nils-herrmann/clean_scirepeval huggingface dataset:
Silicon carbide pinched barrier rectifier (PBR)

In this paper, a new rectifier structure in silicon carbide (SiC) is presented for the first time. The proposed structure involves neither Schottky contact nor minor carrier injection via P-N junction. With adjacent P+ areas placed sufficiently close, pinched barrier is formed for rectifier purpose. Numerical simulations are carried out to verify its function, and optimize its performance. Based on the simulation results, a good trade-off can be achieved between forward drop and reverse leakage, provided that this new structure offers flexibility of controlling onset voltage by adjusting channel parameters continuously.
Real-Time Procedural Generation of Personalized Facade and Interior Appearances Based on Semantics

This article presents a computational model for procedural generation of customized façade and interior styles of buildings for use in three-dimensional virtual environments of games and simulations. The model makes use of two types of input information: geometric and semantic. The geometric information is related to the two-dimensional floor plan of a building, with its parts and dimensions, as well as positions for doors and windows. The semantic information enables the creation of architectural styles, enabling variations for materials and textures for the façade and inner parts, as well as for the shapes and dimensions of doors and windows. Changing one or more input parameters modifies the final appearance of the result. The proposed computational model can be used to generate large virtual environments, as it allows mixing different floor plans and architectural styles to achieve visual diversity. The main characteristics of the work are the realtime procedural generation of 3D buildings, the customization of façades and building interiors, as well as the use of semantic to assign meaning to the different elements of the house.
Seating arrangements that promote positive academic and behavioural outcomes : a review of empirical research

Seating arrangements are important classroom setting events because they have the potential to help prevent problem behaviours that decrease student attention and diminish available instructional time. The purpose of this synthesis of empirical literature is to determine which arrangements of desks best facilitate positive academic and behavioural outcomes for primary through secondary high school students with a range of characteristics. Eight studies that investigated at least two of three common arrangements (i.e., rows, groups or semi-circles) were considered. Results indicate that teachers should let the nature of the task dictate seating arrangements. Evidence supports the idea that students display higher levels of appropriate behaviour during individual tasks when they are seated in rows, with disruptive students benefiting the most.
The microbiome-gut-brain axis: from bowel to behavior.

The ability of gut microbiota to communicate with the brain and thus modulate behavior is emerging as an exciting concept in health and disease. The enteric microbiota interacts with the host to form essential relationships that govern homeostasis. Despite the unique enteric bacterial fingerprint of each individual, there appears to be a certain balance that confers health benefits. It is, therefore, reasonable to note that a decrease in the desirable gastrointestinal bacteria will lead to deterioration in gastrointestinal, neuroendocrine or immune relationships and ultimately disease. Therefore, studies focusing on the impact of enteric microbiota on the host and in particular on the central nervous system are essential to our understanding of the influence of this system. Recent studies published in this Journal demonstrate that germ-free mice display alterations in stress-responsivity, central neurochemistry and behavior indicative of a reduction in anxiety in comparison to conventional mice. Such data offer the enticing proposition that specific modulation of the enteric microbiota may be a useful strategy for stress-related disorders and for modulating the co-morbid aspects of gastrointestinal disorders such as irritable bowel syndrome and inflammatory bowel disease.
Real-time Joint Tracking of a Hand Manipulating an Object from RGB-D Input

Real-time simultaneous tracking of hands manipulating and interacting with external objects has many potential applications in augmented reality, tangible computing, and wearable computing. However, due to difficult occlusions, fast motions, and uniform hand appearance, jointly tracking hand and object pose is more challenging than tracking either of the two separately. Many previous approaches resort to complex multi-camera setups to remedy the occlusion problem and often employ expensive segmentation and optimization steps which makes real-time tracking impossible. In this paper, we propose a real-time solution that uses a single commodity RGB-D camera. The core of our approach is a 3D articulated Gaussian mixture alignment strategy tailored to handobject tracking that allows fast pose optimization. The alignment energy uses novel regularizers to address occlusions and hand-object contacts. For added robustness, we guide the optimization with discriminative part classification of the hand and segmentation of the object. We conducted extensive experiments on several existing datasets and introduce a new annotated hand-object dataset. Quantitative and qualitative results show the key advantages of our method: speed, accuracy, and robustness.
Time-Varying Copula Models for Financial Time Series

We perform an analysis of the potential time inhomogeneity in the dependence between multiple financial time series. To this end, we use the framework of copula theory and tackle the question of whether dependencies in such a case can be assumed constant throughout time or rather have to be modeled in a time-inhomogeneous way. We focus on parametric copula models and suitable inference techniques in the context of a special copula-based multivariate time series model. A recent result due to Chan et al. (2009) is used to derive the joint limiting distribution of local maximum-likelihood estimators on overlapping samples. By restricting the overlap to be fixed, we establish the limiting law of the maximum of the estimator series. Based on the limiting distributions, we develop statistical homogeneity tests, and investigate their local power properties. A Monte Carlo simulation study demonstrates that bootstrapped variance estimates are needed in finite samples. Empirical analyses on real-world financial data finally confirm that time-varying parameters are an exception rather than the rule.
A Study on Using AD5933 to Realize Soft-Sensing Measurement of Conductance and Capacitance

A conductance and capacitance test system was designed in this paper. AD5933 a new chip for impedance testing was used in it. By designing the test system, and testing the impedance of a model, which is a resistor in parallel with a capacitor, under different frequency (range from1000Hz to 100kHz). The information of impedance, real part, and imaginary part of the model under the frequency points were get. Based on this, the internal relations between conductance, capacitance, impedance, real part and imaginary part were carefully analyzed, using the soft-sensing method established the capacitance support vector machine (SVM) regression model on MATLAB. The regression model is the relation between resistance, impedance and capacitance. The test results showed that the test relative error is small in the capacitance range (0.1 pf~33 pf) and the conductance range (9.05*10s~3.8*10s), the biggest, smallest and average test error of capacitance are -2.9%, 0.2%, 1.27% respectively. The study showed that using AD5933 and the soft-sensing method to realize the multi-parameter test is feasible.
Sentry-Based Power Management in Wireless Sensor Networks

This paper presents a sentry-based approach to power management in wireless sensor networks for applications such as intruder detection and tracking. To minimize average power consumption while maintaining sufficient node density for coarse sensing, nodes are partitioned dynamically into two sets: sentries and non-sentries. Sentry nodes provide sufficient coverage for continuous monitoring and basic communication services. Non-sentry nodes sleep for designated periods of time to conserve power, and switch to full power only when needed to provide more refined sensing for tracking. Non-sentry nodes check for beacons from sentry nodes to determine when they should remain on. Experimental results are presented demonstrating trade-offs between power savings and tracking performance for a network of seventeen nodes using the first implementation of a basic sentry-based power management scheme. The paper concludes with a brief description of a full set of power-management services being implemented as middle-ware for general wireless sensor applications.
Schema Conversion Model of SQL Database to NoSQL

With the increasing maturity of NoSQL databases as well as the situation of reading more than writing on large volumes of data, many applications turn to NoSQL and pick it as data storage system. Migrating from SQL database to NoSQL and providing efficient query become growing imperative. However, join operation is not supported in NoSQL database and read separately for multiple times which brings poor performance is unavoidable. This paper proposes a schema conversion model for transforming SQL database to NoSQL which can provide high performance of join query with nesting relevant tables, and a graph transforming algorithm for containing all required content of join query in a table by offering correctly nested sequence. A rigorous proof about the transform algorithm has been conducted and the experiment has verified the correctness of the conversion model and the high performance of join operation.
A Configurable 12–237 kS/s 12.8 mW Sparse-Approximation Engine for Mobile Data Aggregation of Compressively Sampled Physiological Signals

Compressive sensing (CS) is a promising technology for realizing low-power and cost-effective wireless sensor nodes (WSNs) in pervasive health systems for 24/7 health monitoring. Due to the high computational complexity (CC) of the reconstruction algorithms, software solutions cannot fulfill the energy efficiency needs for real-time processing. In this paper, we present a 12-237 kS/s 12.8 mW sparse-approximation (SA) engine chip that enables the energy-efficient data aggregation of compressively sampled physiological signals on mobile platforms. The SA engine chip integrated in 40 nm CMOS can support the simultaneous reconstruction of over 200 channels of physiological signals while consuming (1% of a smartphone's power budget. Such energyefficient reconstruction enables two-to-three times energy saving at the sensor nodes in a CS-based health monitoring system as compared to traditional Nyquist-based systems, while providing timely feedback and bringing signal intelligence closer to the user.
Model selection in ecology and evolution.

Recently, researchers in several areas of ecology and evolution have begun to change the way in which they analyze data and make biological inferences. Rather than the traditional null hypothesis testing approach, they have adopted an approach called model selection, in which several competing hypotheses are simultaneously confronted with data. Model selection can be used to identify a single best model, thus lending support to one particular hypothesis, or it can be used to make inferences based on weighted support from a complete set of competing models. Model selection is widely accepted and well developed in certain fields, most notably in molecular systematics and mark-recapture analysis. However, it is now gaining support in several other areas, from molecular evolution to landscape ecology. Here, we outline the steps of model selection and highlight several ways that it is now being implemented. By adopting this approach, researchers in ecology and evolution will find a valuable alternative to traditional null hypothesis testing, especially when more than one hypothesis is plausible.
An Adaptive Nonlocal Regularized Shadow Removal Method for Aerial Remote Sensing Images

Shadows are evident in most aerial images with high resolutions, particularly in urban scenes, and their existence obstructs the image interpretation and the following application, such as classification and target detection. Most current shadow removal methods were proposed for natural images, whereas shadows in remote sensing images show distinct characteristics. We have therefore analyzed the characteristics of shadows in aerial images, and in this paper, we propose a new shadow removal method for aerial images, using nonlocal (NL) operators. In the proposed method, the soft shadow is introduced to replace the traditional binary hard shadow. NL operators are used to regularize the shadow scale and the updated shadow-free image. Furthermore, a spatially adaptive NL regularization is introduced to handle compound shadows. The combination of the soft shadow and NL operators yields satisfying shadow-free results, preserving textures and holding regular color. Different types of shadowed aerial images are employed to verify the proposed method, and the results are compared with two other methods. The experimental results confirm the validity of the proposed method and the advantage of the soft-shadow approach.
Network Traffic Fingerprinting Based on Approximated Kernel Two-Sample Test

Many applications and communication protocols exhibit unique communication patterns that can be exploited to identify them in network traffic. This paper proposes a method to represent these patterns compactly, such that they can be used in different analytical tasks. The method treats each communication as a set of observations of a random variable with unknown probability distribution. This view allows us to derive the representation from a distance between two probability distributions used in maximum mean discrepancy—a non-parametric kernel test. The representation (and distance) can be then easily used in various algorithms for identification of communicating application and data analysis, independently of the specific type of input data.
Greedy Sampling of Graph Signals

Sampling is a fundamental topic in graph signal processing, having found applications in estimation, clustering, and video compression. In contrast to traditional signal processing, the irregularity of the signal domain makes selecting a sampling set nontrivial and hard to analyze. Indeed, though conditions for graph signal interpolation from noiseless samples exist, they do not lead to a unique sampling set. The presence of noise makes choosing among these sampling sets a hard combinatorial problem. Although greedy sampling schemes are commonly used in practice, they have no performance guarantee. This work takes a twofold approach to address this issue. First, universal performance bounds are derived for the Bayesian estimation of graph signals from noisy samples. In contrast to currently available bounds, they are not restricted to specific sampling schemes and hold for any sampling sets. Second, this paper provides near-optimal guarantees for greedy sampling by introducing the concept of approximate submodularity and updating the classical greedy bound. It then provides explicit bounds on the approximate supermodularity of the interpolation mean-square error showing that it can be optimized with worst case guarantees using greedy search even though it is not supermodular. Simulations illustrate the derived bound for different graph models and show an application of graph signal sampling to reduce the complexity of kernel principal component analysis.
Relationship between Joint Position Sense, Force Sense, and Muscle Strength and the Impact of Gymnastic Training on Proprioception

The aims of this study were (1) to assess the relationship between joint position (JPS) and force sense (FS) and muscle strength (MS) and (2) to evaluate the impact of long-term gymnastic training on particular proprioception aspects and their correlations. 17 elite adult gymnasts and 24 untrained, matched controls performed an active reproduction (AR) and passive reproduction (PR) task and a force reproduction (FR) task at the elbow joint. Intergroup differences and the relationship between JPS, FS, and MS were evaluated. While there was no difference in AR or PR between groups, absolute error in the control group was higher during the PR task (7.15 ± 2.72°) than during the AR task (3.1 ± 1.93°). Mean relative error in the control group was 61% higher in the elbow extensors than in the elbow flexors during 50% FR, while the gymnast group had similar results in both reciprocal muscles. There was no linear correlation between JPS and FS in either group; however, FR was negatively correlated with antagonist MS. In conclusion, this study found no evidence for a relationship between the accuracy of FS and JPS at the elbow joint. Long-term gymnastic training improves the JPS and FS of the elbow extensors.
Real-time canny edge detection parallel implementation for FPGAs

Edge detection is one of the most fundamental algorithms in digital image processing. The Canny edge detector is the most implemented edge detection algorithm because of its ability to detect edges even in images that are intensely contaminated by noise. However, this is a time consuming algorithm and therefore its implementations are difficult to reach real time response speeds. Especially nowadays where the demand for high resolution image processing is constantly increasing, the need for fast and efficient edge detector implementations is ever so present. A new parallel Canny edge detector FPGA implementation is proposed in this paper to answer this demand. This design takes advantage of 4-pixel parallel computations to achieve high throughput without increasing the on-chip memory demands. Synthesis and simulation results are presented to prove the design's efficiency and high frames per second rate.
Detecting cyberbullying: query terms and techniques

In this paper we describe a close analysis of the language used in cyberbullying. We take as our corpus a collection of posts from Formspring.me. Formspring.me is a social networking site where users can ask questions of other users. It appeals primarily to teens and young adults and the cyberbullying content on the site is dense; between 7% and 14% of the posts we have analyzed contain cyberbullying content.
 The results presented in this article are two-fold. Our first experiments were designed to develop an understanding of both the specific words that are used by cyberbullies, and the context surrounding these words. We have identified the most commonly used cyberbullying terms, and have developed queries that can be used to detect cyberbullying content. Five of our queries achieve an average precision of 91.25% at rank 100.
 In our second set of experiments we extended this work by using a supervised machine learning approach for detecting cyberbullying. The machine learning experiments identify additional terms that are consistent with cyberbullying content, and identified an additional querying technique that was able to accurately assign scores to posts from Formspring.me. The posts with the highest scores are shown to have a high density of cyberbullying content.
ontoAGA: Ontology to Support Educational Systems Interoperability

Looking forward to promote the exchange and integration of information in order to build a unique reference model for the universities it is essencial to assure commom concepts of higher education institutions in Brazil. This paper proposes an University Support Management Ontology, named ontoAGA, to help the operation of legacy data in Semantic Web in a standardized manner and to allow the integration of databases with different technologies.To evaluate the research questions two usages cenarios were defined and the results point to the proposal viability.  Para promover o intercâmbio e a integração de informações de forma a se construir um modelo de referência para as universidades há necessidade de se estruturar conceitos comuns às instituições de ensino superior no Brasil. Este artigo propõe uma ontologia, no domínio da gestão acadêmica nas universidades, denominada ontoAGA, para ajudar a exploração de dados legados na web Semântica de forma padronizada e para possibilitar a integração de bases de dados de diferentes tecnologias. Para avaliação das questões de pesquisa foram definidos dois cenários de uso, cujos resultados apontam para a viabilidade da proposta.
The world of emotions is not two-dimensional.

For more than half a century, emotion researchers have attempted to establish the dimensional space that most economically accounts for similarities and differences in emotional experience. Today, many researchers focus exclusively on two-dimensional models involving valence and arousal. Adopting a theoretically based approach, we show for three languages that four dimensions are needed to satisfactorily represent similarities and differences in the meaning of emotion words. In order of importance, these dimensions are evaluation-pleasantness, potency-control, activation-arousal, and unpredictability. They were identified on the basis of the applicability of 144 features representing the six components of emotions: (a) appraisals of events, (b) psychophysiological changes, (c) motor expressions, (d) action tendencies, (e) subjective experiences, and (f) emotion regulation.
COMPOSING THE REFLECTED BEST-SELF PORTRAIT : BUILDING PATHWAYS FOR BECOMING EXTRAORDINARY IN WORK ORGANIZATIONS

We present a theory of how individuals compose their reflected best-self portrait, which we define as a changing self-knowledge structure about who one is at one’s best. We posit that people compose their reflected best-self portrait through social experiences that draw on intrapsychic and interpersonal resources. By weaving together microlevel theories of personal change and macrolevel theories of human resource development, our theory reveals an important means by which work organizations affect people’s capacity to realize their potential.
Community Detection based on Distance Dynamics

In this paper, we introduce a new community detection algorithm, called Attractor, which automatically spots communities in a network by examining the changes of "distances" among nodes (i.e. distance dynamics). The fundamental idea is to envision the target network as an adaptive dynamical system, where each node interacts with its neighbors. The interaction will change the distances among nodes, while the distances will affect the interactions. Such interplay eventually leads to a steady distribution of distances, where the nodes sharing the same community move together and the nodes in different communities keep far away from each other. Building upon the distance dynamics, Attractor has several remarkable advantages: (a) It provides an intuitive way to analyze the community structure of a network, and more importantly, faithfully captures the natural communities (with high quality). (b) Attractor allows detecting communities on large-scale networks due to its low time complexity (O(|E|)).  Extensive experiments show that our algorithm allows the effective and efficient community detection and has good performance compared to state-of-the-art algorithms.
Quadrifilar helical antennas : Wideband and multiband behavior for GPS applications

A novel multi-frequency printed quadrifilar helix antenna based on Multiple arm technique is presented in this paper. Double frequency and satisfactory antenna characteristics are achieved. The antenna has relatively compact size and hemispherical pattern with excellent circularly polarized coverage. The antenna is designed and simulated with the application of HFSS software. The simulation results and analyses are presented.