Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Aggregation on Chip Multiprocessors

The recent introduction of commodity chip multiprocessors requires that the design of core database operations be carefully examined to take full advantage of on-chip parallelism. In this paper we examine aggregation in a multi-core environment, the Sun UltraSPARC T1, a chip multiprocessor with eight cores and a shared L2 cache. Aggregation is an important aspect of query processing that is seemingly easy to understand and implement. Our research, however, demonstrates that a chip multiprocessor adds new dimensions to understanding hash-based aggregation performance---concurrent sharing of aggregation data structures and contentious accesses to frequently used values. We also identify a trade off between private data structures assigned to each thread versus shared data structures for aggregation. Depending on input characteristics, different aggregation strategies are optimal and choosing the wrong strategy can result in a performance penalty of over an order of magnitude. We provide a thorough explanation of the factors affecting aggregation performance on chip multiprocessors and identify three key input characteristics that dictate performance: (1) average run length of identical group-by values, (2) locality of references to the aggregation hash table, and (3) frequency of repeated accesses to the same hash table location. We then introduce an adaptive aggregation operator that performs lightweight sampling of the input to choose the correct aggregation strategy with high accuracy. Our experiments verify that our adaptive algorithm chooses the highest performing aggregation strategy on a number of common input distributions.
Human arm motion modeling and long-term prediction for safe and efficient Human-Robot-Interaction

Modeling and predicting human behavior is indispensable when industrial robots interacting with human operators are to be manipulated safely and efficiently. One challenge is that human operators tend to follow different motion patterns, depending on their intention and the structure of the environment. This precludes the use of classical estimation techniques based on kinematic or dynamic models, especially for the purpose of long-term prediction. In this paper, we propose a method based on Hidden Markov Models to predict the region of the workspace that is possibly occupied by the human within a prediction horizon. In contrast to predictions in the form of single points such as most likely human positions as obtained from previous approaches, the regions obtained here may serve as safety constraints when the robot motion is planned or optimized. This way one avoids collisions with a probability not less than a predefined threshold. The practicability of our method is demonstrated by successfully and accurately predicting the motion of a human arm in two scenarios involving multiple motion patterns.
Gender And Learning Styles In Saudi Arabia Schools

The purpose of this study was to investigate the differences between males and females student in Saudi preparatory schools. 399 students from eight schools in Jeddah city was responded to VARK questioner younger version. Firstly, students were classified according to their learning styles into two groups, multimodal style (M) and single style (S). The multimodal style group consisted of students who used more than one style to learn, whereas the single style category consisted of students who depended only on one style. The second classification method, labelled as VARK7G, categorised students into seven learning style groups that consisted of visual, aural, read/write, kinaesthetic, bi, tri and quad styles. The result indicated a significant relationship between gender and learning style (multimodal or single). However, in VARK7G the result of showed there is no relationship between gender and learning style.
Scalable Human-based Computation

Human computation is becoming an essential component of many computer systems architectures, resulting in what can be referred to as Human-Machine Systems. In order to foster this adoption, the Human computation component should deliver close to predictable performance, both in terms of speed and quality. My dissertation work aims at addressing some of the current shortcomings of paid micro-task crowdsourcing – a popular form of human computation – with the goal of bridging the gap between the inherent differences of computer systems and human computation, for a seamless integration.
Analyses des classes et des transitions latentes : des outils pour documenter le parcours de développement des compétences.

Our proposal is that the process of validating the construct of a competency inferred from an observation grid raises a multivariate and multi-dimensional latent classification problem. The analysis models a given competency based on the identification of discrete classes using latent classification (LCA) and latent transition (LTA) models. Then, the approach is illustrated in the context of the growth of the morphosyntax competency of learners of French as a second language. Advantages and limits of the analysis are outlined. Résumé Notre proposition est que le processus de validation du construit d’une compétence inférée à partir d’une grille d’observation soulève un problème multivarié et multidimensionnel de classification latente. L’analyse modélise une compétence à partir de l’identification de stades discrets et de transitions d’un stade à un autre par le biais de modèles de classes latentes (LCA) et de transitions latentes (LTA). L’approche est par la suite illustrée dans le contexte du développement de la compétence morphosyntaxique chez des apprenants de français langue seconde. Les limites et les avantages de l’analyse sont discutés.
Interactive Software Visualization within the RECONSTRUCTOR Project (RECONSTRUCTOR: Reconstructing Software Architectures for System Evaluation Purposes)

A software architecture is an abstraction of a software system, which is indispensable for many software engineering tasks. Unfortunately, in many cases architectural information is not available, outdated, or inappropriate for the task at hand. Software architecture reconstruction concerns obtaining architectural information from an existing system. Currently, this process is unstructured, requires much manual effort, leads to results that are hard to understand, and does not provide quantitative information on software quality aspects. The RECONSTRUCTOR project aims at raising the state of the art in software architecture reconstruction. My research within RECONSTRUCTOR focuses on interactive visualization of software systems and it is basically concerned with the following questions: “How can architectural information, represented in different, interrelated views, be visualized and manipulated? ” and “How can the visualization support the iterative reconstruction process and help to interpret and validate the findings of a software architecture evaluation? ”
Help yourself: a virtual self-assist system

In this work, we describe an unsupervised framework for creating self-assist systems which can serve as virtual call center agents to guide the customer in performing different domain-dependent tasks (like troubleshooting a problem, changing settings etc.). We describe a framework for creating an intent graph from a corpus of knowledge articles from a given domain which is used in creating the dialogue system. To the best of our knowledge, this is the first work in creating virtual self-assist agents.
An integrated approach to reduce the impact of minor allele frequency and linkage disequilibrium on variable importance measures for genome-wide data

MOTIVATION There is growing momentum to develop statistical learning (SL) methods as an alternative to conventional genome-wide association studies (GWAS). Methods such as random forests (RF) and gradient boosting machine (GBM) result in variable importance measures that indicate how well each single-nucleotide polymorphism (SNP) predicts the phenotype. For RF, it has been shown that variable importance measures are systematically affected by minor allele frequency (MAF) and linkage disequilibrium (LD). To establish RF and GBM as viable alternatives for analyzing genome-wide data, it is necessary to address this potential bias and show that SL methods do not significantly under-perform conventional GWAS methods. RESULTS Both LD and MAF have a significant impact on the variable importance measures commonly used in RF and GBM. Dividing SNPs into overlapping subsets with approximate linkage equilibrium and applying SL methods to each subset successfully reduces the impact of LD. A welcome side effect of this approach is a dramatic reduction in parallel computing time, increasing the feasibility of applying SL methods to large datasets. The created subsets also facilitate a potential correction for the effect of MAF using pseudocovariates. Simulations using simulated SNPs embedded in empirical data-assessing varying effect sizes, minor allele frequencies and LD patterns-suggest that the sensitivity to detect effects is often improved by subsetting and does not significantly under-perform the Armitage trend test, even under ideal conditions for the trend test. AVAILABILITY Code for the LD subsetting algorithm and pseudocovariate correction is available at http://www.nd. edu/~glubke/code.html.
A Novel Interface Circuit for Capacitive Sensors Using Correlated Double Sampling Demodulation Technique

The capacitive readout circuit with CMOS-base (complementary metal oxide semiconductor) MEMS (microelectromechanical systems) devices for micro sensor applications has been developed. This study presents a novel method to make a low noise CMOS-MEMS readout circuit that can prevent the noise from electronic components and reduce the AC distortion due to sensing capacitance mismatch. Here, a significant method of correlated double sampling (CDS) technique can be employed to the proposed sample-and-hold demodulation structure which can diminish the problem of capacitance mismatch. The read out circuit is fabricated using 0.35 mum 2P4M CMOS process for demonstration. Simulation result indicates that the sensing circuits can achieve low power consumption of 3.78 mW with a sampling frequency of 4 MHz, and the overall sensitivity of the proposed circuit is 155 mV/fF. Also, a spurious free dynamic range (SFDR) of 39.4 dB is simulated in this design and shows a great linearity.
Influence of Temperature on Reactive Extraction of Propionic Acid using Tri-n-butyl Phosphate (TBP) dissolved in Inert Diluents and 1-Decanol

This paper deals with propionic acid extraction by tri-n-butylphosphate (TBP), a phosphorusbonded, oxygen-containing extractant dissolved in inert diluent (decane and kerosene) and a modifier (1decanol) at different temperature in the range of 298 K to 333 K. Active diluents (1-decanol) that enhance the extracting power of amines and phosphorus-bonded, oxygen-containing extractants are more favorable than inert diluents in the extraction of monocarboxylic acids, hydroxycarboxylic acids and dicarboxylic acids. Chemical equilibrium experiments have been carried out to investigate the effect of temperature on the extraction efficiency of tri-n-butylphosphate (TBP) as well as on the equilibrium extraction constant. It was found that distribution coefficient, KD decreased with increasing the temperature. However, an improved separation of the phases is observed. The extraction equilibrium constants have been determined using a mathematical model (mass action law) of recative extraction, taking into account formation of (1:1), acid:amine complexes. The apparent enthalpy and entropy of the reactive extraction with systems (decane/TBP/1-decanol and kerosene/TBP/1-decanol) are also determined. These data will be useful in the design of extractive fermentation processes for production of propionic acid.
Study on the Sensing Coating of the Optical Fibre CO2 Sensor

Optical fibre carbon dioxide (CO2) sensors are reported in this article. The principle of operation of the sensors relies on the absorption of light transmitted through the fibre by a silica gel coating containing active dyes, including methyl red, thymol blue and phenol red. Stability of the sensor has been investigated for the first time for an absorption based CO2 optical fiber sensor. Influence of the silica gel coating thickness on the sensitivity and response time has also been studied. The impact of temperature and humidity on the sensor performance has been examined too. Response times of reported sensors are very short and reach 2–3 s, whereas the sensitivity of the sensor ranges from 3 to 10 for different coating thicknesses. Reported parameters make the sensor suitable for indoor and industrial use.
Magnetic Circuit Analysis of Permanent-Magnet-Assisted Salient-Pole Synchronous Machines Under Steady States

Permanent-magnet-assisted salient-pole synchronous machines (PMa-SMs) are a new type of salient-pole synchronous machines that has PMs between the adjacent pole shoes. The PMs have the ability to improve the output power and the terminal voltage. In this paper, a simple magnetic circuit of PMa-SMs is presented. Using the magnetic circuit, the electric and magnetic basic characteristics of PMa-SMs are calculated. The results are compared with the results by measurements and finite-element analysis (FEA). They have shown that the magnetic circuit calculates the characteristics of PMa-SMs with satisfactory accuracy and that the computational time of the magnetic circuit is much smaller than that of FEA. Influence of the pole-shoe magnetic reluctances on the calculation accuracy by the magnetic circuit is also discussed, and some guidelines to enhance the accuracy have been presented.
The Remarkable Mechanism of Prostaglandin E2 on Synaptic Plasticity

Prostanoids have a broad spectrum of biological activities in a variety of organs including the brain. However, their effects on synaptic plasticity in the brain, which have been recently revealed, are ambiguous in comparison to those in the other organs. Prostaglandin E2 (PGE2) is a prostanoid produced from arachidonic acid in the cellular membrane, and knowledge about its functions is increasing. Recently, a novel function of PGE2 in the brain has shed light on aspects of synaptic plasticity such as long-term potentiation (LTP). More recently, we have proposed a hypothesis for the mechanisms of this PGE2-related form of synaptic plasticity in the visual cortex. This involves the dynamics of two subtypes of PGE2 receptors that have opposing functions in intracellular signal transduction. Consequently, mechanisms that increase the level of cyclic AMP in the cytosol may explain for the mechanisms of LTP in the visual cortex. The current notion of bidirectional trafficking of PGE2 receptors under this hypothesis is reminiscent of the “silent synapse” mechanism of LTP on the trafficking of the AMPA receptors between the membrane and cytosol. Moreover, we propose the hypothesis that PGE2 acts as a “post-to-postsynaptic messenger” for the induction of LTP in the visual cortex. This review describes a complex mode of action of PGE2 receptors in synaptic plasticity in the brain.
Entailment-based Fully Automatic Technique for Evaluation of Summaries

We propose a fully automatic technique for evaluating text summaries without the need to prepare the gold standard summaries man- ually. A standard and popular summary evaluation techniques or tools are not fully automatic; they all need some manual process or manual reference summary. Using recognizing textual entailment (TE), automat- ically generated summaries can be evaluated completely automatically without any manual preparation process. We use a TE system based on a combination of lexical entailment module, lexical distance module, Chunk module, Named Entity module and syntactic text entailment (TE) module. The documents are used as text (T) and summary of these doc- uments are taken as hypothesis (H). Therefore, the more information of the document is entailed by its summary the better the summary. Com- paring with the ROUGE 1.5. 5 evaluation scores over TAC 2008 (for- merly DUC, conducted by NIST) dataset, the proposed evaluation tech- nique predicts the ROUGE scores with a accuracy of 98.25% with respect to ROUGE-2 and 95.65% with respect to ROUGE-SU4.
IVA algorithms using a multivariate Student's t source prior for speech source separation in real room environments

The independent vector analysis (IVA) algorithm employs a multivariate source prior to retain the dependency between different frequency bins of each source and thereby avoids the permutation problem that is inherent to blind source separation (BSS). In this paper, a multivariate Student's t distribution is adopted as the source prior, which because of its heavy tail nature can better model the large amplitude information in the frequency bins. Therefore it can improve the separation performance and the convergence speed of the IVA and fast version of the IVA (FastIVA) algorithms as compared with the IVA algorithm based on another multivariate super Gaussian source prior. Separation performance with real binaural room impulse responses (BRIRs) is evaluated by detailed simulation studies when using the different source priors, and the experimental results confirm that the IVA and the FastIVA with the proposed multivariate Student's t source prior can consistently achieve improved and faster separation performance.
Ontology based framework for data integration

One of the most complex issues of the data integration is the case where there are multiple sources for the same data element. It is not easy to generate and maintain the integrated scheme. In this paper we describe a framework which encompasses the entire data integration process. The data source schemas as well as the integrated schema are expressed using an extension of an ontology definition language which allows the incorporation of metadata to support the integration process. The proposed model allows the user to concentrate in modeling the problem itself and not in the issues of dealing with the temporal and the spatial aspects concerning to many of the data sources usually used in the enterprises information systems.
A Regulatory Polymorphism in HAVCR2 Modulates Susceptibility to HIV-1 Infection

The HAVCR2 gene encodes TIM-3, an immunoglobulin superfamily member expressed by exhausted CD8+ T cells during chronic viral infection. We investigated whether genetic variation at HAVCR2 modulates the susceptibility to HIV-1 acquisition; specifically we focused on a 3′ UTR variant (rs4704846, A/G) that represents a natural selection target. We genotyped rs4704846 in three independent cohorts of HIV-1 exposed seronegative (HESN) individuals with different geographic origin (Italy and Spain) and distinct route of exposure to HIV-1 (sexual and injection drug use). Matched HIV-1 positive subjects and healthy controls were also analyzed. In all case-control cohorts the minor G allele at rs4704846 was more common in HIV-1 infected individuals than in HESN, with healthy controls showing intermediate frequency. Results from the three association analyses were combined through a random effect meta-analysis, which revealed no heterogeneity among samples (Cochrane's Q, p value = 0.89, I2 = 0) and yielded a p value of 6.8 ×10−4. The minor G allele at rs4704846 was found to increase HAVCR2 expression after in vitro HIV-1 infection. Thus, a positively selected polymorphism in the 3′ UTR, which modulates HAVCR2 expression, is associated with the susceptibility to HIV-1 infection. These data warrant further investigation into the role of TIM-3 in the prevention and treatment of HIV-1/AIDS.
3 AFP 2006 Program committee :

This year's program reflects the wide variety of exciting new approaches that have recently emerged for gene and protein function prediction. The Program Committee and the Scientific Committee were tasked with selecting 19 presentations from the 32 submitted. We have striven to provide a mix of scientific excellence and variety of approaches, and we believe that this effort is reflected in the program you are now holding. Our keynote speakers will address the wide variety of problems associated with automated function prediction. New to this year, we have introduced a discussion panel engaging the audience with the keynote speakers. We are especially excited about discussing our plans for 2007, which include a Critical Assessment of Function Annotations, in the spirit of ongoing activities in the fields of computational structure prediction, and the prediction of protein-protein interactions. Please join us for this discussion on the second day of the conference. Special thanks to John Wooley, Associate Vice Chancellor for Research at UCSD for securing conference funds and for his ongoing and enthusiastic backing of this endeavor. Finally, we would like to thank everyone who supported this conference financially. AFP 2006 was made possible by grants from the We hope you will find AFP 2006 a stimulating and fruitful meeting. Identifying meaningful functional modules in the yeast protein protein interaction network
The Role of Sleep and Sleep Disorders in the Development, Diagnosis, and Management of Neurocognitive Disorders

It is becoming increasingly apparent that sleep plays an important role in the maintenance, disease prevention, repair, and restoration of both mind and body. The sleep and wake cycles are controlled by the pacemaker activity of the superchiasmic nucleus in the hypothalamus but can be disrupted by diseases of the nervous system causing disordered sleep. A lack of sleep has been associated with an increase in all-cause mortality. Likewise, sleep disturbances and sleep disorders may disrupt neuronal pathways and have an impact on neurological diseases. Sleep deprivation studies in normal subjects demonstrate that a lack of sleep can cause attention and working memory impairment. Moreover, untreated sleep disturbances and sleep disorders such as obstructive sleep apnoe (OSA) can also lead to cognitive impairment. Poor sleep and sleep disorders may present a significant risk factor for the development of dementia. In this review, the underlying mechanisms and the role of sleep and sleep disorders in the development of neurocognitive disorders [dementia and mild cognitive impairment (MCI)] and how the presence of sleep disorders could direct the process of diagnosis and management of neurocognitive disorders will be discussed.
Standards-based external exams and students’ science-related career expectations: an international perspective

ABSTRACT Students’ science-related career expectations are important for predicting their future science, technology, engineering, and mathematics (STEM)-related educational and occupational attainments. This study examines the degree to which standards-based external examinations are associated with a student’s propensity for pursuing science-related professional occupations. The science-related fields included in the analysis are mathematics, physical and life science, and engineering/computing. Three-level hierarchical generalized linear models are employed to analyse international survey and student achievement data from the Programme for International Student Assessment (PISA). The analyses show that students in national education systems that require standards-based external examinations have lower expectations for science-related professional careers than students in the systems that do not use such examinations. This negative association remained constant by gender as well as across levels of science performance. From an educational policy point of view, the results suggest the negative consequence of standards-based external exams in fostering students’ interests in pursuing science-related careers.