Miracle-dz/UniFAR-FTUs huggingface dataset:
An Analytical Approach to the Design of Parallel Block Cipher Encryption/Decryption: A CPU/GPU Case Study

GPUs are at the fore-front of a radical transformation that is taking place in software design. The ability to process multiple data streams simultaneously is delivering substantial benefits to a large collection of domains. Depending on the application, these benefits can be expanded by utilizing the not-insignificant power of traditional CPUs. Multi-core CPUs with a hierarchy of large and fast caches, can provide significant performance especially if data transfers dominate execution time, or branching does not allow for the uninterrupted execution on all GPU cores. In this paper we present an analytical framework that can be used to optimally consolidate CPU and GPU resources for the encryption/decryption of block ciphers such as AES. A rigorous set of experiments concludes the paper, showcasing the benefits and overall characteristics of the proposed framework. Our tests also bring to the surface important aspects of GPU operation that have to be taken into consideration upon the design of any successful scheduling scheme involving GPUs as a system component.
Gaussian kernel GARCH models

This paper aims to investigate a Bayesian sampling approach to parameter estimation in the GARCH model with an unknown conditional error density, which we approximate by a mixture of Gaussian densities centered at individual errors and scaled by a common standard deviation. This mixture density has the form of a kernel density estimator of the errors with its bandwidth being the standard deviation. This study is motivated by the lack of robustness in GARCH models with a parametric assumption for the error density when used for error-density based inference such as value-at-risk (VaR) estimation. A contribution of the paper is to construct the likelihood and posterior of the model and bandwidth parameters under the kernel-form error density, and to derive the one-step-ahead posterior predictive density of asset returns. We also investigate the use and benefit of localized bandwidths in the kernel-form error density. A Monte Carlo simulation study reveals that the robustness of the kernel-form error density compensates for the loss of accuracy when using this density. Applying this GARCH model to daily return series of 42 assets in stock, commodity and currency markets, we find that this GARCH model is favored against the GARCH model with a skewed Student t error density for all stock indices, two out of 11 currencies and nearly half of the commodities. This provides an empirical justification for the value of the proposed GARCH model.
West Nile Virus

In early August 2000, infectious disease specialists in hospitals in central Israel noted an increasing number of elderly patients admitted for encephalitis. By mid-August, when several blood and cerebrospinal fluid (CSF) samples tested positive for antibodies for West Nile (WN) virus, an outbreak was suspected. Eventually a WN fever epidemic that affected the entire country was recognized. This article outlines the epidemiologic aspects of this WN fever epidemic; Chowers et al. (1) details the clinical characteristics. These are the first in-depth descriptions of an outbreak caused by WN virus involving an entire country.
Therapeutic effects of umbilical cord blood plasma in a rat model of acute ischemic stroke

Umbilical cord blood plasma (UCB-PL) contains various cytokines, growth factors, and immune modulatory factors that regulate the proliferation and function of immune cells and adult stem cells. Despite its therapeutic potential, the effects of UCB-PL treatment in conditions of ischemic brain injury have yet to be investigated. In this study, we demonstrated that both behavioral and structural impairments resulting from ischemic brain injury were significantly prevented/reversed after intravenous administration of UCB-PL relative to the vehicle control. As early as 1-week post-ischemia, an increased number of newborn cells in the subventricular zone and a reduced number of activated microglial cells in the peri-infarct area were observed in the UCB-PL group, suggesting that enhanced neurogenesis and/or the suppression of inflammation may have contributed to functional protection/recovery. Moreover, UCB-PL was more effective than plasma derived from a 65-year-old healthy adult for the treatment of ischemia-related structural and functional deficits, indicating that UCB-PL had greater therapeutic potential. This study provides valuable insights into the development of a safe, effective, and cell-free strategy for the treatment of ischemic brain damage and a much-needed alternative for patients who are ineligible for thrombolytic therapy.
Automatic CT Image Segmentation of the Lungs with Region Growing Algorithm

Computer aided diagnosis of lung CT image has been a revolutionary step in the early diagnosing of lung diseases. The best method of implementing computer aided diagnosis for medical image analysis is first to preprocess the image in order to segment it. The first step in computer aided diagnosis of lung computed tomography patient image is generally to first segment the region of interest, in this case lung, and then analyze separately each area obtained, for a tumor, cancer, node detection or other pathology for diagnosis. This is generally much easier approach, because the area used for setting the right diagnosis, is getting smaller with the process of segmentation, so the radiologist can focus his observation only on specific data inside the specific region. In this paper we proposed lung segmentation technique to accurately segment the lung parenchyma of lung CT images, which can help radiologist in early diagnosing lung diseases, but the algorithm can also be used to early diagnose other benign or malignant pathologies in other organs, such as liver, brain or spine.
How Well Can We Predict the Toxicity of Pesticide Mixtures to Aquatic Life?

Abstract Results of published pesticide mixture toxicity experiments conducted with aquatic organisms were compiled and evaluated to assess the accuracy of predictive mixture models. Three types of models were evaluated: Concentration addition (CA), independent action (IA), and simple interaction (SI). The CA model was the most often tested (207 experiments), followed by SI (59) and IA (37). The reviewed experiments are listed in the Supplemental material to provide a resource for future investigators. The predictive accuracy of each model was quantified for each experiment by the model deviation ratio (MDR), which was calculated by dividing the predicted toxicity by the observed toxicity. Eighty-eight percent of all experiments that evaluated the CA model had observed effective concentrations within a factor of 2 of predicted values (MDR values from 0.5–2.0). The median MDR was 1, about 5% of MDRs were less than 0.5, and about 5% were greater than 2, indicating unbiased estimates overall. The predictive accuracy of CA and IA models was influenced, however, by the different modes of action (MOA) of the pesticides. For experiments with pesticides with the same MOA, CA more accurately predicted effective concentrations for more experiments compared to IA, which tended to underpredict toxicity. The IA model was somewhat more accurate than the CA model for most mixtures with different MOAs, but in most cases there were relatively small differences between the models. Additionally, 80% of SI experiments had an MDR value below 2.0 despite a bias towards experiments that are likely to have an interaction. Thus, results indicate that the CA model may be used as a slightly conservative, but broadly applicable model with a relatively small likelihood of underestimating effects due to interactions.
Distorsions cognitives des joueurs de jeux de hasard et d’argent hors ligne et mixtes@@@Cognitive distortions of off-line and mixed gamblers and gamers@@@Distorsiones cognitivas de los jugadores de juegos de azar y de dinero fuera de línea y mixtos

Aim: The aim of this study is to compare and trace profiles of individuals who go to casinos, hippodromes, etc. to gamble (offline gamblers) and those who in addition to it, gamble online (mixed gamblers). Method; A total of 608 players responded to a battery of questionnaire. It consisted of; 1) sociodemographic data (gender, age, family situation, education); 2) measures of gambling; 3) alcohol consumption; 4) measures of anxiety end depression and 5) socio-cognitive measures (cognitive distortions and self-confidence). Results: We notice that our population in majority, consists of women (39% men, 60% women) with a mean age of 53 [Agemin: 17 years, Agemax: 76 years, S.D. : 15.97); 55% were married or living together. Only 23.7% (N = 144) of subjects are in/off gamblers. With respect to consumption of alcohol, 31.5% (N = 191) have problems with alcohol; 19.6% (N = 109) are at moderate risk of pathological gambling and 9% N = 55) are pathological gamblers. Mixed gamblers are significantly younger, play more, have higher scores on problematic gambling, are more anxious and have higher levels of alcohol consumption. They also have significantly higher scores on cognitive distortions (particularly on the dimension of predictive control) but on the other hand, have lower levels of confidence with respect to their capacity to stop gambling as compared to offline gamblers. Conclusion: Mixed gamblers are characterized by higher intensity with respect to pathological gambling end cognitive biases. Therefore, special attention should be paid to this fragile population. Some interventional and preventive approaches are proposed in the conclusion section. (PsycINFO Database Record (c) 2015 APA, all rights reserved). (journal abstract)
Testbed for microgrid with multi-energy Generators

A typical micro-grid with multi-energy generators (MMEG) is composed of renewable energy generation systems including wind, photovoltaic, biomass etc., and fuel cells, energy storage and local loads. As a cost-effective research method, testbeds for MMEG in lab have been developed in some key projects all over world. This paper presents a comprehensive testbed for MMEG (TB-MMEG) in lab built in Hefei University of Technology(HFUT), China, including hardware and software design scheme. The features of the TB-MMEG are single-phase, three-phase and multi-AC buses with energy management system (EMS) that conforms to the IEC61970 standards.
Local Surface Density of the Galactic Disk from a Three-Dimensional Stellar Velocity Sample

We have reestimated the surface density of the Galactic disk in the solar neighborhood within ±0.4 kpc of the Sun using the parallaxes and proper motions of a kinematically and spatially unbiased sample of 1476 old bright red giant stars from the Hipparcos catalog with measured radial velocities from Barbier-Brossat & Figon. We determine the vertical distribution of the red giants as well as the vertical velocity dispersion of the sample (14.4 ± 0.3 km s-1) and combine these to derive the surface density of the gravitating matter in the Galactic disk as a function of the Galactic coordinate z. The surface density of the disk increases from 10.5 ± 0.5 M⊙ pc-2 within ±50 pc to 42 ± 6 M⊙ pc-2 within ±350 pc. The estimated volume density of the Galactic disk within ±50 pc is about 0.1 M⊙ pc-3 which is close to the volume density estimates of the observed baryonic matter in the solar neighborhood.
Optimal Satisficing

Herbert Simon introduced the notion of satisficing to explain how boundedly rational agents might approach difficult sequential decision problems. His satisficing decision makers were offered as an alternative to optimizers, who have impressive computational capacities which allow them to maximize. There is no reason, however, why satisficers can not do their task optimally. In this paper, we present a simplified sequential search problem for a satisficing decision maker, and show how to compute its optimal satisficing search policies. Our findings demonstrate that satisficing, when done properly, can be a quite effective search policy.
Application of Segmented 2-D Probabilistic Occupancy Maps for Robot Sensing and Navigation

The concept of probabilistic occupancy maps was introduced by the end of the 1980s. Over the years, research has focused on the definition of the representation, the data fusion, and the generation of such occupancy models. However, few considerations have been given to processing occupancy maps as textured images to extract meaningful information that is required for robot navigation. This paper investigates the application of modern segmentation techniques over 2-D probabilistic occupancy maps that are encoded as textured images. Enhancements are proposed to a uniformity estimation technique based on local binary pattern and contrast (LBP/C) to achieve the robust segmentation of occupancy maps that typically result from range sensors with limited resolution. The enhanced LBP/C segmentation technique handles occupancy uncertainty and subdivides the space in regions that are characterized by three deterministic occupancy states, which are defined as free, unknown, and occupied. The approach is also extended to increase the number of classification levels, which provides the necessary flexibility to automatically select the regions that are characterized by a given range of occupancy states. The use of these extensions, along with the accuracy of the segmented 2-D occupancy maps, is first experimentally demonstrated on ground-based probabilistic grids for application in mobile robot navigation with collision avoidance. The potential of the proposed approach is also evaluated on aerial and satellite images for which it provides stable results and can find applications for unmanned aerial vehicle navigation.
Control of Delay-ODE , Delay-PDE , and PDE-ODE Cascades Miroslav

I. INTRODUCTION Delay systems occupy a special place within the field of distributed parameter systems. Certain problems for this class of distributed parameter systems lend themselves to more elegant, or even explicit solutions. An enormous wealth of knowledge and research results exists for control of systems with state delays and input delays. Problems with long input delays, for unstable plants, represent a particular challenge. It has been known for about three decades now [1], [2] that the system
Introduction to compressed sensing

Compressed sensing (CS) is an exciting, rapidly growing, field that has attracted considerable attention in signal processing, statistics, and computer science, as well as the broader scientific community. Since its initial development only a few years ago, thousands of papers have appeared in this area, and hundreds of conferences, workshops, and special sessions have been dedicated to this growing research field. In this chapter, we provide an up-to-date review of the basics of the theory underlying CS. This chapter should serve as a review to practitioners wanting to join this emerging field, and as a reference for researchers. We focus primarily on the theory and algorithms for sparse recovery in finite dimensions. In subsequent chapters of the book, we will see how the fundamentals presented in this chapter are expanded and extended in many exciting directions, including new models for describing structure in both analog and discrete-time signals, new sensing design techniques, more advanced recovery results and powerful new recovery algorithms, and emerging applications of the basic theory and its extensions. Introduction We are in the midst of a digital revolution that is driving the development and deployment of new kinds of sensing systems with ever-increasing fidelity and resolution. The theoretical foundation of this revolution is the pioneering work of Kotelnikov, Nyquist, Shannon, and Whittaker on sampling continuous-time bandlimited signals [162, 195, 209, 247]. Their results demonstrate that signals, images, videos, and other data can be exactly recovered from a set of uniformly spaced samples taken at the so-called Nyquist rate of twice the highest frequency present in the signal of interest.
Silencing of Retrotransposons in Arabidopsis and Reactivation by the ddm1 Mutation

Gene silencing associated with repeated DNA sequences has been reported for many eukaryotes, including plants. However, its biological significance remains to be determined. One important function that has been proposed is the suppression of transposons. Here, we address transposon suppression by examining the behavior of the tobacco retrotransposon Tto1 and endogenous retrotransposons in Arabidopsis. After an initial increase in copy number because of active transposition in the Arabidopsis genome, Tto1 became silent. The amount of transcript was reduced, and the inactivated Tto1 became methylated. This silencing correlated with an increase in copy number. These phenomena mimic repeat-induced gene silencing. The homozygous ddm1 (for decrease in DNA methylation) mutation of Arabidopsis results in genomic DNA hypomethylation and the release of silencing in repeated genes. To investigate the role of DNA methylation and the gene-silencing machinery in the suppression of Tto1, we introduced the ddm1 mutation into an Arabidopsis line carrying inactivated Tto1 copies. In the homozygous ddm1 background, Tto1 became hypomethylated and transcriptionally and transpositionally active. In addition, one of the newly isolated endogenous Arabidopsis retrotransposon families, named Tar17, also became hypomethylated and transcriptionally active in the ddm1 mutant background. Our results suggest that the inactivation of retrotransposons and the silencing of repeated genes have mechanisms in common.
Analysis of reference normal values of diadochokinetic rate and U.S. China comparison

In this paper, based on Motor Speech Profile and combined with the normative data of DDK parameters in American adults, we tested 11 parameters of DDK, such as Average DDK Period, Average DDK Rate, Standard Deviation of DDK Period, Coeff.of Variation of DDK Period, Perturbation of DDK Period, Average DDK Peak Intensity and so on. According to our researches on the characteristics of normal motor speech in our college students and the differences of the norm values between Chinese and Americans, we verified the conclusion abroad that there is no relativity between genders and DDK parameters and found out the significant differences between Chinese and Americans. This provides foundation for formulating the normative data of diadochokinetic parameters suitable to Chinese language.
Journal of Machine Learning Research ? ? ? ( ? ? ? ) ? ? ? Submitted ? ? ? ; Published ? ? ? Dynamic Policy Programming

In this paper, we propose a novel policy iteration method, called dynamic policy programming (DPP), to estimate the optimal policy in the infinite-horizon Markov decision processes. DPP is an incremental algorithm that forces a gradual change in policy update. This allows to prove finite-iteration and asymptotic l∞-norm performance-loss bounds in the presence of approximation/estimation error which depend on the average accumulated error as opposed to the standard bounds which are expressed in terms of the supremum of the errors. The dependency on the average error is important in problems with limited number of samples per iteration, for which the average of the errors can be significantly smaller in size than the supremum of the errors. Based on these theoretical results, we prove that a sampling-based variant of DPP (DPP-RL) asymptotically converges to the optimal policy. Finally, we illustrate numerically the applicability of these results on some benchmark problems and compare the performance of the approximate variants of DPP with some existing reinforcement learning (RL) methods.
Cognitive set-shifting in anorexia nervosa.

OBJECTIVE Adult anorexia nervosa (AN) is associated with inefficient cognitive flexibility and set-shifting. Whether such inefficiencies also characterize adolescent AN is an important area of research. METHOD Adolescents with AN and matched controls were administered a computerized task that required initial learning of an explicit rule using corrective feedback and learning of a new rule after a set number of trials. Adult patients with AN and controls were also examined. RESULTS Adolescents with AN did not differ from matched controls with respect to set-shifting cost (decrease in performance after rule change), whereas adults with AN had significantly greater set-shifting cost compared with controls. DISCUSSION This study suggests that set-shifting inefficiencies may not be a vulnerability factor for AN development in adolescents with AN, but might become an important aspect of the disorder at later age, and could point towards developmental neurobiologic brain changes that could affect AN at different ages.
Characterization of Levels and Cellular Transfer of Circulating Lipoprotein-Bound MicroRNAs

Objective—MicroRNAs are important intracellular regulators of gene expression, but also circulate in the blood being protected by extracellular vesicles, proteins, or high-density lipoprotein (HDL). Here, we evaluate the regulation and potential function of HDL- and low-density lipoprotein–bound miRs isolated from healthy subjects and patients with coronary artery disease. Approach and Results—HDL-bound miRs with known effects in the cardiovascular system were analyzed in HDL isolated from healthy subjects (n=10), patients with stable coronary artery disease (n=10), and patients with an acute coronary syndrome (n=10). In HDL from healthy subjects, miR-223 was detected at concentrations >10 000 copies/µg HDL, and miR-126 and miR-92a at about 3000 copies/µg HDL. Concentrations of most miRs were substantially higher in HDL as compared with low-density lipoprotein. However, HDL-bound miR-223 contributed to only 8% of the total circulating miRs. The signatures of miRs varied only slightly in HDL derived from patients with coronary artery disease. We did not observe a significant uptake of HDL-bound miRs into endothelial cells, smooth muscle cells, or peripheral blood mononuclear cells. However, patient-derived HDL transiently reduced miR expression particularly when incubated with smooth muscle and peripheral blood mononuclear cells. Conclusions—Circulating miRs are detected in HDL and to a lesser extent in low-density lipoprotein, and the miR-signatures are only slightly altered in patients with coronary artery disease. Lipoprotein-bound miRs were not efficiently delivered to endothelial, smooth muscle, and peripheral blood mononuclear cells suggesting that the lipoprotein-associated pool of miRs is not regulating the function of the studied cells in vitro.
Src family protein tyrosine kinase signaling mediates monosodium urate crystal‐induced IL‐8 expression by monocytic THP‐1 cells

Neutrophil‐dependent inflammation dependent on monosodium urate (MSU)crystal‐induced IL‐8 expression occurs in gout. MSU crystals activatephagocyte Src family tyrosine kinases and the serine/threonine kinasep70s6k. Thus, using monocytic THP‐1 cells, we assessed the potentialfor Src family kinases and p70s6k to mediate MSU‐induced IL‐8expression. MSU crystals induced phosphorylation of p70s6k and the Srckinases c‐Src, Lyn, Hck, and Fyn. IL‐8 expression was attenuated moreby the Src kinase inhibitor PP1 than by the p70s6k inhibitor rapamycin.PP1 inhibited crystal‐induced phosphorylation of ERK1/2 and IκBαand suppressed IκB kinase (IKK) activation and NF‐κB binding to theIL‐8 promoter, signals that mediate MSU‐induced IL‐8 expression.Transfection of the native Src inhibitor, C‐terminal Src kinase (Csk),also suppressed crystal‐induced c‐Src, ERK1/2, and IκBαphosphorylation and IL‐8 expression. We conclude that Src familytyrosine kinase signaling plays a significant role in MSUcrystal‐induced IL‐8 expression via stimulation of ERK1/2 pathway andNF‐κB activation.