arXiv subjects

Rui Zhang

Publications and source records attributed to Rui Zhang.

At least 919 records · Page 51Linked to original sources

arXiv2021

Hierarchical Temperature Imaging Using Pseudo-Inversed Convolutional Neural Network Aided TDLAS Tomography

As an in situ combustion diagnostic tool, Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography has been widely used for imaging of two-dimensional temperature distributions in reactive flows. Compared with the computational tomographic algorithms, Convolutional Neural Networks (CNNs) have been proofed to be more robust and accurate for image reconstruction, particularly in case of limited access of laser beams in the Region of Interest (RoI). In practice, flame in the RoI that requires to be reconstructed with good spatial resolution is commonly surrounded by low-temperature background. Although the background is not of high interest, spectroscopic absorption still exists due to heat dissipation and gas convection. Therefore, we propose a Pseudo-Inversed CNN (PI-CNN) for hierarchical temperature imaging that (a) uses efficiently the training and learning resources for temperature imaging in the RoI with good spatial resolution, and (b) reconstructs the less spatially resolved background temperature by adequately addressing the integrity of the spectroscopic absorption model. In comparison with the traditional CNN, the newly introduced pseudo inversion of the RoI sensitivity matrix is more penetrating for revealing the inherent correlation between the projection data and the RoI to be reconstructed, thus prioritising the temperature imaging in the RoI with high accuracy and high computational efficiency. In this paper, the proposed algorithm was validated by both numerical simulation and lab-scale experiment, indicating good agreement between the phantoms and the high-fidelity reconstructions.

eess.IV↗

arXiv2021

Performance Analysis and User Association Optimization for Wireless Network Aided by Multiple Intelligent Reflecting Surfaces

Intelligent reflecting surface (IRS) is deemed as a promising solution to improve the spectral and energy efficiency of wireless communications cost-effectively. In this paper, we consider a wireless network where multiple base stations (BSs) serve their respective users with the aid of distributed IRSs in the downlink communication. Specifically, each IRS assists in the transmission from its associated BS to user via passive beamforming, while in the meantime, it also randomly scatters the signals from other co-channel BSs, thus resulting in additional signal as well as interference paths in the network. As such, a new IRS-user/BS association problem arises pertaining to optimally balance the passive beamforming gains from all IRSs among different BS-user communication links. To address this new problem, we first derive a tractable lower bound of the average signal-to-interference-plus-noise ratio (SINR) at the receiver of each user, termed average-signal-to-average-interference-plus-noise ratio (ASAINR), based on which two ASAINR balancing problems are formulated to maximize the minimum ASAINR among all users by optimizing the IRS-user associations without and with BS transmit power control, respectively. We also characterize the scaling behavior of user ASAINRs with the increasing number of IRS reflecting elements to investigate the different effects of IRS-reflected signal versus interference power. Moreover, to solve the two ASAINR balancing problems that are both non-convex optimization problems, we propose an optimal solution to the problem without BS power control and low-complexity suboptimal solutions to both problems by applying the branch-and-bound method and exploiting new properties of the IRS-user associations, respectively. Numerical results verify our performance analysis and also demonstrate significant performance gains of the proposed solutions over benchmark schemes.

cs.IT↗

arXiv2021

Cross-language Sentence Selection via Data Augmentation and Rationale Training

This paper proposes an approach to cross-language sentence selection in a low-resource setting. It uses data augmentation and negative sampling techniques on noisy parallel sentence data to directly learn a cross-lingual embedding-based query relevance model. Results show that this approach performs as well as or better than multiple state-of-the-art machine translation + monolingual retrieval systems trained on the same parallel data. Moreover, when a rationale training secondary objective is applied to encourage the model to match word alignment hints from a phrase-based statistical machine translation model, consistent improvements are seen across three language pairs (English-Somali, English-Swahili and English-Tagalog) over a variety of state-of-the-art baselines.

cs.CL↗

arXiv2021

Noise Doesn't Lie: Towards Universal Detection of Deep Inpainting

Deep image inpainting aims to restore damaged or missing regions in an image with realistic contents. While having a wide range of applications such as object removal and image recovery, deep inpainting techniques also have the risk of being manipulated for image forgery. A promising countermeasure against such forgeries is deep inpainting detection, which aims to locate the inpainted regions in an image. In this paper, we make the first attempt towards universal detection of deep inpainting, where the detection network can generalize well when detecting different deep inpainting methods. To this end, we first propose a novel data generation approach to generate a universal training dataset, which imitates the noise discrepancies exist in real versus inpainted image contents to train universal detectors. We then design a Noise-Image Cross-fusion Network (NIX-Net) to effectively exploit the discriminative information contained in both the images and their noise patterns. We empirically show, on multiple benchmark datasets, that our approach outperforms existing detection methods by a large margin and generalize well to unseen deep inpainting techniques. Our universal training dataset can also significantly boost the generalizability of existing detection methods.

cs.CV↗

arXiv2021

UAV Aided Over-the-Air Computation

Over-the-air computation (AirComp) seamlessly integrates communication and computation by exploiting the waveform superposition property of multiple-access channels. Different from the existing works that focus on transceiver design of AirComp over static networks, this paper considers an unmanned aerial vehicle (UAV) aided AirComp system, where the UAV as a flying base station aggregates data from mobile sensors. The trajectory design of the UAV provides an additional degree of freedom to improve the performance of AirComp. Our goal is to minimize the time-averaged mean-squared error (MSE) of AirComp by jointly optimizing the UAV trajectory, receive normalizing factors, and sensors' transmit power. To this end, we first propose a novel and equivalent problem transformation by introducing intermediate variables. This reformulation leads to a convex subproblem when fixing any other two blocks of variables, thereby enabling efficient algorithm design based on the principle of block coordinate descent and alternating direction method of multipliers (ADMM) techniques. In particular, we derive the optimal closed-form solutions for normalizing factors and intermediate variables optimization subproblems. We also recast the convex trajectory design subproblem into an ADMM form and obtain the closed-form expressions for each variable updating. Simulation results show that the proposed algorithm achieves a smaller time-averaged MSE while reducing the simulation time by orders of magnitude compared to state-of-the-art algorithms.

eess.SP↗

arXiv2021

Target-Dependent Chemical Species Tomography with Hybrid Meshing of Sensing Regions

This paper develops a hybrid-size meshing scheme for target-dependent imaging in Chemical Species Tomography (CST). The traditional implementation of CST generally places the target field in the central region of laser sensing, the so-called Region of Interest (RoI), with uniform-size meshes. The centre of the RoI locates at the midpoint between the laser emitters and receivers, while the size of the RoI is empirically determined by the optical layout. A too small RoI cannot make the most use of laser beams, while a too large one leads to much severer rank deficiency in CST. To solve the above-mentioned issues, we introduce hybrid-size meshing, for the first time, by reforming the density of the pixels in the entire sensing region of CST. This development alleviates the ill-posedness of the CST inverse problem by detailing the target flow field with dense pixels in the RoI and fully considering the complete physical absorption model with sparse pixels out of the RoI. The proposed scheme was both numerically and experimentally validated using a CST sensor with 32 laser beams using a variety of computational tomographic algorithms. The images reconstructed using the hybrid-size meshing scheme show better accuracy and finer profile of the target flow, compared with those reconstructed using the traditionally uniform-size meshing. The proposed hybrid-size meshing scheme significantly facilitates the industrial application of CST towards practical combustors, in which the combustion zone is bypassed by cooling air. In these scenarios, the proposed scheme can better characterise the combustion zone with dense meshes, while maintaining the integrity of the physical model by considering the absorption in the bypass air with sparse meshes.

eess.IV↗

arXiv2021

Consistent and Flexible Selectivity Estimation for High-Dimensional Data

Selectivity estimation aims at estimating the number of database objects that satisfy a selection criterion. Answering this problem accurately and efficiently is essential to many applications, such as density estimation, outlier detection, query optimization, and data integration. The estimation problem is especially challenging for large-scale high-dimensional data due to the curse of dimensionality, the large variance of selectivity across different queries, and the need to make the estimator consistent (i.e., the selectivity is non-decreasing in the threshold). We propose a new deep learning-based model that learns a query-dependent piecewise linear function as selectivity estimator, which is flexible to fit the selectivity curve of any distance function and query object, while guaranteeing that the output is non-decreasing in the threshold. To improve the accuracy for large datasets, we propose to partition the dataset into multiple disjoint subsets and build a local model on each of them. We perform experiments on real datasets and show that the proposed model consistently outperforms state-of-the-art models in accuracy in an efficient way and is useful for real applications.

cs.DB↗

arXiv2021

Nucleon Gluon Distribution Function from 2+1+1-Flavor Lattice QCD

The parton distribution functions (PDFs) provide process-independent information about the quarks and gluons inside hadrons. Although the gluon PDF can be obtained from a global fit to experimental data, it is not constrained well in the large-$x$ region. Theoretical gluon-PDF studies are much fewer than those of the quark PDFs. In this work, we present the first lattice-QCD results that access the $x$-dependence of the gluon unpolarized PDF of the nucleon. The lattice calculation is carried out with nucleon momenta up to 2.16 GeV, lattice spacing $a\approx0.12$ fm, with valence pion masses of 310 and 690 MeV. We use reduced Ioffe-time distributions to cancel the renormalization and implement a one-loop perturbative pseudo-PDF matching to the lightcone distribution. Our matrix element results in coordinate space are consistent with those obtained from the global PDF fits of CT18 NNLO and NNPDF3.1 NNLO. Our fitted gluon PDF extrapolated to the physical pion mass gives consistent results in the $x > 0.3$ region.

hep-lat↗

arXiv2021

Defect Spirograph: Dynamical Behavior of Defects in Spatially Patterned Active Nematics

Topological defects in active liquid crystals can be confined by introducing gradients of activity. Here, we examine the dynamical behavior of two defects confined by a sharp gradient of activity that separates an active circular region and a surrounding passive nematic material. Continuum simulations are used to explain how the interplay among energy injection into the system, hydrodynamic interactions, and frictional forces governs the dynamics of topologically required self-propelling $+1/2$ defects. Our findings are rationalized in terms of a phase diagram for the dynamical response of defects in terms of activity and frictional damping strength. Different regions of the underlying phase diagram correspond to distinct dynamical modes, namely immobile defects (ID), steady rotation of defects (SR), bouncing defects (TB), bouncing-cruising defects (BC), dancing defects (DA), and multiple defects with irregular dynamics (MD). These dynamic states raise the prospect of generating synchronized defect arrays for microfluidic applications.

cond-mat.soft↗

arXiv2021

Detecting Beneficial Feature Interactions for Recommender Systems

Feature interactions are essential for achieving high accuracy in recommender systems. Many studies take into account the interaction between every pair of features. However, this is suboptimal because some feature interactions may not be that relevant to the recommendation result, and taking them into account may introduce noise and decrease recommendation accuracy. To make the best out of feature interactions, we propose a graph neural network approach to effectively model them, together with a novel technique to automatically detect those feature interactions that are beneficial in terms of recommendation accuracy. The automatic feature interaction detection is achieved via edge prediction with an L0 activation regularization. Our proposed model is proved to be effective through the information bottleneck principle and statistical interaction theory. Experimental results show that our model (i) outperforms existing baselines in terms of accuracy, and (ii) automatically identifies beneficial feature interactions.

cs.LG↗

arXiv2021

Hybrid Active/Passive Wireless Network Aided by Intelligent Reflecting Surface: System Modeling and Performance Analysis

Intelligent reflecting surface (IRS) is a new and promising paradigm to substantially improve the spectral and energy efficiency of wireless networks, by constructing favorable communication channels via tuning massive low-cost passive reflecting elements. Despite recent advances in the link-level performance optimization for various IRS-aided wireless systems, it still remains an open problem whether the large-scale deployment of IRSs in wireless networks can be a cost-effective solution to achieve their sustainable capacity growth in the future. To address this problem, we study in this paper a new hybrid wireless network comprising both active base stations (BSs) and passive IRSs, and characterize its achievable spatial throughput in the downlink as well as other pertinent key performance metrics averaged over both channel fading and random locations of the deployed BSs/IRSs therein based on stochastic geometry. Compared to prior works on characterizing the performance of wireless networks with active BSs only, our analysis needs to derive the power distributions of both the signal and interference reflected by distributed IRSs in the network under spatially correlated channels, which exhibit channel hardening effects when the number of IRS elements becomes large. Extensive numerical results are presented to validate our analysis and demonstrate the effectiveness of deploying distributed IRSs in enhancing the hybrid network throughput against the conventional network without IRS, which significantly boosts the signal power but results in only marginally increased interference in the network. Moreover, it is unveiled that there exists an optimal IRS/BS density ratio that maximizes the hybrid network throughput subject to a total deployment cost given their individual costs, while the conventional network without IRS is generally suboptimal in terms of throughput per unit cost.

cs.IT↗

arXiv2021

A Conversational Agent System for Dietary Supplements Use

Dietary supplements (DS) have been widely used by consumers, but the information around the efficacy and safety of DS is disparate or incomplete, thus creating barriers for consumers to find information effectively. Conversational agent (CA) systems have been applied to the healthcare domain, but there is no such a system to answer consumers regarding DS use, although widespread use of DS. In this study, we develop the first CA system for DS use

cs.CL↗

arXiv2021

Domain-Specific Suppression for Adaptive Object Detection

Domain adaptation methods face performance degradation in object detection, as the complexity of tasks require more about the transferability of the model. We propose a new perspective on how CNN models gain the transferability, viewing the weights of a model as a series of motion patterns. The directions of weights, and the gradients, can be divided into domain-specific and domain-invariant parts, and the goal of domain adaptation is to concentrate on the domain-invariant direction while eliminating the disturbance from domain-specific one. Current UDA object detection methods view the two directions as a whole while optimizing, which will cause domain-invariant direction mismatch even if the output features are perfectly aligned. In this paper, we propose the domain-specific suppression, an exemplary and generalizable constraint to the original convolution gradients in backpropagation to detach the two parts of directions and suppress the domain-specific one. We further validate our theoretical analysis and methods on several domain adaptive object detection tasks, including weather, camera configuration, and synthetic to real-world adaptation. Our experiment results show significant advance over the state-of-the-art methods in the UDA object detection field, performing a promotion of $10.2\sim12.2\%$ mAP on all these domain adaptation scenarios.

cs.CV↗

arXiv2021

De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks

Machine learning techniques have been widely applied to various applications. However, they are potentially vulnerable to data poisoning attacks, where sophisticated attackers can disrupt the learning procedure by injecting a fraction of malicious samples into the training dataset. Existing defense techniques against poisoning attacks are largely attack-specific: they are designed for one specific type of attacks but do not work for other types, mainly due to the distinct principles they follow. Yet few general defense strategies have been developed. In this paper, we propose De-Pois, an attack-agnostic defense against poisoning attacks. The key idea of De-Pois is to train a mimic model the purpose of which is to imitate the behavior of the target model trained by clean samples. We take advantage of Generative Adversarial Networks (GANs) to facilitate informative training data augmentation as well as the mimic model construction. By comparing the prediction differences between the mimic model and the target model, De-Pois is thus able to distinguish the poisoned samples from clean ones, without explicit knowledge of any ML algorithms or types of poisoning attacks. We implement four types of poisoning attacks and evaluate De-Pois with five typical defense methods on different realistic datasets. The results demonstrate that De-Pois is effective and efficient for detecting poisoned data against all the four types of poisoning attacks, with both the accuracy and F1-score over 0.9 on average.

cs.CR↗

arXiv2021

Unifying the Global and Local Approaches: An Efficient Power Iteration with Forward Push

Personalized PageRank (PPR) is a critical measure of the importance of a node t to a source node s in a graph. The Single-Source PPR (SSPPR) query computes the PPR's of all the nodes with respect to s on a directed graph $G$ with $n$ nodes and $m$ edges, and it is an essential operation widely used in graph applications. In this paper, we propose novel algorithms for solving two variants of SSPPR: (i) high-precision queries and (ii) approximate queries. For high-precision queries, Power Iteration (PowItr) and Forward Push (FwdPush) are two fundamental approaches. Given an absolute error threshold $λ$, the only known bound of FwdPush is $O(\frac{m}λ)$, much worse than the $O(m \log \frac{1}λ)$-bound of PowItr. Whether FwdPush can achieve the same running time bound as PowItr does still remains an open question in the research community. We give a positive answer to this question by showing that the running time of a common implementation of FwdPush is actually bounded by $O(m \cdot \log \frac{1}λ)$.Based on this finding, we propose a new algorithm, called Power Iteration with Forward Push (PowerPush), which incorporates the strengths of both PowItr and FwdPush. For approximate queries (with a relative error $ε$), we propose a new algorithm, called SpeedPPR, with overall expected time bounded by $O(n \cdot \log n \cdot \log \frac{1}ε)$ on scale-free graphs. This bound greatly improves the $O(\frac{n \cdot \log n}ε)$ bound of a state-of-the-art algorithm FORA.

cs.DS↗

arXiv2021

Optimal Curing Strategy for Competing Epidemics Spreading over Complex Networks

Optimal curing strategy of suppressing competing epidemics spreading over complex networks is a critical issue. In this paper, we first establish a framework to capture the coupling between two epidemics, and then analyze the system's equilibrium states by categorizing them into three classes, and deriving their stability conditions. The designed curing strategy globally optimizes the trade-off between the curing cost and the severity of epidemics in the network. In addition, we provide structural results on the predictability of epidemic spreading by showing the existence and uniqueness of the solution. We also demonstrate the robustness of curing strategy by showing the continuity of epidemic severity with respect to the applied curing effort. A gradient descent algorithm based on a fixed-point iterative scheme is proposed to find the optimal curing strategy. Depending on the system parameters, the curing strategy can lead to switching between equilibria of the epidemic network as the control cost varies. Finally, we use case studies to corroborate and illustrate the obtained theoretical results.

eess.SY↗

arXiv2021

Valence-Quark Distribution of the Kaon and Pion from Lattice QCD

We present the first lattice-QCD calculation of the kaon valence-quark distribution functions using the large-momentum effective theory (LaMET) approach. The calculation is performed with multiple pion masses with the lightest one around 220 MeV, 2 lattice spacings $a=0.06$ and 0.12 fm, $(M_π)_\text{min} L \approx 5.5$, and high statistics ranging from 11,600 to 61,312 measurements. We also calculate the valence-quark distribution of pion and find it to be consistent with the FNAL E615 experimental results, and our ratio of the $u$ quark PDF in the kaon to that in the pion agrees with the CERN NA3 experiment. We also make predictions of the strange-quark distribution of the kaon.

hep-lat↗

arXiv2021

DART: Open-Domain Structured Data Record to Text Generation

We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-Text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures. To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and dialogue-act-based meaning representation tasks by utilizing techniques such as: tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing. We present systematic evaluation on DART as well as new state-of-the-art results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2) facilitates out-of-domain generalization. Our data and code can be found at https://github.com/Yale-LILY/dart.

cs.CL↗