Reviewerly/exHarmony huggingface dataset:
Style Normalization and Restitution for Generalizable Person Re-Identification

Existing fully-supervised person re-identification (ReID) methods usually suffer from poor generalization capability caused by domain gaps. The key to solving this problem lies in filtering out identity-irrelevant interference and learning domain-invariant person representations. In this paper, we aim to design a generalizable person ReID framework which trains a model on source domains yet is able to generalize/perform well on target domains. To achieve this goal, we propose a simple yet effective Style Normalization and Restitution (SNR) module. Specifically, we filter out style variations (e.g., illumination, color contrast) by Instance Normalization (IN). However, such a process inevitably removes discriminative information. We propose to distill identity-relevant feature from the removed information and restitute it to the network to ensure high discrimination. For better disentanglement, we enforce a dual causal loss constraint in SNR to encourage the separation of identity-relevant features and identity-irrelevant features. Extensive experiments demonstrate the strong generalization capability of our framework. Our models empowered by the SNR modules significantly outperform the state-of-the-art domain generalization approaches on multiple widely-used person ReID benchmarks, and also show superiority on unsupervised domain adaptation.
Sending-or-Not-Sending with Independent Lasers: Secure Twin-Field Quantum Key Distribution over 509 km

Twin field quantum key distribution promises high key rates at long distance to beat the rate distance limit. Here, applying the sending or not sending TF QKD protocol, we experimentally demonstrate a secure key distribution breaking the absolute key rate limit of repeaterless QKD over 509 km, 408 km ultra-low loss optical fibre and 350 km standard optical fibre. Two independent lasers are used as the source with remote frequency locking technique over 500 km fiber distance; Practical optical fibers are used as the optical path with appropriate noise filtering; And finite key effects are considered in the key rate analysis. The secure key rates obtained at different distances are more than 5 times higher than the conditional limit of repeaterless QKD, a bound value assuming the same detection loss in the comparison. The achieved secure key rate is also higher than that a traditional QKD protocol running with a perfect repeaterless QKD device and even if an infinite number of sent pulses. Our result shows that the protocol and technologies applied in this experiment enable TF QKD to achieve high secure key rate at long distribution distance, and hence practically useful for field implementation of intercity QKD.
CARAFE: Content-Aware ReAssembly of FEatures

Feature upsampling is a key operation in a number of modern convolutional network architectures, e.g. feature pyramids. Its design is critical for dense prediction tasks such as object detection and semantic/instance segmentation. In this work, we propose Content-Aware ReAssembly of FEatures (CARAFE), a universal, lightweight and highly effective operator to fulfill this goal. CARAFE has several appealing properties: (1) Large field of view. Unlike previous works (e.g. bilinear interpolation) that only exploit subpixel neighborhood, CARAFE can aggregate contextual information within a large receptive field. (2) Content-aware handling. Instead of using a fixed kernel for all samples (e.g. deconvolution), CARAFE enables instance-specific content-aware handling, which generates adaptive kernels on-the-fly. (3) Lightweight and fast to compute. CARAFE introduces little computational overhead and can be readily integrated into modern network architectures. We conduct comprehensive evaluations on standard benchmarks in object detection, instance/semantic segmentation and inpainting. CARAFE shows consistent and substantial gains across all the tasks (1.2% AP, 1.3% AP, 1.8% mIoU, 1.1dB respectively) with negligible computational overhead. It has great potential to serve as a strong building block for future research. Code and models are available at https://github.com/open-mmlab/mmdetection.
MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance

A robust evaluation metric has a profound impact on the development of text generation systems.A desirable metric compares system output against references based on their semantics rather than surface forms.In this paper we investigate strategies to encode system and reference texts to devise a metric that shows a high correlation with human judgment of text quality.We validate our new metric, namely MoverScore, on a number of text generation tasks including summarization, machine translation, image captioning, and data-to-text generation, where the outputs are produced by a variety of neural and non-neural systems.Our findings suggest that metrics combining contextualized representations with a distance measure perform the best.Such metrics also demonstrate strong generalization capability across tasks.For ease-of-use we make our metrics available as web service. 1
Food Safety Traceability System Based on Blockchain and EPCIS

In recent years, food safety issues have drawn growing concerns from society.In order to efficiently detect and prevent food safety problems and trace the accountability, building a reliable traceability system is indispensable.It is especially essential to accurately record, share, and trace the specific data within the whole food supply chain, including the process of production, processing, warehousing, transportation, and retail.The traditional traceability systems have issues, such as data invisibility, tampering, and sensitive information disclosure.The blockchain is a promising technology for the food safety traceability system because of the characteristics, such as the irreversible time vector, smart contract, and consensus algorithm.This paper proposes a food safety traceability system based on the blockchain and the EPC Information Services and develops a prototype system.The management architecture of on-chain & off-chain data is proposed as well, through which the traceability system can alleviate the data explosion issue of the blockchain for the Internet of Things.Furthermore, the enterprise-level smart contract is designed to prevent data tampering and sensitive information disclosure during information interaction among participants.The prototype system was implemented based on the Ethereum.According to the test results, the average time of information query response is around 2 ms, while the amount of on-chain data and query counts are 1 GB and 1000 times/s, respectively.
CNN-RNN Based Intelligent Recommendation for Online Medical Pre-Diagnosis Support

The rapidly developed Health 2.0 technology has provided people with more opportunities to conduct online medical consultation than ever before. Understanding contexts within different online medical communications and activities becomes a significant issue to facilitate patients' medical decision making process. As a subcategory of machine learning, neural networks have drawn increasing attentions in natural language processing applications. In this article, we focus on modeling and analyzing the patient-physician-generated data based on an integrated CNN-RNN framework, in order to deal with the situation that patients' online inquiries are usually not very long. A so-called DP-CRNN algorithm is developed with a newly designed neural network structure, to extract and highlight the combination of semantic and sequential features in terms of patient's inquiries. An intelligent recommendation method is then proposed to provide patients with automatic clinic guidance and pre-diagnosis suggestions, in which a clustering mechanism is utilized to refine the learning process with more precise diagnosis scope and more representative features. Experiments based on the collected real world data demonstrate the effectiveness of our proposed model and method for intelligent pre-diagnosis service in online medical environments.
Demonstration of the trapped-ion quantum CCD computer architecture

The trapped-ion quantum charge-coupled device (QCCD) proposal1,2 lays out a blueprint for a universal quantum computer that uses mobile ions as qubits. Analogous to a charge-coupled device (CCD) camera, which stores and processes imaging information as movable electrical charges in coupled pixels, a QCCD computer stores quantum information in the internal state of electrically charged ions that are transported between different processing zones using dynamic electric fields. The promise of the QCCD architecture is to maintain the low error rates demonstrated in small trapped-ion experiments3-5 by limiting the quantum interactions to multiple small ion crystals, then physically splitting and rearranging the constituent ions of these crystals into new crystals, where further interactions occur. This approach leverages transport timescales that are fast relative to the coherence times of the qubits, the insensitivity of the qubit states of the ion to the electric fields used for transport, and the low crosstalk afforded by spatially separated crystals. However, engineering a machine capable of executing these operations across multiple interaction zones with low error introduces many difficulties, which have slowed progress in scaling this architecture to larger qubit numbers. Here we use a cryogenic surface trap to integrate all necessary elements of the QCCD architecture-a scalable trap design, parallel interaction zones and fast ion transport-into a programmable trapped-ion quantum computer that has a system performance consistent with the low error rates achieved in the individual ion crystals. We apply this approach to realize a teleported CNOT gate using mid-circuit measurement6, negligible crosstalk error and a quantum volume7 of 26 = 64. These results demonstrate that the QCCD architecture provides a viable path towards high-performance quantum computers.
3D Morphable Face Models—Past, Present, and Future

In this article, we provide a detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed. The challenges in building and applying these models, namely, capture, modeling, image formation, and image analysis, are still active research topics, and we review the state-of-the-art in each of these areas. We also look ahead, identifying unsolved challenges, proposing directions for future research, and highlighting the broad range of current and future applications.
Generation of multicomponent atomic Schrödinger cat states of up to 20 qubits

Entanglement goes large The success of quantum computing relies on the ability to entangle large-scale systems. Various platforms are being pursued, with architectures based on superconducting qubits and trapped atoms being the most advanced. By entangling up to 20 qubits, Omran et al. and Song et al. —working with Rydberg atom qubits and superconducting qubits, respectively—demonstrate how far these platforms have reached. The demonstrated controllable generation and detection of entanglement on such quantum systems is promising for the development of large-scale quantum processors. Science , this issue p. 570 , p. 574
Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings

Knowledge Graphs (KG) are multi-relational graphs consisting of entities as nodes and relations among them as typed edges.Goal of the Question Answering over KG (KGQA) task is to answer natural language queries posed over the KG.Multi-hop KGQA requires reasoning over multiple edges of the KG to arrive at the right answer.KGs are often incomplete with many missing links, posing additional challenges for KGQA, especially for multi-hop KGQA.Recent research on multihop KGQA has attempted to handle KG sparsity using relevant external text, which isn't always readily available.In a separate line of research, KG embedding methods have been proposed to reduce KG sparsity by performing missing link prediction.Such KG embedding methods, even though highly relevant, have not been explored for multi-hop KGQA so far.We fill this gap in this paper and propose EmbedKGQA.EmbedKGQA is particularly effective in performing multi-hop KGQA over sparse KGs.EmbedKGQA also relaxes the requirement of answer selection from a prespecified neighborhood, a sub-optimal constraint enforced by previous multi-hop KGQA methods.Through extensive experiments on multiple benchmark datasets, we demonstrate EmbedKGQA's effectiveness over other stateof-the-art baselines.
Deep Multimodal Representation Learning: A Survey

Multimodal representation learning, which aims to narrow the heterogeneity gap among different modalities, plays an indispensable role in the utilization of ubiquitous multimodal data. Due to the powerful representation ability with multiple levels of abstraction, deep learning-based multimodal representation learning has attracted much attention in recent years. In this paper, we provided a comprehensive survey on deep multimodal representation learning which has never been concentrated entirely. To facilitate the discussion on how the heterogeneity gap is narrowed, according to the underlying structures in which different modalities are integrated, we category deep multimodal representation learning methods into three frameworks: joint representation, coordinated representation, and encoder-decoder. Additionally, we review some typical models in this area ranging from conventional models to newly developed technologies. This paper highlights on the key issues of newly developed technologies, such as encoder-decoder model, generative adversarial networks, and attention mechanism in a multimodal representation learning perspective, which, to the best of our knowledge, have never been reviewed previously, even though they have become the major focuses of much contemporary research. For each framework or model, we discuss its basic structure, learning objective, application scenes, key issues, advantages, and disadvantages, such that both novel and experienced researchers can benefit from this survey. Finally, we suggest some important directions for future work.
When Blockchain Meets Supply Chain: A Systematic Literature Review on Current Development and Potential Applications

This study aims to explore the current status, potential applications, and future directions of blockchain technology in supply chain management. A literature survey, along with an analytical review, of blockchain-based supply chain research was conducted to better understand the trajectory of related research and shed light on the benefits, issues, and challenges in the blockchain-supply-chain paradigm. A selected corpus comprising 106 review articles was analyzed to provide an overview of the use of blockchain and smart contracts in supply chain management. The diverse industrial applications of these technologies in various sectors have increasingly received attention by researchers, engineers, and practitioners. Four major issues: traceability and transparency, stakeholder involvement and collaboration, supply chain integration and digitalization, and common frameworks on blockchain-based platforms, are critical for future orientation. Traditional supply chain activities involve several intermediaries, trust, and performance issues. The potential of blockchain can be leveraged to disrupt supply chain operations for better performance, distributed governance, and process automation. This study contributes to the comprehension of blockchain applications in supply chain management and provides a blueprint for these applications from the perspective of literature analysis. Future efforts regarding technical adoption/diffusion, block-supply chain integration, and their social impacts were highlighted to enrich the research scope.
Robust Graph Convolutional Networks Against Adversarial Attacks

Graph Convolutional Networks (GCNs) are an emerging type of neural network model on graphs which have achieved state-of-the-art performance in the task of node classification. However, recent studies show that GCNs are vulnerable to adversarial attacks, i.e. small deliberate perturbations in graph structures and node attributes, which poses great challenges for applying GCNs to real world applications. How to enhance the robustness of GCNs remains a critical open problem. To address this problem, we propose Robust GCN (RGCN), a novel model that "fortifies'' GCNs against adversarial attacks. Specifically, instead of representing nodes as vectors, our method adopts Gaussian distributions as the hidden representations of nodes in each convolutional layer. In this way, when the graph is attacked, our model can automatically absorb the effects of adversarial changes in the variances of the Gaussian distributions. Moreover, to remedy the propagation of adversarial attacks in GCNs, we propose a variance-based attention mechanism, i.e. assigning different weights to node neighborhoods according to their variances when performing convolutions. Extensive experimental results demonstrate that our proposed method can effectively improve the robustness of GCNs. On three benchmark graphs, our RGCN consistently shows a substantial gain in node classification accuracy compared with state-of-the-art GCNs against various adversarial attack strategies.
Blockchain for smart communities: Applications, challenges and opportunities

Since the success of Bitcoin, there have been increased focus of studying the application of blockchain in a broad range of applications, such as in solutions facilitating identity privacy and transaction security using a decentralized architecture via different consensus mechanisms (e.g. proof-of-work) between different geo-located IoT devices/nodes in our increasingly digitalized society (e.g. smart city). In this paper, we survey the usage of blockchain technology for smart communities, focusing on key components of the blockchain applications. We also study the various process models used in the execution of secure transactions. Specifically, we present a detailed taxonomy on the applications, process models used, and communication infrastructure support needed to execute various applications.
Blockchain for Large-Scale Internet of Things Data Storage and Protection

With the dramatically increasing deployment of IoT devices, storing and protecting the large volume of IoT data has become a significant issue. Traditional cloud-based IoT structures impose extremely high computation and storage demands on the cloud servers. Meanwhile, the strong dependencies on the centralized servers bring significant trust issues. To mitigate these problems, we propose a distributed data storage scheme employing blockchain and cetrificateless cryptography. Our scheme eliminates the traditional centralized servers by leveraging the blockchain miners who perform “transaction” verifications and records audit with the help of certificateless cryptography. We present a clear definition of the transactions in a non-cryptocurrency system and illustrate how the transactions are processed. To the best of our knowledge, this is the first work designing a secure and accountable IoT storage system using blockchain. Additionally, we extend our scheme to enable data trading and elaborate how data trading can be efficiently and effectively achieved.
A study of graph-based system for multi-view clustering

This paper studies clustering of multi-view data, known as multi-view clustering. Among existing multi-view clustering methods, one representative category of methods is the graph-based approach. Despite its elegant and simple formulation, the graph-based approach has not been studied in terms of (a) the generalization of the approach or (b) the impact of different graph metrics on the clustering results. This paper extends this important approach by first proposing a general Graph-Based System (GBS) for multi-view clustering, and then discussing and evaluating the impact of different graph metrics on the multi-view clustering performance within the proposed framework. GBS works by extracting data feature matrix of each view, constructing graph matrices of all views, and fusing the constructed graph matrices to generate a unified graph matrix, which gives the final clusters. A novel multi-view clustering method that works in the GBS framework is also proposed, which can (1) construct data graph matrices effectively, (2) weight each graph matrix automatically, and (3) produce clustering results directly. Experimental results on benchmark datasets show that the proposed method outperforms state-of-the-art baselines significantly.
Impact of fully connected layers on performance of convolutional neural networks for image classification

The Convolutional Neural Networks (CNNs), in domains like computer vision, mostly reduced the need for handcrafted features due to its ability to learn the problem-specific features from the raw input data. However, the selection of dataset-specific CNN architecture, which mostly performed by either experience or expertise is a time-consuming and error-prone process. To automate the process of learning a CNN architecture, this paper attempts at finding the relationship between Fully Connected (FC) layers with some of the characteristics of the datasets. The CNN architectures, and recently datasets also, are categorized as deep, shallow, wide, etc. This paper tries to formalize these terms along with answering the following questions. (i) What is the impact of deeper/shallow architectures on the performance of the CNN w.r.t. FC layers?, (ii) How the deeper/wider datasets influence the performance of CNN w.r.t. FC layers?, and (iii) Which kind of architecture (deeper/shallower) is better suitable for which kind of (deeper/wider) datasets. To address these findings, we have performed experiments with four CNN architectures having different depths. The experiments are conducted by varying the number of FC layers. We used four widely used datasets including CIFAR-10, CIFAR-100, Tiny ImageNet, and CRCHistoPhenotypes to justify our findings in the context of image classification problem. The source code of this work is available at https://github.com/shabbeersh/Impact-of-FC-layers.
SoftTriple Loss: Deep Metric Learning Without Triplet Sampling

Distance metric learning (DML) is to learn the embeddings where examples from the same class are closer than examples from different classes. It can be cast as an optimization problem with triplet constraints. Due to the vast number of triplet constraints, a sampling strategy is essential for DML. With the tremendous success of deep learning in classifications, it has been applied for DML. When learning embeddings with deep neural networks (DNNs), only a mini-batch of data is available at each iteration. The set of triplet constraints has to be sampled within the mini-batch. Since a mini-batch cannot capture the neighbors in the original set well, it makes the learned embeddings sub-optimal. On the contrary, optimizing SoftMax loss, which is a classification loss, with DNN shows a superior performance in certain DML tasks. It inspires us to investigate the formulation of SoftMax. Our analysis shows that SoftMax loss is equivalent to a smoothed triplet loss where each class has a single center. In real-world data, one class can contain several local clusters rather than a single one, e.g., birds of different poses. Therefore, we propose the SoftTriple loss to extend the SoftMax loss with multiple centers for each class. Compared with conventional deep metric learning algorithms, optimizing SoftTriple loss can learn the embeddings without the sampling phase by mildly increasing the size of the last fully connected layer. Experiments on the benchmark fine-grained data sets demonstrate the effectiveness of the proposed loss function.