nils-herrmann/clean_scirepeval huggingface dataset:
Tetrabenazine in Huntington’s Disease Chorea

Huntington’s disease (HD) is a heredodegenerative neurological disorder with chorea and other hyperkinetic movement disorders being part of the disease spectrum. These along with cognitive and neurobehavioral manifestations contribute significantly to patient’s disability. Several classes of drugs have been used to treat the various symptoms of HD. These include typical and atypical neuroleptics along with dopamine depletors for treatment of chorea and antidepressants, GABA agonists, antiepileptic medications, cholinesterase inhibitors, antiglutamatergic drugs and botulinum toxin for treatment of other manifestations. Tetrabenazine (TBZ), a dopamine depleting medication was recently approved by the US FDA for treatment of chorea in HD. The purpose of this article is to briefly review information regarding HD and current treatments for chorea and specifically focus on TBZ and review the literature related to its use in HD chorea.
An Algorithmic Framework to Control Bias in Bandit-based Personalization

Personalization is pervasive in the online space as it leads to higher efficiency and revenue by allowing the most relevant content to be served to each user. However, recent studies suggest that personalization methods can propagate societal or systemic biases and polarize opinions; this has led to calls for regulatory mechanisms and algorithms to combat bias and inequality. Algorithmically, bandit optimization has enjoyed great success in learning user preferences and personalizing content or feeds accordingly. We propose an algorithmic framework that allows for the possibility to control bias or discrimination in such bandit-based personalization. Our model allows for the specification of general fairness constraints on the sensitive types of the content that can be displayed to a user. The challenge, however, is to come up with a scalable and low regret algorithm for the constrained optimization problem that arises. Our main technical contribution is a provably fast and low-regret algorithm for the fairness-constrained bandit optimization problem. Our proofs crucially leverage the special structure of our problem. Experiments on synthetic and real-world data sets show that our algorithmic framework can control bias with only a minor loss to revenue. ∗A short version of this paper appeared in the FAT/ML 2017 workshop  1 ar X iv :1 80 2. 08 67 4v 1 [ cs .L G ] 2 3 Fe b 20 18
Transformer and Inductor Design Handbook, 3rd Ed. [Book Review]

6 1 and cost of the product. Not all materials can be scaled-up with the same mixing process. Frequently, scaling-up the mixing process from small research batches to large quantities, necessary for production, can lead to unexpected problems. This reference book is intended to help the reader both identify and solve mixing problems. It is a comprehensive handbook that provides excellent coverage on the fundamentals, design, and applications of current mixing technology in general. Although this book includes many technology areas, one of main areas of interest to our readers would be in the polymer processing area. This would include the first eight chapters in the book and a specific application chapter on polymer processing. These cover the fundamentals of mixing technology, important to polymer processing, including residence time distributions and laminar mixing techniques. In the experimental section of the book, some of the relevant tools and techniques cover flow visualization technologies, lab scale mixing, flow and torque measurements, CFD coding, and numerical methods. There is a good overview of various types of mixers used for polymer processing in a dedicated applications chapter on mixing high viscosity materials such as polymers. There are many details given on the differences between the mixing blades in various types of high viscosity mixers and suggestions for choosing the proper mixer for high viscosity applications. The majority of the book does, however, focus on the chemical, petroleum, and pharmaceutical industries that generally process materials with much lower viscosity than polymers. The reader interested in learning about the fundamentals of mixing in general as well as some specifics on polymer processing would find this book to be a useful reference.
When complexity science meets implementation science: a theoretical and empirical analysis of systems change

Implementation science has a core aim - to get evidence into practice. Early in the evidence-based medicine movement, this task was construed in linear terms, wherein the knowledge pipeline moved from evidence created in the laboratory through to clinical trials and, finally, via new tests, drugs, equipment, or procedures, into clinical practice. We now know that this straight-line thinking was naïve at best, and little more than an idealization, with multiple fractures appearing in the pipeline.



The knowledge pipeline derives from a mechanistic and linear approach to science, which, while delivering huge advances in medicine over the last two centuries, is limited in its application to complex social systems such as healthcare. Instead, complexity science, a theoretical approach to understanding interconnections among agents and how they give rise to emergent, dynamic, systems-level behaviors, represents an increasingly useful conceptual framework for change. Herein, we discuss what implementation science can learn from complexity science, and tease out some of the properties of healthcare systems that enable or constrain the goals we have for better, more effective, more evidence-based care. Two Australian examples, one largely top-down, predicated on applying new standards across the country, and the other largely bottom-up, adopting medical emergency teams in over 200 hospitals, provide empirical support for a complexity-informed approach to implementation. The key lessons are that change can be stimulated in many ways, but a triggering mechanism is needed, such as legislation or widespread stakeholder agreement; that feedback loops are crucial to continue change momentum; that extended sweeps of time are involved, typically much longer than believed at the outset; and that taking a systems-informed, complexity approach, having regard for existing networks and socio-technical characteristics, is beneficial.



Construing healthcare as a complex adaptive system implies that getting evidence into routine practice through a step-by-step model is not feasible. Complexity science forces us to consider the dynamic properties of systems and the varying characteristics that are deeply enmeshed in social practices, whilst indicating that multiple forces, variables, and influences must be factored into any change process, and that unpredictability and uncertainty are normal properties of multi-part, intricate systems.
Deep Neural Networks for Voice Quality Assessment Based on the GRBAS Scale

In the field of voice therapy, perceptual evaluation is widely used by expert listeners as a way to evaluate pathological and normal voice quality. This approach is understandably subjective as it is subject to listeners’ bias which high interand intra-listeners variability can be found. As such, research on automatic assessment of pathological voices using a combination of subjective and objective analyses emerged. The present study aimed to develop a complementary automatic assessment system for voice quality based on the well-known GRBAS scale by using a battery of multidimensional acoustical measures through Deep Neural Networks. A total of 44 dimensionality parameters including Mel-frequency Cepstral Coefficients, Smoothed Cepstral Peak Prominence and Long-Term Average Spectrum was adopted. In addition, the state-of-the-art automatic assessment system based on Modulation Spectrum (MS) features and GMM classifiers was used as comparison system. The classification results using the proposed method revealed a moderate correlation with subjective GRBAS scores of dysphonic severity, and yielded a better performance than MS-GMM system, with the best accuracy around 81.53%. The findings indicate that such assessment system can be used as an appropriate evaluation tool in determining the presence and severity of voice disorders.
Rule-Based Control of Battery Energy Storage for Dispatching Intermittent Renewable Sources

Integrating a battery energy storage system (BESS) with a solar photovoltaic (PV) system or a wind farm can make these intermittent renewable energy sources more dispatchable. This paper focuses on the development of a control strategy for optimal use of the BESS for this purpose. The paper considers a rule-based control scheme, which is the solution of the optimal control problem defined, to incorporate the operating constraints of the BESS, such as state of charge limits, charge/discharge current limits, and lifetime. The goal of the control is to have the BESS provide as much smoothing as possible so that the renewable resource can be dispatched on an hourly basis based on the forecasted solar/wind conditions. The effectiveness of this control strategy has been tested by using an actual PV system and wind farm data and it is shown that the BESS can indeed help to cope with variability in wind's and solar's generation.
China's Internet: Topology mapping and geolocating

We perform a large-scale topology mapping and geolocation study for China's Internet. To overcome the limited number of Chinese PlanetLab nodes and looking glass servers, we leverage several unique features in China's Internet, including the hierarchical structure of the major ISPs and the abundance of IDCs. Using only 15 vantage points, we design a traceroute scheme that finds significantly more interfaces and links than iPlane with significantly fewer traceroute probes. We then consider the problem of geolocating router interfaces and end hosts in China. We develop a heuristic for clustering the interface topology of a hierarchical ISP, and then apply the heuristic to the major Chinese ISPs. We show that the clustering heuristic can geolocate router interfaces with significantly more detail and accuracy than can the existing geoIP databases in isolation, and the resulting clusters expose the major ISPs' provincial structure. Finally, using the clustering heuristic, we propose a methodology for improving commercial geoIP databases.
Chest X-rays Classification: A Multi-Label and Fine-Grained Problem

The widely used ChestX-ray14 dataset addresses an important medical image classification problem and has the following caveats: 1) many lung pathologies are visually similar, 2) a variant of diseases including lung cancer, tuberculosis, and pneumonia are present in a single scan, i.e. multiple labels and 3) The incidence of healthy images is much larger than diseased samples, creating imbalanced data. These properties are common in medical domain. Existing literature uses stateof-the-art DensetNet/Resnet models being transfer learned where output neurons of the networks are trained for individual diseases to cater for multiple diseases labels in each image. However, most of them don’t consider relationship between multiple classes. In this work we have proposed a novel error function, Multi-label Softmax Loss (MSML), to specifically address the properties of multiple labels and imbalanced data. Moreover, we have designed deep network architecture based on fine-grained classification concept that incorporates MSML. We have evaluated our proposed method on various network backbones and showed consistent performance improvements of AUC-ROC scores on the ChestX-ray14 dataset. The proposed error function provides a new method to gain improved performance across wider medical datasets.
A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues

With the widespread adoption of the Internet of Things (IoT), the number of connected devices is growing at an exponential rate, which is contributing to ever-increasing, massive data volumes. Real-time analytics on the massive IoT data, referred to as the “real-time IoT analytics” in this paper, is becoming the mainstream with an aim to provide an immediate or non-immediate actionable insights and business intelligence. However, the analytics network of the existing IoT systems does not adequately consider the requirements of the real-time IoT analytics. In fact, most researchers overlooked an appropriate design of the IoT analytics network while focusing much on the sensing and delivery networks of the IoT system. Since much of the IoT analytics network has often been taken as granted, the survey, in this paper, we aim to review the state-of-the-art of the analytics network methodologies, which are suitable for real-time IoT analytics. In this vein, we first describe the basics of the real-time IoT analytics, use cases, and software platforms, and then explain the shortcomings of the network methodologies to support them. To address those shortcomings, we then discuss the relevant network methodologies which may support the real-time IoT analytics. Also, we present a number of prospective research problems and future research directions focusing on the network methodologies for the real-time IoT analytics.
PROS AND CONS OF CONNECTIVISM AS A LEARNING THEORY

It is a clear fact that nowadays knowledge is growing faster and with the spreading of information and communication technology, the dream of network learning has become a reality, at least technically, and now a vast amount of spontaneous knowledge exchange is possible. Younger and older learners need to generate new ideas and new products that are to be innovative. In this context, this study aims to explore the nature of Connectivism (Siemens, 2004) using available literature as a traditional qualitative method. The second issue is the advantages and disadvantages of Connectivism as it is concieved by the educationalists. For this, a focus group discussion was used to obtain data. The data obtained formed the following categories: Shortness of traditional theories, the tools of Connectivism, digital literacy, flexible learning time and ecenomc competetion, learning to learn, media psychology, need for expertise, dependence on electricity and available sources. Since half of what is known today was not known 10 years ago, there should be more researches about the use, benefits and drawbacks of Connectivism in the cotext of formal and informal lerning.
Techno-economic analysis of PV / H 2 systems

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Techno-economic analysis of PV/H2 systems C Darras, G Bastien, M Muselli, P Poggi, B Champel, P Serre-Combe
Do Deep Convolutional Nets Really Need to be Deep and Convolutional

Yes, they do. This paper provides the first empirical demonstration that deep convolutional models really need to be both deep and convolutional, even when trained with methods such as distillation that allow small or shallow models of high accuracy to be trained. Although previous research showed that shallow feed-forward nets sometimes can learn the complex functions previously learned by deep nets while using the same number of parameters as the deep models they mimic, in this paper we demonstrate that the same methods cannot be used to train accurate models on CIFAR-10 unless the student models contain multiple layers of convolution. Although the student models do not have to be as deep as the teacher model they mimic, the students need multiple convolutional layers to learn functions of comparable accuracy as the deep convolutional teacher.
Automatically Classifying Self-Rated Personality Scores from Speech

Automatic personality recognition is useful for many computational applications, including recommendation systems, dating websites, and adaptive dialogue systems. There have been numerous successful approaches to classify the “Big Five” personality traits from a speaker’s utterance, but these have largely relied on judgments of personality obtained from external raters listening to the utterances in isolation. This work instead classifies personality traits based on self-reported personality tests, which are more valid and more difficult to identify. Our approach, which uses lexical and acoustic-prosodic features, yields predictions that are between 6.4% and 19.2% more accurate than chance. This approach predicts Opennessto-Experience and Neuroticism most successfully, with less accurate recognition of Extroversion. We compare the performance of classification and regression techniques, and also explore predicting personality clusters.
Microring Weight Banks

Microring weight banks could enable novel signal processing approaches in silicon photonics. We analyze factors limiting channel count in microring weight banks, which are central to analog wavelength-division multiplexed processing networks in silicon. We find that microring weight banks require a fundamentally different analysis compared to other wavelength-division multiplexing circuits (e.g., demultiplexers). By introducing a quantitative description of independent weighting, we establish performance tradeoffs between channel count and power penalty. This performance is significantly affected by coherent multiresonator interactions through bus waveguides. We experimentally demonstrate these effects in a fabricated device. Analysis relies on the development of a novel simulation technique combining parametric programming with generalized transmission theory. Experimental measurement fitting of an 8-channel weight bank is presented as an example of another application of the simulator.
VISUAL PERCEPTION IN JAPANESE ROCK

We present an investigation into the relation between design principles in Japanese gardens, and their associated perceptual effects. This leads to the realization that a set of design principles described in a Japanese gardening text by Shingen (1466), shows many parallels to the visual effects of perceptual grouping, studied by the Gestalt school of psychology. Guidelines for composition of rock clusters closely relate to perception of visual figure. Garden design elements are arranged into patterns that simplify figure-ground segmentation, while seemingly balancing the visual salience of subparts and the global arrangement. Visual ‘ground’ is analyzed via medial axis transformation (MAT), often associated with shape perception in humans. MAT analysis reveals implicit structure in the visual ground of a quintessential rock garden design. The MAT structure enables formal comparison of structure of figure and ground. They share some aesthetic qualities, with interesting differences. Both contain naturalistic asymmetric, self-similar, branching structures. While the branching pattern of the ground converges towards the viewer, that of the figure converges in the opposite direction.
A Framework to avoid Scholar Desertion using Artificial Intelligence

In fact, nowadays there are sophisticated computer sciences capable of identifying and prevent the academic desertion by means of using predictive algorithms which allow being aware about the requirements and needs of the students. In this sense, the present paper proposes a new methodology to reduce the students desertion using the advantages of the data mining theory jointly with advanced artificial intelligence techniques such that, fuzzy logic. Such methodology aims to detect in a suitable moment, when a student tends to leave their studies. The design and some relative works are detailed in order to emphasize the relevance of this promising research area. To the end, some conclusions and a discussion is presented to propose news paths for the implementation of the proposed methodology and future work.
Display-camera calibration using eye reflections and geometry constraints

In this paper, we describe a novel method for calibrating display-camera setups from reflections in a user’s eyes. Combining both devices creates a capable controlled illumination system that enables a range of interesting vision applications in non-professional environments, including object/face reconstruction and human–computer interaction. One major issue barring such systems from average homes is the geometric calibration to obtain the pose of the display which requires special hardware and tedious user interaction. Our proposed approach eliminates this requirement by introducing the novel idea of analyzing screen reflections in the cornea of the human eye, a mirroring device that is always available. We employ a simple shape model to recover pose and reflection characteristics of the eye. Thorough experimental evaluation shows that the basic strategy results in a large error and discusses possible reasons. Based on the findings, a non-linear optimization strategy is developed that exploits geometry constraints within the system to considerably improve the initial estimate. It further allows to automatically resolve an inherent ambiguity that arises in image-based eye pose estimation. The strategy may also be integrated to improve spherical mirror calibration. We describe several comprehensive experimental studies which show that the proposed method performs stably with respect to varying subjects, display poses, eye positions, and gaze directions. The results are feasible and should be sufficient for many applications. In addition, the findings provide general insight on the application of eye reflections for geometric reconstruction.
Mechanical Rubbing of Blood Clots Using Helical Robots Under Ultrasound Guidance

A simple way to mitigate the potential negative side-effects associated with chemical lysis of a blood clot is to tear its fibrin network via mechanical rubbing using a helical robot. Here, we achieve mechanical rubbing of blood clots under ultrasound guidance and using external magnetic actuation. Position of the helical robot is determined using ultrasound feedback and used to control its motion toward the clot, whereas the volume of the clots is estimated simultaneously using visual feedback. We characterize the shear modulus and ultimate shear strength of the blood clots to predict their removal rate during rubbing. Our <italic>in vitro</italic> experiments show the ability to move the helical robot controllably toward clots using ultrasound feedback with average and maximum errors of <inline-formula> <tex-math notation="LaTeX">${\text{0.84}\pm \text{0.41}}$</tex-math></inline-formula> and 2.15 mm, respectively, and achieve removal rate of <inline-formula><tex-math notation="LaTeX">$-\text{0.614} \pm \text{0.303}$</tex-math> </inline-formula> mm<inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula>/min at room temperature (<inline-formula><tex-math notation="LaTeX">${\text{25}}^{\circ }$</tex-math></inline-formula>C) and <inline-formula><tex-math notation="LaTeX">$-\text{0.482} \pm \text{0.23}$</tex-math></inline-formula> mm <inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula>/min at body temperature (37 <inline-formula><tex-math notation="LaTeX">$^{\circ}$</tex-math></inline-formula>C), under the influence of two rotating dipole fields at frequency of 35 Hz. We also validate the effectiveness of mechanical rubbing by measuring the number of red blood cells and platelets past the clot. Our measurements show that rubbing achieves cell count of <inline-formula><tex-math notation="LaTeX">$(\text{46} \pm \text{10.9}) \times \text{10}^{4}$</tex-math> </inline-formula> cell/ml, whereas the count in the absence of rubbing is <inline-formula><tex-math notation="LaTeX"> $(\text{2} \pm \text{1.41}) \times \text{10}^{4}$</tex-math></inline-formula> cell/ml, after 40 min.