Miracle-dz/UniFAR-FTUs huggingface dataset:
Spike-based Decision Learning of Nash Equilibria in Two-Player Games

Humans and animals face decision tasks in an uncertain multi-agent environment where an agent's strategy may change in time due to the co-adaptation of others strategies. The neuronal substrate and the computational algorithms underlying such adaptive decision making, however, is largely unknown. We propose a population coding model of spiking neurons with a policy gradient procedure that successfully acquires optimal strategies for classical game-theoretical tasks. The suggested population reinforcement learning reproduces data from human behavioral experiments for the blackjack and the inspector game. It performs optimally according to a pure (deterministic) and mixed (stochastic) Nash equilibrium, respectively. In contrast, temporal-difference(TD)-learning, covariance-learning, and basic reinforcement learning fail to perform optimally for the stochastic strategy. Spike-based population reinforcement learning, shown to follow the stochastic reward gradient, is therefore a viable candidate to explain automated decision learning of a Nash equilibrium in two-player games.
Intravenous Adenosine During Atrioventricular Reentrant Tachycardia: Induction of Atrial Fibrillation with Rapid Conduction Over an Accessory Pathway

Adenosine is considered to be a safe agent for termination of orthodromic atrioventricular reentrant tachycardia in patients with accessory pathways. A case with initially successful accessory pathway ablation and without preexcitation during sinus rhythm is presented, in which intravenous adenosine (6 mg) during orthodromic tachycardia was followed by atrial fibrillation and sudden onset of preexcitation with subsequent rapid ventricular response with moderate hemodynamic compromise.
Unsupervised Clustering of Gene Expression Time Series with Conditional Random Fields

A key challenge of gene expression time series research is the development of efficient and reliable probabilistic models. In response, we propose an unsupervised conditional random fields (CRFs) model for gene expression time series clustering. Conditional random fields have demonstrated superior performance over generative models such as hidden Markov models (HMMs) in terms of computational efficiency on many sequence-data-based tasks. Yet their potential has not been previously explored in this field. In the proposed model, time series data are allowed to interact with each other via a voting pool scheme while clusters are progressively formed. Experiments based on both biological data and simulated data verify the suitability of our model to gene expression data analysis via the comparison with a recent work.
Superhydrophobic surfaces of the water bug Notonecta glauca: a model for friction reduction and air retention

Superhydrophobic surfaces of plants and animals are of great interest for biomimetic applications. Whereas the self-cleaning properties of superhydrophobic surfaces have been extensively investigated, their ability to retain an air film while submerged under water has not, in the past, received much attention. Nevertheless, air retaining surfaces are of great economic and ecological interest because an air film can reduce friction of solid bodies sliding through the water. This opens perspectives for biomimetic applications such as low friction fluid transport or friction reduction on ship hulls. For such applications the durability of the air film is most important. While the air film on most superhydrophobic surfaces usually lasts no longer than a few days, a few semi-aquatic plants and insects are able to hold an air film over a longer time period. Currently, we found high air film persistence under hydrostatic conditions for the elytra of the backswimmer Notonecta glauca which we therefore have chosen for further investigations. In this study, we compare the micro- and nanostructure of selected body parts (sternites, upper side of elytra, underside of elytra) in reference to their air retaining properties. Our investigations demonstrate outstanding air film persistence of the upper side of the elytra of Notonecta glauca under hydrostatic and hydrodynamic conditions. This hierarchically structured surface was able to hold a complete air film under hydrostatic conditions for longer than 130 days while on other body parts with simple structures the air film showed gaps (underside of elytra) or even vanished completely after a few days (sternites). Moreover, the upper side of the elytra was able to keep an air film up to flow velocities of 5 m/s. Obviously the complex surface structure with tiny dense microtrichia and two types of larger specially shaped setae is relevant for this outstanding ability. Besides high air film persistence, the observation of a considerable fluid velocity directly at the air–water interface indicates the ability to reduce friction significantly. The combination of these two abilities makes these hierarchically structured surfaces extremely interesting as a biomimetic model for low friction fluid transport or drag reduction on ship hulls.
Balancing apparel assembly lines through adaptive ant colony optimization

The goal of this study was to introduce an effective optimization method to balancing one-piece flow assembly lines by rearranging manufacturing tasks necessary for an apparel production to minimize the number of workstations and the idle time of the assembly line under certain conditions. In this paper, we firstly define an assembly-line-balancing (ALB) problem in apparel production and established an objective function for minimizing the smoothness index, a term that measures the difference between the worktime and the targeted cycle time of each workstation on an assembly line. We then illustrate an adaptive ant colony (AAC) algorithm with modifications made on the traditional ant colony algorithm for solving the ALB problem. Finally, we present the experiments using real-world data provided by an apparel manufacturer to compare the results generated from the AAC and the prior practice of the company. It was demonstrated that the AAC could help the company reduce the number of workstations on its assembly lines and cut the idle time at most of the workstations.
Performance Improvement of a Centrifugal Compressor Stage by Increasing Degree of Reaction and Optimizing Blade Loading of a 3D-Impeller

Performance improvement of 3D impellers in a high specific speed range was investigated using computational fluid dynamics (CFD) analyses and experimental tests. In order to reduce the loss production within the stator passages, the backsweep angle of the impellers was increased. At the same time, the inlet-to-exit relative velocity diffusion ratio was also increased by increasing impeller exit width to prevent the reduction in pressure ratio. Moreover, the blade loading distribution at the impeller shroud side was optimized to suppress the surge margin reduction caused by the increased relative velocity diffusion ratio. Five types of unshrouded impellers were designed, manufactured and tested to evaluate the effects of blade loading, backsweep angle and relative velocity diffusion ratio on compressor performance. The design suction flow coefficient was 0.125 and the machine Mach number was 0.87. Test results showed that compressor stage efficiency was increased by 5% compared to the base design without reducing the pressure coefficient and surge margin. It was concluded that an increased relative velocity diffusion ratio coupled with large backsweep angle was a very effective way to improve compressor stage efficiency. An appropriate blade loading distribution was also important in order to achieve a wide operating range as well as high efficiency.Copyright © 2009 by ASME
A SystemC platform for Network-on-Chip performance/power evaluation and comparison

In modern system-on-chips the communication architecture is a crucial part of the design for performances and power constraints because the number of modules is increasing due to continuous integration scale. Network-on-Chip (NoC) architecture lets to obtain greater performances over the traditional communication architectures. NoC design is more complex because there are more components and networks have more configuration parameters, so designers must explore a wider solutions space in the fastest way possible; fast tools for early stage NoC performance/power analysis are necessary. We propose a SystemC class library and relative tools for comparing NoC and investigating communication bottlenecks. NoC are compared by packet delay statistical analysis and power dissipation is evaluated on NoC components activities; probabilistic investigations, temporal evolutions and a packet tracing tool allow to discover, identify and localize bottleneck.
Online Classification Using a Voted RDA Method

We propose a voted dual averaging method for on- line classification problems with explicit regularization. This method employs the update rule of the regularized dual averaging (RDA) method proposed by Xiao, but only on the subsequence of training examples where a classification error is made. We derive a bound on the number of mistakes made by this method on the training set, as well as its generalization error rate. We also intro- duce the concept of relative strength of regularization, and show how it affects the mistake bound and gener- alization performance. We examine the method using l1-regularization on a large-scale natural language pro- cessing task, and obtained state-of-the-art classification performance with fairly sparse models.
Innovative holographic particle velocimeter: a multibeam technique.

An innovative multibeam holographic particle velocimetry technique is described for three-dimensional flow-velocity measurement. Characterized by suppressed speckle noise, decreased image depth of field, and efficient use of laser energy, the multibeam holographic particle velocimeter overcomes drawbacks of techniques that are based on conventional in-line and off-axis holography.
Beyond active noun tagging: Modeling contextual interactions for multi-class active learning

We present an active learning framework to simultaneously learn appearance and contextual models for scene understanding tasks (multi-class classification). Existing multi-class active learning approaches have focused on utilizing classification uncertainty of regions to select the most ambiguous region for labeling. These approaches, however, ignore the contextual interactions between different regions of the image and the fact that knowing the label for one region provides information about the labels of other regions. For example, the knowledge of a region being sea is informative about regions satisfying the “on” relationship with respect to it, since they are highly likely to be boats. We explicitly model the contextual interactions between regions and select the question which leads to the maximum reduction in the combined entropy of all the regions in the image (image entropy). We also introduce a new methodology of posing labeling questions, mimicking the way humans actively learn about their environment. In these questions, we utilize the regions linked to a concept with high confidence as anchors, to pose questions about the uncertain regions. For example, if we can recognize water in an image then we can use the region associated with water as an anchor to pose questions such as “what is above water? ”. Our active learning framework also introduces questions which help in actively learning contextual concepts. For example, our approach asks the annotator: “What is the relationship between boat and water? ” and utilizes the answer to reduce the image entropies throughout the training dataset and obtain more relevant training examples for appearance models.
SINR-Based Random Access for Cognitive Radio: Distributed Algorithm and Coalitional Game

In this paper, we study the problem of multi-channel medium access control (MAC) in cognitive radio (CR) networks. While most of the previously proposed MAC protocols for CR networks are heuristic and are based on the simplistic protocol model, we design a distributed MAC protocol using the more accurate signal-to-interference-plus-noise-ratio (SINR) model. First, we assume that the secondary users are cooperative and formulate the problem of assigning transmission and listening probabilities for random access as a non-convex network utility maximization problem. We propose a three-phase algorithm that converges to a near-optimal solution after solving a number of convex optimization problems distributively. Simulation results show that our proposed algorithm based on the SINR model achieves a higher aggregate throughput than other schemes which are based on the protocol model. Then, we consider the case that the secondary users are rational. We use coalitional game theory to study the incentive issues of user cooperation in a given channel for the SINR model. In particular, we use the solution concept of the core to analyze the stability of the grand coalition, and the solution concept of the Shapley value to fairly divide the payoff among the users. We show that the Shapley value lies in the core when all the users are one-hop neighbours of each other. We illustrate the Shapley value and the core with a numerical example.
A Signcryption Scheme from Certificateless to Identity-based Environment for WSNs into IoT

sensor network (WNS) is now an inevitable component of the internet of things (IoT), this integration creates new security challenges that exist between the sensor nodes and the internet host, thus, issue regarding setting up a non-compromised channel between these two ends. In this scheme we required that the sender of the message belongs to the internet host where huge computation can be done without incurring any delays or computational problem while the receiver belongs to the sensor node. The scheme is shown to be suitable and secure using random oracle of bilinear Diffie- Hellman assumption hence providing strong security for wireless sensors into internet of things. Keywordssensor network, Internet of things, security,
Race does not explain genetic heterogeneity in pharmacogenomic pathways.

INTRODUCTION Polymorphic alleles in the human genome have been identified as affecting numerous drug responses. Currently, genotyping of all patients before starting a drug regimen is impractical. Since many polymorphisms occur at varying rates in different racial groups, we investigated whether a patient's race could predict presence of drug-relevant genetic variants well enough to be used as a substitute for individual genotyping. METHODS We performed hierarchical clustering and principal components analysis on tagSNPs from three pathways (irinotecan, 5-fluorouracil and insulin) across 270 individuals from four racial groups available from the International HapMap Project. RESULTS For the drug pathways, irinotecan and 5-fluorouracil, individuals from each race were widely dispersed, although several subclusters consisted entirely of individuals from a single racial group. Principal components analysis confirmed race was not a major contributor to the SNP data variance. Interestingly, individuals tended to cluster more by race across the endogenous insulin signaling pathway SNPs. CONCLUSIONS Most genetic variation was determined by individual variation, not racial grouping, indicating race is not adequate as a surrogate to individualized therapy.
Evolving levels for Super Mario Bros using grammatical evolution

This paper presents the use of design grammars to evolve playable 2D platform levels through grammatical evolution (GE). Representing levels using design grammars allows simple encoding of important level design constraints, and allows remarkably compact descriptions of large spaces of levels. The expressive range of the GE-based level generator is analyzed and quantitatively compared to other feature-based and the original level generators by means of aesthetic and similarity based measures. The analysis reveals strengths and shortcomings of each generator and provides a general framework for comparing content generated by different generators. The approach presented can be used as an assistive tool by game designers to compare and analyze generators' capabilities within the same game genre.
Experimental evaluation of the tribological behaviour of CeO2 nanolubricants under extreme pressures

Lubrication of moving components is critical for lowering friction and reducing energy consumption. Nanoparticles have been recently studied as additives for improving the load-carrying capacity and overall tribological performance of lubricants employed for metal-mechanic applications. Cerium oxide is a lanthanide metal oxide (rare earth) with environmentally friendly characteristics that is widely used for consumer products, and most recently for enhancing the tribological performance of composite materials and lubricants. In this work, formulated polymeric lubricants employed for metal forming processes with dispersed cerium oxide (CeO2) nanoparticles were characterized under extreme pressure (EP) conditions. Nanoparticle concentrations of 0.01, 0.05, and 0.10 wt. % were dispersed by homogenization and ultrasonication in the selected lubricant. Tribological characterization under EP was performed with a T-02 four-ball tribometer according to the ITeE-PIB Polish method for testing lubricants under conditions of scuffing. This method applies and increasing load of 0-7200 N to the tribopair in a very short amount of time (18s) and is employed to find the maximum pressure a lubricant film can withstand. The scuffing load (Pt), and pressure loss limit or load carrying capacity (poz) were determined after each test. Wear scar diameters (WSDs) of worn ball materials were measured with an Alicona optical 3D measurement system. The anti-wear (AW) and friction-reducing characteristics of CeO2 nanolubricants were determined with a T-05 block-on-ring test. No seizure was found for all lubricants likely due to the presence of additives in the polymeric lubricant. Nanoparticles were found to delay scuffing initiation at concentrations up to 0.05 wt. %, and the load required for scuffing increased by 35 %. The load-carrying capacity was enhanced by up to 84% with only 0.01 wt. % CeO2. Higher concentrations (0.10 wt.%) had a detrimental effect, most likely due to nanoparticle agglomeration. The coefficient of friction (COF) was lowered from 0.035 for the polymeric lubricant to 0.006 with a concentration of 0.01 wt. % CeO2.The tribological results obtained by both tests demonstrate that CeO2 nanoparticles have friction-reducing and EP characteristics, making them suitable as lubricant additives in the metal-mechanic industry.
Medication adherence and its determinants among psychiatric patients in an Ethiopian referral hospital

Background The degree to which an individual follows medical advice is a major concern in every medical specialty. Non-adherence to psychiatric treatment regimens has a profound impact on the disease course, relapse, future recovery, cost of health care, and the outcome for the patient. The aim of this study was to assess medication adherence and its correlates among psychiatric patients at Ayder Referral Hospital, Northern Ethiopia. Methods A cross-sectional study was conducted from June to September 2013 at Ayder Referral Hospital, where 423 patients were selected by a systematic random sampling technique from all patients attending the psychiatric clinic at the hospital. Data were collected by trained data collectors through interview of the patients using a structured questionnaire. The collected data were entered into Epi Info version 7 and analyzed by Statistical Package for the Social Sciences version 16 software. Logistic regression was used to assess independent predictors of adherence. Results A total of 387 patients completed the interview. Two hundred and sixteen (55.8%) and 113 (29.2%) were patients with a diagnosis of schizophrenia and mood disorder, respectively, while 35 (9%) and 23 (5.9%) had a diagnosis of drug addiction and autistic disorder. Two hundred and seven (71.6%) patients were found to be adherent to their medication. When adherence rates were observed according to type of disorder, 60 (53.1%), 24 (68.6%), 149 (69%), and 18 (78.3%) of patients with mood disorder, drug addiction, schizophrenia, and autism, respectively, were adherent to their medications. Female gender (adjusted odds ratio [AOR] 2.34; 95% confidence interval [CI] 1.45–3.74), tertiary education (AOR 2.69; 95% CI 1.46–4.85), living with family (AOR 2.57; 95% CI 1.66–4.58), and shorter treatment duration (AOR 1.82; 95% CI 1.21–2.84) were among the variables associated with better adherence. Conclusion Suboptimal adherence was observed among psychiatric patients in this study. Health professionals in the psychiatric clinic and pharmacists need to focus on and counsel patients about adherence and its implications for their clinical outcome.
CimS - The CIS and IMS Joint Submission to WMT 2015 addressing morphological and syntactic differences in English to German SMT

We present the CimS submissions to the WMT 2015 Shared Task for the translation direction English to German. Similar to our previous submissions, all of our systems are aware of the complex nominal morphology of German. In this paper, we combine source-side reordering and target-side compound processing with basic morphological processing in order to obtain improved translation results. We also report on morphological processing for English to French.
Cardiac Autonomic Dysfunction: Particulate Air Pollution Effects Are Modulated by Epigenetic Immunoregulation of Toll‐like Receptor 2 and Dietary Flavonoid Intake

Background Short‐term fine particles (PM2.5) exposure is associated with reduced heart rate variability, a strong predictor of cardiac mortality among older people. Identifying modifiable factors that confer susceptibility is essential for intervention. We evaluated whether Toll‐like receptor 2 (TLR2) methylation, a reversible immune‐epigenetic process, and its dietary modulation by flavonoids and methyl nutrients, modify susceptibility to heart rate variability effects following PM2.5 exposure. Methods and Results We measured heart rate variability and PM2.5 repeatedly over 11 years (1275 total observations) among 573 elderly men from the Normative Aging Study. Blood TLR2 methylation was analyzed using pyrosequencing. Daily flavonoid and methyl nutrients intakes were assessed through the Food Frequency Questionnaire (FFQ). Every 10 μg/m3 increase in 48‐hour PM2.5 moving average was associated with 7.74% (95% CI: −1.21% to 15.90%; P=0.09), 7.46% (95% CI: 0.99% to 13.50%; P=0.02), 14.18% (95% CI: 1.14% to 25.49%; P=0.03), and 12.94% (95% CI: −2.36% to 25.96%; P=0.09) reductions in root mean square of successive differences, standard deviation of normal‐to‐normal intervals, low‐frequency power, and high‐frequency power, respectively. Higher TLR2 methylation exacerbated the root mean square of successive differences, standard deviation of normal‐to‐normal intervals, low‐frequency, and high‐frequency reductions associated with heightened PM2.5 (Pinteraction=0.006, 0.03, 0.05, 0.04, respectively). Every interquartile‐range increase in flavonoid intake was associated with 5.09% reduction in mean TLR2 methylation (95% CI: 0.12% to 10.06%; P=0.05) and counteracted the effects of PM2.5 on low frequency (Pinteraction=0.05). No significant effect of methyl nutrients on TLR2 methylation was observed. Conclusions Higher TLR2 methylation may confer susceptibility to adverse cardiac autonomic effects of PM2.5 exposure in older individuals. Higher flavonoid intake may attenuate these effects, possibly by decreasing TLR2 methylation.