nils-herrmann/clean_scirepeval huggingface dataset:
Player Load, Acceleration, and Deceleration During Forty-Five Competitive Matches of Elite Soccer.

The use of time-motion analysis has advanced our understanding of position-specific work rate profiles and the physical requirements of soccer players. Still, many of the typical soccer activities can be neglected, as these systems only examine activities measured by distance and speed variables. This study used triaxial accelerometer and time-motion analysis to obtain new knowledge about elite soccer players' match load. Furthermore, we determined acceleration/deceleration profiles of elite soccer players and their contribution to the players' match load. The data set includes every domestic home game (n = 45) covering 3 full seasons (2009, 2010, and 2011) for the participating team (Rosenborg FC), and includes 8 central defenders (n = 68), 9 fullbacks (n = 83), 9 central midfielders (n = 70), 7 wide midfielders (n = 39), and 5 attackers (A, n = 50). A novel finding was that accelerations contributed to 7-10% of the total player load for all player positions, whereas decelerations contributed to 5-7%. Furthermore, the results indicate that other activities besides the high-intensity movements contribute significantly to the players' total match workload. Therefore, motion analysis alone may underestimate player load because many high-intensity actions are without a change in location at the pitch or they are classified as low-speed activity according to current standards. This new knowledge may help coaches to better understand the different ways players achieve match load and could be used in developing individualized programs that better meet the "positional physical demands" in elite soccer.
Browsing, Posting, and Liking on Instagram: The Reciprocal Relationships Between Different Types of Instagram Use and Adolescents' Depressed Mood

Although studies have shown that Instagram use and young adults' mental health are cross-sectionally associated, longitudinal evidence is lacking. In addition, no study thus far examined this association, or the reverse, among adolescents. To address these gaps, we set up a longitudinal panel study among 12- to 19-year-old Flemish adolescents to investigate the reciprocal relationships between different types of Instagram use and depressed mood. Self-report data from 671 adolescent Instagram users (61% girls; MAge = 14.96; SD = 1.29) were used to examine our research question and test our hypotheses. Structural equation modeling showed that Instagram browsing at Time 1 was related to increases in adolescents' depressed mood at Time 2. In addition, adolescents' depressed mood at Time 1 was related to increases in Instagram posting at Time 2. These relationships were similar among boys and girls. Potential explanations for the study findings and suggestions for future research are discussed.
Cell-free DNA fragment-size distribution analysis for non-invasive prenatal CNV prediction

Non-invasive detection of aneuploidies in a fetal genome through analysis of cell-free DNA circulating in the maternal plasma is becoming a routine clinical test. Such tests, which rely on analyzing the read coverage or the allelic ratios at single-nucleotide polymorphism (SNP) loci, are not sensitive enough for smaller sub-chromosomal abnormalities due to sequencing biases and paucity of SNPs in a genome.



We have developed an alternative framework for identifying sub-chromosomal copy number variations in a fetal genome. This framework relies on the size distribution of fragments in a sample, as fetal-origin fragments tend to be smaller than those of maternal origin. By analyzing the local distribution of the cell-free DNA fragment sizes in each region, our method allows for the identification of sub-megabase CNVs, even in the absence of SNP positions. To evaluate the accuracy of our method, we used a plasma sample with the fetal fraction of 13%, down-sampled it to samples with coverage of 10X-40X and simulated samples with CNVs based on it. Our method had a perfect accuracy (both specificity and sensitivity) for detecting 5 Mb CNVs, and after reducing the fetal fraction (to 11%, 9% and 7%), it could correctly identify 98.82-100% of the 5 Mb CNVs and had a true-negative rate of 95.29-99.76%.



Our source code is available on GitHub at https://github.com/compbio-UofT/FSDA  brudno@cs.toronto.edu.
The Oxford handbook of computational linguistics

This collection of invited papers covers a lot of ground in its nearly 800 pages, so any review of reasonable length will necessarily be selective. However, there are a number of features that make the book as a whole a comparatively easy and thoroughly rewarding read. Multiauthor compendia of this kind are often disjointed, with very little uniformity from chapter to chapter in terms of breadth, depth, and format. Such is not the case here. Breadth and depth of treatment are surprisingly consistent, with coherent formats that often include both a little history of the field and some thoughts about the future. The volume has a very logical structure in which the chapters flow and follow on from each other in an orderly fashion. There are also many cross-references between chapters, which allow the authors to build upon the foundation of one another's work and eliminate redundancies. Specifically, the contents consist of 38 survey papers grouped into three parts: Fundamentals; Processes, Methods, and Resources; and Applications. Taken together, they provide both a comprehensive introduction to the field and a useful reference volume. In addition to the usual author and subject matter indices, there is a substantial glossary that students will find invaluable. Each chapter ends with a bibliography, together with tips for further reading and mention of other resources, such as conferences , workshops, and URLs. Part I covers the full spectrum of linguistic levels of analysis from a largely theoretical point of view, including phonology, morphology, lexicography, syntax, semantics, discourse, and dialogue. The result is a layered approach to the subject matter that allows each new level to take the previous level for granted. However, the authors do not typically restrict themselves to linguistic theory. For example, Hanks's chapter on lexicography characterizes the deficiencies of both hand-built and corpus-based dictionaries , as well as discussing other practical problems, such as how to link meaning and use. The phonology and morphology chapters provide fine introductions to these topics, which tend to receive short shrift in many NLP and AI texts. Part I ends with two chapters, one on formal grammars and one on complexity, which round out the computational aspect. This is an excellent pairing, with Martín-Vide's thorough treatment of regular and context-free languages leading into Carpen-ter's masterly survey of problem complexity and practical efficiency. Part II is more task based, with a focus on such activities as text segmentation, …
Histotopographic study of the fibroadipose connective cheek system.

The purpose of this study was to investigate the morphology of the superficial musculoaponeurotic system (SMAS). Eight embalmed cadavers were analyzed: one side of the face was macroscopically dissected; on the other side, full-thickness samples of the parotid, zygomatic, nasolabial fold and buccal regions were taken. In all specimens, a laminar connective tissue layer (SMAS) bounding two different fibroadipose connective layers was identified. The superficial fibroadipose layer presented vertically oriented fibrous septa, connecting the dermis with the superficial aspect of the SMAS. In the deep fibroadipose connective layer, the fibrous septa were obliquely oriented, connecting the deep aspect of the SMAS to the parotid-masseteric fascia. This basic arrangement shows progressive thinning of the SMAS from the preauricular district to the nasolabial fold (p < 0.05). In the parotid region, the mean thicknesses of the superficial and deep fibroadipose connective tissues were 1.63 and 0.8 mm, respectively, whereas in the region of the nasolabial fold the superficial layer is not recognizable and the mean thickness of the deep fibroadipose connective layer was 2.9 mm. The connective subcutaneous tissue of the face forms a three-dimensional network connecting the SMAS to the dermis and deep muscles. These connective laminae connect adipose lobules of various sizes within the superficial and deep fibroadipose tissues, creating a three-dimensional network which modulates transmission of muscle contractions to the skin. Changes in the quantitative and qualitative characteristics of the fibroadipose connective system, reducing its viscoelastic properties, may contribute to ptosis of facial soft tissues during aging.
A Drift Detection Method Based on Active Learning

Several real-world prediction problems are subject to changes over time due to their dynamic nature. These changes, named concept drift, usually lead to immediate and disastrous loss in classifier's performance. In order to cope with such a serious problem, drift detection methods have been proposed in the literature. However, current methods cannot be widely used since they are based either on performance monitoring or on fully labeled data, or even both. Focusing on overcoming these drawbacks, in this work we propose using density variation of the most significant instances as an explicit unsupervised trigger for concept drift detection. Here, density variation is based on Active Learning, and it is calculated from virtual margins projected onto the input space according to classifier confidence. In order to investigate the performance of the proposed method, we have carried out experiments on six databases, precisely four synthetic and two real databases focusing on setting up all parameters involved in our method and on comparing it to three baselines, including two supervised drift detectors and one Active Learning-based strategy. The obtained results show that our method, when compared to the supervised baselines, reached better recognition rates in the majority of the investigated databases, while keeping similar or higher detection rates. In terms of the Active Learning-based strategies comparison, our method outperformed the baseline taking into account both recognition and detection rates, even though the baseline employed much less labeled samples. Therefore, the proposed method established a better trade-off between amount of labeled samples and detection capability, as well as recognition rate.
A Performance Comparison of CUDA and OpenCL

CUDA and OpenCL offer two different interfaces for programming GPUs. OpenCL is an open standard that can be used to program CPUs, GPU s, and other devices from different vendors, while CUDA is specific to NVIDIA GPUs. Alt hough OpenCL promises a portable language for GPU programming, its generali ty may entail a performance penalty. In this paper, we compare the performance of CUDA and OpenCL using complex, near-identical kernels. We show that when using NVIDIA compiler tools, converting a CUDA kernel to an OpenCL kernel involv es minimal modifications. Making such a kernel compile with ATI’s build tools involves more modifications. Our performance tests measure and compare data transfer times to and from the GPU, kernel execution times, and end-to-end application executi on times for both CUDA and OpenCL.
Bayesian Checking for Topic Models

Real document collections do not fit the independence assumptions asserted by most statistical topic models, but how badly do they violate them? We present a Bayesian method for measuring how well a topic model fits a corpus. Our approach is based on posterior predictive checking, a method for diagnosing Bayesian models in user-defined ways. Our method can identify where a topic model fits the data, where it falls short, and in which directions it might be improved.
Arteriovenous malformation and gastrointestinal bleeding in patients with the HeartMate II left ventricular assist device.

In this study we investigated gastrointestinal (GI) bleeding and its relationship to arteriovenous malformations (AVMs) in patients with the continuous-flow HeartMate II (HMII) left ventricular assist device (LVAD).



The records of 172 patients who received HMII support between November 2003 and June 2010 were reviewed. Patients were considered to have GI bleeding if they had 1 or more of the following symptoms: guaiac-positive stool; hematemesis; melena; active bleeding at the time of endoscopy or colonoscopy; and blood within the stomach at endoscopy or colonoscopy. The symptom(s) had to be accompanied by a decrease of >1 g/dl in the patient's hemoglobin level. The location of the bleeding was identified as upper GI tract, lower GI tract or both according to esophagogastroduodenoscopy, colonoscopy, small-bowel enteroscopy or mesenteric angiography. Post-LVAD implantation anti-coagulation therapy consisted of warfarin, aspirin and dipyridamole.



Thirty-two of the 172 patients (19%) had GI bleeding after 63 ± 62 (range 8 to 241) days of HMII support. Ten patients had GI bleeding from an AVM; these included 3 patients who had 2 bleeding episodes and 2 patients who had 5 episodes each. Sixteen patients had upper GI bleeding (10 hemorrhagic gastritis, 4 gastric AVM, 2 Mallory-Weiss syndrome), 15 had lower GI bleeding (6 diverticulosis, 6 jejunal AVM, 1 drive-line erosion of the colon, 1 sigmoid polyp, 1 ischemic colitis) and 1 had upper and lower GI bleeding (1 colocutaneous and gastrocutaneous fistula). All GI bleeding episodes were successfully managed medically.



Arteriovenous malformations can cause GI bleeding in patients with continuous-flow LVADs. In all cases in this series, GI bleeding was successfully managed without the need for surgical intervention.
Recessive Resistance to Plant Viruses: Potential Resistance Genes Beyond Translation Initiation Factors

The ability of plant viruses to propagate their genomes in host cells depends on many host factors. In the absence of an agrochemical that specifically targets plant viral infection cycles, one of the most effective methods for controlling viral diseases in plants is taking advantage of the host plant's resistance machinery. Recessive resistance is conferred by a recessive gene mutation that encodes a host factor critical for viral infection. It is a branch of the resistance machinery and, as an inherited characteristic, is very durable. Moreover, recessive resistance may be acquired by a deficiency in a negative regulator of plant defense responses, possibly due to the autoactivation of defense signaling. Eukaryotic translation initiation factor (eIF) 4E and eIF4G and their isoforms are the most widely exploited recessive resistance genes in several crop species, and they are effective against a subset of viral species. However, the establishment of efficient, recessive resistance-type antiviral control strategies against a wider range of plant viral diseases requires genetic resources other than eIF4Es. In this review, we focus on recent advances related to antiviral recessive resistance genes evaluated in model plants and several crop species. We also address the roles of next-generation sequencing and genome editing technologies in improving plant genetic resources for recessive resistance-based antiviral breeding in various crop species.
Brain , Brow , and Booty : Latina Iconicity in U . S . Popular Culture

This stamp, honoring a Mexican artist who has transcended “la frontera” and has become and icon to Hispanics, feminists, and art lovers, will be a further reminder of the continuous cultural contributions of Latinos to the United States. (Cecilia Alvear, President of National Association of Hispanic Journalists (NAHJ) on the occasion of the introduction of the Frida Kahlo U.S. postage stamp; 2001; emphasis added)
Supporting compressed-sparse activations and weights on SIMD-like accelerator for sparse convolutional neural networks

Sparsity is widely observed in convolutional neural networks by zeroing a large portion of both activations and weights without impairing the result. By keeping the data in a compressed-sparse format, the energy consumption could be considerably cut down due to less memory traffic. However, the wide SIMD-like MAC engine adopted in many CNN accelerators can not support the compressed input due to the data misalignment. In this work, a novel Dual Indexing Module (DIM) is proposed to efficiently handle the alignment issue where activations and weights are both kept in compressed-sparse format. The DIM is implemented in a representative SIMD-like CNN accelerator, and able to exploit both compressed-sparse activations and weights. The synthesis results with 40nm technology have shown that DIM can enhance up to 46% of energy consumption and 55.4% Energy-Delay-Product (EDP).
A 70 mΩ Intelligent High Side Switch with Full Diagnostics

A new smart power switch for industrial, automotive and computer applications developed in BCD (Bipolar, CMOS, DMOS) technology is described. It consists of an on-chip 70 mΩ power DMOS transistor connected in high side configuration and its driver makes the device virtually indestructible and suitable to drive any kind of load with an output current of 2.5 A. If the load is inductive, an internal voltage clamp allows fast demagnetization down to 55 V under the supply voltage. The device includes novel structures for the driver, the fully integrated charge pump circuit and its oscillator. These circuits have specifically been designed to reduce ElectroMagnetic Interference (EMI) thanks to an accurate control of the output voltage slope and the reduction of the output voltage ripple caused by the charge pump itself (several patents pending). An innovative open load circuit allows the detection of the open load condition with high precision (2 to 4 mA within the temperature range and including process spreads). The quiescent current has also been reduced to 600 uA. Diagnostics for CPU feedback is available at the external connections of the chip when the following fault conditions occur: open load; output short circuit to supply voltage; overload or output short circuit to ground; over temperature; under voltage supply.
Big Data Processing for Full-Text Search and Visualization with Elasticsearch

In this paper, the task of using Big Data to identify specific individuals on the indirect grounds of their interaction with information resources is considered. Possible sources of Big Data and problems related to its processing are analyzed. Existing means of data clustering are considered. Available software for full-text search and data visualization is analyzed, and a system based on Elasticsearch engine and MapReduce model is proposed for the solution of user verification problem. Big Data processing; verification; elasticsearch; MapReduce; data clustering
Implication of Abscisic Acid on Ripening and Quality in Sweet Cherries: Differential Effects during Pre- and Post-harvest

Sweet cherry, a non-climacteric fruit, is usually cold-stored during post-harvest to prevent over-ripening. The aim of the study was to evaluate the role of abscisic acid (ABA) on fruit growth and ripening of this fruit, considering as well its putative implication in over-ripening and effects on quality. We measured the endogenous concentrations of ABA during the ripening of sweet cherries (Prunus avium L. var. Prime Giant) collected from orchard trees and in cherries exposed to 4°C and 23°C during 10 days of post-harvest. Furthermore, we examined to what extent endogenous ABA concentrations were related to quality parameters, such as fruit biomass, anthocyanin accumulation and levels of vitamins C and E. Endogenous concentrations of ABA in fruits increased progressively during fruit growth and ripening on the tree, to decrease later during post-harvest at 23°C. Cold treatment, however, increased ABA levels and led to an inhibition of over-ripening. Furthermore, ABA levels positively correlated with anthocyanin and vitamin E levels during pre-harvest, but not during post-harvest. We conclude that ABA plays a major role in sweet cherry development, stimulating its ripening process and positively influencing quality parameters during pre-harvest. The possible influence of ABA preventing over-ripening in cold-stored sweet cherries is also discussed.
A Cognitive Model of Episodic Memory Integrated with a General Cognitive Architecture

Episodic memory provides a mechanism for accessing past experiences and has been relatively ignored in comp utational models of cognition. In this paper, we present a fr amework for describing the functional stages for computatio nal models of episodic memory: encoding, storage, retrieval an d use of the retrieved memories. We present two implementati ons of a computational model of episodic memory in Soar. We demonstrate all four stages of the model for a simp le interactive task.
Towards automatic analysis of gestures and body expressions in depression

Depression is a common mental disorder and one of the main causes of disease burden worldwide. Several studies in depression address the relation between non-verbal cues and different levels of depression. Manual coding of non-verbal cues is the common practice for running such studies, which is time consuming and non-objective. Recent research has looked into automatic detection of cues associated with depression. However, most of the work has focussed on facial cues such as facial expressions, gaze and head pose. Few studies have looked into multimodal features for analysis of depression, mainly focusing on facial movements, head movements and vocal prosody. Body gestures are an understudied modality in that field. We propose to investigate assessment of depression using automatic detection of nonverbal signals of body gestures. Moreover, we propose the use of multimodal fusion of features to incorporate body as well as face and head for better inference of depression level. Automatic analysis of such body cues can serve as a tool for experimental psychologists. Also, it can assist physicians in diagnosing by providing quantitative measures after or during face to face sessions or telemedicine sessions or even in systems like a virtual coach.
Detection of Stress using Wearable Sensors in IoT Platform

Stress is a natural reaction to various stress-inducing factors which can lead to physiological and behavioral changes. If persists for a longer period, stress can cause harmful effects on our body. The body sensors along with the concept of the Internet of Things can provide rich information about one's mental and physical health. The proposed work concentrates on developing an IoT system which can efficiently detect the stress level of a person and provide a feedback which can assist the person to cope with the stressors. The system consists of a smart band module and a chest strap module which can be worn around wrist and chest respectively. The system monitors the parameters such as Electro dermal activity and Heart rate in real time and sends the data to a cloud-based ThingSpeak server serving as an online IoT platform. The computation of the data is performed using a ‘MATLAB Visualization’ application and the stress report is displayed. The authorized person can log in, view the report and take actions such as consulting a medical person, perform some meditation or yoga exercises to cope with the condition.