nils-herrmann/clean_scirepeval huggingface dataset:
Photorealistic rendering of mixed reality scenes

Photo-realistic rendering of virtual objects into real scenes is one of the most important research problems in computer graphics. Methods for capture and rendering of mixed reality scenes are driven by a large number of applications, ranging from augmented reality to visual effects and product visualization. Recent developments in computer graphics, computer vision, and imaging technology have enabled a wide range of new mixed reality techniques including methods for advanced image based lighting, capturing spatially varying lighting conditions, and algorithms for seamlessly rendering virtual objects directly into photographs without explicit measurements of the scene lighting. This report gives an overview of the state-of-the-art in this field, and presents a categorization and comparison of current methods. Our in-depth survey provides a tool for understanding the advantages and disadvantages of each method, and gives an overview of which technique is best suited to a specific problem.
Screen Media Usage, Sleep Time and Academic Performance in Adolescents: Clustering a Self-Organizing Maps Analysis

Screen media usage, sleep time and socio-demographic features are related to adolescents' academic performance, but interrelations are little explored. This paper describes these interrelations and behavioral profiles clustered in low and high academic performance. A nationally representative sample of 3,095 Spanish adolescents, aged 12 to 18, was surveyed on 15 variables linked to the purpose of the study. A Self-Organizing Maps analysis established non-linear interrelationships among these variables and identified behavior patterns in subsequent cluster analyses. Topological interrelationships established from the 15 emerging maps indicated that boys used more passive videogames and computers for playing than girls, who tended to use mobile phones to communicate with others. Adolescents with the highest academic performance were the youngest. They slept more and spent less time using sedentary screen media when compared to those with the lowest performance, and they also showed topological relationships with higher socioeconomic status adolescents. Cluster 1 grouped boys who spent more than 5.5 hours daily using sedentary screen media. Their academic performance was low and they slept an average of 8 hours daily. Cluster 2 gathered girls with an excellent academic performance, who slept nearly 9 hours per day, and devoted less time daily to sedentary screen media. Academic performance was directly related to sleep time and socioeconomic status, but inversely related to overall sedentary screen media usage. Profiles from the two clusters were strongly differentiated by gender, age, sedentary screen media usage, sleep time and academic achievement. Girls with the highest academic results had a medium socioeconomic status in Cluster 2. Findings may contribute to establishing recommendations about the timing and duration of screen media usage in adolescents and appropriate sleep time needed to successfully meet the demands of school academics and to improve interventions targeting to affect behavioral change.
Topic-based PageRank on author cocitation networks

Ranking authors is vital for identifying a researcher’s impact and his standing within a scientific field. There are many different ranking methods (e.g., citations, publications, h-index, PageRank, and weighted PageRank), but most of them are topic-independent. This paper proposes topic-dependent ranks based on the combination of a topic model and a weighted PageRank algorithm. The Author-Conference-Topic (ACT) model was used to extract topic distribution of individual authors. Two ways for combining the ACT model with the PageRank algorithm are proposed: simple combination (I_PR) or using a topic distribution as a weighted vector for PageRank (PR_t). Information retrieval was chosen as the test field and representative authors for different topics at different time phases were identified. Principal Component Analysis (PCA) was applied to analyze the ranking difference between I_PR and PR_t.
The Evolution of Human Language: Your theory of language evolution depends on your theory of language

This paper is more about the questions for a theory of language evolution than about the answers. I’d like to ask what there is for a theory of the evolution of language to explain, and I want to show how this depends on what you think language is. So, what is language? Everybody recognizes that language is partly culturally dependent: there is a huge variety of disparate languages in the world, passed down through cultural transmission. If that’s all there is to language, a theory of the evolution of language has nothing at all to explain. We need only explain the cultural evolution of languages: English, Dutch, Mandarin, Hausa, etc. are products of cultural history. However, most readers of the present volume probably subscribe to the contemporary scientific view of language, which goes beneath the cultural differences among languages. It focuses on individual language users and asks:
Digital Counts of Maize Plants by Unmanned Aerial Vehicles (UAVs)

Precision phenotyping, especially the use of image analysis, allows researchers to gain information on plant properties and plant health. Aerial image detection with unmanned aerial vehicles (UAVs) provides new opportunities in precision farming and precision phenotyping. Precision farming has created a critical need for spatial data on plant density. The plant number reflects not only the final field emergence but also allows a more precise assessment of the final yield parameters. The aim of this work is to advance UAV use and image analysis as a possible highthroughput phenotyping technique. In this study, four different maize cultivars were planted in plots with different seeding systems (in rows and equidistantly spaced) and different nitrogen fertilization levels (applied at 50, 150 and 250 kg N/ha). The experimental field, encompassing 96 plots, was overflown at a 50-m height with an octocopter equipped with a 10-megapixel camera taking a picture every 5 s. Images were recorded between BBCH 13–15 (it is a scale to identify the phenological development stage of a plant which is here the 3to 5-leaves development stage) when the color of young leaves differs from older leaves. Close correlations up to R2 = 0.89 were found between in situ and image-based counted plants adapting a decorrelation stretch contrast enhancement procedure, which enhanced color differences in the images. On average, the error between visually and digitally counted plants was ≤5%. Ground cover, as determined by analyzing green pixels, ranged between 76% and 83% at these stages. However, the correlation between ground cover and digitally counted plants was very low. The presence of weeds and blurry effects on the images represent possible errors in counting plants. In conclusion, the final field emergence of maize can rapidly be assessed and allows more precise assessment of the final yield parameters. The use of UAVs and image processing has the potential to optimize farm management and to support field experimentation for agronomic and breeding purposes.
Towards rational management of the patent ductus arteriosus: the need for disease staging.

P atent ductus arteriosus (PDA) is common problem, with rates of 40– 55% in babies born less than 29 weeks’ gestation, 2 yet decisions related to management remain highly controversial. Despite numerous studies on the topic there remains uncertainty with respect to diagnosis, assignment of clinical importance, whether treatment is indicated and if so the preferred treatment modality. The most fundamental question remains unanswered: does a PDA cause acute physiological or clinical change that either acutely or chronically leads to organ damage, which further leads to important neonatal morbidities? Put simply is the PDA an ‘‘innocent bystander’’ or is it pathological to the extent that early detection and intervention is warranted to prevent neonatal morbidity? It is physiologically plausible that a major systemic to pulmonary (left-toright) shunt can lead to considerable postnatal morbidities in extremely low birthweight (ELBW) infants, either from pulmonary overcirculation (eg, chronic lung disease (CLD)) and/or systemic hypoperfusion (eg, necrotising enterocolitis (NEC), acute renal impairment). The lack of evidence supporting causality, 5 failure of medical treatment in some cases and the inherent risks of medical 7 or surgical treatment options has led some investigators to question whether intervention is necessary. In contrast, studies of prophylactic indometacin show reduced rates of PDA ligation, early major pulmonary haemorrhage and serious (grades III–IV) intracranial haemorrhage. 10 This strategy does, however, expose 40% of babies, in whom spontaneous PDA closure would have occurred, to any adverse effects of treatment. The medical community is becoming increasingly divided on the question of treatment of the PDA. Laughon and Bose highlight some important gaps in our knowledge with respect to therapeutic intervention. They emphasise that recent trials of non-steroidal anti-inflammatory agents have not led to any detectable reduction in neonatal morbidities that may be related to ongoing ductal patency. They propose the need for further trials of treatment to assess risks versus beneficial effects and suggest the need for a more restrained approach to management. Although we are in agreement that refinement of target populations requiring intervention is needed, it is unlikely and potentially dangerous to consider the solution to treatment decisions through an ‘‘all or none’’ framework. The traditional definition of a PDA, which forms the basis of clinical trials conducted to date, does not take into account physiological variability or the magnitude of the clinical effects resulting from the ductal shunt. Rather, the ‘‘pathological’’ PDA probably represents a continuum of clinical effects that lead to neonatal morbidities of varying importance. Here we examine the physiological and clinical consequences of transductal shunting, highlight the challenges of current approaches to management and propose a more rational approach to treatment.
On prediction of resource consumption of service requests in cloud environments

The functional efficiency of cloud solutions depends on their responsive allocation of processing resources to individual tasks according to their predefined completion times. An optimum usage of resources requires an accurate prediction of resource consumption. In this paper we introduce a novel method for predicting the resource consumption of processing requests in diverse consumption classes in a system consisting of a plentitude of processing units. We further enhance the prediction algorithm with different mechanisms to determine the resource consumption of a variety of request classes in order to increase the precision of the prediction. We also show that the prediction algorithm is robust against abrupt and large variations of consumption and recovers rapidly.
Contactless payment systems based on RFID technology

Contactless payment systems represent cashless payments that do not require physical contact between the devices used in consumer payment and POS terminals by the merchant. Radio frequency identification (RFID) devices can be embedded in the most different forms, as the form of cards, key rings, built into a watch, mobile phones. This type of payment supports the three largest payment system cards: Visa (Visa Contactless), MasterCard (MasterCard PayPass) and American Express (ExpressPay). All these products are compliant with international ISO 14443 standard, which provides a unique system for payment globally. Implementation of contactless payment systems are based on same infrastructure that exists for the payment cards with magnetic strips and does not require additional investments by the firm and financial institutions, other than upgrading the existing POS terminals. Technological solutions used for the implementation are solutions based on ISO 14443 standard, Sony FeliCa technology, RFID tokens and NFC (Near Field Communication) systems. This paper describes the advantages of introducing contactless payment system based on RF technology through pilot projects conducted by VISA, MasterCard and American Express Company in order to confirm in practice the applicability of this technology.
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum]

Over the last three decades, a large number of evolutionary algorithms have been developed for solving multi-objective optimization problems. However, there lacks an upto-date and comprehensive software platform for researchers to properly benchmark existing algorithms and for practitioners to apply selected algorithms to solve their real-world problems. The demand of such a common tool becomes even more urgent, when the source code of many proposed algorithms has not been made publicly available. To address these issues, we have developed a MATLAB platform for evolutionary multi-objective optimization in this paper, called PlatEMO, which includes more than 50 multiobjective evolutionary algorithms and more than 100 multi-objective test problems, along with several widely used performance indicators. With a user-friendly graphical user interface, PlatEMO enables users to easily compare several evolutionary algorithms at one time and collect statistical results in Excel or LaTeX files. More importantly, PlatEMO is completely open source, such that users are able to develop new algorithms on the basis of it. This paper introduces the main features of PlatEMO and illustrates how to use it for performing comparative experiments, embedding new algorithms, creating new test problems, and developing performance indicators. Source code of PlatEMO is now available at: http://bimk.ahu.edu.cn/index.php?s=/Index/Software/index.html.
Anomaly Machine Component Detection by Deep Generative Model with Unregularized Score

One of the most common needs in manufacturing plants is rejecting products not coincident with the standards as anomalies. Accurate and automatic anomaly detection improves product reliability and reduces inspection cost. Probabilistic models have been employed to detect test samples with lower likelihoods as anomalies in unsupervised manner. Recently, a probabilistic model called deep generative model (DGM) has been proposed for end-to-end modeling of natural images and already achieved a certain success. However, anomaly detection of machine components with complicated structures is still challenging because they produce a wide variety of normal image patches with low likelihoods. For overcoming this difficulty, we propose unregularized score for the DGM. As its name implies, the unregularized score is the anomaly score of the DGM without the regularization terms. The unregularized score is robust to the inherent complexity of a sample and has a smaller risk of rejecting a sample appearing less frequently but being coincident with the standards.
Vibro-Acoustic Simulation and Optimization of a Claw-Pole Alternator

This study aims at minimizing the acoustic noise from a magnetic origin of a claw-pole alternator. This optimization is carried out through a multiphysics simulation, which includes the computation of magnetic forces, vibrations, and the resulting noise. Therefore, a mechanical model of the alternator has to be developed to determine its main modes. Predicted modal parameters are checked against experimental results. Based on this model, the sound power level is simulated and compared with measurements. Finally, the rotor shape is optimized and a significant reduction of the noise level is found by simulation.
Overview of Current Biomedical Image Segmentation Methods

Medical image processing is a very active and fast-growing field that has evolved into an established discipline. Accurate segmentation of medical images is a fundamental step in clinical studies for diagnosis, monitoring, and treatment planning. Manual segmentation of medical images is a time consuming and a tedious task. Therefore the automated segmentation algorithms with high accuracy are of interest. There are several critical factors that determine the performance of a segmentation algorithm. Examples are: the area of application of segmentation technique, reproducibility of the method, accuracy of the results, etc. The purpose of this review is to provide an overview of current image segmentation methods. Their relative efficiency, advantages, and the problems they encounter are discussed. In order to evaluate the segmentation results, some popular benchmark measurements are presented.
Face liveness detection under bad illumination conditions

Spoofing face recognition systems with photos or videos of someone else is not difficult. Sometimes, all one needs is to display a picture on a laptop monitor or a printed photograph to the biometric system. In order to detect this kind of spoofs, in this paper we present a solution that works either with printed or LCD displayed photographs, even under bad illumination conditions without extra-devices or user involvement. Tests conducted on large databases show good improvements of classification accuracy as well as true positive and false positive rates compared to the state-of-the-art.
Calcium transients in astrocyte endfeet cause cerebrovascular constrictions

Cerebral blood flow (CBF) is coupled to neuronal activity and is imaged in vivo to map brain activation. CBF is also modified by afferent projection fibres that release vasoactive neurotransmitters in the perivascular region, principally on the astrocyte endfeet that outline cerebral blood vessels. However, the role of astrocytes in the regulation of cerebrovascular tone remains uncertain. Here we determine the impact of intracellular Ca2+ concentrations ([Ca2+]i) in astrocytes on the diameter of small arterioles by using two-photon Ca2+ uncaging to increase [Ca2+]i. Vascular constrictions occurred when Ca2+ waves evoked by uncaging propagated into the astrocyte endfeet and caused large increases in  The vasoactive neurotransmitter noradrenaline increased [Ca2+]i in the astrocyte endfeet, the peak of which preceded the onset of arteriole constriction. Depressing increases in astrocyte [Ca2+]i with BAPTA inhibited the vascular constrictions in noradrenaline. We find that constrictions induced in the cerebrovasculature by increased [Ca2+]i in astrocyte endfeet are generated through the phospholipase A2–arachidonic acid pathway and 20-hydroxyeicosatetraenoic acid production. Vasoconstriction by astrocytes is a previously unknown mechanism for the regulation of CBF.
Common questions about chronic low back pain.

More than 30% of U.S. adults report having experienced low back pain within the preceding three months. Although most low back pain is nonspecific and self-limiting, a subset of patients develop chronic low back pain, defined as persistent symptoms for longer than three months. Low back pain is categorized as nonspecific low back pain without radiculopathy, low back pain with radicular symptoms, or secondary low back pain with a spinal cause. Imaging should be reserved for patients with red flags for cauda equina syndrome, recent trauma, risk of infection, or when warranted before treatment (e.g., surgical, interventional). Prompt recognition of cauda equina syndrome is critical. Patient education should be combined with evidence-guided pharmacologic therapy. Goals of therapy include reducing the severity of pain symptoms, pain interference, and disability, as well as maximizing activity. Validated tools such as the Oswestry Disability Index can help assess symptom severity and functional change in patients with chronic low back pain. Epidural steroid injections do not improve pain or disability in patients with spinal stenosis. Spinal manipulation therapy produces small benefits for up to six months. Because long-term data are lacking for spinal surgery, patient education about realistic outcome expectations is essential.
SSReq: A method for designing Star Schemas from decisional requirements

The Data Warehouse (DW) design remains a great challenge process for DW designers. As well, so far, there is no strong method to support the requirements analysis process in DW projects. The literature approaches try to solve this tedious and important issue; however, many of these approaches ignore or bypass the requirements elicitation phase. In this paper, we propose a method to generate multidimensional schemas from decisional requirements. We elected natural language (NL) like syntax for expressing decisional/business users' needs. Our approach distinguishes from existing ones in that it: i) is NL-based for requirements elicitation; ii) uses a matrix representation to normalize users' requirements, iii) automates the generation of star schemas relying on eight specific heuristics. We developed SSReq (Star Schemas from Requirements) prototype to demonstrate the feasibility of our approach illustrated with a real case study.
Pediatric first aid knowledge and attitudes among staff in the preschools of Shanghai, China

Unintentional injury remains the leading cause of morbidity and mortality among children worldwide. The aims of this study were to assess a baseline level of first aid knowledge and overall attitudes regarding first aid among staff members in Shanghai preschools.



A cross-sectional study was carried out among the staff members at selected preschools. A stratified random sampling method was first used to identify suitable subjects. Data were obtained using a multiple-choice questionnaire. A standardized collection of demographics was performed and participants were given the aforementioned questionnaire to indicate knowledge of and attitudes toward first aid.



1067 subjects completed the questionnaire. None of the surveyed employees answered all questions correctly; only 39 individuals (3.7%) achieved passing scores. The relative number of correct answers to specific questions ranged from 16.5% to 90.2%. In particular, subjects lacked knowledge regarding first aid for convulsive seizures (only 16.5% answered correctly), chemical injuries to the eye (23%), inhaled poison (27.6%), and choking and coughing (30.1%). A multiple linear regression analysis showed scores were significantly higher among staff members with more education, those who had received first aid training before or were already healthcare providers, younger employees, and staff members from rural districts. Most employees agreed that giving first aid was helpful; the vast majority felt that it was important and useful for them to learn pediatric first aid.



The level of first-aid knowledge among preschool staffs in Shanghai was low. There is an urgent need to educate staff members regarding first aid practices and the various risk factors relating to specific injuries.
Data Augmentation Using Multi-Input Multi-Output Source Separation for Deep Neural Network Based Acoustic Modeling

We investigate the use of local Gaussian modeling (LGM) based source separation to improve speech recognition accuracy. Previous studies have shown that the LGM based source separation technique has been successfully applied to the runtime speech enhancement and the speech enhancement of training data for deep neural network (DNN) based acoustic modeling. In this paper, we propose a data augmentation method utilizing the multi-input multi-output (MIMO) characteristic of LGM based source separation. We first investigate the difference between unprocessed multi-microphone signals and multi-channel output signals from LGM based source separation as augmented training data for DNN based acoustic modeling. Experimental results using the third CHiME challenge dataset show that the proposed data augmentation outperforms the conventional data augmentation. In addition, we experiment the beamforming applied to the source separated signals as runtime speech enhancement. The results show that the proposed runtime beamforming further improves the speech recognition accuracy.