Miracle-dz/UniFAR-FTUs huggingface dataset:
A Farewell to Arms: Sequential Reward Maximization on a Budget with a Giving Up Option

We consider a sequential decision-making problem where an agent can take one action at a time and each action has a stochastic temporal extent, i.e., a new action cannot be taken until the previous one is finished. Upon completion, the chosen action yields a stochastic reward. The agent seeks to maximize its cumulative reward over a finite time budget, with the option of "giving up" on a current action -- hence forfeiting any reward -- in order to choose another action. We cast this problem as a variant of the stochastic multi-armed bandits problem with stochastic consumption of resource. For this problem, we first establish that the optimal arm is the one that maximizes the ratio of the expected reward of the arm to the expected waiting time before the agent sees the reward due to pulling that arm. Using a novel upper confidence bound on this ratio, we then introduce an upper confidence based-algorithm, WAIT-UCB, for which we establish logarithmic, problem-dependent regret bound which has an improved dependence on problem parameters compared to previous works. Simulations on various problem configurations comparing WAIT-UCB against the state-of-the-art algorithms are also presented.
An Efficient and Effective Second-Order Training Algorithm for LSTM-Based Adaptive Learning

We study adaptive (or online) nonlinear regression with Long-Short-Term-Memory (LSTM) based networks, i.e., LSTM-based adaptive learning. In this context, we introduce an efficient Extended Kalman filter (EKF) based second-order training algorithm. Our algorithm is truly online, i.e., it does not assume any underlying data generating process and future information, except that the target sequence is bounded. Through an extensive set of experiments, we demonstrate significant performance gains achieved by our algorithm with respect to the state-of-the-art methods. Here, we mainly show that our algorithm consistently provides 10 to 45% improvement in the accuracy compared to the widely-used adaptive methods Adam, RMSprop, and DEKF, and comparable performance to EKF with a 10 to 15 times reduction in the run-time.
Favoritism in Research Assistantship Selection in Turkish Academia

This article analyzes the procedure for the initial employment of research assistants in Turkish universities to see if it complies with the rules and regulations. We manually collected 2409 applicant data from 53 Turkish universities to see if applicants are ranked according to the rules suggested by the Higher Education Council of Turkey. The rulebook states that applicants should be ranked according to a final score based on the weighted average of their GPA, graduate examination score, academic examination score, and foreign language skills score. Thus, the research assistant selection is supposed to be a fair process where each applicant is evaluated based on objective metrics. However, our analysis of data suggests that the final score of the applicants is almost entirely based on the highly subjective academic examination conducted by the hiring institution. Thus, the applicants GPA, standardized graduate examination score, standardized foreign language score are irrelevant in the selection process, making it a very unfair process based on favoritism.
Efficient mate finding in planktonic copepods swimming in turbulence

Zooplankton live in dynamic environments where turbulence may challenge their limited swimming abilities. How this interferes with fundamental behavioral processes remains elusive. We reconstruct simultaneously the trajectories of flow tracers and calanoid copepods and we quantify their ability to find mates when ambient flow imposes physical constrains on their motion and impairs their olfactory orientation. We show that copepods achieve high encounter rates in turbulence due to the contribution of advection and vigorous swimming. Males further convert encounters within the perception radius to contacts and then to mating via directed motion toward nearby organisms within the short time frame of the encounter. Inertial effects do not result in preferential concentration, reducing the geometric collision kernel to the clearance rate, which we model accurately by superposing turbulent velocity and organism motion. This behavioral and physical coupling mechanism may account for the ability of copepods to reproduce in turbulent environments.
Proposal of a New Model for Social Navigation Based on Extraction of Social Contexts from Ontology in Service Robots

With the growing presence of service robots in social environments, research into the development of social behavior in robots is becoming increasingly necessary. Service robotics and social navigation are research areas that have been growing in recent years. However, helping humans with everyday tasks as well as people who need special attention, the elderly and children are still challenging issues in both the domestic and commercial environments. In this paper we present the current problem of social navigation in service robots, a state-of-the-art review of social navigation models, and we propose a new social navigation model using context extraction from ontology. This is expected to improve both the naturalness and the sociability of the robot as well as the comfort of the human being. This project intends to use current detection and learning techniques and tools such as OpenPose, as well as semantic mapping and deep learning. Navigation experiments were performed in simulated environment and selection of a navigation method. Also started implementing people tracking using OpenPose with satisfactory preliminary results. It is hoped that with this project, we can collaborate with the field of social navigation research using ontology approach.
Power Quality Improvement in Multilevel Inverters using Open-end Winding Motor Scheme

This paper presents a power quality improvement scheme on a 3-level inverter using induction-motor drive with open stator. Open-end induction motor(OEIM) is obtained by opening the star connected primary winding and feeding from both ends using two separate voltage-source inverters. A main 3level inverter, obtained by linking two 2-level inverters in cascade supplies the OEIM from one end. The other end of OEIM is fed with a supplementary inverter which works a series active filter to compensate for harmonics produced by the main 3-level inverter. The main inverter is space-vector modulation (SVM) controlled and the supplementary 2-level inverter is controlled with an active filtering control technique to reducing the voltage harmonic distortions produced by the main inverter. As the harmonic distortions in the motor voltage and current waveforms are reduced, efficiency of the drive will be improved.
Association Between Socio-Cultural Factors and HIV And AIDS

Background: society is a web of relationship and cultural is manmade. Social change depends on the socio-cultural norms and values. With changing of society, prevalence of HIV and AIDS came in existence from 1988 in Nepal and brought the disturbance on social harmony. This study investigated the roles of socio-cultural factors increasing the risk of HIV and AIDS in Nepalese context. Methods: study was non-experimental cross sectional. In total 404 respondents were randomly selected from the workers of long route transport, brick factory, and garment factory and health sectors. The study was conducted from Oct 2012 to Mar 2013 in Kathmandu valley. Result: There was association found between stigma (p=.033), human trafficking (p=.000), trust on sex partners (p=.001), gender based violence (p=.000), early marriage (p=.000), gender inequality (p=.000) and polygamy (p=.002) and HIV and AIDS. 56.7%, 93.6%, 77.0% and 94.3% respondents agreed on association between the stigma and discrimination, human trafficking, trust on sex partners and polygamy respectively with HIV and AIDS. Similarly, 52% respondents were found disagree on association between early marriage with risk of HIV transmission. Majority of respondents are not sure to say the association of genders based violence (36.7%) and gender inequality (36.1%) with risk of HIV and AIDS. Conclusion: Significant association found between the socio-cultural factors (such stigma and discrimination, human trafficking, trust on sex partners, gender based violence, early marriage system, gender inequality and polygamy) and HIV and AIDS.
AN INVESTIGATION IN TO THE RISK FACTORS ASSOCIATED WITH CARDIOVASCULAR DISORDERS AMONG THE PAKHTUN POPULATION OF KHYBER PAKHTUNKHWA, PAKISTAN

OBJECTIVE: To determine demographic, dietary and psychological factors leading to coronary heart disease in Khyber Pakhtunkhwa, Pakistan.METHODS: This cross-sectional retrospective study was conducted atthe cardiac unit of Hayatabad Medical Complex, Peshawar from September 2012 to December 2012. A random sample of 200 Pakhtun cardiac patients were thoroughly investigated for demographic parameters, anthropometric measurement, food frequency record, and “Depression Anxiety Stress Scales” was submitted for the prevalence of depression,anxiety and stress.RESULTS: In factors assessed; rural background (75%), complex family structures (71%), illiteracy (68.5%), male smokers (64.75%), large families (9.84±2.57 members), low income (Rs. 8610±953) and less activity were identified as major risk factors. Being overweight (BM1=31.2±5.58 males and 27.98±1.92 females) with low High-density lipoproteins (HDL) (16.85±5.6), higher blood cholesterol (325±13.5 mg/dl), high triglycerides levels (232±10.7), diabetes mellitus (45%) and hypertension (29.5%) were the major biological factors. Dietary intake revealed high beef intake per week (65.5%), milk (97%), carbohydrates mostly wheat as staple food (90.5%) and low consumption of fresh vegetables and fruits. Daily intake of saturated fat (Ghee) (92%), animal fat, while fried and bakery items were also frequently consumed. The study also found a greater percentage of patients having moderate to mild anxiety, depression and stress posing to be health risks in CVD.CONCLUSION: Coronary heart disease follows a multidimensionalscenario in KP which needs to be addressed to prevent people of thisregion from vicious cycle of disease.KEY WORDS: Risk factors (Non-MeSH), demographic factor, dietaryintake patterns, biological factors, DAAS psychological test
Features of Longitudinal Variations of Geomagnetic and Ionospheric Parameters during Severe Magnetic Storms in 2015

The paper was carried out as a part of the project devoted to a comprehensive study of the longitudinal variations of the Earth's magnetic field in middle and high latitudes of the Northern hemisphere and related variations in the midlatitude ionosphere in disturbed and quiet geomagnetic conditions in 2011–2017. The analyzed period covers the years of growth, maximum and decline of the 24th cycle of solar activity. To study the geomagnetic field variations, data from the INTERMAGNET global network of magnetometers in the Northern hemisphere were used. In the analyzed time period 122 events of magnetic storms were identified: 102 moderate $(-100 < Dst < -50\ \mathrm{nT})$, 18 strong $(-200 < Dst < -100\ \mathrm{NT})$ and 2 severe $(Dst < -200)$. A statistical analysis of the distribution of magnetic storms by years and seasons was performed. New interesting results are obtained on the irregular structure of the longitudinal variability of the geomagnetic field components in quiet conditions due to the discrepancy between the Northern geographical and geomagnetic poles, as well as the presence of anomalies of different spatial scales in the background magnetic field of the Earth. The longitudinal-temporal variations of the geomagnetic field components under disturbed conditions also show a strong dependence on the individual features of the magnetic storms. On the case study of the development of two severe magnetic storms in 2015, the longitudinal-temporal variations of the parameters of the midlatitude ionosphere over the Eurasian continent were analyzed based on the analysis of the ionosonde chain data. The presence of longitudinal features of the background structure and variations of the geomagnetic field leads to the fact that the registered ionospheric effects also exhibit a significant longitudinal inhomogeneity. We assume that the storm-associated ionosphere dynamics is determined by the lower thermosphere disturbances due to the auroral electrojet amplifications producing the enhanced neutral wind and turbulences; these, in turn, uplift the molecular gas to the ionospheric heights. This process decreases the [O]/[N2] ratio and, consequently, the electron density.
A Theory and a Case Study on Regenerative Energy Compensation in Railway Systems

This paper presents a novel theory to evaluate the use of regenerative energy in traction systems by introducing the concept of regenerative compensation factor, which can be seen as analogous to the concepts of reactive energy and power factor in AC power systems. The proposed theory is applied to a regenerative energy case study in Metro de Medellin, using computational tools (OpenTrack and OpenPowerNet), and field measurements of the operation of two regenerative compensation prototypes. The results show that the proposed compensation factor provides technical guidelines on regenerative energy management, and is useful to identify potential energy savings, to determine a better use of installed railway facilities, and to propose improvements in conception, design and operation of an electric traction system.
A Realtime Q-Learning Method for Unmanned Surface Vehicle Target Tracking

Target tracking is a key problem of motion planning for unmanned surface vehicle (USV). This paper presented a Q-learning based method for real time USV target tracking planning in an uncertain environment. In this work, the improved Q-learning reinforcement learning algorithm was proposed. In additional, the Q-learning based motion planning model of USV was implemented, in which the continuous reinforcement strategy was designed to determine the training mode between learning and exploration period. Off-line and on-line real time simulation test was carried out to verify the learning ability and availability of the Q-learning algorithm. The result shows that this Q-learning based method can optimize a high rewarded path autonomously and navigate USV to fixed or moving targets in real time.
Trajectory and Communication Protocol for Efficient Data Collecting in UAV-Enabled WSN

A low power consumption and efficient communication scheme is crucial to boost the data collecting effectivity in a WSN (Wireless Sensor Network) with a large number of SNs (Sensor Nodes). To enhance the received signal power and avoid frequent signal collision, RADMA (Rotational Angle Division Multiple Access) has been proposed. In RADMA, a UAV-BS (Unmanned Aerial Vehicle-Base Station) equipped with ULA (Uniformly Linear Array antenna) is deployed as the mobile data collector, To improve the performance of RADMA when serving a larger area, it is required to design an effective trajectory to dynamically deploy the UAV-BS within the area. In this paper, a simple UAV-BS trajectory decision method based on sectorized K-means clustering is proposed to provide uniform QoS (Quality-of-Service) among all SNs. First, the area is sectorized with equal sector size. Then, the initial centroid coordinate of each cluster is randomly generated within each sector area to avoid biased centroids. Finally, the trajectory is formed by connecting the centroids of the SNs clusters. The numerical results have validated that combining RADMA with sectorized k-means clustering-based trajectory enables all SNs to transmit all the data within a predetermined mission time and with low transmission energy.
Evaluation of the Pressure-Flow Relationship in a Boom of an Autonomous Robotic Agricultural Sprayer

Agriculture has undergone changes in order to reduce the labor force and increase the productivity due to the technological advance. However, farmers argue that it is impossible to produce in medium or large-scale without agrochemicals. On the other hand, the need to reduce the amount of pesticides applied is evident, not just for the risk of overapplication fines, but also for potential savings. In this context, pesticide application has been revolutionized with the use of intelligent sprayers able to control even the places where the spraying is really necessary. Some manufacturers already sell solutions to meet these requirements, but the cost sometimes is so high as to make their use prohibitive in small production properties. Therefore, this work is part of a project started to develop low-cost solutions of precision agriculture, robotics, and autonomous systems accessible to smallholders and family farming. Thus, this paper presents the mathematical modeling to allow pressure and flow control along a boom sprayer with individual nozzle on-off control and serial feed. Laboratory characterizations and tests are performed on a seven nozzles boom, in various scenarios of opening and closing nozzles. Experimental results show that the pressure drop along the boom varied under extreme conditions from one to all open nozzles, affecting droplet size.
Faster R-CNN for Detection of Carotid Plaque on Ultrasound Images

Carotid plaque is an important indicator in clinical diagnosis to whether it causes cardiovascular and cerebrovascular diseases. Due to the complexity of the principle of US imaging, doctors are cumbersome and mechanical in identifying plaque hardening inside blood vessels, it takes doctors some time to identify carotid plaque. In this study, Inception network is used as a pre-training network to extract the feature map of the image, and the recommendation information of plaque or sclerosis is extracted by the RPN network (Region Proposal Network) in Faster R-CNN to realize the automated target detection of carotid US image plaque or sclerosis. The carotid US dataset (687 images) was made and the marker categories were divided into: plaque, sclerosis, psudomorph and normal, enabling endto-end training in convolutional neural networks, with the two category of psudomorph and normal is to make the model more fault tolerant. Adjust the parameters, configuration, and network structure of the network to analyze the plaque target detection performance. Finally, the Average Precision(AP) with carotid plaque on the carotid US test set was 91.09%, and the mean Average Precision(mAP) was 58.62%. The stability and detection rate of the model met certain application requirements, and the better automatic target detection effect could be achieved.
La prise en charge du patient d’âge pédiatrique victime d’un traumatisme crânien aigu

Resume Le traumatisme crânien responsable de lesions cerebrales est une cause importante de morbidite et de mortalite pendant l’enfance. La gravite de la lesion depend du mecanisme du traumatisme, qui varie lui-meme selon l’âge. Chez les patients d’âge pediatrique, la majorite des traumatismes crâniens sont legers, n’exigent pas de traitement particulier et ne laissent aucune sequelle. Cependant, il est important de determiner les personnes vulnerables aux lesions graves, qui ont donc besoin d’une evaluation particuliere. Le present document de principes vise a decrire les enjeux lies aux traumatismes crâniens chez les nourrissons, les enfants et les adolescents, y compris les manifestations cliniques, les priorites initiales de prise en charge, les directives en matiere d’imagerie ainsi que l’observation et le traitement subsequents. Il traite de l’evaluation des patients victimes d’un traumatisme crânien aigu au moment de l’evaluation initiale, mais ne decrit pas la prise en charge subsequente de ces patients a l’unite de soins intensifs pediatriques.
Design of Low Profile and Wideband End-Fire Antenna Using Metasurface

In this paper, a metasurfing (MS) concept is demonstrated and applied in the design of the low profile and wideband endfire antenna on metallic surface environments. The MS comprises an array of varying patch printed on homogeneous host medium, and fed by a surface wave launcher (SWL). Each row patch is designed according to the operating wavelength and by altering the surface reactance of the MS the surface-wave mode can be manipulated into the free-space wave mode. Meanwhile, two row of rectangle patches with same size are located on between the surface wave launcher (SWL) and non-uniform MS, which is regarded as an impedance modulation to obtain a good impedance matching. The VSWR of the proposed antenna is below 2.5 from 3.8 GHz to 16.7 GHz in the measured results, which are in good agreement with the simulated results and the thickness of proposed antenna is only 5 mm (<inline-formula> <tex-math notation="LaTeX">$0.065\lambda _{L}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\lambda _{L}$ </tex-math></inline-formula> is the free-space wavelength at the lowest operating frequency). Moreover, a stable end-fire beam and low side lobe level (SLL) is obtained in a wide frequency band, and the group delay and the time-domain result also are shown to prove the good wideband transmission.
Efficient False Alarm Probability Identification for Linear System with Uncertain Measurement

This paper focuses on quickly and analytically identifying the unknown or time-varying false alarm probability (FAP) of the measurements uncertainty or missing in the linear networked multi-sensor system by resorting to the efficient implementation of maximization likelihood (ML) estimation. Firstly, the full-probability likelihood computation is equivalently transformed into a log-likelihood function summation form parameterized by FAP through Bayes’ rule. Secondly, the computation of the log-likelihood function is further transferred by skillfully introducing Jessen’s inequality for facilitating the rapid and analytical maximization. Thirdly, the analytical identification result of FAP is obtained by constructing Lagrange operator to maximize the transferred log-likelihood with the parameter constraint. Naturally, such analytical result is so simple that it can be efficiently carried out, and has no precision loss for meeting the high performance. Finally, an example motivated by the target tracking application is presented to demonstrate the superiority of the new method.
Ascent Adaptive Guidance for Power System Fault of Heavy Lift Launch Vehicle

In response to future large payload launches, the increasing complexity of the power system leads to a higher probability of power system fault. For the power system fault in the ascent phase of the heavy lift launch vehicle, this paper presents a guidance method that can autonomously reconstruct the trajectory, where different methods are applied based on the power system fault moment. As to the exo-atmospheric fault, the orbit injection point adaptive updated iterative guidance method is adopted, where the argument of latitude is adjusted online to improve the accuracy of other five orbital elements. For the endo-atmospheric fault, the online trajectory optimization closed-loop guidance algorithm based on the indirect method is applied. The necessary conditions of the optimal control problem are re-derived based on the calculus of variations. To satisfy the real-time requirement, finite element method is used to transform the optimal control problem into root-finding problem of algebraic equations, which is finally solved through Newton iteration, by discretizing the necessary conditions. A series of cases for a two stage heavy lift launch vehicle are tested, with variations on the degree of the engine failures. The results showed that the proposed guidance algorithm effectively improve the reliability against power system faults. The impact of different degrees of failure on fuel consumption is analysed, which will be beneficial to heavy lift launch vehicle design for future missions.