arXiv subjects

Jie Xu

Publications and source records attributed to Jie Xu.

At least 397 records · Page 22Linked to original sources

arXiv2022

Well-posedness of frame hydrodynamics for biaxial nematic liquid crystals

We consider the hydrodynamics for the biaxial nematic phase characterized by a field of orthonormal frame, which can be derived from a molecular-theory-based tensor model. In dimension two and three, we establish the local well-posedness and the blow-up criterion for smooth solutions to the frame hydrodynamic model. Furthermore, we prove the global existence of weak solutions in $\mathbb{R}^2$ which are nonsmooth at finitely many singular times.

math.AP↗

arXiv2022

Rigorous biaxial limit of a molecular-theory-based two-tensor hydrodynamics

We consider a two-tensor hydrodynamics derived from the molecular model, where high-order tensors are determined by closure approximation through the maximum entropy state or the quasi-entropy. We prove the existence and uniqueness of local in time smooth solutions to the two-tensor system. Then, we rigorously justify the connection between the molecular-theory-based two-tensor hydrodynamics and the biaxial frame hydrodynamics. More specifically, in the framework of Hilbert expansion, we show the convergence of the solution to the two-tensor hydrodynamics to the solution to the frame hydrodynamics.

math.AP↗

arXiv2022

FedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning and Negative Learning

Federated learning (FL), a popular decentralized and privacy-preserving machine learning (FL) framework, has received extensive research attention in recent years. The majority of existing works focus on supervised learning (SL) problems where it is assumed that clients carry labeled datasets while the server has no data. However, in realistic scenarios, clients are often unable to label their data due to the lack of expertise and motivation while the server may host a small amount of labeled data. How to reasonably utilize the server labeled data and the clients' unlabeled data is thus of paramount practical importance. In this paper, we propose a new FL algorithm, called FedSEAL, to solve this Semi-Supervised Federated Learning (SSFL) problem. Our algorithm utilizes self-ensemble learning and complementary negative learning to enhance both the accuracy and the efficiency of clients' unsupervised learning on unlabeled data, and orchestrates the model training on both the server side and the clients' side. Our experimental results on Fashion-MNIST and CIFAR10 datasets in the SSFL setting validate the effectiveness of our method, which outperforms the state-of-the-art SSFL methods by a large margin.

cs.LG↗

arXiv2022

Vertical Federated Edge Learning with Distributed Integrated Sensing and Communication

This letter studies a vertical federated edge learning (FEEL) system for collaborative objects/human motion recognition by exploiting the distributed integrated sensing and communication (ISAC). In this system, distributed edge devices first send wireless signals to sense targeted objects/human, and then exchange intermediate computed vectors (instead of raw sensing data) for collaborative recognition while preserving data privacy. To boost the spectrum and hardware utilization efficiency for FEEL, we exploit ISAC for both target sensing and data exchange, by employing dedicated frequency-modulated continuous-wave (FMCW) signals at each edge device. Under this setup, we propose a vertical FEEL framework for realizing the recognition based on the collected multi-view wireless sensing data. In this framework, each edge device owns an individual local L-model to transform its sensing data into an intermediate vector with relatively low dimensions, which is then transmitted to a coordinating edge device for final output via a common downstream S-model. By considering a human motion recognition task, experimental results show that our vertical FEEL based approach achieves recognition accuracy up to 98\% with an improvement up to 8\% compared to the benchmarks, including on-device training and horizontal FEEL.

eess.SP↗

arXiv2022

Impact of Third Order Dispersion on Dissipative Soliton Resonance

Dissipative soliton resonance (DSR) is a promising way for high-energy pulse generation typically having a symmetrical square pulse profile. While this method is well known, the impact of third order dispersion (TOD) on DSR is yet to be fully addressed in the literature. In this article, the impact of TOD on DSR is numerically investigated under the frame of the complex cubic-quintic Ginzburg-Landau equation (CQGLE). Our numerical investigations indicate that DSR can stably exist under TOD with nearly the same pulse amplitude, but with a (significantly) different pulse duration. Depending on the value of chromatic dispersion, the pulse duration can be notably longer or shorter due to the presence of TOD. The TOD effect also alters the dependence of pulse duration on the nonlinear gain. Another impact of TOD on DSR is that the DSR exists with an asymmetric pulse profile, leading to steepening of one edge of the DSR pulse, while flattening of the other. Our results indicate that TOD has a critical role for realizing DSR in mode-locked lasers and it should be taken into consideration during design and development of DSR-based lasers.

physics.optics↗

arXiv2022

Microwave excitations and hysteretic magnetization dynamics of stripe domain films

FeNi films with the stripe domain (SD) pattern are prepared by electrodeposition and sputtering methods. The magnetic domain, static magnetic parameters, and quality factor, as well as dynamic properties of the two films, are respectively performed. The results show the magnetizations of the film were dependent on the direction of SD, and the rotation of the SD is lagging behind the magnetization reversal. The microwave properties of the SD emerge dynamic hysteresis before the saturation magnetic field. These microwave properties are selectively excited with acoustic mode, optical mode, and spin-wave mode. The frequency and intensity of different resonance modes of stripe domain are determined by the local magnetization. The magnetization variations and the rotation of SD of different modes are further illuminated by the micromagnetic simulation. The magnetic anisotropy and the resonance intensity of permeability of different modes were finally described by the modified resonance equations.

cond-mat.mtrl-sci↗

arXiv2022

Near-Field Wideband Extremely Large-scale MIMO Transmission with Holographic Metasurface Antennas

Extremely large-scale multiple-input multiple-output (XL-MIMO) is the development trend of future wireless communications. However, the extremely large-scale antenna array could bring inevitable nearfield and dual-wideband effects that seriously reduce the transmission performance. This paper proposes an algorithmic framework to design the beam combining for the near-field wideband XL-MIMO uplink transmissions assisted by holographic metasurface antennas (HMAs). Firstly, we introduce a spherical-wave-based channel model that simultaneously takes into account both the near-field and dual-wideband effects. Based on such a model, we then formulate the HMA-based beam combining problem for the proposed XL-MIMO communications, which is challenging due to the nonlinear coupling of high dimensional HMA weights and baseband combiners. We further present a sum-mean-square-error-minimization-based algorithmic framework. Numerical results showcase that the proposed scheme can effectively alleviate the sum-rate loss caused by the near-field and dual-wideband effects in HMA-assisted XL-MIMO systems. Meanwhile, the proposed HMA-based scheme can achieve a higher sum rate than the conventional phase-shifter-based hybrid analog/digital one with the same array aperture.

cs.IT↗

arXiv2022

Realization of broadband index-near-zero modes in nonreciprocal magneto-optical heterostructures

Epsilon-near-zero (ENZ) metamaterial with the relative permittivity approaching zero has been a hot research subject in the past decades. The wave in the ENZ region has infinite phase velocity ($v=1/\sqrt{\varepsilonμ}$), whereas it cannot efficiently travel into the other devices or air due to the impedance mismatch or near-zero group velocity. In this paper, we demonstrate that the tunable index-near-zero (INZ) modes with vanishing wavenumbers ($k=0$) and nonzero group velocities ($v_\mathrm{g} \neq 0$) can be achieved in nonreciprocal magneto-optical systems. This kind of INZ modes has been experimentally demonstrated in the photonic crystals at Dirac point frequencies and that impedance-matching effect has been observed as well. Our theoretical analysis reveals that the INZ modes exhibit tunability when changing the parameter of the one-way (nonreciprocal) waveguides. Moreover, owing to the zero-phase-shift characteristic and decreasing $v_\mathrm{g}$ of the INZ modes, several perfect optical buffers (POBs) are proposed in the microwave and terahertz regimes. The theoretical results are further verified by the numerical simulations performed by the finite element method. Our findings may open the new avenues for research in the areas of ultra -strong or -fast nonlinearity, perfect cloaking, high-resolution holographic imaging and wireless communications.

physics.optics↗

arXiv2022

Accelerated Policy Learning with Parallel Differentiable Simulation

Deep reinforcement learning can generate complex control policies, but requires large amounts of training data to work effectively. Recent work has attempted to address this issue by leveraging differentiable simulators. However, inherent problems such as local minima and exploding/vanishing numerical gradients prevent these methods from being generally applied to control tasks with complex contact-rich dynamics, such as humanoid locomotion in classical RL benchmarks. In this work we present a high-performance differentiable simulator and a new policy learning algorithm (SHAC) that can effectively leverage simulation gradients, even in the presence of non-smoothness. Our learning algorithm alleviates problems with local minima through a smooth critic function, avoids vanishing/exploding gradients through a truncated learning window, and allows many physical environments to be run in parallel. We evaluate our method on classical RL control tasks, and show substantial improvements in sample efficiency and wall-clock time over state-of-the-art RL and differentiable simulation-based algorithms. In addition, we demonstrate the scalability of our method by applying it to the challenging high-dimensional problem of muscle-actuated locomotion with a large action space, achieving a greater than 17x reduction in training time over the best-performing established RL algorithm.

cs.LG↗

arXiv2022

An Integrated Design Pipeline for Tactile Sensing Robotic Manipulators

Traditional robotic manipulator design methods require extensive, time-consuming, and manual trial and error to produce a viable design. During this process, engineers often spend their time redesigning or reshaping components as they discover better topologies for the robotic manipulator. Tactile sensors, while useful, often complicate the design due to their bulky form factor. We propose an integrated design pipeline to streamline the design and manufacturing of robotic manipulators with knitted, glove-like tactile sensors. The proposed pipeline allows a designer to assemble a collection of modular, open-source components by applying predefined graph grammar rules. The end result is an intuitive design paradigm that allows the creation of new virtual designs of manipulators in a matter of minutes. Our framework allows the designer to fine-tune the manipulator's shape through cage-based geometry deformation. Finally, the designer can select surfaces for adding tactile sensing. Once the manipulator design is finished, the program will automatically generate 3D printing and knitting files for manufacturing. We demonstrate the utility of this pipeline by creating four custom manipulators tested on real-world tasks: screwing in a wing screw, sorting water bottles, picking up an egg, and cutting paper with scissors.

cs.RO↗

arXiv2022

Cellular-Connected UAV with Adaptive Air-to-Ground Interference Cancellation and Trajectory Optimization

This letter studies a cellular-connected unmanned aerial vehicle (UAV) scenario, in which a UAV user communicates with ground base stations (GBSs) in cellular uplink by sharing the spectrum with ground users (GUs). To deal with the severe air-to-ground (A2G) co-channel interference, we consider an adaptive interference cancellation (IC) approach, in which each GBS can decode the GU's messages by adaptively switching between the modes of IC (i.e., precanceling the UAV's resultant interference) and treating interference as noise (TIN). By designing the GBSs' decoding modes, jointly with the wireless resource allocation and the UAV's trajectory control, we maximize the UAV's data-rate throughput over a finite mission period, while ensuring the minimum data-rate requirements at individual GUs. We propose an efficient algorithm to solve the throughput maximization problem by using the techniques of alternating optimization and successive convex approximation (SCA). Numerical results show that our proposed design significantly improves the UAV's throughput as compared to the benchmark schemes without the adaptive IC and/or trajectory optimization.

cs.IT↗

arXiv2022

Multi-level Feature Learning for Contrastive Multi-view Clustering

Multi-view clustering can explore common semantics from multiple views and has attracted increasing attention. However, existing works punish multiple objectives in the same feature space, where they ignore the conflict between learning consistent common semantics and reconstructing inconsistent view-private information. In this paper, we propose a new framework of multi-level feature learning for contrastive multi-view clustering to address the aforementioned issue. Our method learns different levels of features from the raw features, including low-level features, high-level features, and semantic labels/features in a fusion-free manner, so that it can effectively achieve the reconstruction objective and the consistency objectives in different feature spaces. Specifically, the reconstruction objective is conducted on the low-level features. Two consistency objectives based on contrastive learning are conducted on the high-level features and the semantic labels, respectively. They make the high-level features effectively explore the common semantics and the semantic labels achieve the multi-view clustering. As a result, the proposed framework can reduce the adverse influence of view-private information. Extensive experiments on public datasets demonstrate that our method achieves state-of-the-art clustering effectiveness.

cs.LG↗

arXiv2022

Coordinated Power Control for Network Integrated Sensing and Communication

This correspondence paper studies a network integrated sensing and communication (ISAC) system that unifies the interference channel for communication and distributed radar sensing. In this system, a set of distributed ISAC transmitters send individual messages to their respective communication users (CUs), and at the same time cooperate with multiple sensing receivers to estimate the location of one target. We exploit the coordinated power control among ISAC transmitters to minimize their total transmit power while ensuring the minimum signal-to-interference-plus-noise ratio (SINR) constraints at individual CUs and the maximum Cramér-Rao lower bound (CRLB) requirement for target location estimation. Although the formulated coordinated power control problem is non-convex and difficult to solve in general, we propose two efficient algorithms to obtain high-quality solutions based on the semi-definite relaxation (SDR) and CRLB approximation, respectively. Numerical results show that the proposed designs achieve substantial performance gains in terms of power reduction, as compared to the benchmark with a heuristic separate communication-sensing design.

cs.IT↗

arXiv2022

Observation of anomalous amplitude modes in the kagome metal CsV$_3$Sb$_5$

The charge-density wave (CDW) phase is often accompanied by the condensation of a soft acoustic phonon mode, giving rise to lattice distortion and charge density modulation. This picture was challenged for the recently discovered kagome metal CsV$_3$Sb$_5$, based on the evidence of absence of soft phonons. Here we report the observation of Raman-active CDW amplitude modes in this material, which are collective excitations typically thought to emerge out of frozen soft phonons. The amplitude modes strongly hybridize with other superlattice modes, imparting them with clear temperature-dependent frequency shift and broadening, rarely seen in other known CDW materials. Both the mode mixing and the large amplitude mode frequencies suggest that the CDW exhibits the character of strong electron-phonon coupling, a regime in which acoustic phonon softening can cease to exist. The observation of amplitude modes in the absence of soft phonons highlights the unconventional nature of the CDW in CsV$_3$Sb$_5$.

cond-mat.mtrl-sci↗

arXiv2022

Derivative-Free Placement Optimization for Multi-UAV Wireless Networks with Channel Knowledge Map

This paper studies a multi-UAV wireless network, in which multiple UAV users share the same spectrum to send individual messages to their respectively associated ground base stations (GBSs). The UAV users aim to optimize their locations to maximize the weighted sum rate. While most existing work considers simplified line-of-sight (LoS) or statistic air-to-ground (A2G) channel models, we exploit the location-specific channel knowledge map (CKM) to enhance the placement performance in practice. However, as the CKMs normally contain discrete site- and location-specific channel data without analytic model functions, the corresponding weighted sum rate function becomes non-differentiable in general. In this case, conventional optimization techniques relying on function derivatives are inapplicable to solve the resultant placement optimization problem. To address this issue, we propose a novel iterative algorithm based on the derivative-free optimization. In each iteration, we first construct a quadratic function to approximate the non-differentiable weighted sum rate under a set of interpolation conditions, and then update the UAVs' placement locations by maximizing the approximate quadratic function subject to a trust region constraint. Numerical results show the convergence of the proposed algorithm. It is also shown that the proposed algorithm achieves a weighted sum rate close to the optimal design based on exhaustive search with much lower implementation complexity, and it significantly outperforms the conventional optimization method based on simplified LoS channel models and the heuristic design with each UAV hovering above its associated GBS.

cs.IT↗

arXiv2022

Over-the-Air Computation with Imperfect Channel State Information

This paper investigates the effect of imperfect channel state information (CSI) on the over-the-air computation (AirComp) system, in which multiple wireless devices (WDs) send individual messages to one access point (AP) for distributed functional computation. By particularly considering the channel estimation errors, we jointly design the transmit coefficients at the WDs and the receive strategy at the AP, for minimizing the computation mean squared error (MSE). First, we consider the single-input single-output (SISO) case with each WD and AP equipped with one single antenna, in which the globally optimal solution to the computation MSE minimization problem is obtained in closed form. Next, we consider the single-input multiple-output (SIMO) case with multiple receive antennas at the AP, in which a high-quality solution is obtained based on alternating optimization and convex optimization. For both cases, the optimized power control solution at the WDs follows a threshold-based regularized channel inversion structure; while for the SIMO case, the receive beamforming at the AP follows a sum-minimum MSE (MMSE) structure. It is shown that with finite receive antennas, a non-zero computation MSE is inevitable due to the channel estimation errors even when the WDs' transmit powers become infinity; while with massive receive antennas, a vanishing MSE is achievable when the channel vectors are independent and identically distributed. Finally, numerical results are provided to demonstrate the effectiveness of the proposed designs.

cs.IT↗

arXiv2022

Joint transmit and reflective beamforming for IRS-assisted integrated sensing and communication

This paper studies an intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) system, in which one IRS is deployed to not only assist the wireless communication from a multi-antenna base station (BS) to a single-antenna communication user (CU), but also create virtual line-of-sight (LoS) links for sensing targets at areas with LoS links blocked. We consider that the BS transmits combined information and sensing signals for ISAC. Under this setup, we jointly optimize the transmit information and sensing beamforming at the BS and the reflective beamforming at the IRS, to maximize the IRS's minimum beampattern gain towards the desired sensing angles, subject to the minimum signal-to-noise ratio (SNR) requirement at the CU and the maximum transmit power constraint at the BS. Although the formulated SNR-constrained beampattern gain maximization problem is non-convex and difficult to solve, we present an efficient algorithm to obtain a high-quality solution using alternating optimization and semi-definite relaxation (SDR). Numerical results show that the proposed joint beamforming design achieves improved sensing performance while ensuring the communication requirement as compared to benchmarks without such joint optimization. It is also shown that the use of dedicated sensing beams is beneficial in enhancing the performance for IRS-assisted ISAC.

eess.SP↗

arXiv2022

On Federated Learning with Energy Harvesting Clients

Catering to the proliferation of Internet of Things devices and distributed machine learning at the edge, we propose an energy harvesting federated learning (EHFL) framework in this paper. The introduction of EH implies that a client's availability to participate in any FL round cannot be guaranteed, which complicates the theoretical analysis. We derive novel convergence bounds that capture the impact of time-varying device availabilities due to the random EH characteristics of the participating clients, for both parallel and local stochastic gradient descent (SGD) with non-convex loss functions. The results suggest that having a uniform client scheduling that maximizes the minimum number of clients throughout the FL process is desirable, which is further corroborated by the numerical experiments using a real-world FL task and a state-of-the-art EH scheduler.

eess.SP↗