Miracle-dz/UniFAR-FTUs huggingface dataset:
Bidirectional Motion Estimation Algorithm for B Picture in H.264/AVC

Abstract-Bidirectional motion estimation is one of the important features of B picture coding in H.264/AVC. This paper discusses the procedure of bidirectional integer pixel motion search and fractional pixel motion search. Integer motion search is to get the integer bidirectional motion vectors based on four times iterations. Fractional motion search should be executed twice to refine the two pairs of bi-directional motion vectors to quarter precision. Computer simulation results demonstrate that with this technique the prediction error in some scenes is significantly reduced. Joint estimation of forward and backward motion vectors used in the bi-predictive pictures (B pictures) can efficiently improve the coding performance of B pictures.
An Ensemble Method for Spelling Correction in Consumer Health Questions

Orthographic and grammatical errors are a common feature of informal texts written by lay people. Health-related questions asked by consumers are a case in point. Automatic interpretation of consumer health questions is hampered by such errors. In this paper, we propose a method that combines techniques based on edit distance and frequency counts with a contextual similarity-based method for detecting and correcting orthographic errors, including misspellings, word breaks, and punctuation errors. We evaluate our method on a set of spell-corrected questions extracted from the NLM collection of consumer health questions. Our method achieves a F1 score of 0.61, compared to an informed baseline of 0.29, achieved using ESpell, a spelling correction system developed for biomedical queries. Our results show that orthographic similarity is most relevant in spelling error correction in consumer health questions and that frequency and contextual information are complementary to orthographic features.
Using Data Mining for Life Insurance Network Optimization

Managing the large amounts of information and efficiently using this information in improved decision making has become increasingly challenging. In this paper we will demonstrate how genetic algorithms integrated in MATLAB help in optimizing a life insurance network. We will analyze the case of study of a life insurance company which wants to enlarge its network. We will explain how we have calculated the fitness function and how we have implemented the data mining optimizing technique of cluster analysis. We will use MATLAB to deliver the results of our study, accompanied them with appropriate explanations. We will conclude arguing the benefits of our study.
A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models

This paper presents a novel distributed variational inference framework that unifies many parallel sparse Gaussian process regression (SGPR) models for scalable hyperparameter learning with big data. To achieve this, our framework exploits a structure of correlated noise process model that represents the observation noises as a finite realization of a high-order Gaussian Markov random process. By varying the Markov order and covariance function for the noise process model, different variational SGPR models result. This consequently allows the correlation structure of the noise process model to be characterized for which a particular variational SGPR model is optimal. We empirically evaluate the predictive performance and scalability of the distributed variational SGPR models unified by our framework on two real-world datasets.
Model and Program Repair via SAT Solving

We consider the following \emph{model repair problem}: given a finite Kripke structure $M$ and a specification formula $\eta$ in some modal or temporal logic, determine if $M$ contains a substructure $M'$ (with the same initial state) that satisfies $\eta$. Thus, $M$ can be ``repaired'' to satisfy the specification $\eta$ by deleting some transitions. We map an instance $(M, \eta)$ of model repair to a boolean formula $\repfor(M,\eta)$ such that $(M, \eta)$ has a solution iff $\repfor(M,\eta)$ is satisfiable. Furthermore, a satisfying assignment determines which transitions must be removed from $M$ to generate a model $M'$ of $\eta$. Thus, we can use any SAT solver to repair Kripke structures. Furthermore, using a complete SAT solver yields a complete algorithm: it always finds a repair if one exists. We extend our method to repair finite-state shared memory concurrent programs, to solve the discrete event supervisory control problem \cite{RW87,RW89}, to check for the existence of symmettric solutions \cite{ES93}, and to accomodate any boolean constraint on the existence of states and transitions in the repaired model. Finally, we show that model repair is NP-complete for CTL, and logics with polynomial model checking algorithms to which CTL can be reduced in polynomial time. A notable example of such a logic is Alternating-Time Temporal Logic (ATL).
APPLICATIONS OF RANDOM INTERSECTION GRAPHS TO SECURE SENSOR NETWORKS – CONNECTIVITY RESULTS

Random intersection graphs was introduced by SingerCohen [6]. These graphs have recently received considerable attention in the literature and been used in diverse applications [1,2,7–13]. In a general random intersection graph, each node is assigned a set of items in some random manner, and any two nodes establish an undirected edge in between if and only if they have at least a certain number of items in common. In this paper, we consider random s-intersection graphs defined as follows. In a random s-intersection graph with n nodes, each node selects Kn distinct items uniformly at random from the same item pool that has Pn different items, and any two nodes have an edge in between upon sharing at least s items, where 1 ≤ s ≤ Kn ≤ Pn holds, and Kn and Pn are functions of n for generality. We denote a random s-intersection graph by Gs(n,Kn, Pn). Random intersection graphs have numerous application areas including secure wireless sensor networks [8, 10, 11, 13], social networks [1], cryptanalysis [2], and clustering [12]. We detail the use of random intersection graphs to model secure sensor networks.
A Survey On Detection And Mitigation Of Misbehavior In Disruption Tolerant Networks

Denial of Service (DoS) attack is one of the major problem in today's Internet. It constitutes the hardest security problems nowadays. Particularly Distributed Denial of Service (DDoS) attack have severe impact. The main aim of a DoS is the disruption of services by attempting to control access to a system or service instead of overthrowing the service itself. Disruption-Tolerant Networks (DTNs) deliver data in network environments composed of intermittently connected nodes. Malicious nodes within a DTN may attempt to delay or destroy data in transit to its destination. Such attacks include dropping data, flooding the network with extra messages, corrupting routing tables, and counterfeiting network acknowledgments. Packet dropping in DTNs can be detected in a distributed way by observing signed contact records. To mitigate such routing misbehavior a scheme is proposed in which the number of packets forwarded to the misbehaving nodes is limited. When one or more nodes are malicious, they may prevent correct message routing. Alternate routing paths can be used to circumvent them. In this paper attacks aimed at preventing correct message delivery in structured peerto-peer overlays and defenses to these attacks are discussed. This survey entitles the above mentioned problem and solutions proposed.
Synergistic Antitumor Activity of Ixabepilone (BMS-247550) Plus Bevacizumab in Multiple In vivo Tumor Models

Purpose: Angiogenesis is a critical step in the establishment, growth, and metastasis of solid tumors, and combination of antiangiogenic agents with chemotherapy is an attractive therapeutic option. We investigated the potential of ixabepilone, the first in a new class of antineoplastic agents known as epothilones, to synergize with antiangiogenic agents to inhibit tumor growth. Experimental Design:In vitro and in vivo cytotoxicity of ixabepilone as single agent and in combination with two targeted antiangiogenic agents, bevacizumab or sunitinib, were examined in preclinical tumor models. Direct effects of the agents against endothelial cells was also examined and compared with the effects of paclitaxel as single agent and in combination with bevacizumab. Results: Ixabepilone showed robust synergistic antitumor activity in combination with bevacizumab and sunitinib in preclinical in vivo models derived from breast, colon, lung, and kidney cancers. The synergistic antitumor effect was greater with ixabepilone compared with paclitaxel. Furthermore, ixabepilone was more effective than paclitaxel at killing endothelial cells expressing P-glycoprotein in vitro and inhibiting endothelial cell proliferation and tumor angiogenesis in vivo. Conclusions: Ixabepilone may enhance the antitumor effects of antiangiogenic therapy by direct cytotoxicity and also indirectly via the killing of tumor-associated endothelial cells. Given that ixabepilone has reduced susceptibility to drug efflux pumps compared with taxanes, these data may explain the increased antiangiogenic and antitumor activity of ixabepilone in combination with antiangiogenic agents. Phase II studies to assess the efficacy and safety of ixabepilone plus bevacizumab in locally recurrent or metastatic breast cancer are planned.
Does personality provide unique explanations for behaviour? Personality as cross‐person variability in general principles

I propose that personality does not provide unique explanations for human behaviour. Two principles, accessibility as a ‘cognitive’ principle and regulatory focus as a ‘motivational’ principle, are used to illustrate how personality can be reconceptualized as a cross‐person source of variability in the functioning of general psychological principles that have situational sources of variability as well. For each of these principles, evidence is presented that ‘persons’ and ‘situations’ as sources of variability have similar effects. I then provide some other examples of psychological principles having similar effects when either persons or situations are the source of variability. I discuss the utility of a ‘general principles’ perspective for understanding the many ways that persons, groups, and situations can contribute to manifesting the same pattern of principles, and how some patterns are more adaptive than others. The implications of there being multiple ways of manifesting the same pattern are then considered for the classic issues of when personality is revealed and what is its range of applicability. Copyright © 2000 John Wiley & Sons, Ltd.
Modeling within-word and cross-word pronunciation variation to improve the performance of a Dutch CSR

This paper describes how the performance of a continuous speech recognizer for Dutch has been improved by modeling within-word and cross-word pronunciation variation. Within-word variants were automatically generated by applying five phonological rules to the words in the lexicon. For the within-word method, a significant improvement is found compared to the baseline. Cross-word pronunciation variation was modeled using two different methods: 1) adding cross-word variants directly to the lexicon, 2) only adding multi-words and their variants to the lexicon. Overall, cross-word method 2 leads to better results than cross-word method 1. The best results were obtained when cross-word method 2 was combined with the within-word method: a relative improvement of 8.8% WER was found compared to the baseline.
Physical Fitness, Physical Activity and Sedentary Activities of 7 to 11 Year Old Boys with Different Body Mass Indexes

Purpose The purpose of this study was to examine the differences in physical fitness, physical activity, and sedentary activities among 7 to 11 year old boys with varying body mass index in Ardabil, Iran. Methods The sample consisted of 766 boys who participated in eight weight bearing (1 mile walk/run, sit ups, standing long jump, vertical jump, shuttle run 4 × 10 m, 30-meter sprint, pull ups, pushups) and four non-weight-bearing physical fitness tests (flamingo balance, handgrip; sit and reach and finger reaction time). Physical activity and sedentary activities were assessed by questionnaires. Body mass index (BMI) was computed to classify participants into normal weight, underweight, overweight and obese groups. Results The prevalence of underweight, overweight and obesity was 10.7%, 14.1% and 4.2% respectively. Cardiorespiratory fitness (VO2max) decreased progressively as the BMI increased and differences among the all body mass index categories were significant (P<0.01). Obese subjects performed worse in all weight bearing fitness tests than normal weight subjects (P<0.01). Moreover, overweight subjects performed worse in one mile, pushups and pull ups than normal weight subjects (P<0.01). Underweight subjects had significantly lower Pushups than normal weight subjects (P<0.01). Obese subjects had significantly higher sedentary activities and lower physical activity than other counterparts (P<0.01). Conclusions This study showed that weight bearing physical fitness, cardiorespiratory fitness and physical activity progressively decreased as the BMI increased and conversely, sedentary activities increased as the BMI increased.
Variability of DNA Microarray Gene Expression Profiles in Cultured Rat Primary Hepatocytes

DNA microarray is a powerful tool in biomedical research. However, transcriptomic profiling using DNA microarray is subject to many variations including biological variability. To evaluate the different sources of variation in mRNA gene expression profiles, gene expression profiles were monitored using the Affymetrix RatTox U34 arrays in cultured primary hepatocytes derived from six rats over a 26 hour period at 6 time points (0h, 2h, 5h, 8h, 14h and 26h) with two replicate arrays at each time point for each animal. In addition, the impact of sample size on the variability of differentially expressed gene lists and the consistency of biological responses were also investigated. Excellent intra-animal reproducibility was obtained at all time points with 0 out of 370 present probe sets across all time points showing significant difference between the 2 replicate arrays (3-way ANOVA, p ≤ 0.0001). However, large inter-animal biological variation in mRNA expression profiles was observed with 337 out of 370 present probe sets showing significant differences among 6 animals (3-way ANOVA, p ≤ 0.05). Principal Component Analysis (PCA) revealed that time effect (PC1) in this data set accounted for 47.4% of total variance indicating the dynamics of transcriptomics. The second and third largest effects came from animal difference, which accounted for 16.9% (PC2 and PC3) of the total variance. The reproducibility of gene lists and their functional classification was declined considerably when the sample size was decreased. Overall, our results strongly support that there is significant inter-animal variability in the time-course gene expression profiles, which is a confounding factor that must be carefully evaluated to correctly interpret microarray gene expression studies. The consistency of the gene lists and their biological functional classification are also sensitive to sample size with the reproducibility decreasing considerably under small sample size.
Young Women Face Disadvantage to Enrollment in University STEM Coursework Regardless of Prior Achievement and Attitudes

We evaluated STEM (science, technology, engineering, and mathematics) coursework selection by women and men (representative longitudinal sample, 10,370 Australians) in senior high school and university, controlling achievement and expectancy-value variables. A near-zero total effect of gender on high school STEM enrollment reflected pathways favoring boys through achievement and expectancy-value variables, but a counteracting direct effect of gender favoring girls. In contrast, subsequent university STEM enrollment favored boys. In both high school and university, enrollments favored girls in life sciences and boys in physical sciences, but at university there was a leaky pipeline in which girls who qualified to pursue physical sciences opted for non-STEM subjects. Qualitative analysis not only supported quantitative results but also highlighted alternative mechanisms of STEM engagement/disengagement, and mostly supported gender similarities rather than differences.
Ordered Group Interference Cancellation for Quasi-Orthogonal Space Time Block Codes

We consider a multiple input and multiple output system with quasi-orthogonal space time block codes. Quasi-orthogonal space time block code (QOSTBC) is greatly used because it can achieve a good tradeoff between diversity and multiplexing. However, since the transmit code is not orthogonal, there exists interantenna interference at the receiver. By studying the post detection SNR of transmit symbols at each detection step of ordered interference cancellation (OSIC), we propose an ordered group interference cancellation (OGIC) at reduced complexity. Simulation results show that the proposed method can achieve near the performance of OSIC
The Application of Ant Colony Optimization Algorithm in Linear-Combination Blind Source Separation Problem

The paper establishes a mathematical model of the Blind Source Separation (BSS),and introduce the basic theory of Ant Colony Optimization(ACO) algorithm. According to the idea of Independent component analysis (ICA),the paper proposes a linear blind source separation algorithm based on ant colony optimization.the algorithm establishes a cost function based on Minimum Mutual Information (MMI). The paper does the global optimization in cost function by using the the global optimization capability,positive feedback and other characteristics of ACO,and solves a class of BSS problem.The simulation experiment verifies the effectiveness of the algorithm.
Gravitational-wave confusion background from cosmological compact binaries: Implications for future terrestrial detectors

Increasing the sensitivity of a gravitational-wave (GW) detector improves our ability to measure the characteristics of detected sources. It also increases the number of weak signals that contribute to the data. Because GW detectors have nearly all-sky sensitivity, they can be subject to a confusion limit: Many sources which cannot be distinguished may be measured simultaneously, defining a stochastic noise floor to the sensitivity. For GW detectors operating at present and for their planned upgrades, the projected event rate is sufficiently low that we are far from the confusion-limited regime. However, some detectors currently under discussion may have large enough reach to binary inspiral that they enter the confusion-limited regime. In this paper, we examine the binary inspiral confusion limit for terrestrial detectors. We consider a broad range of inspiral rates in the literature, several planned advanced gravitational-wave detectors, and the highly advanced 'Einstein telescope' design. Though most advanced detectors will not be impacted by this limit, the Einstein telescope with a very low-frequency 'seismic wall' may be subject to confusion noise. At a minimum, careful data analysis will be require to separate signals which will appear confused. This result should be borne in mind when designing highly advanced futuremore » instruments. « less
Robust Unsupervised Feature Learning from Time-Series

Feature learning from unlabeled times series data is an important component in data analysis. Shaplets are discriminative sub-sequence of time series that can best predict target variable. Therefore, shaplets discovery is very important for analysis of time-series. Recently, based on optimization model, a novel approach has been proposed to learning shaplets. To make shaplets learning model more robust, in this paper, we propose a new unsupervised shaplets learning model with emphasizing joint l2,1norm minimization on both loss function and regularization, which can efficiently degenerate effect of noisy and outliers. The utilization of constraints can make the pseudo labels learned more accurate. Numeral experiments validate the effectiveness of the proposed method.
An Hybrid Approach for Improving Word Sense Disambiguation and Text Clustering

In this paper we suggest a new approach to represent text document collections, integrating background knowledge to improve clustering effectiveness. Background knowledge is inferred from previous classification tasks on the same collection and it’s used as context to assign semantic values to words. The WordNet ontology acts as a source repository of semantic meanings. More specifically we propose a new approach to Word Semantic Disambiguation, based on the evaluation of functions related with syntactical and statistical properties of the WordNet synsets or with contextual and synonymical informations.
A versatile multichannel direct-synthesized electrical stimulator for FES applications

Functional electrical stimulation has been applied to restore or maintain the muscle activity of paralyzed patients who suffer spinal cord injuries and related neural impairments. A direct-synthesized arbitrary waveform stimulator for multichannel stimulation is developed in this paper. A novel "element-envelope" method is proposed and implemented by a digital signal processor to synthesize required stimulating patterns with high resolution. This method not only generates arbitrary waveforms but also reduces the memory requirements. It preserves the ability to adjust stimulation parameters dynamically. We also design an output stage that can provide bi-phasic, voltage-controlled constant-current patterns while remain high compliance and wide bandwidth. This system can be considered as a full-featured stimulator for various FES applications with its flexibility in pattern generation and feedback processing capabilities.
Sequence Variation in HSP40 Gene among 16 Toxoplasma gondii Isolates from Different Hosts and Geographical Locations

Toxoplasma gondii with worldwide distribution has received substantial medical and scientific attentions as it causes serious clinical and veterinary problems especially for pregnant women and immunocompromised patients. Heat shock protein 40 (HSP40) plays a variety of essential roles in the pathogenesis of this protozoan parasite. In order to detail the genetic diversity of HSP40 gene, 16 T. gondii strains from different hosts and geographical locations were used in this study. Our results showed that HSP40 sequence of the examined strains was between 6621 bp and 6644 bp in length, and their A+T content was from 48.54% to 48.80%. Furthermore, sequence analysis presented 195 nucleotide mutation positions (0.12%–1.14%) including 29 positions in CDS (0.02%–0.12%) compared with T. gondii ME49 strain (ToxoDB: TGME49_265310). Phylogenetic assay revealed that T. gondii strains representing three classical genotypes (Types I, II, and III) were completely separated into different clusters by maximum parsimony (MP) method, but Type II and ToxoDB#9 strains were grouped into the same cluster. These results suggested that HSP40 gene is not a suitable marker for T. gondii population genetic research, though three classical genotypes of T. gondii could be differentiated by restriction enzymes MscI and EarI existing in amplicon C.