Miracle-dz/UniFAR-FTUs huggingface dataset:
Further Insights on Drawing Sound Conclusions from Noisy Judgments

The effectiveness of a search engine is typically evaluated using hand-labeled datasets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less expensive labels (e.g., from crowdsourcing) are used. This introduces errors in the labels, and thus errors in standard effectiveness metrics (such as P@k and DCG). These errors must be taken into consideration when using the metrics. Previous work has approached assessor error by taking aggregates over multiple inexpensive assessors. We take a different approach and introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors. This is especially important when two search engines are compared on their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words, the evidence supporting a statistical difference was illusory and caused by a failure to account for annotation error.
TRANSFAC® and its module TRANSCompel®: transcriptional gene regulation in eukaryotes

The TRANSFAC® database on transcription factors, their binding sites, nucleotide distribution matrices and regulated genes as well as the complementing database TRANSCompel® on composite elements have been further enhanced on various levels. A new web interface with different search options and integrated versions of Match™ and Patch™ provides increased functionality for TRANSFAC®. The list of databases which are linked to the common GENE table of TRANSFAC® and TRANSCompel® has been extended by: Ensembl, UniGene, EntrezGene, HumanPSD™ and TRANSPRO™. Standard gene names from HGNC, MGI and RGD, are included for human, mouse and rat genes, respectively. With the help of InterProScan, Pfam, SMART and PROSITE domains are assigned automatically to the protein sequences of the transcription factors. TRANSCompel® contains now, in addition to the COMPEL table, a separate table for detailed information on the experimental EVIDENCE on which the composite elements are based. Finally, for TRANSFAC®, in respect of data growth, in particular the gain of Drosophila transcription factor binding sites (by courtesy of the Drosophila DNase I footprint database) and of Arabidopsis factors (by courtesy of DATF, Database of Arabidopsis Transcription Factors) has to be stressed. The here described public releases, TRANSFAC® 7.0 and TRANSCompel® 7.0, are accessible under .
A constraint-based analysis of Warker and Dell ’ s connectionist theory

Psycholinguistic and stochastic grammatical theories of sound structure are typically stated in quite different formal vocabularies (i.e., connectionist networks vs. stochastic extensions of Optimality Theory). This commentary argues that this difference obscures a common set of core principles: in each framework, the generation of behavior can be conceptualized as the consequence of the interaction of two broad types of constraints. To support this claim, Warker and Dell’s connectionist proposal is re-cast as a type of constraint-based theory. This reconceptualization not only increases our understanding of how their proposal accounts for empirical data but also allows us to better understand its relationship to stochastic grammatical theories. The connection between these cross-disciplinary perspectives suggests that the substance of theoretical disagreements reflects not principles of processing vs. grammar but rather conflicting claims regarding the precise nature of constraints and their interactions.
Ischemia Activates the ATF6 Branch of the Endoplasmic Reticulum Stress Response*

Stresses that perturb the folding of nascent endoplasmic reticulum (ER) proteins activate the ER stress response. Upon ER stress, ER-associated ATF6 is cleaved; the resulting active cytosolic fragment of ATF6 translocates to the nucleus, binds to ER stress response elements (ERSEs), and induces genes, including the ER-targeted chaperone, GRP78. Recent studies showed that nutrient and oxygen starvation during tissue ischemia induce certain ER stress response genes, including GRP78; however, the role of ATF6 in mediating this induction has not been examined. In the current study, simulating ischemia (sI) in a primary cardiac myocyte model system caused a reduction in the level of ER-associated ATF6 with a coordinate increase of ATF6 in nuclear fractions. An ERSE in the GRP78 gene not previously shown to be required for induction by other ER stresses was found to bind ATF6 and to be critical for maximal ischemia-mediated GRP78 promoter induction. Activation of ATF6 and the GRP78 promoter, as well as grp78 mRNA accumulation during sI, were reversed upon simulated reperfusion (sI/R). Moreover, dominant-negative ATF6, or ATF6-targeted miRNA blocked sI-mediated grp78 induction, and the latter increased cardiac myocyte death upon simulated reperfusion, demonstrating critical roles for endogenous ATF6 in ischemia-mediated ER stress activation and cell survival. This is the first study to show that ATF6 is activated by ischemia but inactivated upon reperfusion, suggesting that it may play a role in the induction of ER stress response genes during ischemia that could have a preconditioning effect on cell survival during reperfusion.
The Calculation of Educational Indicators by Uncertain Gates

Learning Management Systems allow us to retrieve a large scale of data about learners in order to better understand them and how they learn. Thus, it is possible to suggest educational differentiated approaches which take into account the students’ specific needs. The knowledge about the behavior of learners can be extracted by datamining or can be provided by teachers. The available data is often imprecise and incomplete. The possibility theory provides a solution to these problems. The modeling of knowledge can be performed by a possibilistic network but it requires the definition of all Conditional Possibility distributions. This constitutes a limitation for complex knowledge modeling. Uncertain Gates allow, as Noisy Gates in the probability theory, the automatic calculation of Conditional Possibility Tables. The existing Uncertain MIN and Uncertain MAX connectors are not sufficient for applications which need a compromise between both connectors. Therefore we have developed new Uncertain Compromise connectors. In this paper, we will present an experimentation of educational indicator calculation for a decision support system using Uncertain Gates.
Contact homology of Brieskorn manifolds

We give an algorithm for computing the contact homology of some Brieskorn manifolds. As an application, we construct infinitely many contact structures on the class of simply connected contact manifolds that admit nice contact forms (i.e. no Reeb orbits of degree -1,0 or 1) and have index positivity with trivial first Chern class.
Visual Predictive Control of Spiral Motion

This paper deals with constrained image-based visual servoing of circular and conical spiral motion about an unknown object approximating a single image point feature. Effective visual control of such trajectories has many applications for small unmanned aerial vehicles, including surveillance and inspection, forced landing (homing), and collision avoidance. A spherical camera model is used to derive a novel visual-predictive controller (VPC) using stability-based design methods for general nonlinear model-predictive control. In particular, a quasi-infinite horizon visual-predictive control scheme is derived. A terminal region, which is used as a constraint in the controller structure, can be used to guide appropriate reference image features for spiral tracking with respect to nominal stability and feasibility. Robustness properties are also discussed with respect to parameter uncertainty and additive noise. A comparison with competing visual-predictive control schemes is made, and some experimental results using a small quad rotor platform are given.
Public Goods and Ethnic Divisions

The authors present a model that links heterogeneity of preferences across ethnic groups in a city to the amount and type of public good the city supplies. Results show that the shares of spending on productive public goods - education, roads, sewers, and trash pickup _ in U.S. cities (metro areas/urban counties) are inversely related to the city's (metro area's/county's) ethnic fragmentation, even after controlling for other socioeconomic and demographic determinants. They conclude that the ethnic conflict is an important determinant of local public finances. In cities where ethnic groups are polarized, and where politicians have ethnic constituencies, the share of spending that goes to public goods is low. Their results are driven mainly by how white-majority cities react to varying minority-groups sizes. Voters choose lower public goods when a significant fraction of tax revenues collected from one ethnic group is used to provide public goods shared with other ethnic groups.
Distributed Binary Consensus in Networks with Disturbances

This article evaluates convergence rates of binary majority consensus algorithms in networks with different types of disturbances and studies the potential capacity of randomization to foster convergence. Simulation results show that (a) additive noise, topology randomness, and stochastic message loss may improve the convergence rate; (b) presence of faulty nodes degrades the convergence rate; and (c) explicit randomization of consensus algorithms can be exploited to improve the convergence rate. Watts-Strogatz and Waxman graphs are used as underlying network topologies. A consensus algorithm is proposed that exchanges state information with dynamically randomly selected neighbors and, through this randomization, achieves almost sure convergence in some scenarios.
Visualization of Anatomical Structures Based on Implicit Surfaces and Displacement Mapping

Implicit models provide an accurate and flexible representation of a surface. Although they have been widely used in modeling contexts, their application to visualization has been relatively limited due to rendering difficulties. Typical explicit triangulation of the surface, though effective, results in a redundant representation and a high load on the bus between the CPU and the graphics pipeline. In this work, we present an alternative method that allows high quality visualization of iso-surfaces with lower communication requirements. Our proposal is based on the combination of implicit surfaces with displacement mapping techniques. The application of displacement maps on a coarse mesh approximating the implicit model avoids the necessity of explicit triangulation and reduces the bus load without loss of image quality. The examination of the iso-surface contour, by means of a frequency analysis, allows for automatic creation of the displacement map which better suits to the implicit model in question. In this study we demonstrate the validity of the proposal as an efficient alternative to polygonization of implicit surfaces, thus opening many new possibilities to its application to volume visualization.
Affective Development in Mathematics: A Case Study of Two Preservice Primary School Teachers

Preservice primary school teachers come to their initial teacher education programs with beliefs, attitudes, values and feelings about mathematics and mathematics education. These have been formed through their experiences of mathematics prior to tertiary study and they can powerfully impact on their development as a teacher of mathematics. Then, during their teacher education they have another set of experiences of mathematics, which further impact on their views and emotions about the discipline. This study focuses on two preservice primary teachers in the first year of their initial teacher education and through a series of interviews and tasks it explores their affective views towards mathematics. It seems from the study that tertiary mathematics education courses need to explicitly and continually revisit the preservice teachers' affective positions if positive development is to occur. The beliefs, attitudes, values and feelings of preservice and inservice teachers towards mathematics have become a topic of increasing interest for mathematics educators as is evidenced by the growing number of articles and papers published in recent times. A growing awareness of the impact of affective factors combined with the realisation that many teachers have debilitating views of mathematics, mathematics teaching and learning is concerning. The literature includes a number of studies attempting to address the negative cycle of beliefs, attitudes and feelings preservice teachers bring to mathematics. Overall the results are inconclusive with some reporting limited positive change (eg., Aldridge & Bobis, 2001), some lamented little change (eg., Artzt, 1999) and few studies report success in reforming negative or debilitating views. McLeod (1992) notes in his comprehensive review of research in the area that "although affect is a central concern of students and teachers, research on affect in mathematics education continues to reside on the periphery of the field"(p. 575). This paper reports an investigation of the affective positions of two preservice primary school teachers, particularly as they relate to their experiences of mathematics and mathematics education. Initially the study focussed on their experiences prior to commencing their tertiary teacher education. The second phase explored their experiences during their first semester in teacher education and in particular their development through their first course on the teaching of mathematics. Finally, the study explored their beliefs, attitudes and values after their practicum experience.
Features for word spotting in historical manuscripts

For the transition from traditional to digital libraries, the large number of handwritten manuscripts that exist pose a great challenge. Easy access to such collections requires an index, which is currently created manually at great cost. Because automatic handwriting recognizers fail on historical manuscripts, the word spotting technique has been developed: the words in a collection are matched as images and grouped into clusters which contain all instances of the same word. By annotating "interesting" clusters, an index that links words to the locations where they occur can be built automatically. Due to the noise in historical documents, selecting the right features for matching words is crucial. We analyzed a range of features suitable for matching words using dynamic time warping (DTW), which aligns and compares sets of features extracted from two images. Each feature's individual performance was measured on a test set. With an average precision of 72%, a combination of features outperforms competing techniques in speed and precision.
Performance analysis of parallel CBAR in MapReduce environment

Clustering of data set is a very contemporary problem for handling big data and parallelizing the process of clustering helps in improving efficiency for applications which involve frequent searching. Various clustering techniques are used for grouping of data set and CBAR is one such very frequently used technique used for different applications. Parallelization of CBAR is very necessary for handling Bigdata and Hadoop MapReduce platform provides a suitable environment to improve efficiency for any problem dealing with huge volume of data by using appropriate segmentation technique. In this work, we designed and developed a few algorithms for implementing CBAR using MapReduce technique and tested the results in different clusters of up to 4 nodes. Significant improvement has been observed and analysis and explanation on these results have also been presented in our work with suitable example.
Dealing with additive noise in speaker recognition systems based on i-vector approach

In the last years, the i-vector approach became the state-of-the-art in speaker recognition systems. As in previous approaches, i-vector -based systems suffer greatly in presence of additive noise, especially in low SNR cases. In this paper, we will describe a statistical framework allowing to estimate a clean i-vector given the noisy one or to integrate, directly, statistical knowledges about the noise and clean i-vectors in the scoring phase. The proposed procedure is essentially based on a method which enables to produce statistical knowledge about the noise effect in the i-vector domain. The work presented here is based on the hypothesis that the noise effect is Gaussian and additive in the i-vector space. To validate our approach, experiments were carried out on NIST 2008 data (det7). Significant improvement was observed compared to the baseline system and to the "muti-style" backend training technique.
Exploring the Use of Adaptively Restrained Particles for Graphics Simulations

In this paper, we explore the use of Adaptively Restrained (AR) particles for graphics simulations. Contrary to previous methods, Adaptively Restrained Particle Simulations (ARPS) do not adapt time or space sampling, but rather switch the positional degrees of freedom of particles on and off, while letting their momenta evolve. Therefore, inter-particles forces do not have to be updated at each time step, in contrast with traditional methods that spend a lot of time there. We present the initial formulation of ARPS that was introduced for molecular dynamics simulations, and explore its potential for Computer Graphics applications: We first adapt ARPS to particle-based fluid simulations and propose an efficient incremental algorithm to update forces and scalar fields. We then introduce a new implicit integration scheme enabling to use ARPS for cloth simulation as well. Our experiments show that this new, simple strategy for adaptive simulations can provide significant speedups more easily than traditional adaptive models.
PHYTOCHEMICAL ANALYSES, ANTIBACTERIAL, IN VITRO ANTIOXIDANT AND CYTOTOXIC ACTIVITIES OF ETHANOLIC EXTRACT OF SYZYGIUM CUMINI (L.) SEED EXTRACT

Phytochemical constituents present in the aqueous and ethanolic extract of Syzygium cumini seeds were investigated which were found to be responsible for its medicinal efficacy. The major phytoconstituents present in S. cumini seeds were further determined by GCMS analysis which showed the presence of polyphenols, sesquiterpenes, n-alkanes. The antibacterial activity of ethanolic extract of S. cumini seeds were tested against common human pathogens by agar well diffusion method and minimum inhibitory concentration required to inhibit the growth of various pathogens were also evaluated. The seed extract was found to have high antibacterial activity. The total polyphenolic content of the ethanolic extract of S. cumini seed was determined by Folin-Ciocalteau method. The antioxidant activity of S. cumini seed ethanolic extract was evaluated by DPPH free radical scavenging assay, reducing power assay and total antioxidant capacity. S. cumini seed extract was found to have very high and potent antioxidant activity and might be due to the presence of high phenolic components in seed. The cytotoxicity assay of S. cumini seed ethanolic extract was carried out by trypan blue dye exclusion technique where the seed extract showed potential cytotoxic activity against cervical cancer cells. INTRODUCTION: From ancient times, medicinal herbs have been use in one form or another, under indigenous systems of medicine like Ayurveda, Sidha and Unani . Herbal medicines are promising choice over modern synthetic drugs as they show minimum or no side effects and are considered to be safe . According to WHO, 80% of the world’s inhabitants problem should treated by medicinal herbal drug for their primary health care . There are various medicinal plants which have significant medicinal values and are widely used in folklore medicine. The literature reveals that flavonoids, alkaloids, terpenes of plant derived products have received considerable attention in recent years due to their diverse pharmacological properties including cytotoxicity and cancer chemopreventive effects . There are several factors which are responsible for causing various diseases and complications of the human health. In this present study, we have focused on the major two issues related to their complications on human health, multidrug resistant microorganisms and oxidative stress. The present problem in the pharmaceutical industry is the wide prevalence of the multidrug resistant bacteria. The problem of microbial resistance of growing and the outlook for the use of antimicrobial drugs in the future is still uncertain .
Sub-nanometer Atomic Layer Deposition for Spintronics in Magnetic Tunnel Junctions Based on Graphene Spin-Filtering Membranes

We report on the successful integration of low-cost, conformal, and versatile atomic layer deposited (ALD) dielectric in Ni–Al2O3–Co magnetic tunnel junctions (MTJs) where the Ni is coated with a spin-filtering graphene membrane. The ALD tunnel barriers, as thin as 0.6 nm, are grown layer-by-layer in a simple, low-vacuum, ozone-based process, which yields high-quality electron-transport barriers as revealed by tunneling characterization. Even under these relaxed conditions, including air exposure of the interfaces, a significant tunnel magnetoresistance is measured highlighting the robustness of the process. The spin-filtering effect of graphene is enhanced, leading to an almost fully inversed spin polarization for the Ni electrode of −42%. This unlocks the potential of ALD for spintronics with conformal, layer-by-layer control of tunnel barriers in magnetic tunnel junctions toward low-cost fabrication and down-scaling of tunnel resistances.
COGNITIVE FUNCTIONING IN PATIENTS WITH COMPLEX ABSENCE FOLLOWING TREATMENT WITH SODIUM VALPROATE

SUMMARY The association of sodium valproate with cognitive functions was studied in 29 patients with complex absence seizures. Seventeen patients were on monotherapy and twelve on polypharmacy with sodium valproate. Cognitive functions assessed were attention, speech, visuo-speciat perception, memory and intelligence. Behavioral disturbances were also assessed. Two assessments were made six months apart; in the first assessment, attention and speech were adequate, while memory, visuo-spatial perception, and behavioral functioning were impaired. Intelligence was lower in the polypharmacy group, while other functions were similar. In the second assessment, intelligence and visual memory improved in the monotherapy group, while no changes were present in the polypharmacy group.