Miracle-dz/UniFAR-FTUs huggingface dataset:
A two‐stage binaural speech enhancement approach for hearing aids with preserving binaural benefits in noisy environments

Speech enhancement is one of the most crucial functions, if not the most, in hearing aids, as hearing impaired people have great difficulty in understanding speech in noisy environments. In this paper, we propose a two‐stage binaural speech enhancement approach for hearing aids, which consists of interference estimation by pre‐trained adaptive filters and speech enhancement using the Wiener filters. Main attention is then paid to the theoretical analysis of this system and the experimental comparisons with the traditional binaural speech enhancement approaches. The comparisons are conducted with the following two considerations: interference suppression performance and the ability in preserving binaural cues which give birth to listener's own “binaural gain. ” We finally give the general discussion on this proposed binaural speech enhancement algorithm from the viewpoints of theory, through implementation, to evaluation.
Akt Phosphorylation of p300 at Ser-1834 Is Essential for Its Histone Acetyltransferase and Transcriptional Activity

ABSTRACT The PI3K/Akt pathway plays a critical role in the regulation of gene expression induced by numerous stimuli. p300, a transcriptional coactivator, acts in concert with transcription factors to facilitate gene expression. Here, we show that Akt is activated and translocated to the nucleus in response to tumor necrosis factor alpha. Nuclear Akt associates with p300 and phosphorylates its Ser-1834 both in vivo and in vitro. The phosphorylation induces recruitment of p300 to the ICAM-1 promoter, leading to the acetylation of histones in chromatin and association with the basal transcriptional machinery RNA polymerase II. These two events facilitate ICAM-1 gene expression and are abolished by the p300 S1834A mutant, inhibitors of PI3K/Akt, or small interfering RNA of Akt. Histone acetylation is attributed to the Akt-enhanced intrinsic histone acetyltransferase (HAT) activity of p300 and its association with another HAT, p/CAF. Our study provides a new insight into the molecular mechanism by which Akt promotes the transcriptional potential of p300.
Estudo sobre os níveis de disclosure adotados pelas empresas brasileiras e custo de capital

O presente trabalho de pesquisa teve por objetivo principal estudar a associacao existente entre disclosure e custo de capital proprio das empresas brasileiras. Para atingir o objetivo principal foram tracados tres objetivos especificos: avaliar uma metodologia disponivel para testar a associacao entre disclosure e custo de capital, analisar quais modelos de custo de capital poderiam ser adotados para utilizacao no mercado brasileiro e examinar os efeitos da quantidade e da qualidade da informacao apresentada pelas empresas brasileiras no relatorio anual publicado para os acionistas. A metodologia estabelecida para testar a associacao entre disclosure e custo de capital foi o Metodo dos Minimos Quadrados Ordinarios. Como variavel dependente foi adotado o custo de capital e como variaveis explicativas foram analisados tres indices de disclosure e 9 variaveis indicativas de tamanho, risco e variacao nos resultados. Foram adotados tres modelos para calculo do custo de capital: o Modelo de Precificacao de Ativos de Capital (CAPM), o de Dividendos Descontados (MDD) e o de Ohlson e Juettner. Os testes foram efetuados com duas amostras nao probabilisticas com empresas com acoes negociadas na Bolsa de Valores de Sao Paulo. A primeira amostra contou com 131 empresas de diversos segmentos e a segunda com as empresas que apresentaram relatorio para concorrer as premiacoes do 6o. Premio ABRASCA do ano de 2003. Os resultados desta pesquisa evidenciaram que o modelo escolhido para calculo do custo de capital interfere nos resultados obtidos nos testes empiricos, e que a variavel disclosure pode se apresentar significativa em um modelo e nao significativa em outro. Esses resultados estao em concordância com os dados obtidos por Botosan (1997). Dois dos indices de disclosure, o IDISCABR, indice de pontuacao da premiacao ABRASCA, e o INNM, indice representativo das empresas que aderiram a niveis diferenciados de Governanca Corporativa do Novo Mercado, mostraram-se significativos a 5%, evidenciando que as empresas da amostra ABRASCA se beneficiaram de custos menores pela adocao de niveis diferenciados de disclosure. Os testes com a amostra Geral de 131 empresas permitiram obter evidencia de que os testes entre disclosure e custo de capital sao sensiveis a heterogeneidade da amostra, conforme esclareceu Botosan (1997) em seu trabalho pioneiro. As principais contribuicoes deste estudo referem-se aos cuidados que o pesquisador deve observar para escolher o modelo para calculo do custo de capital, a possibilidade de se estabelecerem diferentes indices de disclosure para testar empiricamente custo de capital e disclosure no mercado brasileiro, e a evidencia de que uma amostra de 37 empresas participantes do premio ABRASCA obtiveram reducao em seus custos de capital pela adocao de politicas mais transparentes. Este resultado, apesar de obtido para uma pequena amostra, serve de incentivo para a tomada de decisao de outras empresas brasileiras e para que novas pesquisas sejam elaboradas com relacao ao tema.
A unified approach to scheduling on unrelated parallel machines

We develop a single rounding algorithm for scheduling on unrelated parallel machines; this algorithm works well with the known linear programming-, quadratic programming-, and convex programming-relaxations for scheduling to minimize completion time, makespan, and other well-studied objective functions. This algorithm leads to the following applications for the general setting of unrelated parallel machines: (i) a bicriteria algorithm for a schedule whose weighted completion-time and makespan simultaneously exhibit the current-best individual approximations for these criteria; (ii) better-than-two approximation guarantees for scheduling to minimize the Lp norm of the vector of machine-loads, for all 1 < p < ∞; and (iii) the first constant-factor multicriteria approximation algorithms that can handle the weighted completion-time and any given collection of integer Lp norms. Our algorithm has a natural interpretation as a melding of linear-algebraic and probabilistic approaches. Via this view, it yields a common generalization of rounding theorems due to Karp et al. [1987] and Shmoys & Tardos [1993], and leads to improved approximation algorithms for the problem of scheduling with resource-dependent processing times introduced by Grigoriev et al. [2007].
A Learning Environment for Software Security Education

In recent years, importance on software security has been recognized. Along with the trend, to foster software engineers who possess software security technologies is expressed. The authors have been tackling software engineering education, and have lots of artifacts as the outcomes of the education. The authors propose a learning process that utilizes these artifacts as input for learning of software security and the learners create artifacts for secure software referring to various types of guidelines (methods for secure software development, patterns, standards, and so on). The authors also propose a learning environment to support the process, which adopts a knowledge management approach. The environment enables to store the artifacts, guidelines and rationale, and learners to associate relationships among them.
Adaptive Change Detection for Relay-Like Behaviour

Detecting anomalous behaviour in network flow data is challenging for a number of reasons, including both the computational demand associated with a large corporate network and the peculiar temporal characteristics of flow data. Relay-like behaviour refers to the rapid commencement of an out-going flow from a network device following the completion of an in-coming flow. This paper develops a computationally efficient and temporally adaptive methodology for detecting relay-like behaviour. The methodology is demonstrated on a real example of NETFLOW data. In addition to providing a detector, further uses of the methodology for combining anomalous events are discussed.
System interpretation of causality measures in frequency domain used in eeg analysis

This paper suggests new measures for the evaluation of an absolute, ratio and relative causal relation in frequency domain in the context of multivariate autoregressive models.The idea is explained on four synoptic artificial data experiments. In each example, a model without causal connection is presented and then, a causal connection is added. The influence of this modification is analysed and interpreted in the scope of LTI digital filters. Using the proposed measures, a comparison of results of state-of-the-art frequency domain methods Generalized Partial Directed Coherence (GPDC) and Direct Directed Transfer Function (dDTF) is performed to evaluate their behavior. The concept is demonstrated on real EEG data of awake resting state of human brain.
An influenza reassortant with polymerase of pH1N1 and NS gene of H3N2 influenza A virus is attenuated in vivo

Influenza viruses readily mutate by accumulating point mutations and also by reassortment in which they acquire whole gene segments from another virus in a co-infected host. The NS1 gene is a major virulence factor of influenza A virus. The effects of changes in NS1 sequence depend on the influenza polymerase constellation. Here, we investigated the consequences of a virus with the polymerase of pandemic H1N1 2009 acquiring an NS gene segment derived from a seasonal influenza A H3N2 virus, a combination that might arise during natural reassortment of viruses that currently circulate in humans. We generated recombinant influenza viruses with surface HA and NA genes and matrix M gene segment from A/PR/8/34 virus, but different combinations of polymerase and NS genes. Thus, any changes in phenotype were not due to differences in receptor use, entry, uncoating or virus release. In Madin–Darby canine kidney (MDCK) cells, the virus with the NS gene from the H3N2 parent showed enhanced replication, probably a result of increased control of the interferon response. However, in mice the same virus was attenuated in comparison with the virus containing homologous pH1N1 polymerase and NS genes. Levels of viral RNA during single-cycles of replication were lower for the virus with H3N2 NS, and this virus reached lower titres in the lungs of infected mice. Thus, virus with pH1N1 polymerase genes did not increase its virulence by acquiring the H3N2 NS gene segment, and MDCK cells were a poor predictor of the outcome of infection in vivo.
Incremental Construction of Topic Hierarchies using Hierarchical Term Clustering

Topic hierarchies are very useful for managing, searching and browsing large repositories of text documents. The hierarchical clustering methods are used to support the construction of topic hierarchies in a unsupervised way. However, the traditional methods are ineffective in scenarios with growing text collections. In this paper, an incremental method for the construction of topic hierarchies are presented, allowing the update of a topic hierarchy without repeating the clustering process. The experimental results on several benchmark text collections show that our method obtains topic hierarchies with quality similar to traditional non-incremental algorithms.
Network Effects and Personal Influences: The Diffusion of an Online Social Network

This article discusses the diffusion process in an online social network given the individual connections between members. The authors model the adoption decision of individuals as a binary choice affected by three factors: (1) the local network structure formed by already adopted neighbors, (2) the average characteristics of adopted neighbors (influencers), and (3) the characteristics of the potential adopters. Focusing on the first factor, the authors find two marked effects. First, an individual who is connected to many adopters has a greater adoption probability (degree effect). Second, the density of connections in a group of already adopted consumers has a strong positive effect on the adoption of individuals connected to this group (clustering effect). The article also records significant effects for influencer and adopter characteristics. For adopters, specifically, the authors find that position in the entire network and some demographic variables are good predictors of adoption. Similarly, in the case of already adopted individuals, average demographics and global network position can predict their influential power on their neighbors. An interesting counterintuitive finding is that the average influential power of individuals decreases with the total number of their contacts. These results have practical implications for viral marketing, a context in which a variety of technology platforms are increasingly considering leveraging their consumers' revealed connection patterns. The model performs particularly well in predicting the next set of adopters.
Concept of entrepreneurial success among women entrepreneur in Malaysia

Nowadays, it seems like women`s involvement in entrepreneurship is not as active as they could be since people know little on women entrepreneur compared to men entrepreneur.Many studies are focusing more on men entrepreneur rather than women entrepreneur.Intensive researches on women entrepreneur especially in entrepreneurial success are needed as guidance in helping them to become a successful entrepreneur.Therefore, this research is carried out to identify the factors that might influence women entrepreneur to become successful. Several definitions from previous research will identified to well understand the meaning of entrepreneurial success.Besides, in this paper also, the author will recognize the most suitable dimension to measure the success among women entrepreneurs.Thus, in future it will help to avoid more failure among them since this research has been determine the characteristics and factors needed to be a successful women entrepreneurs.
A role for NANOG in G1 to S transition in human embryonic stem cells through direct binding of CDK6 and CDC25A

In this study, we show that NANOG, a master transcription factor, regulates S-phase entry in human embryonic stem cells (hESCs) via transcriptional regulation of cell cycle regulatory components. Chromatin immunoprecipitation combined with reporter-based transfection assays show that the C-terminal region of NANOG binds to the regulatory regions of CDK6 and CDC25A genes under normal physiological conditions. Decreased CDK6 and CDC25A expression in hESCs suggest that both CDK6 and CDC25A are involved in S-phase regulation. The effects of NANOG overexpression on S-phase regulation are mitigated by the down-regulation of CDK6 or CDC25A alone. Overexpression of CDK6 or CDC25A alone can rescue the impact of NANOG down-regulation on S-phase entry, suggesting that CDK6 and CDC25A are downstream cell cycle effectors of NANOG during the G1 to S transition.
Improvement of Image Contrast with Local Adaptation

Most existing global contrast enhancement algorithm can not deal with high dynamic range images which are often under non-uniform illumination. Thus a simplified logarithmic transformation equation has been proposed in this paper to meet this demand. Furthermore a partially overlapped sub-block method is also adopted to perform local contrast enhancement. For purpose of optimization, two contrast measures are used for finding optimal transformation parameters. They merge into a new algorithm, called POSLT (Partially Overlapped Sub-block Logarithmic transformation). POSLT exhibits good performance and Controllability even if it has high computational complexity. Our experimental results show its local adaptation for contrast enhancement, the results give firmly support to POSLT.
Segregation and the Perception of the Minority

In his seminal work, Schelling (1971) shows that even individual preferences for integration across groups may generate high levels of segregation. However, this theoretical prediction does not match the decreasing levels of segregation observed since the 1970s. We construct a general equilibrium model in which preferences depends on the number of peers and unlike individuals, but also on the benefit (or loss) they attribute to the economic and social life that a minority member brings with him, which we call their “perception of the minority”. In this framework, there always exists a structure of the preferences for which integrated equilibria emerge and are stable. Even when individuals are all prejudiced against other groups, there is still a level of the perception of the minority for which integration is a stable outcome. We then propose an econometric specification in which the structural preference parameters can be identified. In the case of South Africa, our estimates of preferences provide evidence for a dynamics toward increasing integration as the effect of the perception of the minority is found positive and significant, and overcome both racism and homophily by between roughly one and four times.
Preliminary Survey of Multiview Synthesis Technology

With the maturity of digital camera technology, it is feasible to form an array of cameras. The major usage of a camera array is to acquire different views of a scene in one shot. The captured data can be used to analyze the depths of the objects. Once we have the 3D model, we can synthesize virtual views, relight the scene, etc. The potential applications are virtual reality and augmented reality. In order to investigate the multiview technology, we studied the fundamental concepts, including the single lens camera, the eccentric lens camera, the plenoptic camera, and the multiview camera. We also discussed a few application examples for understanding the practical usages. The study showed that the initial depth estimation technology becomes important nowadays in providing photo realistic natural feel to people. The application areas were also extended to entertainment, and also to some crucial tasks such as medical operations and combat missions.
Clorg: collective intelligence in organizations

Web 2.0 tools are penetrating into organizations after their successful adoption in the consumer domain (e.g., social networking; sharing of photos, videos, tags, or bookmarks; wiki-based editing). Some of these new tools and the collaborative processes that they support on the large scale are often referred to as Collective Intelligence (CI). The workshop brings together leading researchers and designers who are studying or developing CI tools aimed at workers in organizations. The goal is to further articulate the emerging research agenda for this new CSCW area and define new observed forms of CI in organization. Studies of communities, CI tools, and new methods are discussed.
A Hardware/Software Approach for Alleviating Scalability Bottlenecks in Transactional Memory Applications

Scaling processor performance with future technology nodes is essential to enable future applications for devices ranging from smart-phones to servers. But the traditional methods of achieving that performance through frequency scaling and single-core architectural enhancements are no longer viable due to fundamental scaling limits. To continue scaling performance, parallel computers in the form of Chip Multi-processors (CMPs) are now prevalent, moving the challenge of parallel programming from a niche to the general domain. One challenging area is scalable synchronization to shared data structures using traditional methods. It can take many years for expert programmers using traditional methods to craft a scalable and correct scheme to synchronize access to data-structures in a complex program. Researchers have been searching for methods to make synchronization more tractable. One proposal is to use “Transactional Programming” to abstract synchronization to shared data structures as transactions in a similar fashion as database operations. Transactional programming can be efficiently supported by using a “Transactional Memory” (TM) system. One main problem with TM systems is scalability bottlenecks. When transactional applications are written to emulate future average programmer practices, performance can be worse than a single processor on large CMPs. This should not happen on a system meant to make programming easier. This happens because transactions as represented in the TM system may be dependent on each other—accessing the same data and therefore must serialize—without the programmer being knowledgable about these dependencies due to the abstraction hiding system details. This thesis develops a hardware/software approach to alleviate scalability bottlenecks in TM systems, while maintaining the level of abstraction presented in transactional programming. I first introduce “Proactive Transaction Scheduling” (PTS), a technique that profiles parallel code at runtime to determine orders transactions should execute in to maintain acceptable forward progress. I then propose using PTS to automatically determine transactions causing large amounts of serialization. These transactions are then accelerated using an asymmetric CMP to get better performance. I also show PTS can be used to partition resources in a Multi-threaded processor core for better overall performance over a fair partitioning of resources.
NONPARAMETRIC KERNEL‐BASED SEQUENTIAL INVESTMENT STRATEGIES

The purpose of this paper is to introduce sequential investment strategies that guarantee an optimal rate of growth of the capital, under minimal assumptions on the behavior of the market. The new strategies are analyzed both theoretically and empirically. The theoretical results show that the asymptotic rate of growth matches the optimal one that one could achieve with a full knowledge of the statistical properties of the underlying process generating the market, under the only assumption that the market is stationary and ergodic. The empirical results show that the performance of the proposed investment strategies measured on past nyse and currency exchange data is solid, and sometimes even spectacular.
Artery phantoms for intravascular optical coherence tomography: healthy arteries

We present a method to make phantoms of coronary arteries for intravascular optical coherence tomography (IV-OCT). The phantoms provide a calibrated OCT response similar to the layered structure of arteries. The optical properties of each layer are achieved with specific concentrations of alumina and carbon black in a silicone matrix. This composition insures high durability and also approximates the elastic properties of arteries. The phantoms are fabricated in a tubular shape by the successive deposition and curing of liquid silicone mixtures on a lathe setup.
Ranking Scientific Articles by Exploiting Citations, Authors, Journals, and Time Information

Ranking scientific articles is an important but challenging task, partly due to the dynamic nature of the evolving publication network. In this paper, we mainly focus on two problems: (1) how to rank articles in the heterogeneous network; and (2) how to use time information in the dynamic network in order to obtain a better ranking result. To tackle the problems, we propose a graph based ranking method, which utilizes citations, authors, journals/conferences and the publication time information collaboratively. The experiments were carried out on two public datasets. The result shows that our approach is practical and ranks scientific articles more accurately than the state-of-art methods.