nils-herrmann/clean_scirepeval huggingface dataset:
User-transparent Distributed TensorFlow

Deep Learning (DL) algorithms have become the de facto choice for data analysis. Several DL implementations – primarily limited to a single compute node – such as Caffe, TensorFlow, Theano and Torch have become readily available. Distributed DL implementations capable of execution on large scale systems are becoming important to address the computational needs of large data produced by scientific simulations and experiments. Yet, the adoption of distributed DL implementations faces significant impediments: 1) most implementations require DL analysts to modify their code significantly – which is a showstopper, 2) several distributed DL implementations are geared towards cloud computing systems – which is inadequate for execution on massively parallel systems such as supercomputers. This work addresses each of these problems. We provide a distributed memory DL implementation by incorporating required changes in the TensorFlow runtime itself. This dramatically reduces the entry barrier for using a distributed TensorFlow implementation. We use Message Passing Interface (MPI) – which provides performance portability, especially since MPI specific changes are abstracted from users. Lastly – and arguably most importantly – we make our implementation available for broader use, under the umbrella of Machine Learning Toolkit for Extreme Scale (MaTEx) at http://hpc.pnl.gov/matex. We refer to our implementation as MaTEx-TensorFlow.
Soft Hair on Black Holes.

It has recently been shown that Bondi-van der Burg-Metzner-Sachs supertranslation symmetries imply an infinite number of conservation laws for all gravitational theories in asymptotically Minkowskian spacetimes. These laws require black holes to carry a large amount of soft (i.e., zero-energy) supertranslation hair. The presence of a Maxwell field similarly implies soft electric hair. This Letter gives an explicit description of soft hair in terms of soft gravitons or photons on the black hole horizon, and shows that complete information about their quantum state is stored on a holographic plate at the future boundary of the horizon. Charge conservation is used to give an infinite number of exact relations between the evaporation products of black holes which have different soft hair but are otherwise identical. It is further argued that soft hair which is spatially localized to much less than a Planck length cannot be excited in a physically realizable process, giving an effective number of soft degrees of freedom proportional to the horizon area in Planck units.
Layer-Centric Memory Reuse and Data Migration for Extreme-Scale Deep Learning on Many-Core Architectures

Due to the popularity of Deep Neural Network (DNN) models, we have witnessed extreme-scale DNN models with the continued increase of the scale in terms of depth and width. However, the extremely high memory requirements for them make it difficult to run the training processes on single many-core architectures such as a Graphic Processing Unit (GPU), which compels researchers to use model parallelism over multiple GPUs to make it work. However, model parallelism always brings very heavy additional overhead. Therefore, running an extreme-scale model in a single GPU is urgently required. There still exist several challenges to reduce the memory footprint for extreme-scale deep learning. To address this tough problem, we first identify the memory usage characteristics for deep and wide convolutional networks, and demonstrate the opportunities for memory reuse at both the intra-layer and inter-layer levels. We then present Layrub, a runtime data placement strategy that orchestrates the execution of the training process. It achieves layer-centric reuse to reduce memory consumption for extreme-scale deep learning that could not previously be run on a single GPU. Experiments show that, compared to the original Caffe, Layrub can cut down the memory usage rate by an average of 58.2% and by up to 98.9%, at the moderate cost of 24.1% higher training execution time on average. Results also show that Layrub outperforms some popular deep learning systems such as GeePS, vDNN, MXNet, and Tensorflow. More importantly, Layrub can tackle extreme-scale deep learning tasks. For example, it makes an extra-deep ResNet with 1,517 layers that can be trained successfully in one GPU with 12GB memory, while other existing deep learning systems cannot.
V-Band Separation Shock Characteristics

The V-band systems have been widely used in the aerospace industry for securing spacecraft inside the launch vehicle payload fairing. Separation is initiated by firing pyro-devices to rapidly release the tension bands. A significant shock transient is expected as a result of the band separation. The shock environment is defined with the assumption that the shock events due to the band separation are associated with the rapid release of the strain energy from the preload tension of the restraining band.
Gait Planning of Quadruped Walking and Climbing Robot for Locomotion in 3D Environment

One of the traditional problems in the walking and climbing robot moving in the 3D environment is how to negotiate the boundary of two plain surfaces such as corners, which may be convex or concave. In this paper a practical gait planning algorithm in the transition region of the boundary is proposed in terms of a geometrical view. The trajectory of the body is derived from the geometrical analysis of the relationship between the robot and the environment. And the position of each foot is determined by using parameters associated with the hip and the ankle of the robot. In each case of concave or convex boundaries, the trajectory that the robot moves along is determined in advance and the foot positions of the robot associated with the trajectory are computed, accordingly. The usefulness of the proposed method is confirmed through simulations and demonstrations with a walking and climbing robot.
Heterogeneous Teams of Modular Robots for Mapping and Exploration

Most of the research in the field of robotics is focussed on solving the problem of Simultaneous Localization and Mapping(SLAM). In general the problem is solved using a single robot. In the article written by R. Grabowski, C. Paredis and P. Hkosla, called ”Heterogeneous Teams of Modular Robots for Mapping end Exploration” a novel localization method is presented based on multiple robots.[Grabowski, 2000] For this purpose the relative distance between the different robots is calculated. These measurements, together with the positions estimated using dead reckoning, are used to determine the most likely new positions of the agents. Knowing the positions is essential when pursuing accurate (team) mapping capabilities. The proposed method makes it possible for heterogeneous team of modular centimeter-scale robots to collaborate and map unexplored environments.
What is the common thread of creativity? Its dialectical relation to intelligence and wisdom.

Creativity refers to the potential to produce novel ideas that are task-appropriate and high in quality. Creativity in a societal context is best understood in terms of a dialectical relation to intelligence and wisdom. In particular, intelligence forms the thesis of such a dialectic. Intelligence largely is used to advance existing societal agendas. Creativity forms the antithesis of the dialectic, questioning and often opposing societal agendas, as well as proposing new ones. Wisdom forms the synthesis of the dialectic, balancing the old with the new. Wise people recognize the need to balance intelligence with creativity to achieve both stability and change within a societal context.
Resource Allocation Strategy in Fog Computing Based on Priced Timed Petri Nets

Fog computing, also called “clouds at the edge,” is an emerging paradigm allocating services near the devices to improve the quality of service (QoS). The explosive prevalence of Internet of Things, big data, and fog computing in the context of cloud computing makes it extremely challenging to explore both cloud and fog resource scheduling strategy so as to improve the efficiency of resources utilization, satisfy the users’ QoS requirements, and maximize the profit of both resource providers and users. This paper proposes a resource allocation strategy for fog computing based on priced timed Petri nets (PTPNs), by which the user can choose the satisfying resources autonomously from a group of preallocated resources. Our strategy comprehensively considers the price cost and time cost to complete a task, as well as the credibility evaluation of both users and fog resources. We construct the PTPN models of tasks in fog computing in accordance with the features of fog resources. Algorithm that predicts task completion time is presented. Method of computing the credibility evaluation of fog resource is also proposed. In particular, we give the dynamic allocation algorithm of fog resources. Simulation results demonstrate that our proposed algorithms can achieve a higher efficiency than static allocation strategies in terms of task completion time and price.
Performance comparison of intrusion detection systems and application of machine learning to Snort system

This study investigates the performance of two open source intrusion detection systems (IDSs) namely Snort and Suricata for accurately detecting the malicious traffic on computer networks. Snort and Suricata were installed on two different but identical computers and the performance was evaluated at 10 Gbps network speed. It was noted that Suricata could process a higher speed of network traffic than Snort with lower packet drop rate but it consumed higher computational resources. Snort had higher detection accuracy and was thus selected for further experiments. It was observed that Snort triggered a high rate of false positive alarms. To solve this problem a Snort adaptive plug-in was developed. To select the best performing algorithm for the Snort adaptive plug-in, an empirical study was carried out with different learning algorithms and Support Vector Machine (SVM) was selected. A hybrid version of SVM and Fuzzy logic produced a better detection accuracy. But the best result was achieved using an optimized SVM with the firefly algorithm with FPR (false positive rate) as 8.6% and FNR (false negative rate) as 2.2%, which is a good result. The novelty of this work is the performance comparison of two IDSs at 10 Gbps and the application of hybrid and optimized machine learning algorithms to Snort.
How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0

We are living in the era of the fourth industrial revolution, namely Industry 4.0. This paper presents the main aspects related to Industry 4.0, the technologies that will enable this revolution, and the main application domains that will be affected by it. The effects that the introduction of Internet of Things (IoT), Cyber-Physical Systems (CPS), crowdsensing, crowdsourcing, cloud computing and big data will have on industrial processes will be discussed. The main objectives will be represented by improvements in: production efficiency, quality and cost-effectiveness; workplace health and safety, as well as quality of working conditions; products’ quality and availability, according to mass customisation requirements. The paper will further discuss the common denominator of these enhancements, i.e., data collection and analysis. As data and information will be crucial for Industry 4.0, crowdsensing and crowdsourcing will introduce new advantages and challenges, which will make most of the industrial processes easier with respect to traditional technologies.
Teacher attitudes toward dyslexia: effects on teacher expectations and the academic achievement of students with dyslexia.

The present study examined teacher attitudes toward dyslexia and the effects of these attitudes on teacher expectations and the academic achievement of students with dyslexia compared to students without learning disabilities. The attitudes of 30 regular education teachers toward dyslexia were determined using both an implicit measure and an explicit, self-report measure. Achievement scores for 307 students were also obtained. Implicit teacher attitudes toward dyslexia related to teacher ratings of student achievement on a writing task and also to student achievement on standardized tests of spelling but not math for those students with dyslexia. Self-reported attitudes of the teachers toward dyslexia did not relate to any of the outcome measures. Neither the implicit nor the explicit measures of teacher attitudes related to teacher expectations. The results show implicit attitude measures to be a more valuable predictor of the achievement of students with dyslexia than explicit, self-report attitude measures.
Direct Marketing, Indirect Profits: A Strategic Analysis of Dual - Channel Supply - Chain Design

Wei-yu Kevin Chiang • Dilip Chhajed • James D. Hess Department of Information Systems, University of Maryland at Baltimore County, Baltimore, Maryland 21250 Department of Business Administration, University of Illinois at Urbana–Champaign, Champaign, Illinois 61820 Department of Business Administration, University of Illinois at Urbana–Champaign, Champaign, Illinois 61820 kevin@wchiang.net • chhajed@uiuc.edu •
Nutrigenomics and nutrigenetics: the emerging faces of nutrition.

The recognition that nutrients have the ability to interact and modulate molecular mechanisms underlying an organism's physiological functions has prompted a revolution in the field of nutrition. Performing population-scaled epidemiological studies in the absence of genetic knowledge may result in erroneous scientific conclusions and misinformed nutritional recommendations. To circumvent such issues and more comprehensively probe the relationship between genes and diet, the field of nutrition has begun to capitalize on both the technologies and supporting analytical software brought forth in the post-genomic era. The creation of nutrigenomics and nutrigenetics, two fields with distinct approaches to elucidate the interaction between diet and genes but with a common ultimate goal to optimize health through the personalization of diet, provide powerful approaches to unravel the complex relationship between nutritional molecules, genetic polymorphisms, and the biological system as a whole. Reluctance to embrace these new fields exists primarily due to the fear that producing overwhelming quantities of biological data within the confines of a single study will submerge the original query; however, the current review aims to position nutrigenomics and nutrigenetics as the emerging faces of nutrition that, when considered with more classical approaches, will provide the necessary stepping stones to achieve the ambitious goal of optimizing an individual's health via nutritional intervention.
ISOODL: Instance segmentation of overlapping biological objects using deep learning

Image segmentation is an important first step for the quantitative analysis of biomedical images. We present a method to simultaneously segment and classify translucent overlapping objects in 2D images. For this we propose an approach using a fully-convolutional neural network simultaneously solving two tasks: object detection and instance segmentation. Object detection predicts reference points, object class labels and sizes. To solve the problem of multiple labels per location, we lift our label-space from 2D to 3D, resulting in a non-overlapping representation of the instance masks. To our knowledge it is the first method that handles overlapping biological objects using deep learning making it easily applicable to a large variety of challenging datasets.
State of the evidence 2017: an update on the connection between breast cancer and the environment

In this review, we examine the continually expanding and increasingly compelling data linking radiation and various chemicals in our environment to the current high incidence of breast cancer. Singly and in combination, these toxicants may have contributed significantly to the increasing rates of breast cancer observed over the past several decades. Exposures early in development from gestation through adolescence and early adulthood are particularly of concern as they re-shape the program of genetic, epigenetic and physiological processes in the developing mammary system, leading to an increased risk for developing breast cancer. In the 8 years since we last published a comprehensive review of the relevant literature, hundreds of new papers have appeared supporting this link, and in this update, the evidence on this topic is more extensive and of better quality than that previously available.



Increasing evidence from epidemiological studies, as well as a better understanding of mechanisms linking toxicants with development of breast cancer, all reinforce the conclusion that exposures to these substances - many of which are found in common, everyday products and byproducts - may lead to increased risk of developing breast cancer. Moving forward, attention to methodological limitations, especially in relevant epidemiological and animal models, will need to be addressed to allow clearer and more direct connections to be evaluated.
Hand-Eye Calibration of SCARA Robots

In SCARA robots, which are often used in industrial applications, all joint axes are parallel, covering three degrees of freedom in translation and one degree of freedom in rotation. Therefore, conventional approaches for the handeye calibration of articulated robots cannot be used for SCARA robots. In this paper, we present a new linear method that is based on dual quaternions and extends the work of  for SCARA robots. To improve the accuracy, a subsequent nonlinear optimization is proposed. We address several practical implementation issues and show the effectiveness of the method by evaluating it on synthetic and real data.
3D segmentation of mandible from multisectional CT scans by convolutional neural networks

Segmentation of mandibles in CT scans during virtual surgical planning is crucial for 3D surgical planning in order to obtain a detailed surface representation of the patients bone. Automatic segmentation of mandibles in CT scans is a challenging task due to large variation in their shape and size between individuals. In order to address this challenge we propose a convolutional neural network approach for mandible segmentation in CT scans by considering the continuum of anatomical structures through different planes. The proposed convolutional neural network adopts the architecture of the U-Net and then combines the resulting 2D segmentations from three different planes into a 3D segmentation. We implement such a segmentation approach on 11 neck CT scans and then evaluate the performance. We achieve an average dice coefficient of 0.89 on two testing mandible segmentation. Experimental results show that our proposed approach for mandible segmentation in CT scans exhibits high accuracy.
Data Selection Strategies for Multi-Domain Sentiment Analysis

Domain adaptation is important in sentiment analysis as sentiment-indicating words vary between domains. Recently, multi-domain adaptation has become more pervasive, but existing approaches train on all available source domains including dissimilar ones. However, the selection of appropriate training data is as important as the choice of algorithm. We undertake – to our knowledge for the first time – an extensive study of domain similarity metrics in the context of sentiment analysis and propose novel representations, metrics, and a new scope for data selection. We evaluate the proposed methods on two largescale multi-domain adaptation settings on tweets and reviews and demonstrate that they consistently outperform strong random and balanced baselines, while our proposed selection strategy outperforms instance-level selection and yields the best score on a large reviews corpus. All experiments are available at url_redacted1
Investigating interference between LoRa and IEEE 802.15.4g networks

The rapid growth of new radio technologies for Smart City/Building/Home applications means that models of cross-technology interference are needed to inform the development of higher layer protocols and applications. We systematically investigate interference interactions between LoRa and IEEE 802.15.4g networks. Our results show that LoRa can obtain high packet reception rates, even in presence of strong IEEE 802.15.4g interference. IEEE 802.15.4g is also shown to have some resilience to LoRa interference. Both effects are highly dependent on the LoRa radio's spreading factor and bandwidth configuration, as well as on the channelization. The results are shown to arise from the interaction between the two radios' modulation schemes. The data have implications for the design and analysis of protocols for both radio technologies.
Design of a Single-Switch DC/DC Converter for a PV-Battery-Powered Pump System With PFM+PWM Control

A single-switch nonisolated dc/dc converter for a stand-alone photovoltaic (PV)-battery-powered pump system is proposed in this paper. The converter is formed by combining a buck converter with a buck-boost converter. This integration also resulted in reduced repeated power processing, hence improving the conversion efficiency. With only a single transistor, the converter is able to perform three tasks simultaneously, namely, maximum-power-point tracking (MPPT), battery charging, and driving the pump at constant flow rate. To achieve these control objectives, the two inductors operate in different modes such that variable switching frequency control and duty cycle control can be used to manage MPPT and output voltage regulation, respectively. The battery in the converter provides a more steady dc-link voltage as compared to that of a conventional single-stage converter and hence mitigates the high voltage stress problem. Experimental results of a 14-W laboratory prototype converter with a maximum efficiency of 92% confirmed the performance of the proposed converter when used in a PV-battery pump system.
Dynamic modeling and optimal control strategy of waste heat recovery Organic Rankine Cycles

Organic Rankine Cycles (ORCs) are particularly suitable for recovering energy from low-grade heat sources. This paper describes the behavior of a small-scale ORC used to recover energy from a variable flow rate and temperature waste heat source. A traditional static model is unable to predict transient behavior in a cycle with a varying thermal source, whereas this capability is essential for simulating an appropriate cycle control strategy during part-load operation and start and stop procedures. A dynamic model of the ORC is therefore proposed focusing specifically on the time-varying performance of the heat exchangers, the dynamics of the other components being of minor importance. Three different control strategies are proposed and compared. The simulation results show that a model predictive control strategy based on the steady-state optimization of the cycle under various conditions is the one showing the best results.