nils-herrmann/clean_scirepeval huggingface dataset:
The Role of Prefrontal Cortex in Working Memory: A Mini Review

A prominent account of prefrontal cortex (PFC) function is that single neurons within the PFC maintain representations of task-relevant stimuli in working memory. Evidence for this view comes from studies in which subjects hold a stimulus across a delay lasting up to several seconds. Persistent elevated activity in the PFC has been observed in animal models as well as in humans performing these tasks. This persistent activity has been interpreted as evidence for the encoding of the stimulus itself in working memory. However, recent findings have posed a challenge to this notion. A number of recent studies have examined neural data from the PFC and posterior sensory areas, both at the single neuron level in primates, and at a larger scale in humans, and have failed to find encoding of stimulus information in the PFC during tasks with a substantial working memory component. Strong stimulus related information, however, was seen in posterior sensory areas. These results suggest that delay period activity in the PFC might be better understood not as a signature of memory storage per se, but as a top down signal that influences posterior sensory areas where the actual working memory representations are maintained.
Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose Estimation

This paper focuses on structured-output learning using deep neural networks for 3D human pose estimation from monocular images. Our network takes an image and 3D pose as inputs and outputs a score value, which is high when the image-pose pair matches and low otherwise. The network structure consists of a convolutional neural network for image feature extraction, followed by two sub-networks for transforming the image features and pose into a joint embedding. The score function is then the dot-product between the image and pose embeddings. The image-pose embedding and score function are jointly trained using a maximum-margin cost function. Our proposed framework can be interpreted as a special form of structured support vector machines where the joint feature space is discriminatively learned using deep neural networks. We also propose an efficient recurrent neural network for performing inference with the learned image-embedding. We test our framework on the Human3.6m dataset and obtain state-of-the-art results compared to other recent methods. Finally, we present visualizations of the image-pose embedding space, demonstrating the network has learned a high-level embedding of body-orientation and pose-configuration.
How to design informative tutoring feedback for multi-media learning

This paper outlines a conceptual framework for the design of psychologically well-founded informative tutoring feedback forms. This framework is on the one hand based on the large body of research on multiple elaborated feedback types. On the other hand cognitive task and error analyses serve as roots for the development of systematic design strategies. Furthermore, the present paper illustrates how these principles can be applied to the design, implementation and evaluation of informative tutoring feedback for a multimedia learning environment on written subtraction tasks. The impact of the developed informative tutoring feedback forms on learning and motivation was examined in two computer-based learning experiments. The results of these studies show that systematically designed informative feedback has positive effects on achievement and motivation. Informative feedback has to be considered an important factor in order to support efficient learning processes in computer-based learning or multi-media learning environments. Hence, the effects of multiple types and forms of elaborated feedback have been investigated in a large variety of instructional contexts. Widely used and examined feedback forms are outcome feedback types such as knowledge of result or response (KR, informs whether a response is correct or incorrect), knowledge of the correct response (KCR, provides the correct response or solution) or answer until correct (AUC, provides KR and requires to remain on the same item until it is answered correctly). There also exists a large body of research on elaborated feedback (EF). Unfortunately, the findings on the effects of multiple feedback forms are rather inconsistent (see reviews by In order to derive guidelines for the systematic design of elaborated informative feedback messages this body of research must be considered in detail. However, this detailed consideration is not always possible because it is rather costly. Thus, the design and implementation of informative feedback in multi-media learning environments is often more based on intuition than on psychologically well-founded design principles. The purposes of the present paper are therefore (a) to outline a conceptual framework for the design of psychologically well-founded informative tutoring feedback messages based on the large body of research on elaborated feedback and on approaches of cognitive task analysis (e.g. Jonassen, Tessmer & Hannum, 1999), (b) to illustrate how to apply these principles in order to design informative tutoring feedback messages for multimedia learning environments, and (c) to summarize the results of two evaluative studies that investigated the impact of the developed informative feedback forms on …
Fides: remote anomaly-based cheat detection using client emulation

As a result of physically owning the client machine, cheaters in online games currently have the upper-hand when it comes to avoiding detection. To address this problem and turn the table on cheaters, this paper presents Fides, an anomaly-based cheat detection approach that remotely validates game execution. With Fides, a server-side Controller specifies how and when a client-side Auditor measures the game. To accurately validate measurements, the Controller partially emulates the client and collaborates with the server. This paper examines a range of cheat methods and initial measurements that counter them, showing that a Fides prototype is able to efficiently detect several existing cheats, including one state-of-the-art cheat that is advertised as "undetectable".
Lifestyle choices and mental health: a longitudinal survey with German and Chinese students

A healthy lifestyle can be beneficial for one's mental health. Thus, identifying healthy lifestyle choices that promote psychological well-being and reduce mental problems is useful to prevent mental disorders. The aim of this longitudinal study was to evaluate the predictive values of a broad range of lifestyle choices for positive mental health (PMH) and mental health problems (MHP) in German and Chinese students.



Data were assessed at baseline and at 1-year follow-up. Samples included 2991 German (Mage = 21.69, SD = 4.07) and 12,405 Chinese (Mage = 20.59, SD = 1.58) university students. Lifestyle choices were body mass index, frequency of physical and mental activities, frequency of alcohol consumption, smoking, vegetarian diet, and social rhythm irregularity. PMH and MHP were measured with the Positive Mental Health Scale and a 21-item version of the Depression Anxiety and Stress Scale. The predictive values of lifestyle choices for PMH and MHP at baseline and follow-up were assessed with single-group and multi-group path analyses.



Better mental health (higher PMH and fewer MHP) at baseline was predicted by a lower body mass index, a higher frequency of physical and mental activities, non-smoking, a non-vegetarian diet, and a more regular social rhythm. When controlling for baseline mental health, age, and gender, physical activity was a positive predictor of PMH, smoking was a positive predictor of MHP, and a more irregular social rhythm was a positive predictor of PMH and a negative predictor of MHP at follow-up. The good fit of a multi-group model indicated that most lifestyle choices predict mental health comparably across samples. Some country-specific effects emerged: frequency of alcohol consumption, for example, predicted better mental health in German and poorer mental health in Chinese students.



Our findings underline the importance of healthy lifestyle choices for improved psychological well-being and fewer mental health difficulties. Effects of lifestyle on mental health are comparable in German and Chinese students. Some healthy lifestyle choices (i.e., more frequent physical activity, non-smoking, regular social rhythm) are related to improvements in mental health over a 1-year period.
DELIBERATE PRACTICE 1 Deliberate Practice : Is That All It Takes To Become An Expert ?

Twenty years ago, Ericsson, Krampe, and Tesch-Römer (1993) proposed that expert performance reflects a long period of deliberate practice rather than innate ability, or “talent.” Ericsson et al. found that elite musicians had accumulated thousands of hours more deliberate practice than less accomplished musicians, and concluded that deliberate practice provides “a sufficient account of the major facts about the nature and scarcity of exceptional performance” (p. 392). The deliberate practice view has since gained popularity as a theoretical account of expert performance, but here we show that deliberate practice is not sufficient to explain individual differences in performance in the two most widely studied domains in expertise research—chess and music. For researchers interested in advancing the science of expert performance, the task now is to develop and rigorously test theories that take into account as many potentially relevant explanatory constructs as possible.
Modeling Lithium Ion battery degradation in electric vehicles

A new aging model for Lithium Ion batteries is proposed based on theoretical models of crack propagation. This provides an exponential dependence of aging on stress such as depth of discharge. A measure of stress is derived from arbitrary charge and discharge histories to include mixed use in vehicles or vehicle to grid operations. This aging model is combined with an empirical equivalent circuit model, to provide time and state of charge dependent charge and discharge characteristics at any rate and temperature. This choice of model results in a cycle life prediction with few parameters to be fitted to a particular cell.
An Adaptive SVR for High-Frequency Stock Price Forecasting

In order to mitigate investments, stock price forecasting has attracted more attention in recent years. Aiming at the discreteness, non-normality, high-noise in high-frequency data, a support vector machine regression (SVR) algorithm is introduced in this paper. However, the characteristics in different periods of the same stock, or the same periods of different stocks are significantly different. So, SVR with fixed parameters is difficult to satisfy with the constantly changing data flow. To tackle this problem, an adaptive SVR was proposed for stock data at three different time scales, including daily data, 30-min data, and 5-min data. Experiments show that the improved SVR with dynamic optimization of learning parameters by particle swarm optimization can get a better result than compared methods including SVR and back-propagation neural network.
Putting education in "educational" apps: lessons from the science of learning.

Children are in the midst of a vast, unplanned experiment, surrounded by digital technologies that were not available but 5 years ago. At the apex of this boom is the introduction of applications ("apps") for tablets and smartphones. However, there is simply not the time, money, or resources available to evaluate each app as it enters the market. Thus, "educational" apps-the number of which, as of January 2015, stood at 80,000 in Apple's App Store (Apple, 2015)-are largely unregulated and untested. This article offers a way to define the potential educational impact of current and future apps. We build upon decades of work on the Science of Learning, which has examined how children learn best. From this work, we abstract a set of principles for two ultimate goals. First, we aim to guide researchers, educators, and designers in evidence-based app development. Second, by creating an evidence-based guide, we hope to set a new standard for evaluating and selecting the most effective existing children's apps. In short, we will show how the design and use of educational apps aligns with known processes of children's learning and development and offer a framework that can be used by parents and designers alike. Apps designed to promote active, engaged, meaningful, and socially interactive learning-four "pillars" of learning-within the context of a supported learning goal are considered educational.
Phishing on Mobile Devices

We assess the risk of phishing on mobile platforms. Mobile operating systems and browsers lack secure application identity indicators, so the user cannot always identify whether a link has taken her to the expected application. We conduct a systematic analysis of ways in which mobile applications and web sites link to each other. To evaluate the risk, we study 85 web sites and 100 mobile applications and discover that web sites and applications regularly ask users to type their passwords into contexts that are vulnerable to spoofing. Our implementation of sample phishing attacks on the Android and iOS platforms demonstrates that attackers can spoof legitimate applications with high accuracy, suggesting that the risk of phishing attacks on mobile platforms is greater than has previously been appreciated.
WikiTalk: A Spoken Wikipedia-based Open-Domain Knowledge Access System

WikiTalk is an open-domain knowledge access system that talks about topics using Wikipedia articles as its knowledge source. Based on Constructive Dialogue Modelling theory, WikiTalk exploits the concepts of Topic and NewInfo to manage topic-tracking and topic-shifting. As the currently talked-about Topic can be any Wikipedia topic, the system is truly open-domain. NewInfos, the pieces of new information to be conveyed to the partner, are associated with hyperlinks extracted from the Wikipedia texts. Using these hyperlinks the system can change topics smoothly according to the user’s changing interests. As well as user-initiated topics, the system can suggest new topics using for example the daily "Did you know?" items in Wikipedia. WikiTalk can be employed in different environments. It has been demonstrated on Windows, with an open-source robotics simulator, and with the Aldebaran Nao humanoid robot.
Penoplasty for buried penis in infants and children: report of 100 cases

The aim of this study is to report single surgeon’s experience in treatment of buried penis in children and describe the surgical technique which was developed by the senior author. Described surgical technique avoids circumferential incision at the base of the penis and thus prevents formation of post-operative lymphedema. Repair is based on a vertical incision in median raphe, complete degloving of penis and tacking its base to prepubic fascia. Shaft skin is attached to base of penis with vertical mattress sutures. Patient age varied from 1 month to 11.4 years (mean 1.9 years). All patients had good to excellent outcome with uniformly improved visualization of penile shaft post-operatively. There was one case of wound infection successfully treated with oral antibiotics. Revisions were needed in 4% patients. Surgical correction of buried penis in infants and children is safe and effective. Described technique is applicable for essentially all cases of congenital buried penis as well as for iatrogenically entrapped penis after circumcision. In our experience there were no additional procedures required to assure skin coverage of penile shaft.
Polysemy or generality ?

This chapter proposes a conception of lexical meaning as use-potential, in contrast to prevailing atomistic and reificational views. The issues are illustrated on the example of spatial expressions, pre-eminently prepositions. It is argued that the dichotomy between polysemy and semantic generality is a false one, with expressions occupying points on a continuum from full homonymy to full monosemy, and with typical cases of polysemy falling in between. The notion of use-potential is explored in connectionist models of spatial categorization. Some possible objections to the use-potential approach are also addressed.
Antennas for Global Navigation Satellite System (GNSS)

Global Navigation Satellite System (GNSS) will in effect be fully deployed and operational in a few years, even with the delays in Galileo as a consequence of European Union's financial difficulties. The vastly broadened GNSS spectra, spread densely across 1146-1616 MHz, versus the narrow Global Positioning System (GPS) L1 and L2 bands, together with a constellation of over 100 Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites versus GPS' 24 MEO satellites, are revolutionizing the design of GNSS receive antennas. For example, a higher elevation cutoff angle will be preferred. As a result, fundamental changes in antenna design, new features and applications, as well as cost structures are ongoing. Existing GNSS receive antenna technologies are reviewed and design challenges are discussed.
Learning with Random Learning Rates

In neural networks, the learning rate of the gradient descent strongly affects performance. This prevents reliable out-of-the-box training of a model on a new problem. We propose the All Learning Rates At Once (Alrao) algorithm: each unit or feature in the network gets its own learning rate sampled from a random distribution spanning several orders of magnitude, in the hope that enough units will get a close-to-optimal learning rate. Perhaps surprisingly, stochastic gradient descent (SGD) with Alrao performs close to SGD with an optimally tuned learning rate, for various network architectures and problems. In our experiments, all Alrao runs were able to learn well without any tuning.
Compact ASIC Architectures for the 512-Bit Hash Function Whirlpool

Compact hardware architectures are proposed for the ISO/IEC 10118-3 standard hash function Whirlpool. In order to reduce the circuit area, the 512-bit function block ρ[k] for the main datapath is divided into smaller sub-blocks with 256-, 128-, or 64-bit buses, and the sub-blocks are used iteratively. Six architectures are designed by combining the three different datapath widths and two data scheduling techniques: interleave and pipeline. The six architectures in conjunction with three different types of S-box were synthesized using a 90-nm CMOS standard cell library, with two optimization options: size and speed. A total of 18 implementations were obtained, and their performances were compared with conventional designs using the same standard cell library. The highest hardware efficiency (defined by throughput per gate) of 372.3 Kbps/gate was achieved by the proposed pipeline architecture with the 256-bit datapath optimized for speed. The interleaved architecture with the 64-bit datapath optimized for size showed the smallest size of 13.6 Kgates, which requires only 46% of the resources of the conventional compact architecture.
Robust Multi-Frame Super-Resolution Based on Spatially Weighted Half-Quadratic Estimation and Adaptive BTV Regularization

Multi-frame image super-resolution focuses on reconstructing a high-resolution image from a set of low-resolution images with high similarity. Combining image prior knowledge with fidelity model, the Bayesian-based methods have been considered as an effective technique in super-resolution. The minimization function derived from maximum a posteriori probability (MAP) is composed of a fidelity term and a regularization term. In this paper, based on the MAP estimation, we propose a novel initialization method for super-resolution imaging. For the fidelity term in our proposed method, the half-quadratic estimation is used to choose error norm adaptively instead of using fixed  $L_{1}$  and  $L_{2}$  norms. Besides, a spatial weight matrix is used as a confidence map to scale the estimation result. For the regularization term, we propose a novel regularization method based on adaptive bilateral total variation (ABTV). Both the fidelity term and the ABTV regularization guarantee the robustness of our framework. The fidelity term is mainly responsible for dealing with misregistration, blur, and other kinds of large errors, while the ABTV regularization aims at edge preservation and noise removal. The proposed scheme is tested on both synthetic data and real data. The experimental results illustrate the superiority of our proposed method in terms of edge preservation and noise removal over the state-of-the-art algorithms.
Pharmacometric Approaches to Personalize Use of Primarily Renally Eliminated Antibiotics in Preterm and Term Neonates.

Sepsis remains a major cause of mortality and morbidity in neonates, and, as a consequence, antibiotics are the most frequently prescribed drugs in this vulnerable patient population. Growth and dynamic maturation processes during the first weeks of life result in large inter- and intrasubject variability in the pharmacokinetics (PK) and pharmacodynamics (PD) of antibiotics. In this review we (1) summarize the available population PK data and models for primarily renally eliminated antibiotics, (2) discuss quantitative approaches to account for effects of growth and maturation processes on drug exposure and response, (3) evaluate current dose recommendations, and (4) identify opportunities to further optimize and personalize dosing strategies of these antibiotics in preterm and term neonates. Although population PK models have been developed for several of these drugs, exposure-response relationships of primarily renally eliminated antibiotics in these fragile infants are not well understood, monitoring strategies remain inconsistent, and consensus on optimal, personalized dosing of these drugs in these patients is absent. Tailored PK/PD studies and models are useful to better understand relationships between drug exposures and microbiological or clinical outcomes. Pharmacometric modeling and simulation approaches facilitate quantitative evaluation and optimization of treatment strategies. National and international collaborations and platforms are essential to standardize and harmonize not only studies and models but also monitoring and dosing strategies. Simple bedside decision tools assist clinical pharmacologists and neonatologists in their efforts to fine-tune and personalize the use of primarily renally eliminated antibiotics in term and preterm neonates.