Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of the initial stage of the discharge in the tokamak T-15

Building T-15 tokamak highlights the problem of clarifying the discharge scenarios foreseen for this installation. An analysis of the transition to the divertor configuration (0.7-1.2 sec.) and the stationary phase was performed in the works [1-3]. It has been shown that in these stages the vertical plasma instability is suppressed by the feedback system, and that the system of magnetic diagnostics restores the plasma boundary to the specified accuracy if the sensors measurement error does not exceed 1-3 percent. Calculations have shown a good degree of compliance with the results obtained using the DINA and TOKSCEN codes that indicates the study reliability of these stages of the discharge. Thus, the most urgent task today is to study the initial stage of the discharge, from the current breakdown before the start of the process of forming the divertor configuration (approximately 0.7 sec.). The study means: 1) the clarification of the time dependence of the current magnitude in the coils of the poloidal magnetic system, 2) the comparison of the results of calculations on the various codes, 3) the clarification of the restoration accuracy of the plasma boundary in the initial stage of the discharge. The aim of the work was to conduct such an analysis. The calculations use numerical codes DINA [4], TOKSCEN [5] and RPB (restoration of the plasma boundary) [6]. The calculations could find a realistic scenario of an initial stage of the discharge. This work was supported by grant from Russian Scientific Foundation (project 14-22-00193).
Ultra-high sensitivity graphene photosensors

Graphene's advantages for sensor applications are offset by its low electric impedance and consequently low signal-to-noise ratio. We have improved the sensitivity of graphene based photosensors by integrating an energy filtering barrier as an amplifying element. The resulting graphene devices exhibit the highest reported photosensitivity (Ion/Ioff ∼ 4000, gain ∼ 10 A/W) while retaining broad band detection and high speed response. In addition to this unprecedented photosensitivity, the proposed device structure renders the complete graphene photosensitive which enables large scale and flexible photodetectors. The presented approach represents a universal route to enhancing the performance of graphene sensors.
ISAR Imaging of Multiple Targets Based on Particle Swarm Optimization and Hough Transform

This paper proposes an accurate method to obtain Inverse Synthetic Aperture Radar (ISAR) images of multiple targets. The existing method using the Hough transform and edge detection has a demerit that its fitting ability of flight trajectory is not sufficient if a range profile has high order terms. For better quality of ISAR imaging, we use particle swarm optimization (PSO) to find residual high order coefficients. Simulation results show that two moving targets are successfully separated from each other.
Prague at EPE 2017: The UDPipe System

We present our contribution to The First Shared Task on Extrinsic Parser Evaluation (EPE 2017). Our participant system, the UDPipe, is an open-source pipeline performing tokenization, morphological analysis, part-of-speech tagging, lemmatization and dependency parsing. It is trained in a language agnostic manner for 50 languages of the UD version 2. With a relatively limited amount of training data (200k tokens of English UD) and without any English specific tuning, the system achieves overall score 56.05, placing as the 7 participant system.
Using NLP Approach for Analyzing Customer Reviews

The Web considers one of the main sources of customer opinions and reviews which they are represented in two formats; structured data (numeric ratings) and unstructured data (textual comments). Millions of textual comments about goods and services are posted on the web by customers and every day thousands are added, make it a big challenge to read and understand them to make them a useful structured data for customers and decision makers. Sentiment analysis or Opinion mining is a popular technique for summarizing and analyzing those opinions and reviews. In this paper, we use natural language processing techniques to generate some rules to help us understand customer opinions and reviews (textual comments) written in the Arabic language for the purpose of understanding each one of them and then convert them to a structured data. We use adjectives as a key point to highlight important information in the text then we work around them to tag attributes that describe the subject of the reviews, and we associate them with their values (adjectives).
Linking Indigenous Knowledge and Observed Climate Change Studies

We present indigenous knowledge narratives and explore their connections to documented temperature and other climate changes and observed climate change impact studies. We then propose a framework for enhancing integration of these indigenous narratives of observed climate change with global assessments. Our aim is to contribute to the thoughtful and respectful integration of indigenous knowledge with scientific data and analysis, so that this rich body of knowledge can inform science, and so that indigenous and traditional peoples can use the tools and methods of science for the benefit of their communities if they choose to do so. Enhancing ways of understanding such connections are critical as the Intergovernmental Panel on Climate Change Fifth Assessment process gets underway.
Framework for Analyzing Twitter to Detect Community Suspicious Crime Activity

This research work discusses how an integrated open source intelligence framework can help the law enforcements and government entities who are investigating crimes based on statistical and graph analysis on Twitter data. The solution supports a real-time and off-line analysis of the tweets collections. The framework employs tools that support big data processing capabilities, to collect, process and analyze a huge amount of data. The outline solution supports content and textual based analysis, helping the investigators to dig into a person and the community linked to that person based on a tweet. Our solution supports an investigative processes composed of the following phases (i) find suspicious tweets and individuals based on hash tags analysis (ii) classify the user profile based on Twitter features (iii) identify influencers in the FOAF networks of the senders (iiii) analyze these influencers’ background and history to find hints of past or current criminal activity.
What Can This Robot Do? Learning Capability Models from Appearance and Experiments

As autonomous robots become increasingly multifunctional and adaptive, it becomes difficult to determine the extent of their capabilities, i.e. the tasks they can perform and their strengths and limitations at these tasks. A robot’s appearance can provide cues to its physical as well as cognitive capabilities. We present an algorithm that builds on these cues and learns models of a robot’s ability to perform different tasks through active experimentation. These models not only capture the robot’s inherent abilities but also incorporate the effect of relevant extrinsic factors on a robot’s performance. Our algorithm would find use as a tool for humans in determining ”What can this robot do? ”. We applied our algorithm in modelling a NAO and a Pepper robot at two different tasks. We first illustrate the advantages of our active experimentation approach over building models through passive observations. Next, we show the utility of such models in identifying scenarios a robot is well suited for in performing a task. Finally, we demonstrate the use of such models in a collaborative human-robot task.
Ranging Using WTR Method n NLOS Environment

Time reversal technique as an excellent channel equalization technology which can restrain multipath effectively to implementation time focusing and can improve the distribution of energy in space to implementation spatial focusing.This paper aim to improve the shortcomings with poo r accuracy of pos it io ning, a larg e amo unt of calculation,sensitive to the environment for the existing location method indoor.what make full use of strongly time resolution and spatiotemporal focusing characteristics of TR in UWB.From the perspective of collection multipath energy,which brings together time reversal theory,UWB technology TOA to research the effect factor of obstacles in NLOS environment.This paper introduce ranging process of windowed time reversal in indoor environment.With a large number of ranging experiment are carried out,analysed the sources of measurement error in NLOS environment and compared its effects.Simulation results show that the influence of obstacles on ranging.From large to small in turn as the length,width and position which lay the foundation for the next positon. Keywords-time reversal;spatiotemporal focusing;UWB;TOA;windowed time reversal.
Microhabitat Suitability and Niche Breadth of Common and Imperiled Atlantic Slope Freshwater Mussels

ABSTRACT Knowledge of the habitat suitability of freshwater mussels (family Unionidae) is necessary for effective decision making in conservation and management. We empirically measured microhabitat use for 10 unionid mussel species, including the U.S. federally endangered Alasmidonta heterodon, at 20 sites in the Tar River basin, North Carolina, USA. We also quantified habitat availability at each site, and calculated habitat suitability for each mussel species. The majority of available habitat across all sites consisted of shallow, slow-moving water with penetrable silt or sand substrate. Among species, mean water depth of occupied habitats ranged 0.23 – 0.54 m, mean bottom velocity ranged 0.001 – 0.055 m/s, average mean-column velocity ranged 0 – 0.055 m/s, and mean substrate penetrability ranged 0.11 – 11.67 on an index scale. The most commonly measured dominant substrate materials were silt, sand, very coarse sand, pea gravel, and coarse gravel. The most common cover types were coarse woody debris and fine woody debris. These findings revealed a relationship between the niche breadth and conservation status of four species. Federally endangered A. heterodon consistently showed a narrower suite of suitable microhabitats than the common mussel Elliptio complanata. The range of suitable habitat characteristics for Fusconaia masoni and Villosa constricta, listed as North Carolina (USA) state endangered and special concern, respectively, was typically narrower than those of E. complanata and wider than those of A. heterodon. These habitat suitability criteria and relationships will be useful to guide identification of suitable sites for habitat protection, mussel relocation, or site restoration.
Oligothiophene quinoids containing a benzo[c]thiophene unit for the stabilization of the quinoidal electronic structure

Bis(dicyanomethylene)-substituted quinoidal π-conjugated systems possess a high electron-accepting nature and thus have been extensively investigated for application as n-type semiconductors. We focus on the utilization of benzene-annelation for the stabilization of the quinoidal electronic structure against the biradicaloid structure by designing quinoidal thiophenes 3-mer (BTQ) and 6-mer (BTQ-6) that have benzo[c]thiophene units. We also develop quinoidal oligothiophenes (BTQ-F) consisting of both benzo[c]thiophene and fluorinated thiophene units. The influence of benzo[c]thiophene on the quinoidal electronic structure is investigated by theoretical studies and property measurements. The molecular structure of BTQ-F is unambiguously confirmed through single-crystal X-ray diffraction. Analyses of cyclic voltammetry reveal that the lowest unoccupied molecular orbital energy levels of these compounds lie below −4.0 eV, leading to good electron-transporting characteristics even under ambient conditions in organic field-effect transistors (OFETs). Due to an increased highest occupied molecular orbital energy level, ambipolar transport is observed in BTQ-6, indicating the versatility of quinoidal π-conjugated systems incorporating benzo[c]thiophene.
Design and Analysis of DKRINGA Protocol for Location Privacy in Trusted Environment

Originally K-anonymity principle was first used in relational databases to tackle the problem of data anonymity. In earlier protection techniques K threshold is used as personalization factor for mobile users. In case, K users are not present around needy client mobile user, query can be delayed and thus it will not help to achieve the Quality of service parameter. Moreover, authors have adopted methodology that if K-1 additional travelling users or queries are not seen by needy users, dummies are populated in the environment to improve the quality of service. Earlier architectures shows poor usage of K-principle, cryptography and cloaking space, which leads to threat during communication, more communication cost, more computation cost. We present here enhanced privacy model in a trustworthy third party privacy context that employs the notion of K-anonymity. In this work, enhanced algorithms are introduced, that guarantees a success of Location Based Services (LBS) query replies coming back to mobile client. Client sends the query to the anonymization server (AS), where this server cloaks the users with other at least K users. Our novelty in the experiment is that we have introduced cryptography from client to AS, modified earlier algorithms for Ring-Band approach, smart location updates and simulated the scaled experiment in populated cities environment. The AS add the dummies but creates ring-band cloaking area and sends it to LBS server. Cryptography adds some time however ring-band approach reduces communication overhead. We have studied the performance with variation of different parameters. The response from LBS comes to AS with Point of Interests (POIs) along the ring-band. After which AS filters for precise POIs and sends reply to mobile client. With ring-band approach we may also skip the AS and have client to LBS approach directly but without identity protection.
Multi Objective Load Shedding Framework

In this paper, a multi-objective load shedding framework on the power system is presented. The frame work is useable in any kind of smart power systems; the word of smart here refers to the availability of data transmission infrastructure (like PLC or power line carrier) in the system, in order to carry the system data to the load shedding framework. This is an open framework that means it can optimize load shedding problem by considering unlimited number of objective functions, in other word, the number of objectives can be as much as the operator decides, finally in the end of frame work just one matrix breaker state is chosen in a way of having the most compatibility with the operator ideas which are determined by objectives importance percentage which are one input groups of the framework. A two-stage methodology is used for the optimal load shedding problem. In the first stage, Discrete Multi-objective Particle Swarm Optimization method is used to find a collection of the best states of load shedding (Pareto front). In the second stage, the fuzzy logic is used as a Pareto front inference engine. Fuzzy selection algorithm (FSA) is designed in a way that it can infer according to the operator’s opinion without the expert interference that means rule base is formed automatically by fuzzy algorithm. FSA is consisted of two parts. Membership functions and rules base are formed automatically in the first part, the former in accordance with the costs of Pareto front particles and the latter in correspondence with importance percentage of objectives which are entered to FSA by operator; in other word, decision matrix is formed automatically in the algorithm according to the cost of Pareto front particles and importance percentage of objectives. In the Second part, Mamdani inference engine scrutinizes the Pareto front particles by the use of formed membership functions and rules base to know if they are compatible to operator’s opinion or not. Getting this approach, cost functions of each particle are considered as the inputs of (FSA), then a fuzzy combined fitness (FCF) is allocated to each Pareto front particle by Mamdani inference engine. In other word, FCF shows how much the particle is compatible to the operator’s opinion. Finding minimum FCF, final inference is done. The proposed method is tested on 30-bus, and 118-bus IEEE systems by considering two or three objective functions and the results are presented.
Alcohol Withdrawal Syndrome in Trauma Patients: A Prospective Cohort Study

Introduction: Trauma patients with a positive blood alcohol concentration (BAC) are often believed to be at high risk for the alcohol withdrawal syndrome (AWS). Therefore some centers prophylaxis all BAC positive patients. This study prospectively measures the incidence of AWS among trauma patients admitted to the hospital who have consumed alcohol and determines their risk factors for AWS. Methods: A cohort of trauma patients admitted to a non-ICU hospital setting was prospectively monitored for the development of AWS during the first 10 days of hospitalization. The 10-item Alcohol Use Disorders Identification Test (AUDIT) and questions about alcohol withdrawal history were administered on the first day and the revised Clinical Institute for Withdrawal of Alcohol Scale (CIWA-Ar) was administered daily. Results: 113 patients were followed through discharge or for the first 10 days of hospitalization. 74.3% (n = 84) reported drinking alcohol. Of the 89 patients with a measured BAC, 25 (28%) were positive. Mean BAC for positive patients was 187.7 mg/dl. No person who denied drinking had a measurable BAC or developed AWS. Among the 84 drinkers, 3 were diagnosed with AWS by CIWA-Ar (3.6% risk), giving an incidence rate of 1.4 episodes per 100 patient days. All patients developing AWS admitted to a previous history of AWS symptoms upon stopping drinking. All AWS patients drank at least 2-3 times per week compared to only 37% of drinkers who did not develop AWS (p = .05). Positive response to dependence items from the AUDIT were highly associated with AWS risk (67% AWS vs 16% non-AWS, p = .005). Implementation of a prophylaxis protocol for all positive BAC would have resulted in 88% (22/25) of BAC positive patients receiving unwarranted medication. Conclusion: AWS has a low incidence rate among intoxicated trauma patients admitted to a non-ICU setting. It is associated with frequent drinking and is found in patients who report dependence symptoms. Patients can reliably tell physicians whether they are at risk for AWS. Routine prophylaxis for positive BAC patients will likely result in substantial excess medication use.
Playing Location-based Games on Geographically Distributed Game Boards

Virtually all multiplayer location-based games require the players to meet in the same geographic place during the same period of time. As a consequence, significant organizational effort is associated with setting up a game event. In this paper we show how players who are located in different places can nevertheless play a location-based game together. We discuss which characteristics make a multiplayer location-based game concept well-suited for spatially distributed gaming. Playing in two different places implies playing in heterogeneous worlds. We show how a simple measure can be used to determine the similarity of the static structure of real world game boards. With a case study of the distributed location-based Geogame CityPoker played in two cities separated by 500 km distance we demonstrate the feasibility of distributed location-based gaming. Finally, we discuss the requirements for an online match-making platform where players from all over the world can meet to play together.
TWO TIME SCALING METHODS

The two-source energy balance (TSEB) model calculates the energy balance of the soil-canopy-atmosphere continuum, where transpiration is initially determined by the Priestley-Taylor equation. The TSEB was revised recently using the Penman-Monteith equation to replace the Priestley-Taylor formulation, thus better accounting for the impact of large and varying vapor pressure deficits (VPD) typical of advective, semiarid climates. This study is a comparison of the PriestleyTaylor and Penman-Monteith versions of the TSEB (termed TSEB-PT and TSEB-PM, respectively). Evaporation (E), transpiration (T), and evapotranspiration (ET) calculated by the TSEB-PT and TSEB-PM versions were compared to measurements obtained with microlysimeters, sap flow gauges, and weighing lysimeters, respectively, for fully irrigated cotton (Gossypium hirsutum L.) at Bushland, Texas. Radiometric surface temperature (TR) was used to calculate E, T, and ET in both TSEB versions in 15 min intervals and summed to intervals coinciding with times of measurements. In addition, a one-time-of-day TR measurement was used (9:45, 11:15, 12:45, 14:15, or 15:45 CST), and E, T, and ET were calculated for the appropriate measurement interval (i.e., daytime, nighttime, and 24 h) using the time scaling methods based on reference ET (TSCET) and reference temperature (TSCTEMP). Measured average values of E, T, and ET during the study period were 0.94 mm (24 h), 6.9 mm (7:00 to 22:00 CST), and 7.2 mm (24 h), respectively. The TSEB-PT consistently overestimated E and underestimated T, with RMSE/MBE of up to 2.8/1.8 mm and 4.1/-3.9 mm, respectively. In comparison, the TSEB-PM greatly reduced discrepancies between calculations and measurements, with respective RMSE/MBE for E and T of only up to 1.5/0.79 mm and 1.3/±0.76 mm, respectively. For 24 h ET, the TSEB-PT resulted in maximum RMSE/MBE of 3.2/-1.9 mm, and the TSEB-PM had maximum RMSE/MBE of 1.7/0.95 mm. Daytime ET model agreement was very similar for both model versions (RMSE/MBE usually <1.1/<±1.0 mm). However, the TSEB-PT consistently calculated negative nighttime ET of up to 2.0 mm. Summed 15 min calculations generally had better agreement with measurements than did the TSCET or TSCTEMP methods, and results did not greatly differ for TSCET or TSCTEMP. Both time scaling methods were not very sensitive to the TR measurement time used, although morning (9:45 CST) TR measurement times did not perform as well as the other times.
STRONG CONVERGENCE THEOREMS FOR COMMUTATIVE FAMILIES OF LINEAR CONTRACTIVE OPERATORS IN BANACH SPACES

In this paper, we study nonlinear analytic methods for linear contractive operators in Banach spaces. Using these results, we obtain some new strong convergence theorems for commutative families of linear contractive operators in Banach spaces. In the results, the limit points are characterized by sunny generalized nonexpansive retractions.