Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Main Expert-Finding Algorithms in Social Network in Order to Rank the Top Algorithms

The ubiquity of the Internet and social networks have turned question and answer communities into an environment suitable for users to ask their questions about anything or to share their knowledge by providing answers to other users’ questions. These communities designed for knowledge-sharing aim to improve user knowledge, making it imperative to have a mechanism that can evaluate users’ knowledge level or in other words “to find experts”. Experts are people who are highly talented in a specific field like technology, languages, cooking and etc, and we focused on specific fields in programming languages. There is a need for expert-finding algorithms in social networks or any other knowledge sharing environment like question and answer communities. So companies that looking for hiring programmers can easily find people suit their needs. There are various content analysis and link analysis methods for expert-finding in social networks. Experts can be identified by their behaviors in forums and their relationships in proportion to the questions and answers they share. Therefore, analyzing social networks can provide us with appropriate information. This paper aims to challenge four algorithms by applying them to our dataset and analyze the results in order to compare the algorithms. The algorithms suitable for expert finding has been found and ranked. Based on the results and tests, it is concluded that the Z-score algorithm has a better performance than others. The outcome of this article for enthusiasts is top algorithms for expert finding in question-answer communities. In this paper, first, we will start by introducing the problem of expert finding problem the related works will be presented then algorithms and dataset are introduced and finally results are illustrated.
PRBM_A_212923 861..875

Marie Ociskova 1 Jan Prasko 1 Martin Kupka 1Department of Psychiatry, Faculty of Medicine and Dentistry, Palacky University in Olomouc, University Hospital Olomouc, Olomouc, The Czech Republic; 2Department of Psychology, Faculty of Arts, Palacky University in Olomouc, Olomouc, The Czech Republic Purpose: Self-relation is a profound connection that influences one’s life. As such, it presents an important topic for various areas of research and psychotherapy. The goal of this study was to develop and evaluate a brief scale that would assess positive aspects of self-relation. Sample and methods: The Positive Self-Relation Scale (PSRS) consists of 16 items divided into 4 subscales – Self-Acceptance, Self-Confidence, Authenticity and Assertiveness, and Fulfilled Experience. Two samples were used for the evaluation of its content and factor structure. The final sample’s data used for validation assessment. This sample consisted of 1234 adults from a general population (mean age 34.8±14.2 years, 70.8% women). All participants completed PSRS. Subgroups also filled in Rosenberg Self-Esteem Scale (RSES), Adult Dispositional Hope Scale (ADHS), Beck Depression Inventory-II (BDI-II), Liebowitz Social Anxiety Scale (LSAS), and Dissociative Experiences Scale (DES). Results: Differences in scores among demographic groups were small to none. Internal consistency was good (Composite Reliability Coefficients – the whole scale: 0.93; the subscales: 0.73–0.80). Temporal stability, assessed 2 weeks apart, was satisfactory (intraclass correlation coefficients – the whole scale: 0.86, the subscales: 0.60–0.82). Factor loadings in confirmatory factor analysis were 0.45–0.80, fit indices mostly showed an adequate model. The correlation coefficients between PSRS and RSES/ADHS were strong (r=0.79/0.55). The scale also strongly correlated with LSAS, BDI-II, and DES (r=−0.61/-0.48/-0.30, all ps<0.001). Conclusion: PSRS showed adequate psychometric properties in the general population. Future studies should include clinical samples. The areas of application lie mainly in research and psychotherapy.
Analysis of Verbal Fluency Output on Semantic Categories of ‘Food’ and ‘Vehicle’ in Typically Developing Malayalam Speaking Children

Abstract In the wake of limited knowledge on verbal fluency performance in typically developing children, the present study aims at investigating the semantic fluency performance of Malayalam speaking children across age, gender and tasks. Using a cross-sectional study design, semantic fluency performance (on food and vehicle fluency tasks) was investigated in 1015 Malayalam speaking typically developing children aged 5 to 15 years. The findings revealed the positive influence of age and task with no substantial difference between gender groups, with good inter-rater and intra-rater reliability. The study outcomes depicted a distinct pattern of continuous and linear developmental trend in organizational strategies, with no specific age band showing any dramatic increase in performance. Semantic fluency as a task has great potential within the developmental context for understanding the highly language, culture, and task based word retrieval mechanism.
October 2007 Openness , Technology Capital , and Development

In this paper, we extend the growth model to include firm-specific technology capital and use it to assess the gains from opening to foreign direct investment. A firm’s technology capital is its unique know-how from investing in research and development, brands, and organization capital. What distinguishes technology capital from other forms of capital is the fact that a firm can use it simultaneously in multiple domestic and foreign locations. Foreign technology capital is exploited by permitting foreign direct investment by multinationals. In both steady-state and transitional analyses, the extended growth model predicts large gains to being open. ∗Corresponding author: Ellen McGrattan, Research Department, Federal Reserve Bank of Minneapolis, 90 Hennepin Avenue, Minneapolis, MN 55401-2171, USA, erm@ellen.mpls. frb.fed. us, 612-204-5523, fax 612-2045515. We thank the National Science Foundation for financial support for this research (grant SES-0422539) and Loris Rubini, Johanna Wallenius, and particularly Simona Cociuba for exceptional research assistance. This paper was presented at the April 2007 conference at the University of Chicago honoring the contributions of Robert E. Lucas, Jr. Discussions by Bob Lucas and Paul Romer were extremely helpful for us in revising an earlier draft of this paper. The views expressed herein are those of the authors and not necessarily those of the Federal Reserve Bank of Minneapolis or the Federal Reserve System.
Extended Bayesian framework for automatic tuning of kernel data-mining methods

Kernel methods like e.g. Support vector machines (SVM) and Relevance vector machines (RVM) are widely used as data-mining tools. The concept of Bayesian learning exploited in RVMleads to Automatic relevance determination (ARD) which provides sparsity in resulting decision rules. This concept also sets all regularization coefficients without involving computationally expensive cross-validation methods. In this paper we suggest an extension of Bayesian maximal evidence framework which allows to set kernel function most appropriate for the particular task. We propose a local evidence estimation method which establishes a compromise between accuracy and stability of algorithm. In the paper we first briefly describe maximal evidence principle, present model of kernel algorithms as well as our approximations for evidence estimation, and then give results of experimental evaluation. Both classification and regression cases are considered.
Feasibility of Large-Scale Ocean CO2 Sequestration Final Report

In this report we detail research carried out in the period October 1, 2007 through September 30, 2008. The primary body of work is contained in a formal publication attached as Appendix 1 to this report. In brief we have surveyed the recent literature with respect to the natural occurrence of clathrate hydrates (with a special emphasis on methane hydrates), the tools used to investigate them and their potential as a new source of natural gas for energy production.
INTROGRESSION OF STRIGA RESISTANCE INTO POPULAR SUDANESE SORGHUM VARIETIES USING MARKER ASSISTED SELECTION

Witchweed ( Striga spp.) is one of the most important cereals production constraints globally and is projected to worsen with anticipated climate change. It is especially a devastating parasitic weed in Sub-Saharan Africa and parts of Asia. Integrated management strategies that depend mainly on host plant resistance provide the most effective control mechanism for Striga . We used molecular marker-assisted backcrossing to introgress Striga resistance from a resistant genotype, N13, into agronomically important genetic backgrounds (Tabat, Wad and Ahmed). Backcross populations BC 3 S 3 were generated and genotyped using Simple Sequence Repeat (SSR) and Diversity Arrays Technology (DArT) markers. A total of 17 promising backcross progenies were selected and screened in Striga infested field alongside their parents. The Area Under Striga Progress Curve (AUSPC) showed significant decrease in Striga count (920-7.5) resulting in a 97-189% increase in yield under Striga pressure. Our results demonstrate the practical application of marker assisted selection (MAS) to generate farmer-preferrd Striga resistant lines in Sudan
A Refined 3D Pose Dataset for Fine-Grained Object Categories

Most Existing 3D pose datasets of object categories are limited to generic object types and lack fine-grained information. In this work, we introduce a new large-scale dataset that consists of 409 fine-grained categories and 31,881 images with refined 3D pose annotation. Specifically, we augment three existing fine-grained object recognition datasets (StanfordCars, CompCars and FGVC-Aircraft) by finding a specific 3D model for each sub-category from ShapeNet and manually annotating each 2D image by adjusting a full set of 7 continuous perspective parameters. Since the fine-grained shapes allow the projection of 3D models to better fit the 2D images, we further improve the annotation quality by initializing from the human annotation and conducting local search of the pose parameters with the objective of maximizing the IoUs between the projected mask and the segmentation reference estimated from state-of-the-art segmentation networks. We provide a full statistics of the annotations with qualitative and quantitative comparisons suggesting that our dataset can be a complementary source for studying 3D pose estimation. The dataset can be downloaded at http://users.umiacs. umd.edu/~wym/3dpose.html.
Sound pressure levels and sound source components for orchestra musicians

The presented study aimed to do investigations on the balance of the sound components found at the ears of orchestra musicians. This was interesting as orchestra musicians are exposed to much sound in their vocational life and one would like to find the primary sources inducing the sound. Measurements were performed on four instruments. The trumpet was chosen as a representative for wind instruments, the oboe for woodwind instruments, and the violin together with the cello represented the string instruments. For the overall results, it was found that the direct sound of the self was likely to often be dominant or almost dominant at the ears of the trumpeter. The balance of the sound components shifted between dominance of the direct sound of the self-played instrument and the background, consisting of direct sound from others and the reverberant sound. The reverberant sound component was found to be less dominant and within 2-20% for the oboe, the trumpet and the violin. It was found that the balance of the sound components was the most consistent for the trumpet, although more varied musical contents ought to be further tested. The found results of the short-term Foreground-to-Background Balance was statistically best described by the table included. Median μ σ No. of observations Cello -7.1 -7.5 6.6 116 Oboe -6.5 -5.4 7.6 111 Trumpet -1.3 -0.4 4.6 119 Violin -2.7 -2.7 6.4 259
Habitat heterogeneity effects on macro- and meiofauna (especially nematodes) in Punta Francés cora l reef (SW Cuban Archipelago)

Se describen los patrones de di stribucion de la macro- y meiofauna en un a rrecife de coral (Punta Frances, Archipielago Cubano, Mar Caribe). La densidad y la comp osicion por taxa para la macrofauna y meio fauna y la diversidad de especies y de caracteres biologicos para nematodos se compararon entre cuat ro habitats: pastos marinos, arenazo, coral muerto y fondo duro. Se detectaron consistentes patrones espaciales de distribucion los cuales fueron explicados por una combinacion compleja de caracteristicas del habitat. La relacion senal/ ruido en los datos dedistribucion de la macrofauna fue lo suficientemente fuerte para detectar cambios entre habitats pero el tamano de los dispositivos demuestreo no fue adecuado para representar los patrones reales en el medio natural. Los patrones de distribucion de la meiofauna fueron afectados por el tipo de sustrato (sedimento versus fo ndo duro) el cual a su vez seguramente depende del regimen hidrodinamico. Los nematodos dominaron en los pastos marinos y los copepodos en lo s otros tres habitats. La densidad de especies e individuos y la riqueza de especie de nematodos fueron mas altas en los habitats sedimentarios debidos probablemente a mayor estabilidad fisica, mayor nicho espacial y mas disponibilidad de a limento. La variacion en la dive rsidad fue alta y similar ent re pares de habitats con la excepcion de los habitats en fond o duro que tuvieron menor diferenciacion. La mayoria de los nematodos que habitan sobre sustratos du ros mostraron rasgos morf ologicos que les permiten adaptarse al regimen hidrodinamico y a la disponibilidad de al imento (biofilm/algas), es to es cuerpo pequeno y robusto, dientes para raspar, cuticula ornamentada y capsula cefalica. Fue evidente un co ntrol fisico sobre las comunidades de nematodos pero las interacciones bioticas pueden jugar un papel mas importante en pastos marinos y arenazos comp
DDDT_A_223153 3647..3655

Giuseppe Derosa Angela D'Angelo Pamela Maffioli 1Center of Diabetes and Metabolic Diseases, Department of Internal Medicine and Therapeutics, University of Pavia and Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; 2Laboratory of Molecular Medicine, University of Pavia, Pavia, Italy; 3Center for Prevention, Surveillance, Diagnosis and Treatment of Rare Diseases, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy Introduction: Statin-associated myalgia occurs in about 1–3% of patients in the medical literature. Plasma CoQ10 levels are reduced in patients undergoing statin. Objective: The primary outcome was the detection of clinical symptoms and the perception of pain evaluated throughout specific questionnaires. The secondary outcome was the variation in lipid profile and the variation in safety parameters. Methods: We enrolled 60 Caucasian patients, intolerant to statins. During the run-in period, patients underwent a 1-month wash-out period during which statins were stopped. At the end of the wash-out period, if CPK and/or transaminases returned within an acceptable range, statins were re-introduced at half of the previously taken dose. After one month, patients were randomized to take either a liquid CoQ10 supplement or a placebo for three months at 100 mg/day. Results: The Clinical Index Score for myalgia assessment was lower after 3 months with CoQ10, while it did not change with the placebo. The VAS score was lower after 3 months of CoQ10 supplementation, while no variation was recorded with the placebo. In the group treated with the dietary supplement, CoQ10 plasma concentrations were inversely correlated with CPK levels, Clinical Index Score absolute values, and VAS. Conclusion: The addition of CoQ10 with half dosage statin in patients with previous intolerance to statins improves the perception of clinical symptoms such as asthenia, myalgia or pain.
Some Recent Developments of Microstrip Antenna

Although the microstrip antenna has been extensively studied in the past few decades as one of the standard planar antennas, it still has a huge potential for further developments. The paper suggests three areas for further research based on our previous works on microstrip antenna elements and arrays. One is exploring the variety of microstrip antenna topologies to meet the desired requirement such as ultrawide band (UWB), high gain, miniaturization, circular polarization, multipolarized, and so on. Another is to apply microstrip antenna to form composite antenna which is more potent than the individual antenna. The last is growing towards highly integration of antenna/array and feeding network or operating at relatively high frequencies, like sub-millimeter wave or terahertz (THz) wave regime, by using the advanced machining techniques. To support our points of view, some examples of antennas developed in our group are presented and discussed.
Breast Carcinomas − / HER-2 − / Progesterone Receptor − Triple-Negative Estrogen Receptor Is Highly Expressed in Chk 1 The E 2 F-Regulated Gene

We previously showed that checkpoint kinase 1 (Chk1) and Claspin , two DNA-damage checkpoint proteins, were downregulated by 1,25-dihydroxyvitamin D3, a known inhibitor of cell proliferation. In the present study, we aimed to investigate the transcriptional regulation of Chk1 and Claspin and to study their expression levels in human breast cancer tissue. Transient transfection experiments in MCF-7 breast cancer cells showed that promoter activities of Chk1 and Claspin were regulated by the E2F family of transcription factors. Subsequently, transcript levels of Chk1, Claspin , and E2F1 were determined by quantitative reverse transcriptase-PCR analysis in 103 primary invasive breast carcinomas and were compared with several clinicopathologic variables in breast cancer. A strong correlation was found between Chk1 and Claspin transcript levels. Transcript levels of Chk1, Claspin , and E2F1 were highest in histologic grade 3 tumors and in tumors in which the expression of estrogen receptor (ER) and progesterone receptor (PR) was lost. Moreover, Chk1 expression was significantly elevated in grade 3 breast carcinomas showing a triple-negative ER /PR /HER-2 phenotype compared with other grade 3 tumors. Further research is warranted to validate the use of Chk1 inhibitors in triplenegative breast carcinomas for which treatment strategies are limited at present. [Cancer Res 2007;67(14):6574–81]
Human Observers Apply Visual Stress Estimate Perceptual Threshold Dataset of Visual Stress Human Annotated Dataset Computational Method Stressed Image Symmetry Detection

Symmetry is one of the most frequently observed fundamental regularities in the visual characteristic of real-world objects. The human brain has been trained to respond quickly to symmetry patterns, organizing them as salient clues for the unique description of objects. Recently, automatic symmetry detection methods have been widely introduced in computer vision and graphics fields for 2dimensional and 3-dimensional object data including reflection, translation, and rotation symmetry patterns. Researchers have invented features inspired by a human vision system and have adopted deep learning approaches. On the other side, traditional performance evaluations have been conducted on a unified test dataset containing random degrees of diverse visual challenges. However, they ignore observing the insight of usability and practicality of the methods in higher level tasks such as object recognition. In this work, we carefully organize visual stress dataset for reflection symmetry detection evaluation proposing a novel evaluation framework. The state-of-the-art reflection symmetry detection methods are re-evaluated and analyzed in human perception perspective. INDEX TERMS reflection symmetry, performance evaluation, human symmetry perception, visual stresses, psychophysics
A Comparative Exergoeconomic Analysis of Waste Heat Recovery from a GT-MHR using Organic Rankine Cycles

Abstract: A comparative exergoeconomic analysis is reported of waste heat recovery from a Gas Turbine-Modular Helium Reactor (GT-MHR) using different arrangements of Organic Rankine Cycles (ORCs) for electrical power production. The considered organic Rankine cycles are: Simple Organic Rankine Cycle (SORC), ORC with internal heat exchanger (HORC) and Regenerative Organic Rankine Cycle (RORC). The exergoeconomic analysis is performed based on the specific exergy costing (SPECO) approach. For this purpose, the combined cycles are ﬁrst thermodynamically analyzed through energy and exergy. Then cost balances and auxiliary equations are applied to subsystems and exergoeconomic parameters are calculated for the components and entire combined cycles. Based on fixed operating conditions for the GT-MHR cycle, the three combined cycles are compared. Finally a parametric study is performed to reveal the effects on the exergoeconomic performance of the combined cycles of such significant parameters as compressor pressure ratio, turbine inlet temperature and evaporator temperature. The results show that the GT-MHR/RORC has the lowest unit cost of electricity produced by the
Multi-Level and Multi-Scale Spatial and Spectral Fusion CNN for Hyperspectral Image Super-Resolution

Hyperspectral imaging simultaneously captures images of the same scene across many numbers of spectral channels, and has different applications from agriculture, astronomy to surveillance and mineralogy, to name a few. However, due to various hardware limitations, the current hyperspectral sensor only provides low-resolution (LR) hyperspectral images compared with the RGB images obtained from a common color camera. Thus fusing a LR hyperspectral image with the corresponding high-resolution (HR) RGB image to recover a HR hyperspectral image has attracted much attention, and is usually solved as an optimization problem with prior-knowledge constraints such as sparsity representation and spectral physical properties. Motivated by the great success of deep convolutional neural network (DCNN) in many computer vision tasks, this study aims to design a novel DCNN architecture for effectively fusing the LR hyperspectral and HR-RGB images. Taking consideration of the large resolution difference in spatial domain of the observed RGB and hyperspectral images, we propose a multi-scale DCNN via gradually reducing the feature sizes of the RGB images and increasing the feature sizes of the hyperspectral image for fusion. Furthermore, we integrate multi-level cost functions into the proposed multi-scale fusion CNN architecture for alleviating the gradient vanish problem in training procedure. Experiment results on benchmark datasets validate that the proposed multi-level and multi-scale spatial and spectral fusion CNNs outperforms the state-of-the-art methods in both quantitative values and visual qualities.
An Adaptive Transport Management Approach using Imitation Learning

The area of Intelligent Transport Systems has been critical in traffic management and intelligent systems for the past decades. In this paper, we introduce a novel approach to traffic management. We develop a process that that starts with the development of a "game" based upon different road networks that are used to gather data based upon user actions. The user’s decision directly affect how traffic light states change. This data is then passed to an Imitation Learning model that can observe actions and imitate the same decisions on a similar road network.
Isotopes and individuals: diet and mobility among the medieval Bishops of Whithorn

Stable isotopes get personal in this analysis of burials at a medieval cathedral. Compared with the local meat-eating rank and file, those people identified as bishops consumed significantly more fish and were incomers from the east. These results, while not so surprising historically, lend much increased confidence that isotope analysis can successfully read the status and mobility of individuals in a cemetery.