nils-herrmann/clean_scirepeval huggingface dataset:
Neural dynamics of planned arm movements: emergent invariants and speed-accuracy properties during trajectory formation.

A real-time neural network model, called the vector-integration-to-endpoint (VITE) model is developed and used to simulate quantitatively behavioral and neural data about planned and passive arm movements. Invariants o farm movements emerge through network interactions rather than through an explicitly precomputed trajectory. Motor planning occurs in the form of a target position command (TPC), which specifies where the arm intends to move, and an independently controlled GO command, which specifies the movement's overall speed. Automatic processes convert this information into an arm trajectory with invariant properties. These automatic processes include computation of a present position command (PPC) and a difference vector (DV). The DV is the difference between the PPC and the TPC at any time. The PPC is gradually updated by integrating the DV through time. The GO signal multiplies the DV before it is integrated by the PPC. The PPC generates an outflow movement command to its target muscle groups. Opponent interactions regulate the PPCs to agonist and antagonist muscle groups. This system generates synchronous movements across synergetic muscles by automatically compensating for the different total contractions that each muscle group must undergo. Quantitative simulations are provided of Woodworth's law, of the speed-accuracy trade-offknown as Fitts's law, of isotonic arm-movement properties before and after deafferentation, of synchronous and compensatory "central-error-correction" properties of isometric contractions, of velocity amplification during target switching, of velocity profile invariance and asymmetry, of the changes in velocity profile asymmetry at higher movement speeds, of the automarie compensation for staggered onset times of synergetic muscles, of vector cell properties in precentral motor cortex, of the inverse relation between movement duration and peak velocity, and of peak acceleration as a function of movement amplitude and duration. It is shown that TPC, PPC, and DV computations are needed to actively modulate, or gate, the learning of associative maps between TPCs of different modalities, such as between the eye-head system and the hand-arm system. By using such an associative map, looking at an object can activate a TPC of the hand-arm system, as Piaget noted. Then a VITE circuit can translate this TPC into an invariant movement trajectory. An auxiliary circuit, called the Passive Update of Position (PUP) model is described for using inflow signals to update the PPC during passive arm movements owing to external forces. Other uses of outflow and inflow signals are also noted, such as for adaptive linearization of a nonlinear muscle plant, and sequential readout of TPCs during a serial plan, as in reaching and grasping. Comparisons are made with other models of motor control, such as the mass-spring and minimumjerk models.
High voltage subnanosecond silicon carbide opening switch

We discuss the results of the design and examination of high-voltage superfast opening switches - the drift step recovery diodes (DSRD) based on silicon carbide. The fabricated diodes have p+-p-n+ structure with relatively thick low-doped base region, which allows switching voltage of 1.8 kV in less than 500 ps. With 4H-SiC DSRD operating as an opening switch in an ultrashort pulse generator circuit in pulse repetition mode, maximal pulse repetition frequency is 500 kHz in continuous operation mode and 10 MHz in burst mode.
BUSI NESS INTELLIGENCE SYSTEMS: STATE-OF-THE-ART REVIEW AND CONTEMPORARY APPLICATIONS

Recently business intelligence (BI) applications have been the primary agenda for many CIOs. However, the concept of BI is fairly new and to date there is no commonly agreed definition of BI. This paper explores the nebulous definitions and the various applications of BI through a comprehensive review of academic as well as practitioner’s literature. As a result, three main perspectives of BI have been identified, namely the management aspect, the technological aspect, and the product aspect. This categorization gives researchers, practitioners, and BI vendors a better idea of how different parties have approached BI thus far and is valuable in their, design, planning, and implementation of a contemporary BI system in the future. The categorization may even be a first effort towards a commonly agreed definition of BI.
S-band internally harmonic matched GaN FET with 330W output power and 62% PAE

In this paper, an S-band internally harmonic matched GaN FET is presented, which is designed so that up to third harmonic impedance is tuned to high efficiency condition. Harmonic load pull measurements were done for a small transistor cell at first. It was found that power added efficiency (PAE) of 78% together with 6W output power can be obtained by tuning impedances up to 3rd harmonic for both input and output sides. Then matching circuit was designed for large gate periphery multi-cell transistor. To make the circuit size small, harmonic matching was done in a hermetically sealed package. With total gate width of 64mm, 330W output power and 62% PAE was successfully obtained.
Back Flashover phenomenon analysis in power transmission substation for insulation coordination

Back Flashover (BFO) is one of the several phenomenon which decreases power transmission lines reliability. It occurs as a result of direct lightning stroke to the tower structure or guard wires and injects wave currents with high amplitude and very high steepness to the phase conductors, injection of these wave currents produce voltages with high amplitude and very high steepness, which in turn cause phase to ground faults in power transmission lines. In order to treat with this phenomenon, it is necessary to analysis its mechanism and effective parameters on its occurring probability. In this paper, the mechanism of phenomenon has been analyzed and effective parameters on its probability of occurring are evaluated. Then several methods in order to decrease this probability are suggested. Finally, related simulations with insulation coordination of a 765 kV transmission substation also have been done and the results have been presented.
Deep Learning-Based Food Calorie Estimation Method in Dietary Assessment

Obesity treatment requires obese patients to record all food intakes per day. Computer vision has been applied to estimate calories from food images. In order to increase detection accuracy and reduce the error of volume estimation in food calorie estimation, we present our calorie estimation method in this paper. To estimate calorie of food, a top view and side view are needed. Faster R-CNN is used to detect each food and calibration object. GrabCut algorithm is used to get each food’s contour. Then each food’s volume is estimated by volume estimation formulas. Finally we estimate each food’s calorie. And the experiment results show our estimation method is effective.
Decentralized Group Sparse Beamforming for Multi-Cloud Radio Access Networks

Recent studies on cloud-radio access networks (CRANs) assume the availability of a single processor (cloud) capable of managing the entire network performance; inter-cloud interference is treated as background noise. This paper considers the more practical scenario of the downlink of a CRAN formed by multiple clouds, where each cloud is connected to a cluster of multiple-antenna base stations (BSs) via high-capacity wireline backhaul links. The network is composed of several disjoint BSs' clusters, each serving a pre-known set of single-antenna users. To account for both inter- cloud and intra-cloud interference, the paper considers the problem of minimizing the total network power consumption subject to quality of service constraints, by jointly determining the set of active BSs connected to each cloud and the beamforming vectors of every user across the network. The paper solves the problem using Lagrangian duality theory through a dual decomposition approach, which decouples the problem into multiple and independent subproblems, the solution of which depends on the dual optimization problem. The solution then proceeds in updating the dual variables and the active set of BSs at each cloud iteratively. The proposed approach leads to a distributed implementation across the multiple clouds through a reasonable exchange of information between adjacent clouds. The paper further proposes a centralized solution to the problem. Simulation results suggest that the proposed algorithms significantly outperform the conventional per-cloud update solution, especially at high signal-to-interference-plus- noise ratio (SINR) target.
Building theory in the sciences of the artificial

This essay extends Simon's arguments in the Sciences of the Artificial to a critical examination of how theorizing in Information Technology disciplines should occur. The essay is framed around a number of fundamental questions that relate theorizing in the artificial sciences to the traditions of the philosophy of science. Theorizing in the artificial sciences is contrasted with theorizing in other branches of science and the applicability of the scientific method is questioned. The paper argues that theorizing should be considered in a holistic manner that links two modes of theorizing: an interior mode with the how of artifact construction studied and an exterior mode with the what of existing artifacts studied. Unlike some representations in the design science movement the paper argues that the study of artifacts once constructed can not be passed back uncritically to the methods of traditional science. Seven principles for creating knowledge in IT disciplines are derived: (i) artifact system centrality; (ii) artifact purposefulness; (iii) need for design theory; (iv) induction and abduction in theory building; (v) artifact construction as theory building; (vi) interior and exterior modes for theorizing; and (viii) issues with generality. The implicit claim is that consideration of these principles will improve knowledge creation and theorizing in design disciplines, for both design science researchers and also for researchers using more traditional methods. Further, attention to these principles should lead to the creation of more useful and relevant knowledge.
Posture and balance control for biped robots based on contact force optimization

This paper presents a new balancing control approach for regulating the center of mass position and trunk orientation of a bipedal robot in a compliant way. The controller computes a desired wrench (force and torque) required to recover the posture when an unknown external perturbation has changed the posture of the robot. This wrench is later distributed as forces at predefined contact points via a constrained optimization, which aims at achieving the desired wrench while minimizing the Euclidean norm of the contact forces. The formulation of the force distribution as an optimization problem is adopted from the grasping literature and allows to consider restrictions coming from the friction between the contact points and the ground.
Microfluidics meets MEMS

The use of planar fluidic devices for performing small-volume chemistry was first proposed by analytical chemists, who coined the term “miniaturized total chemical analysis systems” ( TAS) for this concept. More recently, the TAS field has begun to encompass other areas of chemistry and biology. To reflect this expanded scope, the broader terms “microfluidics” and “lab-on-a-chip” are now often used in addition to TAS. Most microfluidics researchers rely on micromachining technologies at least to some extent to produce microflow systems based on interconnected micrometer-dimensioned channels. As members of the microelectromechanical systems (MEMS) community know, however, one can do more with these techniques. It is possible to impart higher levels of functionality by making features in different materials and at different levels within a microfluidic device. Increasingly, researchers have considered how to integrate electrical or electrochemical function into chips for purposes as diverse as heating, temperature sensing, electrochemical detection, and pumping. MEMS processes applied to new materials have also resulted in new approaches for fabrication of microchannels. This review paper explores these and other developments that have emerged from the increasing interaction between the MEMS and microfluidics worlds.
k-Space Deep Learning for Accelerated MRI

The annihilating filter-based low-rank Hanel matrix approach (ALOHA) is one of the state-of-the-art compressed sensing approaches that directly interpolates the missing k-space data using low-rank Hankel matrix completion. Inspired by the recent mathematical discovery that links deep neural networks to Hankel matrix decomposition using data-driven framelet basis, here we propose a fully data-driven deep learning algorithm for k-space interpolation. Our network can be also easily applied to non-Cartesian k-space trajectories by simply adding an additional re-gridding layer. Extensive numerical experiments show that the proposed deep learning method significantly outperforms the existing image-domain deep learning approaches.
Validity of the Executive Function Theory of Attention-Deficit/Hyperactivity Disorder: A Meta-Analytic Review

One of the most prominent neuropsychologic theories of attention-deficit/hyperactivity disorder (ADHD) suggests that its symptoms arise from a primary deficit in executive functions (EF), defined as neurocognitive processes that maintain an appropriate problem-solving set to attain a later goal. To examine the validity of the EF theory, we conducted a meta-analysis of 83 studies that administered EF measures to groups with ADHD (total N = 3734) and without ADHD (N = 2969). Groups with ADHD exhibited significant impairment on all EF tasks. Effect sizes for all measures fell in the medium range (.46-.69), but the strongest and most consistent effects were obtained on measures of response inhibition, vigilance, working memory, and planning. Weaknesses in EF were significant in both clinic-referred and community samples and were not explained by group differences in intelligence, academic achievement, or symptoms of other disorders. ADHD is associated with significant weaknesses in several key EF domains. However, moderate effect sizes and lack of universality of EF deficits among individuals with ADHD suggest that EF weaknesses are neither necessary nor sufficient to cause all cases of ADHD. Difficulties with EF appear to be one important component of the complex neuropsychology of ADHD.
A vehicle license plate detection method using region and edge based methods

In this paper, a license plate detection system is implemented. For this purpose, we improve the contrast at possible locations where there might be a license plate, we propose a filtering method called ''region-based'' in order to smooth the uniform and background areas of an image, we apply the Sobel operator and morphological filtering to extract the vertical edges and the candidate regions respectively, and finally, we segment the plate region by considering some geometrical features. In fact the novelty and the strength of our license plate detection system is in applying the region-based filtering that decreases the run time and increases the accuracy in the two final stages: morphological filtering and using the geometrical features. The experimental results show that our proposed method achieves appropriate performance in different scenarios. We should mention that our system is reliable because the accuracy is above 92% in average for different scenarios and it is also practical because of the low computational cost. An intelligent transportation system (ITS) is an important tool for analyzing and controlling the moving vehicles in cities and highways  and in recent years much research on ITS has been carried out. Nowadays automatic vehicle license plate (VLP) recognition is a key ingredient for any ITS such as security control of restricted areas, traffic law enforcements, automatic payment of tolls on highways and parking management systems. In these examples, a camera captures the vehicle images and a computer processes the captured images, detects the car license plate from the input image and then reads the information on the license plate by applying various image processing and optical character recognition techniques. Generally , an automatic VLP recognition system is made up of four modules: image pre-processing, license plate detection, character segmentation and character recognition . Among these four modules  The most common solutions for VLP detection include analyzing the texture [1,2], edge extraction [3–6], morphological filtering [7,8], color feature [7,9], combining of edge statistics and color feature [10], combining of edge and morphology operations [11], Hough transform , neural networks [6], learning-based approaches [13], Gabor filtering [14] and Wave-let analysis . An edge approach is normally simple and fast.  and edge based methods [3–6] are widely used …
SWI-Prolog version 7 extensions

SWI-Prolog version 7 extends the Prolog language as a general purpose programming language that can be used as ‘glue’ between components written in different languages. Taking this role rather than that of a domain specific language (DSL) inside other IT components has always been the design objective of SWI-Prolog as illustrated by XPCE (its object oriented communication to the OS and graphics), the HTTP server library and the many interfaces to external systems and file formats. In recent years, we started extending the language itself, notably to accommodate expressing syntactic constructs of other languages such a HTML and JavaScript. This resulted in an extended notion of operators and quasi quotations. SWI-Prolog version 7 takes this one step further by extending the primitive data types of Prolog. This article describes and motivates these extensions.
Set-Based Concurrent Engineering process within the LeanPPD environment

This paper presents a newly defined set-based concurrent engineering process, which the authors believe addresses some of the key challenges faced by engineering enterprises in the 21 century. The main principles of Set-Based Concurrent Engineering (SBCE) have been identified via an extensive literature review. Based on these principles the SBCE baseline model was developed. The baseline model defines the stages and activities which represent the product development process to be employed in the LeanPPD (lean product and process development) project. The LeanPPD project is addressing the needs of European manufacturing companies for a new model that extends beyond lean manufacturing, and incorporates lean thinking in the product design development process.
A 4×1 L-probe fed inverted hybrid E-H microstrip patch antenna array for 3G application

This paper presents a compact and broadband micro strip patch antenna array (BMPAA) with uniform linear array configuration of 4x1 elemnt for 3G applications. The 4×1 BMPAA was designed and developed by integrating new patch sha pe Hybrid E-H shape, L-probe feeding, inverted patch structure with air filled dielectric microstr ip patch antenna (MPA) element. The array was constructed using two dielectric layer arrangement with a thick air-filled substrate sandwiched betwee n a top-loaded dielectric substrate ( RT 5880) with inverting radiating patch and a ground plane . The Lprobe fed inverted hybrid E-H (LIEH) shaped BMPAA a chieves an impedance bandwidth of 17.32% referenced to the center frequency at 2.02 GHz (at VSWR ≤ 1.5), maximum achievable gain of 11.9±1dBi, and 23 dB crosspolarization level.
MEDAL: A Cost-Effective High-Frequency Energy Data Acquisition System for Electrical Appliances

Traditional energy measurement fails to provide support to consumers to make intelligent decisions to save energy. Non-intrusive load monitoring is one solution that provides disaggregated power consumption profiles. Machine learning approaches rely on public datasets to train parameters for their algorithms, most of which only provide low-frequency appliance-level measurements, thus limiting the available feature space for recognition.
 In this paper, we propose a low-cost measurement system for high-frequency energy data. Our work utilizes an off-the-shelf power strip with a voltage-sensing circuit, current sensors, and a single-board PC as data aggregator. We develop a new architecture and evaluate the system in real-world environments. The self-contained unit for six monitored outlets can achieve up to 50 kHz for all signals simultaneously. A simple design and off-the-shelf components allow us to keep costs low. Equipping a building with our measurement systems is more feasible compared to expensive existing solutions. We used the outlined system architecture to manufacture 20 measurement systems to collect energy data over several months of more than 50 appliances at different locations, with an aggregated size of 15 TB.
Deep Convolutional Compressed Sensing for LiDAR Depth Completion

In this paper we consider the problem of estimating a dense depth map from a set of sparse LiDAR points. We use techniques from compressed sensing and the recently developed Alternating Direction Neural Networks (ADNNs) to create a deep recurrent auto-encoder for this task. Our architecture internally performs an algorithm for extracting multi-level convolutional sparse codes from the input which are then used to make a prediction. Our results demonstrate that with only two layers and 1800 parameters we are able to out perform all previously published results, including deep networks with orders of magnitude more parameters.
RF Energy Harvesting System at 2.67 and 5.8GHz

An RF energy harvesting system using a gap coupled microstrip antenna, designed and fabricated for 2.67 and 5.8GHz, is presented in this paper. Gain of 8.6dB and bandwidth of 100MHz has been achieved for antenna at 2.67GHz. A gain of 9dB and bandwidth of 690MHz is obtained for antenna at 5.8GHz. A CMOS rectifier topology designed in UMC 180nm CMOS process and working at 2.6 GHz is also presented. This topology works without any external bias circuit. Rectified voltage of 1.04V at 1MΩ load with 5-stage rectifier is achieved. Measured results of rectenna using Schottky diode at 2.67 and 5.8GHz are also presented.
Psychological, physical, and academic correlates of cyberbullying and traditional bullying.

To examine the relationship between children's and adolescents' experiences with cyberbullying and traditional bullying and psychological health, physical health, and academic performance.



Nine hundred thirty-one students in grades 6 through 12 completed an anonymous survey examining their experiences with cyberbullying and traditional bullying. Also included were measures of anxiety, depression, self-esteem, physical well-being, school attendance, and academic performance.



Participants were categorized as belonging to one of four groups: cyber victims, cyberbullies, cyber bully/victims, and those not involved in cyberbullying. A similar categorization was done with traditional bullying. Those in the bully/victim groups (and particularly the cyber bully/victim group) had the most negative scores on most measures of psychological health, physical, health, and academic performance.



There appears to be a substantial, although not perfect, overlap between involvement in traditional bullying and cyberbullying. Additionally, the physical, psychological, and academic correlates of the two types of bullying resembled one another.