Miracle-dz/UniFAR-FTUs huggingface dataset:
The QUT-NOISE-TIMIT corpus for the evaluation of voice activity detection algorithms

The QUT-NOISE-TIMIT corpus consists of 600 hours of noisy speech sequences designed to enable a thorough evaluation of voice activity detection (VAD) algorithms across a wide variety of common background noise scenarios. In order to construct the final mixed-speech database, a collection of over 10 hours of background noise was conducted across 10 unique locations covering 5 common noise scenarios, to create the QUT-NOISE corpus. This background noise corpus was then mixed with speech events chosen from the TIMIT clean speech corpus over a wide variety of noise lengths, signal-to-noise ratios (SNRs) and active speech proportions to form the mixed-speech QUT-NOISE-TIMIT corpus. The evaluation of five baseline VAD systems on the QUT-NOISE-TIMIT corpus is conducted to validate the data and show that the variety of noise available will allow for better evaluation of VAD systems than existing approaches in the literature.
Laplace transform integration of the shallow‐water equations. Part I: Eulerian formulation and Kelvin waves

A filtering integration scheme is developed, using a modification of the contour used to invert the Laplace transform (LT). It is shown to eliminate components with frequencies higher than a specified cut‐off value. Thus it is valuable for integrations of the equations governing atmospheric flow. The scheme is implemented in a shallow‐water model with an Eulerian treatment of advection. It is compared to a reference model using the semi‐implicit (SI) scheme. The LT scheme is shown to treat dynamically important Kelvin waves more accurately than the SI scheme. Copyright © 2011 Royal Meteorological Society
Towards automatic parallelization of sequential programs and efficient use of resources in HPC centers

High-Performance Computing (HPC) is becoming increasingly required by scientists of all branches in order to achieve their desired research results. However, carrying out their research in an HPC center can be a difficult task when they are new to parallel programming. These users need support in the parallelization and optimization of their codes, in order to obtain reliable results as well as make efficient use of the available resources. For this purpose, a novel code analyzer for automatic parallelization of sequential codes is presented, focused on resource management of a supercomputing center, where efficient scheduling decisions and energy saving become key challenges. Thus, this paper aims to introduce the analyzer so as to demonstrate the importance of using it, specially in terms of efficiency, when running parallel codes in HPC centers.
Instantons, fermions and Chern-Simons terms

In five spacetime dimensions, instantons are finite energy, solitonic particles. We describe the dynamics of these objects in the presence of a Chern-Simons interaction. For U(N) instantons, we show that the 5d Chern-Simons term induces a corresponding Chern-Simons term in the ADHM quantum mechanics. For SU(N) instantons, we provide a description in terms of geodesic motion on the instanton moduli space, modified by the presence of a magnetic field. We show that this magnetic field is equal to the first Chern character of an index bundle. All of these results are derived by a simple method which follows the fate of zero modes as fermions are introduced, made heavy, and subsequently integrated out.
Impact of the Physical Layout of High-Current Rectifiers on Current Division and Magnetic Field Using PEEC Method

This paper addresses some problems linked to the physical layout of high-current rectifiers: paralleling components, magnetic field close to the rectifier, and also the validation of the physical layout at reduced current. Even if the impact of cabling stray inductance is well known, some new tools and methodology are available today to design quicker and safer physical layout of such high-current rectifiers.
GEMiner: Mining Social and Programming Behaviors to Identify Experts in Github

Hosting over 10 million repositories, GitHub becomes the largest open source community in the world. Besides sharing code, Github is also a social network, in which developers can follow others or keep track of their interested projects. Considering the multi-roles of Github, integrating heterogenous data of each developer to identify experts is a challenging task. In this paper, we propose GEMiner, a novel approach to identify experts for some specific programming languages in Github. Different from previous approaches, GEMiner analyzes the social behaviors and programming behaviors of a developer to determine the expertise of the developer. When modeling social behaviors of developers, to integrate heterogenous social networks in Github, GEMiner implements a Multi-Sources PageRank algorithm. Also, GEMiner analyzes the behaviors of developers when they are programming (e.g., their commit activities and their preferred programming languages) to model programming behaviors of them. Based on our expertise models and our extracted programming languages data, GEMiner can then identify experts for some specific programming languages in Github. We conducted experiments on a real data set, and our results show that GEMiner identifies experts with 60% accuracy higher than the state-of-the-art algorithms.
AN EFFICIENT APPROACH TO PERFORM PRE-PROCESSING

Nowadays, WWW (World Wide Web) becomes more popular and user friendly for transferring information. Therefore people are more interested in analyzing log files which can offer more useful insight into web site usage. Web usage mining is one of the data mining fields, which deals with the discovery and extract useful information from web logs. There are three phases in web usage mining, preprocessing, Pattern Discovery and Pattern Analysis. This paper describes the importance of preprocessing methods and steps involved in retrieving the required information effectively.
Iron Superoxide Dismutases Targeted to the Glycosomes of Leishmania chagasi Are Important for Survival

ABSTRACT Kinetoplastid glycosomes contain a variety of metabolic activities, such as glycolysis, β-oxidation of fatty acids, lipid biosynthesis, and purine salvage. One advantage of sequestering metabolic activities is the avoidance of cellular oxidative damage by reactive oxygen species produced as a by-product of metabolism. Little is known about how glycosomes themselves withstand these toxic metabolites. We previously isolated an iron superoxide dismutase from Leishmania chagasi that is expressed at low levels in the early logarithmic promastigote stage and increases toward the stationary promastigote and amastigote stages. We have since identified a second highly homologous Lcfesodb gene that is expressed at high levels in the early logarithmic promastigote stage and decreases toward the stationary promastigote and amastigote stages. Localization studies using green fluorescent protein fusions have revealed that LcFeSODB1 and LcFeSODB2 are localized within the glycosomes by the last three amino acids of their carboxyl termini. To better understand the specific role that FeSODB plays in parasite growth and survival, a single-allele knockout of the Lcfesodb1 gene was generated. The parasites with these genes exhibited a significant reduction in growth when endogenous superoxide levels were increased with paraquat in culture. Furthermore, the FeSODB1-deficient parasites exhibited a significant reduction in survival within human macrophages. Our results suggest that LcFeSODB plays an important role in parasite growth and survival by protecting glycosomes from superoxide toxicity.
Enhanced Cortico-Amygdala Efficacy and Suppressed Fear in Absence of Rap1

Auditory fear conditioning, a model for fear learning, is thought to be mediated by synaptic changes in the cortical and thalamic inputs to the lateral amygdala (LA); however, the specific roles of both pathways are still debated. Here, we report that a CaMKII-α-Cre-mediated knock-out (KO) of the rap1a and rap1b genes impaired synaptic plasticity and increased basal synaptic transmission in the cortical but not thalamic input to the LA via presynaptic changes: increases in glutamate release probability and the number of glutamate quanta released by a single action potential. Moreover, KO mice with alterations in the cortico-LA pathway had impaired fear learning, which could be rescued by training with a more aversive unconditional stimulus. These results suggest that Rap1-mediated suppression of synaptic transmission enables plasticity in the cortico-amygdala pathway, which is required for fear learning with a moderately aversive unconditional stimulus.
Low mislabeling rates indicate marked improvements in European seafood market operations

Over the span of a decade, genetic identification methods have progressively exposed the inadequacies of the seafood supply chain, revealing previously unrecognized levels of seafood fraud, raising awareness among the public, and serving as a warning to industry that malpractice will be detected. Here we present the outcome of the latest and largest multi-species, transnational survey of fish labeling accuracy to date, which demonstrates an apparent sudden reduction of seafood mislabeling in Europe. We argue that recent efforts in legislation, governance, and outreach have had a positive impact on industry regulation. Coordinated, technology-based, policy-oriented actions can play a pivotal role in shaping a transparent, sustainable global seafood market and in bolstering healthier oceans.
UNIT VECTOR CONTROL OF MULTIVARIABLE SYSTEMS

This paper presents a unit vector based output-feedback model-reference adaptive control (UV-MRAC) for uncertain multi-input-multi-output (MIMO) linear systems. Some features of this new algorithm are: (i) Less restrictive a priori knowledge about the high frequency gain matrix of the plant is required. (ii) It applies to plants of any uniform relative degree. (iii) The closed loop system is exponentially stable with respect to some small residual set. (iv) The controller is free of peaking.
A comparative study of unscented and extended Kalman filter for position and velocity estimation of Stewart platform manipulator

The estimation of position and velocity of Stewart Manipulator using low cost inertial sensors (accelerometers and gyroscope) and Leg length measurements will be considered. The estimation is based on the integration of different sensors by means of Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). The results of the evaluation of these filter estimation techniques are compared with each other. A proposed computed torque controller is simulated and verified using the estimated states from UKF with 6 degrees of freedom Stewart platform using MATLAB SimMechanics toolbox.
A cluster based multiple ontology parallel merge method

Ontologies are an inevitable aspect of knowledge representation, processing, sharing and reuse and this in itself is the essence and significance of ontology. The number of ontologies available online is growing at a rapid pace. This conspicuous growth of ontologies enables sharing of knowledge in distributed and heterogeneous systems. The tremendous growth of ontologies has implied the popping up of multiple ontologies of the same domain. Applications developed based on these ontologies cannot achieve interoperability. To conquer this problem one of the optimum solution is to merge the ontologies in to a single general complete ontology. This brings up the need for a method which merges multiple ontologies, producing consistent, complete ontology. Another decisive factor worth consideration is the performance of multiple ontology merge process in terms of time. The current research work proposes a new cluster based multiple ontology parallel merge process. The performance of the multiple ontology merge process is fine-tuned by considering the similarity measure and parallel merge process. The proposed method produces consistent, optimum and complete knowledge of the domain as a single ontology with reduced time, which can be used by heterogeneous applications. The proposed method is implemented and a comparison of time complexity of the processing is presented at the end.
CloudSec: A security monitoring appliance for Virtual Machines in the IaaS cloud model

The Infrastructure-as-a-Service (IaaS) cloud computing model has become a compelling computing solution with a proven ability to reduce costs and improve resource efficiency. Virtualization has a key role in supporting the IaaS model. However, virtualization also makes it a target for potent rootkits because of the loss of control problem over the hosted Virtual Machines (VMs). This makes traditional in-guest security solutions, relying on operating system kernel trustworthiness, no longer an effective solution to secure the virtual infrastructure of the IaaS model. In this paper, we explore briefly the security problem of the IaaS cloud computing model, and present CloudSec, a new virtualization-aware monitoring appliance that provides active, transparent and real-time security monitoring for hosted VMs in the IaaS model. CloudSec utilizes virtual machine introspection techniques to provide fine-grained inspection of VM's physical memory without installing any monitoring code inside the VM. It actively reconstructs and monitors the dynamically changing kernel data structures instances, as a prior step to enable providing protection for kernel data structures. We have implemented a proof-of-concept prototype using VMsafe libraries on a VMware ESX platform. We have evaluated the system monitoring accuracy and the performance overhead of CloudSec.
Making Sense of Distributed Leadership: How Secondary School Educators Look at Job Redesign

This paper examines how teachers and administrators who were involved in a multi-year effort to engage in distributed leadership interpreted their experiences. We lay out and apply an argument for using an interpretive perspective to study distributed leadership.Collective sensemaking around distributed leadership is illustrated by an in-depth analysis of a single high school. The school was part of a larger study of six schools, and was selected to illustrate sensemaking over time in a large, complex school. There were three years of on-site interviews, observations and document analysis. We found that distributed leadership is a potential “disruption” to traditional patterns of leadership, work performance and influence in high schools. One-quarter of the school’s faculty engaged with the “disruption” but all had a chance to process the change. The end result was that many became sense-givers and kept the momentum for teacher leadership going during significant personnel turnover among faculty and administration. The success of the efforts to create more broadly distributed leadership was facilitated by its integration into an existing improvement initiative.
PROBLEM CLASSIFICATION FOR A PLASMA PROCESS

The use of first-principles based models of plasma processes is discussed in this paper. Such processes are essentially highly nonlinear, feature complex interactions and are difficult to analyse. As an illustration of the nature of firstprinciple based models, a characterisation of a simple plasma process is presented in this work. Quantification of the nonlinearity, in terms of steady-state and dynamics behavior, is carried out for the studied process. The use of Hammerstein model and its applicability to the plasma process is also investigated. A basic stabilising controller for the simple plasma process is designed and its performance is analysed. Copyright c ©2005 IFAC
Fast Asynchronous Parallel Stochastic Gradient Decent

Stochastic gradient descent~(SGD) and its variants have become more and more popular in machine learning due to their efficiency and effectiveness. To handle large-scale problems, researchers have recently proposed several parallel SGD methods for multicore systems. However, existing parallel SGD methods cannot achieve satisfactory performance in real applications. In this paper, we propose a fast asynchronous parallel SGD method, called AsySVRG, by designing an asynchronous strategy to parallelize the recently proposed SGD variant called stochastic variance reduced gradient~(SVRG). Both theoretical and empirical results show that AsySVRG can outperform existing state-of-the-art parallel SGD methods like Hogwild! in terms of convergence rate and computation cost.
Robust Incremental Parsing using Human-Like Memory Constraints

Psycholinguistic studies suggest a model of human language processing that 1) performs incremental interpretation of spoken utterances or written text , 2) preserves ambiguity by maintaining competing analyses in parallel, and 3) operates within a sev erely constrained short-term memory store – possibly constrained to as few as four distinct eleme nts. The first two observations are sometimes taken to endorse a probabilistic beam-search mod el, similar to some existing systems. But the last observation imposes a restriction that has not u til now been evaluated in a corpus study. This paper first describes a relatively simple statis tical model of incremental language processing that meets all three of the above desiderata. The pap r then evaluates the coverage of an implementation of this model, and the accuracy with which this implementation can analyze a large syntactically-annotated corpus of English.
Herbicide tolerant soybeans: why growers are adopting Roundup Ready varieties

Soybean growers have adopted herbicide tolerant varieties at a rapid rate. While some potential cost savings may have driven adoption, the primary reason growers are switching to new programs is the simplicity and flexibility of a weed control program that relies on one herbicide to control a broad spectrum of weeds without crop injury or crop rotation restrictions. Roundup Ready weed control programs for soybeans fit into on-going trends towards postemergence weed control, adoption of conservation tillage practices and narrow row spacing. Roundup Ready systems have also solved some of the problems growers faced in conventional weed management systems.
Expression and function of lysophosphatidic acid receptors (LPARs) 1 and 3 in human hepatic cancer progenitor cells

Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver and is characterized by rapid tumor expansion and metastasis. Lysophosphatidic acid (LPA) signaling, via LPA receptors 1–6 (LPARs1–6), regulates diverse cell functions including motility, migration, and proliferation, yet the role of LPARs in hepatic tumor pathology is poorly understood. We sought to determine the expression and function of endothelial differentiation gene (EDG) LPARs (LPAR1–3) in human HCC and complimentary in vitro models. Human HCC were characterized by significantly elevated LPAR1/LPAR3 expression in the microenvironment between the tumor and non-tumor liver (NTL), a finding mirrored in human SKHep1 cells. Analysis of human tissue and human hepatic tumor cells in vitro revealed cells that express LPAR3 (HCC-NTL margin in vivo and SKHep1 in vitro) also express cancer stem cell markers in the absence of hepatocyte markers. Treatment of SKHep1 cells with exogenous LPA led to significantly increased cell motility but not proliferation. Using pharmacological agents and cells transfected to knock-down LPAR1 or LPAR3 demonstrated LPA-dependent cell migration occurs via an LPAR3-Gi-ERK-pathway independent of LPAR1. These data suggest cells that stain positive for both LPAR3 and cancer stem cell markers are distinct from the tumor mass per se, and may mediate tumor invasiveness/expansion via LPA-LPAR3 signaling.
Effect of Metal Artifacts on Detection of Vertical Root Fractures Using Two Cone Beam Computed Tomography Systems

Introduction: Vertical root fracture (VRF) is common in endodontically treated teeth. Conventional and digital radiographies have limitations for detection of VRFs. Cone-beam computed tomography (CBCT) offers greater detection accuracy of VRFs in comparison with conventional radiography. This study compared the effects of metal artifacts on detection of VRFs by using two CBCT systems. Methods and Materials: Eighty extracted premolars were selected and sectioned at the level of the cemento enamel junction (CEJ). After preparation, root canals were filled with gutta-percha. Subsequently, two thirds of the root fillings were removed for post space preparation and a custom-made post was cemented into each canal. The teeth were randomly divided into two groups (n=40). In the test group, root fracture was created with Instron universal testing machine. The control teeth remained intact. CBCT scans of all teeth were obtained with either New Tom VGI or Soredex Scanora 3D. Three observers analyzed the images for detection of VRF. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for VRF detection and percentage of probable cases were calculated for each imaging system and compared using non-parametric tests considering the non-normal distribution of data. The inter-observer reproducibility was calculated using the weighted kappa coefficient. Results: There were no statistically significant differences in sensitivity, specificity, PPV and NPV between the two CBCT systems. Conclusion: The effect of metal artifacts on VRF detection was not significantly different between the two CBCT systems.
Two-dimensional electronic spectroscopy of the B800–B820 light-harvesting complex

Emerging nonlinear optical spectroscopies enable deeper insight into the intricate world of interactions and dynamics of complex molecular systems. 2D electronic spectroscopy appears to be especially well suited for studying multichromophoric complexes such as light-harvesting complexes of photosynthetic organisms as it allows direct observation of couplings between the pigments and charts dynamics of energy flow on a 2D frequency map. Here, we demonstrate that a single 2D experiment combined with self-consistent theoretical modeling can determine spectroscopic parameters dictating excitation energy dynamics in the bacterial B800–B820 light-harvesting complex, which contains 27 bacteriochlorophyll molecules. Ultrafast sub-50-fs dynamics dominated by coherent intraband processes and population transfer dynamics on a picosecond time scale were measured and modeled with one consistent set of parameters. Theoretical 2D spectra were calculated by using a Frenkel exciton model and modified Förster/Redfield theory for the calculation of dynamics. They match the main features of experimental spectra at all population times well, implying that the energy level structure and transition dipole strengths are modeled correctly in addition to the energy transfer dynamics of the system.
Modeling the Listeria monocytogenes survival/death curves using wavelet neural networks

The development of accurate models to describe and predict pressure inactivation kinetics of microorganisms is very beneficial to the food industry for optimization of process conditions. The need for “intelligent” methods to model highly nonlinear systems is long established. Feed-forward neural networks have been successfully used for modeling of nonlinear systems. The objective of this research is to investigate the capabilities of a new wavelet neural network, to predicting of survival curves of Listeria monocytogenes inactivated by high hydrostatic pressure in UHT whole milk. The performance of the proposed scheme has been compared against a dynamic neural network and classic statistical models used in food microbiology.