nils-herrmann/clean_scirepeval huggingface dataset:
ABF + + : Fast and Robust Angle Based Flattening Temporary version-In print

Conformal parameterization of mesh models has numerous applications in geometry processing. Conformality is desirable for remeshing, surface reconstruction, and many other mesh processing applications. Subject to the conformality requirement, these applications typically bene t from parameterizations with smaller stretch. The Angle Based Flattening (ABF) method, presented a few years ago, generates provably valid conformal parameterizations with low stretch. However, it is quite time consuming and becomes error prone for large meshes due to numerical error accumulation. This work presents ABF++, a highly e cient extension of the ABF method that overcomes these drawbacks, while maintaining all the advantages of ABF. ABF++ robustly parameterizes meshes of hundreds of thousands and millions of triangles within minutes. It is based on two main components: (1) a new numerical solution technique that dramatically reduces the dimension of the linear systems solved at each iteration, speeding up the solution; (2) an e cient hierarchical solution technique. The speedup with (1) does not come at the expense of greater distortion. The hierarchical technique (2) enables parameterization of models with millions of faces in seconds, at the expense of a minor increase in parametric distortion. The parameterization computed by ABF++ are provably valid, i.e. they contain no ipped triangles. As a result of these extensions, the ABF++ method is extremely suitable for robustly and e ciently parameterizing models for geometry processing applications.
Sexting among young adults.

Sexting has stirred debate over its legality and safety, but few researchers have documented the relationship between sexting and health. We describe the sexting behavior of young adults in the United States, and examine its association with sexual behavior and psychological well-being.



Using an adapted Web version of respondent-driven sampling, we recruited a sample of U.S. young adults (aged 18-24 years, N = 3,447). We examined participant sexting behavior using four categories of sexting: (1) nonsexters, (2) receivers, (3) senders, and (4) two-way sexters. We then assessed the relationships between sexting categories and sociodemographic characteristics, sexual behavior, and psychological well-being.



More than half (57%) of the respondents were nonsexters, 28.2% were two-way sexters, 12.6% were receivers, and 2% were senders. Male respondents were more likely to be receivers than their female counterparts. Sexually active respondents were more likely to be two-way sexters than non-sexually active ones. Among participants who were sexually active in the past 30 days, we found no differences across sexting groups in the number of sexual partners or the number of unprotected sex partners in the past 30 days. We also found no relationship between sexting and psychological well-being.



Our results suggest that sexting is not related to sexual risk behavior or psychological well-being. We discuss the findings of this study and propose directions for further research on sexting.
Extreme-angle broadband metamaterial lens.

For centuries, the conventional approach to lens design has been to grind the surfaces of a uniform material in such a manner as to sculpt the paths that rays of light follow as they transit through the interfaces. Refractive lenses formed by this procedure of bending the surfaces can be of extremely high quality, but are nevertheless limited by geometrical and wave aberrations that are inherent to the manner in which light refracts at the interface between two materials. Conceptually, a more natural--but usually less convenient--approach to lens design would be to vary the refractive index throughout an entire volume of space. In this manner, far greater control can be achieved over the ray trajectories. Here, we demonstrate how powerful emerging techniques in the field of transformation optics can be used to harness the flexibility of gradient index materials for imaging applications. In particular we design and experimentally demonstrate a lens that is broadband (more than a full decade bandwidth), has a field-of-view approaching 180 degrees and zero f-number. Measurements on a metamaterial implementation of the lens illustrate the practicality of transformation optics to achieve a new class of optical devices.
Silicon carbide power MOSFETs: Breakthrough performance from 900 V up to 15 kV

Since Cree, Inc.'s 2<sup>nd</sup> generation 4H-SiC MOSFETs were commercially released with a specific on-resistance (R<sub>ON, SP</sub>) of 5 mΩ·cm<sup>2</sup> for a 1200 V-rating in early 2013, we have further optimized the device design and fabrication processes as well as greatly expanded the voltage ratings from 900 V up to 15 kV for a much wider range of high-power, high-frequency, and high-voltage energy-conversion and transmission applications. Using these next-generation SiC MOSFETs, we have now achieved new breakthrough performance for voltage ratings from 900 V up to 15 kV with a R<sub>ON, SP</sub> as low as 2.3 mΩ·cm<sup>2</sup> for a breakdown voltage (BV) of 1230 V and 900 V-rating, 2.7 mΩ·cm<sup>2</sup> for a BV of 1620 V and 1200 V-rating, 3.38 mΩ·cm<sup>2</sup> for a BV of 1830 V and 1700 V-rating, 10.6 mΩ·cm<sup>2</sup> for a BV of 4160 V and 3300 V-rating, 123 mΩ·cm<sup>2</sup> for a BV of 12 kV and 10 kV-rating, and 208 mΩ·cm<sup>2</sup> for a BV of 15.5 kV and 15 kV-rating. In addition, due to the lack of current tailing during the bipolar device switching turn-off, the SiC MOSFETs reported in this work exhibit incredibly high frequency switching performance over their silicon counter parts.
Strength-based interventions : Their importance in application to the gifted

Positive psychology has revived psychology’s abandoned interest in the study of morally positively valued traits (the so-called character strengths) and virtues. We review literature generated on strength-based approaches and focus on applications in the so-called positive psychology interventions. There seems to be great potential in this approach for research in the field of giftedness and, of course, also when practically working with gifted children and adolescents. We highlight some ideas for future research directions.  Originally published at: Proyer, René T; Gander, Fabian; Tandler, Nancy (2017). Strength-based interventions: Their importance in application to the gifted. Gifted Education International, 33(2):118-130. DOI: https://doi.org/10.1177/0261429416640334 Strength-based interventions: Their importance in application to the gifted René T. Proyer Martin-Luther University Halle-Wittenberg, Germany University of Zurich, Switzerland Fabian Gander University of Zurich, Switzerland Nancy Tandler Martin-Luther University Halle-Wittenberg, Germany René T. Proyer is at the Department of Psychology at the Martin-Luther University Halle-Wittenberg (Germany) and the Department of Psychology at the University of Zurich (Switzerland). Fabian Gander is at Department of Psychology at the University of Zurich (Switzerland). Nany Tandler is at the Department of Psychology at the Martin-Luther University Halle-Wittenberg (Germany) The preparation of this paper has been facilitated by research grants of the Swiss National Science Foundation (SNSF; 100014_132512 and 100014_149772) awarded to RTP. Correspondence concerning this article should be addressed to René Proyer, MartinLuther University Halle-Wittenberg (Germany), Emil-Abderhalden-Straße 26-27, 06108 Halle (Saale), Germany; E-mail: rene.proyer@psych.uni-halle.de
Data quality management in the public domain: a case study within the Dutch justice system

The need for anonymity preservation within the justice domain requires the introduction of a trusted third party as an intermediary, while integrating individual databases within its boundaries. After the trusted third party encrypts records, it is no longer possible to perform checks on the data quality and correct data anomalies. Therefore, this research examines the concepts of data quality, data integration, record linkage and trusted third party and, then, combines these with four expert interviews in order to identify ways to assess and improve data quality while linking privacy-sensitive data. Next, the trusted data linkage framework (TDLF) is presented to aid data quality management while combining citizens’ privacy-sensitive data from different organisations. Finally, we evaluate the framework in a case study to demonstrate how the quality of structured judicial data can be managed prior to its encryption, while using multiple databases as sources of data and a different final recipient.
Fear of being laughed at and social anxiety : A preliminary psychometric study

The present study examines the relationship between questionnaire measures of social phobia and gelotophobia. A sample of 211 Colombian adults filled in Spanish versions of the Social Anxiety and Distress scale (SAD; Watson & Friend, 1969), the Fear of Negative Evaluation scale (FNE; Watson & Friend, 1969) and the GELOPH<15> (Ruch & Proyer, 2008). Results confirmed that both Social Anxiety and Distress and Fear of Negative Evaluation scale overlapped with the fear of being laughed at without being identical with it. The SAD and FNE correlated highly with the GELOPH<15> but not all high scorers in these scales expressed a fear of being laughed at. Furthermore, an item factor analysis yielded three factors that were mostly loaded by items of the respective scales. This three-factor structure was verified using confirmatory factor analysis. A measure model where one general factor of social anxiety was specified, or another one where two different factors were defined (gelotophobia vs. social anxiety assessed by SAD and FNE) showed a very poor fit to the data. It is concluded that the fear of being laughed cannot fully be accounted for by these measures of social phobia.
A refined PWM scheme for voltage and current source converters

A pulse-width-modulation (PWM) scheme that uses the converter switching frequency to minimize unwanted load current harmonics is described. This results in the reduction of the number of switch communications per cycle. The method is suitable for high-performance variable-speed AC-motor drives that require high-output switching frequencies for near-silent operation. It is also applicable, without change, to voltage or current-source inverters and to two and four-quadrant three-phase PWM rectifiers. Key predicted results have been verified experimentally on a 5 kVA inverter breadboard.<<ETX>>
Colonic Polyp Classification with Convolutional Neural Networks

Texture patch classification is an important task in many different computer-aided medical systems. Convolutional Neural Networks (CNN's) have become state-of-the-art for many computer vision tasks in recent years. In this paper, we propose the use of CNN's for the automated classification of colonic mucosa for colon polyp staging in the context of colon cancer screening. This deep learning approach has the property of extracting features and classifying images in the same architecture by exploiting directly the input image pixels being successful in handling distortions such as different light conditions, presence of partial occlusions, etc. For this type of deep learning approach it is common to require that the database contains large amounts of data, which is quite rare in the medical field. The method proposed allows the use of small patches (subimages) to increase the size of the database as well to classify different regions in the same image. We show experimentally that this model is more efficient than some of the commonly used features for colonic polyp classification.
Domain Adaptation with Adversarial Neural Networks and Auto-encoders

Domain adaptation focuses on the situation where we have data generated from multiple domains, which are assumed to be different, but similar, in a certain sense. In this work we focus on the case where there are two domains, known as the source and the target domain. The source domain is assumed to have a large amount of labeled data while labeled data in the target domain is scarce. The goal of domain adaptation algorithms is to generalize better on the target domain by exploiting the large amount of labeled data in the related source domain.
Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation

Dynamic treatment recommendation systems based on large-scale electronic health records (EHRs) become a key to successfully improve practical clinical outcomes. Prior relevant studies recommend treatments either use supervised learning (e.g. matching the indicator signal which denotes doctor prescriptions), or reinforcement learning (e.g. maximizing evaluation signal which indicates cumulative reward from survival rates). However, none of these studies have considered to combine the benefits of supervised learning and reinforcement learning. In this paper, we propose Supervised Reinforcement Learning with Recurrent Neural Network (SRL-RNN), which fuses them into a synergistic learning framework. Specifically, SRL-RNN applies an off-policy actor-critic framework to handle complex relations among multiple medications, diseases and individual characteristics. The "actor'' in the framework is adjusted by both the indicator signal and evaluation signal to ensure effective prescription and low mortality. RNN is further utilized to solve the Partially-Observed Markov Decision Process (POMDP) problem due to lack of fully observed states in real world applications. Experiments on the publicly real-world dataset, i.e., MIMIC-3, illustrate that our model can reduce the estimated mortality, while providing promising accuracy in matching doctors' prescriptions.
Adult content consumption in online social networks

Users in online social networks naturally organize themselves into overlapping and interlinked communities that are formed around common identity or shared topical interests. Some communities gather people around specific deviant behaviors, conducts that are commonly considered inappropriate with respect to the society’s norms or moral standards such as drug use, eating disorders, and pornographic content consumption. From a network analysis perspective, the set of interactions between members of these communities form deviant networks that map how the deviant content is shared and consumed. It is commonly believed that deviant networks are small and isolated from the mainstream social media life; accordingly, most research studies have considered them in isolation. We focus on adult content consumption networks, which is one deviant network with a significant presence in online social media and in the Web in general. We investigate two large online social networks and discuss the following insights. Deviant networks are limited in size, tightly connected and structured in subgroups. Nevertheless, content originated in deviant networks spreads widely across the whole social graph possibly touching a large number of unintentionally exposed users, such that the average local perception is that neighboring users share more deviant content. Finally, we investigate how content production and consumption vary with age and show that the consumption rate is very similar between male and female users up to the age of 25. We conclude that deviant communities are deeply rooted into the relational fabric of a social network, and that a deeper understanding of how their activity impacts on every other user is required.
Design of Hybrid Excited Synchronous Machine for Electrical Vehicles

This paper deals with the optimal design of a new synchronous motor particularly suitable for electric vehicles. The unique advantage of this motor is its possibility to realize field weakening at high speeds, which is the most important demand for such drives. The main design goal is to identify the dimensions of certain crucial regions of the machine in order to avoid saturation of the iron and to minimize the mass to volume ratio, while simultaneously maximizing the torque. The originality of the contribution is manifold: formulating the design problem in multiobjective terms, solving it by means of evolutionary computing, solving the associated direct problem based on a three-dimensional finite element analysis (3-D-FEA) field model, considering a reduced set of design variables by means of response surface method, referring to a real-life problem.
Active Sampler: Light-weight Accelerator for Complex Data Analytics at Scale

Recent years have witnessed amazing outcomes from “Big Models” trained by “Big Data”. Most popular algorithms for model training are iterative. Due to the surging volumes of data, we can usually afford to process only a fraction of the training data in each iteration. Typically, the data are either uniformly sampled or sequentially accessed. In this paper, we study how the data access pattern can affect model training. We propose an Active Sampler algorithm, where training data with more “learning value” to the model are sampled more frequently. The goal is to focus training effort on valuable instances near the classification boundaries, rather than evident cases, noisy data or outliers. We show the correctness and optimality of Active Sampler in theory, and then develop a light-weight vectorized implementation. Active Sampler is orthogonal to most approaches optimizing the efficiency of large-scale data analytics, and can be applied to most analytics models trained by stochastic gradient descent (SGD) algorithm. Extensive experimental evaluations demonstrate that Active Sampler can speed up the training procedure of SVM, feature selection and deep learning, for comparable training quality by 1.6-2.2x.
Time Series Forecasting as a Measure

In this paper, the time series prediction is as a measure. At the same time, the optimal combination forecast using each method can be defined as the actual impact measurement value of true. Effect of its theoretical estimation has error correlation coefficient values. The optimal weighted linear combination is the theoretical prediction which can be proved, also, simple averaging method is linear combination forecasting optimal weights. Especially, based on the robust statistic theory, the mathematical derivation and numerical tests on the superiority is simple.
Smoothing LUT classifiers for robust face detection

Look-up table (LUT) classifiers are often used to construct concise classifiers for rapid object detection due to their favorable convergent ability. However, their poor generalization ability imposes restrictions on their applications. A novel improvement to LUT classifiers is proposed in this paper where the new confident of each partition is recalculated by smoothing the old ones within its neighbor partitions. The new confidents are more generalizable than the old ones because each of the new predicts is supported by more training samples and the high frequency components in the old predict sequences are suppressed greatly through smoothing operation. Both weight sum smoothing method and confident smoothing method are introduced here, which all bring negligible extra computation cost in training time and no extra cost in test time. Experimental results in the domain of upright frontal face detection using smoothed LUT classifiers with identical smoothing width and smoothing factor for all partitions based on Haar-like rectangle features show that smoothed LUT classifiers generalize much better and also converge more or less worse than unsmooth LUT classifiers. Specifically, smoothed LUT classifiers can delicately balance between generalization ability and convergent ability through carefully set smoothing parameters.
Natural scene text detection based on SWT, MSER and candidate classification

This paper presents a novel scene text detection algorithm based on Stroke Width Transform (SWT), Maximally Extremal Regions (MSER) and candidate classification. Firstly, utilize the SWT and MSER to extract the candidate characters at the same time. Secondly, preliminary filtering the candidate connected components based on heuristic rules. Thirdly, using mutual verification and integration to class all candidate into two categories: strong candidates, weak candidates. If the weak candidate has similar properties with strong candidate, then the weak candidate is changed into strong candidate. Finally, the text area is aggregated into text lines by text line aggregation algorithm. The experiment results on public datasets show that the proposed method can detect text lines effectively.
Map quality evaluation for visual localization

A variety of end-user devices involving keypoint-based mapping systems are about to hit the market e.g. as part of smartphones, cars, robotic platforms, or virtual and augmented reality applications. Thus, the generated map data requires automated evaluation procedures that do not require experienced personnel or ground truth knowledge of the underlying environment. A particularly important question enabling commercial applications is whether a given map is of sufficient quality for localization. This paper proposes a framework for predicting localization performance in the context of visual landmark-based mapping. Specifically, we propose an algorithm for predicting performance of vision-based localization systems from different poses within the map. To achieve this, a metric is defined that assigns a score to a given query pose based on the underlying map structure. The algorithm is evaluated on two challenging datasets involving indoor data generated using a handheld device and outdoor data from an autonomous fixed-wing unmanned aerial vehicle (UAV). Using these, we are able to show that the score provided by our method is highly correlated to the true localization performance. Furthermore, we demonstrate how the predicted map quality can be used within a belief based path planning framework in order to provide reliable trajectories through high-quality areas of the map.
Performance evaluation of pattern classifiers for handwritten character recognition

This paper describes a performance evaluation study in which some efficient classifiers are tested in handwritten digit recognition. The evaluated classifiers include a statistical classifier (modified quadratic discriminant function, MQDF), three neural classifiers, and an LVQ (learning vector quantization) classifier. They are efficient in that high accuracies can be achieved at moderate memory space and computation cost. The performance is measured in terms of classification accuracy, sensitivity to training sample size, ambiguity rejection, and outlier resistance. The outlier resistance of neural classifiers is enhanced by training with synthesized outlier data. The classifiers are tested on a large data set extracted from NIST SD19. As results, the test accuracies of the evaluated classifiers are comparable to or higher than those of the nearest neighbor (1-NN) rule and regularized discriminant analysis (RDA). It is shown that neural classifiers are more susceptible to small sample size than MQDF, although they yield higher accuracies on large sample size. As a neural classifier, the polynomial classifier (PC) gives the highest accuracy and performs best in ambiguity rejection. On the other hand, MQDF is superior in outlier rejection even though it is not trained with outlier data. The results indicate that pattern classifiers have complementary advantages and they should be appropriately combined to achieve higher performance.