010 e, sua relacao com o desempenho economico financeiro, de mercado e alavancagem. A literatura sugere que a propriedade do governo afeta o desempenho economico financeiro, de mercado e o nivel de alavancagem da firma e, que este efeito sera negativo em niveis majoritarios de participacao do governo e positivo quando a participacao do governo for minoritaria. Baseado nisto, o estudo explora e discute os constructos relacionados a conexoes politicas em estruturas de propriedade, Utilizando quatro formas de identificacao do governo como acionista, direta ou indireta, majoritaria ou minoritaria. Esta dissertacao se caracteriza como um estudo longitudinal, pois, analisa as estruturas de propriedade das empresas nao financeiras, listadas na BM&FBOVESPA entre 1999 e 2010, dividido em tres periodos de analise, correspondentes aos mandatos presidenciais da epoca. As hipoteses derivadas do arcabouco teorico foram testadas por intermedio de regressoes lineares multiplas, em que, os principais resultados indicam que a participacao do governo como acionista afeta o desempenho economico financeiro, de mercado e o nivel de alavancagem das empresas. Os resultados sugerem que a participacao majoritaria do governo, sendo ela direta ou indireta, surte efeito negativo no desempenho economico financeiro da empresa e, surte efeito positivo no valor de mercado. Em referencia a participacao minoritaria, foram encontradas evidencias do efeito positivo no desempenho economico financeiro e de mercado para a participacao direta e, igualmente, efeito positivo no valor de mercado para a participacao indireta, nao havendo indicios da posicao acionaria indireta afetar o desempenho economico financeiro das empresas. A alavancagem das empresas se mostrou afetada apenas pela participacao majoritaria direta do governo em que se observou o efeito negativo%%%%With the growing interest in the economic literature on the activism of the government, it is seen that the government has an increasing influence on productive and economic activity in Brazil. Under the integration of two theoretical perspectives - theory of corporate political strategy and agency theory - this study seeks to explain the effects of political connections through the ownership structure on corporate performance. The main objective of this study is to analyze the structure of stateowned direct or indirect non-financial Brazilian companies traded on the BM&FBOVESPA listed from 1999…