nils-herrmann/clean_scirepeval huggingface dataset:
Short circuit current comparison of DFIG during symmetrical faults with different wind speeds

The short-circuit current calculation of any equipment in the power system is very important for selection of appropriate relay characteristics and circuit breaker for the protection of the system. The power system is undergoing changes because of large scale penetration of renewable energy sources in the conventional system. Major renewable sources which are included in the power system are wind energy and solar energy sources. The wind energy is supplied by wind turbine generators of various types. Type III generators i.e. Doubly Fed Induction Generator (DFIG) is the most common types of generator employed offering different behavior compared to conventionally employed synchronous generators. In this paper; the short circuit current contribution of DFIG is calculated analytically and the same is validated by PSCAD/EMTDC software under various wind speeds and by considering certain voltage drops of the generator output.
Stress responsive neurohormones in depression and anxiety.

Clinical and preclinical studies have gathered substantial evidence that stress response alterations play a major role in the development of major depression, panic disorder and posttraumatic stress disorder. The stress response, the hypothalamic pituitary adrenocortical (HPA) system and its modulation by CRH, corticosteroids and their receptors as well as the role of natriuretic peptides and neuroactive steroids are described. Examplarily, we review the role of the HPA system in major depression, panic disorder and posttraumatic stress disorder as well as its possible relevance for treatment. Impaired glucocorticoid receptor function in major depression is associated with an excessive release of neurohormones, like CRH to which a number of signs and symptoms characteristic of depression can be ascribed. In panic disorder, a role of central CRH in panic attacks has been suggested. Atrial natriuretic peptide (ANP) is causally involved in sodium lactate-induced panic attacks. Furthermore, preclinical and clinical data on its anxiolytic activity suggest that non-peptidergic ANP receptor ligands may be of potential use in the treatment of anxiety disorders. Recent data further suggest a role of 3alpha-reduced neuroactive steroids in major depression, panic attacks and panic disorder. Posttraumatic stress disorder is characterized by a peripheral hyporesponsive HPA-system and elevated CRH concentrations in CSF. This dissociation is probably related to an increased risk for this disorder. Antidepressants are effective both in depression and anxiety disorders and have major effects on the HPA-system, especially on glucocorticoid and mineralocorticoid receptors. Normalization of HPA-system abnormalities is a strong predictor of the clinical course, at least in major depression and panic disorder. CRH-R1 or glucorticoid receptor antagonists and ANP receptor agonists are currently being studied and may provide future treatment options more closely related to the pathophysiology of the disorders.
Online multi-object tracking with efficient track drift and fragmentation handling.

This paper addresses the problem of multi-object tracking in complex scenes by a single, static, uncalibrated camera. Tracking-by-detection is a widely used approach for multi-object tracking. Challenges still remain in complex scenes, however, when this approach has to deal with occlusions, unreliable detections (e.g., inaccurate position/size, false positives, or false negatives), and sudden object motion/appearance changes, among other issues. To handle these problems, this paper presents a novel online multi-object tracking method, which can be fully applied to real-time applications. First, an object tracking process based on frame-by-frame association with a novel affinity model and an appearance update that does not rely on online learning is proposed to effectively and rapidly assign detections to tracks. Second, a two-stage drift handling method with novel track confidence is proposed to correct drifting tracks caused by the abrupt motion change of objects under occlusion and prolonged inaccurate detections. In addition, a fragmentation handling method based on a track-to-track association is proposed to solve the problem in which an object trajectory is broken into several tracks due to long-term occlusions. Based on experimental results derived from challenging public data sets, the proposed method delivers an impressive performance compared with other state-of-the-art methods. Furthermore, additional performance analysis demonstrates the effect and usefulness of each component of the proposed method.
Exploring Intra-Urban Accessibility and Impacts of Pollution Policies with an Agent-Based Simulation Platform: GaMiroD

In this work we address the issue of sustainable cities by focusing on one of their very central components: daily mobility. Indeed, if cities can be interpreted as spatial organizations allowing social interactions, the number of daily movements needed to reach this goal is continuously increasing. Therefore, improving urban accessibility merely results in increasing traffic and its negative externalities (congestion, accidents, pollution, noise, etc.), while eventually reducing the quality of life of people in the city. This is why several urban-transport policies are implemented in order to reduce individual mobility impacts while maintaining equitable access to the city. This challenge is however non-trivial and therefore we propose to investigate this issue from the complex systems point of view. The real spatial-temporal urban accessibility of citizens cannot be approximated just by focusing on space and implies taking into account the space-time activity patterns of individuals, in a more dynamic way. Thus, given the importance of local interactions in such a perspective, an agent based approach seems to be a relevant solution. This kind of individual based and “interactionist” approach allows us to explore the possible impact of individual behaviors on the overall dynamics of the city but also the possible impact of global measures on individual behaviors. In this paper, we give an overview of the Miro Project and then focus on the GaMiroD model design from real data analysis to model exploration tuned by transportation-oriented scenarios. Among them, we start with the the impact of a LEZ (Low Emission Zone) in the city center.
Design of low inertia manipulator with high stiffness and strength using tension amplifying mechanisms

This paper presents a novel manipulator for human-robot interaction that has low mass and inertia without losing stiffness and payload performance. A lightweight tension amplifying mechanism that increases the joint stiffness in quadratic order is proposed. High stiffness is essential for precise and rapid manipulation, and low mass and inertia are important factors for safety due to low stored kinetic energy. The proposed tension amplifying mechanism was applied to a 1-DOF elbow joint and then extended to a 3-DOF wrist joint. The developed manipulator was analyzed in terms of inertia, stiffness, and strength properties. Its moving part weighs 3.37 kg, and its inertia is 0.57 kg·m2, which is similar to that of a human arm. The stiffness of the developed elbow joint is 1440Nm/rad, which is comparable to that of the joints with rigid components in industrial manipulators. A detailed description of the design is provided, and thorough analysis verifies the performance of the proposed mechanism.
Training Medical Image Analysis Systems like Radiologists

The training of medical image analysis systems using machine learning approaches follows a common script: collect and annotate a large dataset, train the classifier on the training set, and test it on a hold-out test set. This process bears no direct resemblance with radiologist training, which is based on solving a series of tasks of increasing difficulty, where each task involves the use of significantly smaller datasets than those used in machine learning. In this paper, we propose a novel training approach inspired by how radiologists are trained. In particular, we explore the use of meta-training that models a classifier based on a series of tasks. Tasks are selected using teacher-student curriculum learning, where each task consists of simple classification problems containing small training sets. We hypothesize that our proposed meta-training approach can be used to pre-train medical image analysis models. This hypothesis is tested on the automatic breast screening classification from DCE-MRI trained with weakly labeled datasets. The classification performance achieved by our approach is shown to be the best in the field for that application, compared to state of art baseline approaches: DenseNet, multiple instance learning and multi-task learning.
Traditional chinese medicine in treatment of metabolic syndrome.

In management of metabolic syndrome, the traditional Chinese medicine (TCM) is an excellent representative in alternative and complementary medicines with a complete theory system and substantial herb remedies. In this article, basic principle of TCM is introduced and 25 traditional Chinese herbs are reviewed for their potential activities in the treatment of metabolic syndrome. Three herbs, ginseng, rhizoma coptidis (berberine, the major active compound) and bitter melon, were discussed in detail on their therapeutic potentials. Ginseng extracts made from root, rootlet, berry and leaf of Panax quinquefolium (American ginseng) and Panax ginseng (Asian ginseng), are proved for anti-hyperglycemia, insulin sensitization, islet protection, anti-obesity and anti-oxidation in many model systems. Energy expenditure is enhanced by ginseng through thermogenesis. Ginseng-specific saponins (ginsenosides) are considered as the major bioactive compounds for the metabolic activities of ginseng. Berberine from rhizoma coptidis is an oral hypoglycemic agent. It also has anti-obesity and anti-dyslipidemia activities. The action mechanism is related to inhibition of mitochondrial function, stimulation of glycolysis, activation of AMPK pathway, suppression of adipogenesis and induction of low-density lipoprotein (LDL) receptor expression. Bitter melon or bitter gourd (Momordica charantia) is able to reduce blood glucose and lipids in both normal and diabetic animals. It may also protect beta cells, enhance insulin sensitivity and reduce oxidative stress. Although evidence from animals and humans supports the therapeutic activities of ginseng, berberine and bitter melon, multi-center large-scale clinical trials have not been conducted to evaluate the efficacy and safety of these herbal medicines.
Classification of orchid species using Neural Network

Orchid species have a largest families among the botanical plant. Basically, a species of orchid are visually recognizing from its color, root, petal shape or even the size. However, there are several orchid species that really look alike and the type could be falsely classified. The aim of this paper is to classify two species of orchids which are physically look identical, i.e. Dendrobium Madame Pampadour and Dendrobium Cqompactum using image processing techniques. Using Neural Network, the classification rate is 85.7%.
Facebook addiction and loneliness in the post-graduate students of a university in southern India.

Facebook is a social networking site (SNS) for communication, entertainment and information exchange. Recent research has shown that excessive use of Facebook can result in addictive behavior in some individuals.



To assess the patterns of Facebook use in post-graduate students of Yenepoya University and evaluate its association with loneliness.



A cross-sectional study was done to evaluate 100 post-graduate students of Yenepoya University using Bergen Facebook Addiction Scale (BFAS) and University of California and Los Angeles (UCLA) loneliness scale version 3. Descriptive statistics were applied. Pearson's bivariate correlation was done to see the relationship between severity of Facebook addiction and the experience of loneliness.



More than one-fourth (26%) of the study participants had Facebook addiction and 33% had a possibility of Facebook addiction. There was a significant positive correlation between severity of Facebook addiction and extent of experience of loneliness ( r = .239, p = .017).



With the rapid growth of popularity and user-base of Facebook, a significant portion of the individuals are susceptible to develop addictive behaviors related to Facebook use. Loneliness is a factor which influences addiction to Facebook.
Interactions between Narrative Schemas and Document Categories

The unsupervised extraction of narrative schemas—sets of events with associated argument chains—has been explored and evaluated from many angles (Chambers and Jurafsky, 2009; Jans et al. 2012; Balasubramanian et al., 2013; Pichotta and Mooney 2014). While the extraction process and evaluation of the products has been well-researched and debated, little insight has been garnered on properties of narrative schemas themselves. We examine how well extracted narrative schemas align with existing document categories using a novel procedure for retrieving candidate category alignments. This was tested against alternative baseline alignment procedures that disregard some of the complex information the schemas contain. We find that a classifier built with all available information in a schema is more precise than a classifier built with simpler subcomponents. Coreference information plays an crucial role in schematic knowledge.
Satellite image-based localization via learned embeddings

We propose a vision-based method that localizes a ground vehicle using publicly available satellite imagery as the only prior knowledge of the environment. Our approach takes as input a sequence of ground-level images acquired by the vehicle as it navigates, and outputs an estimate of the vehicle's pose relative to a georeferenced satellite image. We overcome the significant viewpoint and appearance variations between the images through a neural multi-view model that learns location-discriminative embeddings in which ground-level images are matched with their corresponding satellite view of the scene. We use this learned function as an observation model in a filtering framework to maintain a distribution over the vehicle's pose. We evaluate our method on different benchmark datasets and demonstrate its ability localize ground-level images in environments novel relative to training, despite the challenges of significant viewpoint and appearance variations.
Robust Speech Recognition Using Generalized Distillation Framework

In this paper, we propose a noise robust speech recognition system built using generalized distillation framework. It is assumed that during training, in addition to the training data, some kind of ”privileged” information is available and can be used to guide the training process. This allows to obtain a system which at test time outperforms those built on regular training data alone. In the case of noisy speech recognition task, the privileged information is obtained from a model, called ”teacher”, trained on clean speech only. The regular model, called ”student”, is trained on noisy utterances and uses teacher’s output for the corresponding clean utterances. Thus, for this framework a parallel clean/noisy speech data are required. We experimented on the Aurora2 database which provides such kind of data. Our system uses hybrid DNN-HMM acoustic model where neural networks provide HMM state probabilities during decoding. The teacher DNN is trained on the clean data, while the student DNN is trained using multi-condition (various SNRs) data. The student DNN loss function combines the targets obtained from forced alignment of the training data and the outputs of the teacher DNN when fed with the corresponding clean features. Experimental results clearly show that distillation framework is effective and allows to achieve significant reduction in the word error rate.
The molecular mechanisms of hemodialysis vascular access failure

The arteriovenous fistula has been used for more than 50 years to provide vascular access for patients undergoing hemodialysis. More than 1.5 million patients worldwide have end stage renal disease and this population will continue to grow. The arteriovenous fistula is the preferred vascular access for patients, but its patency rate at 1 year is only 60%. The majority of arteriovenous fistulas fail because of intimal hyperplasia. In recent years, there have been many studies investigating the molecular mechanisms responsible for intimal hyperplasia and subsequent thrombosis. These studies have identified common pathways including inflammation, uremia, hypoxia, sheer stress, and increased thrombogenicity. These cellular mechanisms lead to increased proliferation, migration, and eventually stenosis. These pathways work synergistically through shared molecular messengers. In this review, we will examine the literature concerning the molecular basis of hemodialysis vascular access malfunction.
Context-aware Image Generation by Using Generative Adversarial Networks

Automatically generating images from text by using generative adversarial networks (GANs) has been actively investigated. To the best of our knowledge, there is no method of generating images with consideration of the given text and its context; therefore, representing a story describing a series of related actions is insufficient for applications such as generating image sequences. In this paper, we propose a method of automatically tuning the noise parameter of GANs and a context-aware GAN model to generate images from a series of text and image pairs. Our method and model can be used for automatically generating visual stories.
Morphology: Sindhi morphological analysis for natural language processing applications

In linguistics, morphology refers to the mental system involved in word formation or to the branch of linguistics that deals with words, their internal structure, and how they are formed. Morphological Analysis is very essential for various automatic natural language processing applications. Sindhi Morphology is much more complex due to the large number of morphological variants. This paper presents the morphological analysis of Sindhi language in which important areas of Sindhi morphemes including structure, function, & nature, categories of words like compound words, prefix words, suffix Words & prefix-suffix words, and writing system are analyzed and reviewed. Moreover, comparative analysis is also carried out to comprehend the formation of Sindhi Morphology. Presented work will help to understand the internal structure of Sindhi words and beneficial for the software developers of Sindhi natural language and speech processing applications.
5 G MIMO Data for Machine Learning : Application to Beam-Selection using Deep Learning

The increasing complexity of configuring cellular networks suggests that machine learning (ML) can effectively improve 5G technologies. Deep learning has proven successful in ML tasks such as speech processing and computational vision, with a performance that scales with the amount of available data. The lack of large datasets inhibits the flourish of deep learning applications in wireless communications. This paper presents a methodology that combines a vehicle traffic simulator with a raytracing simulator, to generate channel realizations representing 5G scenarios with mobility of both transceivers and objects. The paper then describes a specific dataset for investigating beamselection techniques on vehicle-to-infrastructure using millimeter waves. Experiments using deep learning in classification, regression and reinforcement learning problems illustrate the use of datasets generated with the proposed methodology.
DIFFUSING POLICIES : TOWARDS WASSERSTEIN POLICY GRADIENT FLOWS

Policy gradients methods often achieve better performance when the change in policy is limited to a small Kullback-Leibler divergence. We derive policy gradients where the change in policy is limited to a small Wasserstein distance (or trust region). This is done in the discrete and continuous multi-armed bandit settings with entropy regularisation. We show that in the small steps limit with respect to the Wasserstein distance W2, policy dynamics are governed by the heat equation, following the Jordan-Kinderlehrer-Otto result. This means that policies undergo diffusion and advection, concentrating near actions with high reward. This helps elucidate the nature of convergence in the probability matching setup, and provides justification for empirical practices such as Gaussian policy priors and additive gradient noise.
CALIBRATION OF ACTIVATED SLUDGE MODELS: A CRITICAL REVIEW OF EXPERIMENTAL DESIGNS

This review begins with an overview of literature data on methodologies that have been applied in other studies to calibrate Activated Sludge Model No. 1 (ASM1). An attempt was made to gather and summarise the information needed to achieve a successful model calibration, and based on this a general model calibration procedure is proposed. The main part of the literature review is devoted to the different methods that have been developed and applied for the characterisation of wastewater and reaction kinetics in relation to ASM1. The methodologies are critically discussed and it is attempted to illustrate the power of the different methods for characterisation, all within the frame of ASM1 calibration. Finally, it is discussed which wastewater components and parameters are most relevant to be characterised via lab-scale experiments. This discussion also includes the problem of transferability between lab-scale and full-scale observations, and potentially different model concepts. One of the most discussed experimental factors determining the experimental response is the ratio between initial substrate and biomass concentration (S(0)/X(0)). A separate section is focusing upon this factor.
Cloud forensics definitions and critical criteria for cloud forensic capability: An overview of survey results

With the rapid growth of cloud adoption in both private and public sectors globally, cloud computing environments have become a new battlefield for cyber crime. In this paper, the researchers present the results and analysis of a survey that had been widely circulated among digital forensic experts and practitioners worldwide on cloud forensics and critical criteria for cloud forensic capability in order to better understand the key fundamental issues of cloud forensics such as its definition, scope, challenges, opportunities as well as missing capabilities based on the 257 collected responses of the survey.
Cloud tracking with optical flow for short-term solar forecasting

A method for tracking and predicting cloud movement using ground based sky imagery is presented. Sequences of partial sky images, with each image taken one second apart with a size of 640 by 480 pixels, were processed to determine the time taken for clouds to reach a user defined region in the image or the Sun. The clouds were first identified by segmenting the image based on the difference between the blue and red colour channels, producing a binary detection image. Good features to track were then located in the image and tracked utilising the Lucas-Kanade method for optical flow. From the trajectory of the tracked features and the binary detection image, cloud signals were generated. The trajectory of the individual features were used to determine the risky cloud signals (signals that pass over the user defined region or Sun). Time to collision estimates were produced based on merging these risky cloud signals. Estimates of times up to 40 seconds were achieved with error in the estimate increasing when the estimated time is larger. The method presented has the potential for tracking clouds travelling in different directions and at different velocities.