Miracle-dz/UniFAR-FTUs huggingface dataset:
Autotuning in High-Performance Computing Applications

Autotuning refers to the automatic generation of a search space of possible implementations of a computation that are evaluated through models and/or empirical measurement to identify the most desirable implementation. Autotuning has the potential to dramatically improve the performance portability of petascale and exascale applications. To date, autotuning has been used primarily in high-performance applications through tunable libraries or previously tuned application code that is integrated directly into the application. This paper draws on the authors’ extensive experience applying autotuning to high-performance applications, describing both successes and future challenges. If autotuning is to be widely used in the HPC community, researchers must address the software engineering challenges, manage configuration overheads, and continue to demonstrate significant performance gains and portability across architectures. In particular, tools that configure the application must be integrated into the application build process so that tuning can be reapplied as the application and target architectures evolve.
Evaluation of Geometric Asian Power Options under Fractional Brownian Motion

Modern option pricing techniques are often considered among the most mathematical complex of all applied areas of financial mathematics. In particular, the fractional Brownian motion is proper to model the stock dynamics for its long-range dependence. In this paper, we evaluate the price of geometric Asian options under fractional Brownian motion framework. Furthermore, the options are generalized to those with the added feature whose payoff is a power function. Based on the equivalent martingale theory, a closed form solution has been derived under the risk neutral probability.
Carotenoid coloration in great black-backed gull Larus marinus reflects individual quality

Carotenoids are a large group of biochemicals, with similar properties, synthesised by bacteria, fungi, algae and plants. Vertebrates obtain these biologically active pigments through the diet, and they are a disproportionately common component of animal colour signals and play important roles in immune functions and as antioxidants. Carotenoids are believed to be a limited resource and because of the trade-off between allocation of carotenoids to signals and to other functions, carotenoid based signals are often thought to be handicap signals. The purpose of this study was to investigate the signalling potential of carotenoid-based tissue coloration in the great black-backed gull Larus marinus. The intensity of carotenoid-based coloration in bill, gape and eye-ring coloration was investigated in relation to body condition, reproductive parameters, levels of immune activity, and sexual dimorphism. In males there was a positive relationship between colour intensity and body condition, but in females no such relationship was found. However, females with high colour intensity had larger eggs and clutches. Additionally, females with high red scores tended to have high density of circulating lymphocytes. There was no sexual dimorphism in coloration and there was a negative relationship between colour intensity and sampling time, which indicates that this coloration is most intensely expressed early in the breeding season. The results in this study suggest that carotenoid-based coloration in great black-backed gull are partly condition dependent and reveal information about individual quality in both males an females. Hence, it might have evolved as an important signal for assessing the quality of potential mates.
CRS-based Seismic Imaging in Complex Marine Geology

The present work represents an extension of the efforts described previously by the authors, now to marine data, and we demonstrate that Common Reflection Surface (CRS) stack and imaging gives results that clearly improves: (1) the signal-to-noise ratio; (2) the continuity of reflection events; (3) enhances the surface multiple; and (4) that the CRS operator approximates a pseudo-migration. The workflow is summarized as: (1) Preprocessing; (2) CRS stack; (3) Residual static correction; (4) Prestack data enhancement; (5) CRS stack; (6) Pseudo-migration. The CRS stack has been described by several authors, and the tecnology supports subsequent processing based on the wavefront attributes. The preprocessing steps were performed with the free seismic processing package CWP/SU. Marine geological structural features of the survey area are described for sedimentary basins of the passive continental shelf of the Brazilian South Atlantic.
The Dutch famine of 1944-45 as a human laboratory: changes in the early life environment and adult health

Studies of men and women exposed to the Dutch famine of 1944-1945 (also known as the Dutch ‘Hunger winter’) during different periods of life are important because they provide an opportunity to look at long-term effects of disturbances in the early life environment. For ethical and practical reasons, such studies could not otherwise be carried out in humans. At the time of the Dutch famine, civilian starvation was caused by conditions of war and the impact can be documented of extreme changes in nutrition not normally seen in human populations. We present an overview of studies conducted on the Dutch famine using military examination records, psychiatric hospital records, population surveys, and famine birth cohorts followed to the present day, for medical examinations and DNA analysis. Of all reported outcomes, associations between prenatal famine and adult body size, diabetes, and schizophrenia show the most consistent pattern. For other outcomes, the pattern is more variable and inconsistent. There are also associations between prenatal famine and long-lasting epigenetic changes in DNA regulation. These need replication but could provide a potential mechanism to explain other observations.
Regular Expression Search in Compressed Text Extended Abstract

Companies like Facebook, Google or Amazon store and process huge amounts of data. Lossless compression algorithms such as brotli1 and zstd2 are used to store textual data in a cost-effective way. On the other hand, it is common to process textual data using regular expression engines as evidenced by the number of highlyperformant engines under development such asHyperscan3 or RE24. For the scenario in which the input to the regular expression engine is only available in compressed form, the state of the art approach is to feed the output of the decompressor into the regular expression engine. We challenge this approach by searching directly on the compressed file. The experiments show that our purely sequential implementation, called zearch, outperforms the state of the art even when decompressor and search engine each have a distinct computing unit. Based on theoretical studies that suggest zearch’s algorithm is easily parallelizable, we take the challenge to boost the performance of zearch using parallel processing capabilities of modern architectures: SIMD instructions, multi-threading, multiprocessing and general-purpose computing on GPUs.
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms

Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. The paper closes with some discussion of ongoing and future work.
3D scanning and rendering cultural heritage artifacts on a low budget

1. ABSTRACT The recent evolution of graphics technology has been impressive, and the management of very complex models is now possible on inexpensive platforms. Moreover, automatic scanning devices (often called 3D scanners) allow to build highly accurate models of real 3D objects in a costand time-effective manner. This paper discusses some issues in the use of this technology by taking into account a particular application context: the acquisition of Cultural Heritage artifacts. Specific needs of this domain are: a high accuracy, a lower cost of the scanning devices and an improved usability of the data produced. We will present the results of two projects, aimed at: the design of a low-cost scanner based on structured light; the design of a data simplification tool which enables complex dataset visualization on low cost computers.
Histopathological Changes in Liver, Gills and Intestine of Labeo rohita Inhabiting Industrial Waste Contaminated Water of River Ravi

Histopathological changes have been recorded in the gills, liver and intestinal tissue of Labeo rohita from River Ravi receiving industrial waste. Varying concentrations of Pb and Cu were recorded in the liver, gills and intestine of Labeo rohita. The pronounced histopathological changes like degeneration, necrosis and disorganization of lamellae and hyperplasia of epithelial cells was observed in the gills of the fish collected from a polluted site as compared to the fish from a control site. The liver sections of Labeo rohita exhibited aggregation, hypertrophy of hepatocytes, cytoplasmic degeneration, hemolysis between hepatocytes, focal necrosis and degeneration. In the intestinal tissue of Labeo rohita prominent histopathological changes, including edema, atrophy in the mucosal layer, hemorrhage between blood vessels, blood congestion and aggregations of inflammatory cells were observed. The polluted water has caused more histopathological alterations in Labeo rohita. Comparison of histopathology in each organ of Labeo rohita showed a same degree of tissue damage in the gills, while aggregation of hepatocytes in the liver, and atrophy of the mucosal layer in intestinal tissue of L. rohita were more evident.
Optimization of Extraction Method and Qualitative FT-NMR Analysis on Andrographis Paniculata Leaves

A study was conducted to optimize the extraction conditions of Andrographis paniculata leaves. Four different solvents, methanol (MeOH), dichloromethane (DCM), ethyl acetate (EtOAc) and water (H2O) were used in a period of 1, 3, 5 or 7 hours at set water-bath temperature of 25, 30, 40 or 60°C. The extraction dried mass yield of about 200 ± 0.1 mg fresh cut leaves was measured, and 1D-Nuclear Magnetic Resonance (NMR) profiles were employed to correlate the crude extract weight to the percentage of the main compound, andrographolide, present. Methanol was shown to be the best solvent with 13.75% yield in 3 hours extraction at 40°C. The NMR peak intensity analysis of the major compound (andrographolide) also in support of these obtained parameters.
Merkle Signatures for Real-World Use

Hash-based signatures have seen little use in practice. We explore the possibility of using hash-based signatures as the signature mechanism used in certificates on the Web. After optimizing the parameters for these settings, in both a stateful and stateless setup, we experiment with an implementation of these schemes and report on their performance and resulting signature size. We also explore an approach to further improve these signatures.
Batch Normalized Deep Kernel Learning for Weight Uncertainty

There has been recent interest in Gaussian processes with neural network parameterized kernels, a framework referred to as deep kernel learning [1, 2, 3, 4, 5]. While the idea of ’deep-ifying‘ kernels is not new [6, 7], only recently have they been trained end-to-end. This is largely due to advances in inducing point [8] and variational [9] methods which allow for scalable inference. However, current methods fail to propagate the uncertainty in neural network weights during training. While the Gaussian process layer in deep kernel learning can provide uncertainty estimates at testing, the estimates also fail to account for uncertainty in the neural network weights.
Interactive comment on “ Atmospheric sulphuric acid and aerosol formation : implications from atmospheric measurements for nucleation and early growth mechanisms ” by S .-L . Sihto et al .

The main question pointed out by the referee was, how we can be sure that the nucleation takes place close to the ground based measurements. In fact, we do not say that nucleation and subsequent growth takes place at the measurement station. Particle formation process is expected to be a large scale phenomenon that extends over several hundreds of kilometers where meteorological conditions are more or less uniform. Small, freshly nucleated particles (∼ 3 nm) are formed near the measurement site, but bigger particles are advected from a longer distance depending on the wind speed. For
Detecting Sarcasm is Extremely Easy ;-)

Detecting sarcasm in text is a particularly challenging problem in computational semantics, and its solution may vary across different types of text. We analyze the performance of a domain-general sarcasm detection system on datasets from two very different domains: Twitter, and Amazon product reviews. We categorize the errors that we identify with each, and make recommendations for addressing these issues in NLP systems in the future.
A Report on the 2018 VUA Metaphor Detection Shared Task

As the community working on computational approaches to figurative language is growing and as methods and data become increasingly diverse, it is important to create widely shared empirical knowledge of the level of system performance in a range of contexts, thus facilitating progress in this area. One way of creating such shared knowledge is through benchmarking multiple systems on a common dataset. We report on the shared task on metaphor identification on the VU Amsterdam Metaphor Corpus conducted at the NAACL 2018 Workshop on Figurative Language Processing.
Three Boundary Meshless Methods for Heat Conduction Analysis in Nonlinear FGMs with Kirchhoff and Laplace Transformation

This paper presents three boundary meshless methods for solving prob- lems of steady-state and transient heat conduction in nonlinear functionally graded materials (FGMs). The three methods are, respectively, the method of fundamen- tal solution (MFS), the boundary knot method (BKM), and the collocation Trefftz method (CTM) in conjunction with Kirchhoff transformation and various variable transformations. In the analysis, Laplace transform technique is employed to han- dle the time variable in transient heat conduction problem and the Stehfest nu- merical Laplace inversion is applied to retrieve the corresponding time-dependent solutions. The proposed MFS, BKM and CTM are mathematically simple, easy- to-programming, meshless, highly accurate and integration-free. Three numerical examples of steady state and transient heat conduction in nonlinear FGMs are con- sidered, and the results are compared with those from meshless local boundary integral equation method (LBIEM) and analytical solutions to demonstrate the effi- ciency of the present schemes.
Analysis of the keeper-dependent strategy in the soccer penalty kick

The penalty kick plays a decisive role in the outcome of many Association Football (soccer) matches, and it is important for the kicker to choose the best strategy to score. This paper looked at the keeper-dependent strategy in which the kicker reacts to the goalkeeper's movement, and determined the amount of time needed before foot-ball contact for a player to successfully adjust their kicking direction. Field tests were conducted with 8 participants using lights to simulate the time and direction of a keeper's dive. A second experiment involving 6 participants determined simple, choice and discriminative reaction times for leg movements. It was found that the critical time needed to react and shoot the ball to the opposite side of the keeper was approximately 0.3 seconds. For three different strategies, involving different initial shooting directions, chance performance was found when stimuli were presented 300-400 ms before contact and full success rate was achieved when more than 500 ms were available. There were no significant differences between the strategies with regard to success. A large part of the pre-contact time was needed for reacting to the stimulus, and the time in which the adjustment could be made was approximately 135 ms.
PREDICTORS OF GPA FOR VOCATIONAL ADULT AND TRADITIONAL LEARNERS by LaChelle

Vocational education has gained support and endorsement from the Obama Administration as a way to advance the American public in economic and social prosperity in this coming age. Time and resources are crucial as traditional and adult learners seek the skills they need for competitive employment. The purpose of this quantitative study was to examine the predictive relationship between learning and study strategies and academic performance (GPA) among first semester vocational adult learners and vocational traditional learners. The participants for this study were selected from a convenience sample of vocational college students at a northwestern, rural, four-year college with an academic and vocational division. The sample included traditional learners, students between the age of 18 to 24 years-old and adult learners who completed the Learning and Study Strategies Inventory (LASSI) during Spring 2013, Fall 2013, Spring 2014, and Fall 2014 vocational student success enrollment interviews. Adult learners included students who were 25 and older, and younger students who had adult responsibilities such as full-time employment or dependents. LASSI subscale scores were matched with corresponding first semester cumulative grades and demographics. Two multiple regression analyses were conducted on the predictive relationship between subscales of the LASSI and GPA for Vocational Adult Learners and Vocational Traditional Learners. As a result, it was identified that there were no predictive relationships between subscales of the LASSI and GPA for Vocational Adult Learners or Vocational Traditional Learners. This study failed to reject both null hypotheses.
Functional knockout of FUT8 in Chinese hamster ovary cells using CRISPR/Cas9 to produce a defucosylated antibody

We report the adaptation of the new CRISPR/Cas9 (clustered regularly interspaced short palindromic repeat/CRISPR‐associated protein 9) system to disrupt the gene encoding fucosyltransferase 8 (FUT8), an α1,6‐fucosyltransferase that directs fucose addition to derived antibody Fc region asparagine 297, in Chinese hamster ovary (CHO) cells. Compared to previously reported homologous recombination or zinc‐finger nucleases (ZFNs) applications in CHO cells, CRISPR/Cas9 demonstrated higher targeting efficiency and easier customization. FUT8 disruptive clones (FUT8−/−) were obtained within 3 weeks at indel frequencies ranging from 9 to 25%, which could be enhanced to 52% with Lens culinaris agglutinin (LCA) selection. Based on the lectin blot method, the derived FUT8−/− clone had the ability to produce defucosylated therapeutic mAb with no detrimental effects on cell growth, viability, or product quality. The clone had the potential of industrial application for therapeutic antibodies manufacturing. We have demonstrated functionally that a gene related to product synthesis could be mutated using CRISPR/Cas9 technology, and consequently the glycan profile of expressed mAb was alternated. We believe that with its robustness and effectiveness, CRISPR/Cas9 can be widely applicable in cell line development leading to higher productivity and better quality of mAbs and other biological therapeutics.
The Value of Neutrophil Lymphocyte Ratio in Predicting the Degree of Insulin Resistance and β Cell Dysfunction in Type 2 Diabetic Patients

Objective:Insulin resistance and progressive pancreatic β-cell failure are key factors in the development of type 2 diabetes.The aim of the present study was to define the relationship between Neutrophil Lymphocyte Ratio (NLR) and the degree of insulin resistance and β cell dysfunction in Type 2Diabetus Mellitus.Methods: This study was carried out on seventy diabetic patient attended the outpatient clinic of the diabetes and metabolism unit in Alexandria Main University Hospital (AMUH)and thirty t healthy subjects with matched age, sex and socioeconomic status as a control group. Subjects were interviewed in order to collect data using predesigned questionnaire, anthropometric measures were measured, blood samples were collected from each subject for chemical analysis. Results: NLR was higher in diabetic subjects than non-diabetic one. NLR showed significant positive correlation with HOMA-IR and no significant negative correlation with HOMA-β. Conclusion: the relation between inflammation and adipose tissue is extremely complex in diabetic patients. However, simple calculation of NLR and measurement of waist circumference can reveal inflammation in the diabetic population.
Interaction of a graphene sheet with a ferromagnetic metal plate

Dispersion forces, such as Casimir and van der Waals forces, can have a significant influence on the fabrication, handling, and assembly processes as well as the performance of micro- and nanodevices. In this paper, we investigate the Casimir force between a graphene sheet and a ferromagnetic metallic substrate in vacuum. The reflection coefficients, entering the expression for the force, are dependent on the graphene conductivity, which is described by the Kubo formalism. It is found that the magnetic response of the ferromagnet plays a significant role at low temperatures or high value of the graphene chemical potential. The numerical results also demonstrate that the thickness of the metallic plate has a minor influence on the Casimir force. This study maybe useful for the design and operation of micro- and nanodevices.
Bayesian Structural Equation Modeling: A Business Culture Application in Kenya

Structural equation modeling (SEM) is a multivariate method that incorporates regression, path-analysis and factor analysis. Classical SEM requires the assumption of multivariate normality to be met and large sample size, also choice is made either to ignore uncertainties or treat the latent variables as observed. National culture Data gathered in a study or survey may be inform of ordered categories and may not follow the assumptions of multivariate normality. This restricts the use of frequentist method of estimation. A Bayesian approach to SEM allows inclusion of this uncertainty and directly models the uncertainties in predictive models. In addition Bayesian SEM does not require constant variance normal disturbances and the sample size can be a small number. The development and application of Bayesian SEM has been relatively slow but it has been made possible by Gibbs sampler. The main purpose of the study was model National Culture in Kenya based on Hofstede model and business performance. Maximum likelihood Estimation was used to estimate the parameters in Classical SEM. Gibbs sampler algorithm was employed in Bayesian approach to SEM. This study used non-informative priors. The convergence of parameter was evaluated using proportional scale reduction procedure and trace and density plots. Data was gathered from employees in Nairobi through structured questionnaires. Bayesian SEM with non-informative prior gave the best estimates indicating that personal distance, individualism and long term orientation were significantly related to business performance. However, Uncertainty Avoidance had no significant relationship with business performance.