nils-herrmann/clean_scirepeval huggingface dataset:
Optimality principles in sensorimotor control

The sensorimotor system is a product of evolution, development, learning and adaptation—which work on different time scales to improve behavioral performance. Consequently, many theories of motor function are based on 'optimal performance': they quantify task goals as cost functions, and apply the sophisticated tools of optimal control theory to obtain detailed behavioral predictions. The resulting models, although not without limitations, have explained more empirical phenomena than any other class. Traditional emphasis has been on optimizing desired movement trajectories while ignoring sensory feedback. Recent work has redefined optimality in terms of feedback control laws, and focused on the mechanisms that generate behavior online. This approach has allowed researchers to fit previously unrelated concepts and observations into what may become a unified theoretical framework for interpreting motor function. At the heart of the framework is the relationship between high-level goals, and the real-time sensorimotor control strategies most suitable for accomplishing those goals.
HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction

Recently, microRNAs (miRNAs) have drawn more and more attentions because accumulating experimental studies have indicated miRNA could play critical roles in multiple biological processes as well as the development and progression of human complex diseases. Using the huge number of known heterogeneous biological datasets to predict potential associations between miRNAs and diseases is an important topic in the field of biology, medicine, and bioinformatics. In this study, considering the limitations in the previous computational methods, we developed the computational model of Heterogeneous Graph Inference for MiRNA-Disease Association prediction (HGIMDA) to uncover potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally verified miRNA-disease associations into a heterogeneous graph. HGIMDA obtained AUCs of 0.8781 and 0.8077 based on global and local leave-one-out cross validation, respectively. Furthermore, HGIMDA was applied to three important human cancers for performance evaluation. As a result, 90% (Colon Neoplasms), 88% (Esophageal Neoplasms) and 88% (Kidney Neoplasms) of top 50 predicted miRNAs are confirmed by recent experiment reports. Furthermore, HGIMDA could be effectively applied to new diseases and new miRNAs without any known associations, which overcome the important limitations of many previous computational models.
NADO: network anomaly detection using outlier approach

Anomaly detection, which is an important task in any Network Intrusion Detection System (NIDS), enables discovery of known as well as unknown attacks. Anomaly detection using outlier approach is a successful network anomaly identification technique. In this paper, we describe NADO (Network Anomaly Detection using Outlier approach), an effective outlier based approach for detection of anomalies in networks. It initially clusters the normal data using a variant of the k-means clustering technique for high dimensional data. Then it calculates the reference point from each cluster and builds profiles for each cluster. Finally, it calculates the score for each candidate point w.r.t the reference points and reports as anomaly if it exceeds a user defined threshold value. We evaluate the performance of our approach with KDDcup99 intrusion dataset and other real life datasets. We show that NADO has high detection rate and low false positive rate.
Bayesian policy reuse

A long-lived autonomous agent should be able to respond online to novel instances of tasks from a familiar domain. Acting online requires ‘fast’ responses, in terms of rapid convergence, especially when the task instance has a short duration such as in applications involving interactions with humans. These requirements can be problematic for many established methods for learning to act. In domains where the agent knows that the task instance is drawn from a family of related tasks, albeit without access to the label of any given instance, it can choose to act through a process of policy reuse from a library in contrast to policy learning. In policy reuse, the agent has prior experience from the class of tasks in the form of a library of policies that were learnt from sample task instances during an offline training phase. We formalise the problem of policy reuse and present an algorithm for efficiently responding to a novel task instance by reusing a policy from this library of existing policies, where the choice is based on observed ‘signals’ which correlate to policy performance. We achieve this by posing the problem as a Bayesian choice problem with a corresponding notion of an optimal response, but the computation of that response is in many cases intractable. Therefore, to reduce the computation cost of the posterior, we follow a Bayesian optimisation approach and define a set of policy selection functions, which balance exploration in the policy library against exploitation of previously tried policies, together with a model of expected performance of the policy library on their corresponding task instances. We validate our method in several simulated domains of interactive, short-duration episodic tasks, showing rapid convergence in unknown task variations.
Executive functioning as a predictor of children's mathematics ability: inhibition, switching, and working memory.

Children's mathematical skills were considered in relation to executive functions. Using multiple measures--including the Wisconsin Card Sorting Task (WCST), dual-task performance, Stroop task, and counting span-it was found that mathematical ability was significantly correlated with all measures of executive functioning, with the exception of dual-task performance. Furthermore, regression analyses revealed that each executive function measure predicted unique variance in mathematics ability. These results are discussed in terms of a central executive with diverse functions (Shallice & Burgess, 1996) and with recent evidence from Miyake, et al. (2000) showing the unity and diversity among executive functions. It is proposed that the particular difficulties for children of lower mathematical ability are lack of inhibition and poor working memory, which result in problems with switching and evaluation of new strategies for dealing with a particular task. The practical and theoretical implications of these results are discussed, along with suggestions for task changes and longitudinal studies that would clarify theoretical and developmental issues related to executive functioning.
Double-sided cooling and thermo-electrical management of power transients for silicon chips on DCB-substrates for converter applications: Design, technology and test

This paper deals with the system design, technology and test of a novel concept of integrating Si and SiC power dies along with thermo-electric coolers in order to thermally manage transients occurring during operation. The concept features double-sided cooling as well as new materials and joining technologies to integrate the dies such as transient liquid phase bonding/soldering and sintering. Coupled-field simulations are used to predict thermal performance and are verified by especially designed test stands to very good agreement. This paper is the second in a series of publications on the ongoing work.
Simple Personalized Search Based on Long-Term Behavioral Signals

Extensive research has shown that content-based Web result ranking can be significantly improved by considering personal behavioral signals (such as past queries) and global behavioral signals (such as global click frequencies). In this work we present a new approach to incorporating click behavior into document ranking, using ideas of click models as well as learning to rank. We show that by training a click model with pairwise loss, as is done in ranking problems, our approach achieves personalized reranking performance comparable to the stateof-the-art while eliminating much of the complexity required by previous models. This contrasts with other approaches that rely on complex feature engineering.
A Decomposition Storage Model

This report examines the relative advantages of a storage model based on decomposition (of community view relations into binary relations containing a surrogate and one attribute) over conventional n-ary storage models There seems to be a general consensus among the database community that the n-ary approach is better This conclusion is usually based on a consideration of only one or two dimensions of a database system The purpose of this report is not to claim that decomposition is better Instead, we claim that the consensus opinion is not well founded and that neither is clearly better until a closer analysis is made along the many dimensions of a database system The purpose of this report is to move further in both scope and depth toward such an analysis We examine such dimensions as simplicity, generality, storage requirements, update performance and retrieval performance
The Psychotomimetic Effects of Intravenous Delta-9-Tetrahydrocannabinol in Healthy Individuals: Implications for Psychosis

Recent advances in the understanding of brain cannabinoid receptor function have renewed interest in the association between cannabinoid compounds and psychosis. In a 3-day, double-blind, randomized, and counterbalanced study, the behavioral, cognitive, and endocrine effects of 0, 2.5, and 5 mg intravenous delta-9-tetrahydrocannabinol (Δ-9-THC) were characterized in 22 healthy individuals, who had been exposed to cannabis but had never been diagnosed with a cannabis abuse disorder. Prospective safety data at 1, 3, and 6 months poststudy was also collected. Δ-9-THC (1) produced schizophrenia-like positive and negative symptoms; (2) altered perception; (3) increased anxiety; (4) produced euphoria; (5) disrupted immediate and delayed word recall, sparing recognition recall; (6) impaired performance on tests of distractibility, verbal fluency, and working memory (7) did not impair orientation; (8) increased plasma cortisol. These data indicate that Δ-9-THC produces a broad range of transient symptoms, behaviors, and cognitive deficits in healthy individuals that resemble some aspects of endogenous psychoses. These data warrant further study of whether brain cannabinoid receptor function contributes to the pathophysiology of psychotic disorders.
Improved power transformer winding fault detection using FRA diagnostics – part 1: axial displacement simulation

Frequency response analysis (FRA) has become a widely accepted tool to detect power transformer winding deformation due to the development of FRA test equipment. Because FRA relies on graphical analysis, interpretation of its signature is a very specialized area that calls for skilled personnel, as so far, there is no reliable standard code for FRA signature identification and quantification. Many researchers investigated the impact of various mechanical winding deformations on the transformer FRA signature using simulation analysis by altering particular electrical parameters of the transformer equivalent electrical circuit. None of them however, investigated the impact of various physical fault levels on the corresponding change in the equivalent circuit parameters. In this paper, the physical geometrical dimension of a single-phase transformer is simulated using 3D finite element analysis to emulate the real transformer operation. A physical axial displacement of different fault levels is simulated in both low voltage and high voltage windings. The impact of each fault level on the electrical parameters of the equivalent circuit is investigated. A key contribution of this paper is the charts it introduces to correlate various axial displacement levels with the percentage change of all transformer equivalent circuit parameters due to the axial displacement fault. In contrary with other researchers who only considered mutual inductance between low voltage and high voltage windings, simulation results shown in this paper reveal that other circuit parameters should be changed by a particular percentage to accurately simulate particular fault level of transformer winding axial displacement. Results of this paper aid to precisely simulating winding axial displacement using transformer equivalent circuit that facilitates accurate qualitative and quantitative analysis of transformer FRA signatures.
Filtering of Video using Guided Filter

Image processing is one of the most important areas of signal processing. Image processing is applicable for the improvement of pictorial improvement for human perception and autonomous machine application also the efficient storage and transmission. In this paper guided filter is proposed which can be applicable to both images as well as to video. This guided filter has many advantages over the previous version of filter like mean filter, median filter, sobel filter, bilateral filter, etc. Among of those bilateral filter is more popular. But this bilateral filter has certain limitations like gradient reversal effect this introduces false edges in the image. Proposed guided filter can smooth image/video and also preserved the edge that is information at the edges of image/video but this filter has better behaviors near edges. Also the fast implementation of guided filter is possible. This filter also has algorithm implementation regardless of kernel size. Guided filter gives better output than that of bilateral filter after filtering this bilateral filter gives output like cartoon this also a problem with this. The guided filter is both effective and efficient in many applications of computer vision and computer graphics, etc. Filtering techniques are used in image and video processing applications for various technologies.
Attention Guided Deep Imitation Learning

When a learning agent attempts to imitate human visuomotor behaviors, it may benefit from knowing the human demonstrator's visual attention. Such information could clarify the goal of the demonstrator, i.e., the object being attended is the most likely target of the current action. Hence it could help the agent better infer and learn the demonstrator's underlying state representation for decision making. We collect human control actions and eye-tracking data for playing Atari games. We train a deep neural network to predict human actions, and show that including gaze information significantly improves the prediction accuracy. In addition, more biologically correct representation enhances prediction accuracy.
A Novel Control Strategy for a Variable-Speed Wind Turbine With a Permanent-Magnet Synchronous Generator

This paper presents a novel control strategy for the operation of a direct-drive permanent-magnet synchronous-generator-based stand-alone variable-speed wind turbine. The control strategy for the generator-side converter with maximum power extraction is presented. The stand-alone control is featured with output voltage and frequency controller that is capable of handling variable load. The potential excess of power is dissipated in the dump-load resistor with the chopper control, and the dc-link voltage is maintained. Dynamic representation of dc bus and small-signal analysis are presented. Simulation results show that the controllers can extract maximum power and regulate the voltage and frequency under varying wind and load conditions. The controller shows very good dynamic and steady-state performance.
Dragonfly wing nodus: A one-way hinge contributing to the asymmetric wing deformation.

Dragonfly wings are highly specialized locomotor systems, which are formed by a combination of several structural components. The wing components, also known as structural elements, are responsible for the various aspects of the wing functionality. Considering the complex interactions between the wing components, modelling of the wings as a whole is only possible with inevitable huge oversimplifications. In order to overcome this difficulty, we have recently proposed a new approach to model individual components of complex wings comparatively. Here, we use this approach to study nodus, a structural element of dragonfly wings which has been less studied to date. Using a combination of several imaging techniques including scanning electron microscopy (SEM), wide-field fluorescence microscopy (WFM), confocal laser scanning microscopy (CLSM) and micro-computed tomography (micro-CT) scanning, we aim to characterize the spatial morphology and material composition of fore- and hindwing nodi of the dragonfly Brachythemis contaminata. The microscopy results show the presence of resilin in the nodi, which is expected to help the deformability of the wings. The computational results based on three-dimensional (3D) structural data suggest that the specific geometry of the nodus restrains its displacements when subjected to pressure on the ventral side. This effect, resulting from an interlocking mechanism, is expected to contribute to the dorso-ventral asymmetry of wing deformation and to provide a higher resistance to aerodynamic forces during the downstroke. Our results provide an important step towards better understanding of the structure-property-function relationship in dragonfly wings.



In this study, we investigate the wing nodus, a specialized wing component in dragonflies. Using a combination of modern imaging techniques, we demonstrate the presence of resilin in the nodus, which is expected to facilitate the wing deformability in flight. The specific geometry of the nodus, however, seems to restrain its displacements when subjected to pressure on the ventral side. This effect, resulting from an interlocking mechanism, is suggested to contribute to dorso-ventral asymmetry of wing deformations and to provide a higher resistance to aerodynamic forces during the downstroke. Our results provide an important step towards better understanding of the structure-property-function relationship in dragonfly wings and might help to design more efficient wings for biomimetic micro-air vehicles.
Meta-Lamarckian learning in memetic algorithms

Over the last decade, memetic algorithms (MAs) have relied on the use of a variety of different methods as the local improvement procedure. Some recent studies on the choice of local search method employed have shown that this choice significantly affects the efficiency of problem searches. Given the restricted theoretical knowledge available in this area and the limited progress made on mitigating the effects of incorrect local search method choice, we present strategies for MA control that decide, at runtime, which local method is chosen to locally improve the next chromosome. The use of multiple local methods during a MA search in the spirit of Lamarckian learning is here termed Meta-Lamarckian learning. Two adaptive strategies for Meta-Lamarckian learning are proposed in this paper. Experimental studies with Meta-Lamarckian learning strategies on continuous parametric benchmark problems are also presented. Further, the best strategy proposed is applied to a real-world aerodynamic wing design problem and encouraging results are obtained. It is shown that the proposed approaches aid designers working on complex engineering problems by reducing the probability of employing inappropriate local search methods in a MA, while at the same time, yielding robust and improved design search performance.
Interlocking Folded Plate - Integrated Mechanical Attachment for Structural Wood Panels

Automatic joinery has become a common technique for the jointing of beams in timber framing and roofing. It has revived traditional, integrated joints such as mortise and tenon connections. Similarly, but only recently, the automatic fabrication of traditional cabinetmaking joints has been introduced for the assembly of timber panel shell structures. First prototypes have used such integrated joints for the alignment and assembly of components, while additional adhesive bonding was used for the load-bearing connection. However, glued joints cannot be assembled on site, which results in several design constraints. In this paper, we propose the use of dovetail joints without adhesive bonding, on the case study of a timber folded plate structure. Through their single-degree-of-freedom (1DOF) geometry, these joints block the relative movement of two parts in all but one direction. This presents the opportunity for an interlocking connection of plates, as well as a challenge for the assembly of folded plate shells, where multiple non-parallel edges per plate must be jointed simultaneously.
Emotional intelligence as a moderator in the stress-burnout relationship: a questionnaire study on nurses.

To investigate inter-relationships between emotional intelligence (EI), work stress and burnout in a group of nurses in the Western Cape Province, South Africa. The moderating effect of EI in the stress-burnout relationship and group differences (nurses working in different wards) in burnout were also investigated.



Stress and subsequent burnout commonly threaten the occupational health and well-being of nurses in South Africa and elsewhere. Developing EI in nurses may increase individual stress resistance and combat burnout.



A cross-sectional research design with anonymous questionnaires was conducted. Self-report data were used.



Survey data were collected from 122 nurses working in different wards at four hospitals from a private hospital group. The Swinburne University Emotional Intelligence Test, Sources of Work Stress Inventory and Maslach Burnout Inventory were used to measure EI, stress and burnout, respectively.



Consistent inverse relationships between emotional control and management as dimensions of EI, and stress and burnout emerged. A differential effect of high vs. low EI on the stress-burnout relationship was evident. Workload and the work/family interface emerged as significant predictors of burnout. Respondents working in maternity, paediatric and ER wards reported more feelings of personal accomplishment than those working in general wards.



Higher EI is significantly related with lower stress and burnout in a sample of South African nurses. The moderator effect of EI in the stress-burnout relationship suggests that enhanced EI may help diminish burnout development when chronic stress is experienced.


 TO CLINICAL PRACTICE
EI developmental interventions, if introduced in nursing curricula, may increase emotional coping resources and enhanced social skills, which may benefit the long-term occupational health of nurses. This may be relevant in developing countries, where environmental stressors related to the organisational context (budget constraints) and wider social factors (shortage of qualified nurses) are difficult to address.
Cloud-Based Software Platform for Big Data Analytics in Smart Grids

This article focuses on a scalable software platform for the Smart Grid cyber-physical system using cloud technologies. Dynamic Demand Response (D2R) is a challenge-application to perform intelligent demand-side management and relieve peak load in Smart Power Grids. The platform offers an adaptive information integration pipeline for ingesting dynamic data; a secure repository for researchers to share knowledge; scalable machine-learning models trained over massive datasets for agile demand forecasting; and a portal for visualizing consumption patterns, and validated at the University of Southern California's campus microgrid. The article examines the role of clouds and their tradeoffs for use in the Smart Grid Cyber-Physical Sagileystem.