nils-herrmann/clean_scirepeval huggingface dataset:
A General Framework For Text Semantic Analysis And Clustering On Yelp Reviews

Millions of user reviews have been posted through Yelp. Automatic extraction of useful information from these reviews can be very beneficial for both users and businesses. Recent success in understanding the meaning of a word within the context of natural language processing (NLP) has shed a light on such a practice. Word2vec, an implementation of neural network based wordembedding approaches, has shown its ability to accurately capture the semantic similarity among words. The transition from word2vec to doc2vec (document to vector) or text2vec (text to vector), however, has remained an active research. In this study, a word2vec based framework for learning Yelp reviews to yield vector/matrix representation of Yelp reviews and Yelp businesses has been developed. It's application in automatic recognition of similarity among different reviews or different businesses has been shown to be successful. Furthermore, the framework is shown to be able to handle practical tasks including businesses recommendation, businesses clustering and reviews clustering.
A review of the relationships between drought and forest fire in the United States.

The historical and presettlement relationships between drought and wildfire are well documented in North America, with forest fire occurrence and area clearly increasing in response to drought. There is also evidence that drought interacts with other controls (forest productivity, topography, fire weather, management activities) to affect fire intensity, severity, extent, and frequency. Fire regime characteristics arise across many individual fires at a variety of spatial and temporal scales, so both weather and climate - including short- and long-term droughts - are important and influence several, but not all, aspects of fire regimes. We review relationships between drought and fire regimes in United States forests, fire-related drought metrics and expected changes in fire risk, and implications for fire management under climate change. Collectively, this points to a conceptual model of fire on real landscapes: fire regimes, and how they change through time, are products of fuels and how other factors affect their availability (abundance, arrangement, continuity) and flammability (moisture, chemical composition). Climate, management, and land use all affect availability, flammability, and probability of ignition differently in different parts of North America. From a fire ecology perspective, the concept of drought varies with scale, application, scientific or management objective, and ecosystem.
A Novel Co-immunoprecipitation Protocol Based on Protoplast Transient Gene Expression for Studying Protein–protein Interactions in Rice

Protein–protein interactions constitute the regulatory network that coordinates diverse cellular functions. Co-immunoprecipitation (co-IP) is a widely used and effective technique to study protein–protein interactions in living cells. However, the time and cost for the preparation of a highly specific antibody is the major disadvantage associated with this technique. In the present study, a co-IP system was developed to detect protein–protein interactions based on an improved protoplast transient expression system by using commercially available antibodies. This co-IP system eliminates the need for specific antibody preparation and transgenic plant production. Leaf sheaths of rice green seedlings were used for the protoplast transient expression system which demonstrated high transformation and co-transformation efficiencies of plasmids. The transient expression system developed by this study is suitable for subcellular localization and protein detection. This work provides a rapid, reliable, and cost-effective system to study transient gene expression, protein subcellular localization, and characterization of protein–protein interactions in vivo.
Improved percolation-based method for crack detection in concrete surface images

This paper presents a highly accurate and efficient method for crack detection using percolation-based image processing. The detection of cracks in concrete surfaces during the maintenance and diagnosis of concrete structures is important to ensure the safety of these structures. Recently, the image-based crack detection method has attracted considerable attention due to its low cost and objectivity. However, there are several problems in the practical application of image processing for crack detection since real concrete surface images have noises such as concrete blebs, stains, and shadings of several sizes. In order to resolve these problems, our proposed method focuses on the number of pixels in a crack and the connectivity of the pixels. Our method employs a percolation model for crack detection in order to consider the features of the cracks. Through experiments using real concrete surface images, we demonstrate the accuracy and efficiency of our method.
24 GHz circularly polarized Doppler radar with a single antenna

24 GHz circularly polarized Doppler front-end with a single antenna is developed. The radar system is composed of 24 GHz circularly polarized Doppler radar module, signal conditioning block, DAQ unit, and signal processing program. 24 GHz Doppler radar receiver front-end IC which is comprised of 3-stage LNA, single-ended mixer, and Lange coupler is fabricated with commercial InGaP/GaAs HBT technology. To reduce the chip size and suppress self-mixing, single-ended mixer which uses Tx leakage as a LO signal of the mixer is used. The operation of the developed radar front-end is demonstrated by measuring human vital signal. Compact size and high sensitivity can be achieved at the same time with the circularly polarized Doppler radar with a single antenna.
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances

In the last 5 years there have been a large number of new time series classification algorithms proposed in the literature. These algorithms have been evaluated on subsets of the 47 data sets in the University of California, Riverside time series classification archive. The archive has recently been expanded to 85 data sets, over half of which have been donated by researchers at the University of East Anglia. Aspects of previous evaluations have made comparisons between algorithms difficult. For example, several different programming languages have been used, experiments involved a single train/test split and some used normalised data whilst others did not. The relaunch of the archive provides a timely opportunity to thoroughly evaluate algorithms on a larger number of datasets. We have implemented 18 recently proposed algorithms in a common Java framework and compared them against two standard benchmark classifiers (and each other) by performing 100 resampling experiments on each of the 85 datasets. We use these results to test several hypotheses relating to whether the algorithms are significantly more accurate than the benchmarks and each other. Our results indicate that only nine of these algorithms are significantly more accurate than both benchmarks and that one classifier, the collective of transformation ensembles, is significantly more accurate than all of the others. All of our experiments and results are reproducible: we release all of our code, results and experimental details and we hope these experiments form the basis for more robust testing of new algorithms in the future.
Variable resolution SAR ADC architecture with 99.6% reduction in switching energy over conventional scheme

A novel energy-efficient switching method for variable resolution successive approximation register (SAR) analogue-to-digital converters (ADCs) is presented. The proposed switching scheme achieves switching energy inspired by the early reset merged capacitor switching algorithm (EMCS) and monotonic capacitor switching procedure. Besides, the dummy capacitors are used to further reduce power consumption and area. When sized for the same static linearity as the conventional SAR ADC, the proposed method enhances the efficiency of switching energy by 99.6% and reduces the total area by 93.75%. Furthermore, the proposed scheme can achieve a variable resolution for SAR ADCs.
Lateral epicondylitis: a review of the literature.

Lateral epicondylitis (Tennis Elbow) is the most frequent type of myotendinosis and can be responsible for substantial pain and loss of function of the affected limb. Muscular biomechanics characteristics and equipment are important in preventing the conditions. This article present on overview of the current knowledge on lateral Epicondylitis and focuses on Etiology, Diagnosis and treatment strategies, conservative treatment are discussed and recent surgical techniques are outlined. This information should assist health care practitioners who treat patients with this disorder.
Extending UTAUT2 to Explore Pervasive Information Systems

The increasing emergence of pervasive information systems requires a clearer understanding of the underlying characteristics in relation to user acceptance. Based on the integration of UTAUT2 and three pervasiveness constructs, we derived a comprehensive research model to account for pervasive information systems. Data collected from 346 participants in an online survey was analyzed to test the developed model using structural equation modeling and taking into account multigroup analysis. The results confirm the applicability of the integrated UTAUT2 model to measure pervasiveness. Implications for research and practice are discussed together with future research opportunities.
Management of status epilepticus in adults.

Status epilepticus (SE) may cause death or severe sequelae unless seizures are terminated promptly. Various types of SE exist, and treatment should be adjusted to the specific type. Yet some basic guiding principles are broadly applicable: (1) early treatment is most effective, (2) benzodiazepines are the best first line agents, (3) electroencephalography should be used to confirm the termination of seizures in patients who are not alert and to monitor therapy in refractory cases, and (4) close attention to the appearance of systemic complications (from the SE per se or from the medications used to treat it) is essential. This article expands on these principles and summarizes current knowledge on the definition, classification, diagnosis, and treatment of SE.
Learning Graph Representations with Embedding Propagation

We propose Embedding Propagation (EP), an unsupervised learning framework for graph-structured data. EP learns vector representations of graphs by passing two types of messages between neighboring nodes. Forward messages consist of label representations such as representations of words and other attributes associated with the nodes. Backward messages consist of gradients that result from aggregating the label representations and applying a reconstruction loss. Node representations are finally computed from the representation of their labels. With significantly fewer parameters and hyperparameters an instance of EP is competitive with and often outperforms state of the art unsupervised and semi-supervised learning methods on a range of benchmark data sets.
A peer-to-peer spatial cloaking algorithm for anonymous location-based service

This paper tackles a major privacy threat in current location-based services where users have to report their exact locations to the database server in order to obtain their desired services. For example, a mobile user asking about her nearest restaurant has to report her exact location. With untrusted service providers, reporting private location information may lead to several privacy threats. In this paper, we present a peer-to-peer (P2P)spatial cloaking algorithm in which mobile and stationary users can entertain location-based services without revealing their exact location information. The main idea is that before requesting any location-based service, the mobile user will form a group from her peers via single-hop communication and/or multi-hop routing. Then,the spatial cloaked area is computed as the region that covers the entire group of peers. Two modes of operations are supported within the proposed P2P s patial cloaking algorithm, namely, the on-demand mode and the proactive mode. Experimental results show that the P2P spatial cloaking algorithm operated in the on-demand mode has lower communication cost and better quality of services than the proactive mode, but the on-demand incurs longer response time.
Continuum robots for space applications based on layer-jamming scales with stiffening capability

Continuum robots, which have continuous mechanical structures comparable to the flexibility in elephant trunks and octopus arms, have been primarily geared toward the medical and defense communities. In space, however, NASA projects these robots to have a place in irregular inspection routines. The inherent compliance and bending of these continuum arms are especially suitable for inspection in obstructed spaces to ensure proper equipment functionality. In this paper, we propose a new solution that improves on the functionality of previous continuum robots, via a novel mechanical scaly layer-jamming design. Layer-jamming assisted continuum arms have previously required pneumatic sources for actuation, which limit their portability and usage in aerospace applications. This paper combines the compliance of continuum arms and stiffness modulation of the layer jamming mechanism to design a new hybrid layer jamming continuum arm. The novel design uses an electromechanical actuation which eliminates the pneumatic actuation therefore making it compact and portable.
Approximating Explicit Model Predictive Control Using Constrained Neural Networks

This paper presents a method to compute an approximate explicit model predictive control (MPC) law using neural networks. The optimal MPC control law for constrained linear quadratic regulator (LQR) systems is piecewise affine on polytopes. However, computing this optimal control law becomes computationally intractable for large problems, and motivates the application of reinforcement learning techniques using neural networks with rectified linear units. We introduce a modified reinforcement learning policy gradient algorithm that utilizes knowledge of the system model to efficiently train the neural network. We guarantee that the network generates feasible control inputs by projecting onto polytope regions derived from the maximal control invariant set of the system. Finally, we present numerical examples that demonstrate the characteristics and performance of our algorithm.
The Effect of Maximal- and Explosive-Strength Training on Performance Indicators in Cyclists.

Cycling economy (CE), power output at maximal oxygen uptake (WV̇O2max), and anaerobic function (ie, sprinting ability) are considered the best physiological performance indicators in elite road cyclists. In addition to cardiovascular function, these physiological indicators are partly dictated by neuromuscular factors. One technique to improve neuromuscular function in athletes is through strength training. The aim of this study was to investigate the effect of a 20-wk maximal- and explosive-strength-training intervention on strength (maximal strength, explosive strength, and bike-specific explosive strength), WV̇O2max, CE, and body composition (body mass, fat and lean mass) in cyclists. Fifteen competitive road cyclists were divided into an intervention group (endurance training and strength training: n = 6; age, 38.0 ± 10.2 y; weight, 69.1 ± 3.6 kg; height, 1.77 ± 0.04 m) and a control group (endurance training only: n = 9; age, 34.8 ± 8.5 y; weight, 72.5 ± 7.2 kg; height, 1.78 ± 0.05 m). The intervention group strength-trained for 20 wk. Each participant completed 3 assessments: physiology (CE, WV̇O2max, power at 2 and 4 mmol/L blood lactate), strength (isometric midthigh pull, squat-jump height, and 6-s bike-sprint peak power), and body composition (body mass, fat mass, overall leanness, and leg leanness). The results showed significant between- and within-group changes in the intervention group for maximal strength, bike-specific explosive strength, absolute WV̇O2max, body mass, overall leanness, and leg leanness at wk 20 (P < .05). The control group showed no significant within-group changes in measures of strength, physiology, or body composition. This study demonstrates that 20 wk of strength training can significantly improve maximal strength, bike-specific explosive strength, and absolute WV̇O2max in competitive road cyclists.
A Fast , Adaptive Variant of the Goemans-Williamson Scheme for the Prize-Collecting Steiner Tree Problem

We introduce a new variant of the Goemans-Williamson (GW) scheme for the Prize-Collecting Steiner Tree Problem (PCST). Motivated by applications in signal processing, the focus of our contribution is to construct a very fast algorithm for the PCST problem that still achieves a provable approximation guarantee. Our overall algorithm runs in time O(dm logn) on a graph with m edges, where all edge costs and node prizes are specified with d bits of precision. Moreover, our algorithm maintains the Lagrangian-preserving factor-2 approximation guarantee of the GW scheme. Similar to [Cole, Hariharan, Lewenstein, and Porat, ], we use dynamic edge splitting in order to efficiently process all cluster merge and deactivation events in the moat-growing stage of the GW scheme. Our edge splitting rules are more adaptive to the input, thereby reducing the amount of time spent on processing intermediate edge events. Numerical experiments based on the public DIMACS test instances show that our edge splitting rules are very effective in practice. In most test cases, the number of edge events processed per edge is less than 2 on average. On a laptop computer from 2010, the longest running time of our implementation on a DIMACS challenge instance is roughly 1.3 seconds (the corresponding instance has about 340,000 edges). Since the running time of our algorithm scales nearly linearly with the input size and exhibits good constant factors, we believe that our algorithm could potentially be useful in a variety of applied settings.
Monosomy 18p

Monosomy 18p refers to a chromosomal disorder resulting from the deletion of all or part of the short arm of chromosome 18. The incidence is estimated to be about 1:50,000 live-born infants. In the commonest form of the disorder, the dysmorphic syndrome is very moderate and non-specific. The main clinical features are short stature, round face with short philtrum, palpebral ptosis and large ears with detached pinnae. Intellectual deficiency is mild to moderate. A small subset of patients, about 10-15 percent of cases, present with severe brain/facial malformations evocative of holoprosencephaly spectrum disorders. In two-thirds of the cases, the 18p- syndrome is due to a mere terminal deletion occurring de novo, in one-third the following are possible: a de novo translocation with loss of 18p, malsegregation of a parental translocation or inversion, or a ring chr18. Parental transmission of the 18p- syndrome has been reported. Cytogenetic analysis is necessary to make a definite diagnosis. Recurrence risk for siblings is low in de novo deletions and translocations, but is significant if a parental rearrangement is present. Deletion 18p can be detected prenatally by amniocentesis or chorionic villus sampling and cytogenetic testing. Differential diagnosis may include a wide number of syndromes with short stature and mild intellectual deficiency. In young children, deletion 18p syndrome may be vaguely evocative of either Turner syndrome or trisomy 21. No specific treatment exists but speech therapy and early educational programs may help to improve the performances of the children. Except for the patients with severe brain malformations, the life expectancy does not seem significantly reduced.
Exploiting Social Internet of Things Features in Cognitive Radio

Cognitive radio (CR) represents the proper technological solution in case of radio resources scarcity and availability of shared channels. For the deployment of CR solutions, it is important to implement proper sensing procedures, which are aimed at continuously surveying the status of the channels. However, accurate views of the resources status can be achieved only through the cooperation of many sensing devices. For these reasons, in this paper, we propose the utilization of the Social Internet of Things (SIoT) paradigm, according to which objects are capable of establishing social relationships in an autonomous way, with respect to the rules set by their owners. The resulting social network enables faster and trustworthy information/service discovery exploiting the social network of “friend” objects. We first describe the general approach according to which members of the SIoT collaborate to exchange channel status information. Then, we discuss the main features, i.e., the possibility to implement a distributed approach for a low-complexity cooperation and the scalability feature in heterogeneous networks. Simulations have also been run to show the advantages in terms of increased capacity and decreased interference probability.
An online rotor time constant estimator for induction machine

Indirect field oriented control for induction machine requires the knowledge of rotor time constant to estimate the rotor flux linkages. Here an online method for estimating the rotor time constant and stator resistance is presented. The problem is formulated as a nonlinear least-squares problem and a procedure is presented that guarantees the minimum is found in a finite number of steps. Experimental results are presented. Two different approaches to implementing the algorithm online are discussed. Simulations are also presented to show how the algorithm works online
Verification and validation of bioinformatics software without a gold standard: a case study of BWA and Bowtie

Bioinformatics software quality assurance is essential in genomic medicine. Systematic verification and validation of bioinformatics software is difficult because it is often not possible to obtain a realistic "gold standard" for systematic evaluation. Here we apply a technique that originates from the software testing literature, namely Metamorphic Testing (MT), to systematically test three widely used short-read sequence alignment programs. MT alleviates the problems associated with the lack of gold standard by checking that the results from multiple executions of a program satisfy a set of expected or desirable properties that can be derived from the software specification or user expectations. We tested BWA, Bowtie and Bowtie2 using simulated data and one HapMap dataset. It is interesting to observe that multiple executions of the same aligner using slightly modified input FASTQ sequence file, such as after randomly re-ordering of the reads, may affect alignment results. Furthermore, we found that the list of variant calls can be affected unless strict quality control is applied during variant calling. Thorough testing of bioinformatics software is important in delivering clinical genomic medicine. This paper demonstrates a different framework to test a program that involves checking its properties, thus greatly expanding the number and repertoire of test cases we can apply in practice.