nils-herrmann/clean_scirepeval huggingface dataset:
A comparative study of Cutibacterium (Propionibacterium) acnes clones from acne patients and healthy controls.

Cutibacterium (Propionibacterium) acnes is assumed to play an important role in the pathogenesis of acne.



To examine if clones with distinct virulence properties are associated with acne.



Multiple C. acnes isolates from follicles and surface skin of patients with moderate to severe acne and healthy controls were characterized by multilocus sequence typing. To determine if CC18 isolates from acne patients differ from those of controls in the possession of virulence genes or lack of genes conducive to a harmonious coexistence the full genomes of dominating CC18 follicular clones from six patients and five controls were sequenced.



Individuals carried one to ten clones simultaneously. The dominating C. acnes clones in follicles from acne patients were exclusively from the phylogenetic clade I-1a and all belonged to clonal complex CC18 with the exception of one patient dominated by the worldwide-disseminated and often antibiotic resistant clone ST3. The clonal composition of healthy follicles showed a more heterogeneous pattern with follicles dominated by clones representing the phylogenetic clades I-1a, I-1b, I-2 and II. Comparison of follicular CC18 gene contents, allelic versions of putative virulence genes and their promoter regions, and 54 variable-length intragenic and inter-genic homopolymeric tracts showed extensive conservation and no difference associated with the clinical origin of isolates.



The study supports that C. acnes strains from clonal complex CC18 and the often antibiotic resistant clone ST3 are associated with acne and suggests that susceptibility of the host rather than differences within these clones may determine the clinical outcome of colonization.
CUDAAdvisor: LLVM-based runtime profiling for modern GPUs

General-purpose GPUs have been widely utilized to accelerate parallel applications. Given a relatively complex programming model and fast architecture evolution, producing efficient GPU code is nontrivial. A variety of simulation and profiling tools have been developed to aid GPU application optimization and architecture design. However, existing tools are either limited by insufficient insights or lacking in support across different GPU architectures, runtime and driver versions. This paper presents CUDAAdvisor, a profiling framework to guide code optimization in modern NVIDIA GPUs. CUDAAdvisor performs various fine-grained analyses based on the profiling results from GPU kernels, such as memory-level analysis (e.g., reuse distance and memory divergence), control flow analysis (e.g., branch divergence) and code-/data-centric debugging. Unlike prior tools, CUDAAdvisor supports GPU profiling across different CUDA versions and architectures, including CUDA 8.0 and Pascal architecture. We demonstrate several case studies that derive significant insights to guide GPU code optimization for performance improvement.
Autonomous Learning of State Representations for Control: An Emerging Field Aims to Autonomously Learn State Representations for Reinforcement Learning Agents from Their Real-World Sensor Observations

This article reviews an emerging field that aims for autonomous reinforcement learning (RL) directly on sensor-observations. Straightforward end-to-end RL has recently shown remarkable success, but relies on large amounts of samples. As this is not feasible in robotics, we review two approaches to learn intermediate state representations from previous experiences: deep auto-encoders and slow-feature analysis. We analyze theoretical properties of the representations and point to potential improvements.
The consequences of unintended births for maternal and child health in India.

Data from the Indian National Family Health Survey, 2005-06 were used to explore how pregnancy intention at the time of conception influences a variety of maternal and child health and health care outcomes. Results indicate that mistimed children are more likely than wanted children to be delivered without a skilled attendant present (OR = 1.3), to not receive all recommended vaccinations (OR = 1.4), and to die during the neonatal and postneonatal periods (OR = 1.8 and 2.6, respectively). Unwanted children are more likely than wanted children to not receive all recommended vaccinations (OR = 2.2), to be stunted (OR = 1.3), and to die during the neonatal, postneonatal, and early childhood periods (OR = 2.2, 3.6, and 5.9, respectively). Given the high levels of unintended fertility in India (21 per cent of all births), these are striking findings that underscore the importance of investments in family planning.
Detecting Faces Using Region-based Fully Convolutional Networks

Face detection has achieved great success using the region-based methods. In this report, we propose a region-based face detector applying deep networks in a fully convolutional fashion, named Face R-FCN. Based on Region-based Fully Convolutional Networks (R-FCN), our face detector is more accurate and computationally efficient compared with the previous R-CNN based face detectors. In our approach, we adopt the fully convolutional Residual Network (ResNet) as the backbone network. Particularly, we exploit several new techniques including position-sensitive average pooling, multi-scale training and testing and on-line hard example mining strategy to improve the detection accuracy. Over two most popular and challenging face detection benchmarks, FDDB and WIDER FACE, Face R-FCN achieves superior performance over state-of-the-arts.
Radio Frequency Energy Harvesting and Management for Wireless Sensor Networks

Radio Frequency (RF) Energy Harvesting holds a pro mising future for generating a small amount of electrical power to drive partial circuits in wirelessly communicating electronics devices. Reducing power consumption has become a major challenge in wireless sensor networks. As a vital factor affecting system cost and lifetime, energy consumption in wireless sensor networks is an emerging and active res arch area. This chapter presents a practical approach for RF Energy harvesting and man agement of the harvested and available energy for wireless sensor networks using the Impro ved Energy Efficient Ant Based Routing Algorithm (IEEABR) as our proposed algorithm. The c hapter looks at measurement of the RF power density, calculation of the received power, s torage of the harvested power, and management of the power in wireless sensor networks . The routing uses IEEABR technique for energy management. Practical and real-time implemen tatio s of the RF Energy using PowercastTM harvesters and simulations using the ene rgy model of our Libelium Waspmote to verify the approach were performed. The chapter con cludes with performance analysis of the harvested energy, comparison of IEEABR and other tr aditional energy management techniques, while also looking at open research areas of energy harvesting and management for wireless sensor networks.
Measuring service quality in E-retailing using SERVQUAL model

Practitioners and academics are eager on measuring service quality accurately in order to have better understanding of its indispensable antecedent and consequences, and eventually ascertain methods for improving and measuring service quality in search for competitive advantage. The aim of this study is to rank the dimensions of service quality that affect the customerspsila expectation in online purchasing in Iran from the customerspsila perspective. A questionnaire used in this study was published in Cloob.com which is an Iranian virtual society Web site. The measurements used were based on the widely accepted SERVQUAL model which is the most common method for measuring service quality. This study also examined the service quality gap by comparing customerspsila expectations and their actual perceptions. The results of the study indicated that all of the service quality factors are important. Tangibility was rated as the most important dimension followed by assurance, reliability, responsiveness, and empathy.
Marketing mix, customer value, and customer loyalty in social commerce: A stimulus-organism-response perspective

Based on stimulus-organism-response model, the purpose of this paper is to develop an integrated model to explore the effects of six marketing-mix components (stimuli) on consumer loyalty (response) through consumer value (organism) in social commerce (SC).  In order to target online social buyers, a web-based survey was employed. Structural equation modeling with partial least squares (PLS) is used to analyze valid data from 599 consumers who have repurchase experience via Facebook.  The results from PLS analysis show that all components of SC marketing mix (SCMM) have significant effects on SC consumer value. Moreover, SC customer value positively influences SC customer loyalty (CL).  The data for this study are collected from Facebook only and the sample size is limited; thus, replication studies are needed to improve generalizability and data representativeness of the study. Moreover, longitudinal studies are needed to verify the causality among the constructs in the proposed research model.  SC sellers should implement more effective SCMM strategies to foster SC CL through better SCMM decisions.  The SCMM components represent the collective benefits of social interaction, exemplifying the importance of effective communication and interaction among SC customers.  This study develops a parsimonious model to explain the over-arching effects of SCMM components on CL in SC mediated by customer value. It confirms that utilitarian, hedonic, and social values can be applied to online SC and that SCMM can be leveraged to achieve these values.
Integrating Software Security into Agile-Scrum Method

Scrum is one of the most popular and efficient agile development methods. However, like other agile methods such as Extreme Programming (XP), Feature Driven Development (FDD), and the Dynamic Systems Development Method (DSDM), Scrum has been criticized because of lack of support to develop secure software. Thus, in 2011, we published research proposing the idea of a security backlog (SB). This paper represents the continuation of our previous research, with a focus on the evaluation in industry-based case study. Our findings highlight an improved agility in Scrum after the integration of SB. Furthermore, secure software can be developed quickly, even in situations involving requirement changes of software. Based on our experimental findings, we noticed that, when integrating SB, it is quite feasible to develop secure software using an agile Scrum model.
Predictive testing: amplifying the effectiveness of software testing

Testing with manually generated test cases often results in poor coverage and fails to discover many corner case bugs and security vulnerabilities. Automated test generation techniques based on static or symbolic analysis usually do not scale beyond small program units. We propose predictive testing, a new method for amplifying the effectiveness of existing test cases using symbolic analysis. We assume that a software system has an associated test suite consisting of a set of test inputs and a set of program invariants, in the form of a set of assert statements that the software must always satisfy. Predictive testing uses a combination of concrete and symbolic execution, similar to concolic execution, on the provided test inputs to discover if any of the assertions encountered along a test execution path could be violated for some closely related inputs. We extend predictive testing to catch bugs related to memory-safety violations, integer overflows, and string-related vulnerabilities. Furthermore, we propose a novel technique that leverages the results of unit testing to hoist assertions located deep inside the body of a unit function to the beginning of the unit function. This enables predictive testing to encounter assertions more often in test executions and thereby significantly amplifies the effectiveness of testing.
Preoperative asymmetry of upper eyelid thickness in young Chinese women undergoing double eyelid blepharoplasty.

Asymmetry of the eyelid crease is a major complication following double eyelid blepharoplasty; the reasons are multivariate. This study presents, for the first time, a novel method, based on high-definition magnetic resonance imaging and high-precision weighing of tissue, for quantitating preoperative asymmetry of eyelid thickness in young Chinese women presenting for blepharoplasty. From 1 January 2008 to 1 October 2011, we studied 1217 women requesting double eyelid blepharoplasty. The patients ranged in age from 17 to 24 years (average 21.13 years). All patients were of Chinese Han nationality. Soft-tissue thickness at the tarsal plate superior border was 5.05 ± 1.01 units on the right side and 4.12 ± 0.96 units on the left. The submuscular fibro-adipose tissue area was 95.12 ± 23.27 unit(2) on the right side and 76.05 ± 21.11 unit(2) on the left. The pre-aponeurotic fat pad area was 112.33 ± 29.16 unit(2) on the right side and 91.25 ± 27.32 unit(2) on the left. The orbicularis muscle resected weighed 0.185 ± 0.055 g on the right side and 0.153 ± 0.042 g on the left; the orbital fat resected weighed 0.171 ± 0.062 g on the right side and 0.106 ± 0.057 g on the left. In conclusion, upper eyelid thickness asymmetry is a common phenomenon in young Chinese women who wish to undertake double eyelid blepharoplasty. We have demonstrated that the orbicularis muscle and orbital fat pad are consistently thicker on the right side than on the left.
Text to Speech Conversion Using FLITE Algorithm

Speech synthesis is the artificial production of human voice. A computer system used for this task is called a speech synthesizer. Anyone can use this synthesizer in software or hardware products. The main aim of text-to-speech (TTS) system is to convert normal language text into speech. Synthesized speech can be produced by concatenating pieces of recorded speech that are stored in a database. TTS Systems differ in size of the stored speech units. A system which stores phones or diphones provides the largest output range, but this may give low clarity. For specific application domains, the storage of entire words or sentences allows for highquality output. Alternatively, a synthesizer can constitute a model of the vocal tract and other human voice characteristics to create a fully synthetic voice output. The quality of a speech synthesizer is decided by its naturalness or simillarity to the human voice and by its ability to be understood clearly. This paper summarizes the published literatures on Text to Speech (TTS), with discussing about the efforts taken in each paper. This system will be more helpful for an illiterate and visually impaired people to hear and understand the text.
Alkamides Activate Jasmonic Acid Biosynthesis and Signaling Pathways and Confer Resistance to Botrytis cinerea in Arabidopsis thaliana

Alkamides are fatty acid amides of wide distribution in plants, structurally related to N-acyl-L-homoserine lactones (AHLs) from Gram-negative bacteria and to N- acylethanolamines (NAEs) from plants and mammals. Global analysis of gene expression changes in Arabidopsis thaliana in response to N-isobutyl decanamide, the most highly active alkamide identified to date, revealed an overrepresentation of defense-responsive transcriptional networks. In particular, genes encoding enzymes for jasmonic acid (JA) biosynthesis increased their expression, which occurred in parallel with JA, nitric oxide (NO) and H₂O₂ accumulation. The activity of the alkamide to confer resistance against the necrotizing fungus Botrytis cinerea was tested by inoculating Arabidopsis detached leaves with conidiospores and evaluating disease symptoms and fungal proliferation. N-isobutyl decanamide application significantly reduced necrosis caused by the pathogen and inhibited fungal proliferation. Arabidopsis mutants jar1 and coi1 altered in JA signaling and a MAP kinase mutant (mpk6), unlike salicylic acid- (SA) related mutant eds16/sid2-1, were unable to defend from fungal attack even when N-isobutyl decanamide was supplied, indicating that alkamides could modulate some necrotrophic-associated defense responses through JA-dependent and MPK6-regulated signaling pathways. Our results suggest a role of alkamides in plant immunity induction.
Predicting How People Play Games : Reinforcement Learning i Experimental Games with Unique , Mixed Strategy Equilibria

We examine learning in all experiments we could locate involving 100 periods or more of games with a unique equilibn'um in mixed strategies, and in a new experiment. We study both the ex post (" best fit ") akscrtptive power of learning models, and their ex ante predictive power, by simulating each experiment using parameters estimated from the other experiments. Even a one-parameter reinforcement learning model robustly outpelfonns the equilibrium predictions. Predictive power is improved by adding ' yorgemng " and " experimentation, " or by allowing greater ra-tionality as in probabilistic jictitious play. Implications for developing a low-rationality, cognitive game theory are discussed ( i Game theory has traditionally been developed as a theory of strategic interaction among players who are perfectly rational, and who (consequently) exhibit equilibrium behavior. This approach has been complemented by evolutionary game theory, which, motivated by biological evolution, seeks to understand how equilibria could arise in the long term by selection among generations of players who need not be rational or even conscious decision makers. Somewhere in between are models of learning, which consider the adaptive behavior of goal-oriented players who may not be highly rational, both to provide foundations also contributed to the design and programming of the new experiment. We are indebted to Barry O'Neill, Jack Ochs, and Amnon Rapoport for access to unpublished parts of their data. The present version reflects numerous comments by three anonymous referees on several earlier drafts. for theories of equilibrium and to model em-: pirically observed behavior. The present paper considers how well ~im-.~'! ple learning models, motivated by the pay-' chology of learning, can model the interaction :. of players who must learn about the game and. C each other in the course of playing the game, over time spans that may not be long enough to lead to equilibrium. Our goal will be to model observed behavior, starting with behavior observed in experimental settings. (In the conclusion we will also consider the implications of this approach for applied economics in naturally occurring, nonexperimental settings .) We will show that a wide range of experimental data can be both well described ex post and robustly predicted ex ante by a very simple family of learning theories. Economists have traditionally avoided explaining behavior as less than rational for fear of developing many fragmented theories of mistakes. Part of the attraction of highly rational models …
The Use of Facebook for Educational Purposes in EFL Classrooms

The objective of this study is to analyze the possibility of blending conventional instruction with online instruction via a social networking website, Facebook, in EFL classrooms in order to motivate students and improve their English language learning. Thus, this paper seeks to examine specific ways in which EFL teachers can use Facebook as an educational tool, describing the benefits of this technological instrument and analyzing the potential pitfalls and challenges that it could create. Besides, it includes practical strategies that teachers can apply in order to overcome these pitfalls and get the most out of this social network.
Imaging in breast cancer: Magnetic resonance spectroscopy

A technique called in vivo magnetic resonance spectroscopy (MRS) can be performed along with magnetic resonance imaging (MRI) to obtain information about the chemical content of breast lesions. This information can be used for several clinical applications, such as monitoring the response to cancer therapies and improving the accuracy of lesion diagnosis. Initial MRS studies of breast cancer show promising results, and a growing number of research groups are incorporating the technique into their breast MRI protocols. This article introduces 1H-MRS of the breast, reviews the literature, discusses current methods and technical issues, and describes applications for treatment monitoring and lesion diagnosis.
Open Issues in Evolutionary Robotics

One of the long-term goals in evolutionary robotics is to be able to automatically synthesize controllers for real autonomous robots based only on a task specification. While a number of studies have shown the applicability of evolutionary robotics techniques for the synthesis of behavioral control, researchers have consistently been faced with a number of issues preventing the widespread adoption of evolutionary robotics for engineering purposes. In this article, we review and discuss the open issues in evolutionary robotics. First, we analyze the benefits and challenges of simulation-based evolution and subsequent deployment of controllers versus evolution on real robotic hardware. Second, we discuss specific evolutionary computation issues that have plagued evolutionary robotics: (1) the bootstrap problem, (2) deception, and (3) the role of genomic encoding and genotype-phenotype mapping in the evolution of controllers for complex tasks. Finally, we address the absence of standard research practices in the field. We also discuss promising avenues of research. Our underlying motivation is the reduction of the current gap between evolutionary robotics and mainstream robotics, and the establishment of evolutionary robotics as a canonical approach for the engineering of autonomous robots.
Acquiring scattering properties of participating media by dilution

The visual world around us displays a rich set of volumetric effects due to participating media. The appearance of these media is governed by several physical properties such as particle densities, shapes and sizes, which must be input (directly or indirectly) to a rendering algorithm to generate realistic images. While there has been significant progress in developing rendering techniques (for instance, volumetric Monte Carlo methods and analytic approximations), there are very few methods that measure or estimate these properties for media that are of relevance to computer graphics. In this paper, we present a simple device and technique for robustly estimating the properties of a broad class of participating media that can be either (a) diluted in water such as juices, beverages, paints and cleaning supplies, or (b) dissolved in water such as powders and sugar/salt crystals, or (c) suspended in water such as impurities. The key idea is to dilute the concentrations of the media so that single scattering effects dominate and multiple scattering becomes negligible, leading to a simple and robust estimation algorithm. Furthermore, unlike previous approaches that require complicated or separate measurement setups for different types or properties of media, our method and setup can be used to measure media with a complete range of absorption and scattering properties from a single HDR photograph. Once the parameters of the diluted medium are estimated, a volumetric Monte Carlo technique may be used to create renderings of any medium concentration and with multiple scattering. We have measured the scattering parameters of forty commonly found materials, that can be immediately used by the computer graphics community. We can also create realistic images of combinations or mixtures of the original measured materials, thus giving the user a wide flexibility in making realistic images of participating media.