Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequence and Structural Analysis of 3' Untranslated Region of Hepatitis C Virus, Genotype 3a, From Pakistani Isolates

Background Hepatitis C virus (HCV) is the cause of high morbidity and mortality worldwide, inflicting around one million people in Pakistan alone. The HCV genomic RNA harbors conserved structural elements that are indispensable for its replication. The 3’ untranslated region (UTR) contains several of these elements essentially involved in regulating the major steps of the viral life cycle. Objectives Differences in regulatory elements of HCV may contribute towards differential infectivity of local isolates. The present study explicates sequence analysis and secondary structure prediction of HCV 3'UTR region of subtype 3a from Pakistan to characterize this particular region. Patients and Methods HCV 3'UTR region was amplified, cloned and sequenced from five different patients. Sequence and structural analysis was performed and phylogenetic analysis was carried out using the 3'UTR sequence reported in NCBI nucleotide data base (http://www.ncbi.nlm.nih.gov/nuccore) by other studies. Results Sequence analysis of the amplified fragment from five patients indicated that the 3'UTR is composed of 214-235 nts. Its sequence contains a type-specific variable region followed by a poly U/UC region and a highly conserved X-tail of 98 nts. The variable region reported here has 26 nts and one stem loop at the secondary structure that differentiate it from HCV genotype 1a ( GT1a) 3'UTR which contains additional 14 nts and two stem loops. The poly U/UC region varied in length (100-79 nts) and nucleotide sequence within the Pakistani isolates, and among different genotypes. Some substitutions found in the X-tail do not affect secondary structure of this element suggesting that this region might play an important role in replication, stabilization and packaging of HCV genome. Additionally, U residues are not present at the end of the X-tail in Pakistani 3a isolates as otherwise reported for the variants of genotype 1b. Conclusions Sequence and structural diversity of the 3'UTR variable region and Poly U/UC region found in the local isolates indicate specificity in the regulating elements of 3'UTR that might be associated with differential replication efficacy of the HCV Pakistani isolates. The study necessitates functional characterization of these regulating elements to elucidate variable viral efficiency and pathogenicity associated with inter-geographical isolates.
A Theoretical Model of the Effects and Consequences of Playing Video Games.

Though there has been considerable discussion of video game effects in several research literatures, theoretical integrations have been somewhat rare. Our own empirical work has focused primarily on the effects of violent video games on those who play them (e.g., Anderson et al., 2004; Anderson & Dill, 2000). That work has been framed in terms of the General Aggression Model, an integrative social-cognitive model designed to handle all influences on all types of human aggression (e.g., Anderson B Anderson & Camagey, 2004; Anderson & Huesmann, 2003). But that model itself can be further generalized to account for nonviolent effects of video games, many of which may well be quite beneficial to the individual as well as to larger society. Our primary goal in this chapter is to elucidate such a general model of video game effects.
Robust stability and stabilization of large-scale descriptor systems with norm-bounded uncertainties

This paper presents a strategy to deal with the problem of robust stability and stabilization of large-scale descriptor systems with uncertainties in both the derivative and state matrices. Using a parameter dependent Lyapunov function, we derive a LMI based sufficient condition for the stability and stabilization of the system. By solving the LMIs, a state feedback decentralized controller is obtained for the closed-loop systems to be quadratically stable. Finally, the numerical example is given to show the effectiveness of the proposed theorems.
A continuum of theories of lambda calculus without semantics

In this paper, we give a topological proof of the following result: there exist 2¿(/spl aleph//sub 0/) lambda theories of the untyped lambda calculus without a model in any semantics based on D.S. Scott's (1972, 1981) view of models as partially ordered sets and of functions as monotonic functions. As a consequence of this result, we positively solve the conjecture, stated by O. Bastonero and X. Gouy (1999) and by C. Berline (2000), that the strongly stable semantics is incomplete.
Word Categorization Using Clustering Ensemble

Entropy model is the base structure of automated word categorizing. In this model, words appear consecutive frequently will place in different groups. Although this method is not correct always, in the most cases, obtained results simulate real situation. Because of NP-complete structure of clustering problems, the entropy model cannot be solved by an optimal algorithm, so a number of heuristic algorithms were developed to solve this problem. Some examples of these heuristics are artificial neural networks, genetic algorithms, greedy algorithms and so on. In this paper, we used a clustering ensemble method for word categorization. The method uses a new feature space generated by k-means for clustering. The results show that an improvement in categorization is obtained.
The use of categorization information in language models for question retrieval

Community Question Answering (CQA) has emerged as a popular type of service meeting a wide range of information needs. Such services enable users to ask and answer questions and to access existing question-answer pairs. CQA archives contain very large volumes of valuable user-generated content and have become important information resources on the Web. To make the body of knowledge accumulated in CQA archives accessible, effective and efficient question search is required. Question search in a CQA archive aims to retrieve historical questions that are relevant to new questions posed by users. This paper proposes a category-based framework for search in CQA archives. The framework embodies several new techniques that use language models to exploit categories of questions for improving question-answer search. Experiments conducted on real data from Yahoo! Answers demonstrate that the proposed techniques are effective and efficient and are capable of outperforming baseline methods significantly.
Lyapunov stability solutions of fractional integrodifferential equations

Lyapunov stability and asymptotic stability conditions for the solutions of the fractional integrodiffrential equations x(α)(t) = f (t, x(t)) + t t0 K(t, s, x(s))ds ,0 <α ≤ 1, with the initial condition x (α−1) (t0) = x0, have been investigated. Our methods are applications of Gronwall’s lemma and Schwartz inequality.
Mitochondrial association, protein phosphorylation, and degradation regulate the availability of the active Rab GTPase Ypt11 for mitochondrial inheritance

There are conflicting models regarding the role of the Ypt11 GTPase in mitochondrial inheritance during yeast budding. This study demonstrates that Ypt11 function requires mitochondrial membrane targeting and GTPase domain–dependent effector interactions. In addition, the abundance of active Ypt11 forms is controlled by phosphorylation and degradation.
Using Hypersets to Represent Semistructured Data

This paper presents a new metamodel based on partially ordered hypersets. The metamodel is minimalist, its only primitives being atoms, potentially infinite hypersets and partial order. The type system is very loose and supports concurrent extensional and intensional definition of types. Every element of the metamodel can be directly referenced, thanks to a powerful automated reification facility. These characteristics make the metamodel well adapted to integrating semistructured data. The metamodel is also compared to a few other information representation formalisms to demonstrate that it subsumes them all and to exhibit some of their problems.
TEXTURE ANALYSIS OF X-RAY IMAGES FOR DETECTION OF CHANGES IN BONE MASS AND STRUCTURE

An attempt is made to apply digital image analysis techniques to the evaluation of bone mass and internal structure, based on X-ray images. Images of distal forearm bone are investigated. A calibration phantom is included in the image to equalize the image intensity. Both raw and equalized images are analyzed and results are compared. A number of first-order texture parameters and fractal dimensions are evaluated. These texture-derived image features are correlated with the bone mineral density estimated by means of dual-photon absorptiometry (DXA), a standard diagnostic technique. The effect of image blur and noise on texture parameters is investigated as well, showing significant influence of image distortion on fractal features, and thus indicating the need for image preprocessing before texture analysis.
TKNN: An Improved KNN Algorithm Based on Tree Structure

Text classification is the process of assigning document to a set of previously fixed categories. It is widely used in many applications, such as web page categorization, email spam filtering, and document indexing, etc. Many popular algorithms for text classification have been proposed, such as Naive Bayes, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). However, these classification approaches do not perform well in multi-class text classification because they are well relied on linear classifiers. KNN is a simple and mature algorithm, but it cannot effectively solve the problem of overlapped categories borders, unbalanced class samples, k value determination, and overlarge search space. In this paper, we propose a new TKNN that absorb tree structure and adaptive k value method based on classical KNN algorithm. TKNN can overcome the shortcoming of KNN and improve the performance of multi-class text classification. Then the theoretical analysis and experimental results show TKNN can greatly enhance the classification efficiency than KNN.
Gene conversion and deletion frequencies during double-strand break repair in human cells are controlled by the distance between direct repeats

Homologous recombination (HR) repairs DNA double-strand breaks and maintains genome stability. HR between linked, direct repeats can occur by gene conversion without an associated crossover that maintains the gross repeat structure. Alternatively, direct repeat HR can occur by gene conversion with a crossover, or by single-strand annealing (SSA), both of which delete one repeat and the sequences between the repeats. Prior studies of different repeat structures in yeast and mammalian cells revealed disparate conversion:deletion ratios. Here, we show that a key factor controlling this ratio is the distance between the repeats, with conversion frequency increasing linearly with the distances from 850 to 3800 bp. Deletions are thought to arise primarily by SSA, which involves extensive end-processing to reveal complementary single-strands in each repeat. The results can be explained by a model in which strand-invasion leading to gene conversion competes more effectively with SSA as more extensive end-processing is required for SSA. We hypothesized that a transcription unit between repeats would inhibit end-processing and SSA, thereby increasing the fraction of conversions. However, conversion frequencies were identical for direct repeats separated by 3800 bp of transcriptionally silent or active DNA, indicating that end-processing and SSA are not affected by transcription.
Distribution of exchanges upon homologous recombination of exogenous DNA in Xenopus laevis oocytes.

Homologous recombination between DNA molecules injected into Xenopus oocyte nuclei was investigated by examining the recovery of information from differentially marked parental sequences. The injected recombination substrate was a linear DNA with terminal direct repeats of 1246 bp; one repeat differed from the other by eight single base-pair substitutions, distributed throughout the region of homology, each of which created or destroyed a restriction enzyme site. Recombination products were recovered and analyzed for their content of the diagnostic sites, either directly by Southern blot-hybridization or after cloning in bacteria. The majority (76%) of the cloned products appeared to be the result of simple exchanges-i.e., there was one sharp transition from sequences derived from one parent to sequences derived from the other. These simple exchanges were concentrated near the ends of the homologous interval and, thus, near the sites of the original molecular ends. Placing marked sites on only one side of the homologous overlap showed that marker recovery was governed largely by the positions of the molecular ends and not by the markers themselves. When a terminal nonhomology was present at one end of the substrate, the yield of recombinants was sharply decreased, but the pattern of exchanges was not affected, suggesting that products from end-blocked substrates arise by the same recombination pathway. Because of considerable evidence supporting a nonconservative, resection-annealing mechanism for recombination in oocytes, we interpret the distribution of exchanges as resulting from long-patch repair of extensive heteroduplex intermediates.
Contour extraction and tracking in video using a joint similarity measure

In this paper, an active contour model based on the gradient vector flow of the gray-level and the motion similarity measure (MGVF-Snake) is introduced after analyzing the performance of the traditional Snake and Snake based on gradient vector flow (GVF-Snake), the algorithm is proposed to extract and track the Video Object (VO) automatically. The MGVF-Snake overcomes the shortcoming of the GVF-Snake that could not fine the VO contour precisely in the complex background. In allusion to the problem that the traditional GVF-Snake easily makes mistake when tracing the VO moving rapidly, the scheme takes advantage of the redundancy and makes the tracking more accurately and rapidly by adjusting the previous VO contour with the motion vector as the current initial contour. The algorithm is validated with the video sequences, the results of the experimentation show that it not only extract VO contour automatically, but also track accurately.
Interoperability-Enriched App Recommendation

At present, there are three main mobile apps marketplaces, iTunes App Store, Android Market and Windows Phone Store. With app recommendation technology, users not only discover more relevant apps, but they're also more likely to be engaged with those apps on a higher level because they are relevant to their interests in the first place. Collaborative filtering (CF) methods had been applied to recommender systems, but the CF techniques do not handle sparse dataset well, especially in the case of the cold start problem where there is no enough interaction for apps. To conquer this constraint, we propose a novel recommending model: Interoperability-Enriched Recommendation (IER) that is an interoperability-enriched collaborative filtering method for multi-marketplace app recommendation based on the global app ecosystem. Experimental results on the known marketplaces app dataset demonstrate that the proposed IER method significantly outperforms the state-of-the-art CF method and context-aware recommendations (CAR) method for app recommendation, especially in the cold start scenario.
Hill-Climbing Attacks on Multibiometrics Recognition Systems

Biometric recognition systems, despite the advantages provided with respect to traditional authentication methods, have some peculiar weaknesses which may allow an attacker being falsely recognized or accessing users' personal data. Among such vulnerabilities, in this paper, we speculate on the hill-climbing attack, i.e., the possibility for an attacker to exploit the scores produced by the matcher with the goal of generating synthetic biometric data, which could allow a false acceptance. More in detail, we focus on multibiometrics systems and investigate about the robustness of different system architectures, both parallel and serial fusion schemes, against the hill-climbing attack. Nonuniform quantization is also evaluated as a possible countermeasure for limiting the effectiveness of the considered attacks in terms of recognition success rate and average number of required attempts without affecting the recognition performance.
Evaluating community structure in the large network with random walks

Community structure is one of the most important properties of networks. Most community algorithms are not suitable for large networks because of their time consuming. In fact there are lots of networks with millions even billions of nodes. In such case, most algorithms running in time O(n2logn) or even larger are not practical. What we need are linear or approximately linear time algorithm. Rising in response to such needs, we propose a quick method to evaluate community structure in networks and then put forward a local community algorithm with nearly linear time based on random walks. Using our community evaluating measure, we could find some difference results from measures used before, i.e., the Newman Modularity. Our algorithm are effective in small benchmark networks with small less accuracy than more complex algorithms but a great of advantage in time consuming for large networks, especially super large networks.
Trajectory-based Comparison of SLAM Algorithms

In this paper, we address the problem of creating an objective benchmark for comparing SLAM approaches. We propose a framework for analyzing the results of SLAM approaches based on a metric for measuring the error of the corrected trajectory. The metric uses only relative relations between poses and does not rely on a global reference frame. The idea is related to graph-based SLAM approaches, namely to consider the energy that is needed to deform the trajectory estimated by a SLAM approach into the ground truth trajectory. Our method enables us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot. We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the SLAM community. The relations have been obtained by manually matching laser-range observations to avoid the errors caused by matching algorithms. Our benchmark framework allows the user an easy analysis and objective comparisons between different SLAM approaches.
A Fast Parsimonious Maximum Likelihood Approach for Predicting Outcome Variables from a Large Number of Predictors

A new model with K correlated components is presented for predicting outcome variables where the number of predictors G may exceed the total sample size N. A fast maximum likelihood algorithm provides closed-form expressions for the model parameters and statistical tests for determining the number of components. We also propose a way to reduce the number of predictors in a stepwise fashion, at each step eliminating the least important predictor based on a new measure of predictor importance. When at least one suppressor variable is included among the predictors, the new model predicts and validates better than traditional models, especially when G is large.
The error-related potential and BCIs

The error-related potential is an event-related potential triggered by errors. Recently it has been the subject of many attentions notably for its possible use in BCI systems. Since it is linked to error occurrence, it could be used in the design of control loop to build more robust systems. In this paper we studied the characteristics of the error potential and present how it could be used for BCI systems improvement.
Finding Optimized Deployment Strategy for Multitenant Services by Iterative Staging

A serious challenge that confronts multi-tenant service systems is finding an optimized deployment strategy according to their business scale and operating characteristics. The tenants want to rent high performance services and services providers demand minimizing cost at the same time of meeting the requirements of tenants. But there are often contradictions between high performance and low cost. Therefore, in order to balance the contradiction, this paper proposes a staging-based optimized deployment method. This method performs iterative optimization based on customized workload generation, continuously emulation and evaluation in a benchmark suite. We demonstrate our method by a case study on a multi-tenant Supplier Business Management (SBM) service, as well as evaluate the capability of our benchmark suite through two sets of experiments. Results from these experiments characterized the relationship between workloads and performance, which can help find optimized deployment strategies for multi-tenant applications. In the case study on multi-tenant SBM service system, we gain an optimized strategy that satisfies the requirement of tenants and makes the maximum use of the resources, which can give useful recommendations in real service instances deployment stage.
System Description for the 2011 NIST Language Recognition Evaluation Contributors in alphabetical order :

The Institute for Infocomm Research (IIR) team submitted two systems, namely the primary iir_primary_llr and the contrastive iir_contrast1_llr, to the 2011 NIST Language Recognition Evaluation (LRE). Both systems are based on the fusion of multiple classifiers. These classifiers are broadly divided into two groups: acoustic and phonotactic. Included in the submission are the result files: iir1/iir_primary_llr. out iir1/iir_contrast1_llr. out
MAC LAYER PROTOCOLS FOR LINEAR WIRELESS SENSOR NETWORKS : A SURVEY

With the proliferation of new devices and connection technologies, the number and types of wireless sensor networks (WSN) applications have skyrocketed. This diversity has created the need to take a more systematic approach in defining the specifics and requirements of different types of WSNs. A major criterion is the topology of a WSN, which plays an important role in the trade-off between ensuring high performance and extending the lifetime of the network. In this paper we address a group of WSNs that require linear or chain topology, called Linear WSN (LWSN). Linear topology creates addition problems like increased delays, uneven load distribution between the nodes (“relay burden problem”) but also provides unique possibilities to address the issues of delay control and energy efficiency at the media access (MAC) layer. Thus in our survey we present a overview of the latest MAC protocols, developed especially for LWSN, discuss their specifics and summarize the related research challenges and open research issues. Key-Words: Wireless Sensor Networks, MAC Protocols, Linear Topology, Chain Networks, Energy Efficiency, Delay, Relay Burden Problem.
On the Blind Separation of Signals from Moving Sources Using Switching and Tuning

In this paper, we present a conceptually simple solution to the problem of blind separation of signals whose sources are moving. Essentially, a set of beams are cast, one for each signal. These are based on an adaptive version of the minimum-variance distortion less response beam former. Each beam pattern is such that the gain is a maximum along the direction-of-arrival of a signal, and nulls along the directions of all other signals. The weights are switched whenever there is spatial coherence between the signals. The arrival-directions of all signals are determined using the ESPRIT algorithm.