Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing Low Cost Error Correction Schemes for Improving Memory Reliability

Memory systems are becoming increasingly error-prone, and thus guaranteeing their reliability is a major challenge. In this dissertation, new techniques to improve the reliability of both 2D and 3D dynamic random access memory (DRAM) systems are presented. The proposed schemes have higher reliability than current systems but with lower power, better performance and lower hardware cost. First, a low overhead solution that improves the reliability of commodity DRAM systems with no change in the existing memory architecture is presented. Specifically, five erasure and error correction (E-ECC) schemes are proposed that provide at least Chipkill-Correct protection for x4 (Schemes 1, 2 and 3), x8 (Scheme 4) and x16 (Scheme 5) DRAM systems. All schemes have superior error correction performance due to the use of strong symbol-based codes. In addition, the use of erasure codes extends the lifetime of the 2D DRAM systems. Next, two error correction schemes are presented for 3D DRAM memory systems. The first scheme is a rate-adaptive, two-tiered error correction scheme (RATT-ECC) that provides strong reliability (10x reduction in raw FIT rate) for an HBM-like 3D DRAM system that services CPU applications. The rate-adaptive feature of RATTECC enables permanent bank failures to be handled through sparing. It can also be used to significantly reduce the refresh power consumption without decreasing the reliability and timing performance. The second scheme is a two-tiered error correction scheme (Config-ECC) that supports different sized accesses in GPU applications with strong reliability. It addresses the mismatch between data access size and fixed sized ECC scheme by designing a product code based flexible scheme. Config-ECC is built around a core unit designed for 32B access with a simple extension to support 64B and 128B accesses. Compared to fixed 32B and 64B ECC schemes, Config-ECC reduces the failure in time (FIT)
Title Complexity of the Maximum k-Path Vertex Cover Problem

This paper introduces the maximum version of the k-path vertex cover problem, called the Maximum k-Path Vertex Cover problem (MaxPkVC for short): A path consisting of k vertices, i.e., a path of length k − 1 is called a k-path. If a k-path Pk includes a vertex v in a vertex set S, then we say that S or v covers Pk. Given a graph G = (V,E) and an integer s, the goal of MaxPkVC is to find a vertex subset S ⊆ V of at most s vertices such that the number of k-paths covered by S is maximized. MaxPkVC is generally NP-hard. In this paper we consider the tractability/intractability of MaxPkVC on subclasses of graphs: We prove that MaxP3VC and MaxP4VC remain NP-hard even for split graphs and for chordal graphs, respectively. Furthermore, if the input graph is restricted to graphs with constant bounded treewidth, then MaxP3VC can be solved in polynomial time.
Aliases and catching errors AOF 2 is an alias for KDM 1 A . A search for KDM 1 A works fine . A search for AOF 2 using the search box on the website or using returns a blank

With the availability of numerous curated databases, researchers are now able to efficiently use the multitude of biological data by integrating these resources via hyperlinks and cross-references. A large proportion of bioinformatics research tasks, however, may include labor-intensive tasks such as fetching, parsing, and merging datasets and functional annotations from distributed multi-domain databases. This data integration issue is one of the key challenges in bioinformatics. We aim to provide an identifier conversion and data aggregation system as a part of solution to solve this problem with a service named G-Links, 1) by gathering resource URI information from 130 databases and 30 web services in a gene-centric manner so that users can retrieve all available links about a given gene, 2) by providing RESTful API for easy retrieval of links including facet searching based on keywords and/or predicate types, and 3) by producing a variety of outputs as visual HTML page, tab-delimited text, and in Semantic Web formats such as Notation3 and RDF. G-Links as well as other relevant documentation are available at http://link.g-language.org/
The Relation of Personality and Intelligence—What Can the Brunswik Symmetry Principle Tell Us?

Personality and intelligence are defined as hierarchical constructs, ranging from broad g-factors to (domain-)specific constructs. The present study investigated whether different combinations of hierarchical levels lead to different personality-intelligence correlations. Based on the integrative data analysis approach, we combined a total of five data sets. The focus of the first study (N = 682) was an elaborated measurement of personality (NEO-PI-R), which was applied with a relatively short intelligence test (Intelligence Structure Test 2000 R). In the second study (N = 413), a comprehensive measurement of intelligence (Berlin Intelligence Structure test) was used with a shorter personality questionnaire (NEO-FFI). In line with the Brunswik symmetry principle, the findings emphasize that personality-intelligence correlations varied greatly across the hierarchical levels of constructs considered in the analysis. On average, Openness showed the largest relation with intelligence. We recommend for future studies to investigate personality-intelligence relations at more fine-grained levels based on elaborated measurements of both personality and intelligence.
000-000 Management Quality , Certification , And Initial Public Offerings ♣

We empirically examine the relationship between the quality and reputation of a firm’s management and various aspects of its IPO and post-IPO performance, a relationship that has so far received little attention in the literature. We hypothesize that better and more reputable managers are able to convey the intrinsic value of their firm more credibly to outsiders, thereby reducing the information asymmetry facing their firm in the equity market. Therefore, IPOs of firms with higher management quality will be characterized by lower underpricing, greater institutional interest, more reputable underwriters, and smaller underwriting expenses. Further, if higher management quality is associated with lower heterogeneity in investor valuations, firms with better managers will have greater long-term stock returns. Finally, since better managers are likely to select better projects (having a larger NPV for any given scale) and implement them more ably, higher management quality will also be associated with larger IPO offer sizes and stronger post-IPO operating performance. We present evidence consistent with the above hypotheses. © 2004 Elsevier Science B.V. All rights reserved. JEL classification: G24; G32
Hanging Out with the Cool Guys ! Making music as motivation for teenagers who use AAC

IN THIS ISSUE Hanging Out with the Cool Guys! Making music as motivation for teenage AAC users Intermediary Witness Scheme It's Never too Late to Learn: Picture-think Measuring Outcomes Preparation and Practise: The key to success? SCAMP: Specialist Communication Access Mobility Potential Survey and Evaluation of the AAC Strategies used by School-aged Children with Learning Disabilities TRAINS: Visual supports for communication, participation and independence Voice Output Technology and Children with Autism who use exchange-based communication
Classification-based Segmentation for Rehabilitation Exercise Monitoring

Introduction Exercise segmentation, the process of isolating individual repetitions from continuous time series measurement of human motion, is key to providing online feedback to patients during rehabilitation and enables the computation of useful metrics such as joint velocity and range of motion that are otherwise difficult to measure in the clinical setting. Methods This paper proposes a classifier-based approach, where the motion segmentation problem is formulated as a two-class classification problem, classifying between segment and non-segment points. The proposed approach does not require domain knowledge of the exercises and generalizes to groups of participants and exercises that were not part of the training set, allowing for more robustness in clinical applications. Results Using only data from healthy participants for training, the proposed algorithm achieves an average segmentation accuracy of 92% on a 30-participant healthy dataset and 87% on a 44-patient rehabilitation dataset. Conclusion A real-time approach for segmentation of rehabilitation exercises is proposed, based on two-class classification approach. The method is validated on both healthy and rehabilitation motion datasets and generalizes to a variety of demographics and exercises not part of the training set.
Predicting Survived and Killed Mutants

Mutation Testing (MT) is a state-of-the-art technique for assessing test suite effectiveness. The MT principle is to inject variants, known as mutants, into the System Under Test (SUT). Then, the behaviour of the original SUT is compared to that of the mutated SUT when running the same test suite. If no difference in behaviour is observed, the mutant is said to have survived; otherwise, it is said to have been killed. Despite its strengths, the applicability of MT in practice has been limited by its high computational cost. To mitigate this problem, Predictive Mutation Testing (PMT) has been proposed. PMT uses a classification model based on features related to the mutated code and the test suite to predict the execution results of a mutant without actually executing it. In other words, PMT predicts whether a mutant will be killed or will survive. In previous studies, PMT has been evaluated on several projects in two application scenarios, involving cross-project and crossversion learning. The goal of our research is to investigate how well the proposed PMT method, which has been evaluated on Java, can be extended to other programming languages. For that purpose, we first replicated the previous study and then extended the PMT approach to a single C program. We used random forrest classifiers as our supervised learning approach of choice. Our results indicate that PMT is able to predict the execution results of mutants with high accuracy. On the Java projects, we achieved Area Under Curve (AUC) values above 0.90 with a Prediction Error (PE) below 10%. On the C project, we achieved an AUC value above 0.90 with a PE below 1%. In our analyses we also investigated how sensitive the performance of PMT is to the set of selected features. In particular, we wanted to understand whether adding programming language specific features to a language independent core set of features significantly improve the performance of PMT. Our results are an indicator that, overall, PMT has potential to be applied across programming languages and is robust when dealing with imbalanced data.
Fraud Detection in Automobile Insurance using a Data Mining Based Approach

nsurance industry is one of the most important issues in both economy and human being life in modern societies which awards peace and safety to the people by compensating the financial risk of detriments and losses. This industry, like others, requires to choose some strategies to obtain desired ranking and remain in competitive market. One of efficient factors which affects enormous decision makings in insurance is paying attention to important information of customers and bazar that each insurance company stores it in its own database. But with daily increasing data in databases, although hidden knowledge and pattern discovery using usual statistical methods is not impossible, it is so complicated and time-consuming. In this paper we employ data mining as a powerful approach for extracting hidden knowledge and patterns on massive data to guide insurance industry. For example, one of the greatest deleterious challenges here is interacting between insurance companies and policyholders which creates a feasible situation for fraudulent claims. Due to importance of this issue, after investigating different ways of fraudulent crimes in insurance, we use K-Means clustering technique to find fraud patterns in automobile insurance include body and third-party. Our experimental results indicate a high accuracy when have been compared with statistical information extracted from data sets. Outcomes show significant relations among efficient factors in similar fraud cases.
Application of the Analytical Expressions of Fields to MOT Solution of Time Domain EFIE and CFIE

The effects of use of the exact closed-form expressions of the electric and magnetic fields (potentials) due to impulsively excited RWG basis functions on the Marching on-in-Time (MOT) solutions of the EFIE and CFIE are presented. The solutions via analytical expressions of fields are verified and compared with conventional (numerical) MOT solutions. It is shown that the accuracy and stability of the solutions obtained with the analytical-based method are better than those obtained with numerical-based conventional method. Also the dependency of the MOT solution of the time domain EFIE to time step size is investigated and it is shown that the analytical based solutions are less sensitive to selection of the time step size.
ESTIMATE OF REAL PRESSURES OF DENTAL OCCLUSAL CONTACT FROM THE STUDY OF SURFACE TEXTURE PARAMETERS

1,2 Agnes Meireles, 3 Flávia Bastos 2 Estevam las Casas 2 G. Cristina Godoy and 2 Tulimar Cornacchia 1 Federal University of Minas Gerais (UFMG), Belo Horizonte, Minas Gerais, Brazil email: agnesbm@ufmg.br 2 Bioengeneereing Group, (UFMG), Belo Horizonte, Minas Gerais, Brazil web: www. www.dees. ufmg.br/biomec 3 NUMEC, Federal University of Juiz de Fora, Juiz de Fora, Minas Gerais, Brazil web: www.ufjf. br/numec
Application of an artificial neural network model for prediction of diesel engine heat using nano-additives in diesel-biodiesel blends

Biodiesel is a renewable clean bioenergy produced from vegetable oils, animal fats, and micro-algal oil. It can be applied to instead of diesel fuel without any special modifications to the engines. In recent years, nano-catalysts or nano-additives was added to fuels to improve the thermo-physical properties of them. In this study, the carbon nanotubes (CNTs) as additives were mixed with the B5 and B10 fuel blends to evaluate the temperature of the cylinder head and cylinder block of a CI single-cylinder engine through artificial neural network (ANN). The CNTs with concentrations of 30, 60, and 90 ppm were used for each fuel blends, and engine was running at three speeds of 1800, 2300, and 2800 rpm under full load. The results of optimum ANN model showed that the training algorithm of Back-Propagation with 24 neurons in a hidden layer was enough to predict temperature of engine’s cylinder head and cylinder block for different modes. The mean square error (MSE) for training, validation and testing of optimum ANN model were 0.00095, 10.40, and 9.71, respectively. The corresponding R-values were 0.9999, 0.9487 and 0.9726, respectively. It can be concluded that neural network is a powerful tool to predict the temperature of diesel engines’ cylinder head and cylinder block with a reasonable accuracy.
Evaluation of antimicrobial, antioxidant and cytotoxic activities and characterization of bioactive substances from freshwater blue-green algae

Organic solvent extracts of three cyanobacterial species (Anabaena oryzae, Oscillatoria sp. and Stigonema ocellatum) were tested for antimicrobial activity against human pathogenic fungal and bacterial strains as well as for antioxidant and cytotoxic activity against human breast adenocarcinoma (MCF-7). The acetone extract of Anabaena oryzae was found to be the most active one against tested fungal and bacterial strains. It showed a maximum antimicrobial activity against Serratia marcescens and Candida albicans. The methanol extract of Oscillatoria sp. exhibited the best total antioxidant capacity compared to the other solvents and algal species. Acetone and methanol extracts of Anabaena oryzae exhibited high toxicity against MCF-7 cell line with IC50 of 45.1 and 44.4 µg/L, respectively. Acetone was the best solvent for extracting the active material. The acetone extracts were characterized by Gas Chromatography Mass Spectrophotometer (GC–MS) to identify the compounds responsible for such activities. Pharmaceutical important compounds in the acetone extract of cyanobacterial species like diacetone alcohol, acetic acid butyl ester mesityl oxide and heptadecane were present as a major component. These results indicate that extracts of studied cyanobacterial species exhibited appreciable antimicrobial, antioxidant and cytotoxic activity and could be a source of valuable bioactive materials for health products.
in press-uncorrected proof Multicentre evaluation of the Tosoh HbA 1 c G 8 analyser

Background: We report a Dutch–Belgian multicentre evaluation of the Tosoh HLC-723G8 glycohaemoglobin analyser, an ion-exchange HPLC instrument for the separation and quantification of haemoglobin A1c (HbA1c) in whole blood. Methods: We evaluated the analytical performances of the Tosoh G8 analyser and compared the results for blood samples with its predecessor, the Tosoh G7, and with two other widely used analysers, the Bio-Rad Variant II and Adams Arkray HA-8160. Results: Withinand between-batch imprecision wcoefficient of variation (CVs)x was -0.5% and 2%, respectively, and compared favourably with the G7. The excellent performances in terms of speed (1.6 min/analysis) did not result in increased variability of the results or carry-over between samples. The method shows no interference from carbamylated haemoglobin, and recognises the presence of haemoglobinopathies, which triggers the correction of the HbA1c result. Comparison with established methods showed good correlation, not only with the G7 but also with the Variant II and HA-8160 systems. Conclusions: With respect to reproducibility, chromatographic resolution, speed of analysis and identification of Hb variants, the Tosoh G8 analyser can be considered to be state of the art. Clin Chem Lab Med 2010;48:365–71.
Growth and Productivity of Pomegranate Trees under Different Irrigation Levels . III : Leaf Pigments , Proline and Mineral Content

This investigation was carried out during 2007 and 2008 seasons on trees of Manfalouty pomegranate cultivar (Punica granatum. L). The trees were grown at El-Kassasien Research Station, Ismailia Governorate in sandy soil under drip irrigation system. The trees received the following five irrigation levels: 7 or 9 or 11 (control) or 13 and 15m /tree/season. The data indicated that, the reduction in irrigation rate was concurrent with 3 decrease in chlorophyll a, b and carotenoids in both seasons. Irrigation level at 7m /tree/season gave the 3 highest significant leaf proline, while the farm control (11m /tree/season) showed an intermediate leaf proline. 3 The lowest significant proline was resulted from the highest irrigation level (15m /tree/season). Leaf nitrogen 3 percentage increased by increasing irrigation level from 7 m /tree/season to 13m /tree/ season, but a slight 3 3 reduction in N percentage was observed by increasing irrigation rate to 15m /tree/season. The highest leaf P 3 percentage was recorded by irrigation at 13m /tree/season sequenced by 15m , farm control 11m then 3 3 3 9m /tree/season. Meanwhile, irrigation at 7m /tree/season gave the lowest significant value of P percentage. 3 3 As the level of irrigation supply increased, a subsequent increment was observed in plant K percentage. Ca percentage increased by increasing irrigation levels, the highest significant Ca-percentage was recorded with highest irrigation level (15m /tree/season) followed by 13, 11 then 9m /tree/season. On the other hand, the 3 3 lowest Ca-percentage was recorded by irrigation level 7m /tree/season. 3
Reason Label Description Issues with the Question-Image ( QI ) pair Low Quality

Visual question answering is the task of returning the answer to a question about an image. A challenge is that different people often provide different answers to the same visual question. To our knowledge, this is the first work that aims to understand why. We propose a taxonomy of nine plausible reasons, and create two labelled datasets consisting of ∼45,000 visual questions indicating which reasons led to answer differences. We then propose a novel problem of predicting directly from a visual question which reasons will cause answer differences as well as a novel algorithm for this purpose. Experiments demonstrate the advantage of our approach over several related baselines on two diverse datasets. We publicly share the datasets and code at https://vizwiz.org.
Incentives for technological innovation: a study of the public policy of tax exemption in Brazil

Resumo Este artigo tem por objetivo explicar relações entre os investimentos em pesquisa e desenvolvimento (P&D) e os resultados da inovação entre os anos de 2009 e 2013 em empresas beneficiárias dos incentivos para inovação tecnológica da Lei n. 11.196 (2005) - a chamada Lei do Bem. São estimados modelos econométricos logit para efeitos fixos e modelos de probabilidade linear com efeitos fixos. Os resultados indicam que a probabilidade das beneficiárias inovarem em produtos, processos ou serviços, ou apenas em produtos, depende do tamanho da empresa e do montante investido em P&D, principalmente em recursos humanos (RH). As chances das empresas inovarem em processos se relacionam ao tamanho da empresa, aos gastos e aos programas de desenvolvimento de RH. Logo, os resultados confirmam que os investimentos em P&D das beneficiárias geram resultados de inovação tecnológica.
ASSESSING MOTION BLUR WITHOUT REFERENCE

The expansion of mobile devices such as tablets and smartphones has resulted in an increase in the capture and distribution of multimedia content. The image quality can be degraded when motion blur occurs due to the movement of camera induced by handshake. Various sharpness assessment techniques have been designed to measure defocus blur, but these do not handle motion blur well. We propose a method for no-reference sharpness prediction of motionblurred images based on their spectral distribution.
Tax effects on the trading behavior of private investors: evidence from individual

We study the effects of capital-gains taxation on the trading behavior of individual investors. We use confidential portfolio-level panel data provided by a large commercial bank in Germany which include daily information about individual trading behavior. For causal identification, we exploit a large capital-gains tax reform in Germany. Prior to 2009, any gains from sold shares with a holding period of less than one year were subject to effectively half the personal income tax rate whereas gains from shares that were held for more than a year remained tax free. This so-called ’speculation tax’ was abolished in 2009 and replaced by a system where the holding period was not tax relevant anymore. Using bunching techniques and hazard-rate regressions, we show that the realized holding periods and sale probabilities were considerably affected by the tax prior to 2009. In particular, we see a large spike in sales and increased selling probabilities of shares with a gain just after the 365-days threshold. Deductible losses spike just before the 365-days threshold. We do not see any spikes or irregular selling probabilities around the threshold in the post-reform years. We further show that the well-known disposition effect is attenuated by capital gains taxes. This is in line with the rationale that capital gains taxes induce investors to defer the sale of appreciated shares which counters the disposition effect that investors generally have a larger propensity to realize gains rather than losses. JEL Classification: H20, C41, D14, G11