nils-herrmann/clean_scirepeval huggingface dataset:
A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning

Learning sparse combinations is a frequent theme in machine learning. In this paper, we study its associated optimization problem in the distributed setting where the elements to be combined are not centrally located but spread over a network. We address the key challenges of balancing communication costs and optimization errors. To this end, we propose a distributed Frank-Wolfe (dFW) algorithm. We obtain theoretical guarantees on the optimization error and communication cost that do not depend on the total number of combining elements. We further show that the communication cost of dFW is optimal by deriving a lowerbound on the communication cost required to construct an -approximate solution. We validate our theoretical analysis with empirical studies on synthetic and real-world data, which demonstrate that dFW outperforms both baselines and competing methods. We also study the performance of dFW when the conditions of our analysis are relaxed, and show that dFW is fairly robust.
Fifty Years of Classification and Regression Trees 1

Fifty years have passed since the publication of the first regression tree algorithm. New techniques have added capabilities that far surpass those of the early methods. Modern classification trees can partition the data with linear splits on subsets of variables and fit nearest neighbor, kernel density, and other models in the partitions. Regression trees can fit almost every kind of traditional statistical model, including least-squares, quantile, logistic, Poisson, and proportional hazards models, as well as models for longitudinal and multiresponse data. Greater availability and affordability of software (much of which is free) have played a significant role in helping the techniques gain acceptance and popularity in the broader scientific community. This article surveys the developments and briefly reviews the key ideas behind some of the major algorithms.
Arbitrary-Oriented Vehicle Detection in Aerial Imagery with Single Convolutional Neural Networks

Vehicle detection with orientation estimation in aerial images has received widespread interest as it is important for intelligent traffic management. This is a challenging task, not only because of the complex background and relatively small size of the target, but also the various orientations of vehicles in aerial images captured from the top view. The existing methods for oriented vehicle detection need several post-processing steps to generate final detection results with orientation, which are not efficient enough. Moreover, they can only get discrete orientation information for each target. In this paper, we present an end-to-end single convolutional neural network to generate arbitrarily-oriented detection results directly. Our approach, named Oriented_SSD (Single Shot MultiBox Detector, SSD), uses a set of default boxes with various scales on each feature map location to produce detection bounding boxes. Meanwhile, offsets are predicted for each default box to better match the object shape, which contain the angle parameter for oriented bounding boxes’ generation. Evaluation results on the public DLR Vehicle Aerial dataset and Vehicle Detection in Aerial Imagery (VEDAI) dataset demonstrate that our method can detect both the location and orientation of the vehicle with high accuracy and fast speed. For test images in the DLR Vehicle Aerial dataset with a size of 5616× 3744, our method achieves 76.1% average precision (AP) and 78.7% correct direction classification at 5.17 s on an NVIDIA GTX-1060.
HARP: Hierarchical Representation Learning for Networks

We present HARP, a novel method for learning low dimensional embeddings of a graph’s nodes which preserves higherorder structural features. Our proposed method achieves this by compressing the input graph prior to embedding it, effectively avoiding troublesome embedding configurations (i.e. local minima) which can pose problems to non-convex optimization. HARP works by finding a smaller graph which approximates the global structure of its input. This simplified graph is used to learn a set of initial representations, which serve as good initializations for learning representations in the original, detailed graph. We inductively extend this idea, by decomposing a graph in a series of levels, and then embed the hierarchy of graphs from the coarsest one to the original graph. HARP is a general meta-strategy to improve all of the stateof-the-art neural algorithms for embedding graphs, including DeepWalk, LINE, and Node2vec. Indeed, we demonstrate that applying HARP’s hierarchical paradigm yields improved implementations for all three of these methods, as evaluated on classification tasks on real-world graphs such as DBLP, BlogCatalog, and CiteSeer, where we achieve a performance gain over the original implementations by up to 14% Macro F1.
Plastic particles in coastal pelagic ecosystems of the Northeast Pacific ocean.

The purpose of this study was to examine the distribution, abundance and characteristics of plastic particles in plankton samples collected routinely in Northeast Pacific ecosystems, and to contribute to the development of ideas for future research into the occurrence and impact of small plastic debris in marine pelagic ecosystems. Plastic debris particles were assessed from zooplankton samples collected as part of the National Oceanic and Atmospheric Administration's (NOAA) ongoing ecosystem surveys during two research cruises in the Southeast Bering Sea in the spring and fall of 2006 and four research cruises off the U.S. west coast (primarily off southern California) in spring, summer and fall of 2006, and in January of 2007. Nets with 0.505 mm mesh were used to collect surface samples during all cruises, and sub-surface samples during the four cruises off the west coast. The 595 plankton samples processed indicate that plastic particles are widely distributed in surface waters. The proportion of surface samples from each cruise that contained particles of plastic ranged from 8.75 to 84.0%, whereas particles were recorded in sub-surface samples from only one cruise (in 28.2% of the January 2007 samples). Spatial and temporal variability was apparent in the abundance and distribution of the plastic particles and mean standardized quantities varied among cruises with ranges of 0.004-0.19 particles/m³, and 0.014-0.209 mg dry mass/m³. Off southern California, quantities for the winter cruise were significantly higher, and for the spring cruise significantly lower than for the summer and fall surveys (surface data). Differences between surface particle concentrations and mass for the Bering Sea and California coast surveys were significant for pair-wise comparisons of the spring but not the fall cruises. The particles were assigned to three plastic product types: product fragments, fishing net and line fibers, and industrial pellets; and five size categories: <1 mm, 1-2.5 mm, >2.5-5 mm, >5-10 mm, and >10 mm. Product fragments accounted for the majority of the particles, and most were less than 2.5 mm in size. The ubiquity of such particles in the survey areas and predominance of sizes <2.5 mm implies persistence in these pelagic ecosystems as a result of continuous breakdown from larger plastic debris fragments, and widespread distribution by ocean currents. Detailed investigations of the trophic ecology of individual zooplankton species, and their encounter rates with various size ranges of plastic particles in the marine pelagic environment, are required in order to understand the potential for ingestion of such debris particles by these organisms. Ongoing plankton sampling programs by marine research institutes in large marine ecosystems are good potential sources of data for continued assessment of the abundance, distribution and potential impact of small plastic debris in productive coastal pelagic zones.
Comparative Evaluation of Spin-Transfer-Torque and Magnetoelectric Random Access Memory

Spin-transfer torque random access memory (STT-RAM), as a promising nonvolatile memory technology, faces challenges of high write energy and low density. The recently developed magnetoelectric random access memory (MeRAM) enables the possibility of overcoming these challenges by the use of voltage-controlled magnetic anisotropy (VCMA) effect and achieves high density, fast speed, and low energy simultaneously. As both STT-RAM and MeRAM suffer from the reliability problem of write errors, we implement a fast Landau-Lifshitz-Gilbert equation-based simulator to capture their write error rate (WER) under process and temperature variation. We utilize a multi-write peripheral circuit to minimize WER and design reliable STT-RAM and MeRAM. With the same acceptable WER, MeRAM shows advantages of 83% faster write speed, 67.4% less write energy, 138% faster read speed, and 28.2% less read energy compared with STT-RAM. Benefiting from the VCMA effect, MeRAM also achieves twice the density of STT-RAM with a 32 nm technology node, and this density difference is expected to increase with technology scaling down.
Creating healthy food and eating environments: policy and environmental approaches.

Food and eating environments likely contribute to the increasing epidemic of obesity and chronic diseases, over and above individual factors such as knowledge, skills, and motivation. Environmental and policy interventions may be among the most effective strategies for creating population-wide improvements in eating. This review describes an ecological framework for conceptualizing the many food environments and conditions that influence food choices, with an emphasis on current knowledge regarding the home, child care, school, work site, retail store, and restaurant settings. Important issues of disparities in food access for low-income and minority groups and macrolevel issues are also reviewed. The status of measurement and evaluation of nutrition environments and the need for action to improve health are highlighted.
Composite Common Spatial Pattern for Subject-to-Subject Transfer

Common spatial pattern (CSP) is a popular feature extraction method for electroencephalogram (EEG) classification. Most of existing CSP-based methods exploit covariance matrices on a subject-by-subject basis so that inter-subject information is neglected. In this paper we present modifications of CSP for subject-to-subject transfer, where we exploit a linear combination of covariance matrices of subjects in consideration. We develop two methods to determine a composite covariance matrix that is a weighted sum of covariance matrices involving subjects, leading to composite CSP. Numerical experiments on dataset IVa in BCI competition III confirm that our composite CSP methods improve classification performance over the standard CSP (on a subject-by-subject basis), especially in the case of subjects with fewer number of training samples.
Learning to detect violent videos using convolutional long short-term memory

Developing a technique for the automatic analysis of surveillance videos in order to identify the presence of violence is of broad interest. In this work, we propose a deep neural network for the purpose of recognizing violent videos. A convolutional neural network is used to extract frame level features from a video. The frame level features are then aggregated using a variant of the long short term memory that uses convolutional gates. The convolutional neural network along with the convolutional long short term memory is capable of capturing localized spatio-temporal features which enables the analysis of local motion taking place in the video. We also propose to use adjacent frame differences as the input to the model thereby forcing it to encode the changes occurring in the video. The performance of the proposed feature extraction pipeline is evaluated on three standard benchmark datasets in terms of recognition accuracy. Comparison of the results obtained with the state of the art techniques revealed the promising capability of the proposed method in recognizing violent videos.
'Theory of mind' in the brain. Evidence from a PET scan study of Asperger syndrome.

The ability to attribute mental states to others ('theory of mind') pervades normal social interaction and is impaired in autistic individuals. In a previous positron emission tomography scan study of normal volunteers, performing a 'theory of mind' task was associated with activity in left medial prefrontal cortex. We used the same paradigm in five patients with Asperger syndrome, a mild variant of autism with normal intellectual functioning. No task-related activity was found in this region, but normal activity was observed in immediately adjacent areas. This result suggests that a highly circumscribed region of left medial prefrontal cortex is a crucial component of the brain system that underlies the normal understanding of other minds.
Integration of RFID and WSN technologies in a Smart Parking System

In this paper, a Smart Parking System (SPS) based on the integration of Ultra-High Frequency (UHF) Radio Frequency Identification (RFID) and IEEE 802.15.4 Wireless Sensor Network (WSN) technologies is presented. The system is able to collect information about the occupancy state of parking spaces, and to direct drivers to the nearest vacant parking spot by using a customized software application. Such application also leverages an NFC-based e-wallet system to allow users to pay for the parking fee. Furthermore, a software application based on RESTful Java and Google Cloud Messaging (GCM) technologies has been installed on a Central Server in order to manage alert events (e.g. improper use of a reserved space or expiration of the purchased time). In such a case, it promptly informs the traffic cops through an Android mobile app, which has been designed ad hoc for the considered scenario. A proof-of-concept has demonstrated that the proposed solution can meet the real requirements of a SPS.
A New High Resolution Depth Map Estimation System Using Stereo Vision and Kinect Depth Sensing

Depth map estimation is an active and long standing problem in image/video processing and computer vision. Conventional depth estimation algorithms which rely on stereo/multi-view vision or depth sensing devices alone are limited by complicated scenes or imperfections of the depth sensing devices. On the other hand, the depth maps obtained from the stereo/multi-view vision and depth sensing devices are de facto complementary to each other. This motivates us to develop in this paper a new system for high resolution and high quality depth estimation by joint fusion of stereo and Kinect data. We modeled the observations using Markov random field (MRF) and formulated the fusion problem as a maximum a posteriori probability (MAP) estimation problem. The reliability and the probability density functions for describing the observations from the two devices are also derived. The MAP problem is solved using a multiscale belief propagation (BP) algorithm. To suppress possible estimation noise, the depth map estimated is further refined by color image guided depth matting and a 2D polynomial regression (LPR)-based filtering. Experimental results and numerical comparisons show that our system can provide high quality and high resolution depth maps, thanks to the complementary strengths of both stereo vision and Kinect depth sensors.
Semi and Weakly Supervised Semantic Segmentation Using Generative Adversarial Network

Semantic segmentation has been a long standing challenging task in computer vision. It aims at assigning a label to each image pixel and needs significant number of pixellevel annotated data, which is often unavailable. To address this lack, in this paper, we leverage, on one hand, massive amount of available unlabeled or weakly labeled data, and on the other hand, non-real images created through Generative Adversarial Networks. In particular, we propose a semi-supervised framework – based on Generative Adversarial Networks (GANs) – which consists of a generator network to provide extra training examples to a multi-class classifier, acting as discriminator in the GAN framework, that assigns sample a label y from theK possible classes or marks it as a fake sample (extra class). The underlying idea is that adding large fake visual data forces real samples to be close in the feature space, enabling a bottom-up clustering process, which, in turn, improves multiclass pixel classification. To ensure higher quality of generated images for GANs with consequent improved pixel classification, we extend the above framework by adding weakly annotated data, i.e., we provide class level information to the generator. We tested our approaches on several challenging benchmarking visual datasets, i.e. PASCAL, SiftFLow, Stanford and CamVid, achieving competitive performance also compared to state-of-the-art semantic segmentation methods.
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient

In this paper, we propose a StochAstic Recursive grAdient algoritHm (SARAH), as well as its practical variant SARAH+, as a novel approach to the finite-sum minimization problems. Different from the vanilla SGD and other modern stochastic methods such as SVRG, S2GD, SAG and SAGA, SARAH admits a simple recursive framework for updating stochastic gradient estimates; when comparing to SAG/SAGA, SARAH does not require a storage of past gradients. The linear convergence rate of SARAH is proven under strong convexity assumption. We also prove a linear convergence rate (in the strongly convex case) for an inner loop of SARAH, the property that SVRG does not possess. Numerical experiments demonstrate the efficiency of our algorithm.
Personality and subjective well-being in orangutans (Pongo pygmaeus and Pongo abelii).

Orangutans (Pongo pygmaeus and Pongo abelii) are semisolitary apes and, among the great apes, the most distantly related to humans. Raters assessed 152 orangutans on 48 personality descriptors; 140 of these orangutans were also rated on a subjective well-being questionnaire. Principal-components analysis yielded 5 reliable personality factors: Extraversion, Dominance, Neuroticism, Agreeableness, and Intellect. The authors found no factor analogous to human Conscientiousness. Among the orangutans rated on all 48 personality descriptors and the subjective well-being questionnaire, Extraversion, Agreeableness, and low Neuroticism were related to subjective well-being. These findings suggest that analogues of human, chimpanzee, and orangutan personality domains existed in a common ape ancestor.
High Step-Up Interleaved Converter With Built-In Transformer Voltage Multiplier Cells for Sustainable Energy Applications

In this paper, the built-in transformer voltage multiplier cell is inserted into each phase of the conventional interleaved boost converter to provide additional control freedom for the voltage gain extension without extreme duty cycle. The voltage multiplier cell is only composed of the built-in transformer windings, diodes and small capacitors. And additional active switches are not required to simplify the circuit configuration. Furthermore, the switch voltage stress and the diode peak current are also minimized due to the built-in transformer voltage multiplier cells to improve the conversion efficiency. Moreover, there is no reverse-recovery problem for the clamp diodes and the reverse-recovery current for the regenerative and output diodes are controlled by the leakage inductance of the built-in transformer to reduce the relative losses. In addition, the switch turn-off voltage spikes are suppressed effectively by the ingenious and inherent passive clamp scheme and zero current switch (ZCS) turn-on is realized for the switches, which can enhance the power device reliability. Finally, a 40 V-input 380 V-output 1 kW prototype is built to demonstrate the clear advantages of the proposed converter.
Report of the American Psychiatric Association Task Force on Treatment of Gender Identity Disorder.

Both the diagnosis and treatment of Gender Identity Disorder (GID) are controversial. Although linked, they are separate issues and the DSM does not evaluate treatments. The Board of Trustees (BOT) of the American Psychiatric Association (APA), therefore, formed a Task Force charged to perform a critical review of the literature on the treatment of GID at different ages, to assess the quality of evidence pertaining to treatment, and to prepare a report that included an opinion as to whether or not sufficient credible literature exists for development of treatment recommendations by the APA. The literature on treatment of gender dysphoria in individuals with disorders of sex development was also assessed. The completed report was accepted by the BOT on September 11, 2011. The quality of evidence pertaining to most aspects of treatment in all subgroups was determined to be low; however, areas of broad clinical consensus were identified and were deemed sufficient to support recommendations for treatment in all subgroups. With subjective improvement as the primary outcome measure, current evidence was judged sufficient to support recommendations for adults in the form of an evidence-based APA Practice Guideline with gaps in the empirical data supplemented by clinical consensus. The report recommends that the APA take steps beyond drafting treatment recommendations. These include issuing position statements to clarify the APA's position regarding the medical necessity of treatments for GID, the ethical bounds of treatments of gender variant minors, and the rights of persons of any age who are gender variant, transgender or transsexual.
Maximum power point tracking of photovoltaic water pumping system using fuzzy logic controller

Diode junction photovoltaic (PV) generators exhibit nonlinear V-I characteristics and the maximum power extractable varies with the intensity of solar radiation, temperature and load conditions. A maximum power point tracking (MPPT) controller is therefore usually employed in PV-generator applications to automatically extract maximum power irrespective of the instantaneous conditions of the PV system. This paper presents a fuzzy logic control (FLC) scheme for extracting the maximum power from a stand-alone PV generator for use in a water pumping system. The PV-generator system comprises a solar panel, DC-DC buck chopper, fuzzy MPP tracker and permanent DC-motor driving a centrifugal pump. The fuzzy controller generates a control signal for the pulse-width-modulation generator which in turn adjusts the duty ratio of the buck chopper to match the load impedance to the PV generator, and consequently maximizes the motor speed and the water discharge rate of a coupled centrifugal pump. The control method has been modelled in Matlab/Simulink and simulation results are presented to confirm its significantly improved power extraction performance under different sunlight conditions, when compared with a directly-connected PV-generator energized pumping system operating.
Context-Sensitive Lexicon Features for Neural Sentiment Analysis

Sentiment lexicons have been leveraged as a useful source of features for sentiment analysis models, leading to the state-of-the-art accuracies. On the other hand, most existing methods use sentiment lexicons without considering context, typically taking the count, sum of strength, or maximum sentiment scores over the whole input. We propose a context-sensitive lexicon-based method based on a simple weighted-sum model, using a recurrent neural network to learn the sentiments strength, intensification and negation of lexicon sentiments in composing the sentiment value of sentences. Results show that our model can not only learn such operation details, but also give significant improvements over state-of-the-art recurrent neural network baselines without lexical features, achieving the best results on a Twitter benchmark.