nils-herrmann/clean_scirepeval huggingface dataset:
Areas of brain activation in males and females during viewing of erotic film excerpts.

Various lines of evidence indicate that men generally experience greater sexual arousal (SA) to erotic stimuli than women. Yet, little is known regarding the neurobiological processes underlying such a gender difference. To investigate this issue, functional magnetic resonance imaging was used to compare the neural correlates of SA in 20 male and 20 female subjects. Brain activity was measured while male and female subjects were viewing erotic film excerpts. Results showed that the level of perceived SA was significantly higher in male than in female subjects. When compared to viewing emotionally neutral film excerpts, viewing erotic film excerpts was associated, for both genders, with bilateral blood oxygen level dependent (BOLD) signal increases in the anterior cingulate, medial prefrontal, orbitofrontal, insular, and occipitotemporal cortices, as well as in the amygdala and the ventral striatum. Only for the group of male subjects was there evidence of a significant activation of the thalamus and hypothalamus, a sexually dimorphic area of the brain known to play a pivotal role in physiological arousal and sexual behavior. When directly compared between genders, hypothalamic activation was found to be significantly greater in male subjects. Furthermore, for male subjects only, the magnitude of hypothalamic activation was positively correlated with reported levels of SA. These findings reveal the existence of similarities and dissimilarities in the way the brain of both genders responds to erotic stimuli. They further suggest that the greater SA generally experienced by men, when viewing erotica, may be related to the functional gender difference found here with respect to the hypothalamus.
Design of Wideband Cavity-Backed Slot Antenna With Multilayer Dielectric Cover

A novel wideband cavity-backed slot antenna operating at X-band is proposed. Compared to conventional cavity-backed slot antennas, both the bandwidth and gain of the proposed antenna are significantly increased by adding a multilayer dielectric cover. This antenna offers a 40% (|S11| <; -10 dB) impedance bandwidth. Meanwhile, the gain of a single antenna within this band is 6-9.5 dBi, 2-3 dB higher than those without that multilayer dielectric cover at most of the bandwidth. To further validate the capability of the proposed antenna, a 4 ×4 antenna array is designed, fabricated, and tested. The measurement results corroborate with simulations well. Taking advantage of its wider bandwidth and higher gain compared to the counterparts, this antenna could find great potential application in the future.
Interpretability of deep learning models: A survey of results

Deep neural networks have achieved near-human accuracy levels in various types of classification and prediction tasks including images, text, speech, and video data. However, the networks continue to be treated mostly as black-box function approximators, mapping a given input to a classification output. The next step in this human-machine evolutionary process — incorporating these networks into mission critical processes such as medical diagnosis, planning and control — requires a level of trust association with the machine output. Typically, statistical metrics are used to quantify the uncertainty of an output. However, the notion of trust also depends on the visibility that a human has into the working of the machine. In other words, the neural network should provide human-understandable justifications for its output leading to insights about the inner workings. We call such models as interpretable deep networks. Interpretability is not a monolithic notion. In fact, the subjectivity of an interpretation, due to different levels of human understanding, implies that there must be a multitude of dimensions that together constitute interpretability. In addition, the interpretation itself can be provided either in terms of the low-level network parameters, or in terms of input features used by the model. In this paper, we outline some of the dimensions that are useful for model interpretability, and categorize prior work along those dimensions. In the process, we perform a gap analysis of what needs to be done to improve model interpretability.
Birth Outcomes for Pregnant Women with HIV Using Tenofovir-Emtricitabine.

In a previous trial of antiretroviral therapy (ART) involving pregnant women with human immunodeficiency virus (HIV) infection, those randomly assigned to receive tenofovir, emtricitabine, and ritonavir-boosted lopinavir (TDF-FTC-LPV/r) had infants at greater risk for very premature birth and death within 14 days after delivery than those assigned to receive zidovudine, lamivudine, and ritonavir-boosted lopinavir (ZDV-3TC-LPV/r).



Using data from two U.S.-based cohort studies, we compared the risk of adverse birth outcomes among infants with in utero exposure to ZDV-3TC-LPV/r, TDF-FTC-LPV/r, or TDF-FTC with ritonavir-boosted atazanavir (ATV/r). We evaluated the risk of preterm birth (<37 completed weeks of gestation), very preterm birth (<34 completed weeks), low birth weight (<2500 g), and very low birth weight (<1500 g). Risk ratios with 95% confidence intervals were estimated with the use of modified Poisson models to adjust for confounding.



There were 4646 birth outcomes. Few infants or fetuses were exposed to TDF-FTC-LPV/r (128 [2.8%]) as the initial ART regimen during gestation, in contrast with TDF-FTC-ATV/r (539 [11.6%]) and ZDV-3TC-LPV/r (954 [20.5%]). As compared with women receiving ZDV-3TC-LPV/r, women receiving TDF-FTC-LPV/r had a similar risk of preterm birth (risk ratio, 0.90; 95% confidence interval [CI], 0.60 to 1.33) and low birth weight (risk ratio, 1.13; 95% CI, 0.78 to 1.64). As compared to women receiving TDF-FTC-ATV/r, women receiving TDF-FTC-LPV/r had a similar or slightly higher risk of preterm birth (risk ratio, 1.14; 95% CI, 0.75 to 1.72) and low birth weight (risk ratio, 1.45; 95% CI, 0.96 to 2.17). There were no significant differences between regimens in the risk of very preterm birth or very low birth weight.



The risk of adverse birth outcomes was not higher with TDF-FTC-LPV/r than with ZDV-3TC-LPV/r or TDF-FTC-ATV/r among HIV-infected women and their infants in the United States, although power was limited for some comparisons.
A Review on Human-Centered IoT-Connected Smart Labels for the Industry 4.0

One of the challenges of Industry 4.0 is the creation of vertical networks that connect smart production systems with design teams, suppliers, and the front office. To achieve such a vision, information has to be collected from machines and products throughout a smart factory. Smart factories are defined as flexible and fully connected factories that are able to make use of constant streams of data from operations and production systems. In such scenarios, the arguably most popular way for identifying and tracking objects is by adding labels or tags, which have evolved remarkably over the last years: from pure hand-written labels to barcodes, QR codes, and RFID tags. The latest trend in this evolution is smart labels which are not only mere identifiers with some kind of internal storage, but also sophisticated context-aware tags with embedded modules that make use of wireless communications, energy efficient displays, and sensors. Therefore, smart labels go beyond identification and are able to detect and react to the surrounding environment. Moreover, when the industrial Internet of Things paradigm is applied to smart labels attached to objects, they can be identified remotely and discovered by other Industry 4.0 systems, what allows such systems to react in the presence of smart labels, thus triggering specific events or performing a number of actions on them. The amount of possible interactions is endless and creates unprecedented industrial scenarios, where items can talk to each other and with tools, machines, remote computers, or workers. This paper, after reviewing the basics of Industry 4.0 and smart labels, details the latest technologies used by them, their applications, the most relevant academic and commercial implementations, and their internal architecture and design requirements, providing researchers with the necessary foundations for developing the next generation of Industry 4.0 human-centered smart label applications.
Study on mechanical behaviors of pneumatic artificial muscle

0020-7225/$ see front matter 2009  Corresponding author.  (T. Leephakp Nowadays, Pneumatic Artificial Muscle (PAM) has become one of the most widely-used fluid-power actuators which yields remarkable muscle-like properties such as high force to weight ratio, soft and flexible structure, minimal compressed-air consumption and low cost. To obtain optimum design and usage, it is necessary to understand mechanical behaviors of the PAM. In this study, the proposed models are experimentally derived to describe mechanical behaviors of the PAMs. The experimental results show a non-linear relationship between contraction as well as air pressure within the PAMs and a pulling force of the PAMs. Three different sizes of PAMs available in industry are studied for empirical modeling and simulation. The case studies are presented to verify close agreement on the simulated results to the experimental results when the PAMs perform under various loads.
Speech enhancement based on Deep Neural Networks with skip connections

Speech enhancement under noise condition has always been an intriguing research topic. In this paper, we propose a new Deep Neural Networks (DNNs) based architecture for speech enhancement. In contrast to standard feed forward network architecture, we add skip connections between network inputs and outputs to indirectly force the DNNs to learn ideal ratio mask. We also show that the performance can be further improved by stacking multiple such network blocks. Experimental results demonstrate that our proposed architecture can achieve considerably better performance than the existing method in terms of three commonly used objective measurements under two real noise conditions.
CAST: Effective and Efficient User Interaction for Context-Aware Selection in 3D Particle Clouds

We present a family of three interactive Context-Aware Selection Techniques (CAST) for the analysis of large 3D particle datasets. For these datasets, spatial selection is an essential prerequisite to many other analysis tasks. Traditionally, such interactive target selection has been particularly challenging when the data subsets of interest were implicitly defined in the form of complicated structures of thousands of particles. Our new techniques SpaceCast, TraceCast, and PointCast improve usability and speed of spatial selection in point clouds through novel context-aware algorithms. They are able to infer a user's subtle selection intention from gestural input, can deal with complex situations such as partially occluded point clusters or multiple cluster layers, and can all be fine-tuned after the selection interaction has been completed. Together, they provide an effective and efficient tool set for the fast exploratory analysis of large datasets. In addition to presenting Cast, we report on a formal user study that compares our new techniques not only to each other but also to existing state-of-the-art selection methods. Our results show that Cast family members are virtually always faster than existing methods without tradeoffs in accuracy. In addition, qualitative feedback shows that PointCast and TraceCast were strongly favored by our participants for intuitiveness and efficiency.
Factors Affecting Consumers ’ purchasing Decision through E-Commerce

the rapid changing Internet environment has formed a competitive business setting, which provides opportunities for conducting businesses online. Availability of online transaction systems enable users to buy and make payment for products and services using the Internet platform. Thus, customers’ involvements in online purchasing have become an important trend. However, since the market is comprised of many different people and cultures, with diverse viewpoints, e-commerce businesses are being challenged by the reality of complex behavior of consumers. Therefore, it is vital to identify the factors that affect consumers purchasing decision through e-commerce in respective cultures and societies. In response to this claim, the purpose of this study is to explore the factors affecting customers’ purchasing decision through e-commerce (online shopping). Several factors such as trust, satisfaction, return policy, cash on delivery, after sale service, cash back warranty, business reputation, social and individual attitude, are considered. At this stage, the factors mentioned above, which are commonly considered influencing purchasing decision through online shopping in literature, are hypothesized to measure the causal relationship within the framework.
Controlling nuclear RNA levels

RNA turnover is an integral part of cellular RNA homeostasis and gene expression regulation. Whereas the cytoplasmic control of protein-coding mRNA is often the focus of study, we discuss here the less appreciated role of nuclear RNA decay systems in controlling RNA polymerase II (RNAPII)-derived transcripts. Historically, nuclear RNA degradation was found to be essential for the functionalization of transcripts through their proper maturation. Later, it was discovered to also be an important caretaker of nuclear hygiene by removing aberrant and unwanted transcripts. Recent years have now seen a set of new protein complexes handling a variety of new substrates, revealing functions beyond RNA processing and the decay of non-functional transcripts. This includes an active contribution of nuclear RNA metabolism to the overall cellular control of RNA levels, with mechanistic implications during cellular transitions. RNA is controlled at various stages of transcription and processing to achieve appropriate gene regulation. Whereas much research has focused on the cytoplasmic control of RNA levels, this Review discusses our emerging appreciation of the importance of nuclear RNA regulation, including the molecular machinery involved in nuclear RNA decay, how functional RNAs bypass degradation and roles for nuclear RNA decay in physiology and disease.
A Novel Compact Monopulse Parallel-Plate Slot Array Antenna

In this letter, the design of a novel radial planar monopulse antenna with two differences and one sum far-field patterns are presented. The antenna consists of the parallel radial line, slot array on the upper plate and feeding network on the underside of the plate. The slots are included of split-ring slots (SRS) that have been designed for circular polarization to work in the X-band. The feeding network is designed by microstrip line to provide the phase excitation for each beam. At 10.58 GHz, the sum pattern gain is 18.1 dB and differences gains are 12 and 11.9 dB for elevation and azimuth beams, respectively. The axial ratio in the main beam of sum pattern is better than 1.2 dB.
Motion Robust Remote-PPG in Infrared

Current state-of-the-art remote photoplethysmography (rPPG) algorithms are capable of extracting a clean pulse signal in ambient light conditions using a regular color camera, even when subjects move significantly. In this study, we investigate the feasibility of rPPG in the (near)-infrared spectrum, which broadens the scope of applications for rPPG. Two camera setups are investigated: one setup consisting of three monochrome cameras with different optical filters, and one setup consisting of a single RGB camera with a visible light blocking filter. Simulation results predict the monochrome setup to be more motion robust, but this simulation neglects parallax. To verify this, a challenging benchmark dataset consisting of 30 videos is created with various motion scenarios and skin tones. Experiments show that both camera setups are capable of accurate pulse extraction in all motion scenarios, with an average SNR of +6.45 and +7.26 dB, respectively. The single camera setup proves to be superior in scenarios involving scaling, likely due to parallax of the multicamera setup. To further improve motion robustness of the RGB camera, dedicated LED illumination with two distinct wavelengths is proposed and verified. This paper demonstrates that accurate rPPG measurements in infrared are feasible, even with severe subject motion.
Energy-free machine learning force field for aluminum

We used the machine learning technique of Li et al. (PRL 114, 2015) for molecular dynamics simulations. Atomic configurations were described by feature matrix based on internal vectors, and linear regression was used as a learning technique. We implemented this approach in the LAMMPS code. The method was applied to crystalline and liquid aluminum and uranium at different temperatures and densities, and showed the highest accuracy among different published potentials. Phonon density of states, entropy and melting temperature of aluminum were calculated using this machine learning potential. The results are in excellent agreement with experimental data and results of full ab initio calculations.
Othering in the nursing context: A concept analysis

'Othering' is described as a social process whereby a dominant group or person uses negative attributes to define and subordinate others. Literature suggests othering creates exclusive relationships and puts patients at risk for suboptimal care. A concept analysis delineating the properties of othering was conducted to develop knowledge to support inclusionary practices in nursing.



Rodgers' Evolutionary Method for concept analysis guided this study.



The following databases were searched spanning the years 1999-2015: CINAHL, PUBMED, PsychINFO and Google. Search terms included "othering", "nurse", "other", "exclusion" and "patient".



Twenty-eight papers were analyzed whereby definitions, related concepts and othering attributes were identified. Findings support that othering in nursing is a sequential process with a trajectory aimed at marginalization and exclusion, which in turn has a negative impact on patient care and professional relationships. Implications are discussed in terms of deriving practical solutions to disrupt othering. We conclude with a conceptual foundation designed to support inclusionary strategies in nursing.
SMT-C: A Semantic Mutation Testing Tools for C

Semantic Mutation Testing (SMT) is a technique that aims to capture errors caused by possible misunderstandings of the semantics of a description language. It is intended to target a class of errors which is different from those captured by traditional Mutation Testing (MT). This paper describes our experiences in the development of an SMT tool for the C programming language: SMT-C. In addition to implementing the essential requirements of SMT (generating semantic mutants and running SMT analysis) we also aimed to achieve the following goals: weak MT/SMT for C, good portability between different configurations, seamless integration into test routines of programming with C and an easy to use front-end.
A Data Quality in Use model for Big Data

Beyond the hype of Big Data, something within business intelligence projects is indeed changing. This is mainly because Big Data is not only about data, but also about a complete conceptual and technological stack including raw and processed data, storage, ways of managing data, processing and analytics. A challenge that becomes even trickier is the management of the quality of the data in Big Data environments. More than ever before the need for assessing the Quality-in-Use gains importance since the real contribution – business value – of data can be only estimated in its context of use. Although there exists different Data Quality models for assessing the quality of regular data, none of them has been adapted to Big Data. To fill this gap, we propose the ‘‘3As Data Quality-in-Use model’’, which is composed of three Data Quality characteristics for assessing the levels of Data Quality-in-Use in Big Data projects: Contextual Adequacy, Operational Adequacy and Temporal Adequacy. The model can be integrated into any sort of BigData project, as it is independent of anypre-conditions or technologies. Thepaper shows the way to use the model with a working example. The model accomplishes every challenge related to Data Quality program aimed for Big Data. The main conclusion is that the model can be used as an appropriate way to obtain the Quality-in-Use levels of the input data of the Big Data analysis, and those levels can be understood as indicators of trustworthiness and soundness of the results of the Big Data analysis.
Rare but serious complications of acupuncture: traumatic lesions.

Acupuncture has a reputation among the public of being safe. Although recently performed prospective studies on the frequency of adverse effects of acupuncture found no severe complication, since 1965 many case reports of serious or even life-threatening incidents caused by acupuncture have appeared in the scientific literature. The most frequently reported complications are pneumothorax and lesions of the spinal cord. Severe injuries of peripheral nerves and blood vessels due to acupuncture seem to be very rare. Although case reports do not produce reliable data on the frequency of adverse events. information on sources of application errors can be extracted to increase the quality of acupuncture in education and therapy. All traumatic injuries described in this article could be avoided if practitioners had better anatomical knowledge, applied existing anatomical knowledge better, or both.
Telugu to English Translation using Direct Machine Translation Approach

The motivation behind working on a translation system from Telugu to English were based on the principles that a) There are many translation systems for translating from English to Indian languages but very few for vice versa. Telugu is a language that exhibits very strong phrasal, word and sentence structures next to Sanskrit, which makes the work organized on one hand but complex in handling on the other. This work demonstrates one such machine translation (MT) system for translating simple and moderately complex sentences from Telugu to English. b) Of the many MT approaches, the direct MT is used for translation between similar or nearly related languages. However, the direct MT has been used in this work for conversion from Telugu to English, which is quite complex compared to other Indian languages. The purpose of using direct MT for development of such a tool was to have the flexibility in usage, keeping it simple, look for rapid development and primarily to have better accuracy than all the known system.  There are very large numbers of elisions/ inflection rules in Telugu requiring complex morphs, like those in Sanskrit. A large number of rules for handling inflections were to be developed along with the grammar rules. The outcomes were compared with Google Translator, a publicly available translation web based system. The outcomes were found to be much better, as much as 90 percent more accurate. This work shall bring forth deeper insights into Telugu MT research. KeywordsMachine translation (MT), direct MT, Telugu to English, natural language processing (NLP), elisions, inflections.
Transient Turbulent Flow in a LiquidMetal Model of Continuous Casting , Including Comparison of Six Different Methods

Computational modeling is an important tool to understand and stabilize transient turbulent fluid flow in the continuous casting of steel in order to minimize defects. The current work combines the predictions of two steady Reynolds-averaged Navier-Stokes (RANS) models, a “filtered” unsteady RANS model, and two large eddy simulation models with ultrasonic Doppler velocimetry (UDV) measurements in a small scale liquid GaInSn model of the continuous casting mold region fed by a bifurcated well-bottom nozzle with horizontal ports. Both mean and transient features of the turbulent flow are investigated.