Miracle-dz/UniFAR-FTUs huggingface dataset:
Noise‐Robust Speaker Recognition Using Subband Likelihoods and Reliable‐Feature Selection

We consider the feature recombination technique in a multiband approach to speaker identification and verification. To overcome the ineffectiveness of conventional feature recombination in broadband noisy environments, we propose a new subband feature recombination which uses subband likelihoods and a subband reliable‐feature selection technique with an adaptive noise model. In the decision step of speaker recognition, a few very low unreliable feature likelihood scores can cause a speaker recognition system to make an incorrect decision. To overcome this problem, reliable‐feature selection adjusts the likelihood scores of an unreliable feature by comparison with those of an adaptive noise model, which is estimated by the maximum a posteriori adaptation technique using noise features directly obtained from noisy test speech. To evaluate the effectiveness of the proposed methods in noisy environments, we use the TIMIT database and the NTIMIT database, which is the corresponding telephone version of TIMIT database. The proposed subband feature recombination with subband reliable‐feature selection achieves better performance than the conventional feature recombination system with reliable‐feature selection.
Modulation of calcium-activated potassium channels from rat brain by protein kinase A and phosphatase 2A

By incorporating plasma membrane vesicles into planar lipid bilayers, we previously characterized a family of four types of Ca(2+)-activated K+ channels from rat brain (Reinhart et al., 1989). Two of these are “large-conductance” or “maxi”-K+ channels, which differ in their gating kinetics and toxin sensitivity and are henceforth referred to as “type 1” and “type 2” channels. Here we show that the gating of these two channel types can be modulated by phosphorylation and dephosphorylation. The effects of cAMP-dependent protein kinase catalytic subunit (PK-A) on type 1 maxi-K+ channels are complex in that, while half of these channels are upregulated by the kinase, about one out of seven channels is downregulated. Thus, there may be several distinct channels within the type 1 category. Type 2 maxi-K+ channels are consistently downregulated by PK-A. The effects of PK-A on both channel types are reversed by the catalytic subunit of protein phosphatase 2A (PP-2A), but not by protein phosphatase 1 (PP-1). Furthermore, some of the type 1 maxi-K+ channels can be modulated by PP- 2A, even without any prior PK-A treatment, indicating they are in a phosphorylated state when they are incorporated into the bilayer. The results demonstrate that (1) type 1 and type 2 maxi-K+ channels are substrates for PK-A; (2) phosphorylation can shift the open probability of channels in either direction, by a mechanism involving multiple phosphorylation sites; (3) phosphorylation alters the Ca2+/voltage sensitivity of these channels; and (4) dephosphorylation of type 1 and type 2 channels is catalyzed by specific phosphatases.
Radial Basis Functions for Process Control

Radial basis function (RBFs) neural networks provide an attractive method for high dimensional nonparametric estimation for use in nonlinear control. They are faster to train than conventional feedforward networks with sigmoidal activation networks (\backpropagation nets"), and provide a model structure better suited for adaptive control. This article gives a brief survey of the use of RBFs and then introduces a new statistical interpretation of radial basis functions and a new method of estimating the parameters, using the EM algorithm. This new statistical interpretation allows us to provide con dence limits on predictions made using the networks.
Semantically Tagging Images of Landmarks

Semantic tagging allows images to be linked with URIs from web resources. Disambiguating URIs which correspond with an image’s visual content is challenging. Previous work has largely failed to effectively contextualise the knowledge provided by the Semantic Web and the user-provided keyword tags in images. We propose an algorithm which uses geographical coordinates and keywords of similar images to recommend semantic tags that describe an image’s visual content. Our algorithm uses the semantic stores YAGO2, DBPedia and Geonames. These stores allow us to handle multi-lingual keyword tags and disambiguate between alternative names for landmarks.
Computer simulation of liquid-crystal surface modification

Metropolis Monte Carlo simulations are used to study the interplay between two different anchoring effects of spherocylinders on a modified surface consisting of hard walls onto which liquid-crystal molecules have been perpendicularly grafted. By varying both the length and grafting density of the surface molecules, a number of different and novel anchoring regimes are observed including: planar, homeotropic, tilted and decoupled planar.
An Industrial Evaluation of DRAM Tests

This paper presents the results of 44 well known memory tests applied to 1896 1M*4 DRAM chips, using up to 96 different stress combinations with each test. The results show the importance of selecting the right stress combination, and that the theoretically better tests (i.e. those covering different functional faults) also have a higher fault coverage. However the currently used fault models still leave much to be explained; e.g., the used data backgrounds and address orders show an unexplainable large variation in fault coverage.
Power allocation algorithms in parallel relay communication networks

We consider a parallel relay communication network based on the nonorthorgonal amplify-and-forward protocol. We design two optimal power allocation schemes: separate power allocation (SPA) of the relays using the improved water-filling algorithm and joint power allocation (JPA) of the source and relays using convex optimization algorithm. Simulation results show that, in the fading channels with path loss, the proposed JPA and SPA have better mutual information performances than equal power allocation and direct transmission without relay.
Did Retirees Save Enough to Compensate for the Increase in Individual Risk Exposure?

The United States experienced an unprecedented financial crisis after 2007. This paper analyzes whether retirees had enough wealth built up to weather the financial risks that materialized in the crisis. Financial risks associated with saving for retirement had increasingly shifted onto individuals and away from the public and employers during the decades before the crisis. This growing personal responsibility should have brought about more saving and less risk taking. I use data from the Federal Reserve's triennial Survey of Consumer Finances first to define an income threshold for retirees, specifically whether annuity income is greater than twice the poverty line: a common proxy for basic income needs. I then calculate the potential retirement income that retirees could expect if they translated all of their wealth into income and if the income is adjusted for market, idiosyncratic, and longevity risks. I compare the potential risk-adjusted income for retirees with annuity income above twice the poverty line to those retirees with annuity income below twice the poverty line. Both groups of retirees should have at least the same level of risk-adjusted potential retirement income. This comparison shows, however, that retirees with annuity income below twice the poverty line did not build up sufficient wealth to compensate for the rising financial risk exposure. Public policy thus should maintain existing sources of annuity income, promote greater annuitization of financial wealth, and encourage additional savings.
An extension theorem for sober spaces and the Goldman topology

Goldman points of a topological space are defined in order to extend the notion of prime G-ideals of a ring. We associate to any topological space a new topology called Goldman topology. For sober spaces, we prove an extension theorem of continuous maps. As an application, we give a topological characterization of the Jacobson subspace of the spectrum of a commutative ring. Many examples are provided to illustrate the theory.
Genetic Characterization of Porcine Circovirus Type 2 from Pigs with Porcine Circovirus Associated Diseases in Argentina

Porcine circovirus type 2 (PCV-2) has been associated with syndromes grouped by the term porcine circovirus associated diseases (PCVAD). The PCV-2 isolates have been grouped into two major groups or genotypes according to their nucleotide sequence of whole genomes and/or ORF-2: PCV-2b, which have, in turn, been subdivided into three clusters (1A–1C), and PCV-2a, which has been subdivided into five clusters (2A–2E). In the present study, we obtained 16 sequences of PCV-2 from different farms from 2003 to 2008, from animals with confirmatory diagnosis of PCVAD. Since results showed an identity of 99.8% among them, they were grouped within a common cluster 1A-B. This preliminary study suggests a stable circulation of PCV-2b among the Argentinean pig population.
A Xen-Based Secure Virtual Disk Access-Control Method

The paper begins with introduction on the xen-based secure computer, the common data sharing methods and virtual disk access control. It puts forward a data sharing method based on virtual disk between guest OSs of Full-Virtualization in order to deal with the shortages of data sharing between virtual guest OSs based on Xen secure computer. It induces further analysis on the Xen driver split mechanism inspiring possibility to build a communication channel from guest OS to host OS. Meanwhile, along with the Xen’s virtual disk control principles, a data access control measure is also brought forward. Finally, it proves through a series of precise experiments, the method to be a cross-level data share, which makes data access more controllable and much safer with a promising efficiency beyond other sharing methods.
Opponent Identity Influences Value Learning in Simple Games

Context plays a pivotal role in many decision-making scenarios, including social interactions wherein the identities and strategies of other decision makers often shape our behaviors. However, the neural mechanisms for tracking such contextual information are poorly understood. Here, we investigated how opponent identity affects human reinforcement learning during a simulated competitive game against two independent computerized opponents. We found that strategies of participants were affected preferentially by the outcomes of the previous interactions with the same opponent. In addition, reinforcement signals from the previous trial were less discriminable throughout the brain after the opponent changed, compared with when the same opponent was repeated. These opponent-selective reinforcement signals were particularly robust in right rostral anterior cingulate and right lingual regions, where opponent-selective reinforcement signals correlated with a behavioral measure of opponent-selective reinforcement learning. Therefore, when choices involve multiple contextual frames, such as different opponents in a game, decision making and its neural correlates are influenced by multithreaded histories of reinforcement. Overall, our findings are consistent with the availability of temporally overlapping, context-specific reinforcement signals. SIGNIFICANCE STATEMENT In real-world decision making, context plays a strong role in determining the value of an action. Similar choices take on different values depending on setting. We examined the contextual dependence of reward-based learning and reinforcement signals using a simple two-choice matching-pennies game played by humans against two independent computer opponents that were randomly interleaved. We found that human subjects' strategies were highly dependent on opponent context in this game, a fact that was reflected in select brain regions' activity (rostral anterior cingulate and lingual cortex). These results indicate that human reinforcement histories are highly dependent on contextual factors, a fact that is reflected in neural correlates of reinforcement signals.
Blind adaptation of stable discrete-time IIR filters in state-space form

Blind deconvolution consists of extracting a source sequence and impulse response of a linear system from their convolution. In the presence of system zeros close to the unit circle, which give rise to very long impulse responses, infinite-impulse-response (IIR) adaptive structures are of use, whose adaptation should be carefully designed in order to guarantee stability. In this paper, we propose a blind-type discrete-time IIR adaptive filter structure realized in state-space form that, with a suitable parameterization of its coefficients, remains stable. The theory is first developed for a two-pole filter, whose numerical behavior is investigated via numerical experiments. The proposed structure/adaptation theory is then extended to a multipole structure realized as a cascade of two-pole filters. Computer-based experiments are proposed and discussed, which aim at illustrating the behavior of the filter cascade on several cases of study. The numerical results obtained show the proposed filters remain stable during adaptation and provide satisfactory deconvolution results
Anomalous Loss Performance for Mixed Real-Time and TCP Traffic in Routers With Very Small Buffers

In the past few years there has been vigorous debate regarding the size of buffers required at core Internet routers. Recent arguments supported by theory and experimentation show that under certain conditions, core router buffer sizes of a few tens of packets suffice for realizing acceptable end-to-end TCP throughputs. This is a significant step toward the realization of optical packet switched (OPS) networks, which are inherently limited in their ability to buffer optical signals. However, prior studies have largely ignored the presence of real-time traffic, which is increasing in importance as a source of revenue for Internet service providers. In this paper, we study the interaction that happens between real-time (open-loop) and TCP (closed-loop) traffic when they multiplex at buffers of very small size (few tens of packets) and make a significant discovery - namely that in a specific range of buffer size, real-time traffic losses increase as buffer size becomes larger. Our contributions pertaining to this anomalous behavior are threefold. First, we exhibit this anomalous loss performance for real-time traffic via extensive simulations using synthetic traffic and real video traces. Second, we develop quantitative models that reveal the dynamics of buffer sharing between real-time and TCP traffic that lead to this behavior. Third, we show how various factors such as the nature of real-time traffic, mixture of long-lived and short-lived TCP flows, and packet sizes impact the severity of the anomaly. Our study is the first to consider interactions between real-time and TCP traffic in very small (potentially all-optical) buffers and informs router manufacturers and network operators of the factors to consider when dimensioning such small buffer sizes for desired performance balance between real-time and TCP traffic.
Phenotypic switching can speed up biological evolution of microbes

Stochastic phenotype switching has been suggested to play a beneficial role in microbial populations by leading to the division of labour among cells, or ensuring that at least some of the population survives an unexpected change in environmental conditions. Here we use a computational model to investigate an alternative possible function of stochastic phenotype switching – as a way to adapt more quickly even in a static environment. We show that when a genetic mutation causes a population to become less fit, switching to an alternative phenotype with higher fitness (growth rate) may give the population enough time to develop compensatory mutations that increase the fitness again. The possibility of switching phenotypes can reduce the time to adaptation by orders of magnitude if the “fitness valley” caused by the deleterious mutation is deep enough. Our work has important implications for the emergence of antibiotic-resistant bacteria. In line with recent experimental findings we hypothesise that switching to a slower growing but less sensitive phenotype helps bacteria to develop resistance by exploring a larger set of beneficial mutations while avoiding deleterious ones.
Identi fi cation and functional characterization of BTas transactivator as a DNA-binding protein

a State Key Laboratory of Elemento-Organic Chemistry and Department of Chemical Biology, Nankai University, Tianjin 300071, China b Key Laboratory of Molecular Microbiology and Biotechnology (Ministry of Education) and Key Laboratory of Microbial Functional Genomics (Tianjin), College of Life Sciences, Nankai University, Tianjin 300071, China c Tianjin Key Lab of Ophthalmology and Visual Science, Tianjin Eye Hospital, Tianjin 300020, China d Key Laboratory of Molecular Microbiology and Biotechnology (Ministry of Education) and Key Laboratory of Microbial Functional Genomics (Tianjin), TEDA School of Biological Sciences and Biotechnology, Nankai University, Tianjin 300457, China
Nearly optimal sample size in hypothesis testing for high-dimensional regression

We consider the problem of fitting the parameters of a high-dimensional linear regression model. In the regime where the number of parameters p is comparable to or exceeds the sample size n, a successful approach uses an ℓ1-penalized least squares estimator, known as Lasso. Unfortunately, unlike for linear estimators (e.g. ordinary least squares), no well-established method exists to compute confidence intervals or p-values on the basis of the Lasso estimator. Very recently, a line of work [8], [7], [13] has addressed this problem by constructing a debiased version of the Lasso estimator. We propose a special debiasing method that is well suited for random designs with sparse inverse covariance. Our approach improves over the state of the art in that it yields nearly optimal average testing power if sample size n asymptotically dominates s0(logp)2, with s0 being the sparsity level (number of non-zero coefficients). Earlier work achieved similar performances only for much larger sample size, namely it requires n to asymptotically dominates (s0 log p)2. We evaluate our method on synthetic data, and compare it with earlier proposals.
The Close Combat Tactical Trainer Program

The Close Combat Tactical Trainer (CCTT) is the first full scale development of a Distributed Interactive Simulations (DIS) training system. CCTT is being developed to support the training requirements of the U.S. Army's combined arms teams, however it also provides an entity-based battlefield which will be able to support higher level training and development activities. This paper will provide an overview of CCTT, discuss how it will use the DIS standard protocols and semi-automated forces within its architecture, the program's development methodology, and its role in future Army training. CCTT builds on the successful technology demonstration of this approach by DARPA's SIMNET project (Thorpe 1987).
RAPPORT Designing Active Objects in DEGAS

This report discusses application design for active databases, in particular for the active object-based database programming language Degas. In Degas one modularisation principle, the object, is applied to all elements of the application, including rules. We discuss a design process consisting of four phases, corresponding with the four kinds of capabilities in a Degas object, attributes, methods, rules, lifecycles. The elements of this design process are similar to those found in a design methodology such as OMT. To illustrate the design process we use the example of workflow management. In addition, it shows that the application of one modularisation to all elements of an active database leads to a clear modularisation of the workflow application, Furthermore, this modularisation facilitates all important workflow evolutions. 1991 Computing Reviews Classification System: H.2. 1 [Information Systems]: Logical Design data models, H.2. 8 [Information Systems]: Database Applications, H.4. 1 [Information Systems]: Office Automation.
The Lasso under Heteroscedasticity

The performance of the Lasso is well understood under the assumptions of the standard linear model with homoscedastic noise. However, in several appli- cations, the standard model does not describe the important features of the data. This paper examines how the Lasso performs on a non-standard model that is mo- tivated by medical imaging applications. In these applications, the variance of the noise scales linearly with the expectation of the observation. Like all heteroscedas- tic models, the noise terms in this Poisson-like model are not independent of the design matrix. More specically, this paper studies the sign consistency of the Lasso under a sparse Poisson-like model. In addition to studying sucient conditions for the sign consistency of the Lasso estimate, this paper also gives necessary conditions for sign consistency. Both sets of conditions are comparable to results for the homoscedastic model, showing that when a measure of the signal to noise ratio is large, the Lasso performs well on both Poisson-like data and homoscedastic data. Simulations reveal that the Lasso performs equally well in terms of model selec- tion performance on both Poisson-like data and homoscedastic data (with properly scaled noise variance), across a range of parameterizations. Taken as a whole, these results suggest that the Lasso is robust to the Poisson-like heteroscedastic noise.
Current Mode Monolithic Active Pixel Sensor With Correlated Double Sampling forCharged Particle Detection

A monolithic active pixel sensor operating in current mode for charged particle detection is described. The sensing element in each pixel is an n-well/p-sub diode with a PMOS transistor integrated in an n-well. The drop of the n-well potential from the collection of charge modulates the transistor channel current. Each pixel features two current mode memory cells. The subtraction of distant-in-time samples frees the signal of fixed pattern noise (FPN) and of the correlated low-frequency temporal noise components, resulting in extraction of the particle footprint. The subtraction circuits are placed at each column end. A transimpedance amplifier, integrating in sequence two current samples and subtracting the results in an arithmetic operation, was adopted. The integrated version of the transimpedance amplifier, designed with a maximized conversion gain, is burdened by a risk of an early saturation, imperiling its operation, if the dispersions of the dc current component are too big. The degree of dispersions could not be estimated during the design. Some number of columns is available as a backup with the direct current readout. An external realization of the subtracting circuit, based on the same principle, is used to process direct output columns. The concept of the data acquisition setup developed, the tested performance of an array of cells, and the processing circuitry are described