Reviewerly/exHarmony huggingface dataset:
Modes and states in quantum optics

A few decades ago, quantum optics stood out as a new domain of physics by exhibiting states of light with no classical equivalent. The first investigations concerned single photons, squeezed states, twin beams and EPR states, that involve only one or two modes of the electromagnetic field. The study of the properties of quantum light then evolved in the direction of more and more complex and rich situations, involving many modes, either spatial, temporal, frequency, or polarization modes. Actually, each mode of the electromagnetic field can be considered as an individual quantum degree of freedom. It is then possible, using the techniques of nonlinear optics, to couple different modes, and thus to build in a controlled way a quantum network in which the nodes are optical modes and that is endowed with a strong multipartite entanglement. In addition, such networks are easily reconfigurable and subject only to weak decoherence. They open indeed many promising perspectives for optical communications and computation. Because of the linearity of Maxwell equations a linear superposition of two modes is another mode. This means that a "modal superposition principle" exists hand in hand with the regular quantum state superposition principle. The purpose of the present review is to show the interest of considering these two aspects of multimode quantum light in a global way. Indeed using different sets of modes allows to consider the same quantum state under different perspectives: a given state can be entangled in one basis, factorized in another. We will show how to produce, characterize and tailor multimode quantum light. Finally we will describe some applications to quantum technologies, such as parameter estimation and measurement-based quantum computation.
Mastering Complex Control in MOBA Games with Deep Reinforcement Learning

We study the reinforcement learning problem of complex action control in the Multi-player Online Battle Arena (MOBA) 1v1 games. This problem involves far more complicated state and action spaces than those of traditional 1v1 games, such as Go and Atari series, which makes it very difficult to search any policies with human-level performance. In this paper, we present a deep reinforcement learning framework to tackle this problem from the perspectives of both system and algorithm. Our system is of low coupling and high scalability, which enables efficient explorations at large scale. Our algorithm includes several novel strategies, including control dependency decoupling, action mask, target attention, and dual-clip PPO, with which our proposed actor-critic network can be effectively trained in our system. Tested on the MOBA game Honor of Kings, the trained AI agents can defeat top professional human players in full 1v1 games.
Asymmetric Co-Teaching for Unsupervised Cross-Domain Person Re-Identification

Person re-identification (re-ID), is a challenging task due to the high variance within identity samples and imaging conditions. Although recent advances in deep learning have achieved remarkable accuracy in settled scenes, i.e., source domain, few works can generalize well on the unseen target domain. One popular solution is assigning unlabeled target images with pseudo labels by clustering, and then retraining the model. However, clustering methods tend to introduce noisy labels and discard low confidence samples as outliers, which may hinder the retraining process and thus limit the generalization ability. In this study, we argue that by explicitly adding a sample filtering procedure after the clustering, the mined examples can be much more efficiently used. To this end, we design an asymmetric co-teaching framework, which resists noisy labels by cooperating two models to select data with possibly clean labels for each other. Meanwhile, one of the models receives samples as pure as possible, while the other takes in samples as diverse as possible. This procedure encourages that the selected training samples can be both clean and miscellaneous, and that the two models can promote each other iteratively. Extensive experiments show that the proposed framework can consistently benefit most clustering based methods, and boost the state-of-the-art adaptation accuracy. Our code is available at https://github.com/FlyingRoastDuck/ACT_AAAI20.
A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.
An Evaluation of Deep Learning Methods for Small Object Detection

Small object detection is an interesting topic in computer vision. With the rapid development in deep learning, it has drawn attention of several researchers with innovations in approaches to join a race. These innovations proposed comprise region proposals, divided grid cell, multiscale feature maps, and new loss function. As a result, performance of object detection has recently had significant improvements. However, most of the state-of-the-art detectors, both in one-stage and two-stage approaches, have struggled with detecting small objects. In this study, we evaluate current state-of-the-art models based on deep learning in both approaches such as Fast RCNN, Faster RCNN, RetinaNet, and YOLOv3. We provide a profound assessment of the advantages and limitations of models. Specifically, we run models with different backbones on different datasets with multiscale objects to find out what types of objects are suitable for each model along with backbones. Extensive empirical evaluation was conducted on 2 standard datasets, namely, a small object dataset and a filtered dataset from PASCAL VOC 2007. Finally, comparative results and analyses are then presented.
Digital contact tracing for COVID-19

KEY POINTS The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exceeded the capacity of many public health agencies to undertake traditional contact tracing. To address the limitations of traditional contact tracing, numerous governments — including those of Alberta in
Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?

While pretrained models such as BERT have shown large gains across natural language understanding tasks, their performance can be improved by further training the model on a data-rich intermediate task, before fine-tuning it on a target task.However, it is still poorly understood when and why intermediate-task training is beneficial for a given target task.To investigate this, we perform a large-scale study on the pretrained RoBERTa model with 110 intermediate-target task combinations.We further evaluate all trained models with 25 probing tasks meant to reveal the specific skills that drive transfer.We observe that intermediate tasks requiring high-level inference and reasoning abilities tend to work best.We also observe that target task performance is strongly correlated with higher-level abilities such as coreference resolution.However, we fail to observe more granular correlations between probing and target task performance, highlighting the need for further work on broad-coverage probing benchmarks.We also observe evidence that the forgetting of knowledge learned during pretraining may limit our analysis, highlighting the need for further work on transfer learning methods in these settings.
Detail-recovery Image Deraining via Context Aggregation Networks

This paper looks at this intriguing question: are single images with their details lost during deraining, reversible to their artifact-free status? We propose an end-to-end detail-recovery image deraining network (termed a DRDNet) to solve the problem. Unlike existing image deraining approaches that attempt to meet the conflicting goal of simultaneously deraining and preserving details in a unified framework, we propose to view rain removal and detail recovery as two seperate tasks, so that each part could specialize rather than trade-off between two conflicting goals. Specifically, we introduce two parallel sub-networks with a comprehensive loss function which synergize to derain and recover the lost details caused by deraining. For complete rain removal, we present a rain residual network with the squeeze-and-excitation (SE) operation to remove rain streaks from the rainy images. For detail recovery, we construct a specialized detail repair network consisting of welldesigned blocks, named structure detail context aggregation block (SDCAB), to encourage the lost details to return for eliminating image degradations. Moreover, the detail recovery branch of our proposed detail repair framework is detachable and can be incorporated into existing deraining methods to boost their performances. DRD-Net has been validated on several well-known benchmark datasets in terms of deraining robustness and detail accuracy. Comparisons show clear visual and numerical improvements of our method over the state-of-the-arts.
Contact tracing apps and values dilemmas: A privacy paradox in a neo-liberal world

Contact tracing apps are presented as a solution, if not the solution, to curb pandemics in the Covid-19 crisis. In France, despite heated public institutional debate on privacy related issues, the app was presented by government as an essential benefit for protecting health and lives, thus avoiding both politicians and citizens to feel morally responsible and looking guilty, and as essential to recover our freedom to move. However we argue that, while detection of cases have still not been reported after 10 days and one million app downloads - a situation comparable to Australia who launched its app a month before -, the adoption of the app generates important risks to our informational privacy, surveillance and habituation to security policies. It also may create discrimination, distrust and generate other health problems such as addiction and others as 5G technology continues to be deployed without prior impact studies. Finally the smartphone app against covid epidemics appears as an extreme case of the privacy paradox where the government plays on the immediate benefits and downplays long-term concerns while inducing a technology of self. Contact tracing apps may become an emblematic case for digital transformation and value changes in the western world.
Deep Learning based Safe Social Distancing and Face Mask Detection in Public Areas for COVID-19 Safety Guidelines Adherence

According to data obtained by the World Health Organization, the global pandemic of COVID-19 has severely impacted the world and has now infected more than eight million people worldwide. Wearing face masks and following safe social distancing are two of the enhanced safety protocols need to be followed in public places in order to prevent the spread of the virus. To create safe environment that contributes to public safety, we propose an efficient computer vision based approach focused on the real-time automated monitoring of people to detect both safe social distancing and face masks in public places by implementing the model on raspberry pi4 to monitor activity and detect violations through camera. After detection of breach, the raspberry pi4 sends alert signal to control center at state police headquarters and also give alarm to public. In this proposed system modern deep learning algorithm have been mixed with geometric techniques for building a robust modal which covers three aspects of detection, tracking, and validation. Thus, the proposed system favors the society by saving time and helps in lowering the spread of corona virus. It can be implemented effectively in current situation when lockdown is eased to inspect persons in public gatherings, shopping malls, etc. Automated inspection reduces manpower to inspect the public and also can be used in any place.
sFuzz

Smart contracts are Turing-complete programs that execute on the infrastructure of the blockchain, which often manage valuable digital assets. Solidity is one of the most popular programming languages for writing smart contracts on the Ethereum platform. Like traditional programs, smart contracts may contain vulnerabilities. Unlike traditional programs, smart contracts cannot be easily patched once they are deployed. It is thus important that smart contracts are tested thoroughly before deployment. In this work, we present an adaptive fuzzer for smart contracts on the Ethereum platform called sFuzz. Compared to existing Solidity fuzzers, sFuzz combines the strategy in the AFL fuzzer and an efficient lightweight multi-objective adaptive strategy targeting those hard-to-cover branches. sFuzz has been applied to more than 4 thousand smart contracts and the experimental results show that (1) sFuzz is efficient, e.g., two orders of magnitude faster than state-of-the-art tools; (2) sFuzz is effective in achieving high code coverage and discovering vulnerabilities; and (3) the different fuzzing strategies in sFuzz complement each other.
A Particle Swarm Optimization-Based Flexible Convolutional Autoencoder for Image Classification

Convolutional auto-encoders have shown their remarkable performance in stacking to deep convolutional neural networks for classifying image data during past several years. However, they are unable to construct the state-of-the-art convolutional neural networks due to their intrinsic architectures. In this regard, we propose a flexible convolutional auto-encoder by eliminating the constraints on the numbers of convolutional layers and pooling layers from the traditional convolutional auto-encoder. We also design an architecture discovery method by using particle swarm optimization, which is capable of automatically searching for the optimal architectures of the proposed flexible convolutional auto-encoder with much less computational resource and without any manual intervention. We use the designed architecture optimization algorithm to test the proposed flexible convolutional auto-encoder through utilizing one graphic processing unit card on four extensively used image classification datasets. Experimental results show that our work in this paper significantly outperform the peer competitors including the state-of-the-art algorithm.
Ensemble feature selection for high-dimensional data: a stability analysis across multiple domains

Selecting a subset of relevant features is crucial to the analysis of high-dimensional datasets coming from a number of application domains, such as biomedical data, document and image analysis. Since no single selection algorithm seems to be capable of ensuring optimal results in terms of both predictive performance and stability (i.e. robustness to changes in the input data), researchers have increasingly explored the effectiveness of “ensemble” approaches involving the combination of different selectors. While interesting proposals have been reported in the literature, most of them have been so far evaluated in a limited number of settings (e.g. with data from a single domain and in conjunction with specific selection approaches), leaving unanswered important questions about the large-scale applicability and utility of ensemble feature selection. To give a contribution to the field, this work presents an empirical study which encompasses different kinds of selection algorithms (filters and embedded methods, univariate and multivariate techniques) and different application domains. Specifically, we consider 18 classification tasks with heterogeneous characteristics (in terms of number of classes and instances-to-features ratio) and experimentally evaluate, for feature subsets of different cardinalities, the extent to which an ensemble approach turns out to be more robust than a single selector, thus providing useful insight for both researchers and practitioners.
Secure data storage based on blockchain and coding in edge computing

Edge computing is an important tool for smart computing, which brings convenience to data processing as well as security problems. In particular, the security of data storage under edge computing has become an obstacle to its widespread use. To solve the problem, the mechanism combing blockchain with regeneration coding is proposed to improve the security and reliability of stored data under edge computing. Our contribution is as follows. 1) According to the three-tier edge computing architecture and data security storage requirements, we proposed hybrid storage architecture and model specifically adapted to edge computing. 2) Making full use of the data storage advantages of edge network devices and cloud storage servers, we build a global blockchain in the cloud service layer and local blockchain is built on the terminals of the Internet of things. Moreover, the regeneration coding is utilized to further improve the reliability of data storage in blockchains. 3) Our scheme provides a mechanism for periodically validating hash values of data to ensure the integrity of data stored in global blockchain.
When a Good Translation is Wrong in Context: Context-Aware Machine Translation Improves on Deixis, Ellipsis, and Lexical Cohesion

Though machine translation errors caused by the lack of context beyond one sentence have long been acknowledged, the development of context-aware NMT systems is hampered by several problems.Firstly, standard metrics are not sensitive to improvements in consistency in document-level translations.Secondly, previous work on context-aware NMT assumed that the sentence-aligned parallel data consisted of complete documents while in most practical scenarios such document-level data constitutes only a fraction of the available parallel data.To address the first issue, we perform a human study on an English-Russian subtitles dataset and identify deixis, ellipsis and lexical cohesion as three main sources of inconsistency.We then create test sets targeting these phenomena.To address the second shortcoming, we consider a set-up in which a much larger amount of sentence-level data is available compared to that aligned at the document level.We introduce a model that is suitable for this scenario and demonstrate major gains over a context-agnostic baseline on our new benchmarks without sacrificing performance as measured with BLEU. 1
STA: Spatial-Temporal Attention for Large-Scale Video-Based Person Re-Identification

In this work, we propose a novel Spatial-Temporal Attention (STA) approach to tackle the large-scale person re-identification task in videos. Different from the most existing methods, which simply compute representations of video clips using frame-level aggregation (e.g. average pooling), the proposed STA adopts a more effective way for producing robust clip-level feature representation. Concretely, our STA fully exploits those discriminative parts of one target person in both spatial and temporal dimensions, which results in a 2-D attention score matrix via inter-frame regularization to measure the importances of spatial parts across different frames. Thus, a more robust clip-level feature representation can be generated according to a weighted sum operation guided by the mined 2-D attention score matrix. In this way, the challenging cases for video-based person re-identification such as pose variation and partial occlusion can be well tackled by the STA. We conduct extensive experiments on two large-scale benchmarks, i.e. MARS and DukeMTMC-VideoReID. In particular, the mAP reaches 87.7% on MARS, which significantly outperforms the state-of-the-arts with a large margin of more than 11.6%.
Fast Learning of Temporal Action Proposal via Dense Boundary Generator

Generating temporal action proposals remains a very challenging problem, where the main issue lies in predicting precise temporal proposal boundaries and reliable action confidence in long and untrimmed real-world videos. In this paper, we propose an efficient and unified framework to generate temporal action proposals named Dense Boundary Generator (DBG), which draws inspiration from boundary-sensitive methods and implements boundary classification and action completeness regression for densely distributed proposals. In particular, the DBG consists of two modules: Temporal boundary classification (TBC) and Action-aware completeness regression (ACR). The TBC aims to provide two temporal boundary confidence maps by low-level two-stream features, while the ACR is designed to generate an action completeness score map by high-level action-aware features. Moreover, we introduce a dual stream BaseNet (DSB) to encode RGB and optical flow information, which helps to capture discriminative boundary and actionness features. Extensive experiments on popular benchmarks ActivityNet-1.3 and THUMOS14 demonstrate the superiority of DBG over the state-of-the-art proposal generator (e.g., MGG and BMN).
Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning

We propose a novel self-supervised method, referred to as Video Cloze Procedure (VCP), to learn rich spatial-temporal representations. VCP first generates “blanks” by withholding video clips and then creates “options” by applying spatio-temporal operations on the withheld clips. Finally, it fills the blanks with “options” and learns representations by predicting the categories of operations applied on the clips. VCP can act as either a proxy task or a target task in self-supervised learning. As a proxy task, it converts rich self-supervised representations into video clip operations (options), which enhances the flexibility and reduces the complexity of representation learning. As a target task, it can assess learned representation models in a uniform and interpretable manner. With VCP, we train spatial-temporal representation models (3D-CNNs) and apply such models on action recognition and video retrieval tasks. Experiments on commonly used benchmarks show that the trained models outperform the state-of-the-art self-supervised models with significant margins.
Cloudburst

Function-as-a-Service (FaaS) platforms and "serverless" cloud computing are becoming increasingly popular due to ease-of-use and operational simplicity. Current FaaS offerings are targeted at stateless functions that do minimal I/O and communication. We argue that the benefits of serverless computing can be extended to a broader range of applications and algorithms while maintaining the key benefits of existing FaaS offerings. We present the design and implementation of Cloudburst, a stateful FaaS platform that provides familiar Python programming with low-latency mutable state and communication, while maintaining the autoscaling benefits of serverless computing. Cloudburst accomplishes this by leveraging Anna, an autoscaling key-value store, for state sharing and overlay routing combined with mutable caches co-located with function executors for data locality. Performant cache consistency emerges as a key challenge in this architecture. To this end, Cloudburst provides a combination of lattice-encapsulated state and new definitions and protocols for distributed session consistency. Empirical results on benchmarks and diverse applications show that Cloudburst makes stateful functions practical, reducing the state-management overheads of current FaaS platforms by orders of magnitude while also improving the state of the art in serverless consistency.