Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing speaker verification in noisy environments using Recursive Least-Squares (RLS) adaptive filter

In this paper, we present a speaker verification system based on the Hidden Markov Model (HMM) technique and Recursive Least Squares (RLS) adaptive filtering. The aim of using RLS adaptive filtering is to improve the HMM performance in noisy environments. A Malay spoken digit database is used for the testing and validation modules. It is shown that, in a clean environment a total success rate (TSR) of 89.97% is achieved using HMM. For speaker verification, the true speaker rejection rate is 25.3% while the impostor acceptance rate is 9.99% and the equal error rate (EER) is 16.66%. In noisy environments without RLS adaptive filtering TSRs of between 43.07%–51.26% are achieved for SNRs of 0–30 dBs. Meanwhile, after RLS filtering, TSRs of between 50.95%–56.75% are achieved for SNRs 0–30 dB.
The Morphology-Density Relation in z ~ 1 Clusters

We measure the morphology-density relation (MDR) and morphology-radius relation (MRR) for galaxies in seven z ~ 1 clusters that have been observed with the Advanced Camera for Surveys (ACS) on board the Hubble Space Telescope. Simulations and independent comparisons of our visually derived morphologies indicate that ACS allows one to distinguish between E, S0, and spiral morphologies down to z850 = 24, corresponding to L/L* = 0.21 and 0.30 at z = 0.83 and 1.24, respectively. We adopt density and radius estimation methods that match those used at lower redshift in order to study the evolution of the MDR and MRR. We detect a change in the MDR between 0.8 < z < 1.2 and that observed at z ~ 0, consistent with recent work; specifically, the growth in the bulge-dominated galaxy fraction, fE+S0, with increasing density proceeds less rapidly at z ~ 1 than it does at z ~ 0. At z ~ 1 and Σ ≥ 500 galaxies Mpc-2, we find ⟨fE+S0⟩ = 0.72 ± 0.10. At z ~ 0, an E+S0 population fraction of this magnitude occurs at densities about 5 times smaller. The evolution in the MDR is confined to densities Σ ≳ 40 galaxies Mpc-2 and appears to be primarily due to a deficit of S0 galaxies and an excess of Sp+Irr galaxies relative to the local galaxy population. The fE-density relation exhibits no significant evolution between z = 1 and 0. We find mild evidence to suggest that the MDR is dependent on the bolometric X-ray luminosity of the intracluster medium. Implications for the evolution of the disk galaxy population in dense regions are discussed in the context of these observations.
SecPlace: A Security-Aware Placement Model for Multi-tenant SaaS Environments

Software-as-a-Service (SaaS) is emerging as a new software delivery model, where the application and its associated data are hosted in the cloud. Due to the nature of SaaS and the cloud in general, where the data and the computation are beyond the control of the user, data privacy and security becomes a vital factor in this new paradigm. In multi-tenant SaaS applications, the tenants (i.e., Companies) become concerned about the confidentiality of their data since several tenants are consolidated onto a shared infrastructure (i.e., Databases). Consequently, two main questions raise. First, how to prohibit a tenant from accessing other's data? Second, how to avoid the security threats from co-located competing tenants? In this paper, we address the second question. We present Sec Place, a resource allocation model designed to increase the level of security for tenants sharing the same infrastructure. Sec Place avoids hosting competing companies on the same database instance. We minimize the risk of co-resident tenants by preventing any two tenants of the same business type to be hosted on the same database server. Sec Place utilizes the usage of tenant subscription data, such as business type and tenant size and place the tenant accordingly. We conduct extensive experiments to validate our approach. The results show that our approach is practical, achieves its goal, and have a moderate complexity.
Action Spaces for Efficient Bayesian Tracking of Human Motion

Bayesian tracking implemented as a particle filter is one of the most used techniques for full-body human tracking. However, given the high-dimensionality of the models to be tracked, the number of required particles to properly populate the space of solutions makes the problem computationally very expensive. To overcome this, we present an efficient scheme which makes use of an action model that guides the prediction step of the particle filter. In this manner, particles are propagated to locations in the search space with most a posteriori information. Hence, we sample from a smooth motion model only those postures which are feasible given a particular action. We show that this scheme improves the efficiency and accuracy of the overall tracking approach
Dynamics of Pneumococcal Acquisition and Carriage in Young Adults during Training in Confined Settings in Israel

Background Outbreaks and sporadic cases of pneumococcal illness occur among young adults in confined settings. Our aim was to characterize pneumococcal acquisition and carriage among healthy young adults in Israel during military training in confined settings. Methods During the years 2007–2008, an observational longitudinal study was conducted in three cohorts of healthy soldiers, during a 7-month basic training period. Epidemiological data, oropharyngeal and nasopharyngeal cultures were sampled on 5 occasions: before and 3, 6, 12 and 24 weeks after start of training. Samples were processed within 2–18 hours. Relatedness of isolates was investigated by capsular typing of all isolates and pulsed-field gel electrophoresis to determine acquisition and transmission. Carriage and acquisition patterns were analyzed and multivariable logistic regression analysis was performed to assess the impact of time on acquisition after mixing, controlling for other covariates. Results Pneumococci were recovered on 202 of 1872 visits among 742 individuals, including 40 different serotypes. Mean carriage prevalence increased in all visits following training initiation. Acquisition during training was high, as 36.9% of individuals acquired pneumococci at least once during training, and for almost one fourth of the whole population this occurred during the first 6 weeks. Significant clustering was noted. Sharing drinking glass/bottle was found to be a significant and common risk factor for pneumococcal acquisition. Conclusions Pneumococcal acquisition is highly frequent when young adults live in close contact in confined settings, especially early after mixing.
The New Economics of Equilibrium Sorting and Policy Evaluation Using Housing Markets

Households "sort" across neighborhoods according to their wealth and their preferences for public goods, social characteristics, and commuting opportunities. The aggregation of these individual choices in markets and in other institutions influences the supply of amenities and local public goods. Pollution, congestion, and the quality of public education are examples. Over the past decade, advances in economic models of this sorting process have led to a new framework that promises to alter the ways we conceptualize the policy evaluation process in the future. These "equilibrium sorting" models use the properties of market equilibria, together with information on household behavior, to infer structural parameters that characterize preference heterogeneity. The results can be used to develop theoretically consistent predictions for the welfare implications of future policy changes. Analysis is not confined to marginal effects or a partial equilibrium setting. Nor is it limited to prices and quantities. Sorting models can integrate descriptions of how nonmarket goods are generated, estimate how they affect decision making, and, in turn, predict how they will be affected by future policies targeting prices or quantities. Conversely, sorting models can predict how equilibrium prices and quantities will be affected by policies that target product quality, information, or amenities generated by the sorting process. These capabilities are just beginning to be understood and used in applied research. This survey article aims to synthesize the state of knowledge on equilibrium sorting, the new possibilities for policy analysis, and the conceptual and empirical challenges that define the frontiers of the literature.
Enhancing complex-network synchronization

Heterogeneity in the degree (connectivity) distribution has been shown to suppress synchronization in networks of symmetrically coupled oscillators with uniform coupling strength (unweighted coupling). Here we uncover a condition for enhanced synchronization in weighted networks with asymmetric coupling. We show that, in the optimum regime, synchronizability is solely determined by the average degree and does not depend on the system size and the details of the degree distribution. In scale-free networks, where the average degree may increase with heterogeneity, synchronizability is drastically enhanced and may become positively correlated with heterogeneity, while the overall cost involved in the network coupling is significantly reduced as compared to the case of unweighted coupling.
Integrating Renewable Energy Using Data Analytics Systems: Challenges and Opportunities

The variable and intermittent nature of many renewable energy sources makes integrating them into the electric grid challenging and limits their penetration. The current grid requires expensive, largescale energy storage and peaker plants to match such supplies to conventional loads. We present an alternative solution, in which supply-following loads adjust their power consumption to match the available renewable energy supply. We show Internet data centers running batched, data analytic workloads are well suited to be such supply-following loads. They are large energy consumers, highly instrumented, agile, and contain much scheduling slack in their workloads. We explore the problem of scheduling the workload to align with the time-varying available wind power. Using simulations driven by real life batch workloads and wind power traces, we demonstrate that simple, supply-following job schedulers yield 40-60% better renewable energy penetration than supply-oblivious schedulers.
Vascular Mimicry: The Next Big Glioblastoma Target

Glioblastoma (GBM), a grade IV glioma classified by World Health Organization (WHO), is considered highly malignant, vascular and invasive subtype [1]. GBM is most lethal during first year after initial diagnosis despite surgical resection, radiotherapy and/or chemotherapy [1,2]. Median survival of patients diagnosed with GBM is only 12 to 15 months [1,2]. Anti-angiogenic therapies (AAT) were used as an adjuvant mainly against vascular endothelial growth factor and its receptors (VEGF-VEGFRs) to normalize tumor vasculatures in GBM patients. However, all of them provided minimal to none effect with no change in overall survival [3]. Hypoxia and neovascularization are histopathologic features of GBM [4]. Hypoxia activated proangiogenic, invasion and metastasis associated gene signatures, enabling tumor to become more angiogenic, invasive and malignant in a compromised microenvironment [5,6]. GBM tumor vessels are tortuous, disorganized, highly permeable, and have abnormal endothelial cells (ECs), pericyte coverage, and basement membrane structure [7,8]. Conventionally, tumor vessel formation occurs through angiogenesis, which is mediated by proliferation and migration of resident ECs [9]. Instead, vasculogenesis originates from circulating bone marrow derived cells (BMDCs) or endothelial progenitor cells (EPCs), which express VEGFR2, are recruited by VEGF followed by differentiation and incorporation into new tumor blood vessels [10].
Hepatobiliary Schistosomiasis

Schistosomiasis is an ancient parasitic disease that has afflicted Egyptians since the time of the pharaohs. The disease is caused by lodged schistosome eggs in the host liver, evoking an immune response and leading in some patients to the development of hepatic granuloma and fibrosis. Here, we review the epidemiology, immunopathogenesis, and clinical profile of schistosomiasis. This information may aid in the development of more efficacious treatments and improved disease prognosis.
Essential dimension and algebraic stacks

We define and study the essential dimension of an algebraic stack. We compute the essential dimension of the stacks Mgn and MgnBar of smooth, or stable, n-pointed curves of genus g. We also prove a general lower bound for the essential dimension of algebraic groups with a non-trivial center. Using this, we find new exponential lower bounds for the essential dimension of spin groups and new formulas for the essential dimension of some finite p-groups. Finally, we apply the lower bound for spin groups to the theory of the Witt ring of quadratic forms over a field k.
A Complete Classification of the Expressiveness of Interval Logics of Allen's Relations over Dense Linear Orders

Interval temporal logics are temporal logics that take time intervals, instead of time instants, as their primitive temporal entities. One of the most studied interval temporal logics is Halpern and Shoham's modal logic of time intervals (HS), which has a distinct modality for each binary relation between intervals over a linear order. As HS turns out to be undecidable over most classes of linear orders, the study of HS fragments, featuring a proper subset of HS modalities, is a major item in the research agenda for interval temporal logics. A characterization of HS fragments in terms of their relative expressive power has been given for the class of all linear orders. Unfortunately, there is no easy way to directly transfer such a result to other meaningful classes of linear orders. In this paper, we provide a complete classification of the expressiveness of HS fragments over the class of (all) dense linear orders.
Refined phenotyping identifies links between preeclampsia and related diseases in a Norwegian preeclampsia family cohort

Objective: Preeclampsia is a complex genetic disease of pregnancy with a heterogenous presentation, unknown cause and potential severe outcomes for both mother and child. Preeclamptic women have increased risk for atherothrombotic cardiovascular disease. We aimed to identify heritabilities and phenotypic correlations of preeclampsia and related conditions in the Norwegian Preeclampsia Family Biobank. Methods: By applying a variance components model, a total of 493 individuals (from 138 families with increased occurrence of preeclampsia) were classified according to 30 disease-related phenotypes. Results: Of parous women, 75.7% (263/338) had experienced preeclampsia and 35.7% of women with and 22.4% without preeclampsia delivered children small for gestational age (SGA). We identified 11 phenotypes as heritable. The increased occurrence of preeclampsia was reflected by the presence [heritability (H2r) = 0.60)] and severity (H2r = 0.15) of preeclampsia and being born in a preeclamptic pregnancy (H2r = 0.25). Other heritable phenotypes identified included SGA (H2r = 0.40), chronic hypertension (H2r = 0.57), severity of atherothrombotic cardiovascular disease (H2r = 0.31), BMI (H2r = 0.60) and pulmonary disease (H2r = 0.91). The heritable phenotype preeclampsia overlapped with SGA (P = 0.03), whereas pulmonary disease was phenotypically correlated with atherothrombotic cardiovascular disease (P < 0.01), SGA (P = 0.02) and BMI (P = 0.02). Conclusion: This is the first study identifying the H2r of a range of health-related conditions in preeclamptic families. Our study demonstrates how refinement of phenotypes leads to better H2r estimation and the identification of a biological relationship between preeclampsia and related traits.
- 1-Peer-to-Peer Overlay Networks : A Survey

P2P network is factually an overlay network for distributed object store, search and sharing. This paper will present a survey about it’s recent. First, we will introduce a simple definition of P2P, and define some common operation procedures in P2P. Second, P2P performance metric will be discussed in order to understand and differentiated the practical P2P architectures and protocols. Third, the current P2P architecture will be classified and compared in detail. Then, object search protocols will be carefully discussed with corresponding P2P architecture. We also review the current P2P modeling outputs and list the general modeling approaches. Finally, several novel applications based on P2P techniques and future research directions of P2P overlay networks are briefly investigated. This paper aims to give a clear and complete survey of P2P networks according to clues of its development and evolution process.
Neurofibromatosis Type 2 Tumor Suppressor Protein, NF2, Induces Proteasome-Mediated Degradation of JC Virus T-Antigen in Human Glioblastoma

Neurofibromatosis type 2 protein (NF2) has been shown to act as tumor suppressor primarily through its functions as a cytoskeletal scaffold. However, NF2 can also be found in the nucleus, where its role is less clear. Previously, our group has identified JC virus (JCV) tumor antigen (T-antigen) as a nuclear binding partner for NF2 in tumors derived from JCV T-antigen transgenic mice. The association of NF2 with T-antigen in neuronal origin tumors suggests a potential role for NF2 in regulating the expression of the JCV T-antigen. Here, we report that NF2 suppresses T-antigen protein expression in U-87 MG human glioblastoma cells, which subsequently reduces T-antigen-mediated regulation of the JCV promoter. When T-antigen mRNA was quantified, it was determined that increasing expression of NF2 correlated with an accumulation of T-antigen mRNA; however, a decrease in T-antigen at the protein level was observed. NF2 was found to promote degradation of ubiquitin bound T-antigen protein via a proteasome dependent pathway concomitant with the accumulation of the JCV early mRNA encoding T-antigen. The interaction between T-antigen and NF2 maps to the FERM domain of NF2, which has been shown previously to be responsible for its tumor suppressor activity. Co-immunoprecipitation assays revealed a ternary complex among NF2, T-antigen, and the tumor suppressor protein, p53 within a glioblastoma cell line. Further, these proteins were detected in various degrees in patient tumor tissue, suggesting that these associations may occur in vivo. Collectively, these results demonstrate that NF2 negatively regulates JCV T-antigen expression by proteasome-mediated degradation, and suggest a novel role for NF2 as a suppressor of JCV T-antigen-induced cell cycle regulation.
Locally Weighted Fusion of Multiple Predictive Models

Fusing the outputs from an ensemble of models in an effective way can often boost overall model accuracy. This paper presents a novel method, called locally weighted fusion, which aggregates the results of multiple predictive models based on local accuracy measures of these models in the neighborhood of the probe point for which we want to make a prediction. While we demonstrate the method in the context of multiple neural network models, the concepts may be applied to other predictive techniques as well. This fusion method is applied to develop highly accurate models for emissions, efficiency, and load prediction in a complex real-world power plant. The locally weighted fusion method boosts the predictive performance by 20-40% over the baseline single model approach for the various prediction targets. Relative to this approach, fusion strategies which apply averaging or globally weighting only produce a 2-6% performance boost over the baseline.
Streaming Universal Distortion-Free Entanglement Concentration

This paper presents a streaming (sequential) protocol for universal entanglement concentration at the Shannon bound. Alice and Bob begin with N identical (but unknown) two-qubit pure states, each containing E ebits of entanglement. They each run a reversible algorithm on their qubits, and end up with Y perfect EPR pairs, where Y=NE ± O(√(N)). Our protocol is streaming, so the N input systems are fed in one at a time, and perfect EPR pairs start popping out almost immediately. It matches the optimal block protocol exactly at each stage, so the average yield after n inputs is 〈 Y〉=nE-O(logn). So, somewhat surprisingly, there is no tradeoff between yield and lag-our protocol optimizes both. In contrast, the optimal N-qubit block protocol achieves the same yield, but since no EPR pairs are produced until the entire input block is read, its lag is O(N). Finally, our algorithm runs in O(log N) space, so a lot of entanglement can be efficiently concentrated using a very small (e.g., current or near-future technology) quantum processor. Along the way, we find an optimal streaming protocol for extracting randomness from classical i.i. d. sources and a more space-efficient implementation of the Schur transform.
Provenance in Games

Serious games have been used to aid the understanding of concepts that are taught in theoretical classes. However, mistakes made by players may result in failure to complete the game objectives. These mistakes, which are usually difficult to spot or reproduce in subsequent trials, directly jeopardize the learning capabilities of the serious games and are usually avoided by the established gameplay. In order to solve this issue, this paper introduces a new concept: provenance in games, which is used to analyze collected data and provide a feedback to the player allowing him to understand and analyze the effects of the decisions he made. To do so, we present a framework that records all useful gameplay data and provides this data to further analysis. We also instantiated this framework in a Software Engineering game as a proof of concept, which allows the player to identify his mistakes and learn through them.
THE RELATIONSHIP BETWEEN AFFECTIVE COMMITMENT AND UNETHICAL PRO-ORGANIZATIONAL BEHAVIOR: THE ROLE OF MORAL DISENGAGEMENT

Purpose - As one of the few studies on unethical pro-organizational behavior (UPB), this study hypothesizes that affective commitment predicts UPB through the mediation of moral disengagement. It was also proposed that ethical leadership affects the hypothesized relationship. Methodology - 208 employees (98 males and 110 females) from different companies located in Istanbul, Turkey formed the sample of this study. A multidimensional questionnaire with seven-point interval scale was applied to measure all substantive variables used in this study. Findings- The results of mediation analysis revealed that people with higher affective commitment engaged in more UPB, and that this effect was mediated by moral disengagement. In addition, the results of moderated mediation analysis showed that the mediation relationship was stronger when employees perceived a lower level of ethical leadership in the organization. Conclusion- We found that affective commitment encourages UPB through the mechanism of moral disengagement. Besides, this effect is stronger when the ethical leadership is perceived to be low as opposed to being high. Our results extend knowledge of UPB by highlighting the importance of ethical leadership, and open up new avenues of research on the ethical challenges of prosocial motives, attitudes, and behaviors. Further theoretical and practical implications are discussed.