nils-herrmann/clean_scirepeval huggingface dataset:
Low-Cost “ball and Plate” design and implementation for learning control systems

Undergraduate engineering students require a practical application of theoretical concepts learned in classrooms in order to appropriate a complete management of them. Our aim is to assist students to learn control systems theory in an engineering context, through the design and implementation of a simple and low cost ball and plate plant. Students are able to apply mathematical and computational modelling tools, control systems design, and real-time software-hardware implementation while solving a position regulation problem. The whole project development is presented and may be assumed as a guide for replicate results or as a basis for a new design approach. In both cases, we end up in a tool available to implement and assess control strategies experimentally.
Preoperative planning for primary total hip arthroplasty.

Preoperative planning is of paramount importance in obtaining reproducible results in modern hip arthroplasty. Planning helps the surgeon visualize the operation after careful review of the clinical and radiographic findings. A standardized radiograph with a known magnification should be used for templating. The cup template should be placed relative to the ilioischial line, the teardrop, and the superolateral acetabular margin, so that the removal of the supportive subchondral bone is minimal and the center of rotation of the hip is restored. When acetabular abnormalities are encountered, additional measures are necessary to optimize cup coverage and minimize the risk of malposition. Templating the femoral side for cemented and cementless implants should aim to optimize limb length and femoral offset, thereby improving the biomechanics of the hip joint. Meticulous preoperative planning allows the surgeon to perform the procedure expediently and precisely, anticipate potential intraoperative complications, and achieve reproducible results.
Soft Clustering for Very Large Data Sets

Clustering is regarded as one of the significant task in data mining and has been widely used in very large data sets. Soft clustering is unlike the traditional hard clustering which allows one data belong to two or more clusters. Soft clustering such as fuzzy c-means and rough k-means have been proposed and successfully applied to deal with uncertainty and vagueness. However, the influx of very large amount of noisy and blur data increases difficulties of parallelization of the soft clustering techniques. The question is how to deploy clustering algorithms for this tremendous amount of data to get the clustering result within a reasonable time. This paper provides an overview of the mainstream clustering techniques proposed over the past decade and the trend and progress of clustering algorithms applied in big data. Moreover, the improvement of clustering algorithms in big data are introduced and analyzed. The possible future for more advanced clustering techniques are illuminated based on today’s information era.
Effects of Ketogenic Diets on Cardiovascular Risk Factors: Evidence from Animal and Human Studies

The treatment of obesity and cardiovascular diseases is one of the most difficult and important challenges nowadays. Weight loss is frequently offered as a therapy and is aimed at improving some of the components of the metabolic syndrome. Among various diets, ketogenic diets, which are very low in carbohydrates and usually high in fats and/or proteins, have gained in popularity. Results regarding the impact of such diets on cardiovascular risk factors are controversial, both in animals and humans, but some improvements notably in obesity and type 2 diabetes have been described. Unfortunately, these effects seem to be limited in time. Moreover, these diets are not totally safe and can be associated with some adverse events. Notably, in rodents, development of nonalcoholic fatty liver disease (NAFLD) and insulin resistance have been described. The aim of this review is to discuss the role of ketogenic diets on different cardiovascular risk factors in both animals and humans based on available evidence.
Design of chatbot with 3D avatar, voice interface, and facial expression

A chatbot is a conventional agent that is able to interact with users in a given subject by using natural language. The conversations in most chatbot are still using a keyboard as the input. Keyboard input is considered ineffective as the conversation is not natural without any saying and a conversation is not just about words. Therefore, this paper propose a design of a chatbot with avatar and voice interaction to make a conversation more alive. This proposed approach method will come from using several API and using its output as another input to next API. It would take speech recognition to take input from user, then proceed it to chatbot API to receive the chatbot reply in a text form. The reply will be processed to text-to-speech recognition and created a spoken, audio version of the reply. Last, the computer will render an avatar whose gesture and lips are sync with the audio reply. This design would make every customer service or any service with human interaction can use it to make interaction more natural. This design can be further explored with additional tool such as web camera to make the agent can analyze the user's emotion and reaction.
Value-added Utilisation of Waste Glass in Concrete

Ahmad SHAYAN Chief Research Scientist ARRB Transport Research Vermont South Vic Aust Ahmad Shayan is a Chief Scientist at ARRB Transport Research. He has over 22 years of experience in the assessment of concrete deterioration and its prevention, and also utilisation of waste materials in concrete and recycling. He has published around 110 papers and written 185 technical reports on these issues. He is a member of several national and international committees.
Postural aberrations in low back pain.

The purpose of this study was to measure and describe postural aberrations in chronic and acute low back pain in search of predictors of low back pain. The sample included 59 subjects recruited to the following three groups: chronic, acute, or no low back pain. Diagnoses included disc disease, mechanical back pain, and osteoarthritis. Lumbar lordosis, thoracic kyphosis, head position, shoulder position, shoulder height, pelvic tilt, and leg length were measured using a photographic technique. In standing, chronic pain patients exhibited an increased lumbar lordosis compared with controls (p < .05). Acute patients had an increased thoracic kyphosis and a forward head position compared with controls (p < .05). In sitting, acute patients had an increased thoracic kyphosis compared with controls (p < .05). These postural parameters identified discrete postural profiles but had moderate value as predictors of low back pain. Therefore other unidentified factors are also important in the prediction of low back pain.
Ultrathin Films of Single-Walled Carbon Nanotubes for Electronics and Sensors : A Review of Fundamental and Applied Aspects

Ultrathin films of single-walled carbon nanotubes (SWNTs) represent an attractive, emerging class of material, with properties that can approach the exceptional electrical, mechanical, and optical characteristics of individual SWNTs, in a format that, unlike isolated tubes, is readily suitable for scalable integration into devices. These features suggest the potential for realistic applications as conducting or semiconducting layers in diverse types of electronic, optoelectronic and sensor systems. This article reviews recent advances in assembly techniques for forming such films, modeling and experimental work that reveals their collective properties, and engineering aspects of implementation in sensors and in electronic devices and circuits with various levels of complexity. A concluding discussion provides some perspectives on possibilities for future work in fundamental and applied aspects.
Practical challenges related to point of care testing

Point of care testing (POCT) refers to laboratory testing that occurs near to the patient, often at the patient bedside. POCT can be advantageous in situations requiring rapid turnaround time of test results for clinical decision making. There are many challenges associated with POCT, mainly related to quality assurance. POCT is performed by clinical staff rather than laboratory trained individuals which can lead to errors resulting from a lack of understanding of the importance of quality control and quality assurance practices. POCT is usually more expensive than testing performed in the central laboratory and requires a significant amount of support from the laboratory to ensure the quality testing and meet accreditation requirements. Here, specific challenges related to POCT compliance with accreditation standards are discussed along with strategies that can be used to overcome these challenges. These areas include: documentation of POCT orders, charting of POCT results as well as training and certification of individuals performing POCT. Factors to consider when implementing connectivity between POCT instruments and the electronic medical record are also discussed in detail and include: uni-directional versus bidirectional communication, linking patient demographic information with POCT software, the importance of positive patient identification and considering where to chart POCT results in the electronic medical record.
Directivity Enhancement to Vivaldi Antennas Using Compactly Anisotropic Zero-Index Metamaterials

The traditional Vivaldi antenna has an ultrawide bandwidth, but low directivity. To enhance the directivity, we propose a high-gain Vivaldi antenna based on compactly anisotropic zero-index metamaterials (ZIM). Such anisotropic ZIM are designed and fabricated using resonant meander-line structures, which are integrated with the Vivaldi antenna smoothly and hence have compact size. Measurement results show that the directivity and gain of the Vivaldi antenna have been enhanced significantly in the designed bandwidth of anisotropic ZIM (9.5-10.5 GHz), but not affected in other frequency bands (2.5-9.5 GHz and 10.5-13.5 GHz).
Fast Marching farthest point sampling for point clouds and implicit surfaces

In a recent paper , the Fast Marching farthest point sampling strategy (FastFPS) for planar domains and curved manifolds was introduced. The version of FastFPS for curved manifolds discussed in the paper [13] deals with surface domains in triangulated form only. Due to a restriction of the underlying Fast Marching method, the algorithm further requires the splitting of any obtuse into acute triangles to ensure the consistency of the Fast Marching approximation. In this paper, we overcome these restrictions by using Mémoli and Sapiro’s [11, 12] extension of the Fast Marching method to the handling of implicit surfaces and point clouds. We find that the extended FastFPS algorithm can be applied to surfaces in implicit or point cloud form without the loss of the original algorithm’s computational optimality and without the need for any preprocessing.
Improving Resource Efficiency of Container-Instance Clusters on Clouds

Cloud computing providers such as Amazon and Google have recently begun offering container-instances, which provide an efficient route to application deployment within a lightweight, isolated and well-defined execution environment. Cloud providers currently offer Container Service Platforms (CSPs), which orchestrate containerised applications. Existing CSP frameworks do not offer any form of intelligent resource scheduling: applications are usually scheduled individually, rather than taking a holistic view of all registered applications and available resources in the cloud. This can result in increased execution times for applications, resource wastage through underutilised container-instances, and a reduction in the number of applications that can be deployed, given the available resources. The research presented in this paper aims to extend existing systems by adding a cloud-based Container Management Service (CMS) framework that offers increased deployment density, scalability and resource efficiency. CMS provides additional functionalities for orchestrating containerised applications by joint optimisation of sets of containerised applications, and resource pool in multiple (geographical distributed) cloud regions. We evaluated CMS on a cloud-based CSP i.e., Amazon EC2 Container Management Service (ECS) and conducted extensive experiments using sets of CPU and Memory intensive containerised applications against the direct deployment strategy of Amazon ECS. The results show that CMS achieves up to 25% higher cluster utilisation, and up to 70% reduction in execution times.
Fast Object Class Labelling via Speech

Object class labelling is the task of annotating images with labels on the presence or absence of objects from a given class vocabulary. Simply asking one yes-no question per class, however, has a cost that is linear in the vocabulary size and is thus inefficient for large vocabularies. Modern approaches rely on a hierarchical organization of the vocabulary to reduce annotation time, but remain expensive (several minutes per image for the 200 classes in ILSVRC). Instead, we propose a new interface where classes are annotated via speech. Speaking is fast and allows for direct access to the class name, without searching through a list or hierarchy. As additional advantages, annotators can simultaneously speak and scan the image for objects, the interface can be kept extremely simple, and using it requires less mouse movement. However, a key challenge is to train annotators to only say words from the given class vocabulary. We present a way to tackle this challenge and show that our method yields high-quality annotations at significant speed gains (2.3− 14.9× faster than existing methods).
Semi-supervised Multitask Learning for Sequence Labeling

Fischler PER •Sequence of tokens mapped to word embeddings. •Bidirectional LSTM builds context-dependent representations for each word. •A small feedforward layer encourages generalisation. •Conditional Random Field (CRF) at the top outputs the most optimal label sequence for the sentence. •Using character-based dynamic embeddings (Rei et al., 2016) to capture morphological patterns and unseen words.
Imagined Visual Representations as Multimodal Embeddings

Language and vision provide complementary information. Integrating both modalities in a single multimodal representation is an unsolved problem with wide-reaching applications to both natural language processing and computer vision. In this paper, we present a simple and effective method that learns a language-to-vision mapping and uses its output visual predictions to build multimodal representations. In this sense, our method provides a cognitively plausible way of building representations, consistent with the inherently reconstructive and associative nature of human memory. Using seven benchmark concept similarity tests we show that the mapped (or imagined) vectors not only help to fuse multimodal information, but also outperform strong unimodal baselines and state-of-the-art multimodal methods, thus exhibiting more human-like judgments. Ultimately, the present work sheds light on fundamental questions of natural language understanding concerning the fusion of vision and language such as the plausibility of more associative and reconstructive approaches.
Disinformation on the Web: Impact, Characteristics, and Detection of Wikipedia Hoaxes

Wikipedia is a major source of information for many people. However, false information on Wikipedia raises concerns about its credibility. One way in which false information may be presented on Wikipedia is in the form of hoax articles, i.e., articles containing fabricated facts about nonexistent entities or events. In this paper we study false information on Wikipedia by focusing on the hoax articles that have been created throughout its history. We make several contributions. First, we assess the real-world impact of hoax articles by measuring how long they survive before being debunked, how many pageviews they receive, and how heavily they are referred to by documents on the Web. We find that, while most hoaxes are detected quickly and have little impact on Wikipedia, a small number of hoaxes survive long and are well cited across the Web. Second, we characterize the nature of successful hoaxes by comparing them to legitimate articles and to failed hoaxes that were discovered shortly after being created. We find characteristic differences in terms of article structure and content, embeddedness into the rest of Wikipedia, and features of the editor who created the hoax. Third, we successfully apply our findings to address a series of classification tasks, most notably to determine whether a given article is a hoax. And finally, we describe and evaluate a task involving humans distinguishing hoaxes from non-hoaxes. We find that humans are not good at solving this task and that our automated classifier outperforms them by a big margin.
Acid rain and its ecological consequences.

Acidification of rain-water is identified as one of the most serious environmental problems of transboundary nature. Acid rain is mainly a mixture of sulphuric and nitric acids depending upon the relative quantities of oxides of sulphur and nitrogen emissions. Due to the interaction of these acids with other constituents of the atmosphere, protons are released causing increase in the soil acidity Lowering of soil pH mobilizes and leaches away nutrient cations and increases availability of toxic heavy metals. Such changes in the soil chemical characteristics reduce the soil fertility which ultimately causes the negative impact on growth and productivity of forest trees and crop plants. Acidification of water bodies causes large scale negative impact on aquatic organisms including fishes. Acidification has some indirect effects on human health also. Acid rain affects each and every components of ecosystem. Acid rain also damages man-made materials and structures. By reducing the emission of the precursors of acid rain and to some extent by liming, the problem of acidification of terrestrial and aquatic ecosystem has been reduced during last two decades.
WINDKESSEL MODEL ANALYSIS IN MATLAB

This paper briefly describes three Windkessel models and demonstrates application of Matlab for mathematical modelling and simulation experiments with the models. Windkessel models are usually used to describe basic properties vascular bed and to study relationships among hemodynamic variables in great vessels. Analysis of a systemic or pulmonary arterial load described by parameters such as arterial compliance and peripheral resistance, is important, for example, in quantifying the effects of vasodilator or vasoconstrictor drugs. Also, a mathematical model of the relationship between blood pressure and blood flow in the aorta and pulmonary artery can be useful, for example, in the design, development and functional analysis of a mechanical heart and/or heart-lung machines. We found that ascending aortic pressure could be predicted better from aortic flow by using the four-element windkessel than by using the three-element windkessel or two-elment windkessel. The root-mean-square errors were smaller for the four-element windkessel.
Life-history evolution and the origin of multicellularity.

The fitness of an evolutionary individual can be understood in terms of its two basic components: survival and reproduction. As embodied in current theory, trade-offs between these fitness components drive the evolution of life-history traits in extant multicellular organisms. Here, we argue that the evolution of germ-soma specialization and the emergence of individuality at a new higher level during the transition from unicellular to multicellular organisms are also consequences of trade-offs between the two components of fitness-survival and reproduction. The models presented here explore fitness trade-offs at both the cell and group levels during the unicellular-multicellular transition. When the two components of fitness negatively covary at the lower level there is an enhanced fitness at the group level equal to the covariance of components at the lower level. We show that the group fitness trade-offs are initially determined by the cell level trade-offs. However, as the transition proceeds to multicellularity, the group level trade-offs depart from the cell level ones, because certain fitness advantages of cell specialization may be realized only by the group. The curvature of the trade-off between fitness components is a basic issue in life-history theory and we predict that this curvature is concave in single-celled organisms but becomes increasingly convex as group size increases in multicellular organisms. We argue that the increasingly convex curvature of the trade-off function is driven by the initial cost of reproduction to survival which increases as group size increases. To illustrate the principles and conclusions of the model, we consider aspects of the biology of the volvocine green algae, which contain both unicellular and multicellular members.
Concatenated p-mean Word Embeddings as Universal Cross-Lingual Sentence Representations

Average word embeddings are a common baseline for more sophisticated sentence embedding techniques. However, they typically fall short of the performances of more complex models such as InferSent. Here, we generalize the concept of average word embeddings to power mean word embeddings. We show that the concatenation of different types of power mean word embeddings considerably closes the gap to state-of-the-art methods monolingually and substantially outperforms these more complex techniques crosslingually. In addition, our proposed method outperforms different recently proposed baselines such as SIF and Sent2Vec by a solid margin, thus constituting a much harder-to-beat monolingual baseline. Our data and code are publicly
The Traditional Medicine and Modern Medicine from Natural Products

Natural products and traditional medicines are of great importance. Such forms of medicine as traditional Chinese medicine, Ayurveda, Kampo, traditional Korean medicine, and Unani have been practiced in some areas of the world and have blossomed into orderly-regulated systems of medicine. This study aims to review the literature on the relationship among natural products, traditional medicines, and modern medicine, and to explore the possible concepts and methodologies from natural products and traditional medicines to further develop drug discovery. The unique characteristics of theory, application, current role or status, and modern research of eight kinds of traditional medicine systems are summarized in this study. Although only a tiny fraction of the existing plant species have been scientifically researched for bioactivities since 1805, when the first pharmacologically-active compound morphine was isolated from opium, natural products and traditional medicines have already made fruitful contributions for modern medicine. When used to develop new drugs, natural products and traditional medicines have their incomparable advantages, such as abundant clinical experiences, and their unique diversity of chemical structures and biological activities.