nils-herrmann/clean_scirepeval huggingface dataset:
Multiple types of skeletal muscle atrophy involve a common program of changes in gene expression.

Skeletal muscle atrophy is a debilitating response to starvation and many systemic diseases including diabetes, cancer, and renal failure. We had proposed that a common set of transcriptional adaptations underlie the loss of muscle mass in these different states. To test this hypothesis, we used cDNA microarrays to compare the changes in content of specific mRNAs in muscles atrophying from different causes. We compared muscles from fasted mice, from rats with cancer cachexia, streptozotocin-induced diabetes mellitus, uremia induced by subtotal nephrectomy, and from pair-fed control rats. Although the content of >90% of mRNAs did not change, including those for the myofibrillar apparatus, we found a common set of genes (termed atrogins) that were induced or suppressed in muscles in these four catabolic states. Among the strongly induced genes were many involved in protein degradation, including polyubiquitins, Ub fusion proteins, the Ub ligases atrogin-1/MAFbx and MuRF-1, multiple but not all subunits of the 20S proteasome and its 19S regulator, and cathepsin L. Many genes required for ATP production and late steps in glycolysis were down-regulated, as were many transcripts for extracellular matrix proteins. Some genes not previously implicated in muscle atrophy were dramatically up-regulated (lipin, metallothionein, AMP deaminase, RNA helicase-related protein, TG interacting factor) and several growth-related mRNAs were down-regulated (P311, JUN, IGF-1-BP5). Thus, different types of muscle atrophy share a common transcriptional program that is activated in many systemic diseases.
A W-Band Power Amplifier Utilizing a Miniaturized Marchand Balun Combiner

A miniaturized Marchand balun combiner is proposed for a W-band power amplifier (PA). The proposed combiner reduces the electrical length of the transmission lines (transmission line) from about 80 <sup>°</sup> to 30 <sup>°</sup>, when compared with a conventional Marchand balun combiner. Implemented in a 1-V 65-nm CMOS process, the presented PA achieves a measured saturated output power of 11.9 dBm and a peak power-added efficiency of 9.0% at 87 GHz. The total chip area (with pads) is 0.77×0.48 mm<sup>2</sup>, where the size of the balun combiner is only 0.36×0.13 mm<sup>2</sup>.
Stochastic Dropout : Activation-level Dropout to Learn Better Neural Language Models

Recurrent Neural Networks are very powerful computational tools that are capable of learning many tasks across different domains. However, it is prone to overfitting and can be very difficult to regularize. Inspired by Recurrent Dropout  and Skip-connections , we describe a new and simple regularization scheme: Stochastic Dropout. It resembles the structure of recurrent dropout, but offers skip-connection over the recurrent depth. We reason the theoretical construct of such method and compare its regularization effectiveness with feedforward dropout and recurrent dropout. We demonstrate that Stochastic Dropout not only offers improvement when applied to vanilla RNN models, but also outperforms feedforward Dropout on word-level language modeling. At last, we show that the model can achieve even better result if stochastic dropout and feedforward dropout are combined.
Semantic Vector Encoding and Similarity Search Using Fulltext Search Engines

Vector representations and vector space modeling (VSM) play a central role in modern machine learning. We propose a novel approach to ‘vector similarity searching’ over dense semantic representations of words and documents that can be deployed on top of traditional inverted-index-based fulltext engines, taking advantage of their robustness, stability, scalability and ubiquity. We show that this approach allows the indexing and querying of dense vectors in text domains. This opens up exciting avenues for major efficiency gains, along with simpler deployment, scaling and monitoring. The end result is a fast and scalable vector database with a tunable tradeoff between vector search performance and quality, backed by a standard fulltext engine such as Elasticsearch. We empirically demonstrate its querying performance and quality by applying this solution to the task of semantic searching over a dense vector representation of the entire English Wikipedia.
MODELING OF GROWTH RATES OF SELECTIVE EPITAXIAL GROWTH (SEG) AND EPITAXIAL LATERAL OVERGROWTH (ELO) OF SILICON IN THE SIH2CL2-HCL-H2 SYSTEM

Kongetira, Poonacha. MSEE., Purdue University, August 1994. Modelling of Selective Epitaxial Growth(SEG) and Epitaxial Lateral Overgrowth( ELO) of Silicon in SiH2C12-HC1-H2 system. Major Professor: Gerold W. Neudeck. A semi-empirical model for the growth rate of selective epitaxial silicon(SEG) in the Dichlorosilane-HC1-Hz system that represents the experimenltal data has been presented. All epitaxy runs were done using a Gemini-I LPCVD pancake reactor. Dichlorosilane was used as the source gas and hydrogen as the carrier gas. Hydrogen Cllloride(HC1) was used to ensure that no nucleation took place on the oxide. The growth rate expression was considered to be the sum of a growth term dependent on the partial pressures of Dichlorosilane and hydrogen, and an etch berm that varies as the partial pressure of HC1. The growth and etch terms were found to have an Arrhenius relation with temperature, with activation energies of 52kcal/mol and 36kcal/mol respectively. Good agreement was obtained with experimental data. The variation of the selectivity threshold was correctly predicted, which had been a problem with earlier models for SEG growth rates. SEG/ELO Silicon was grown from 920-970°C at 40 and 150 torr pressures for a variety of HCI concentrations. In addition previous data collected by our research group at 820-1020°C and 40-150torr were used in the model.
How Many Discoveries Have Been Lost by Ignoring Modem Statistical Methods ?

Hundreds of articles in statistical journals have pointed out that standard analysis of variance, Pearson productmoment correlations, and least squares regression can be highly misleading and can have relatively low power even under very small departures from normality. In practical terms, psychology journals are littered with nonsignificant results that would have been significant if a more modern method had been used. Modern robust techniques, developed during the past 30 years, provide very effective methods for dealing with nonnormality, and they compete very well with conventional procedures when standard assumptions are met. In addition, modern methods provide accurate confidence intervals for a much broader range of situations, they provide more effective methods for detecting and studying outliers, and they can be used to get a deeper understanding of how variables are related. This article outlines and illustrates these results.
Metabolic demands of match performance in young soccer players.

The aim of the present study was to determine metabolic responses, movement patterns and distance covered at running speeds corresponding to fixed blood lactate concentrations (FBLs) in young soccer players during a match play. A further aim of the study was to evaluate the relationships between FBLs, maximal oxygen consumption (VO2max) and distance covered during a game. A multistage field test was administered to 32 players to determine FBLs and VO2max. Blood lactate (LA), heart rate (HR) and rate of perceived exertion (RPE) responses were obtained from 36 players during tournament matches filmed using six fixed cameras. Images were transferred to a computer, for calibration and synchronization. In all players, values for LA and HR were higher and RPE lower during the 1(st) half compared to the 2(nd) half of the matches (p < 0.01). Players in forward positions had higher LA levels than defenders, but HR and RPE values were similar between playing positions. Total distance and distance covered in jogging, low-moderate-high intensity running and low intensity sprint were higher during the 1(st) half (p < 0.01). In the 1(st) half, players also ran longer distances at FBLs [p<0.01; average running speed at 2mmol·L(-1) (FBL2): 3.32 ± 0.31m·s(-1) and average running speed at 4mmol·L(-1) (FBL4): 3.91 ± 0.25m·s(-1)]. There was a significant difference between playing positions in distance covered at different running speeds (p < 0.05). However, when distance covered was expressed as FBLs, the players ran similar distances. In addition, relationships between FBLs and total distance covered were significant (r = 0.482 to 0.570; p < 0.01). In conclusion, these findings demonstrated that young soccer players experienced higher internal load during the 1(st) half of a game compared to the 2(nd) half. Furthermore, although movement patterns of players differed between playing positions, all players experienced a similar physiological stress throughout the game. Finally, total distance covered was associated to fixed blood lactate concentrations during play. Key pointsBased on LA, HR and RPE responses, young top soccer players experienced a higher physiological stress during the 1(st) half of the matches compared to the 2(nd) half.Movement patterns differed in accordance with the players' positions but that all players experienced a similar physiological stress during match play.Approximately one quarter of total distance was covered at speeds that exceeded the 4 mmol·L(-1) fixed LA threshold.Total distance covered was influenced by running speeds at fixed lactate concentrations in young soccer players during match play.
Phantom Limb Pain: Mechanisms and Treatment Approaches

The vast amount of research over the past decades has significantly added to our knowledge of phantom limb pain. Multiple factors including site of amputation or presence of preamputation pain have been found to have a positive correlation with the development of phantom limb pain. The paradigms of proposed mechanisms have shifted over the past years from the psychogenic theory to peripheral and central neural changes involving cortical reorganization. More recently, the role of mirror neurons in the brain has been proposed in the generation of phantom pain. A wide variety of treatment approaches have been employed, but mechanism-based specific treatment guidelines are yet to evolve. Phantom limb pain is considered a neuropathic pain, and most treatment recommendations are based on recommendations for neuropathic pain syndromes. Mirror therapy, a relatively recently proposed therapy for phantom limb pain, has mixed results in randomized controlled trials. Most successful treatment outcomes include multidisciplinary measures. This paper attempts to review and summarize recent research relative to the proposed mechanisms of and treatments for phantom limb pain.
Evolution of Nervous Systems and Brains

C.G. Galizia, P.-M. Lledo (eds.), Neurosciences From Molecule to Behavior: A University Textbook, DOI 10.1007/978-3-642-10769-6_2,  The modern theory of biological evolution, as established by Charles Darwin and Alfred Russel Wallace in the middle of the nineteenth century, is based on three interrelated facts: (i) phylogeny – the common history of organisms on earth stretching back over 3.5 billion years, (ii) evolution in a narrow sense – modi fi cations of organisms during phylogeny and underlying mechanisms, and (iii) speciation – the process by which new species arise during phylogeny. Regarding the phylogeny, it is now commonly accepted that all organisms on Earth are derived from a common ancestor or an ancestral gene pool, while controversies have remained since the time of Darwin and Wallace about the major mechanisms underlying the observed modi fi cations during phylogeny (cf .  ). The prevalent view of neodarwinism (or better “new” or “modern evolutionary synthesis”) is characterized by the assumption that evolutionary changes are caused by a combination of two major processes, (i) heritable variation of individual genomes within a population by mutation and recombination, and (ii) natural selection, i.e., selective environmental or genomic forces leading to better adaptation of those bearing the mutation and as a consequence to their greater differential reproductive success. The modern synthesis holds that evolutionary changes are gradual in the sense that larger, macro-evolutionary, changes are the sum of smaller, micro-evolutionary, changes. Other experts argue that such a gradualistic view of evolution (including “sexual selection” and “genetic drift”) is incomplete; they point to a number of other and perhaps equally important mechanisms such as (i) neutral gene evolution without natural selection, (ii) mass extinctions wiping out up to 90 % of existing species (such as the Cambrian, Devonian, Permian, and Cretaceous-Tertiary mass extinctions) and (iii) genetic and epigenetic-developmental (“ evo devo ”) self-canalization of evolutionary processes  . It remains uncertain as to which of these possible processes principally drive the evolution of nervous systems and brains.
Give Me More Feedback: Annotating Argument Persuasiveness and Related Attributes in Student Essays

Oracle experiment: to understand how well these attributes, when used together, can explain persuasiveness, we train 3 linear SVM regressors, one for each component type, to score an arguments persuasiveness using gold attribute’s as features • Two human annotators who were both native speakers of English were first familiarized with the rubrics and definitions and then trained on five essays • 30 essays were doubly annotated for computing inter-annotator agreement • Each of the remaining essays was annotated by one of the annotators • Score/Class distributions by component type: Give me More Feedback: Annotating Argument Persusiveness and Related Attributes in Student Essays
Combining policy gradient and Q-learning

Policy gradient is an efficient technique for improving a policy in a reinforcement learning setting. However, vanilla online variants are on-policy only and not able to take advantage of off-policy data. In this paper we describe a new technique that combines policy gradient with off-policy Q-learning, drawing experience from a replay buffer. This is motivated by making a connection between the fixed points of the regularized policy gradient algorithm and the Q-values. This connection allows us to estimate the Q-values from the action preferences of the policy, to which we apply Q-learning updates. We refer to the new technique as ‘PGQL’, for policy gradient and Q-learning. We also establish an equivalency between action-value fitting techniques and actor-critic algorithms, showing that regularized policy gradient techniques can be interpreted as advantage function learning algorithms. We conclude with some numerical examples that demonstrate improved data efficiency and stability of PGQL. In particular, we tested PGQL on the full suite of Atari games and achieved performance exceeding that of both asynchronous advantage actor-critic (A3C) and Q-learning.
Applications of Holography in Fluid Mechanics and Particle Dynamics

The quantification of three-dimensional (3D) flow structures and particle dynamics is crucial for unveiling complex interactions in turbulent flows. This review summarizes recent advances in volumetric particle detection and 3D flow velocimetry involving holography. We introduce the fundamental principle of holography and discuss the debilitating depth-of-focus problem, along with methods that have been implemented to circumvent it. The focus of this review is on recent advances in the development of in-line digital holography in general, and digital holographic microscopy in particular. A mathematical background for the numerical reconstruction of digital holograms is followed by a summary of recently introduced 3D particle tracking and velocity measurement techniques. The review concludes with sample applications, including 3D velocity measurements that fully resolve the flow in the inner part of a turbulent boundary layer, the diffusion of oil droplets in high–Reynolds number turbulence, and predator-prey interactions among swimming microorganisms in dense suspensions, as well as oceanic and atmospheric field experiments. 531 A nn u. R ev . F lu id M ec h. 2 01 0. 42 :5 31 -5 55 . D ow nl oa de d fr om w w w .a nn ua lr ev ie w s. or g by T ex as T ec h U ni ve rs ity L ub bo ck o n 08 /2 9/ 14 . F or p er so na l u se o nl y. ANRV400-FL42-22 ARI 13 November 2009 15:8
Collective Classification of Spam Campaigners on Twitter: A Hierarchical Meta-Path Based Approach

Cybercriminals have leveraged the popularity of a large user base available on Online Social Networks (OSNs) to spread spam campaigns by propagating phishing URLs, attaching malicious contents, etc. However, another kind of spam attacks using phone numbers has recently become prevalent on OSNs, where spammers advertise phone numbers to attract users’ attention and convince them to make a call to these phone numbers. The dynamics of phone number based spam is different from URL-based spam due to an inherent trust associated with a phone number. While previous work has proposed strategies to mitigate URL-based spam attacks, phone number based spam attacks have received less attention. In this paper, we aim to detect spammers that use phone numbers to promote campaigns on Twitter. To this end, we collected information (tweets, user meta-data, etc.) about 3, 370 campaigns spread by 670, 251 users. We model the Twitter dataset as a heterogeneous network by leveraging various interconnections between different types of nodes present in the dataset. In particular, we make the following contributions – (i) We propose a simple yet effective metric, called Hierarchical Meta-Path Score (HMPS) to measure the proximity of an unknown user to the other known pool of spammers. (ii) We design a feedback-based active learning strategy and show that it significantly outperforms three state-of-the-art baselines for the task of spam detection. Our method achieves 6.9% and 67.3% higher F1-score and AUC, respectively compared to the best baseline method. (iii) To overcome the problem of less training instances for supervised learning, we show that our proposed feedback strategy achieves 25.6% and 46% higher F1-score and AUC respectively than other oversampling strategies. Finally, we perform a case study to show how our method is capable of detecting those users as spammers who have not been suspended by Twitter (and other baselines) yet.
Deep learning in vision-based static hand gesture recognition

Hand gesture for communication has proven effective for humans, and active research is ongoing in replicating the same success in computer vision systems. Human–computer interaction can be significantly improved from advances in systems that are capable of recognizing different hand gestures. In contrast to many earlier works, which consider the recognition of significantly differentiable hand gestures, and therefore often selecting a few gestures from the American Sign Language (ASL) for recognition, we propose applying deep learning to the problem of hand gesture recognition for the whole 24 hand gestures obtained from the Thomas Moeslund’s gesture recognition database. We show that more biologically inspired and deep neural networks such as convolutional neural network and stacked denoising autoencoder are capable of learning the complex hand gesture classification task with lower error rates. The considered networks are trained and tested on data obtained from the above-mentioned public database; results comparison is then made against earlier works in which only small subsets of the ASL hand gestures are considered for recognition.
EMAIL SPAM DETECTION : A METHOD OF METACLASSIFIERS STACKING

Nowadays, email is one of the fastest ways to conduct communications through sending out information and attachments from one to another. Individuals and organizations are all benefit the convenience from email usage, but at the same time they may also suffer the unexpected user experience of receiving spam email all the time. Spammers flood the email servers and send out mass quantity of unsolicited email to the end users. From a business perspective, email users have to spend time on deleting received spam email which definitely leads to the productivity decrease and cause potential loss for organizations. Thus, how to detect the email spam effectively and efficiently with high accuracy becomes a significant study. In this study, data mining will be utilized to process machine learning by using different classifiers for training and testing and filters for data preprocessing and feature selection. It aims to seek out the optimal hybrid model with higher accuracy or base on other metric’s evaluation. The experiment results show accuracy improvement in email spam detection by using hybrid techniques compared to the single classifiers used in this research. The optimal hybrid model provides 93.00% of accuracy and 7.80% false positive rate for email spam detection.
Functional Foods and Nutraceuticals in the Primary Prevention of Cardiovascular Diseases

Cardiovascular disease (CVD) is now the leading cause of death globally and is a growing health concern. Dietary factors are important in the pathogenesis of CVD and may to a large degree determine CVD risk, but have been less extensively investigated. Functional foods are those that are thought to have physiological benefits and/or reduce the risk of chronic disease beyond their basic nutritional functions. The food industry has started to market products labelled as "functional foods." Although many review articles have focused on individual dietary variables as determinants of CVD that can be modified to reduce the risk of CVD, the aim of this current paper was to examine the impact of functional foods in relation to the development and progression of CVD. Epidemiologic studies have demonstrated the association between certain dietary patterns and cardiovascular health. Research into the cardio-protective potential of their dietary components might support the development of functional foods and nutraceuticals. This paper will also compare the effect of individual bioactive dietary compounds with the effect of some dietary patterns in terms of their cardiovascular protection.
High Performance Fiber Reinforced Cementitious Composites as Durable Material for Concrete Structure Repair

This paper addresses the property requirements of repair materials for high durability performance for concrete structure repair. It is proposed that the high tensile strain capacity of High Performance Fiber Reinforced Cementitious Composites (HPFRCC) makes such materials particularly suitable for repair applications, provided that the fresh properties are also adaptable to those required in placement techniques in typical repair applications. A specific version of HPFRCC, known as Engineered Cementitious Composites (ECC), is described. It is demonstrated that the fresh and hardened properties of ECC meet many of the requirements for durable repair performance. Recent experience in the use of this material in a bridge deck patch repair is highlighted. The origin of this article is a summary of a keynote lecture with the same title given at the Conference on Fiber Composites, High-Performance Concretes and Smart Materials, Chennai, India, Jan., 2004. It is only slightly updated here.