Reviewerly/exHarmony huggingface dataset:
Metrological Nonlinear Squeezing Parameter

The well known metrological linear squeezing parameters (such as quadrature or spin squeezing) efficiently quantify the sensitivity of Gaussian states. Yet, these parameters are insufficient to characterize the much wider class of highly sensitive non-Gaussian states. Here, we introduce a class of metrological nonlinear squeezing parameters obtained by analytical optimization of measurement observables among a given set of accessible (possibly nonlinear) operators. This allows for the metrological characterization of non-Gaussian quantum states of discrete and continuous variables. Our results lead to optimized and experimentally-feasible recipes for high-precision moment-based estimation of a phase parameter and can be used to systematically construct multipartite entanglement and non-classicality witnesses for complex quantum states.
Convergence Rate Analysis of a Stochastic Trust-Region Method via Supermartingales

We propose a novel framework for analyzing convergence rates of stochastic optimization algorithms with adaptive step sizes. This framework is based on analyzing properties of an underlying generic stochastic process; in particular, we derive a bound on the expected stopping time of this process. We utilize this framework to analyze the expected global convergence rates of a stochastic variant of a traditional trust-region method. Although traditional trust-region methods rely on exact computations of the gradient, Hessian, and values of the objective function, this method assumes that these values are available only up to some dynamically adjusted accuracy. Moreover, this accuracy is assumed to hold only with some sufficiently large—but fixed—probability without any additional restrictions on the variance of the errors. This setting applies, for example, to standard stochastic optimization and machine learning formulations. Improving upon prior analysis, we show that the stochastic process defined by the trust-region method satisfies the assumptions of our proposed general framework. The stopping time in this setting is defined by an iterate satisfying a first-order accuracy condition. We demonstrate the first global complexity bound for a stochastic trust-region method under the assumption of sufficiently accurate stochastic gradients. Finally, we apply the same framework to derive second-order complexity bounds under additional assumptions.
Secure Online/Offline Data Sharing Framework for Cloud-Assisted Industrial Internet of Things

Ciphertext-policy attribute-based keyword search (CP-ABKS) schemes facilitate the fine-grained keyword search over encrypted data, such as those sensed/collected from Industrial Internet of Things (IIoT) devices and stored in the cloud. However, existing CP-ABKS schemes generally have significant computation and storage requirements, which are beyond those of resource-constrained IIoT devices. Therefore, in this paper, we design a secure online/offline data sharing framework (DSF), which supports online/offline encryption and outsourced decryption. Using the healthcare setting as a case study, we demonstrate how DSF can be deployed in the cloud-assisted Healthcare IIoT (HealthIIoT) system. We not only prove that the DSF is selectively secure in the chosen access structure security model but also demonstrate its efficiency and feasibility in practical scenarios using experiments.
Towards Understanding Linear Word Analogies

A surprising property of word vectors is that word analogies can often be solved with vector arithmetic.However, it is unclear why arithmetic operators correspond to non-linear embedding models such as skip-gram with negative sampling (SGNS).We provide a formal explanation of this phenomenon without making the strong assumptions that past theories have made about the vector space and word distribution.Our theory has several implications.Past work has conjectured that linear substructures exist in vector spaces because relations can be represented as ratios; we prove that this holds for SGNS.We provide novel justification for the addition of SGNS word vectors by showing that it automatically downweights the more frequent word, as weighting schemes do ad hoc.Lastly, we offer an information theoretic interpretation of Euclidean distance in vector spaces, justifying its use in capturing word dissimilarity.
Single image rain removal via a deep decomposition–composition network

Rain effect in images typically is annoying for many multimedia and computer vision tasks. For removing rain effect from a single image, deep leaning techniques have been attracting considerable attentions. This paper designs a novel multi-task leaning architecture in an end-to-end manner to reduce the mapping range from input to output and boost the performance. Concretely, a decomposition net is built to split rain images into clean background and rain layers. Different from previous architectures, our model consists of, besides a component representing the desired clean image, an extra component for the rain layer. During the training phase, we further employ a composition structure to reproduce the input by the separated clean image and rain information for improving the quality of decomposition. Experimental results on both synthetic and real images are conducted to reveal the high-quality recovery by our design, and show its superiority over other state-of-the-art methods. Furthermore, our design is also applicable to other layer decomposition tasks like dust removal. More importantly, our method only requires about 50ms to process a testing image in VGA resolution on a GTX 1080 GPU with promising rain removal quality, making it attractive for practical use. The synthesized dataset and code are publicly available at https://sites.google.com/view/xjguo/rain.
Using deep convolutional neural networks for multi-classification of thyroid tumor by histopathology: a large-scale pilot study

Background: To explore whether deep convolutional neural networks (DCNNs) have the potential to improve diagnostic efficiency and increase the level of interobserver agreement in the classification of thyroid nodules in histopathological slides. Methods: A total of 11,715 fragmented images from 806 patients’ original histological images were divided into a training dataset and a test dataset. Inception-ResNet-v2 and VGG-19 were trained using the training dataset and tested using the test dataset to determine the diagnostic efficiencies of different histologic types of thyroid nodules, including normal tissue, adenoma, nodular goiter, papillary thyroid carcinoma (PTC), follicular thyroid carcinoma (FTC), medullary thyroid carcinoma (MTC) and anaplastic thyroid carcinoma (ATC). Misdiagnoses were further analyzed. Results: The total 11,715 fragmented images were divided into a training dataset and a test dataset for each pathology type at a ratio of 5:1. Using the test set, VGG-19 yielded a better average diagnostic accuracy than did Inception-ResNet-v2 (97.34% vs. 94.42%, respectively). The VGG-19 model applied to 7 pathology types showed a fragmentation accuracy of 88.33% for normal tissue, 98.57% for ATC, 98.89% for FTC, 100% for MTC, 97.77% for PTC, 100% for nodular goiter and 92.44% for adenoma. It achieved excellent diagnostic efficiencies for all the malignant types. Normal tissue and adenoma were the most challenging histological types to classify. Conclusions: The DCNN models, especially VGG-19, achieved satisfactory accuracies on the task of differentiating thyroid tumors by histopathology. Analysis of the misdiagnosed cases revealed that normal tissue and adenoma were the most challenging histological types for the DCNN to differentiate, while all the malignant classifications achieved excellent diagnostic efficiencies. The results indicate that DCNN models may have potential for facilitating histopathologic thyroid disease diagnosis.
Celeb-DF: A New Dataset for DeepFake Forensics

AI-synthesized face swapping videos, commonly known as the DeepFakes, havebecome an emerging problem recently. Correspondingly, there is an increasinginterest in developing algorithms that can detect them. However, existingdataset of DeepFake videos suffer from low visual quality and abundantartifacts that do not reflect the reality of DeepFake videos circulated on theInternet. In this work, we present a new DeepFake dataset, Celeb-DF, for thedevelopment and evaluation of DeepFake detection algorithms. The Celeb-DFdataset is generated using a refined synthesis algorithm that reduces thevisual artifacts observed in existing datasets. Based on the Celeb-DF dataset,we also benchmark existing DeepFake detection algorithms.
Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning

Joint extraction of aspects and sentiments can be effectively formulated as a sequence labeling problem.However, such formulation hinders the effectiveness of supervised methods due to the lack of annotated sequence data in many domains.To address this issue, we firstly explore an unsupervised domain adaptation setting for this task.Prior work can only use common syntactic relations between aspect and opinion words to bridge the domain gaps, which highly relies on external linguistic resources.To resolve it, we propose a novel Selective Adversarial Learning (SAL) method to align the inferred correlation vectors that automatically capture their latent relations.The SAL method can dynamically learn an alignment weight for each word such that more important words can possess higher alignment weights to achieve finegrained (word-level) adaptation.Empirically, extensive experiments 1 demonstrate the effectiveness of the proposed SAL method.
Multi-model ensemble with rich spatial information for object detection

Due to the development of deep learning networks and big data dimensionality, research on ensemble deep learning is receiving an increasing amount of attention. This paper takes the object detection task as the research domain and proposes an object detection framework based on ensemble deep learning. To guarantee the accuracy as well as real-time detection, the detector uses a Single Shot MultiBox Detector (SSD) as the backbone and combines ensemble learning with context modeling and multi-scale feature representation. Two modes were designed in order to achieve ensemble learning: NMS Ensembling and Feature Ensembling. In addition, to obtain contextual information, we used dilated convolution to expand the receptive field of the network. Compared with state-of-the-art detectors, our detector achieves superior performance on the PASCAL VOC set and the MS COCO set.
Cross-Entropy Adversarial View Adaptation for Person Re-identification

Person re-identification (re-ID) is a task of matching pedestrians under disjoint camera views. To recognize paired snapshots, it has to cope with large cross-view variations caused by the camera view shift. The supervised deep neural networks are effective in producing a set of non-linear projections that can transform cross-view images into a common feature space. However, they typically impose a symmetric architecture, leaving the network ill-conditioned on its optimization. In this paper, we learn view-invariant subspace for person re-ID, and its corresponding similarity metric using an adversarial view adaptation approach. The main contribution is to learn coupled asymmetric mappings regarding view characteristics which are adversarially trained to address the view discrepancy by optimizing the cross-entropy view confusion objective. To determine the similarity value, the network is empowered with a similarity discriminator to promote features that are highly discriminant in distinguishing positive and negative pairs. The other contribution includes an adaptive weighing on the most difficult samples to address the imbalance of within-/between-identity pairs. Our approach achieves notably improved performance in comparison with the state-of-the-arts on benchmark datasets.
Blockchain-Based Dynamic Provable Data Possession for Smart Cities

Smart cities have experienced rapid development with the advances of information and communication technologies such as Internet of Things (IoT). To tackle the data storage issues raised by large-scale data generated by IoT devices, an increasing number of enterprises and individuals prefer to outsource their data to cloud, where data integrity becomes a concern to cloud users. A variety of provable data possession (PDP) protocols have been proposed for centralized cloud storage scenarios so far. However, a centralized cloud relies too much on the trust of the central servers and, thus, is prone to the single point of failure. In this article, we describe a blockchain-based PDP model to realize the decentralized outsourcing storage framework, and then present a concrete construction of decentralized PDP by using multireplica storage tricks. Moreover, our protocol provides dynamic operations for outsourced data and at the same time, guarantees the fairness of all parties involved. We provide a detailed security proof for the proposed protocol and deploy a smart contract for the protocols as well. We finally evaluate the algorithms and the implementation results demonstrate the practicability of the proposed protocol.
Learning Transferable Adversarial Examples via Ghost Networks

Recent development of adversarial attacks has proven that ensemble-based methods outperform traditional, non-ensemble ones in black-box attack. However, as it is computationally prohibitive to acquire a family of diverse models, these methods achieve inferior performance constrained by the limited number of models to be ensembled.In this paper, we propose Ghost Networks to improve the transferability of adversarial examples. The critical principle of ghost networks is to apply feature-level perturbations to an existing model to potentially create a huge set of diverse models. After that, models are subsequently fused by longitudinal ensemble. Extensive experimental results suggest that the number of networks is essential for improving the transferability of adversarial examples, but it is less necessary to independently train different networks and ensemble them in an intensive aggregation way. Instead, our work can be used as a computationally cheap and easily applied plug-in to improve adversarial approaches both in single-model and multi-model attack, compatible with residual and non-residual networks. By reproducing the NeurIPS 2017 adversarial competition, our method outperforms the No.1 attack submission by a large margin, demonstrating its effectiveness and efficiency. Code is available at https://github.com/LiYingwei/ghost-network.
Sending-or-not-sending twin-field quantum key distribution: Breaking the direct transmission key rate

We present improved results of sending-or-not-sending twin-field quantum key distribution by using error rejection through two-way classical communications. Our error rejection method, especially our method of actively odd-parity pairing (AOPP) can drastically improve the performance of sending-or-not-sending twin-field protocol in both secure distance and key rate. Taking a typical experimental parameter setting, our method here improves the secure distance by 70 km to more than 100 km in comparison with the prior art results. Comparative study also shows advantageous in key rates at regime of long distance and large misalignment error rate for our method here. The numerical results show that our method here can significantly exceed the absolute limit of direct transmission key rate, and also have an advantageous key rates higher than various prior art results by 10 to 20 times.
A Prediction-Based Approach to Distributed Filtering With Missing Measurements and Communication Delays Through Sensor Networks

This article addresses the prediction-based distributed filtering problem for a class of time-varying nonlinear stochastic systems with communication delays and missing measurements through the sensor networks. The phenomenon of the missing measurements is depicted by a set of Bernoulli distributed random variables, where each sensor node possesses its own missing probability. The communication delays are taken into account, which commonly occur during the estimation exchanges among the sensor nodes with communication links. A new prediction-based suboptimal distributed filter is designed by taking the missing probabilities and the prediction estimation into account, which has the advantages on the active compensation of the impacts caused by the missing measurements and communication delays. That is, a new compensation filtering method within the time-varying framework is presented based on the predictive estimation and the innovation measurements. A locally minimum upper bound matrix for the estimation error covariance is obtained by properly designing the distributed filter gain at every sampling step. Furthermore, the performance analysis problem of the prediction-based distributed filtering algorithm is discussed by providing the desirable theoretical derivations. Finally, some comparative simulations are used to show the advantages of the presented prediction-based distributed filtering strategy under delay compensation mechanism.
MRI Cross-Modality Image-to-Image Translation

We present a cross-modality generation framework that learns to generate translated modalities from given modalities in MR images. Our proposed method performs Image Modality Translation (abbreviated as IMT) by means of a deep learning model that leverages conditional generative adversarial networks (cGANs). Our framework jointly exploits the low-level features (pixel-wise information) and high-level representations (e.g. brain tumors, brain structure like gray matter, etc.) between cross modalities which are important for resolving the challenging complexity in brain structures. Our framework can serve as an auxiliary method in medical use and has great application potential. Based on our proposed framework, we first propose a method for cross-modality registration by fusing the deformation fields to adopt the cross-modality information from translated modalities. Second, we propose an approach for MRI segmentation, translated multichannel segmentation (TMS), where given modalities, along with translated modalities, are segmented by fully convolutional networks (FCN) in a multichannel manner. Both of these two methods successfully adopt the cross-modality information to improve the performance without adding any extra data. Experiments demonstrate that our proposed framework advances the state-of-the-art on five brain MRI datasets. We also observe encouraging results in cross-modality registration and segmentation on some widely adopted brain datasets. Overall, our work can serve as an auxiliary method in medical use and be applied to various tasks in medical fields.
Dual Learning for Explainable Recommendation: Towards Unifying User Preference Prediction and Review Generation

In many recommender systems, users express item opinions through two kinds of behaviors: giving preferences and writing detailed reviews. As both kinds of behaviors reflect users’ assessment of items, review enhanced recommender systems leverage these two kinds of user behaviors to boost recommendation performance. On the one hand, researchers proposed to better model the user and item embeddings with additional review information for enhancing preference prediction accuracy. On the other hand, some recent works focused on automatically generating item reviews for recommendation explanations with related user and item embeddings. We argue that, while the task of preference prediction with the accuracy goal is well recognized in the community, the task of generating reviews for explainable recommendation is also important to gain user trust and increase conversion rate. Some preliminary attempts have considered jointly modeling these two tasks, with the user and item embeddings are shared. These studies empirically showed that these two tasks are correlated, and jointly modeling them would benefit the performance of both tasks.
Polaritonic Neuromorphic Computing Outperforms Linear Classifiers

Machine learning software applications are nowadays ubiquitous in many fields of science and society for their outstanding capability of solving computationally vast problems like the recognition of patterns and regularities in big datasets.One of the main goals of research is the realization of a physical neural network able to perform data processing in a much faster and energyefficient way than the state-of-the-art technology.Here we show that lattices of exciton-polariton condensates accomplish neuromorphic computing using fast optical nonlinearities and with lower error rate than any previous hardware implementation.We demonstrate that our neural network significantly increases the recognition efficiency compared to the linear classification algorithms on one of the most widely used benchmarks, the MNIST problem, showing a concrete advantage from the integration of optical systems in reservoir computing architectures.
Deep features for person re-identification on metric learning

Person re-identification, a branch of image retrieval, is an increasingly important public safety application. When monitoring larger areas, it is crucial to correctly match the same person in different camera views. With the emergence of deep learning and large-scale data, metric learning has significantly improved person re-identification performance, but the extent to which deep features affect metric learning performance is unknown. However, given the large number of approaches, datasets, evaluation indices, and experimental environments, comparing metric learning methods directly is difficult. To obtain a more comprehensive empirical evaluation of the person re-identification, here we summarize the different types of features and metric learning approaches from a label attributes perspective. Then, by combining advanced approaches to data enhancement and feature extraction, we conduct comprehensive experiments on metric learning methods with two datasets. For fairness, all methods use a unified code library that includes two data enhancement schemes, eight feature extraction algorithms, and eight metric learning methods. Our results show that, the relations of loss function with deep feature space and metric learning.
Faster-YOLO: An accurate and faster object detection method

In the computer vision, object detection has always been considered one of the most challenging issues because it requires classifying and locating objects in the same scene. Many object detection approaches were recently proposed based on deep convolutional neural networks (DCNNs), which have been demonstrated to achieve outstanding object detection performance compared to other approaches. However, the supervised training of DCNNs mostly uses gradient-based optimization criteria, in which all parameters of hidden layers require multiple iterations, and often faces some problems such as local minima, intensive human intervention, time-consuming, etc. In this paper, we propose a new method called Faster-YOLO, which is able to perform real-time object detection. The deep random kernel convolutional extreme learning machine (DRKCELM) and double hidden layer extreme learning machine auto-encoder (DLELM-AE) joint network is used as a feature extractor for object detection, which integrating the advantages of ELM-LRF and ELM-AE. It takes the raw images directly as input and thus is suitable for the different datasets. In addition, most connection weights are randomly generated, so there are few parameter settings and training speed is faster. The experiment results on Pascal VOC dataset show that Faster-YOLO improves the detection accuracy effectively by 1.1 percentage points compared to the original YOLOv2, and an average 2X speedup compared to YOLOv3.