nils-herrmann/clean_scirepeval huggingface dataset:
Metamaterial electromagnetic wave absorbers.

The advent of negative index materials has spawned extensive research into metamaterials over the past decade. Metamaterials are attractive not only for their exotic electromagnetic properties, but also their promise for applications. A particular branch-the metamaterial perfect absorber (MPA)-has garnered interest due to the fact that it can achieve unity absorptivity of electromagnetic waves. Since its first experimental demonstration in 2008, the MPA has progressed significantly with designs shown across the electromagnetic spectrum, from microwave to optical. In this Progress Report we give an overview of the field and discuss a selection of examples and related applications. The ability of the MPA to exhibit extreme performance flexibility will be discussed and the theory underlying their operation and limitations will be established. Insight is given into what we can expect from this rapidly expanding field and future challenges will be addressed.
An Explanatory Analysis of the Geometry of Latent Variables Learned by Variational Auto-Encoders

Variational AutoEncoders are generative models, consisting of two cascading networks: the recognition network and the generative network. Under the framework of variational inference, the original training algorithms of VAEs optimize a lowerbound on the log-likelihood, derived using the Kullback-Leibler divergence. More recent literature focused on improving the log-likelihood using alternative bounds, such as the ones derived from the Rényi divergence and their reformulations in terms of importance sampling. A thorough description of the influence of such bounds on the quality of the latent representation is lacking. Defining what makes a given latent representation better than another is not trivial. Learning adequate such descriptions represents one of the main determinants of the performance of VAEs. Representations in the latent space are reportedly distributed in a coherent way and the sub-manifold of observations appear to be mapped into an affine space. However, the explicit choice of the prior over the latent space remains the only known element in the construction of the geometry of this space. By means of an explanatory analysis, in our work-in-progress paper, we investigate the factors that shape the geometry of the latent space of VAEs. We evaluate the impact of different structural parameters of the model and that of the cost function optimized during training.
Vehicle Localization along a Previously Driven Route Using Image Database

In most autonomous driving applications, such as parking and commuting, a vehicle follows a previously taken route, or almost the same route. In this paper, we propose a method to localize a vehicle along a previously driven route using images. The proposed method consists of two stages: offline creation of a database, and online localization. In the offline stage, a database is created from images that are captured when the vehicle drives a route for the first time. The database consists of images, 3D positions of feature points estimated by structure-from-motion, and a topological graph. In the online stage, the method first identifies the database image that is most similar to the current image by topometric localization, which considers topological information on a metric scale. The vehicle poses are then estimated from the 3D-2D correspondences of matching feature points between the current image and the identified database image. In an experiment, we estimated vehicle poses using images captured in an indoor parking lot.
A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users

Spatial item recommendation has become an important means to help people discover interesting locations, especially when people pay a visit to unfamiliar regions. Some current researches are focusing on modelling individual and collective geographical preferences for spatial item recommendation based on users' check-in records, but they fail to explore the phenomenon of user interest drift across geographical regions, i.e., users would show different interests when they travel to different regions. Besides, they ignore the influence of public comments for subsequent users' check-in behaviors. Specifically, it is intuitive that users would refuse to check in to a spatial item whose historical reviews seem negative overall, even though it might fit their interests. Therefore, it is necessary to recommend the right item to the right user at the right location. In this paper, we propose a latent probabilistic generative model called LSARS to mimic the decision-making process of users' check-in activities both in home-town and out-of-town scenarios by adapting to user interest drift and crowd sentiments, which can learn location-aware and sentiment-aware individual interests from the contents of spatial items and user reviews. Due to the sparsity of user activities in out-of-town regions, LSARS is further designed to incorporate the public preferences learned from local users' check-in behaviors. Finally, we deploy LSARS into two practical application scenes: spatial item recommendation and target user discovery. Extensive experiments on two large-scale location-based social networks (LBSNs) datasets show that LSARS achieves better performance than existing state-of-the-art methods.
Design of a Wideband Waveguide Slot Array Antenna and its Decoupling Method for Synthetic Aperture Radar

For the synthetic aperture radar systems, there is an increasing demand for achieving resolutions of 0.2 mtimes0.2 m. As the range resolution and system bandwidth are inversely proportional, the system bandwidth is expected to be greater than 1 GHz. Thus an antenna with wider band needs to be developed. Waveguide slot antennas have been implemented on several SAR satellites due to its inherent advantages such as high efficiency and power handling capacity, but its bandwidth is quite limited. To avoid the manufacture difficulties of the ridge waveguide, which is capable of broadening the bandwidth of slot antennas, a novel antenna element with conventional waveguide is designed. The bandwidth of VSWR les1.5 is greater than 1 GHz at X-band. To reduce the mutual coupling of closely placed antenna elements, a decoupling method with cavity-like walls inserted between adjacent elements is adopted and their effects on the performance of the antenna are summarized.
Introduction Welcome to this early seminal work , Fostering Resiliency in Kids : Protective Factors in the Family , School and Community

The field of prevention, both research and practice, came a long way in the1980s: from short-term, even one-shot, individual-focused interventions in the school classroom to a growing awareness and beginning implementation of long-term, comprehensive, environmental-focused interventions expanding beyond the school to include the community. Furthermore, in the mid-1980s we finally started to hear preventionists talking about prevention strategies and programs based on research identifying the underlying risk factors for problems such as alcohol and other drug abuse, teen pregnancy, delinquency and gangs, and dropping out (Hawkins, Lishner, and Catalano, 1985). While certainly a giant step in the right direction, the identification of risks does not necessarily provide us with a clear sense of just what strategies we need to implement to reduce the risks. More recently, we are hearing preventionists talk about “protective factors,” about building “resiliency” in youth, about basing our strategies on what research hast old us about the environmental factors that facilitate the development of youth who do not get involved in life-compromising problems (Benard, March 1987). What clearly becomes the challenge for the l990s is the implementation of prevention strategies that strengthen protective factors in our families, schools, an communities. As Gibbs and Bennett (1990) conceptualize the process, we must”turn the situation around by translating negative risk factors into positive action strategies” which are, in essence, protective factors. After a brief overview of the protective factor research phenomenon, this paper will discuss the major protective factors that research has identified as contributing to the development of resiliency in youth and the implications of this for building effective prevention programs.
An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems

Matrix-factorization (MF)-based approaches prove to be highly accurate and scalable in addressing collaborative filtering (CF) problems. During the MF process, the non-negativity, which ensures good representativeness of the learnt model, is critically important. However, current non-negative MF (NMF) models are mostly designed for problems in computer vision, while CF problems differ from them due to their extreme sparsity of the target rating-matrix. Currently available NMF-based CF models are based on matrix manipulation and lack practicability for industrial use. In this work, we focus on developing an NMF-based CF model with a single-element-based approach. The idea is to investigate the non-negative update process depending on each involved feature rather than on the whole feature matrices. With the non-negative single-element-based update rules, we subsequently integrate the Tikhonov regularizing terms, and propose the regularized single-element-based NMF (RSNMF) model. RSNMF is especially suitable for solving CF problems subject to the constraint of non-negativity. The experiments on large industrial datasets show high accuracy and low-computational complexity achieved by RSNMF.
Detecting Malware for Android Platform: An SVM-Based Approach

In recent years, Android has become one of the most popular mobile operating systems because of numerous mobile applications (apps) it provides. However, the malicious Android applications (malware) downloaded from third-party markets have significantly threatened users' security and privacy, and most of them remain undetected due to the lack of efficient and accurate malware detection techniques. In this paper, we study a malware detection scheme for Android platform using an SVM-based approach, which integrates both risky permission combinations and vulnerable API calls and use them as features in the SVM algorithm. To validate the performance of the proposed approach, extensive experiments have been conducted, which show that the proposed malware detection scheme is able to identify malicious Android applications effectively and efficiently.
Neurodegenerative Diseases Monitoring ( NDM ) main Challenges , Tendencies , and Enabling Big Data Technologies : A Survey

Evidence-based health monitoring has been recognized in the past few years as a very prominent solution to cope with continuous monitoring of chronic diseases such as neurodegenerative diseases for example: epilepsy. This has reduced the burden and cost for healthcare agencies and has led to efficient disease tracking, diagnosis, and intervention. Monitoring these diseases requires a long and continuous EEG signals recordings, pre-processing, analytics, and visualization. This will require a comprehensive solution to handle the complexity and time sensitivity of continuous monitoring. Several neurodegenerative disease monitoring (NDM) architectures have been proposed in the literature, however, they diverge on different aspects, such as the way they handle the monitoring processes, and the techniques they used to process, classify, and analyze the data. In this paper, we aim to bridge the gap between these existing NDM solutions. We provide first an overview of a standard NDM system, its main components, and requirements. We then survey and classify the exiting NDM solutions features, and characteristics. Afterwards, we provide a thorough evaluation of existing NDM solutions and we discuss the remaining key research challenges that have to be addressed. Finally, we propose and describe a generic NDM framework incorporating new technologies mainly the Cloud and Big Data to efficiently handle data intensive related processes. We aim by this work to serve researchers in this filed with useful information on NDM and provide direction for future research advancements. KeywordsNeurodegenerative Diseases Monitoring, Brain Informatics, Neuro Informatics, Big data, EEG, Seizure Detection
Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer

Radiomics characterizes tumor phenotypes by extracting large numbers of quantitative features from radiological images. Radiomic features have been shown to provide prognostic value in predicting clinical outcomes in several studies. However, several challenges including feature redundancy, unbalanced data, and small sample sizes have led to relatively low predictive accuracy. In this study, we explore different strategies for overcoming these challenges and improving predictive performance of radiomics-based prognosis for non-small cell lung cancer (NSCLC). CT images of 112 patients (mean age 75 years) with NSCLC who underwent stereotactic body radiotherapy were used to predict recurrence, death, and recurrence-free survival using a comprehensive radiomics analysis. Different feature selection and predictive modeling techniques were used to determine the optimal configuration of prognosis analysis. To address feature redundancy, comprehensive analysis indicated that Random Forest models and Principal Component Analysis were optimum predictive modeling and feature selection methods, respectively, for achieving high prognosis performance. To address unbalanced data, Synthetic Minority Over-sampling technique was found to significantly increase predictive accuracy. A full analysis of variance showed that data endpoints, feature selection techniques, and classifiers were significant factors in affecting predictive accuracy, suggesting that these factors must be investigated when building radiomics-based predictive models for cancer prognosis.
Ant supervised by PSO and 2-Opt algorithm, AS-PSO-2Opt, applied to Traveling Salesman Problem

AS-PSO-2Opt is a new enhancement of the AS-PSO method. In the classical AS-PSO, the Ant heuristic is used to optimize the tour length of a Traveling Salesman Problem, TSP, and PSO is applied to optimize three parameters of ACO, (α, β, ρ). The AS-PSO-2Opt consider a post processing resuming path redundancy, helping to improve local solutions and to decrease the probability of falling in local minimum. Applied to TSP, the method allowed retrieving a valuable path solution and a set of fitted parameters for ACO. The performance of the AS-PSO-2Opt is tested on nine different TSP test benches. Experimental results based on a statistical analysis showed that the new proposal performs better than key state of art methods using Genetic algorithm, Neural Network and ACO algorithm. The AS-PSO-2Opt performs better than close related methods such as PSO-ACO-3Opt  and ACO with ABC [19] for various test benches.
Artificial emotion expression for a robot by dynamic color change

We propose a novel method of expressing emotions for a robot by dynamically changing the color luminosity of its body. To identify color and dynamic parameters for representing emotions, we asked subjects to select hue values and frequencies and waveforms of blinking to represent the 24 emotions of Plutchik's Wheel of Emotions. The results indicate that the hue value represents basic types of emotion, i.e., anger-red, anticipation-amber, joy-yellow, trust-green, fear-violet, surprise-red, sadness-blue, and disgust-purple, and the duration and waveform represent emotion intensity, i.e., a rect-angular waveform with high frequency represents intense emotion. Another experiment validated that emotions of a robot expressed using our model impressed other subjects appropriately.
Daylight Design of Office Buildings : Optimisation of External Solar Shadings by Using Combined Simulation Methods

Integrating daylight and energy performance with optimization into the design process has always been a challenge for designers. Most of the building environmental performance simulation tools require a considerable amount of time and iterations for achieving accurate results. Moreover the combination of daylight and energy performances has always been an issue, as different software packages are needed to perform detailed calculations. A simplified method to overcome both issues using recent advances in software integration is explored here. As a case study; the optimization of external shadings in a typical office space in Australia is presented. Results are compared against common solutions adopted as industry standard practices. Visual comfort and energy efficiency are analysed in an integrated approach. The DIVA (Design, Iterate, Validate and Adapt) plug-in for Rhinoceros/Grasshopper software is used as the main tool, given its ability to effectively calculate daylight metrics (using the Radiance/Daysim engine) and energy consumption (using the EnergyPlus engine). The optimization process is carried out parametrically controlling the shadings’ geometries. Genetic Algorithms (GA) embedded in the evolutionary solver Galapagos are adopted in order to achieve close to optimum results by controlling iteration parameters. The optimized result, in comparison with conventional design techniques, reveals significant enhancement of comfort levels and energy efficiency. Benefits and drawbacks of the proposed strategy are then discussed. OPEN ACCESS Buildings 2015, 5 561
High efficiency voltage-clamped coupled-inductor boost converter

In this paper, a low voltage stress and high efficiency voltage-clamped coupled-inductor boost converter is proposed. The conventional coupled-inductor boost converter has a serious drawback of high voltage stresses across all power semiconductors due to the high surge voltage caused by the resonance between leakage inductor of coupled-inductor and stray capacitors. Therefore, the conventional converter must be equipped with the dissipative snubber for absorbing this surge voltage, which will degrade the overall power conversion efficiency. To overcome these drawbacks, the proposed converter employs the diode-clamping circuit instead of the dissipative snubber. Proposed clamping circuit can clamp voltages across the power switch and output diode on the link capacitor voltage and output voltage, respectively. In addition, the leakage inductor energy is stored in the link capacitor, and then transferred to the output side together with the input energy. Therefore, the proposed converter can achieve the higher efficiency than the conventional coupled-inductor boost converter. The proposed converter is expected to be well suited to various applications demanding the high efficiency and high-voltage gain. To confirm the validity of the proposed circuit, the theoretical analysis and experimental results of the proposed converter are presented.
Event Linking with Sentential Features from Convolutional Neural Networks

Coreference resolution for event mentions enables extraction systems to process document-level information. Current systems in this area base their decisions on rich semantic features from various knowledge bases, thus restricting them to domains where such external sources are available. We propose a model for this task which does not rely on such features but instead utilizes sentential features coming from convolutional neural networks. Two such networks first process coreference candidates and their respective context, thereby generating latent-feature representations which are tuned towards event aspects relevant for a linking decision. These representations are augmented with lexicallevel and pairwise features, and serve as input to a trainable similarity function producing a coreference score. Our model achieves state-of-the-art performance on two datasets, one of which is publicly available. An error analysis points out directions for further research.
Finger Vein Recognition With Anatomy Structure Analysis

Finger vein recognition has received a lot of attention recently and is viewed as a promising biometric trait. In related methods, vein pattern-based methods explore intrinsic finger vein recognition, but their performance remains unsatisfactory owing to defective vein networks and weak matching. One important reason may be the neglect of deep analysis of the vein anatomy structure. By comprehensively exploring the anatomy structure and imaging characteristic of vein patterns, this paper proposes a novel finger vein recognition framework, including an anatomy structure analysis-based vein extraction algorithm and an integration matching strategy. Specifically, the vein pattern is extracted from the orientation map-guided curvature based on the valley- or half valley-shaped cross-sectional profile. In addition, the extracted vein pattern is further thinned and refined to obtain a reliable vein network. In addition to the vein network, the relatively clear vein branches in the image are mined from the vein pattern, referred to as the vein backbone. In matching, the vein backbone is used in vein network calibration to overcome finger displacements. The similarity of two calibrated vein networks is measured by the proposed elastic matching and further recomputed by integrating the overlap degree of corresponding vein backbones. Extensive experiments on two public finger vein databases verify the effectiveness of the proposed framework.
Monostatic and bistatic radar measurements of birds and micro-drone

This paper analyses the experimental results from recent monostatic and bistatic radar measurements of multiple birds as well as a quadcopter micro-drone. The radar system deployed for these measurements was the UCL developed NetRAD system. The aim of this work is to evaluate the key differences observed by a radar system between different birds and a micro-drone. Measurements are presented from simultaneous monostatic co/cross polarized data as well as co-polar bistatic data. The results obtained show comparable signature within the time domain and a marked difference in the Doppler domain, from the various birds in comparison to the micro-drone. The wing beat properties of the birds are shown for some cases which is a stark contrast to the rotor blade micro-Doppler signatures of the drone.