Miracle-dz/UniFAR-FTUs huggingface dataset:
Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods

Quantifying the effects of future changes in the frequency of precipitation extremes is a key challenge in assessing the vulnerability of hydrological systems to climate change but is difficult as climate models do not always accurately simulate daily precipitation. This article compares the performance of four published techniques used to reduce the bias in a regional climate model precipitation output: (1) linear, (2) nonlinear, (3) γ‐based quantile mapping and (4) empirical quantile mapping. Overall performance and sensitivity to the choice of calibration period were tested by calculating the errors in the first four statistical moments of generated daily precipitation time series and using a cross‐validation technique. The study compared the 1961–2005 precipitation time series from the regional climate model HadRM3.0‐PPE‐UK (unperturbed version) with gridded daily precipitation time series derived from rain gauges for seven catchments spread throughout Great Britain. We found that while the first and second moments of the precipitation frequency distribution can be corrected robustly, correction of the third and fourth moments of the distribution is much more sensitive to the choice of bias correction procedure and to the selection of a particular calibration period. Overall, our results demonstrate that, if both precipitation data sets can be approximated by a γ‐distribution, the γ‐based quantile‐mapping technique offers the best combination of accuracy and robustness. In circumstances where precipitation data sets cannot adequately be approximated using a γ‐distribution, the nonlinear method is more effective at reducing the bias, but the linear method is least sensitive to the choice of calibration period. The empirical quantile mapping method can be highly accurate, but results were very sensitive to the choice of calibration time period. However, it should be borne in mind that bias correction introduces additional uncertainties, which are greater for higher order moments. Copyright © 2012 Royal Meteorological Society
Sinc-Windowing and Multiple Correlation Coefficients Improve SSVEP Recognition Based on Canonical Correlation Analysis

Canonical Correlation Analysis (CCA) is an increasingly used approach in the field of Steady-State Visually Evoked Potential (SSVEP) recognition. The efficacy of the method has been widely proven, and several variations have been proposed. However, most CCA variations tend to complicate the method, usually requiring additional user training or increasing computational load. Taking simple procedures and low computational costs may be, however, a relevant aspect, especially in view of low-cost and high-portability devices. In addition, it would be desirable that the proposed variations are as general and modular as possible to facilitate the translation of results to different algorithms and setups. In this work, we evaluated the impact of two simple, modular variations of the classical CCA method. The variations involved (i) the number of canonical correlations used for classification and (ii) the inclusion of a prefiltering step by means of sinc-windowing. We tested ten volunteers in a 4-class SSVEP setup. Both variations significantly improved classification accuracy when they were used separately or in conjunction and led to accuracy increments up to 7-8% on average and peak of 25–30%. Additionally, variations had no (variation (i)) or minimal (variation (ii)) impact on the number of algorithm steps required for each classification. Given the modular nature of the proposed variations and their positive impact on classification accuracy, they might be easily included in the design of CCA-based algorithms that are even different from ours.
CapsNet comparative performance evaluation for image classification

Image classification has become one of the main tasks in the field of computer vision technologies. In this context, a recent algorithm called CapsNet that implements an approach based on activity vectors and dynamic routing between capsules may overcome some of the limitations of the current state of the art artificial neural networks (ANN) classifiers, such as convolutional neural networks (CNN). In this paper, we evaluated the performance of the CapsNet algorithm in comparison with three well-known classifiers (Fisher-faces, LeNet, and ResNet). We tested the classification accuracy on four datasets with a different number of instances and classes, including images of faces, traffic signs, and everyday objects. The evaluation results show that even for simple architectures, training the CapsNet algorithm requires significant computational resources and its classification performance falls below the average accuracy values of the other three classifiers. However, we argue that CapsNet seems to be a promising new technique for image classification, and further experiments using more robust computation resources and re-fined CapsNet architectures may produce better outcomes.
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data

We present a probabilistic framework for studying adversarial attacks on discrete data. Based on this framework, we derive a perturbation-based method, Greedy Attack, and a scalable learning-based method, Gumbel Attack, that illustrate various tradeoffs in the design of attacks. We demonstrate the effectiveness of these methods using both quantitative metrics and human evaluation on various state-of-the-art models for text classification, including a word-based CNN, a character-based CNN and an LSTM. As as example of our results, we show that the accuracy of character-based convolutional networks drops to the level of random selection by modifying only five characters through Greedy Attack.
Ion Storage in Nanoconfined Interstices Between Vertically Aligned Nanotubes in Electric Double-Layer Capacitors

Ionic liquids are considered promising electrolytes for developing electric double-layer capacitors (EDLCs) with high energy density. To identify optimal operating conditions, we performed molecular dynamics simulations of N-methyl-N-propyl pyrrolidinium bis(trifluoromethanesulfonyl)imide (mppyþ TFSI ) ionic liquid confined in the interstices of vertically aligned carbon nanostructures mimicking the electrode structure. We modeled various surface charge densities as well as varied the distance between nanotubes in the array. Our results indicate that high-density ion storage occurs within the noninteracting double-layer region formed in the nanoconfined domain between charged nanotubes. We determined the specific arrangement of these ions relative to the nanotube surface and related the layered configuration to the molecular structure of the ions. The pitch distance of the nanotube array that enables optimal mppyþ TFSI storage and enhanced capacitance is determined to be 16 Å. [DOI: 10.1115/1.4037582]
Arabic senti-lexicon: Constructing publicly available language resources for Arabic sentiment analysis

Sentiment analysis is held to be one of the highly dynamic recent research fields in Natural Language Processing, facilitated by the quickly growing volume of Web opinion data. Most of the approaches in this field are focused on English due to the lack of sentiment resources in other languages such as the Arabic language and its large variety of dialects. In most sentiment analysis applications, good sentiment resources play a critical role. Based on that, in this article, several publicly available sentiment analysis resources for Arabic are introduced. This article introduces the Arabic senti-lexicon, a list of 3880 positive and negative synsets annotated with their part of speech, polarity scores, dialects synsets and inflected forms. This article also presents a Multi-domain Arabic Sentiment Corpus (MASC) with a size of 8860 positive and negative reviews from different domains. In this article, an in-depth study has been conducted on five types of feature sets for exploiting effective features and investigating their effect on performance of Arabic sentiment analysis. The aim is to assess the quality of the developed language resources and to integrate different feature sets and classification algorithms to synthesise a more accurate sentiment analysis method. The Arabic senti-lexicon is used for generating feature vectors. Five well-known machine learning algorithms: naïve Bayes, k-nearest neighbours, support vector machines (SVMs), logistic linear regression and neural network are employed as base-classifiers for each of the feature sets. A wide range of comparative experiments on standard Arabic data sets were conducted, discussion is presented and conclusions are drawn. The experimental results show that the Arabic senti-lexicon is a very useful resource for Arabic sentiment analysis. Moreover, results show that classifiers which are trained on feature vectors derived from the corpus using the Arabic sentiment lexicon are more accurate than classifiers trained using the raw corpus.
DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning

In this paper, we propose a regression system to infer the emotion intensity of a tweet. We develop a multi-aspect feature learning mechanism to capture the most discriminative semantic features of a tweet as well as the emotion information conveyed by each word in it. We combine six types of feature groups: (1) a tweet representation learned by an LSTM deep neural network on the training data, (2) a tweet representation learned by an LSTM network on a large corpus of tweets that contain emotion words (a distant supervision corpus), (3) word embeddings trained on the distant supervision corpus and averaged over all words in a tweet, (4) word and character n-grams, (5) features derived from various sentiment and emotion lexicons, and (6) other hand-crafted features. As part of the word embedding training, we also learn the distributed representations of multi-word expressions (MWEs) and negated forms of words. An SVR regressor is then trained over the full set of features. We evaluate the effectiveness of our ensemble feature sets on the SemEval-2018 Task 1 datasets and achieve a Pearson correlation of 72% on the task of tweet emotion intensity prediction.
Phylogenetic relationships, biofilm formation, motility, antibiotic resistance and extended virulence genotypes among Escherichia coli strains from women with community-onset primitive acute pyelonephritis

The present work set out to search for a virulence repertoire distinctive for Escherichia coli causing primitive acute pyelonephritis (APN). To this end, the virulence potential of 18 E. coli APN strains was genotypically and phenotypically assessed, comparatively with 19 strains causing recurrent cystitis (RC), and 16 clinically not significant (control, CO) strains. Most of the strains belong to phylogenetic group B1 (69.8%; p<0.01), and APN strains showed unique features, which are the presence of phylogroup A, and the absence of phylogroup B2 and non-typeable strains. Overall, the most dominant virulence factor genes (VFGs) were ecpA and fyuA (92.4 and 86.7%, respectively; p<0.05), and the mean number of VFGs was significantly higher in uropathogenic strains. Particularly, papAH and malX were exclusive for uropathogenic strains. APN and RC strains showed a significantly higher prevalence of fyuA, usp, and malX than of CO strains. Compared to RC strains, APN ones showed a higher prevalence of iha, but a lower prevalence of iroN, cnf1, and kpsMT-II. Hierarchical cluster analysis showed a higher proportion of two gene clusters (malX and usp, and fyuA and ecpA) were detected in the APN and RC groups than in CO, whereas iutA and iha clusters were detected more frequently in APN strains. The motility level did not differ among the study-groups and phylogroups considered, although a higher proportion of swarming strains was observed in APN strains. Antibiotic-resistance rates were generally low except for ampicillin (37.7%), and were not associated with specific study- or phylogenetic groups. APN and RC strains produced more biofilm than CO strains. In APN strains, iha was associated with higher biofilm biomass formation, whereas iroN and KpSMT-K1 were associated with a lower amount of biofilm biomass. Further work is needed to grasp the virulence and fitness mechanisms adopted by E. coli causing APN, and hence develop new therapeutic and prophylactic approaches.
Social, Behavioral, and Cognitive Influences on Upper Echelons During Strategy Process

This study reviews research on the social, behavioral, and cognitive influences on CEOs, top management teams (TMTs), and the CEO-TMT interface during strategic decision making. We identify the key issues examined in this research over the past 10 years and relate developments in the field to previous knowledge in this area. We also attempt to identify what constitutes an established body of knowledge in the field and, therefore, areas that need additional examination. Our review indicates that while there has been an explosion of research on the influence of CEO personality and TMT social processes on strategy process, much remains to be done in terms of examining CEO and TMT cognition, particularly at the level of the CEO-TMT interface.
Evaluation of plasma catestatin levels in patient with coronary slow flow

﻿ Objectives: Coronary slow flow (CSF) is characterized by delayed opasification of the coronary arteries in the absence of obstructive coronary disease. Catestatin has several cardiovasculer actions, in addition to diminihed sympato-adrenal flow. The study was to investigated associations between CSF and plasma catestatin levels. Method:. This study included 45 CSF patients (37 male, mean age 48 ± 9.5 years) and 30 control individuals (24 male, mean age 48.4 ± 9 years). Coronary flow was quantified according to the TIMI (Thrombolysis in Myocardial Infarction) frame count method for coronary arteries. Serum catestatin levels taken from blood samples were measured by ELISA method. These parameters were compared between the groups . Results: When compared with to the control group the serum catestatin levels was found higher in the CSF group. In addition to this, mean platelet volume was also significantly higher in patients with coronary slow flow. Conclusions: Our study revealed that catestatin levels are increased in patients with CSF. Coronary slow flow that increased catecholaminergic sympathetic system activities seem to be among the reasons of endothelial dysfunction.
bypasses to lateral gene transfer among prokaryotes Directed networks reveal genomic barriers and DNA repair Material Supplemental

Material Supplemental http://genome.cshlp. org/content/suppl/2011/01/26/gr.115592. 110.DC1. html P<P Published online January 26, 2011 in advance of the print journal. Open Access Freely available online through the Genome Research Open Access option. service Email alerting click here top right corner of the article or Receive free email alerts when new articles cite this article sign up in the box at the
Peripheral T Cells as a Biomarker for Oxygen-Ion-Radiation-Induced Social Impairments

Exposure to galactic cosmic rays (GCR) poses an obstacle to successful deep space missions, including missions to the Moon or Mars. Previously, we and others have identified chronic cognitive impairments associated with GCR in rodent model systems. The persistent cognitive loss previously reported is indicative of global changes in different regions of the brain, including the prefrontal cortex and the hippocampus. It has been shown that both of these brain regions are involved in social functions. Here we demonstrate that four months after a single exposure to oxygen ionizing radiation, which is a component of GCR, adult male mice have social memory deficits. Importantly, we identified circulating levels of CD8 T cells as predictors of social behavioral changes. Thus, CD8 T cells could be used as a potential peripheral biomarker. To the best of our knowledge we demonstrate for the first time that GCR-induced impairments in social behavior are directly linked to peripheral immune changes. These results further advance our understanding of the challenges encountered during space exploration.
Does Attendance Matter? An Examination of Student Attitudes, Participation, Performance and Attendance

Non attendance of lectures and tutorials appears to be a growing trend. The literature suggests many possible reasons including students’ changing lifestyle, attitudes, teaching and technology. This paper looks at the reasons for non attendance of students in the Faculty of Commerce at the University of Wollongong and identifies relationships between attendance, participation and performance. The results indicate that there are valid reasons for non attendance that are both in the control of learners and teachers. There are also clear benefits for students to be gained in attendance; however, changes in the way we learn, teach, assess and use technology are recommended if we wish to reverse the trend.
Evaluation of the effect of hypnobirthing education during antenatal period on fear of childbirth

Objectives: The objective of this research was to analyse the effect of hypnobirthing education given to pregnant women during antenatal period on fear of childbirth. Methods: This is an educational interventional case-control study. A total of 51 pregnant women were studied together during the 12 th week of pregnancy. Ethical Committee and related consents were taken. Data was evaluated by using descriptive statistics. Results: The age average of the pregnant in the control group was 28.70 ± 5.42 years while it was 25.74 ± 5.16 years for the case group. Women's gestational week averages were 20.87 weeks for study and 24.10 weeks for control group. In the study, the pre-training scale scores of the cases and control groups were compared with the average level of birth fear of the groups (t = 1.848, p = 0.073). There was a significant difference between pre- and post-hypnotic scale scores in the birth preparation class (t = -5.329, p < 0.001). Before the training; 48% of the case group. Fifty-two percent of the control group is adequately informed about the hypnobirthing problem. Conclusions: It was found that among the pregnant women who had attended birth preparation classes, positive labour perception of the intervention group pregnant women was higher than that of the control group pregnant women who hadn’t had hypnobirthing education and there was a significant difference between them.
Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning

Applying reinforcement learning in physical-world tasks is extremely challenging. It is commonly infeasible to sample a large number of trials, as required by current reinforcement learning methods, in a physical environment. This paper reports our project on using reinforcement learning for better commodity search in Taobao, one of the largest online retail platforms and meanwhile a physical environment with a high sampling cost. Instead of training reinforcement learning in Taobao directly, we present our environment-building approach: we build Virtual-Taobao, a simulator learned from historical customer behavior data, and then we train policies in Virtual-Taobao with no physical sampling costs. To improve the simulation precision, we propose GAN-SD (GAN for Simulating Distributions) for customer feature generation with better matched distribution; we propose MAIL (Multiagent Adversarial Imitation Learning) for generating better generalizable customer actions. To further avoid overfitting the imperfection of the simulator, we propose ANC (Action Norm Constraint) strategy to regularize the policy model. In experiments, Virtual-Taobao is trained from hundreds of millions of real Taobao customers’ records. Compared with the real Taobao, Virtual-Taobao faithfully recovers important properties of the real environment. We further show that the policies trained purely in Virtual-Taobao, which has zero physical sampling cost, can have significantly superior real-world performance to the traditional supervised approaches, through online A/B tests. We hope this work may shed some light on applying reinforcement learning in complex physical environments.
History and current status of polymyxin B‐immobilized fiber column for treatment of severe sepsis and septic shock

Toraymyxin® (Toray Medical Co., Ltd, Tokyo, Japan) has been developed as a direct hemoperfusion column that contains polymyxin B‐immobilized fiber to bind endotoxins in patients’ blood. Toraymyxin was approved by the Japanese National Health Insurance system for the treatment of endotoxemia and septic shock in 1994. Since then, PMX (defined as direct hemoperfusion with Toraymyxin) has been safely used in more than 100 000 cases in emergency and intensive care units in Japan. Toraymyxin is currently available for use in clinical settings in 12 countries outside of Japan. We reviewed and analyzed the development, clinical use, and efficacy of Toraymyxin, and assessed the current status of Toraymyxin use for the treatment of severe sepsis and septic shock. Our review shows that PMX appeared to be effective in improving hemodynamics and respiratory function in septic shock requiring emergency abdominal surgery. Recent large‐scale ranomized controlled trialscould not demonstrate whether prognosis is improved by PMX. However, the latest meta‐analysis revealed that PMX significantly decreased mortality in patients with severe sepsis and septic shock. Combination of PMX with continuous hemodiafiltration and longer duration of PMX might be an effective strategy to improve survival in such patients.
The Correlation between the Change in Thoracic Fluid Content and the Change in Patient Body Weight in Fontan Procedure

Background The thoracic fluid content (TFC) and its percent change compared to the baseline (TFCd0%) derived from a bioreactance technique using a noninvasive cardiac output monitoring (NICOM) device correlate well with the amount of fluid removal in patients undergoing hemodialysis and with intraoperative fluid balance in pediatric patients undergoing cardiac surgery. We hypothesized that TFC or TFCd0% would also be a useful indicator allowing fluid management in pediatric patients undergoing a Fontan procedure. Methods The medical records of patients who underwent an elective Fontan procedure were reviewed retrospectively. The intraoperative variables recorded at two time points were used in the analysis: when the NICOM data obtained just after anesthesia induction (T0) and just before transfer of the patient from the operating room to the ICU (T1). The analyzed variables were hemodynamic parameters, TFC, TFCd0%, stroke volume variation, body weight gain, change in the central venous pressure, and difference in the TFC (ΔTFC). Results The correlation coefficient between TFCd0% and body weight gain was 0.546 (p = 0.01); between TFCd0% and body weight gain% 0.572 (p = 0.007); and between TFCd0% and intraoperative fluid balance 0.554 (p = 0.009). The coefficient of determination derived from a linear regression analysis of TFCd0% versus body weight gain was 0.30 (p = 0.01); between TFCd0% and body weight gain% 0.33 (p = 0.007); and between TFCd0% and intraoperative fluid balance 0.31 (p = 0.009). Conclusions TFCd0% correlated well with body weight gain, body weight gain%, and intraoperative fluid balance. It is a useful indicator in the intraoperative fluid management of pediatric patients undergoing a Fontan procedure. Trial Registration This trial is registered with Clinical Research Information Service KCT0002062.