arXiv subjects

Jie Xu

Publications and source records attributed to Jie Xu.

At least 469 records · Page 26Linked to original sources

arXiv2020

Federated Learning for Healthcare Informatics

With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies and pharmaceutical industries, among others. This access provides an unprecedented opportunity for data science technologies to derive data-driven insights and improve the quality of care delivery. Healthcare data, however, are usually fragmented and private making it difficult to generate robust results across populations. For example, different hospitals own the electronic health records (EHR) of different patient populations and these records are difficult to share across hospitals because of their sensitive nature. This creates a big barrier for developing effective analytical approaches that are generalizable, which need diverse, "big data". Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy-preservation. The goal of this survey is to provide a review for federated learning technologies, particularly within the biomedical space. In particular, we summarize the general solutions to the statistical challenges, system challenges and privacy issues in federated learning, and point out the implications and potentials in healthcare.

cs.LG↗

arXiv2020

Quasi-entropy by log-determinant covariance matrix and application to liquid crystals

A quasi-entropy is constructed for tensors averaged by a density function on $SO(3)$ using the log-determinant of a covariance matrix. It serves as a substitution of the entropy for tensors derived from a constrained minimization that involves integrals. The quasi-entropy is an elementary function that possesses the essential properties of the original entropy. It constrains the covariance matrix to be positive definite, is strictly convex, and is invariant under rotations. Moreover, when reduced by symmetries, it keeps the vanishing tensors of the symmetry zero. Explicit expressions are provided for axial symmetries up to four-fold, as well as tetrahedral and octahedral symmetries. The quasi-entropy is utilized to discuss phase transitions in several systems. The results are consistent with using the original entropy. Besides, some novel results are presented.

math-ph↗

arXiv2020

When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing

Defense strategies have been well studied to combat Byzantine attacks that aim to disrupt cooperative spectrum sensing by sending falsified versions of spectrum sensing data to a fusion center. However, existing studies usually assume network or attackers as passive entities, e.g., assuming the prior knowledge of attacks is known or fixed. In practice, attackers can actively adopt arbitrary behaviors and avoid pre-assumed patterns or assumptions used by defense strategies. In this paper, we revisit this security vulnerability as an adversarial machine learning problem and propose a novel learning-empowered attack framework named Learning-Evaluation-Beating (LEB) to mislead the fusion center. Based on the black-box nature of the fusion center in cooperative spectrum sensing, our new perspective is to make the adversarial use of machine learning to construct a surrogate model of the fusion center's decision model. We propose a generic algorithm to create malicious sensing data using this surrogate model. Our real-world experiments show that the LEB attack is effective to beat a wide range of existing defense strategies with an up to 82% of success ratio. Given the gap between the proposed LEB attack and existing defenses, we introduce a non-invasive method named as influence-limiting defense, which can coexist with existing defenses to defend against LEB attack or other similar attacks. We show that this defense is highly effective and reduces the overall disruption ratio of LEB attack by up to 80%.

cs.NI↗

arXiv2020

Deep Embedded Multi-view Clustering with Collaborative Training

Multi-view clustering has attracted increasing attentions recently by utilizing information from multiple views. However, existing multi-view clustering methods are either with high computation and space complexities, or lack of representation capability. To address these issues, we propose deep embedded multi-view clustering with collaborative training (DEMVC) in this paper. Firstly, the embedded representations of multiple views are learned individually by deep autoencoders. Then, both consensus and complementary of multiple views are taken into account and a novel collaborative training scheme is proposed. Concretely, the feature representations and cluster assignments of all views are learned collaboratively. A new consistency strategy for cluster centers initialization is further developed to improve the multi-view clustering performance with collaborative training. Experimental results on several popular multi-view datasets show that DEMVC achieves significant improvements over state-of-the-art methods.

cs.LG↗

arXiv2020

Real-Time Resource Allocation for Wireless Powered Multiuser Mobile Edge Computing With Energy and Task Causality

This paper considers a wireless powered multiuser mobile edge computing (MEC) system, in which a multi-antenna hybrid access point (AP) wirelessly charges multiple users, and each user relies on the harvested energy to execute computation tasks. We jointly optimize the energy beamforming and remote task execution at the AP, as well as the local computing and task offloading, aiming to minimize the total system energy consumption over a finite time horizon, subject to causality constraints for both energy harvesting and task arrival at the users. In particular, we consider a practical scenario with casual task state information (TSI) and channel state information (CSI), i.e., only the current and previous TSI and CSI are available, but the future TSI and CSI can only be predicted subject to certain errors. To solve this real-time resource allocation problem, we propose an offline-optimization inspired online design approach. First, we consider the offline optimization case by assuming that the TSI and CSI are perfectly known a-priori. In this case, the energy minimization problem corresponds to a convex problem, for which the semi-closed-form optimal solution is obtained via the Lagrange duality method. Next, inspired by the optimal offline solution, we propose a sliding-window based online resource allocation design in practical cases by integrating with the sequential optimization. Finally, numerical results show that the proposed joint wireless powered MEC designs significantly improve the system's energy efficiency, as compared with the benchmark schemes that consider a sliding window of size one or without such joint optimization.

cs.IT↗

arXiv2020

Optimized Power Control for Over-the-Air Computation in Fading Channels

In this paper, we study the power control problem for Over-the-air computation (AirComp) over fading channels. Our objective is to minimize the computation error by jointly optimizing the transmit power at the power-constrained devices and a signal scaling factor (called denoising factor) at the fusion center (FC). The problem is generally non-convex due to the coupling of the transmit power over devices and denoising factor at the FC. To tackle the challenge, we first consider the special case with static channels, for which we derive the optimal solution in closed form. The optimal power control exhibits a threshold-based structure. Specifically, for each device, if the product of the channel quality and power budget, called quality indicator, exceeds an optimized threshold, this device applies channel-inversion power control; otherwise, it performs full power transmission. Building on the results, we proceed to consider the general case with time-varying channels. To solve the more challenging non-convex power control problem, we use the Lagrange-duality method via exploiting its "time-sharing" property. The derived optimal power control exhibits a regularized channel inversion structure, where the regularization has the function of balancing the tradeoff between the signal-magnitude alignment and noise suppression. Moreover, for the special case with only one device being power limited, we show that the optimal power control for the power-limited device has an interesting channel-inversion water-filling structure, while those for other devices (with sufficient power budgets) reduce to channel-inversion power control over all fading states.

cs.IT↗

arXiv2020

Cooperative Interference Management for Over-the-Air Computation Networks

This paper considers a multi-cell AirComp network and investigates the optimal power control policies over multiple cells to regulate the effect of inter-cell interference. First, we consider the scenario of centralized multi-cell power control, where we characterize the Pareto boundary of the multi-cell MSE region by minimizing the sum MSE subject to a set of constraints on individual MSEs. Though the sum-MSE minimization problem is non-convex and its direct solution intractable, we optimally solve this problem via equivalently solving a sequence of convex second-order cone program feasibility problems together with a bisection search. Next, we consider distributed power control in the other scenario without a centralized controller, for which an alternative IT-based method is proposed to characterize the same MSE Pareto boundary, and enable a decentralized power control algorithm. Accordingly, each AP only needs to individually control the power of its associated devices, but subject to a set of IT constraints on their interference to neighboring cells, while different APs can cooperate in iteratively updating the IT levels by pairwise information exchange, to achieve a Pareto-optimal MSE tuple. Last, simulation results demonstrate that cooperative power control using the proposed algorithms can substantially reduce the sum MSE of AirComp networks.

cs.IT↗

arXiv2020

Iterative Methods for Globally Lipschitz Nonlinear Laplace Equations

We introduce an iterative method to prove the existence and uniqueness of the complex-valued nonlinear elliptic PDE of the form $ -Δu + F(u) = f $ with Dirichlet or Neumann boundary conditions on a precompact domain $ Ω\subset \mathbb{R}^{n}$, where $ F : \mathbb{C} \rightarrow \mathbb{C} $ is Lipschitz. The same method gives a solution to $ - Δ_{g} u + F(u) = f $ for these boundary conditions on a smooth, compact Riemannian manifold $ (M, g) $ with $ \mathcal{C}^{1} $ boundary, where $ - Δ_{g} $ is the Laplace-Beltrami operator. We also apply parametrix methods to discuss an integral version of these PDEs.

math.AP↗

arXiv2020

Unconditionally positivity preserving and energy dissipative schemes for Poisson--Nernst--Planck equations

We develop a set of numerical schemes for the Poisson--Nernst--Planck equations. We prove that our schemes are mass conservative, uniquely solvable and keep positivity unconditionally. Furthermore, the first-order scheme is proven to be unconditionally energy dissipative. These properties hold for various spatial discretizations. Numerical results are presented to validate these properties. Moreover, numerical results indicate that the second-order scheme is also energy dissipative, and both the first- and second-order schemes preserve the maximum principle for cases where the equation satisfies the maximum principle.

math.NA↗

arXiv2020

Adversarial Machine Learning based Partial-model Attack in IoT

As Internet of Things (IoT) has emerged as the next logical stage of the Internet, it has become imperative to understand the vulnerabilities of the IoT systems when supporting diverse applications. Because machine learning has been applied in many IoT systems, the security implications of machine learning need to be studied following an adversarial machine learning approach. In this paper, we propose an adversarial machine learning based partial-model attack in the data fusion/aggregation process of IoT by only controlling a small part of the sensing devices. Our numerical results demonstrate the feasibility of this attack to disrupt the decision making in data fusion with limited control of IoT devices, e.g., the attack success rate reaches 83\% when the adversary tampers with only 8 out of 20 IoT devices. These results show that the machine learning engine of IoT system is highly vulnerable to attacks even when the adversary manipulates a small portion of IoT devices, and the outcome of these attacks severely disrupts IoT system operations.

eess.SP↗

arXiv2020

A Survey of Prototype and Experiment for UAV Communications

Unmanned aerial vehicle (UAV) communications have attracted significant attention from both academia and industry. To facilitate the large-scale usage of UAVs for various applications in practice, we provide a comprehensive survey on the prototype and experiment for UAV communications. To this end, we first provide an overview on the general architecture of the prototype and experiment for UAV communications, and then present experimental verification for air-to-ground channel models and UAV energy consumption models. Next, we discuss measurement experiments on two promising paradigms of UAV communications, namely cellular-connected UAVs and UAV-enabled aerial communication platforms. For the former, we focus on the feasibility study and address the interference mitigation issue. For UAV-enabled aerial communication platforms, we present three scenarios, namely UAV-enabled aerial base stations, UAV-enabled aerial relays and UAV-enabled aerial data collection/dissemination. Finally, we point out some promising future directions for prototype and experimental measurements for UAV communications.

cs.IT↗

arXiv2020

Symmetry-consistent expansion of interaction kernels between rigid molecules

We discuss the expansion of interaction kernels between anisotropic rigid molecules. The expansion decouples the correlated orientational variables so that it can be utilized to derive macroscopic models. Symmetries of two types are considered. First, we examine the symmetry of the interacting cluster, including the translation and rotation of the whole cluster, and label permutation within the cluster. The expansion is expressed by symmetric traceless tensors, and the linearly independent terms are identified. Then, we study the molecular symmetry characterized by a point group in $O(3)$. The proper rotations determine what symmetric traceless tensors can appear. The improper rotations decompose these tensors into two subspaces and determine how the tensors in the two subspaces are coupled. For each point group, we identify the two subspaces, so that the expansion consistent with the point group is established.

math-ph↗

arXiv2020

Hybrid Beamforming for Massive MIMO Over-the-Air Computation

Over-the-air computation (AirComp) has been recognized as a promising technique in Internet-of-Things (IoT) networks for fast data aggregation from a large number of wireless devices. However, as the number of devices becomes large, the computational accuracy of AirComp would seriously degrade due to the vanishing signal-to-noise ratio (SNR). To address this issue, we exploit the massive multiple-input multiple-output (MIMO) with hybrid beamforming, in order to enhance the computational accuracy of AirComp in a cost-effective manner. In particular, we consider the scenario with a large number of multi-antenna devices simultaneously sending data to an access point (AP) equipped with massive antennas for functional computation over the air. Under this setup, we jointly optimize the transmit digital beamforming at the wireless devices and the receive hybrid beamforming at the AP, with the objective of minimizing the computational mean-squared error (MSE) subject to the individual transmit power constraints at the wireless devices. To solve the non-convex hybrid beamforming design optimization problem, we propose an alternating-optimization-based approach. In particular, we propose two computationally efficient algorithms to handle the challenging receive analog beamforming problem, by exploiting the techniques of successive convex approximation (SCA) and block coordinate descent (BCD), respectively. It is shown that for the special case with a fully-digital receiver at the AP, the achieved MSE of the massive MIMO AirComp system is inversely proportional to the number of receive antennas. Furthermore, numerical results show that the proposed hybrid beamforming design substantially enhances the computation MSE performance as compared to other benchmark schemes, while the SCA-based algorithm performs closely to the performance upper bound achieved by the fully-digital beamforming.

eess.SP↗

arXiv2020

Time-Division Energy Beamforming for Multiuser Wireless Power Transfer with Non-Linear Energy Harvesting

Energy beamforming has emerged as a promising technique for enhancing the energy transfer efficiency of wireless power transfer (WPT). However, the performance of conventional energy beamforming may seriously degrade due to the non-linear radio frequency (RF) to direct current (DC) conversion at energy receivers (ERs). To tackle this issue, this letter proposes a new time-division energy beamforming, in which different energy beamforming matrices (of high ranks in general) are time shared to exploit the "convex-concave" shape of the RF-DC power relation at ERs. By considering a particular time duration for WPT, we maximize the minimum harvested DC energy among all ERs, by jointly optimizing the energy beamforming matrices and the corresponding time allocation. In order to solve the non-convex min-DC-energy maximization problem, we propose an efficient solution by using the techniques of alternating optimization and successive convex approximation (SCA). Numerical results show that the proposed time-division energy beamforming design indeed outperforms the conventional multi-beam and time-division-multiple-access (TDMA)-based energy transmissions.

eess.SP↗

arXiv2020

Classifying local anisotropy formed by rigid molecules: symmetries and tensors

We consider an infinitesimal volume where there are many rigid molecules of the same kind, and discuss the description and classification of the local anisotropy in this volume by tensors. First, we examine the symmetry of a rigid molecule, which is described by a point group in $SO(3)$. For each point group in $SO(3)$, we find the tensors invariant under the rotations in the group. These tensors shall be symmetric and traceless. We write down the explicit expressions. The order parameters to describe the local anisotropy are then chosen as some of the invariant tensors averaged about the density function. Next, we discuss the classification of local anisotropy by the symmetry of the whole infinitesimal volume. This mesoscopic symmetry can be recognized by the value of the order parameter tensors in the sense of maximum entropy state. For some sets of order parameter tensors involving different molecular symmetries, we give the classification of mesoscopic symmetries, in which the three-fold, four-fold and polyhedral symmetries are examined.

math-ph↗

arXiv2020

Enabling Cross-chain Transactions: A Decentralized Cryptocurrency Exchange Protocol

Inspired by Bitcoin, many different kinds of cryptocurrencies based on blockchain technology have turned up on the market. Due to the special structure of the blockchain, it has been deemed impossible to directly trade between traditional currencies and cryptocurrencies or between different types of cryptocurrencies. Generally, trading between different currencies is conducted through a centralized third-party platform. However, it has the problem of a single point of failure, which is vulnerable to attacks and thus affects the security of the transactions. In this paper, we propose a distributed cryptocurrency trading scheme to solve the problem of centralized exchanges, which can achieve trading between different types of cryptocurrencies. Our scheme is implemented with smart contracts on the Ethereum blockchain and deployed on the Ethereum test network. We not only implement transactions between individual users, but also allow transactions between multiple users. The experimental result proves that the cost of our scheme is acceptable.

cs.CR↗

arXiv2020

Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective

This paper studies federated learning (FL) in a classic wireless network, where learning clients share a common wireless link to a coordinating server to perform federated model training using their local data. In such wireless federated learning networks (WFLNs), optimizing the learning performance depends crucially on how clients are selected and how bandwidth is allocated among the selected clients in every learning round, as both radio and client energy resources are limited. While existing works have made some attempts to allocate the limited wireless resources to optimize FL, they focus on the problem in individual learning rounds, overlooking an inherent yet critical feature of federated learning. This paper brings a new long-term perspective to resource allocation in WFLNs, realizing that learning rounds are not only temporally interdependent but also have varying significance towards the final learning outcome. To this end, we first design data-driven experiments to show that different temporal client selection patterns lead to considerably different learning performance. With the obtained insights, we formulate a stochastic optimization problem for joint client selection and bandwidth allocation under long-term client energy constraints, and develop a new algorithm that utilizes only currently available wireless channel information but can achieve long-term performance guarantee. Further experiments show that our algorithm results in the desired temporal client selection pattern, is adaptive to changing network environments and far outperforms benchmarks that ignore the long-term effect of FL.

cs.DC↗

arXiv2020

Outage Probability Minimization for UAV-Enabled Data Collection with Distributed Beamforming

This paper studies an unmanned aerial vehicle (UAV)-enabled wireless sensor network, in which one UAV flies in the sky to collect the data transmitted from a set of sensors via distributed beamforming. We consider the delay-sensitive application scenario, in which the sensors transmit the common/shared messages by using fixed data rates and adaptive transmit powers. Under this setup, we jointly optimize the UAV's trajectory design and the sensors' transmit power allocation, in order to minimize the transmission outage probability, subject to the UAV's flight speed constraints and the sensors' individual average power constraints. However, the formulated outage probability minimization problem is non-convex and thus difficult to be optimally solved in general. To tackle this issue, we first consider the special problem in the ideal case with the UAV's flight speed constraints ignored, for which the well-structured optimal solution is obtained to reveal the fundamental performance upper bound. Next, for the general problem with the UAV's flight speed constraints considered, we propose an efficient algorithm to solve it sub-optimally by using the techniques of convex optimization and approximation. Finally, numerical results show that our proposed design achieves significantly reduced outage probability than other benchmark schemes.

cs.IT↗