Miracle-dz/UniFAR-FTUs huggingface dataset:
Block Randomized SVD on GPUs

Fast computation of singular value decomposition (SVD) is of great interest in various machine learning tasks. Recently, SVD methods based on randomized linear algebra have shown significant speedup in this regime. For processing large-scale data, computing systems with accelerators like GPUs have become the mainstream approach. In those systems, access to the input data dominates the overall process time; therefore, it is needed to design a batched algorithm to dispatch the computation into accelerators. This paper proposes an accurate 2-pass randomized SVD, named block randomized SVD (BRSVD), designed for matrices with a slow-decay singular spectrum that is often observed in image data. BRSVD fully utilizes the power of modern computing system architectures and efficiently processes large-scale data in a parallel and batched fashion. Our experiments show that BRSVD outperforms existing randomized SVD methods in terms of speed with retaining similar accuracy. We also show application of randomized SVD to convex robust principal component analysis on a GPU, which shows significant speedup in computer vision applications.
Characterisation of Wings with NACA 0012 Airfoils

Dans cet article, on presente une comparaison des resultats experimentaux des ailes de formes elliptiques, rectangulaires ou trapezoidales de profils NACA 0012. La prediction de la partance et la trainee induite par la theorie de la ligne portante de Prandtl est aussi comparee aux resultats experimentaux. Il parait qu'a de differents nombres de Reynolds, la difference des caracteristiques aerodynamiques entre les trois ailes est tres reduite.
Molecular Identification of Fig Cryptic Virus and Fig Fleck-Associated Virus in Turkey

Recently, several new viruses infecting fig trees were identified. To assess the presence, distribution and genetic diversity of Fig cryptic virus (FCV) and Fig fleck-associated virus (FFkaV) in fig trees of Turkey, a total of 65 fig samples, which show yellowing, chlorotic, necrotic spots and vein clearing symptoms, were collected from Aegean and Mediterranean regions, which are the most important fig growing regions of Turkey, in spring 2014 and tested by molecular analysis. After cDNA synthesis, FCV and FFkaV specific primer sets of RNA dependent RNA polymerase (RdRp) regions were used for reverse transcription-polymerase chain reaction (RT-PCR) analysis and the PCR products were directly sequenced. The obtained sequences were analyzed and nucleotide sequence analysis confirmed the FCV and FFkaV infections. According to the results, some of the fig trees are infected in Turkey by FCV and FFkaV with an incidence of 20 % and 9.2 %, respectively. BLAST analysis of both FCV and FFkaV has shown high identity with Italian isolates (FCV|ref|NC015494.1 and FFkaV|ref|NC015229.1). For FFkaV, the phylogenetic analysis, constructed from partial RdRp nucleotide sequences, clustered the isolates based on their geographical origin. While the correlation between FFkaV isolates and regions was very high, no correlation between collection regions and FCV isolates was observed. It can be concluded that, fig trees from the most important fig growing regions of Turkey are infected by FCV and FFkaV and it is instrumental to overview of the viral control strategies for fig plantations in Turkey.
Screening Fresh, Dry and Processed Turmeric (Curcuma longa L.) Essential Oil Against Pathogenic Bacteria

Turmeric (Curcuma longa L.) is a rhizomatous perennial plant related to Zingiberaceae family, commonly known as Curcuma, Curcum, Haridra and Indian saffron. Curcuminoids represent the key bioactive principles of turmeric, which are of great significance as health beneficial molecules. In the current study, eight cultivars namely, Local (check), Alleppey supreme, Kedaram, Prabha, Prathibha, Suvarna, Suguna and Sudharshana, maintained at Sanjeevani Vatika, Department of Horticulture, UAS (B), GKVK, Bangalore were utilized for the study. Rhizome extract of turmeric in different forms (fresh, dry and Processed) was evaluated for antimicrobial action on Pseudomonas aeruginosa and Escherichia coli by disc diffusion method. The inhibition zone (mm) was compared to standard antibiotics, Ampicillin and Streptomycin.The results uncovered that the dry and processed form of rhizome extract effectively inhibited P.aeruginosa which is resistant to most broad spectrum antibiotics. Turmeric, may thus offer an effective alternative in prevention
INVESTIGATING THE EFFECTIVENESS OF THREE-DIMENSIONAL-PRINTING FOR PRODUCING REALISTIC PHYSICAL SURGICAL TRAINING PHANTOMS

Current simulation methods for surgical training present several restrictions in terms of accessibility, flexibility, costs and ethics. In this research, the potential of ThreeDimensional Printing (3DP) for the manufacturing of biological representative simulation materials for training phantoms has been investigated. The force experienced during surgical dissection of the human sinus has been quantified and used as a benchmark for the optimization of 3DP materials. The research has explored a wide range of properties achievable in 3DP through post-processing methods, which allowed the reproduction of force values in accordance with those registered with cadaveric human tissue.
Identification of the agent of grapevine fleck disease

An antiserum against an Italian isolate of grapevine phloem-limited isometric virus (GPLIV) was used in an ELISA survey carried out for assessing the natural distribution of the virus and its association with fleck disease. A total of 591 vines of Vitis rupestris were checked for the presence of GPLIV. Of 150 plants with fleck symptoms, 138 (92 %) were ELISA-positive and 12 (8 %) negative. Of 441 symptomless V. rupestris , 435 (98,6 %) were ELISA-negative and 6 (1,4 %) positive for GPLIV. The virus was detected in about 30 % of 694 vines of different origin grown in Apulia (Southern Italy). The highest infection (53 %) was in a commercial vineyard of cv. Italia and the lowest (8 %) in a plot of certified and visually selected rootstocks. Fleck-infected, but not fleck-free V. rupestris contained virus particles and vesiculated inclusion bodies in phloem tissues. LN 33 plantlets derived from in vitro culture of meristem tips from ELISA-positive fleck-infected mother plants were found to be free from GPLIV, as ascertained by ELISA and thin-sectioning. These vines failed to induce fleck symptoms when grafted on V. rupestris. It is concluded that GPLIV is the agent of fleck and, therefore, it should be renamed grapevine fleck virus (GFKV).
Active 3D Classification of Multiple Objects in Cluttered Scenes

Autonomous agents that need to effectively move and interact in a realistic environment have to be endowed with robust perception skills. Among many, accurate object classification is an essential supporting element for assistive robotics. However, realistic scenarios often present scenes with severe clutter, that dramatically degrades the performance of current object classification methods. This paper presents an active vision approach that improves the accuracy of 3D object classification through a next-best-view (NBV) paradigm to perform this complex task with ease. The next camera motion is chosen with the criteria that aim to avoid object self-occlusions while exploring as much as possible the surrounding area. An online 3D reconstruction module is exploited in our system in order to obtain a better canonical 3D representation of the scene while moving the sensor. By reducing the impact of occlusions, we show with both synthetic and real-world data that in a few moves the approach can surpass a state-of-the-art method, PointNet with single view object classification from depth data. In addition, we demonstrate our system in a practical scenario where depth sensor moves to search and classify a set of objects in cluttered scenes.
Income and Calorie Intake among Farming Households in Rural Nigeria: Results of Parametric and Nonparametric Analysis

There has been an intense debate in the last two decades on the nature of the relationship between income and calorie intake. This paper relooks at the relationship using parametric and nonparametric techniques. The paper employed a survey data set collected from farm households in rural Nigeria. Our analysis indicates that average per capita daily calorie intake in the study area is 2428 kilocalories, which is below the recommended intake level. We find that income has a significant positive relationship with calorie intake. Nonetheless, the calorie-income elasticity is estimated as 0.181, suggesting that calorie intake does not increase substantially with increases in income. The results also reveal a positive and significant relationship between farm size and calorie intake. A combination of policy strategies, including income growth, agricultural development and targeted food programmes could reduce the problems of inadequate calorie consumption among poor households.
D ETECTION OF S IMILARITIES IN C YBER T HREATS THROUGH OSINT

The real condition is that infringement incidents are automated and intelligent with the rapidly growing number of cyber threats. There are double-sided characteristics that attack tools used for infringement incidents share, but the characteristics of the tool are different for each attacker. As a result, demand on cyber threats information sharing increases, so that the whole world can respond to cyber threats instead of local respond to infringement incidents. This paper proposes a method of calculating a level of similarity by graph kernels among cyber threat information that is shared. And we also propose several techniques for facilitating development based on the proposed method. For example, in selecting targets for comparison, we have limited the input values to execute the delta function in original graph kernels. In order to make similarity more reliable, we adopt a pre-processing procedures by types of threat information shared. This system provides some insight into planning that cyber threats information can be grouped according to characteristics.
Knowledge exploration in public linked data ontologies

A challenge when using big knowledge graphs (KGs) such as Wikidata and DBpedia is to select a proper subset of the KG. This paper compares using entity types and propertyroles as filters for selecting relevant entities in a KG. Propertyroles e.g. performedBy-object (entity is a performer) or genre-subject (entity has a genre) are found by using the most frequent statements for a query entity. The Semantic Artists Similarity dataset contains artists with their top ten most similar artists, by using each artist as a query entity and filtering the KG on the query entity’s types, similar types, and property-roles we measure recall (percentage of similar artists found) and precision (percentage of entities in knowledge graph not satisfying the filter). The results show that using property-roles outperforms entity types in Wikidata, recall 99.5% vs 88% and precision 98.4% vs 84.2% (compared to using the query entity’s types). Interestingly, there is little difference in using property-roles and similar entity-types in DBpedia: 97.6% vs 98.4% recall and 93.1% vs 88.6% precision, likely because of a more well defined type-hierarchy in DBpedia compared to Wikidata.
R . 1 BOUNDARY LAYER EVOLUTION AND PRECIPITATING CLOUDS OVER ASIA MONSOON

K. Krishna Reddy*, Ryuichi Shirooka, Baio Geng, Hiroshi Uyeda, Toshiaki Kozu, Yuichi Ohno, Chen-Jeih Pan, V.K. Anandan, D.Naryana Rao stitute of Observational Research for Global Change (IORGC)/Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Japan, Nagoya University, Nagoya, Japan Shimane University, Matsue, Japan, National Institute of Information and Communications Technology, Tokyo, Japan, 5 National Central University, Chung-Li, Taiwan 6 National MST Radar Facility, Tirupati, India
Using Modflow as a Mangment tool for the Groundwater System in the Area Northeastern Cairo, Egypt

The northeastern part of Greater Cairo is a target for development and anthropogenic activities that stress both the quantitative and qualitative aspects of the groundwater resources. On the other hand, it is a critical area, being adjacent to the Second Egyptian Research Reactor.An annual increase in groundwater levels (0.206 m/year) was estimated by earlier work of Al Gamal, 2011. This represents a crucial problem in the study area where it may influence the environmental and population safety due to the nuclear installation under the effects of potential radiological release. The main aim of this research is to provide an additional insight into the behavior of the groundwater flow system in the study area, and to provide valuable information for the decision-maker to the exploration of alternative management approaches.The main aim of this research is to provide an additional insight into the behavior of the groundwater flow system in the study area. and to provide valuable information for the decision-maker to the exploration of alternative management approaches.The groundwater model MODFLOW has been used as a simulator for groundwater flow both in steady and unsteady state to provide a solution for the problem of expected groundwater increment . The simulated groundwater flow system has been calibrated and production scenarios have been proposed and tested as a solution of the expected groundwater storage increase. One of these scenarios gave accepted results, by using eight production wells withdrawing this water to face such rise of the groundwater level, and using of this water in reclamation of the desert area around the nuclear facility.
The impact of meal experiences and packaging on wine choices

Purpose: We examine the moderating effect of the packaging on wine choices in the context of restaurant meal experiences. The meal experience is analysed using the Five Aspects Meal Model. We document correlative patterns between wine packaging, consumers' wine choice criteria and their meal experience. Design/methodology/approach: We collect field data from four major French cities: Lille, Rennes, Paris and Lyon. Respondents may face 5 types of wine packaging (bottle, glass, carafe, can and PET bottle). We use multi-group analysis to measure the effect of packaging on wine choice, given the meal experience. Findings: A more convivial meal experience is positively correlated with wine choices that are aligned with the host's recommendation. Other types of meal experiences lead to wine choices that give less consideration to the wine's origin. The moderating effect of packaging is validated but mostly depends on non-traditional packaging (PET and can). Practical implications: Fast-food consumers seem to accept more easily non-traditional packaging like the PET bottle and can. For the producer side, the PET could be used in convivial context without any risk to dislike the others guest and it could use to discover a simple wine in an informal context. For the can packaging, the wine will be basic in regular occasions or occasions without pressure (simplicity and informal occasion.
Comprehensive Evaluation Model of Cross-border E-commerce Credit Risk

In order to obtain the ideal cross-border electrician credit risk assessment results, this paper proposes a gray correlation analysis and least squares support vector machine for cross-border e-commerce credit risk assessment model for cross-border e-commerce credit risk assessment The First of all, according to the relevant research to build cross-border e-commerce credit risk assessment indicators, and the use of gray relational analysis of the evaluation results selected the most important evaluation indicators; and then use the least squares support vector machine cross-border e-commerce credit risk assessment samples to learn, and constructs the evaluation model of cross-border e-commerce credit risk. Finally, the validity and superiority of the model are analyzed. The results show that the model can improve the accuracy of cross border ecommerce credit risk and provide valuable reference information for cross border electricity trading.
Cell cycle and physiological characteristics of Synechococcus ( WH 7803 ) in chemostat culture

The marine cyanobacterium Synechococcus spp, is one of the most abundant picoplanktonic photoautotrophs in the world's oceans. When grown in nitrogen-limited chemostat culture under continuous light, Synechococcus strain WH7803 displays a bimodal DNA histogram. Consequently, the duration of each growth phase can be con~puted from the proportion of cells in each cell cycle phase. We measured cellular DNA distribution, carbon and nitrogen content, and pigment composit~on of Synechococcus WH7803 in a series of chemostat experiments with growth rate ranging from 0.1 to 0.9 d-'. Flow cytometric data showed that the duration of each cell cycle phase increased linearly with doubling time, and the fraction of cells in the S and G2 phases, fStG2, was relatively constant at growth rates of less than 1 doubling d-'. Cellular carbon and nitrogen contents were about 200 and 40 fg cell-'. respectively, but were much higher for slowly growing cells. The carbon to nitrogen ratio, the carbon to chlorophyll a ratio, and the ratio of zeaxanthin to chlorophyll a all decreased with increasing growth rate. These observed relationships are useful for applying cell cycle analysis to estimate growth rate, or for using cell abundance or pigment concentrations to estimate biomass and productivity of Synechococcus in the sea.
Credit Risk Model Based on Logistic Regression and Weight of Evidence

Many techniques have been used to build credit risk model. Among them, logistic regression is a more appropriate technique due to its desirable features (e.g., interpretability and prediction accuracy). In this paper, toimplement credit risk assessment quickly, a method for constructing credit risk model (in the form of a scorecard) based on logistic and weight of evidence is proposed.
Learning Affect with Distributional Semantic Models

The affective content of a text depends on the valence and emotion values of its words. At the same time a word distributional properties deeply influence its affective content. For instance a word may become negatively loaded because it tends to co-occur with other negative expressions. Lexical affective values are used as features in sentiment analysis systems and are typically estimated with hand-made resources (e.g. WordNet Affect), which have a limited coverage. In this paper we show how distributional semantic models can effectively be used to bootstrap emotive embeddings for Italian words and then compute affective scores with respect to eight basic emotions. We also show how these emotive scores can be used to learn the positive vs. negative valence of words and model behavioral data.
Social Influence and Household Decision-Making : A Behavioural Analysis of Household Demand

Housing markets are subject to many interrelated sources of instability on both a microeconomic and macroeconomic scale. Housing decisions of different individuals will be interdependent, generating non-linearities, discontinuities and feedback effects. This paper focuses in on some behavioural factors that contribute to complexity in housing demand. In particular, the impact of herding and social influence is captured using a model incorporating the impact of social information on willingness to pay. This model is tested in an experimental context and this experimental evidence confirms first, that social information has a statistically significant impact and, second, this impact is determined by a person’s individual characteristics including gender and personality traits.
Resting-State Functional Network Scale Effects and Statistical Significance-Based Feature Selection in Machine Learning Classification

In recent years, functional brain network topological features have been widely used as classification features. Previous studies have found that network node scale differences caused by different network parcellation definitions significantly affect the structure of the constructed network and its topological properties. However, we still do not know how network scale differences affect the classification accuracy, performance of classification features, and effectiveness of the feature selection strategy using P values in terms of the machine learning method. This study used five scale parcellations, involving 90, 256, 497, 1003, and 1501 nodes. Three local properties of resting-state functional brain networks were selected (degree, betweenness centrality, and nodal efficiency), and the support vector machine method was used to construct classifiers to identify patients with major depressive disorder. We analyzed the impact of the five scales on classification accuracy. In addition, the effectiveness and redundancy of features obtained by the different scale parcellations were compared. Finally, traditional statistical significance (P value) was verified as a feature selection criterion. The results showed that the feature effectiveness of different scales was similar; in other words, parcellation with more regions did not provide more effective discriminative features. Nevertheless, parcellation with more regions did provide a greater quantity of discriminative features, which led to an improvement in the accuracy of the classification. However, due to the close distance between brain regions, the redundancy of parcellation with more regions was also greater. The traditional P value feature selection strategy is feasible with different scales, but our analysis showed that the traditional P < 0.05 threshold was too strict for feature selection. This study provides an important reference for the selection of network scales when applying topological properties of brain networks to machine learning methods.
Evaluation of Genetic Structure of Amaranth Accessions from the United States

Amaranths (Amaranthus sp.), an endemic American crop, are now grown widely across the world. This study used 14 simple sequence repeat (SSR) markers to analyze the genetic diversity of 74 amarnth accessions from the United States, with eight accessions from Australia as controls. One hundred twenty-two alleles, averaging eight alleles per locus, were observed. The average major allele frequency, expected heterozygosity, and polymorphism information content (PIC) were 0.44, 0.69, and 0.65, respectively. The structure analysis based on genetic distance classified 77 accessions (94%) into three clusters, while five accessions (6%) were admixtures. Among the three clusters, Cluster 3 had the highest allele number and PIC values, while Cluster 2 had the lowest. The lowest FST was between Clusters 1 and 3, indicating that these two clusters have higher gene flow between them compared to the others. This finding was reasonable because Cluster 2 included most of the Australian accessions. These results indicated satisfactory genetic diversity among U.S. amaranths. These findings can be used to design effective breeding programs involving different plant characteristics.
Implied Stochastic Volatility Models

This paper proposes “implied stochastic volatility models” designed to fit option-implied volatility data and implements a new estimation method for such models. The method is based on explicitly linking observed shape characteristics of the implied volatility surface to the coefficient functions that define the stochastic volatility model. The method can be applied to estimate a fully flexible nonparametric model, or to estimate by the generalized method of moments any arbitrary parametric stochastic volatility model, affine or not. Empirical evidence based on S&P 500 index options data show that the method is stable and performs well out of sample.