nils-herrmann/clean_scirepeval huggingface dataset:
Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex

Rapid variations in cortical state during wakefulness have a strong influence on neural and behavioural responses and are tightly coupled to changes in pupil size across species. However, the physiological processes linking cortical state and pupil variations are largely unknown. Here we demonstrate that these rapid variations, during both quiet waking and locomotion, are highly correlated with fluctuations in the activity of corticopetal noradrenergic and cholinergic projections. Rapid dilations of the pupil are tightly associated with phasic activity in noradrenergic axons, whereas longer-lasting dilations of the pupil, such as during locomotion, are accompanied by sustained activity in cholinergic axons. Thus, the pupil can be used to sensitively track the activity in multiple neuromodulatory transmitter systems as they control the state of the waking brain.
Making Scheduling "Cool": Temperature-Aware Workload Placement in Data Centers

Trends towards consolidation and higher-density computing configurations make the problem of heat management one of the critical challenges in emerging data centers. Conventional approaches to addressing this problem have focused at the facilities level to develop new cooling technologies or optimize the delivery of cooling. In contrast to these approaches, our paper explores an alternate dimension to address this problem, namely a systems-level solution to control the heat generation through temperatureaware workload placement. We first examine a theoretic thermodynamic formulation that uses information about steady state hot spots and cold spots in the data center and develop real-world scheduling algorithms. Based on the insights from these results, we develop an alternate approach. Our new approach leverages the non-intuitive observation that the source of cooling inefficiencies can often be in locations spatially uncorrelated with its manifested consequences; this enables additional energy savings. Overall, our results demonstrate up to a factor of two reduction in annual data center cooling costs over location-agnostic workload distribution, purely through software optimizations without the need for any costly capital investment.
Learning how to match fresco fragments

One of the main problems faced during reconstruction of fractured archaeological artifacts is sorting through a large number of candidate matches between fragments to find the relatively few that are correct. Previous computer methods for this task provided scoring functions based on a variety of properties of potential matches, including color and geometric compatibility across fracture surfaces. However, they usually consider only one or at most a few properties at once, and therefore provide match predictions with very low precision. In this article, we investigate a machine learning approach that computes the probability that a match is correct based on the combination of many features. We explore this machine learning approach for ranking matches in three different sets of fresco fragments, finding that classifiers based on many match properties can be significantly more effective at ranking proposed matches than scores based on any single property alone. Our results suggest that it is possible to train a classifier on match properties in one dataset and then use it to rank predicted matches in another dataset effectively. We believe that this approach could be helpful in a variety of cultural heritage reconstruction systems.
Normally-off AlGaN/GaN HFET with p-type GaN gate and AlGaN buffer

A 1.5 A normally-off GaN transistor for power applications in p-type GaN gate technology with a modified epitaxial concept is presented. A higher threshold voltage is achieved while keeping the on-state resistance low by using an Al-GaN buffer instead of a GaN buffer. Additionally, the AlGaN buffer acts as a back-barrier and suppresses source-drain punch-through currents in the off-state. P-GaN gate GaN transistors with AlGaN buffer will therefore yield higher breakdown voltages as compared to standard GaN buffer versions which results in an excellent VBr-to-RON ratio. The proposed normally-off technology shows save operation under elevated ambient temperature up to 200 °C without thermal runaway. In contrast to standard normally-on AlGaN/GaN HEMTs, a reverse diode operation is possible for offstate conditions which may enable improved inverter circuits.
Text feature extraction based on deep learning: a review

Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.
Complexities in Internet peering: Understanding the “Black” in the “Black Art”

Peering in the Internet interdomain network has long been considered a “black art”, understood in-depth only by a select few peering experts while the majority of the network operator community only scratches the surface employing conventional rules-of-thumb to form peering links through ad hoc personal interactions. Why is peering considered a black art? What are the main sources of complexity in identifying potential peers, negotiating a stable peering relationship, and utility optimization through peering? How do contemporary operational practices approach these problems? In this work we address these questions for Tier-2 Network Service Providers. We identify and explore three major sources of complexity in peering: (a) inability to predict traffic flows prior to link formation (b) inability to predict economic utility owing to a complex transit and peering pricing structure (c) computational infeasibility of identifying the optimal set of peers because of the network structure. We show that framing optimal peer selection as a formal optimization problem and solving it is rendered infeasible by the nature of these problems. Our results for traffic complexity show that 15% NSPs lose some fraction of customer traffic after peering. Additionally, our results for economic complexity show that 15% NSPs lose utility after peering, approximately, 50% NSPs end up with higher cumulative costs with peering than transit only, and only 10% NSPs get paid-peering customers.
Modern Release Engineering in a Nutshell -- Why Researchers Should Care

The release engineering process is the process that brings high quality code changes from a developer's workspace to the end user, encompassing code change integration, continuous integration, build system specifications, infrastructure-as-code, deployment and release. Recent practices of continuous delivery, which bring new content to the end user in days or hours rather than months or years, have generated a surge of industry-driven interest in the release engineering pipeline. This paper argues that the involvement of researchers is essential, by providing a brief introduction to the six major phases of the release engineering pipeline, a roadmap of future research, and a checklist of three major ways that the release engineering process of a system under study can invalidate the findings of software engineering studies. The main take-home message is that, while release engineering technology has flourished tremendously due to industry, empirical validation of best practices and the impact of the release engineering process on (amongst others) software quality is largely missing and provides major research opportunities.
A bias for posterior α-band power suppression versus enhancement during shifting versus maintenance of spatial attention

Voluntarily directing visual attention to a cued position in space leads to improved processing of forthcoming visual stimuli at the attended position, and attenuated processing of competing stimuli elsewhere, due to anticipatory tuning of visual cortex activity. In EEG, recent evidence points to a determining role of modulations of posterior alpha-band activity (8-14 Hz) in such anticipatory facilitation (alpha-power decreases) versus inhibition (alpha-power increases). Yet, while such alpha-modulations are a common finding, the direction of modulation varies to a great extent across studies implying dependence on task demands. Here, we reveal opposite modulation of posterior alpha-power with early/initiation versus later/sustained processes of anticipatory attention orienting. Marked alpha-decreases were observed during shifting of attention (initial 700 ms) over occipito-parietal areas processing to-be-attended visual space, while alpha-increases dominated in the subsequent maintenance phase (>700 ms) over occipito-parietal cortex tuned to unattended positions. Notably, the presence of alpha-modulation strongly depended on individual resting alpha-power. Overall, this provides further support to an active facilitative versus inhibitory role of alpha-power decreases and increases and suggests that these attention-related changes are differentially deployed during anticipatory attention orienting to prepare versus maintain the cortex for optimal target processing.
Affective Color in Visualization

Communicating the right affect, a feeling, experience or emotion, is critical in creating engaging visual communication. We carried out three studies examining how different color properties (lightness, chroma and hue) and different palette properties (combinations and distribution of colors) contribute to different affective interpretations in information visualization where the numbers of colors is typically smaller than the rich palettes used in design. Our results show how color and palette properties can be manipulated to achieve affective expressiveness even in the small sets of colors used for data encoding in information visualization.
Bridging Music and Image via Cross-Modal Ranking Analysis

Human perceptions of music and image are closely related to each other, since both can inspire similar human sensations, such as emotion, motion, and power. This paper aims to explore whether and how music and image can be automatically matched by machines. The main contributions are three aspects. First, we construct a benchmark dataset composed of more than 45 000 music-image pairs. Human labelers are recruited to annotate whether these pairs are well-matched or not. The results show that they generally agree with each other on the matching degree of music-image pairs. Secondly, we investigate suitable semantic representations of music and image for this cross-modal matching task. In particular, we adopt lyrics as a middle-media to connect music and image, and design a set of lyric-based attributes for image representation. Thirdly, we propose cross-modal ranking analysis (CMRA) to learn the semantic similarity between music and image with ranking labeling information. CMRA aims to find the optimal embedding spaces for both music and image in the sense of maximizing the ordinal margin between music-image pairs. The proposed method is able to learn the non-linear relationship between music and image, and to integrate heterogeneous ranking data from different modalities into a unified space. Experimental results demonstrate that the proposed method outperforms state-of-the-art cross-modal methods in the music-image matching task, and achieves a consistency rate of 91.5% with human labelers.
Sliding mode control design principles and applications to electric drives

The paper deals with the basic concepts, mathematics, and design aspects of variable structure systems as well as sliding modes as a principle operation mode. The main arguments in favor of sliding mode control are order reduction, decoupling design procedure, disturbance rejection, insensitivity to parameter variations, and simple implementation by means of power converters. The control algorithms and data processing used in variable structure systems are analyzed. The potential of sliding mode control methodology is demonstrated for versatility of electric drives and functional goals of control.
Rare adipose disorders (RADs) masquerading as obesity

Rare adipose disorders (RADs) including multiple symmetric lipomatosis (MSL), lipedema and Dercum's disease (DD) may be misdiagnosed as obesity. Lifestyle changes, such as reduced caloric intake and increased physical activity are standard care for obesity. Although lifestyle changes and bariatric surgery work effectively for the obesity component of RADs, these treatments do not routinely reduce the abnormal subcutaneous adipose tissue (SAT) of RADs. RAD SAT likely results from the growth of a brown stem cell population with secondary lymphatic dysfunction in MSL, or by primary vascular and lymphatic dysfunction in lipedema and DD. People with RADs do not lose SAT from caloric limitation and increased energy expenditure alone. In order to improve recognition of RADs apart from obesity, the diagnostic criteria, histology and pathophysiology of RADs are presented and contrasted to familial partial lipodystrophies, acquired partial lipodystrophies and obesity with which they may be confused. Treatment recommendations focus on evidence-based data and include lymphatic decongestive therapy, medications and supplements that support loss of RAD SAT. Associated RAD conditions including depression, anxiety and pain will improve as healthcare providers learn to identify and adopt alternative treatment regimens for the abnormal SAT component of RADs. Effective dietary and exercise regimens are needed in RAD populations to improve quality of life and construct advanced treatment regimens for future generations.
FADE: Secure Overlay Cloud Storage with File Assured Deletion

While we can now outsource data backup to third-party cloud storage services so as to reduce data management costs, security concerns arise in terms of ensuring the privacy and integrity of outsourced data. We design FADE, a practical, implementable, and readily deployable cloud storage system that focuses on protecting deleted data with policy-based file assured deletion. FADE is built upon standard cryptographic techniques, such that it encrypts outsourced data files to guarantee their privacy and integrity, and most importantly, assuredly deletes files to make them unrecoverable to anyone (including those who manage the cloud storage) upon revocations of file access policies. In particular, the design of FADE is geared toward the objective that it acts as an overlay system that works seamlessly atop today’s cloud storage services. To demonstrate this objective, we implement a working prototype of FADE atop Amazon S3, one of today’s cloud storage services, and empirically show that FADE provides policy-based file assured deletion with a minimal trade-off of performance overhead. Our work provides insights of how to incorporate value-added security features into current data outsourcing applications.
Neural Domain Adaptation for Biomedical Question Answering

Factoid question answering (QA) has recently benefited from the development of deep learning (DL) systems. Neural network models outperform traditional approaches in domains where large datasets exist, such as SQuAD (≈ 100, 000 questions) for Wikipedia articles. However, these systems have not yet been applied to QA in more specific domains, such as biomedicine, because datasets are generally too small to train a DL system from scratch. For example, the BioASQ dataset for biomedical QA comprises less then 900 factoid (single answer) and list (multiple answers) QA instances. In this work, we adapt a neural QA system trained on a large open-domain dataset (SQuAD, source) to a biomedical dataset (BioASQ, target) by employing various transfer learning techniques. Our network architecture is based on a state-of-theart QA system, extended with biomedical word embeddings and a novel mechanism to answer list questions. In contrast to existing biomedical QA systems, our system does not rely on domain-specific ontologies, parsers or entity taggers, which are expensive to create. Despite this fact, our systems achieve state-of-the-art results on factoid questions and competitive results on list questions.
Moving Obstacle Detection using Monocular Vision

This paper proposes a method for detecting moving obstacles on roads, by using a vehicle mounted monocular camera. To detect various moving obstacles, such as vehicles and pedestrians, the ego-motion of the vehicle is initially estimated from images captured by the camera. There are two problems in ego-motion estimation. Firstly, a typical road scene contains moving obstacles. This causes false estimation of the ego-motion. Secondly, roads possess fewer features, when compared to the number associated with background structures. This reduces the accuracy of ego-motion estimation. In our approach, the ego-motion is estimated from the correspondences of dispersed feature points extracted from various regions other than those that contain moving obstacles. After estimating the ego-motion, any moving obstacles are detected by tracking the feature points over consecutive frames. In our experiments, it has been shown that the proposed method is able to detect moving obstacles
What does media use reveal about personality and mental health? An exploratory investigation among German students

The present study aimed to investigate the relationship between personality traits, mental health variables and media use among German students. The data of 633 participants were collected. Results indicate a positive association between general Internet use, general use of social platforms and Facebook use, on the one hand, and self-esteem, extraversion, narcissism, life satisfaction, social support and resilience, on the other hand. Use of computer games was found to be negatively related to these personality and mental health variables. The use of platforms that focus more on written interaction (Twitter, Tumblr) was assumed to be negatively associated with positive mental health variables and significantly positively with depression, anxiety, and stress symptoms. In contrast, Instagram use, which focuses more on photo-sharing, correlated positively with positive mental health variables. Possible practical implications of the present results for mental health, as well as the limitations of the present work are discussed.
Driving Behavior Analysis for Smartphone-based Insurance Telematics

Insurance telematics programs are continuously gaining market shares in the automotive insurance industry. By recording data on drivers' behavior, the information asymmetry between the policyholder and the insurer is reduced, enabling a granular risk differentiation based on the true risk levels of the drivers. However, the growth of the insurance telematics industry is being held up by large logistic costs associated with the process of acquiring data. As a result, several market participants have started looking towards smartphone-based solutions, which have the potential of easing and improving the data collection process for both policyholders and insurers.
 In this paper, we present a unified framework highlighting the challenges of smartphone-based driver behavior analysis. Since all driver behavior analysis relies on access to accurate navigation data, we first address the intermediate step of smartphone-based automotive navigation. The considered topics include estimation of the smartphone's orientation with respect to the vehicle, classification of the smartphone owner as a passenger or driver, and navigation in GNSS-challenged areas. Once a driver-specific high-performance navigation solution has been obtained, it can be used to extract information on the driver's behavior. We review the most commonly employed driving events, and discuss some of the difficulties inherent in detecting these events.
3D Simulation for Robot Arm Control with Deep Q-Learning

Intelligent control of robotic arms has huge potential over the coming years, but as of now will often fail to adapt when presented with new and unfamiliar environments. Recent trends to solve this problem have seen a shift to endto-end solutions using deep reinforcement learning to learn policies from visual input, rather than relying on a handcrafted, modular pipeline. Building upon the recent success of deep Q-networks, we present an approach which uses threedimensional simulations to train a 7-DOF robotic arm in a robot arm control task without any prior knowledge. Policies accept images of the environment as input and output motor actions. However, the high-dimensionality of the policies as well as the large state space makes policy search difficult. This is overcome by ensuring interesting states are explored via intermediate rewards that guide the policy towards higher reward states. Our results demonstrate that deep Q-networks can be used to learn policies for a task that involves locating a cube, grasping, and then finally lifting. The agent is able to learn to deal with a range of starting joint configurations and starting cube positions when tested in simulation. Moreover, we show that policies trained via simulation have the potential to be directly applied to real-world equivalents without any further training. We believe that robot simulations can decrease the dependency on physical robots and ultimately improve productivity of training robot control tasks.
A business driven cloud optimization architecture

In this paper, we discuss several facets of optimization in cloud computing, the corresponding challenges and propose an architecture for addressing those challenges. We consider a layered cloud where various cloud layers virtualize parts of the cloud infrastructure. The architecture takes into account different stakeholders in the cloud (infrastructure providers, platform providers, application providers and end users). The architecture supports self-management by automating most of the activities pertaining to optimization: monitoring, analysis and prediction, planning and execution.
Design of a transmit-receive (T/R) switch in TSMC 180nm RF CMOS process for 2.4GHz transceiver

Design and analysis of a RF transmit/receive switch in 180nm CMOS RF process for 2.4GHz transceiver is presented. LC resonance along with stacked gate architecture is presented for designing the T/R switch. The switch exhibited excellent performance in terms of insertion loss, isolation linearity and gain. The switch added minimum noise by itself during the input and output switching and achieved stability throughout the operating rang at 2.4GHz. The designed switch exhibits -48.73dB and -79.04dB insertion loss during receiver and transmitter mode respectively. The isolation between non-used ports is -96.27 in both mode of operation. The designed switch handles more than 20 dBm power at 2.4 GHz frequency.