nils-herrmann/clean_scirepeval huggingface dataset:
Multi-modal Factorized Bilinear Pooling with Co-attention Learning for Visual Question Answering

Visual question answering (VQA) is challenging because it requires a simultaneous understanding of both the visual content of images and the textual content of questions. The approaches used to represent the images and questions in a fine-grained manner and questions and to fuse these multimodal features play key roles in performance. Bilinear pooling based models have been shown to outperform traditional linear models for VQA, but their high-dimensional representations and high computational complexity may seriously limit their applicability in practice. For multimodal feature fusion, here we develop a Multi-modal Factorized Bilinear (MFB) pooling approach to efficiently and effectively combine multi-modal features, which results in superior performance for VQA compared with other bilinear pooling approaches. For fine-grained image and question representation, we develop a ‘co-attention’ mechanism using an end-to-end deep network architecture to jointly learn both the image and question attentions. Combining the proposed MFB approach with co-attention learning in a new network architecture provides a unified model for VQA. Our experimental results demonstrate that the single MFB with co-attention model achieves new state-of-theart performance on the real-world VQA dataset. Code available at https://github.com/yuzcccc/mfb.
Recovery of central and peripheral neuromuscular fatigue after exercise.

Sustained physical exercise leads to a reduced capacity to produce voluntary force that typically outlasts the exercise bout. This "fatigue" can be due both to impaired muscle function, termed "peripheral fatigue," and a reduction in the capacity of the central nervous system to activate muscles, termed "central fatigue." In this review we consider the factors that determine the recovery of voluntary force generating capacity after various types of exercise. After brief, high-intensity exercise there is typically a rapid restitution of force that is due to recovery of central fatigue (typically within 2 min) and aspects of peripheral fatigue associated with excitation-contraction coupling and reperfusion of muscles (typically within 3-5 min). Complete recovery of muscle function may be incomplete for some hours, however, due to prolonged impairment in intracellular Ca2+ release or sensitivity. After low-intensity exercise of long duration, voluntary force typically shows rapid, partial, recovery within the first few minutes, due largely to recovery of the central, neural component. However, the ability to voluntarily activate muscles may not recover completely within 30 min after exercise. Recovery of peripheral fatigue contributes comparatively little to the fast initial force restitution and is typically incomplete for at least 20-30 min. Work remains to identify what factors underlie the prolonged central fatigue that usually accompanies long-duration single joint and locomotor exercise and to document how the time course of neuromuscular recovery is affected by exercise intensity and duration in locomotor exercise. Such information could be useful to enhance rehabilitation and sports performance.
Skip N-grams and Ranking Functions for Predicting Script Events

In this paper, we extend current state-of-theart research on unsupervised acquisition of scripts, that is, stereotypical and frequently observed sequences of events. We design, evaluate and compare different methods for constructing models for script event prediction: given a partial chain of events in a script, predict other events that are likely to belong to the script. Our work aims to answer key questions about how best to (1) identify representative event chains from a source text, (2) gather statistics from the event chains, and (3) choose ranking functions for predicting new script events. We make several contributions, introducing skip-grams for collecting event statistics, designing improved methods for ranking event predictions, defining a more reliable evaluation metric for measuring predictiveness, and providing a systematic analysis of the various event prediction models.
A Latent Variable Model Approach to PMI-based Word Embeddings

Semantic word embeddings represent the meaning of a word via a vector, and are created by diverse methods. Many use nonlinear operations on co-occurrence statistics, and have hand-tuned hyperparameters and reweighting methods. This paper proposes a new generative model, a dynamic version of the log-linear topic model of Mnih and Hinton (2007). The methodological novelty is to use the prior to compute closed form expressions for word statistics. This provides a theoretical justification for nonlinear models like PMI, word2vec, and GloVe, as well as some hyperparameter choices. It also helps explain why low-dimensional semantic embeddings contain linear algebraic structure that allows solution of word analogies, as shown by Mikolov et al. (2013a) and many subsequent papers. Experimental support is provided for the generative model assumptions, the most important of which is that latent word vectors are fairly uniformly dispersed in space.
Evolution-Guided Policy Gradient in Reinforcement Learning

Deep Reinforcement Learning (DRL) algorithms have been successfully applied to a range of challenging control tasks. However, these methods typically suffer from three core difficulties: temporal credit assignment with sparse rewards, lack of effective exploration, and brittle convergence properties that are extremely sensitive to hyperparameters. Collectively, these challenges severely limit the applicability of these approaches to real-world problems. Evolutionary Algorithms (EAs), a class of black box optimization techniques inspired by natural evolution, are well suited to address each of these three challenges. However, EAs typically suffer from high sample complexity and struggle to solve problems that require optimization of a large number of parameters. In this paper, we introduce Evolutionary Reinforcement Learning (ERL), a hybrid algorithm that leverages the population of an EA to provide diversified data to train an RL agent, and reinserts the RL agent into the EA population periodically to inject gradient information into the EA. ERL inherits EA’s ability of temporal credit assignment with a fitness metric, effective exploration with a diverse set of policies, and stability of a population-based approach and complements it with off-policy DRL’s ability to leverage gradients for higher sample efficiency and faster learning. Experiments in a range of challenging continuous control benchmarks demonstrate that ERL significantly outperforms prior DRL and EA methods.
Revised diagnostic criteria for neuromyelitis optica.

The authors previously proposed diagnostic criteria for neuromyelitis optica (NMO) that facilitate its distinction from prototypic multiple sclerosis (MS). However, some patients with otherwise typical NMO have additional symptoms not attributable to optic nerve or spinal cord inflammation or have MS-like brain MRI lesions. Furthermore, some patients are misclassified as NMO by the authors' earlier proposed criteria despite having a subsequent course indistinguishable from prototypic MS. A serum autoantibody marker, NMO-IgG, is highly specific for NMO. The authors propose revised NMO diagnostic criteria that incorporate NMO-IgG status.



Using final clinical diagnosis (NMO or MS) as the reference standard, the authors calculated sensitivity and specificity for each criterion and various combinations using a sample of 96 patients with NMO and 33 with MS. The authors used likelihood ratios and logistic regression analysis to develop the most practical and informative diagnostic model.



Fourteen patients with NMO (14.6%) had extra-optic-spinal CNS symptoms. NMO-IgG seropositivity was 76% sensitive and 94% specific for NMO. The best diagnostic combination was 99% sensitive and 90% specific for NMO and consisted of at least two of three elements: longitudinally extensive cord lesion, onset brain MRI nondiagnostic for MS, or NMO-IgG seropositivity.



The authors propose revised diagnostic criteria for definite neuromyelitis optica (NMO) that require optic neuritis, myelitis, and at least two of three supportive criteria: MRI evidence of a contiguous spinal cord lesion 3 or more segments in length, onset brain MRI nondiagnostic for multiple sclerosis, or NMO-IgG seropositivity. CNS involvement beyond the optic nerves and spinal cord is compatible with NMO.
Workload characterization on a production Hadoop cluster: A case study on Taobao

MapReduce is becoming the state-of-the-art computing paradigm for processing large-scale datasets on a large cluster with tens or thousands of nodes. It has been widely used in various fields such as e-commerce, Web search, social networks, and scientific computation. Understanding the characteristics of MapReduce workloads is the key to achieving better configuration decisions and improving the system throughput. However, workload characterization of MapReduce, especially in a large-scale production environment, has not been well studied yet. To gain insight on MapReduce workloads, we collected a two-week workload trace from a 2,000-node Hadoop cluster at Taobao, which is the biggest online e-commerce enterprise in Asia, ranked 14th in the world as reported by Alexa. The workload trace covered 912,157 jobs, logged from Dec. 4 to Dec. 20, 2011. We characterized the workload at the granularity of job and task, respectively and concluded with a set of interesting observations. The results of workload characterization are representative and generally consistent with data platforms for e-commerce websites, which can help other researchers and engineers understand the performance and job characteristics of Hadoop in their production environments. In addition, we use these job analysis statistics to derive several implications for potential performance optimization solutions.
Full Resolution Image Compression with Recurrent Neural Networks

This paper presents a set of full-resolution lossy image compression methods based on neural networks. Each of the architectures we describe can provide variable compression rates during deployment without requiring retraining of the network: each network need only be trained once. All of our architectures consist of a recurrent neural network (RNN)-based encoder and decoder, a binarizer, and a neural network for entropy coding. We compare RNN types (LSTM, associative LSTM) and introduce a new hybrid of GRU and ResNet. We also study one-shot versus additive reconstruction architectures and introduce a new scaled-additive framework. We compare to previous work, showing improvements of 4.3%&#x2013;8.8% AUC (area under the rate-distortion curve), depending on the perceptual metric used. As far as we know, this is the first neural network architecture that is able to outperform JPEG at image compression across most bitrates on the rate-distortion curve on the Kodak dataset images, with and without the aid of entropy coding.
Exact sampling with coupled Markov chains and applications to statistical mechanics

For many applications it is useful to sample from a nite set of objects in accordance with some particular distribution. One approach is to run an ergodic (i.e., irreducible aperiodic) Markov chain whose stationary distribution is the desired distribution on this set; after the Markov chain has run for M steps, with M suuciently large, the distribution governing the state of the chain approximates the desired distribution. Unfortunately it can be diicult to determine how large M needs to be. We describe a simple variant of this method that determines on its own when to stop, and that outputs samples in exact accordance with the desired distribution. The method uses couplings, which have also played a role in other sampling schemes; however, rather than running the coupled chains from the present into the future, one runs from a distant point in the past up until the present, where the distance into the past that one needs to go is determined during the running of the algorithm itself. If the state space has a partial order that is preserved under the moves of the Markov chain, then the coupling is often particularly eecient. Using our approach one can sample from the Gibbs distributions associated with various statistical mechanics models (including Ising, random-cluster, ice, and dimer) or choose uniformly at random from the elements of a nite distributive lattice.
EPITOMIC VARIATIONAL AUTOENCODER

In this paper, we propose epitomic variational autoencoder (eVAE), a probabilistic generative model of high dimensional data. eVAE is composed of a number of sparse variational autoencoders called ‘epitome’ such that each epitome partially shares its encoder-decoder architecture with other epitomes in the composition. We show that the proposed model greatly overcomes the common problem in variational autoencoders (VAE) of model over-pruning. We substantiate that eVAE is efficient in using its model capacity and generalizes better than VAE, by presenting qualitative and quantitative results on MNIST and TFD datasets.
Cloning Strategies from Trading Records using Agent-based Reinforcement Learning Algorithm

Investment decision making is considered as a series of complicated processes, which are difficult to be analyzed and imitated. Given large amounts of trading records with rich expert knowledge in financial domain, extracting its original decision logics and cloning the trading strategies are also quite challenging. In this paper, an agent-based reinforcement learning (RL) system is proposed to mimic professional trading strategies. The concept of continuous Markov decision process (MDP) in RL is similar to the trading decision making in financial time series data. With the specific-designed RL components, including states, actions, and rewards for financial applications, policy gradient method can successfully imitate the expert's strategies. In order to improve the convergence of RL agent in such highly dynamic environment, a pre-trained model based on supervised learning is transferred to the deep policy networks. The experimental results show that the proposed system can reproduce around eighty percent trading decisions both in training and testing stages. With the discussion of the tradeoff between explorations and model updating, this paper tried to fine-tuning the system parameters to get reasonable results. Finally, an advanced strategy is proposed to dynamically adjust the number of explorations in each episode to achieve better results.
swDNN: A Library for Accelerating Deep Learning Applications on Sunway TaihuLight

To explore the potential of training complex deep neural networks (DNNs) on other commercial chips rather than GPUs, we report our work on swDNN, which is a highly-efficient library for accelerating deep learning applications on the newly announced world-leading supercomputer, Sunway TaihuLight. Targeting SW26010 processor, we derive a performance model that guides us in the process of identifying the most suitable approach for mapping the convolutional neural networks (CNNs) onto the 260 cores within the chip. By performing a systematic optimization that explores major factors, such as organization of convolution loops, blocking techniques, register data communication schemes, as well as reordering strategies for the two pipelines of instructions, we manage to achieve a double-precision performance over 1.6 Tflops for the convolution kernel, achieving 54% of the theoretical peak. Compared with Tesla K40m with cuDNNv5, swDNN results in 1.91-9.75x performance speedup in an evaluation with over 100 parameter configurations.
3D body scanning technology for fashion and apparel industry

This paper presents an overview of 3D body scanning technologies with applications to the fashion and apparel industry. Complete systems for the digitization of the human body exist since more than fifteen years. One of the main users of this technology with application in the textile field was the military industry. In fact, body scanning technology is being successfully employed since many years in military bases for a fast selection of the correct size of uniforms for the entire staff. Complete solutions were especially developed for this field of application. Many different research projects were issued for the exploitation of the same technology in the commercial field. Experiments were performed and start-up projects are to time running in different parts of the world by installing full body scanning systems in various locations such as shopping malls, boutiques or dedicated scanning centers. Everything is actually ready to be exploited and all the required hardware, software and solutions are available: full body scanning systems, software for the automatic and reliable extraction of body measurements, e-kiosk and web solutions for the presentation of garments, high-end and low-end virtual-try-on systems. However, complete solutions in this area have still not yet found the expected commercial success. Today, with the on-going large cost reduction given by the appearance of new competitors, methods for digitization of the human body becomes more interesting for the fashion and apparel industry. Therefore, a large expansion of these technologies is expected in the near future. To date, different methods are used commercially for the measurement of the human body. These can be divided into three major distinguished groups: laser-scanning, projection of light patterns, combination modeling and image processing. The different solutions have strengths and weaknesses that profile their suitability for specific applications. This paper gives an overview of their differences and characteristics and expresses clues for the selection of the adequate method. A special interest is given to practical examples of the commercial exploitation of human body digitization with applications to the fashion and apparel industry.
Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema

Universal schema predicts the types of entities and relations in a knowledge base (KB) by jointly embedding the union of all available schema types—not only types from multiple structured databases (such as Freebase or Wikipedia infoboxes), but also types expressed as textual patterns from raw text. This prediction is typically modeled as a matrix completion problem, with one type per column, and either one or two entities per row (in the case of entity types or binary relation types, respectively). Factorizing this sparsely observed matrix yields a learned vector embedding for each row and each column. In this paper we explore the problem of making predictions for entities or entity-pairs unseen at training time (and hence without a pre-learned row embedding). We propose an approach having no per-row parameters at all; rather we produce a row vector on the fly using a learned aggregation function of the vectors of the observed columns for that row. We experiment with various aggregation functions, including neural network attention models. Our approach can be understood as a natural language database, in that questions about KB entities are answered by attending to textual or database evidence. In experiments predicting both relations and entity types, we demonstrate that despite having an order of magnitude fewer parameters than traditional universal schema, we can match the accuracy of the traditional model, and more importantly, we can now make predictions about unseen rows with nearly the same accuracy as rows available at training time.
Suspicious human activity recognition: a review

Suspicious human activity recognition from surveillance video is an active research area of image processing and computer vision. Through the visual surveillance, human activities can be monitored in sensitive and public areas such as bus stations, railway stations, airports, banks, shopping malls, school and colleges, parking lots, roads, etc. to prevent terrorism, theft, accidents and illegal parking, vandalism, fighting, chain snatching, crime and other suspicious activities. It is very difficult to watch public places continuously, therefore an intelligent video surveillance is required that can monitor the human activities in real-time and categorize them as usual and unusual activities; and can generate an alert. Recent decade witnessed a good number of publications in the field of visual surveillance to recognize the abnormal activities. Furthermore, a few surveys can be seen in the literature for the different abnormal activities recognition; but none of them have addressed different abnormal activities in a review. In this paper, we present the state-of-the-art which demonstrates the overall progress of suspicious activity recognition from the surveillance videos in the last decade. We include a brief introduction of the suspicious human activity recognition with its issues and challenges. This paper consists of six abnormal activities such as abandoned object detection, theft detection, fall detection, accidents and illegal parking detection on road, violence activity detection, and fire detection. In general, we have discussed all the steps those have been followed to recognize the human activity from the surveillance videos in the literature; such as foreground object extraction, object detection based on tracking or non-tracking methods, feature extraction, classification; activity analysis and recognition. The objective of this paper is to provide the literature review of six different suspicious activity recognition systems with its general framework to the researchers of this field.
SOME RADAR TOPICS: WAVEFORM DESIGN, RANGE CFAR AND TARGET RECOGNITION

The first RADAR patent was applied for by Christian Huelsmeyer on April 30, 1904 at the patent office in Berlin, Germany. He was motivated by a ship accident on the river Weser and called his experimental system ”Telemobiloscope”. In this chapter some important and modern topics in radar system design and radar signal processing will be discussed. Waveform design is one innovative topic where new results are available for special applications like automotive radar. Detection theory is a fundamental radar topic which will be discussed in this chapter for new range CFAR schemes which are essential for all radar systems. Target recognition has for many years been the dream of all radar engineers. New results for target classification will be discussed for some automotive radar sensors.
Augmented Reality vs . Virtual Reality : Differences and Similarities

This paper presents an overview of basic aspects of Augmented Reality (AR) and Virtual Reality(VR) . It describes the main fields in which Augmented and Virtual Reality is applied nowadays and important AR and VR devices. Some difference and similarities of Augmented Reality and Virtual Reality will be discussed and this paper will provide an overview of them. KeywordsAugmented Reality, Virtual Reality, AR device ,VR device.
A Process for Data Protection Impact Assessment Under the European General Data Protection Regulation

With the General Data Protection Regulation there will be a legal obligation for controllers to conduct a Data Protection Impact Assessment for the first time. This paper examines the new provisions in detail and examines ways for their successful implementation. It proposes a process which operationalizes established requirements ensuring the appropriate attention to fundamental rights as warranted by the GDPR, incorporates the legislation’s new requirements and can be adapted to suit the controller’s needs.