Miracle-dz/UniFAR-FTUs huggingface dataset:
A Randomized Controlled Trial of Inhibitory Control Training for the Reduction of Alcohol Consumption in Problem Drinkers

Objective: We conducted a randomized controlled trial to compare the effects of three types of Internet-delivered Inhibitory Control Training (ICT) with each other and with an active control intervention on alcohol consumption in a community sample of problem drinkers. Method: Two hundred and 46 heavy drinkers, who were motivated to reduce their alcohol consumption (mean age 41.32, 130 female) self-monitored their alcohol consumption for 1 week before being randomized to receive 1 of 3 variants of ICT (Associative No-Go, Associative Stop Signal, General Inhibition) or an active control. Participants then completed up to 14 ICT/control sessions on the Internet over a 4-week period, while regularly recording their alcohol consumption. Results: There were significant reductions in alcohol consumption across all groups over the 4-week training period (main effect of time, F(2, 402) = 77.12, p < . 01, &eegr;p2 = . 28, BF10 > 99), however there were no differences between ICT groups, or between ICT groups and the active control group (Group × Time interaction, F(6, 402) = 1.10, p = . 36, &eegr;p2 = . 02, BF10 = 0.03). Contrary to hypotheses, there were no changes in general inhibitory control, the disinhibiting effects of alcohol cues, or alcohol affective associations after ICT. Conclusions: In this study, which attempted to translate findings from proof-of-concept laboratory studies into a viable behavior change intervention, we found that multiple sessions of ICT delivered over the Internet did not help heavy drinkers to reduce their alcohol consumption beyond nonspecific effects associated with taking part in a trial.
AlGaN/GaN High Electron Mobility Transistors on Semi-Insulating Ammono-GaN Substrates with Regrown Ohmic Contacts

AlGaN/GaN high electron mobility transistors on semi-insulating bulk ammonothermal GaN have been investigated. By application of regrown ohmic contacts, the problem with obtaining low resistance ohmic contacts to low-dislocation high electron mobility transistor (HEMT) structures was solved. The maximum output current was about 1 A/mm and contact resistances was in the range of 0.3–0.6 Ω·mm. Good microwave performance was obtained due to the absence of parasitic elements such as high access resistance.
FPGA Implementation of LSTM Based on Automatic Speech Recognition

Automatic speech recognition(ASR) remains very popular research area in recent years. Long Short-Term Memory(LSTM) is always used in speech recognition and demonstrated state-of-art accuracy. The high performance LSTM models is becoming increasing demanding in terms of computational and memory load. FPGA-based accelerators have attracted attention of researchers because of its high energy-efficiency and great flexibility. The main work of this paper is an efficient FPGA implementation of LSTM to accelerate the ASR algorithm. The proposed circuit is implemented at Zedboard 100 MHz. The amount of parameters are reduced more than 7 times because of compression method.
Graphical Models Over Heterogeneous Domains and for Multilevel Networks

We review models for analyzing multivariate data of mixed (heterogeneous) domains such as binary, categorical, ordinal, counts, continuous, and/or skewed continuous, and methods for modeling various graphs including multiplex, multilevel, and multilayer networks. Data are modeled with Markov random fields which encode Markov property between nodes: two nodes are not connected with an edge if and only if random variables associated with these nodes are conditionally independent, given the other variables. Inferring dependence structure through graphical models (both directed and undirected) is essential for discovering multivariate interaction among high-dimensional data, which could potentially be associated with several diseases. Networks are modeled with exponential random graph models which encode Markov property between edges: two edges are conditionally dependent, given the rest of the network, if they have a common vertex. Studying and understanding multilayer and/or multilevel representations of various phenomena, including social and natural phenomena, could lead to predictive models of these phenomena. Modeling data of heterogeneous domains and multilevel and/or multilayer networks pose challenges which are reviewed. Addressing these challenges within a unified framework stresses open problems and points out new directions for research.
Design of a Metamaterial Element in Deep Subwavelength Composed of Multilayer Meander Line (MML) Resonator Structures

An extremely compact metamaterial element is proposed with multilayer meander line (MML) resonator inclusions. The MML is featured with multiple single-planar meander line (SML) structures which are connected by via. The effective inductance in the new MML structure is thus multiplied leading to a reduced resonance frequency. A proof MML element is numerically demonstrated with period of 8mm. Very strong electric responses have been observed indicating the MML element is essentially a kind of electrically resonating structure. The proof MML element resonances at 237MHz in VHF band. The electrical period size is 1/158λ0 which is in deep subwavelength. By making the meander line thinner or incorporating more SML layers, the effective inductance can be further enhanced and the structure is more compact which is promising for radio frequency metamaterial designs in future.
A Neuronal Model of the 3D Head Position Based on a Wearable System

Nowadays, the wearable systems start to play an increasing and very important role in the development of many applications. Their ultimate design is intended to provide final users with small, independent, low-power, wearable and unobtrusive solutions for continuous all-day and in any-place monitoring and forecast systems. The main purpose of the current paper is to present the design details, the key technologies and the practical implementation methods used in the developing of a new proposed smart clothing system able to detect, in a non-contact mode, the individual rotation of a subject head: pitch-roll-yaw. Exactly, by using four LC-based capacitive sensors embedded in a shirt collar, the CC2650 SoC, the device-to-gateway communication concept to connect to an Internet cloud service, the Bluetooth low energy standard version 4.2 in order to connect to the gateway, the TI-RTOS as a consistent embedded software platform and a neuronal network approximation model, we succeeded to successfully develop and implement a low-power wearable system, named Intell.TieSens.
Learning Motion Policy for Mobile Robots Using Deep Q-Learning

We present a deep Q-network (DQN) that learns motion policy for multiple mobile robots. Although DQNs have shown huge successes in several domains, there are few applications to learning for multiple robots. We propose the several advantages of deep Q-learning for multi-robots and present our implementation on a physics-based simulator. We show that the robots learned by the proposed DQN can successfully navigate an unseen environment and escape a dead-lock situation.
Achieving Data Truthfulness and Privacy Preservation in Data Markets

As a significant business paradigm, many online information platforms have emerged to satisfy society's needs for person-specific data, where a service provider collects raw data from data contributors, and then offers value-added data services to data consumers. However, in the data trading layer, the data consumers face a pressing problem, i.e., how to verify whether the service provider has truthfully collected and processed data? Furthermore, the data contributors are usually unwilling to reveal their sensitive personal data and real identities to the data consumers. In this paper, we propose TPDM, which efficiently integrates Truthfulness and Privacy preservation in Data Markets. TPDM is structured internally in an Encrypt-then-Sign fashion, using partially homomorphic encryption and identity-based signature. It simultaneously facilitates batch verification, data processing, and outcome verification, while maintaining identity preservation and data confidentiality. We also instantiate TPDM with a profile matching service and a data distribution service, and extensively evaluate their performances on Yahoo! Music ratings dataset and 2009 RECS dataset, respectively. Our analysis and evaluation results reveal that TPDM achieves several desirable properties, while incurring low computation and communication overheads when supporting large-scale data markets.
Economic Analysis in a Diagnosis Related Groups System for Two-stage Exchange of Prosthetic-joint Infections

Background: There is a constant increase of joint arthroplasties performed, with an infectious risk of 1-2%. Different therapeutic options for prosthetic-joint infections exist, but surgery remains essential. With a two-stage exchange procedure, a success rate above 90% can be expected. Currently, there is no consensus regarding the optimal interval duration between explantation and reimplantation. This retrospective study aimed to assess the economic impact of a two-stage exchange from a single-hospital perspective. Methods: 21 patients who have undergone a two-stage exchange of a hip or knee prosthetic-joint infection at the University Hospital of Lausanne (Switzerland) from 2012 to 2013 were included. The revenues earned according to the Swiss Diagnosis Related Groups (SwissDRG) system introduced in 2012 and the costs were compared for each hospital stay. Results: The remuneration ranged from 26'806 to 42'978 Swiss francs (CHF) (~ 22'905-36'723 EUR, median 36'338 CHF, ~ 31'049 EUR). The median total cost per patient was 76'000 CHF (~ 65'000 EUR) (51'151 to 118'263; hip median 79'744, knee median 66'708). The main determinant of the costs was the length of the hospital stay. Revenues never covered all the costs, even with a short-interval procedure. The hospital lost a median of 35'000 CHF per patient (~ 30'000 EUR) (22'280 to 64'666). Conclusion: The current DRG system may not be specific enough for rewarding prosthetic-joint infections. Several options could be considered to act on the length of the hospital stay. In order to cover costs in complicated cases, such as prosthetic-joint infections, more specific DRGs are needed.
Concurrent Control for Three-Phase Four-Wire Five Levels E-Type Inverter for Microgrids

This paper deals with a concurrent control strategy for the three-phase four-wire five-level E-Type Inverter ($3\mathbf{\Phi} 5\mathbf{L}$ E-Type Inverter). The proposed control has a parallel architecture and it is analyzed, implemented and verified with reference to microgrids. Analysis and characteristics of the $3\mathbf{\Phi} 5\mathbf{L}$ E-Type Inverter are described. The proposed control strategy has been implemented using the repetitive controller plus the proportional-integral controller, which allow to adjust the output voltage and the output current in case of either short-circuit or overcurrent conditions. The control strategy has been tested using the cosimulation capabilities offered by National Instruments Multisim and LabVIEW FPGA in order to evaluate its effectiveness and performances.
RepassDroid: Automatic Detection of Android Malware Based on Essential Permissions and Semantic Features of Sensitive APIs

Most current literature on Android malware pays particular attention to the features of applications. Much of them focus on permissions or APIs, neglecting the behavioral semantics of applications, and the literature considering behavioral semantics is often expensive and weak in extendibility. In this paper, we introduce RepassDroid - a relatively coarse-grained but faster tool for automatic Android malware detection. We define Generalized-sensitive API and emphasize on considering if the trigger points of generalized-sensitive APIs are UI-related or not. It analyzes the application by abstracting the generalized sensitive API with its trigger point as the semantic feature, with the addition of Really-essential Permission as the syntax feature. Then it utilizes machine learning to automatically determine whether an application is benign or malicious. We evaluate RepassDroid on 24288 samples in total, 20000 for training and 4288 for test. With the comparative experiments, we find that Random Forest is the optimal classification technique for our feature set, achieving 97.7% accuracy and 0.99 AUC, along with a malware classification precision as high as 99.3%. Our evaluation results confirm that our approach and the feature set are logical and effective for Android malware detection.
Generalized Likelihood Ratio Detection Schemes for Forward Scatter Radar

This paper introduces innovative detection schemes for forward scatter radar (FSR) based on the generalized likelihood ratio test (GLRT) for both cases, where a fixed threshold can be used and where a fully adaptive CFAR scheme is desired. The detection performance of the newly proposed detectors is characterized analytically and compared to the performance of the standard detection scheme. This shows that the new detectors always outperform the standard FSR detector. In most cases the improvement has an upper bound of 3 dB, but there are specific cases where the standard FSR detector shows significant losses, while the new GLRT schemes suffer a much smaller degradation. Finally, simplified equivalent SNR expressions are introduced that relate the GLRT detection performance to the main parameters describing the FSR observation geometry and the target size and motion. These expressions are shown to be useful for the design of effective FSR geometries that guarantee desired detection performance for specific targets.
Concepts Game Theory Applied in Smart Grid

The smart grid is conceived as a large-scale system that includes advanced power, communications, control and information technologies. To adapt these technologies, we must work on mathematical tools and solid techniques that can ensure the exchange of information, an efficient operation, improving in many cases the technical losses of energy and raise their indexes of quality and reliability, making the network capable of to solve any problem especially in the management of demand, customer management, decision making, energy exchange, energy storage, contingency plans, geo-referenced information systems, among others. In this context, this article presents an overview of the potential of applying game theory to address smart electric networks, providing a systematic treatment.
Wideband Bandstop Filter With Extreme Sharp Skirt Selectivity

A novel and simple wideband bandstop filter with extreme sharp skirt selectivity is proposed. The wideband bandstop response with multi-transmission zeros and large skirt-band attenuation rate is realized based on the modified coupled-line sections with open-/short-circuited termination and two quarter-wavelength transmission lines. In order to simultaneously improve stopband rejection (SR) and passband return loss, the gray boxes with different circuit structures are introduced. For demonstration, a designed prototype operating at 1 GHz with 64% relative bandwidth of 17-dB SR is simulated, fabricated, and measured.
Exploiting Visual Contents in Posters and Still Frames for Movie Recommendation

Recommender systems, e.g., movie recommendation, play an important role in our life. However, few movie recommendation methods have considered the rich visual content information in posters and still frames, which can be used to alleviate the data sparsity and cold start problems in recommendation. Moreover, no existing paper has taken visual feature learning and recommendation into a unified optimization process. To this end, in this paper, we focus on how to use visual contents to improve the performance of movie recommendation and propose a novel movie recommendation model named unified visual contents matrix factorization (UVMF) that integrates visual feature extraction and recommendation into a unified framework. Specifically, we integrate convolutional neural network into probabilistic matrix factorization, and the model can be trained end-to-end. Moreover, we unfix weights in the last few layers of VGG16 to learn features and adapt them for the movie recommendation task. Finally, the experimental results on real-world data show that UVMF outperforms other benchmark methods in terms of recommendation accuracy.
On the Scaling of Consequent Pole Vernier Machines With Spoke Type Magnets

This paper thoroughly investigates the scalability problem of consequent pole Vernier machines having a spoke-type permanent magnet (PM) structure, with the aim to lay the basis of design of Vernier PM machines for various applications. The scalability analysis is carried out based on a generic analytical model, observations are obtained considering physics-based key design factors such as: machine geometries and dimensions, slot per pole per phase (SPP) value, magnet dimensions and associated induced air gap flux density. With the help of finite element analysis (FEA), the derived methodologies are verified with a consequent pole spoke type Vernier machine designed for low speed high torque direct drive application, comparisons are made between the analytical sizing equations and FEA calculations, the results suggest good agreement.
A Novel Differential Power Processing Architecture for a Partially Shaded PV String Using Distributed Control

This paper proposes a differential power processing (DPP) architecture applied to a series PV string which enables each PV element to operate at its local maximum power point (MPP) while processing only a small portion of its total generated power through the module integrated converters (MICs). The current processed through each converter is the difference between the local PV element MPP current and the local PV string current. This feature allows for a reduced current stress on the MIC components relative to the most common DPP topology. Thus, a higher system efficiency is realized at a reduced cost. A state space model of the proposed system is derived, and a comparison analysis is carried out with respect to the conventional DPP architectures. Additionally, a modular and compact design is proposed for a large number of PV panels in a series PV string. A hardware prototype is designed and built for 3 PV panels connected in series to validate the proposed architectures effectiveness and experimentally demonstrate its robustness. The modularity of the system is also tested to ensure low current and voltage stress on the MICs.
Analysis on Convergence of Stochastic Processes in Cloud Computing Models

On cloud computing systems consisting of task queuing and resource allocations, it is essential but hard to model and evaluate the global performance. In most of the models, researchers use a stochastic process or several stochastic processes to describe a real system. However, due to the absence of theoretical conclusions of any arbitrary stochastic processes, they approximate the complicated model into simple processes that have mathematical results, such as Markov processes. Our purpose is to give a universal method to deal with common stochastic processes as long as the processes can be expressed in the form of transition matrix. To achieve our purpose, we firstly prove several theorems about the convergence of stochastic matrices to figure out what kind of matrix-defined systems has steady states. Furthermore, we propose two strategies for measuring the rate of convergence which reflects how fast the system would come to its steady state. Finally, we give a method for reducing a stochastic matrix into smaller ones, and perform some experiments to illustrate our strategies in practice.
Velocity Profile Variability at a Tidal-Stream Energy Site (Aldemey Race, France): From Short (Second) to Yearly Time Scales

This paper deals with in situ measurements of waves and currents in Alderney Race. This site is one of the most energetic tidal site in Western Europe with current values up to 5 m.s−1. However, this area is facing open sea to the west and thus strong wind can generate significant wave height interplaying with the tidal currents, which is a critical point for the dimensioning of the tidal turbines. We present our field measurements and our original database. Then, we discuss the preliminary results about the interactions between tidal currents and ocean waves.
Matching the Free-Vibration Response of a Delta Robot with Pick-and-Place Tasks Using Multi-Body Simulation

Nowadays one of the key aspects in the study and design of industrial robots is the energy efficiency. These robots are not only expected to perform the task accurately and rapidly but also to minimize the energy consumption. One method that has shown high potential in reducing the energy consumption of pick-and-place robots is the addition of elastic elements and the use of the natural motion of the system. In this method, the system is designed such that its free-vibration response matches the task frequency and amplitude of oscillation. Consequently, after releasing the robot from the starting position, it moves towards the end position, where the end-effector stops at the given time for the task. In this paper, we propose a method for finding the appropriate springs and the resulting motion of the mechanism using multi-body simulation and a numerical optimization. This method can be applied to different robots and springs configurations, and requires no formulation of the equations of motion.
Compact 8-Port MIMO Antenna System for Dual-Band 5G Mobile Terminals

A compact eight-port multiple input multiple output (MIMO) antenna system operating in the C-band at the 3.5-GHz (3300–3600 MHz) band and the 5-GHz (4800–5000 MHz) band is proposed for future fifth-generation (5G) mobile broadband services. The proposed array is composed of four pairs of orthogonally dual-polarized monopole antenna elements, and each antenna pair is fed by two independent feeding structures to obtain dual-polarized radiation. Every antenna pair has a same structure which contains a longer monopole antenna and a shorter monopole antenna. The longer monopole covers the 3300–3600 MHz band and the shorter monopole covers the 4800–5000 MHz band. The antenna is simulated and optimized, good port parameters and radiating performances indicate that the proposed array is a good candidate for 5G.
Spatial Sequential Recurrent Neural Network for Hyperspectral Image Classification

In hyperspectral image processing, classification is one of the most popular research topics. In recent years, research progress made in deep-learning-based hierarchical feature extraction and classification has shown a great power in many applications. In this paper, we propose a novel local spatial sequential (LSS) method, which is used in a recurrent neural network (RNN). Using this model, we can extract local and semantic information for hyperspectral image classification. First, we extract low-level features from hyperspectral images, including texture and differential morphological profiles. Second, we combine the low-level features together and propose a method to construct the LSS features. Afterwards, we build an RNN and use the LSS features as the input to train the network for optimizing the system parameters. Finally, the high-level semantic features generated by the RNN is fed into a softmax layer for the final classification. In addition, a nonlocal spatial sequential method is presented for the recurrent neural network model (NLSS-RNN) to further enhance the classification performance. NLSS-RNN finds nonlocal similar structures to a given pixel and extracts corresponding LSS features, which not only preserve the local spatial information, but also integrate the information of nonlocal similar samples. The experimental results on three publicly accessible datasets show that our proposed method can obtain competitive performance compared with several state-of-the-art classifiers.