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Jie Li

Publications and source records attributed to Jie Li.

At least 397 records · Page 22Linked to original sources
arXiv2023

Optimization Landscape of Policy Gradient Methods for Discrete-time Static Output Feedback

In recent times, significant advancements have been made in delving into the optimization landscape of policy gradient methods for achieving optimal control in linear time-invariant (LTI) systems. Compared with state-feedback control, output-feedback control is more prevalent since the underlying state of the system may not be fully observed in many practical settings. This paper analyzes the optimization landscape inherent to policy gradient methods when applied to static output feedback (SOF) control in discrete-time LTI systems subject to quadratic cost. We begin by establishing crucial properties of the SOF cost, encompassing coercivity, L-smoothness, and M-Lipschitz continuous Hessian. Despite the absence of convexity, we leverage these properties to derive novel findings regarding convergence (and nearly dimension-free rate) to stationary points for three policy gradient methods, including the vanilla policy gradient method, the natural policy gradient method, and the Gauss-Newton method. Moreover, we provide proof that the vanilla policy gradient method exhibits linear convergence towards local minima when initialized near such minima. The paper concludes by presenting numerical examples that validate our theoretical findings. These results not only characterize the performance of gradient descent for optimizing the SOF problem but also provide insights into the effectiveness of general policy gradient methods within the realm of reinforcement learning.

math.OC↗
arXiv2023

Automatic Change-Point Detection in Time Series via Deep Learning

Detecting change-points in data is challenging because of the range of possible types of change and types of behaviour of data when there is no change. Statistically efficient methods for detecting a change will depend on both of these features, and it can be difficult for a practitioner to develop an appropriate detection method for their application of interest. We show how to automatically generate new offline detection methods based on training a neural network. Our approach is motivated by many existing tests for the presence of a change-point being representable by a simple neural network, and thus a neural network trained with sufficient data should have performance at least as good as these methods. We present theory that quantifies the error rate for such an approach, and how it depends on the amount of training data. Empirical results show that, even with limited training data, its performance is competitive with the standard CUSUM-based classifier for detecting a change in mean when the noise is independent and Gaussian, and can substantially outperform it in the presence of auto-correlated or heavy-tailed noise. Our method also shows strong results in detecting and localising changes in activity based on accelerometer data.

stat.ML↗
arXiv2023

Microwave-optics Entanglement via Cavity Optomagnomechanics

Microwave-optics entanglement is a vital component for building hybrid quantum networks. Here, a new mechanism for preparing stationary entanglement between microwave and optical cavity fields in a cavity optomagnomechanical system is proposed. It consists of a magnon mode in a ferrimagnetic crystal that couples directly to a microwave cavity mode via the magnetic dipole interaction, and indirectly to an optical cavity through the deformation displacement of the crystal. The mechanical displacement is induced by the magnetostrictive force and coupled to the optical cavity via radiation pressure. Both the opto- and magnomechanical couplings are dispersive. Magnon-phonon entanglement is created via magnomechanical parametric down-conversion, which is further distributed to optical and microwave photons via simultaneous optomechanical beamsplitter interaction and electromagnonic state-swap interaction, yielding stationary microwave-optics entanglement. The microwave-optics entanglement is robust against thermal noise, which will find broad potential applications in quantum networks and quantum information processing with hybrid quantum systems.

quant-ph↗
arXiv2023

Learning Optimal Robust Control of Connected Vehicles in Mixed Traffic Flow

Connected and automated vehicles (CAVs) technologies promise to attenuate undesired traffic disturbances. However, in mixed traffic where human-driven vehicles (HDVs) also exist, the nonlinear human-driving behavior has brought critical challenges for effective CAV control. This paper employs the policy iteration method to learn the optimal robust controller for nonlinear mixed traffic systems. Precisely, we consider the H_infty control framework and formulate it as a zero-sum game, the equivalent condition for whose solution is converted into a Hamilton-Jacobi inequality with a Hamiltonian constraint. Then, a policy iteration algorithm is designed to generate stabilizing controllers with desired attenuation performance. Based on the updated robust controller, the attenuation level is further optimized in sum of squares program by leveraging the gap of the Hamiltonian constraint. Simulation studies verify that the obtained controller enables the CAVs to dampen traffic perturbations and smooth traffic flow.

eess.SY↗
arXiv2023

MDS Array Codes With Small Sub-packetization Levels and Small Repair Degrees

High-rate minimum storage regenerating (MSR) codes are known to require a large sub-packetization level, which can make meta-data management difficult and hinder implementation in practical systems. A few maximum distance separable (MDS) array code constructions have been proposed to attain a much smaller sub-packetization level by sacrificing a bit of repair bandwidth. However, to the best of our knowledge, only one construction by Guruswami et al. can support the repair of a failed node without contacting all the surviving nodes. This construction is certainly of theoretical interest but not yet practical due to its requirement for very large code parameters. In this paper, we propose a generic transformation that can convert any $(\overline{n}, \overline{k})$ MSR code with a repair degree of $\overline{d}<\overline{n}-1$ into another $(n=s\overline{n},k)$ MDS array code that supports $d<n-1$ with a small sub-packetization level and $(1+ε)$-optimal repair bandwidth (i.e., $1+ε$ times the optimal value) under a specific condition. We obtain three MDS array codes with small sub-packetization levels and $(1+ε)$-optimal repair bandwidth by applying this transformation to three known MSR codes. All the new MDS array codes have a small repair degree of $d<n-1$ and work for both small and large code parameters.

cs.IT↗
arXiv2023

ChinaTelecom System Description to VoxCeleb Speaker Recognition Challenge 2023

This technical report describes ChinaTelecom system for Track 1 (closed) of the VoxCeleb2023 Speaker Recognition Challenge (VoxSRC 2023). Our system consists of several ResNet variants trained only on VoxCeleb2, which were fused for better performance later. Score calibration was also applied for each variant and the fused system. The final submission achieved minDCF of 0.1066 and EER of 1.980%.

cs.SD↗
arXiv2023

Improving Human-Object Interaction Detection via Virtual Image Learning

Human-Object Interaction (HOI) detection aims to understand the interactions between humans and objects, which plays a curtail role in high-level semantic understanding tasks. However, most works pursue designing better architectures to learn overall features more efficiently, while ignoring the long-tail nature of interaction-object pair categories. In this paper, we propose to alleviate the impact of such an unbalanced distribution via Virtual Image Leaning (VIL). Firstly, a novel label-to-image approach, Multiple Steps Image Creation (MUSIC), is proposed to create a high-quality dataset that has a consistent distribution with real images. In this stage, virtual images are generated based on prompts with specific characterizations and selected by multi-filtering processes. Secondly, we use both virtual and real images to train the model with the teacher-student framework. Considering the initial labels of some virtual images are inaccurate and inadequate, we devise an Adaptive Matching-and-Filtering (AMF) module to construct pseudo-labels. Our method is independent of the internal structure of HOI detectors, so it can be combined with off-the-shelf methods by training merely 10 additional epochs. With the assistance of our method, multiple methods obtain significant improvements, and new state-of-the-art results are achieved on two benchmarks.

cs.CV↗
arXiv2023

A novel integrated method of detection-grasping for specific object based on the box coordinate matching

To better care for the elderly and disabled, it is essential for service robots to have an effective fusion method of object detection and grasp estimation. However, limited research has been observed on the combination of object detection and grasp estimation. To overcome this technical difficulty, a novel integrated method of detection-grasping for specific object based on the box coordinate matching is proposed in this paper. Firstly, the SOLOv2 instance segmentation model is improved by adding channel attention module (CAM) and spatial attention module (SAM). Then, the atrous spatial pyramid pooling (ASPP) and CAM are added to the generative residual convolutional neural network (GR-CNN) model to optimize grasp estimation. Furthermore, a detection-grasping integrated algorithm based on box coordinate matching (DG-BCM) is proposed to obtain the fusion model of object detection and grasp estimation. For verification, experiments on object detection and grasp estimation are conducted separately to verify the superiority of improved models. Additionally, grasping tasks for several specific objects are implemented on a simulation platform, demonstrating the feasibility and effectiveness of DG-BCM algorithm proposed in this paper.

cs.RO↗
arXiv2023

Enhanced Strong Coupling between Spin Ensemble and non-Hermitian Topological Edge States

Light-matter interaction is crucial to both understanding fundamental phenomena and developing versatile applications. Strong coupling, robustness, and controllability are the three most important aspects in realizing light-matter interactions. Topological and non-Hermitian photonics, have provided frameworks for robustness and extensive control freedom, respectively. How to engineer the properties of the edge state such as photonic density of state, scattering parameters by using non-Hermitian engineering while ensuring topological protection has not been fully studied. Here we construct a parity-time-symmetric dimerized photonic lattice and generate complex-valued edge states via spontaneous PT-symmetry breaking. The enhanced strong coupling between the topological photonic edge mode and magnon mode in a ferromagnetic spin ensemble is demonstrated. Our research reveals the subtle non-Hermitian topological edge states and provides strategies for realizing and engineering topological light-matter interactions.

quant-ph↗
arXiv2023

MMNet: Multi-Collaboration and Multi-Supervision Network for Sequential Deepfake Detection

Advanced manipulation techniques have provided criminals with opportunities to make social panic or gain illicit profits through the generation of deceptive media, such as forged face images. In response, various deepfake detection methods have been proposed to assess image authenticity. Sequential deepfake detection, which is an extension of deepfake detection, aims to identify forged facial regions with the correct sequence for recovery. Nonetheless, due to the different combinations of spatial and sequential manipulations, forged face images exhibit substantial discrepancies that severely impact detection performance. Additionally, the recovery of forged images requires knowledge of the manipulation model to implement inverse transformations, which is difficult to ascertain as relevant techniques are often concealed by attackers. To address these issues, we propose Multi-Collaboration and Multi-Supervision Network (MMNet) that handles various spatial scales and sequential permutations in forged face images and achieve recovery without requiring knowledge of the corresponding manipulation method. Furthermore, existing evaluation metrics only consider detection accuracy at a single inferring step, without accounting for the matching degree with ground-truth under continuous multiple steps. To overcome this limitation, we propose a novel evaluation metric called Complete Sequence Matching (CSM), which considers the detection accuracy at multiple inferring steps, reflecting the ability to detect integrally forged sequences. Extensive experiments on several typical datasets demonstrate that MMNet achieves state-of-the-art detection performance and independent recovery performance.

cs.CV↗
arXiv2023

Quantum-Enhanced Diamond Molecular Tension Microscopy for Quantifying Cellular Forces

The constant interplay and information exchange between cells and their micro-environment are essential to their survival and ability to execute biological functions. To date, a few leading technologies such as traction force microscopy, have been broadly used in measuring cellular forces. However, the considerable limitations, regarding the sensitivity and ambiguities in data interpretation, are hindering our thorough understanding of mechanobiology. Herein, we propose an innovative approach, namely quantum-enhanced diamond molecular tension microscopy (QDMTM), to precisely quantify the integrin-based cell adhesive forces. Specifically, we construct a force sensing platform by conjugating the magnetic nanotags labeled, force-responsive polymer to the surface of diamond membrane containing nitrogen vacancy (NV) centers. Thus, the coupled mechanical information can be quantified through optical readout of spin relaxation of NV centers modulated by those magnetic nanotags. To validate QDMTM, we have carefully performed corresponding measurements both in control and real cell samples. Particularly, we have obtained the quantitative cellular adhesion force mapping by correlating the measurement with established theoretical model. We anticipate that our method can be routinely used in studying important issues like cell-cell or cell-material interactions and mechanotransduction.

q-bio.CB↗
arXiv2023

Pressure tunable quantum anomalous Hall states in a topological antiferromagnet

Mechanical modulation of the lattice parameter can modify the electronic structure and manipulate the magnetic coupling of a material without introducing impurities. Inspired by success in pressure-controlled magnetism, we investigate the effect of hydrostatic pressure on quantized Chern states in the antiferromagnetic topological insulator MnBi2Te4, using transport as a probe. We show that pressure can enhance the robustness of quantum anomalous Hall (QAH) phases that are otherwise delicate in 7SL MnBi2Te4 and in the spin-flop (SF) state of 8SL MnBi2Te4. We explain our findings using a coupled Dirac cone model of MnBi2Te4, which identifies stronger hybridization between van der Waals layers as the driver of topological states. We further demonstrate that moderate pressures readily available in laboratory systems can provide reversible control of magnetic and topological phases. Our results reveal a strong connection between the mechanical engineering of band topology and magnetism.

cond-mat.mes-hall↗
arXiv2023

SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal Segmentation

Medical image segmentation based on deep learning often fails when deployed on images from a different domain. The domain adaptation methods aim to solve domain-shift challenges, but still face some problems. The transfer learning methods require annotation on the target domain, and the generative unsupervised domain adaptation (UDA) models ignore domain-specific representations, whose generated quality highly restricts segmentation performance. In this study, we propose a novel Structure-Modal Constrained (SMC) UDA framework based on a discriminative paradigm and introduce edge structure as a bridge between domains. The proposed multi-modal learning backbone distills structure information from image texture to distinguish domain-invariant edge structure. With the structure-constrained self-learning and progressive ROI, our methods segment the kidney by locating the 3D spatial structure of the edge. We evaluated SMC-UDA on public renal segmentation datasets, adapting from the labeled source domain (CT) to the unlabeled target domain (CT/MRI). The experiments show that our proposed SMC-UDA has a strong generalization and outperforms generative UDA methods.

cs.CV↗
arXiv2023

Highly Accurate Prediction of NMR Chemical Shifts from Low-Level Quantum Mechanics Calculations Using Machine Learning

Theoretical predictions of NMR chemical shifts from first-principles can greatly facilitate experimental interpretation and structure identification. However, accurate prediction of chemical shifts using the best coupled cluster methods can be prohibitively expensive for systems larger than ten to twenty non-hydrogen atoms on today's computers. By contrast machine learning methods offer inexpensive alternatives but are hampered by generalization to molecules outside the original training set. Here we propose a novel machine learning feature representation informed by intermediate calculations of atomic chemical shielding tensors within a molecular environment using an inexpensive quantum mechanics method, and training it to predict NMR chemical shieldings of a high-level composite theory that is comparable to CCSD(T) in the complete basis set limit. The inexpensive shift machine learning (iShiftML) algorithm is trained through a new progressive active learning workflow that reduces the total number of expensive calculations required when constructing the dataset, while allowing the model to continuously improve on data it has never seen. Furthermore, we show that the error estimations from our model correlate quite well with actual errors to provide confidence values on new predictions. We illustrate the predictive capacity of iShiftML across gas phase experimental chemical shifts for small organic molecules and much larger and more complex natural products in which we can accurately differentiate between subtle diastereomers based on chemical shift assignments.

physics.chem-ph↗
arXiv2023

Temporal Gradient Inversion Attacks with Robust Optimization

Federated Learning (FL) has emerged as a promising approach for collaborative model training without sharing private data. However, privacy concerns regarding information exchanged during FL have received significant research attention. Gradient Inversion Attacks (GIAs) have been proposed to reconstruct the private data retained by local clients from the exchanged gradients. While recovering private data, the data dimensions and the model complexity increase, which thwart data reconstruction by GIAs. Existing methods adopt prior knowledge about private data to overcome those challenges. In this paper, we first observe that GIAs with gradients from a single iteration fail to reconstruct private data due to insufficient dimensions of leaked gradients, complex model architectures, and invalid gradient information. We investigate a Temporal Gradient Inversion Attack with a Robust Optimization framework, called TGIAs-RO, which recovers private data without any prior knowledge by leveraging multiple temporal gradients. To eliminate the negative impacts of outliers, e.g., invalid gradients for collaborative optimization, robust statistics are proposed. Theoretical guarantees on the recovery performance and robustness of TGIAs-RO against invalid gradients are also provided. Extensive empirical results on MNIST, CIFAR10, ImageNet and Reuters 21578 datasets show that the proposed TGIAs-RO with 10 temporal gradients improves reconstruction performance compared to state-of-the-art methods, even for large batch sizes (up to 128), complex models like ResNet18, and large datasets like ImageNet (224*224 pixels). Furthermore, the proposed attack method inspires further exploration of privacy-preserving methods in the context of FL.

cs.LG↗
arXiv2023

Entangling ferrimagnetic magnons with an atomic ensemble via opto-magnomechanics

We show how to prepare macroscopic entanglement between an atomic ensemble and a large number of magnons in a ferrimagnetic YIG crystal. Specifically, we adopt an opto-magnomechanical configuration where the magnetostriction-induced magnomechanical displacement couples to an optical cavity via radiation pressure, and the latter further couples to an ensemble of two-level atoms that are placed inside the cavity. We show that by properly driving the cavity and magnon modes, optomechanical entanglement is created which is further distributed to the atomic and magnonic systems, yielding stationary entanglement between atoms and magnons. The atom-magnon entanglement is a result of the combined effect of opto- and magnomechanical cooling and optomechanical parametric down-conversion interactions. A competition mechanism between two mechanical cooling channels is revealed. We further show that genuine tripartite entanglement of three massive subsystems, i.e., atoms, magnons and phonons, can also be achieved in the same system. Our results indicate that the hybrid opto-magnomechanical system may become a promising system for preparing macroscopic quantum states involving magnons, photons, phonons and atoms.

quant-ph↗
arXiv2023

Massive white dwarfs in Rastall-Rainbow gravity

We investigate the hydrostatic equilibrium of white dwarfs within the framework of Rastall-Rainbow gravity, aiming to explore the effects of this modified gravitational theory on their properties. By employing the Chandrasekhar equation of state in conjunction with the modified Tolman-Oppenheimer-Volkoff equation, we derive the mass-radius relations for white dwarfs. Our results show that the maximum mass of white dwarfs deviates significantly from the predictions of general relativity, potentially exceeding the Chandrasekhar limit. Furthermore, we discuss other properties of white dwarfs, such as the gravitational redshift, compactness and dynamical stability, shedding light on their behavior within the context of this modified gravitational framework.

gr-qc↗