nils-herrmann/clean_scirepeval huggingface dataset:
A survey on reconfigurable accelerators for cloud computing

Data centers are experiencing an exponential increase in the amount of network traffic that they have to sustain due to cloud computing and several emerging web applications. To face this network load, large data centers are required with thousands of servers interconnected with high bandwidth switches. Current data center, based on general purpose processor, consume excessive power while their utilization is quite low. Hardware accelerators can provide high energy efficiency for many cloud applications but they lack the programming efficiency of processors. In the last few years, there several efforts for the efficient deployment of hardware accelerators in the data centers. This paper presents a thorough survey of the frameworks for the efficient utilization of the FPGAs in the data centers. Furthermore it presents the hardware accelerators that have been implemented for the most widely used cloud computing applications. Furthermore, the paper provides a qualitative categorization and comparison of the proposed schemes based on their main features such as speedup and energy efficiency.
Graph Convolutional Neural Networks for Web-Scale Recommender Systems

Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods practical and scalable to web-scale recommendation tasks with billions of items and hundreds of millions of users remains an unsolved challenge. Here we describe a large-scale deep recommendation engine that we developed and deployed at Pinterest. We develop a data-efficient Graph Convolutional Network (GCN) algorithm, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i.e., items) that incorporate both graph structure as well as node feature information. Compared to prior GCN approaches, we develop a novel method based on highly efficient random walks to structure the convolutions and design a novel training strategy that relies on harder-and-harder training examples to improve robustness and convergence of the model. We also develop an efficient MapReduce model inference algorithm to generate embeddings using a trained model. Overall, we can train on and embed graphs that are four orders of magnitude larger than typical GCN implementations. We show how GCN embeddings can be used to make high-quality recommendations in various settings at Pinterest, which has a massive underlying graph with 3 billion nodes representing pins and boards, and 17 billion edges. According to offline metrics, user studies, as well as A/B tests, our approach generates higher-quality recommendations than comparable deep learning based systems. To our knowledge, this is by far the largest application of deep graph embeddings to date and paves the way for a new generation of web-scale recommender systems based on graph convolutional architectures.
Multiscale texture synthesis

Example-based texture synthesis algorithms have gained widespread popularity for their ability to take a single input image and create a perceptually similar non-periodic texture. However, previous methods rely on single input exemplars that can capture only a limited band of spatial scales. For example, synthesizing a continent-like appearance at a variety of zoom levels would require an impractically high input resolution. In this paper, we develop a multiscale texture synthesis algorithm. We propose a novel example-based representation, which we call an exemplar graph, that simply requires a few low-resolution input exemplars at different scales. Moreover, by allowing loops in the graph, we can create infinite zooms and infinitely detailed textures that are impossible with current example-based methods. We also introduce a technique that ameliorates inconsistencies in the user's input, and show that the application of this method yields improved interscale coherence and higher visual quality. We demonstrate optimizations for both CPU and GPU implementations of our method, and use them to produce animations with zooming and panning at multiple scales, as well as static gigapixel-sized images with features spanning many spatial scales.
Hierarchical protection architecture for 380V DC data center application

The DC distribution system is becoming an appealing spot due to its higher energy efficiency in recent years. Nowadays, it has been already applied in data centers, commercial buildings, electrical vehicles charger station and DC micro grid systems, etc. However, there are a lot of challenges in DC application which is not critical in traditional AC system, such as arcing, capacitive charging and discharging, etc. All of them make the protection strategy and architecture an important issue for DC application. In this paper, one 3-level hierarchy circuit protection architecture is proposed with developed solid state circuit protection hardware. It is designed with considering the power rating for DC data center load conditions. Analysis and experimental results based on 380V DC voltage have been conducted and discussed.
Social Activities, Self-Efficacy, Game Attitudes, and Game Addiction

This study examines whether social activities with parents, online and offline social self-efficacy, and attitudes toward gaming are associated with the degree of game addiction among adolescents. Using data from a survey of 600 middle- and high-school students in South Korea, we tested the relationships of personal characteristics (grade point average and time spent on gaming each day), social self-efficacy (both on- and offline), general social activities (with parents, friends, and teachers), gaming activities with parents, and attitudes toward gaming (those of self, parents, friends, and teachers) with the degree of game addiction. In addition, we conducted ANOVA tests to determine the differences among three groups: non-addicts (NA), possible (mild or moderate) addicts (PA), and Internet addicts (IA). The results show that social self-efficacy in the real world (offline) was negatively related with the degree of game addiction, whereas social self-efficacy in the virtual world (online) indicated a positive association. Social activities with parents are negatively associated with game addiction, although no relationship is found between gaming activities with parents and game addiction. Parental attitude toward gaming has a negative relationship with the addiction. Results and implications are discussed.
Circular Patch Microstrip Array Antenna for KU-band

This paper present a circular patch microstrip array antenna operate in KU-band (10.9GHz – 17.25GHz). The proposed circular patch array antenna will be in light weight, flexible, slim and compact unit compare with current antenna used in KU-band. The paper also presents the detail steps of designing the circular patch microstrip array antenna. An Advance Design System (ADS) software is used to compute the gain, power, radiation pattern, and S11 of the antenna. The proposed Circular patch microstrip array antenna basically is a phased array consisting of ‘n’ elements (circular patch antennas) arranged in a rectangular grid. The size of each element is determined by the operating frequency. The incident wave from satellite arrives at the plane of the antenna with equal phase across the surface of the array. Each ‘n’ element receives a small amount of power in phase with the others. There are feed network connects each element to the microstrip lines with an equal length, thus the signals reaching the circular patches are all combined in phase and the voltages add up. The significant difference of the circular patch array antenna is not come in the phase across the surface but in the magnitude distribution. Circular patch microstrip array antenna, gain, radiation pattern, S-Parameter.
Atoms of recognition in human and computer vision.

Discovering the visual features and representations used by the brain to recognize objects is a central problem in the study of vision. Recently, neural network models of visual object recognition, including biological and deep network models, have shown remarkable progress and have begun to rival human performance in some challenging tasks. These models are trained on image examples and learn to extract features and representations and to use them for categorization. It remains unclear, however, whether the representations and learning processes discovered by current models are similar to those used by the human visual system. Here we show, by introducing and using minimal recognizable images, that the human visual system uses features and processes that are not used by current models and that are critical for recognition. We found by psychophysical studies that at the level of minimal recognizable images a minute change in the image can have a drastic effect on recognition, thus identifying features that are critical for the task. Simulations then showed that current models cannot explain this sensitivity to precise feature configurations and, more generally, do not learn to recognize minimal images at a human level. The role of the features shown here is revealed uniquely at the minimal level, where the contribution of each feature is essential. A full understanding of the learning and use of such features will extend our understanding of visual recognition and its cortical mechanisms and will enhance the capacity of computational models to learn from visual experience and to deal with recognition and detailed image interpretation.
DeNet: Scalable Real-Time Object Detection with Directed Sparse Sampling

We define the object detection from imagery problem as estimating a very large but extremely sparse bounding box dependent probability distribution. Subsequently we identify a sparse distribution estimation scheme, Directed Sparse Sampling, and employ it in a single end-to-end CNN based detection model. This methodology extends and formalizes previous state-of-the-art detection models with an additional emphasis on high evaluation rates and reduced manual engineering. We introduce two novelties, a corner based region-of-interest estimator and a deconvolution based CNN model. The resulting model is scene adaptive, does not require manually defined reference bounding boxes and produces highly competitive results on MSCOCO, Pascal VOC 2007 and Pascal VOC 2012 with real-time evaluation rates. Further analysis suggests our model performs particularly well when finegrained object localization is desirable. We argue that this advantage stems from the significantly larger set of available regions-of-interest relative to other methods. Source-code is available from: https://github.com/lachlants/denet
Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes

Multivariate quantitative traits arise naturally in recent neuroimaging genetics studies, in which both structural and functional variability of the human brain is measured non-invasively through techniques such as magnetic resonance imaging (MRI). There is growing interest in detecting genetic variants associated with such multivariate traits, especially in genome-wide studies. Random forests (RFs) classifiers, which are ensembles of decision trees, are amongst the best performing machine learning algorithms and have been successfully employed for the prioritisation of genetic variants in case-control studies. RFs can also be applied to produce gene rankings in association studies with multivariate quantitative traits, and to estimate genetic similarities measures that are predictive of the trait. However, in studies involving hundreds of thousands of SNPs and high-dimensional traits, a very large ensemble of trees must be inferred from the data in order to obtain reliable rankings, which makes the application of these algorithms computationally prohibitive. We have developed a parallel version of the RF algorithm for regression and genetic similarity learning tasks in large-scale population genetic association studies involving multivariate traits, called PaRFR (Parallel Random Forest Regression). Our implementation takes advantage of the MapReduce programming model and is deployed on Hadoop, an open-source software framework that supports data-intensive distributed applications. Notable speed-ups are obtained by introducing a distance-based criterion for node splitting in the tree estimation process. PaRFR has been applied to a genome-wide association study on Alzheimer's disease (AD) in which the quantitative trait consists of a high-dimensional neuroimaging phenotype describing longitudinal changes in the human brain structure. PaRFR provides a ranking of SNPs associated to this trait, and produces pair-wise measures of genetic proximity that can be directly compared to pair-wise measures of phenotypic proximity. Several known AD-related variants have been identified, including APOE4 and TOMM40. We also present experimental evidence supporting the hypothesis of a linear relationship between the number of top-ranked mutated states, or frequent mutation patterns, and an indicator of disease severity. The Java codes are freely available at http://www2.imperial.ac.uk/~gmontana .
Harmonic Colormaps for Volume Visualization

Color design forms a crucial part in visual aesthetics, and it has been shown that a visually aesthetic visualization will be looked at more carefully. An important role plays here the choice of a colormap that is composed of harmonic colors. This paper presents an interface that allows users to choose harmonic colors in volume visualization applications. In addition, it describes mechanisms by which non-harmonic colormaps can be converted to harmonic ones, but keeping lightness constant to preserve the original contrast relationships. Finally, we also show how harmonic colors can be used for the highlighting of important volume features.
Improved Particle Swarm Optimization Based K-Means Clustering

Clustering is a popular data analysis and data mining technique. K-Means is one of the most popular data mining algorithms for being simple, scalable and easily modifiable to a variety of contexts and application domains. The major issue of traditional K-Means algorithm is that its performance depends on the initialization of centroid and requires the number of clusters to be specified in advance. Many evolutionary based clustering algorithms have been developed in recent years for selecting optimum initial centroid to optimize clustering results. Particle Swarm Optimization algorithm is a population-based memetic-evolution-motivated meta-heuristic algorithm that mimics the capability of swarm. The K-Means algorithm typically uses Euclidean or squared Euclidean distance to measure the distortion between a data object and its cluster centroid. The Euclidean and squared Euclidean distances are usually computed from raw data and not from standardized data. Normalization is one of the important preprocessing steps, to transform values of all attributes. Effective data clustering can only occur if an equally effective technique for normalizing the data is applied. This paper proposes an effective partitional clustering algorithm which is developed by integrating the merits of Particle Swarm Optimization and normalization with traditional K-Means clustering algorithms. Experiments are conducted on real dataset to prove the efficiency of the proposed algorithm.
Normalized Information Distance

The normalized information distance is a universal distance measure for objects of all kinds. It is based on Kolmogorov complexity and thus uncomputable, but there are ways to utilize it. First, compression algorithms can be used to approximate the Kolmogorov complexity if the objects have a string representation. Second, for names and abstract concepts, page count statistics from the World Wide Web can be used. These practical realizations of the normalized information distance can then be applied to machine learning tasks, expecially clustering, to perform feature-free and parameter-free data mining. This chapter discusses the theoretical foundations of the normalized information distance and both practical realizations. It presents numerous examples of successful real-world applications based on these distance measures, ranging from bioinformatics to music clustering to machine translation.
Gratitude influences sleep through the mechanism of pre-sleep cognitions.

To test whether individual differences in gratitude are related to sleep after controlling for neuroticism and other traits. To test whether pre-sleep cognitions are the mechanism underlying this relationship.



A cross-sectional questionnaire study was conducted with a large (186 males, 215 females) community sample (ages=18-68 years, mean=24.89, S.D.=9.02), including 161 people (40%) scoring above 5 on the Pittsburgh Sleep Quality Index, indicating clinically impaired sleep. Measures included gratitude, the Pittsburgh Sleep Quality Index (PSQI), self-statement test of pre-sleep cognitions, the Mini-IPIP scales of Big Five personality traits, and the Social Desirability Scale.



Gratitude predicted greater subjective sleep quality and sleep duration, and less sleep latency and daytime dysfunction. The relationship between gratitude and each of the sleep variables was mediated by more positive pre-sleep cognitions and less negative pre-sleep cognitions. All of the results were independent of the effect of the Big Five personality traits (including neuroticism) and social desirability.



This is the first study to show that a positive trait is related to good sleep quality above the effect of other personality traits, and to test whether pre-sleep cognitions are the mechanism underlying the relationship between any personality trait and sleep. The study is also the first to show that trait gratitude is related to sleep and to explain why this occurs, suggesting future directions for research, and novel clinical implications.
AssistOn-Knee: A self-aligning knee exoskeleton

We present kinematics, actuation, detailed design, characterization results and initial user evaluations of AssistOn-Knee, a novel self-aligning active exoskeleton for robot-assisted knee rehabilitation. AssistOn-Knee can, not only assist flexion/extension movements of the knee joint but also accommodate its translational movements in the sagittal plane. Automatically aligning its joint axes, AssistOn-Knee enables an ideal match between human knee axis and the exoskeleton axis, guaranteeing ergonomy and comfort throughout the therapy. Self-aligning feature significantly shortens the setup time required to attach the patient to the exoskeleton, allowing more effective time spent on exercises. The proposed exoskeleton actively controls the rotational degree of freedom of the knee through a Bowden cable-driven series elastic actuator, while the translational movements of the knee joints are passively accommodated through use of a 3 degrees of freedom planar parallel mechanism. AssistOn-Knee possesses a lightweight and compact design with significantly low apparent inertia, thanks to its Bowden cable based transmission that allows remote location of the actuator and reduction unit. Furthermore, thanks to its series-elastic actuation, AssistOn-Knee enables high-fidelity force control and active backdrive-ability within its control bandwidth, while featuring passive elasticity for excitations above this bandwidth, ensuring safety and robustness throughout the whole frequency spectrum.
Miniaturized electroencephalographic scalp electrode for optimal wearing comfort

Current mainstream EEG electrode setups permit efficient recordings, but are often bulky and uncomfortable for subjects. Here we introduce a novel type of EEG electrode, which is designed for an optimal wearing comfort. The electrode is referred to as C-electrode where "C" stands for comfort.



The C-electrode does not require any holder/cap for fixation on the head nor does it use traditional pads/lining of disposable electrodes - thus, it does not disturb subjects. Fixation of the C-electrode on the scalp is based entirely on the adhesive interaction between the very light C-electrode/wire construction (<35 mg) and a droplet of EEG paste/gel. Moreover, because of its miniaturization, both C-electrode (diameter 2-3mm) and a wire (diameter approximately 50 microm) are minimally (or not at all) visible to an external observer. EEG recordings with standard and C-electrodes were performed during rest condition, self-paced movements and median nerve stimulation.



The quality of EEG recordings for all three types of experimental conditions was similar for standard and C-electrodes, i.e., for near-DC recordings (Bereitschaftspotential), standard rest EEG spectra (1-45 Hz) and very fast oscillations approximately 600 Hz (somatosensory evoked potentials). The tests showed also that once being placed on a subject's head, C-electrodes can be used for 9h without any loss in EEG recording quality. Furthermore, we showed that C-electrodes can be effectively utilized for Brain-Computer Interfacing. C-electrodes proved to posses a high stability of mechanical fixation (stayed attached with 2.5 g accelerations). Subjects also reported not having any tactile sensations associated with wearing of C-electrodes.



C-electrodes provide optimal wearing comfort without any loss in the quality of EEG recordings.



We anticipate that C-electrodes can be used in a wide range of clinical, research and emerging neuro-technological environments.
Learning to push by grasping: Using multiple tasks for effective learning

Recently, end-to-end learning frameworks are gaining prevalence in the field of robot control. These frameworks input states/images and directly predict the torques or the action parameters. However, these approaches are often critiqued due to their huge data requirements for learning a task. The argument of the difficulty in scalability to multiple tasks is well founded, since training these tasks often require hundreds or thousands of examples. But do end-to-end approaches need to learn a unique model for every task? Intuitively, it seems that sharing across tasks should help since all tasks require some common understanding of the environment. In this paper, we attempt to take the next step in data-driven end-to-end learning frameworks: move from the realm of task-specific models to joint learning of multiple robot tasks. In an astonishing result we show that models with multi-task learning tend to perform better than task-specific models trained with same amounts of data. For example, a deep-network learned with 2.5K grasp and 2.5K push examples performs better on grasping than a network trained on 5K grasp examples.