arXiv subjects

Li Yang

Publications and source records attributed to Li Yang.

At least 199 records · Page 11Linked to original sources

arXiv2021

MAVE: A Product Dataset for Multi-source Attribute Value Extraction

Attribute value extraction refers to the task of identifying values of an attribute of interest from product information. Product attribute values are essential in many e-commerce scenarios, such as customer service robots, product ranking, retrieval and recommendations. While in the real world, the attribute values of a product are usually incomplete and vary over time, which greatly hinders the practical applications. In this paper, we introduce MAVE, a new dataset to better facilitate research on product attribute value extraction. MAVE is composed of a curated set of 2.2 million products from Amazon pages, with 3 million attribute-value annotations across 1257 unique categories. MAVE has four main and unique advantages: First, MAVE is the largest product attribute value extraction dataset by the number of attribute-value examples. Second, MAVE includes multi-source representations from the product, which captures the full product information with high attribute coverage. Third, MAVE represents a more diverse set of attributes and values relative to what previous datasets cover. Lastly, MAVE provides a very challenging zero-shot test set, as we empirically illustrate in the experiments. We further propose a novel approach that effectively extracts the attribute value from the multi-source product information. We conduct extensive experiments with several baselines and show that MAVE is an effective dataset for attribute value extraction task. It is also a very challenging task on zero-shot attribute extraction. Data is available at {\it \url{https://github.com/google-research-datasets/MAVE}}.

cs.CL↗

arXiv2021

On the Dilution of Precision for Time Difference of Arrival with Station Deployment

The paper aims to reveal the relationship between the performance of moving object tracking algorithms and the tracking anchors (station) deployment. The Dilution of Precision (DoP) for Time difference of arrival (TDoA) technique with respect to anchor deployment is studied. Linear estimator and non-linear estimator are used for TDoA algorithms. The research findings are: for the linear estimator, the DoP attain a lower value when other anchors are scattered around a central anchor; for the nonlinear estimator, the DoP is optimal when the anchors are scattered around the target tag. Experiments on both of the algorithms are conducted, targeting the location precision related to the anchors' deployment, with practical situations for tracking moving objects integrated with a Kalman Filter (KF) in an Ultra-Wide Band (UWB) based real-time localization system. The work provides a guideline for deploying anchors in UWB-based tracking systems.

eess.SP↗

arXiv2021

Design and Implementation of Real-Time Localization System (RTLS) based on UWB and TDoA Algorithm

Nowadays, accurate localization plays an essential role in many fields, like target tracking and path planning. The challenges of indoor localization include inadequate localization accuracy, unreasonable anchor deployment in complex scenarios, lack of stability, and high cost. So the universal positioning technologies cannot meet the real application requirements scarcely. To overcome these shortcomings, a comprehensive Ultra Wide-Band (UWB) based RTLS is presented in this paper. We first introduce the architecture of the real-time localization system, then propose a new wireless clock synchronization (WCS) scheme, finally discuss the time difference of arrival (TDoA) algorithm. We define the time-base selection strategy for the TDoA algorithm, and analyze the relationship between anchor deployment and positioning accuracy. The Extended Kalman Filter (EKF) method is presented for non-linear dynamic localization estimation, and it performs well in terms of stability and accuracy in moving targets.

eess.SY↗

arXiv2021

The compressibility of quark matter under strong magnetic field in the NJL model

The compressibility of magnetized quark matter is investigated in the SU(2) NJL model. The increases of the chemical potential and the temperature can reduce the compressibility, and lead to the much stiffer equation of state. The variation of the compressibility with the magnetic field will depend on the phase region. Due to the anisotropic structure, the compressibility is different in the directions parallel and perpendicular to the field. The discontinuity of longitudinal compressibility with the chemical potential and the temperature captures the signature of a first-order chiral phase transition and the crossover at high temperature. Moreover, the magnetic-field-and-temperature running coupling would have an important effect on the position of the phase transition. Under the lowest landau level approximation at zero temperature, the longitudinal compressibility has a direct inverse proportional relation to the magnetic field strength and the chemical potential square as $κ^\parallel_{\mathrm{LLL},χ}\propto1/(eB μ^2)$.

hep-ph↗

arXiv2021

Blind VQA on 360° Video via Progressively Learning from Pixels, Frames and Video

Blind visual quality assessment (BVQA) on 360{\textdegree} video plays a key role in optimizing immersive multimedia systems. When assessing the quality of 360{\textdegree} video, human tends to perceive its quality degradation from the viewport-based spatial distortion of each spherical frame to motion artifact across adjacent frames, ending with the video-level quality score, i.e., a progressive quality assessment paradigm. However, the existing BVQA approaches for 360{\textdegree} video neglect this paradigm. In this paper, we take into account the progressive paradigm of human perception towards spherical video quality, and thus propose a novel BVQA approach (namely ProVQA) for 360{\textdegree} video via progressively learning from pixels, frames and video. Corresponding to the progressive learning of pixels, frames and video, three sub-nets are designed in our ProVQA approach, i.e., the spherical perception aware quality prediction (SPAQ), motion perception aware quality prediction (MPAQ) and multi-frame temporal non-local (MFTN) sub-nets. The SPAQ sub-net first models the spatial quality degradation based on spherical perception mechanism of human. Then, by exploiting motion cues across adjacent frames, the MPAQ sub-net properly incorporates motion contextual information for quality assessment on 360{\textdegree} video. Finally, the MFTN sub-net aggregates multi-frame quality degradation to yield the final quality score, via exploring long-term quality correlation from multiple frames. The experiments validate that our approach significantly advances the state-of-the-art BVQA performance on 360{\textdegree} video over two datasets, the code of which has been public in \url{https://github.com/yanglixiaoshen/ProVQA.}

cs.CV↗

arXiv2021

Quantum Miss-in-the-Middle Attack

Traditional cryptography is facing great challenges with the development of quantum computing. Not only public-key cryptography, the applications of quantum algorithms to symmetric cryptanalysis has also drawn more and more attention. In this paper, we apply quantum algorithms to the miss-in-the-middle technique and propose a quantum algorithm for finding impossible differentials of general block ciphers. We prove that, as long as the attacked block cipher satisfies certain algebraic conditions, the outputs of the quantum algorithm will be impossible differentials of it except for a negligible probability. The proposed quantum algorithm has polynomial quantum complexity and does not require any quantum or classical query to the encryption oracle of the block cipher. Compared with traditional miss-in-the-middle technique, which is difficult to find impossible differentials as the number of rounds increases, the quantum version of miss-in-the-middle technique proposed in this paper is more conducive to find impossible differentials when the block cipher has a large number of rounds.

quant-ph↗

arXiv2021

$DA^3$:Dynamic Additive Attention Adaption for Memory-EfficientOn-Device Multi-Domain Learning

Nowadays, one practical limitation of deep neural network (DNN) is its high degree of specialization to a single task or domain (e.g., one visual domain). It motivates researchers to develop algorithms that can adapt DNN model to multiple domains sequentially, while still performing well on the past domains, which is known as multi-domain learning. Almost all conventional methods only focus on improving accuracy with minimal parameter update, while ignoring high computing and memory cost during training, which makes it difficult to deploy multi-domain learning into more and more widely used resource-limited edge devices, like mobile phones, IoT, embedded systems, etc. We observe that large memory used for activation storage is the bottleneck that largely limits the training time and cost on edge devices. To reduce training memory usage, while keeping the domain adaption accuracy performance, we propose Dynamic Additive Attention Adaption ($DA^3$), a novel memory-efficient on-device multi-domain learning method. $DA^3$ learns a novel additive attention adaptor module, while freezing the weights of the pre-trained backbone model for each domain. Differentiating from prior works, such module not only mitigates activation memory buffering for reducing memory usage during training but also serves as a dynamic gating mechanism to reduce the computation cost for fast inference. We validate $DA^3$ on multiple datasets against state-of-the-art methods, which shows great improvement in both accuracy and training time. Moreover, we deployed $DA^3$ into the popular NIVDIA Jetson Nano edge GPU, where the measured experimental results show our proposed $DA^3$ reduces the on-device training memory consumption by 19-37x, and training time by 2x, in comparison to the baseline methods (e.g., standard fine-tuning, Parallel and Series Res. adaptor, and Piggyback).

cs.CV↗

arXiv2021

GROWN: GRow Only When Necessary for Continual Learning

Catastrophic forgetting is a notorious issue in deep learning, referring to the fact that Deep Neural Networks (DNN) could forget the knowledge about earlier tasks when learning new tasks. To address this issue, continual learning has been developed to learn new tasks sequentially and perform knowledge transfer from the old tasks to the new ones without forgetting. While recent structure-based learning methods show the capability of alleviating the forgetting problem, these methods start from a redundant full-size network and require a complex learning process to gradually grow-and-prune or search the network structure for each task, which is inefficient. To address this problem and enable efficient network expansion for new tasks, we first develop a learnable sparse growth method eliminating the additional pruning/searching step in previous structure-based methods. Building on this learnable sparse growth method, we then propose GROWN, a novel end-to-end continual learning framework to dynamically grow the model only when necessary. Different from all previous structure-based methods, GROWN starts from a small seed network, instead of a full-sized one. We validate GROWN on multiple datasets against state-of-the-art methods, which shows superior performance in both accuracy and model size. For example, we achieve 1.0\% accuracy gain on average compared to the current SOTA results on CIFAR-100 Superclass 20 tasks setting.

cs.LG↗

arXiv2021

Quantum Identity-Based Encryption from the Learning with Errors Problem

In order to prevent eavesdropping and tampering, the network security protocols use a handshake with an asymmetric cipher to establish a session-specific shared key with which further communication is encrypted using a symmetric cipher. The commonly used asymmetric algorithms include public key encryption, key exchange and identity-based encryption(IBE). However, the network security protocols based on classic identity-based encryption do not have perfect forward security. To solve the problem, we construct the first quantum IBE (QIBE) scheme based on the learning with errors problem, and prove that our scheme is fully secure under the random oracle. Moreover, we construct the quantum circuit of our QIBE scheme and give an estimate of the quantum resource of our circuit including the numbers of Hadamard gate, phase gate, T gate, CNOT gate and the total qubits used in the circuit, and conclude that the quantum resources required by our scheme increase linearly with the number of bits of the encrypted quantum plaintext. Our scheme exhibits the following advantages: (i) The classic key generation center (KGC) system still can be used for our QIBE scheme to generate and distribute the secret identity keys so that the cost can be reduced when the scheme is implemented. The reason why the classic KGC can be used is that the public and private keys are in the form of classic bits. (ii) The network security protocols using a handshake with our QIBE scheme can provide perfect forward security. In our scheme, the ciphertext is transmitted in the form of a quantum state that is unknown to the adversary and therefore cannot be copied and stored. Thus, in the network security protocols based on our QIBE construction, the adversary cannot decrypt the previous quantum ciphertext to threat the previous session keys even if the identity secret key is threatened.

cs.CR↗

arXiv2021

The Report on China-Spain Joint Clinical Testing for Rapid COVID-19 Risk Screening by Eye-region Manifestations

Background: The worldwide surge in coronavirus cases has led to the COVID-19 testing demand surge. Rapid, accurate, and cost-effective COVID-19 screening tests working at a population level are in imperative demand globally. Methods: Based on the eye symptoms of COVID-19, we developed and tested a COVID-19 rapid prescreening model using the eye-region images captured in China and Spain with cellphone cameras. The convolutional neural networks (CNNs)-based model was trained on these eye images to complete binary classification task of identifying the COVID-19 cases. The performance was measured using area under receiver-operating-characteristic curve (AUC), sensitivity, specificity, accuracy, and F1. The application programming interface was open access. Findings: The multicenter study included 2436 pictures corresponding to 657 subjects (155 COVID-19 infection, 23.6%) in development dataset (train and validation) and 2138 pictures corresponding to 478 subjects (64 COVID-19 infections, 13.4%) in test dataset. The image-level performance of COVID-19 prescreening model in the China-Spain multicenter study achieved an AUC of 0.913 (95% CI, 0.898-0.927), with a sensitivity of 0.695 (95% CI, 0.643-0.748), a specificity of 0.904 (95% CI, 0.891 -0.919), an accuracy of 0.875(0.861-0.889), and a F1 of 0.611(0.568-0.655). Interpretation: The CNN-based model for COVID-19 rapid prescreening has reliable specificity and sensitivity. This system provides a low-cost, fully self-performed, non-invasive, real-time feedback solution for continuous surveillance and large-scale rapid prescreening for COVID-19. Funding: This project is supported by Aimomics (Shanghai) Intelligent

eess.IV↗

arXiv2021

Concept Drift Detection in Federated Networked Systems

As next-generation networks materialize, increasing levels of intelligence are required. Federated Learning has been identified as a key enabling technology of intelligent and distributed networks; however, it is prone to concept drift as with any machine learning application. Concept drift directly affects the model's performance and can result in severe consequences considering the critical and emergency services provided by modern networks. To mitigate the adverse effects of drift, this paper proposes a concept drift detection system leveraging the federated learning updates provided at each iteration of the federated training process. Using dimensionality reduction and clustering techniques, a framework that isolates the system's drifted nodes is presented through experiments using an Intelligent Transportation System as a use case. The presented work demonstrates that the proposed framework is able to detect drifted nodes in a variety of non-iid scenarios at different stages of drift and different levels of system exposure.

cs.LG↗

arXiv2021

PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams

As the number of Internet of Things (IoT) devices and systems have surged, IoT data analytics techniques have been developed to detect malicious cyber-attacks and secure IoT systems; however, concept drift issues often occur in IoT data analytics, as IoT data is often dynamic data streams that change over time, causing model degradation and attack detection failure. This is because traditional data analytics models are static models that cannot adapt to data distribution changes. In this paper, we propose a Performance Weighted Probability Averaging Ensemble (PWPAE) framework for drift adaptive IoT anomaly detection through IoT data stream analytics. Experiments on two public datasets show the effectiveness of our proposed PWPAE method compared against state-of-the-art methods.

cs.LG↗

arXiv2021

Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection

Content delivery networks (CDNs) provide efficient content distribution over the Internet. CDNs improve the connectivity and efficiency of global communications, but their caching mechanisms may be breached by cyber-attackers. Among the security mechanisms, effective anomaly detection forms an important part of CDN security enhancement. In this work, we propose a multi-perspective unsupervised learning framework for anomaly detection in CDNs. In the proposed framework, a multi-perspective feature engineering approach, an optimized unsupervised anomaly detection model that utilizes an isolation forest and a Gaussian mixture model, and a multi-perspective validation method, are developed to detect abnormal behaviors in CDNs mainly from the client Internet Protocol (IP) and node perspectives, therefore to identify the denial of service (DoS) and cache pollution attack (CPA) patterns. Experimental results are presented based on the analytics of eight days of real-world CDN log data provided by a major CDN operator. Through experiments, the abnormal contents, compromised nodes, malicious IPs, as well as their corresponding attack types, are identified effectively by the proposed framework and validated by multiple cybersecurity experts. This shows the effectiveness of the proposed method when applied to real-world CDN data.

cs.CR↗

arXiv2021

Strengthening and Weakening by Dislocations in Monolayer MoS2

Dislocations govern the properties of any crystals. Yet, how dislocation of pentagonheptagon (5|7) in grain boundaries (GBs) affects the mechanical properties of two-dimensional MoS2 crystals remains poorly known. Using atomistic simulations and continuum disclination dipole model, we show that, depending on the tilt angle and 5|7 dislocation arrangement, MoS2 GB strength can be enhanced or reduced with tilt angle. For zigzag-tilt GBs primarily composed of Mo5|7+S5|7 dislocations, GB strength monotonically increases as the square of tilt angle. For armchair-tilt GBs with Mo5|7 or S5|7 dislocations, however, the trend of GB strength breaks down as 5|7 dislocations are non-evenly spaced. Moreover, mechanical failure initiates at the bond shared by 5|7 rings, in contrast to graphene where failure occurs at the bond shared by 6|7 rings. This work provides new insights into mechanical design of synthetic transition metal dichalcogenide crystals via dislocation engineering.

cond-mat.mtrl-sci↗

arXiv2021

Mechanical Ductile Detwinning in CH3NH3PbI3 Perovskite

Twin boundaries (TBs) were identified to show conflicting positive/negative effects on the physical properties of CH3NH3PbI3 perovskite, but their roles on the mechanical properties are pending. Herein, tensile characteristics of a variety of TB-dominated bicrystalline CH3NH3PbI3 perovskites are explored using molecular simulations. TB-contained CH3NH3PbI3 are classified into four types from their tensile ductile detwinning characteristics. Type I is characterized by smooth loading flow stressstrain responses, originating from relatively uniform stress distribution induced gradual amorphization at TB region. Types II and III are represented by sudden drop of loading stresses but then distinct ductile flow stress-strain curves, resulting from limited and large-area amorphizations of TB-involved structures, respectively. However, Type IV is highlighted by double apparent peaks in the loading curve followed by ductile flow response, coming from stress-concentration of localization-to-globalization at TB structure, as well as amorphization. This study provides critical insights into mechanics of CH3NH3PbI3 perovskites, and offers that TB engineering is a promising strategy to design mechanically robust hybrid organic-inorganic perovskites-based device systems

cond-mat.mtrl-sci↗

arXiv2021

Single-Photon Spin-Orbit Entangled States in Optical Fibers

Even and odd vector modes in optical fibers are represented with linear combinations of orbital angular momentum modes, and considered as single-photon spin-orbit entangled states. It renders generating such states with fiber structures possible.

quant-ph↗

arXiv2021

Rapid COVID-19 Risk Screening by Eye-region Manifestations

It is still nontrivial to develop a new fast COVID-19 screening method with the easier access and lower cost, due to the technical and cost limitations of the current testing methods in the medical resource-poor districts. On the other hand, there are more and more ocular manifestations that have been reported in the COVID-19 patients as growing clinical evidence[1]. This inspired this project. We have conducted the joint clinical research since January 2021 at the ShiJiaZhuang City, Heibei province, China, which approved by the ethics committee of The fifth hospital of ShiJiaZhuang of Hebei Medical University. We undertake several blind tests of COVID-19 patients by Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Meantime as an important part of the ongoing globally COVID-19 eye test program by AIMOMICS since February 2020, we propose a new fast screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras. This could reliably make a rapid risk screening of COVID-19 with the sustainable stable high performance in different countries and races. Our model for COVID-19 rapid prescreening have the merits of the lower cost, fully self-performed, non-invasive, importantly real-time, and thus enables the continuous health surveillance. We further implement it as the open accessible APIs, and provide public service to the world. Our pilot experiments show that our model is ready to be usable to all kinds of surveillance scenarios, such as infrared temperature measurement device at airports and stations, or directly pushing to the target people groups smartphones as a packaged application.

eess.IV↗

arXiv2021

Asymptotic behavior of multiscale stochastic partial differential equations

In this paper, we study the asymptotic behavior of a semi-linear slow-fast stochastic partial differential equation with singular coefficients. Using the Poisson equation in Hilbert space, we first establish the strong convergence in the averaging principe, which can be viewed as a functional law of large numbers. Then we study the stochastic fluctuations between the original system and its averaged equation. We show that the normalized difference converges weakly to an Ornstein-Uhlenbeck type process, which can be viewed as a functional central limit theorem. Furthermore, rates of convergence both for the strong convergence and the normal deviation are obtained, and these convergence are shown not to depend on the regularity of the coefficients in the equation for the fast variable, which coincides with the intuition, since in the limit systems the fast component has been totally averaged or homogenized out.

math.PR↗