Miracle-dz/UniFAR-FTUs huggingface dataset:
UKSearch - Web Search with Knowledge-Rich Indices

Standard search engines prove to be very useful if they have access to huge amounts of data. However, a common problem is search over a restricted domain. This paper addresses this problem by indexing the source data in a more elaborate way than in standard search engine technology. This allows us to extract concepts that axe used to create a structure for the documents that is similar to that found in classified directories.
A distributed heuristic for decentralized workflow scheduling in global Grids

Efficient scheduling is a key concern for the effectual execution of performance driven Grid applications, such as workflows. Many list heuristics have been developed for scheduling workflows in centralized Grid environment. However, in this paper, we present a distributed list heuristic for decentralized scheduling of workflow applications in global Grids. The simulation results show that the proposed scheduling approach is scalable with respect to increased workload on the system.
Description of Work : Deliverable 3 . 3 D 3 . 3 : Detailed analyses of the LES and SCM results for ASTEX and the two

Deliverable D3.3 Detailed analyses of the LES and SCM results for ASTEX and the two GPCI columns. Aim Large-eddy simulation and single-column models are used to study Lagrangian transitions of solid subtropical stratocumulus to shallow cumulus as the cloudy air mass is being advected towards the equator over increasingly higher sea surface temperatures. The simulations were carried out not only by all the relevant EUCLIPSE partners, but also by other participants from various weather and climate institutions as well as universities form outside the EUCLIPSE consortium. The first case is based on observations collected during the ASTEX field campaign which allows to examine if models can faithfully represent the observed break-up of a solid stratocumulus cloud deck. Since it is found that LES models agree well with the observations, the range of large-scale forcing conditions that control the timing of the transition is broadened by adding three composite Lagrangian cases representative for the typical locations in the Eastern parts of the Pacific and Atlantic Ocean. In this way we can confront single-column model results with both observations and four high-resolution large-eddy simulation cases, each of which was forced by slightly different boundary conditions. We will particularly focus on the diurnal cycle of the cloud liquid water path and the timing of the break-up of the stratocumulus cloud deck. As this report describes the results of four Lagrangian transition cases in the Hadley cell, we decided to present the modelling results of the Eulerian experiments, i.e. low clouds at fixed points in the Hadley cell, in the report of D3.9.
Preliminary Analysis of a Write Reduction Method for Non-volatile Main Memory on Jikes RVM

There are many researches and developments on byte accessible non-volatile memory (NVM). Byte accessible NVM can be used as not only storage but also main memory. Non-volatile main memory has two significant merits: reducing power consumption of main memory, flexible system standby and fast resume. However, NVM has bottlenecks in writing and limitation on the number of writing. To overcome this problem, there are some researches on hybrid memory architectures, which combine NVM and DRAM. On hybrid memory architectures, it needs to collect program characteristics of write access and allocate DRAM or NVM based on that characteristics. Previous works proposed to collect and allocate at hardware level or operating system level. At hardware or OS level, memory allocators can collect writing information of wide range of processes. However, they do not know contents of written data, thus they cannot allocate memory based on data characteristics. To solve this problem, we propose a method to obtain characteristics of writing and assign DRAM or NVM based on that information in previous work. The method applies write barrier of language runtimes to collect writing characteristics. This paper describes modification of our previous work and preliminary analysis for implementation the method on Jikes RVM.
Sulindac Enhances the Proteasome Inhibitor Bortezomib-Mediated Oxidative Stress and Anticancer Activity

Purpose: The nonsteroidal antiinflammatory drug sulindac is a promising chemopreventive agent against colon cancer. Here, we address whether sulindac enhances the anticancer effects of the proteasome inhibitor bortezomib (PS-341) in colon cancer cells. Experimental Design: The synergistic effects of sulindac with bortezomib were evaluated by cell death, colony formation assay, DNA fragmentation, and tumor progression of DLD-1 xenografts. Reactive oxygen species (ROS) generation was detected using carboxy-H2DCFDA or dihydroethidium. Oxidative stress was evaluated by heme oxygenase-1 induction and stress-activated mitogen-activated protein kinases p38 and c-Jun-NH2-kinase phosphorylation. Oxidative DNA damage was evaluated by histone H2AX phosphorylation and accumulation of 8-hydroxy-2′-deoxyguanosine. Results: Sulindac and its metabolites enhanced the anticancer effects of bortezomib in DLD-1 and BM314 colon cancer cells. Sulindac induced ROS generation and enhanced bortezomib-mediated oxidative stress and subsequent DNA damage. Their combined effects were highly sensitive to free radical scavengers l-N-acetylcysteine and α-tocopherol, but were much less sensitive to a p38 inhibitor SB203580. Conclusion: Sulindac synergistically augments the anticancer effects of bortezomib primarily through cooperative ROS generation and oxidative DNA damage, thereby representing a novel combination therapy against colon cancer.
Illumination and Expression Invariant Face Recognition: Toward Sample Quality-based Adaptive Fusion

The performance of face recognition schemes is adversely affected as a result of significant to moderate variation in illumination, pose, and facial expressions. Most existing approaches to face recognition tend to deal with one of these problems by controlling the other conditions. Beside strong efficiency requirements, face recognition systems on constrained mobile devices and PDA's are expected to be robust against all variations in recording conditions that arise naturally as a result of the way such devices are used. Wavelet-based face recognition schemes have been shown to meet well the efficiency requirements. Wavelet transforms decompose face images into different frequency subbands at different scales, each giving rise to different representation of the face, and thereby providing the ingredients for a multi-stream approach to face recognition which stand a real chance of achieving acceptable level of robustness. This paper is concerned with the best fusion strategy for a multi-stream face recognition scheme. By investigating the robustness of different wavelet subbands against variation in lighting conditions and expressions, we shall demonstrate the shortcomings of current non-adaptive fusion strategies and argue for the need to develop an image quality based, intelligent, dynamic fusion strategy.
False Contagion and False Covergence Clubs in Stochastic Growth Theory

Three hypotheses exist for the bimodal, cross-country, income distributions emerging in the data since 1960 and dubbed convergence clubs. One is that economies in each mode are interacting with one another - true clubs. A second is that the bimodality is caused by lock-in or 'true contagion' - success at innovations breeds further success, failure breeds failure. These are false clubs because no interaction is assumed. A third (ours) generates false clubs but with no lock-in (false contagion), in a model with two stochastic processes, one from the consumer side, the other the usual Poisson process for innovations. Monte Carlo simulations show that bimodals can be caused by effects from either side. With non-conformist consumers for example, as R&D productivity is varied from increasing to decreasing returns, the bimodal distribution can move to an almost normal curve, with both few rich and few poor economies. All the derived Markov transition matrices mimic the dominant diagonals of the post-1960 data so that these are no proof of either clubs or lock-in.
On the role of domain-specific knowledge in the visualization of technical flows

In this paper, we present an overview of a number of existing ﬂow visualization methods, developed by the authors in the recent past, that are speciﬁcally aimed at integrat-ing and leveraging domain-speciﬁc knowledge into the visualization process. These methods transcend the traditional divide between interactive exploration and feature-based schemes and allow a visualization user to beneﬁt from the abstraction properties of feature extraction and topological methods while retaining intuitive and interactive control over the visual analysis process, as we demonstrate on a number of examples.
Fast perception-based depth of field rendering

Current algorithms to create depth of field (DOF) effects are either too costly to be applied in VR systems, or they produce inaccurate results. In this paper, we present a new algorithm to create DOF effects. The algorithm is based on two techniques: one of high accuracy and one of high speed but less accurate. The latter is used to create DOF effects in the peripheral viewing area where accurate results are not necessary. The first is applied to the viewing volume focussed at by the viewer. Both techniques make extensive use of rendering hardware, for texturing as well as image processing. The algorithm presented in this paper is an improvement over other (fast) DOF algorithms in that it is faster and provides better quality DOF effects where it matters most.
It’s not cricket: examining political discussion in nonpolitical online space

ABSTRACT Scholars of digital democracy share enthusiasm about the potential the Internet provides for democratic communication among citizens. Many applaud the prospect of an expanded, digital, public sphere; others are more cautious about whether the Internet may foster deliberative democracy. We attempt to provide a third alternative view by (1) focusing on everyday political talk in nonpolitical online forums and (2) expanding research beyond a singular deliberative model to attend to multiple frameworks for democratic discussion online. In this paper, we examine online political discussion of six globally prominent political issues in two transnational cricket forums. Our findings suggest that deliberative discussion coexists with liberal individualist and communitarian forms of communication in online sports forums. We discuss the implications of our findings for the future of mediated political discussion research.
A Novel Agent Enhances the Chemotherapeutic Efficacy of Doxorubicin in MCF-7 Breast Cancer Cells

We have previously demonstrated that DT-010, a novel conjugate of danshensu (DSS) and tetramethylpyrazine (TMP), displays anti-tumor effects in breast cancer cells both in vitro and in vivo. In the present study, we investigated whether DT-010 enhances the chemotherapeutic effect of doxorubicin (Dox) in MCF-7 breast cancer cells and exerts concurrent cardioprotective benefit at the same time. Our findings showed that DT-010 was more potent than TMP, DSS, or their combination in potentiating Dox-induced toxicity in MCF-7 cells. Co-treatment with DT-010 and Dox increased apoptosis in MCF-7 cells relative to Dox alone. Further study indicated that glycolytic capacity, glycolytic reserve and lactate level of MCF-7 cells were significantly inhibited after DT-010 treatment. DT-010 also increased the expression of the pro-survival protein GRP78, which was inhibited by co-treatment with Dox. Both endoplasmic reticulum stress inhibitor 4-PBA and knockdown of the expression of GRP78 protein potentiated DT-010-mediated apoptosis in MCF-7 cells. Moreover, DT-010 inhibited Dox-induced cardiotoxicity in H9c2 myoblasts. In conclusion, DT-010 and Dox confer synergistic anti-tumor effect in MCF-7 breast cancer cells through downregulation of the glycolytic pathway and inhibition of the expression of GRP78. Meanwhile, DT-010 also protects against Dox-induced cardiotoxicity.
Developing learning strategies for topic-based summarization

Most up-to-date well-behaved topic-based summarization systems are built upon the extractive framework. They score the sentences based on the associated features by manually assigning or experimentally tuning the weights of the features. In this paper, we discuss how to develop learning strategies in order to obtain the optimal feature weights automatically, which can be used for assigning a sound score to a sentence characterized with a set of features. The two fundamental issues are about training data and learning models. To save the costly manual annotation time and effort, we construct the training data by labeling the sentence with a "true" score calculated according to human summaries. The Support Vector Regression (SVR) model is then used to learn how to relate the "true" score of the sentence to its features. Once the relations have been mathematically modeled, SVR is able to predict the "estimated" score for any given sentence. The evaluations by ROUGE-2 criterion on DUC 2006 and DUC 2005 document sets demonstrate the competitiveness and the adaptability of the proposed approaches.
A Fuzzy Approach for Ambiguity Reduction in Text Similarity Estimation (Case Study: Persian Web Contents)

Finding similar web contents have great efficiency in academic community and software systems. There are many methods and metrics in literature to measure the extent of text similarity among various documents and some its application especially in plagiarism detection systems. However, most of them do not take ambiguity inherent in word or text pair‟s comparison that gained form linguistic experts as well as structural feature s into account. As a result, pervious methods did not have enough accuracy to deal vague information. So using structural features and considering ambiguity inherent word improve the identification of similar contents. In this paper, a new method has been proposed that taking lexical and structural features in text similarity measures into consideration. After preprocessing and removing stop words, each text was divided into general words and domain-specific knowledge words. For each part, appropriate features and measures are extracted. Then, the two lexical and structural fuzzy inference systems were designed to assess lexical and structural text similarity respectively. The proposed method has been evaluated on Persian paper abstracts of International Conference on e-Learning and e-Teaching (ICELET) Corpus. The results shows that the proposed method can achieve a rate of 75% in terms of precision and can detect 81% of the similar cases.
The research for storage scheme based on Hadoop

With the rise of big data, people begin to focus on storage of it. In this paper, first, we design four different storage solutions on Hadoop platform with HBase, MySQL, XML and plain text for specific data storage. Data is stored in Hadoop Distributed File System except for MySQL storage solution, in which data is stored directly in the Linux File System. Second, for each solution, we write specific program to meet the needs. Then we run each program respectively, record execution time and monitor system performance - CPU usage, memory usage and CPU load during the whole execution. Through analyzing and comparing the differences among storage solutions, we finally conclude the advantages, disadvantages and features of each one. The results can help us to select the most appropriate storage solution with these characteristics when processing data with Hadoop platform.
Multiparameter prediction model for atrial fibrillation after CABG

The aim of the study was to develop a multiparameter prediction model of atrial fibrillation (AF) after coronary artery bypass grafting (CABG) based on measured P wave parameters. We recorded the standard II lead ECG for at least 48 hours after surgery in 48 patients. In contrast to previous research and in order to enable the analysis of more data we decided to record the ECG continuously. The ECGs were processed offline and a vector of 82 P-wave parameters was calculated for every hour of the record. The segmentation of the ECGs was based on wavelet QRS and P-wave detectors. The calculated P-wave parameters were used for building classification and regression trees. We built several decision trees (models) for discriminating the AF prone patients after CABG. With the best tree model, we were able to achieve specificity (96.55%), sensitivity (54,54%), positive predictivity (85.71%), negative predictivity (84.84 %), accuracy (85,00%).
A multiphase region-based framework for image segmentation based on least square method

We propose a multiphase region-based framework for image segmentation using Least Square Method, by piecewise constant optimal approximations. The basic idea of our model is to build up a minimum error functional by approximating n sub-regions of the original image with n constants respectively. The main contribution of our method is that we introduce weighting matrixes into the region-based model, which can enhance the weight of the specific region while reducing the influence from other regions. Moreover, our method can fast converge, and segment a given image into arbitrary regions under least squares and iterative algorithm. Experimental results show the advantages of our method in terms of accuracy and efficiency in image segmentation.
Development and convergence analysis of training algorithms with local learning rate adaptation

A new theorem for the development and convergence analysis of supervised training algorithms with an adaptive learning rate for each weight is presented. Based on this theoretical result, a strategy is proposed to automatically adapt the search direction, as well as the step-size length along the resultant search direction. This strategy is applied to some well known local learning algorithms to investigate its effectiveness.
Symmetric Statistical Translation Models for Automatic Image Annotation

Automatic image annotation provides means for users to search image collections on the semantic level using natural language queries. In the past, statistical machine translation models have been successfully applied to automatic image annotation. A problem with this approach is that, due to the skewed distribution of term frequency for annotation words, common words have been overly favored, which leaves little room for uncommon words to be used in auto-annotations. In contrast, studies on information retrieval have revealed that uncommon words are at least as important as common words since they are also often used in users’ queries. Unlike the previous studies where a single type of statistical translation model is considered for automatic image annotation, in this paper, we studied two types of statistical translation models: a forward translation model, which translates visual information into textual words, and a backward model, which translates textual words into visual images. In particular, we propose a new statistical translation model, named regularization-based symmetric statistical translation model, which combines strength of forward and backward models to alleviate the problem of overly favoring common words. Our empirical studies with the Corel dataset have shown that the proposed model performs considerably better than the existing translation model and a state-of-the-art approach for automatic image annotation.
Statistics of Stellar Populations of Star Clusters and Surrounding Fields in the Outer Disk of the Large Magellanic Cloud

A comparative analysis of Washington color-magnitude diagrams (CMDs) for 14 star clusters and respective surrounding fields in the Large Magellanic Cloud (LMC) outer disk is presented. Each CCD frame including field and the respective cluster covers an area of 185 arcmin2. The stellar population sampled is of intermediate age and metallicity. CMD radial analysis involving star count ratios, morphologies, and integrated light properties are carried out. Luminosity functions (LFs) are also presented. The two main results are, (1) within the range 4 kpc < R < 8 kpc, the distance from the LMC center is well correlated with the average age in the sense that inner fields are younger and (2) beyond ≈8 kpc the outer fields do not show evidence of a significant intermediate-age component in their stellar populations, as inferred from red giant clump star counts.
Associative pathways between pornography consumption and reduced sexual satisfaction

ABSTRACT Social and clinical psychologists are increasingly examining the influence of pornography on sexual health outcomes. An important sexual health outcome that some scholars have suggested is influenced by pornography is sexual satisfaction. Guided by sexual script theory, social comparison theory, and informed by prior research on pornography, socialization, and sexual satisfaction, the present survey study of heterosexual adults tested a conceptual model linking more frequent pornography consumption to reduced sexual satisfaction via the perception that pornography is a primary source of sexual information, a preference for pornographic over partnered sexual excitement, and the devaluation of sexual communication. The model was supported by the data for both men and women. Pornography consumption frequency was associated with perceiving pornography as a primary source of sexual information, which was associated with a preference for pornographic over partnered sexual excitement and the devaluation of sexual communication. Preferring pornographic to partnered sexual excitement and devaluing sexual communication were both associated with less sexual satisfaction.
Option Pricing with Stochastic Volatility Using Fuzzy Sets Theory

The aim of this paper is to price European options for underlying assets with stochastic volatility (SV) in Heston model in 1993 using fuzzy set theory. The main idea is to transform the probability distribution of stochastic volatility to its possibility distribution (from ‘volatility smile to volatility frown’) and reduce the problem to a fuzzy stochastic process for underlying asset with a new SV as a fuzzy number associated with initial SV. We use then non-linear fuzzy PDE approach to price European options.
Nutrition for Sarcopenia

Aging-related sarcopenia means that muscle mass, strength, and physical performance tend to decline with age, and malnutrition is associated with sarcopenia. Therefore, nutritional interventions may make an important contribution to prevent the development of sarcopenia. Here I reviewed published articles about the effects of nutritional factors on sarcopenia in elderly people. A growing body of evidence suggests that metabolic factors associated with obesity and diabetes induce the progression of sarcopenia. However, the effectiveness and safety of caloric restriction for sarcopenia remained unclear. Protein intake and physical activity are the main anabolic stimuli for muscle protein synthesis. As optimal dietary protein intake, 1.0 - 1.2 g/kg (body weight)/day with an optimal repartition over each daily meal or 25 - 30 g of high quality protein per meal were recommended to prevent sarcopenia, which was supported by some observational studies. Protein supplementation using cheese and milk protein, essential amino acids, leucine, beta-hydroxy-beta-methylbutyrate and vitamin D has been investigated as a potential supplement to improve muscle quality in sarcopenic elderly people.