Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness in open-set speaker identification

This paper presents investigations into the relative effectiveness of two alternative approaches to open-set text-independent speaker identification (OSTI-SI). The methods considered are the recently introduced i-vector and the more traditional GMM-UBM method supported by score normalisation. The study is motivated by the growing need for effective extraction of intelligence and evidence from audio recordings in the fight against crime. OSTI-SI is known to be the most challenging subclass of speaker recognition, and its adoption in criminal investigation applications is further complicated by undesired variations in speech characteristics due to changing levels of environmental noise. In this study, the experimental investigations are conducted using a protocol developed for the identification task, based on the NIST speaker recognition evaluation corpus of 2008. In order to closely cover relevant conditions in the considered application areas and investigate the identification performance in such scenarios, the speech data is contaminated with a range of real-world noise. The paper provides a detailed description of the experimental study and presents a thorough analysis of the results.
Global Financial Crisis, China's Macroeconomic Policies and the Fluctuations of China's Economy

The paper simulates and measures the effects of the international financial crisis of 2008 on China’s economy by employing a small open DSGE-VAR model, with which the efficiencies of the responses of China’s monetary and fiscal policies are assessed, and the determinants of China’s business cycle in the long run are also investigated. The results show that, first, the channels of exchange rate and foreign trade are the main transmission mechanisms of financial crisis’ shocks on China’s economy; second, if the real GDP of USA falls 1 percent caused by the Great Recession, the real output of China will decrease 1.2% without policy’s stimulus, therefore the actual spill-over effect of the international crisis to China can be estimated; third, the stabilizing policy package of China’s government are effective in tackling the international financial shocks; finally, the preference shocks, investment shocks, interest rate shocks and the external shocks from foreign countries by financial crises are identified as main sources of the fluctuations of China’s economy in our simulation.
EFFECTS OF NITROGEN AND PHOSPHORUS FERTILIZERS ON NUTRITIVE VALUE OF SAFFLOWER FORAGE AND SILAGE

This experiment was conducted to determine the effects of nitrogen and phosphorus fertilizers on nutritive value of safflower forage and silage by in situ and in vitro methods. The safflower forage was harvested at flowering stage, chopped to 3-5 cm particles and then ensiled in the laboratory silos for 60 days. The experimental treatments included: 1. Unfertilized forage (control), 2. Phosphorus fertilized forage (100 kg per ha), 3. Nitrogen fertilized forage (300 kg per ha), and 4. Nitrogen and phosphorus fertilized forage (300 kg N and 100 kg P per ha). The effect of Nitrogen and phosphorus fertilizers on reduction of Dry mater, Neutral detergent fiber and Acid detergent fiber and increasing of ash, Calcium and Phosphorus of forage and silage was significant. The Nitrogen fertilizer significantly increased Crude protein, Non-protein nitrogen and Buffer soluble protein and decreased the Acid detergent insoluble nitrogen content of silage. The Nitrogen and phosphorus fertilizers caused the significant increase of the degradability of both safflower forage and silage. The highest production of gas after 96 h of incubation belonged to treatment 4 and the lowest was for treatment 1 (for both forage and silage). The effect of Nitrogen and phosphorus fertilizers on gas production of forage and silage was significant. Applying the Nitrogen and phosphorus fertilizers caused the significant increase of the gas production parameters including b and c fractions and also Metabolism energy content of forage and silage. Nitrogen and phosphorus fertilizers application caused the production of the higher quality forage for making silage and also improving the Dry mater digestibility of silage.
Mechanisms for Priming DNA Synthesis

DNA replication is a semiconservative process in which a DNA polymerase uses one DNA strand as a template for the synthesis of a second, complementary, DNA strand. However, in contrast to RNA polymerases, which can initiate RNA synthesis on a DNA template de novo, all DNA polymerases require a preexisting primer on which to initiate DNA synthesis (Kornberg and Baker 1992). One apparent exception to this rule is a mitochondrial DNA (mtDNA)-encoded reverse transcriptase (RT) in Neurospora (Wang and Lambowitz 1993). Preexisting primers can be classified into four groups. The simplest primer consists of the 3′-hydroxyl (3′-OH) termini of DNA chains that are complementary to the DNA template and thereby form a stable duplex structure at the site where DNA synthesis begins. This primer is used for DNA repair (Friedberg and Wood, this volume), parvovirus DNA replication (Brush and Kelly; Cotmore and Tattersall; both this volume), some RTs. The second type of primer consists of a deoxyribonucleoside monophosphate that is covalently attached to a specific serine, threonine, or tyrosine residue of a protein. Examples are bacteriophage, plasmids, and animal viruses that replicate as a linear DNA genome, and animal viruses such as hepadnaviruses whose genome is partially double-stranded and partially single-stranded. The third type of primer consists of tRNA molecules that anneal to specific sequences in the RNA genomes of retroviruses where their 3′-OH termini are utilized by RT. The fourth class of primers consists of nascent RNA chains. These comprise nascent RNA transcripts that are processed to create a.
Cwp84, a Clostridium difficile cysteine protease, exhibits conformational flexibility in the absence of its propeptide

Two structures of Cwp84, a cysteine protease from the S-layer of C. difficile, are presented after propeptide cleavage. They reveal the movement of three loops, two in the active-site groove and one on the surface of the lectin-like domain, exposing a hydrophobic pocket.
Analysis of sub-threshold swing and performance of various tunnel transistors

In the last few decades, MOSFET scaling has enabled smaller and faster transistors that consume less power per operation. Device dimensions are shrunk into the sub-65-nm regime, However, In order to increase the speed of operation, the sub-threshold swing has to be reduced to below 60 mV/dec. Advanced electron devices such as impact ionization FET, p-i-n and p-n-p-n tunnel FET, double gate tunnel FET, nano electromechanical FET, and FBFET have been proposed recently as novel concepts by many researches to achieve sharper swings below 60 mV/dec. As a result, these devices potentially enable reduced Vdd operation and contribute considerable reduction in system power. In this paper, detailed study of subthreshold swing is done and performances of above mentioned transistors are analyzed. In this analysis it is found that the IMOS device has least sub-threshold swing than other transistors. It was also found that FBFET has low value of loaded and unloaded delay while comparing other transistors. NEMFET and PNPN consume low active power than other transistors.
Touch-Bookmark: a lightweight navigation and bookmarking technique for e-books

The navigation function of an e-book significantly influences its usability. In this paper, we introduce Touch-Bookmark (TB), a multitouch navigation technique for e-books. TB enables users to bookmark a page in a casual manner and return to it quickly when required. Moreover, the users can flip between two remote pages by using simple gestures. In a usability test conducted to evaluate our prototype, users found the technique easy to learn, natural to use, and useful for navigation. Analysis of the patterns of interaction gestures helped identify human factors that should be considered when designing touch interfaces for e-books. The factors include navigation strategies, patterns of interaction gestures, types of books, and motor memory.
High Dimensional Matrix Relevance Learning

In supervised learning the parameters of a parametric Euclidean distance or mahalanobis distance can be effectively learned by so called Matrix Relevance Learning. This adaptation is not only useful to improve the discrimination capabilities of the model, but also to identify relevant features or relevant correlated features in the input data. Classical Matrix Relevance Learning scales quadratic with the number of input dimensions M and becomes prohibitive if M exceeds some thousand input features. We address Matrix Relevance Learning for data with a very large number of input dimensions. Such high dimensional data occur frequently in the life sciences domain e.g. For microarray or spectral data. We derive two respective approximation schemes and show exemplarily the implementation in Generalized Matrix Relevance Learning (GMLVQ) for classification problems. The first approximation scheme is based on Limited Rank Matrix Approximation (LiRaM) LiRaM is a random subspace projection technique which was formerly mainly considered for visualization purposes. The second novel approximation scheme is based on the Nystroem approximation and is exact if the number of Eigen values equals the rank of the Relevance Matrix. Using multiple benchmark problems, we demonstrate that the training process yields fast low rank approximations of the relevance matrices without harming the generalization ability. The approaches can be used to identify discriminative features for high dimensional data sets.
Learning Robot Control - Using Control Policies as Abstract Actions

Autonomous robot systems operating in an uncertain environment have to be able to cope with new situations and task requirements. Important properties of the control architecture of such systems are thus that it is reactive, allows for exible responses to novel situations, and that it adapts to longer lasting changes in the environment or the task requirements. In the extreme case, this learning has to occur without the direct innuence of an outside teacher, making the reinforcement learning paradigm an attractive option since it allows to learn sequences of behavior from simple reinforcement signals 1, 17]. However, while these techniques have been applied to simple robot systems and in simulationn2, 5, 7, 10, 11, 12, 6], the complexity of the primitive action and state spaces of most robots leads to a need for large amounts of experiences to learn a given task, thus rendering these methods impracticable for on-line learning on such systems. Furthermore, most such learning systems do not provide a means for introducing a priori knowledge, thus permitting the occurrence of catastrophic failures which is often not permissible in real world systems which have to learn new tasks in a single trial. To address these issues, the control architecture presented here uses more abstract actions which allow to deene the system as a Discrete Event Dynamic System (DEDS) on an abstract, discrete state space, within which a policy for the given task is learned. To illustrate this, the architecture has been applied to walking tasks on a four-legged walking robot. The use of abstract actions within the reinforcement learning frameworkk14] promises to make it possible to address more complex tasks and platforms. Much of this promise stems from the possibility to treat the resulting system as an event driven system rather than a clock driven one, reducing the set of points at which the learning agent has to consider a new action to the times when certain control or sensor events happen. While this allows for optimal decision points to be missed if the corresponding sensor signals lie outside the scope of the current set of control and sensor alternatives, it also leads to a focus of attention and can dramatically
Running person detection from a community patrol robot

In this paper, a running person detection method is proposed for the community patrol robot. The challenges include the diversity of movement direction and the ego-motion of camera. The diversity of movement direction means that it is difficult to gain high accuracy detection by only using appearance information. The ego-motion of camera means that the motion information contains high noise. To address these limitations, two-stream architecture of convolutional networks, spatial stream and motion stream, is proposed to capture the complementary information on appearance from still frames and motion between frames. In addition, simple but effective filtering based ego-motion elimination technique is applied in motion stream input calculation. We demonstrate the efficiency of the proposed method on the video captured from the community scene and the performance compared with existing methods, which validates that the proposed method detects running person more accurately.
Fast matching pursuit with a multiscale dictionary of Gaussian chirps

We introduce a modified matching pursuit algorithm, called fast ridge pursuit, to approximate N-dimensional signals with M Gaussian chirps at a computational cost O(MN) instead of the expected O(MN/sup 2/logN). At each iteration of the pursuit, the best Gabor atom is first selected, and then, its scale and chirp rate are locally optimized so as to get a "good" chirp atom, i.e., one for which the correlation with the residual is locally maximized. A ridge theorem of the Gaussian chirp dictionary is proved, from which an estimate of the locally optimal scale and chirp is built. The procedure is restricted to a sub-dictionary of local maxima of the Gaussian Gabor dictionary to accelerate the pursuit further. The efficiency and speed of the method is demonstrated on a sound signal.
I Semantic Data Integration in Peer-to-Peer based Digital Libraries

An open and cooperative Digital Library (DL) may consist of a utonomous sub-systems which can have their own metadata form ats. In conventional solutions, amediator is adopted to create and maintain the matching between relevant terms. Concequently, relevant data in disti nct schemas can be integrated according to the mappings in the mediator. However, s uch olutions suffer some problems originated from the static matching in mediat or. For example, the tradeoff is huge in modfiying the global view of the schemas wh en new system in different schema joins. In this paper, we propose a Peer-t o-Peer (P2P) based infrastructure for managing heterogeneous schemas in DLs. We present the advantages of P2P data management over the conventional metho ds with respect to achieving dynamic matching functionality in an open and h eterogeneous environment. Assumptively, peers are clustered according to sh ared topics or interest. Each node can publish its schema to the involved peer communi ty such that other peers can automatically discover the specific schema. A loca l matchmaking engine is adopted as well in order to automatically generate th e relations between its own schema and the retrieved ones. This paper presents also t he h listic system architecture and the proposed mechanisms in dynamically in tegrating schemas in the open and cooperative DL.
Deep Sequencing of Influenza A Virus from a Human Challenge Study Reveals a Selective Bottleneck and Only Limited Intrahost Genetic Diversification

ABSTRACT Knowledge of influenza virus evolution at the point of transmission and at the intrahost level remains limited, particularly for human hosts. Here, we analyze a unique viral data set of next-generation sequencing (NGS) samples generated from a human influenza challenge study wherein 17 healthy subjects were inoculated with cell- and egg-passaged virus. Nasal wash samples collected from 7 of these subjects were successfully deep sequenced. From these, we characterized changes in the subjects' viral populations during infection and identified differences between the virus in these samples and the viral stock used to inoculate the subjects. We first calculated pairwise genetic distances between the subjects' nasal wash samples, the viral stock, and the influenza virus A/Wisconsin/67/2005 (H3N2) reference strain used to generate the stock virus. These distances revealed that considerable viral evolution occurred at various points in the human challenge study. Further quantitative analyses indicated that (i) the viral stock contained genetic variants that originated and likely were selected for during the passaging process, (ii) direct intranasal inoculation with the viral stock resulted in a selective bottleneck that reduced nonsynonymous genetic diversity in the viral hemagglutinin and nucleoprotein, and (iii) intrahost viral evolution continued over the course of infection. These intrahost evolutionary dynamics were dominated by purifying selection. Our findings indicate that rapid viral evolution can occur during acute influenza infection in otherwise healthy human hosts when the founding population size of the virus is large, as is the case with direct intranasal inoculation. IMPORTANCE Influenza viruses circulating among humans are known to rapidly evolve over time. However, little is known about how influenza virus evolves across single transmission events and over the course of a single infection. To address these issues, we analyze influenza virus sequences from a human challenge experiment that initiated infection with a cell- and egg-passaged viral stock, which appeared to have adapted during its preparation. We find that the subjects' viral populations differ genetically from the viral stock, with subjects' viral populations having lower representation of the amino-acid-changing variants that arose during viral preparation. We also find that most of the viral evolution occurring over single infections is characterized by further decreases in the frequencies of these amino-acid-changing variants and that only limited intrahost genetic diversification through new mutations is apparent. Our findings indicate that influenza virus populations can undergo rapid genetic changes during acute human infections.
Modelling the hop-constrained minimum spanning tree problem over a layered graph

The Hop-constrained Minimum Spanning Tree Problem (HMST) i s defined as follows: given a graph G = (V,E) with node setV = {0, 1, . , n} and edge set E as well as a cost ce associated with each edge e of E and a natural number H, we wish to find a spanning tree T of the graph with minimum total cost and such that the unique path from a specified root node, node 0, to any o ther node has no more than H hops (edges).
Quality Education through Child-Friendly Schools: Resource Allocation for the Protection of Children’s Rights (English version)

The paper discusses the idea and purpose of Child-Friendly Schools (CFSs) initiated by the UNICEF. It analyses the implications of CFSs in terms of improving children’s health and nutrition, promoting gender equality, protecting children’s rights, re-defining education quality and creating positive psycho-emotional environment at schools.
Performance and energy characterization of high-performance low-cost cornerness detection on GPUs and multicores

Feature detection and tracking is an important problem in Computer Vision. Corners in an image are a good indication of features to track. Original algorithms may be expensive even on multicore architectures because they require full convolutions to be performed. Although these can be performed in real time in modern GPUs and multicore CPUs, faster solutions are needed for embedded systems and complex algorithms, given that corner detections is just a step of the analysis process. In this paper we evaluate the performance and energy efficiency of the Harris corner detection algorithm as well as an approximation of it, in both desktop and mobile platforms. The purpose of this paper is three-fold: evaluate the performance gains of GPUs vs. CPUs for several mobile and desktop systems, evaluate whether the Harris approximation provides adequate performance gains to justify its use in mobile and desktop system configurations and, finally, determine which configurations provide real-time performance. According to our evaluation (a) the best GPU solution is 16.3 times faster than the best CPU solution for the desktop case while being 2.6 times more energy efficient and (b) the best GPU solution for the mobile case is 1.2 times faster while being 3.6 times more energy efficient than the respective CPU.
Using reaction times and binary responses to estimate psychophysical performance: an information theoretic analysis

As the strength of a stimulus increases, the proportions of correct binary responses increases, which define the psychometric function. Simultaneously, mean reaction times (RT) decrease, which collectively define the chronometric function. However, RTs are traditionally ignored when estimating psychophysical parameters, even though they may provide additional Shannon information. Here, we extend Palmer et al's (2005) proportional-rate diffusion model (PRD) by: (a) fitting individual RTs to an inverse Gaussian distribution, (b) including lapse rate, (c) point-of-subjective-equality (PSE) parameters, and, (d) using a two-alternative forced choice (2AFC) design based on the proportion of times a variable comparison stimulus is chosen. Maximum likelihood estimates of mean RT values (from fitted inverse Gaussians) and binary responses were fitted both separately and in combination to this extended PRD (EPRD) model, to obtain psychophysical parameter values. Values estimated from binary responses alone (i.e., the psychometric function) were found to be similar to those estimated from RTs alone (i.e., the chronometric function), which provides support for the underlying diffusion model. The EPRD model was then used to estimate the mutual information between binary responses and stimulus strength, and between RTs and stimulus strength. These provide conservative bounds for the average amount of Shannon information the observer gains about stimulus strength on each trial. For the human experiment reported here, the observer gains between 2.68 and 3.55 bits/trial. These bounds are monotonically related to a new measure, the Shannon increment, which is the expected value of the smallest change in stimulus strength detectable by an observer.
Motion Planning for a Rod-shaped Robot in Et3: Connecting the Rod-HGVG using the Point-HGVG

This work considers the motion planning for a rod-shaped robot in a three-dimensional space. Our approach is to construct a roadmap in the configuration space of the rod and use it to navigate and map unknown spaces. Previously, we defined the rod-HGVG, which is a roadmap for a rod-shaped robot operating in a three-dimensional space. However, the rod-HGVG is only guaranteed to be connected if the point-GVG is connected. This work extends the rod-HGVG into more general environments where the point-GVG may not be connected but the point-HGVG is connected. For this, we define new components, termed the higher-order edges for the rod-HGVG and linking strategies which are based on the point-HGVG. Just as there is a close relationship between the components of the point-GVG and the rod-HGVG, there is a close relationship between the components of the point-HGVG and the higher-order edges of the rod-HGVG. We provide the construction procedures for the higher-order edges, which, like the components of the rod-HGVG, can be constructed using only sensor-provided information.