arXiv subjects

Hua Chen

Publications and source records attributed to Hua Chen.

At least 73 records · Page 4Linked to original sources

arXiv2025

Nanoscale quantum imaging of field-free deterministic switching of a chiral antiferromagnet

Recently, unconventional spin-orbit torques (SOTs) with tunable spin generation open new pathways for designing novel magnetization control for cutting-edge spintronics innovations. A leading research thrust is to develop field-free deterministic magnetization switching for implementing scalable and energy favorable magnetic recording and storage applications, which have been demonstrated in conventional ferromagnetic and antiferromagnetic material systems. Here we extend this advanced magnetization control strategy to chiral antiferromagnet Mn3Sn using spin currents with out-of-plane canted polarization generated from low-symmetry van der Waals (vdW) material WTe2. Numerical calculations suggest that damping-like SOT of spins injected perpendicular to the kagome plane of Mn3Sn serves as a driving force to rotate the chiral magnetic order, while the field-like SOT of spin currents with polarization parallel to the kagome plane provides the bipolar deterministicity to the magnetic switching. We further introduce scanning quantum microscopy to visualize nanoscale evolutions of Mn3Sn magnetic domains during the field-free switching process, corroborating the exceptionally large magnetic switching ratio up to 90%. Our results highlight the opportunities provided by hybrid SOT material platforms consisting of noncollinear antiferromagnets and low-symmetry vdW spin source materials for developing next-generation, transformative spintronic logic devices.

cond-mat.mtrl-sci↗

arXiv2025

A Practical Introduction to Deep Reinforcement Learning

Deep reinforcement learning (DRL) has emerged as a powerful framework for solving sequential decision-making problems, achieving remarkable success in a wide range of applications, including game AI, autonomous driving, biomedicine, and large language models. However, the diversity of algorithms and the complexity of theoretical foundations often pose significant challenges for beginners seeking to enter the field. This tutorial aims to provide a concise, intuitive, and practical introduction to DRL, with a particular focus on the Proximal Policy Optimization (PPO) algorithm, which is one of the most widely used and effective DRL methods. To facilitate learning, we organize all algorithms under the Generalized Policy Iteration (GPI) framework, offering readers a unified and systematic perspective. Instead of lengthy theoretical proofs, we emphasize intuitive explanations, illustrative examples, and practical engineering techniques. This work serves as an efficient and accessible guide, helping readers rapidly progress from basic concepts to the implementation of advanced DRL algorithms.

cs.LG↗

arXiv2025

Interaction-Driven Intervalley Coherence with Emergent Kekulé Orbitons

The $p$-orbital doublet in a honeycomb lattice is concretely studied with interacting spinless fermions at half filling. The Dirac fermions with linear dispersion at $\pm K$ valleys govern the non-interacting low-energy physics. In the weak-coupling regime, the Dirac fermions are gaped due to the spontaneous generation of mass terms through a uniform axial orbital ordering, rendering the system into the quantum anomalous Hall insulator phase with a nonzero Chern number. Surprisingly, the intermediate many-particle interaction produces the intervalley coherence between $\pm K$ valleys by developing complex polar orbital orderings in a tripled Wigner-Seitz cell. This phase is shown to have a deep connection with the low-energy physical behavior described by the orbital exchange model in the Mott insulating phase. The classical ground-state manifold in the Mott regime enjoys a continuous symmetry characterized by the intervalley coherent phase. Finally, the quantum fluctuation selects a unique ground state with emergent Kekulé orbitons through the order-by-disorder mechanism. Our findings provide insights for a direction of searching for Kekulé distortion in correlated multi-orbital systems.

cond-mat.str-el↗

arXiv2025

Blow up analysis for Keller-Segel system

In this paper we develop a blow up theory for the parabolic-elliptic Keller-Segel system, which can be viewed as a parabolic counterpart to the Liouville equation. This theory is applied to the study of first time singularities, ancient solutions and entire solutions, leading to a description of the blow-up limit in the first problem, and the large scale structure in the other two problems.

math.AP↗

arXiv2025

Beamforming for Movable and Rotatable Antenna Enabled Multi-User Communications

In the development of wireless communication technology, multiple-input multiple-output (MIMO) technology has emerged as a key enabler, significantly enhancing the capacity of communication systems. However, traditional MIMO systems, which rely on fixed-position antennas (FPAs) with spacing limitations, cannot fully exploit the channel variations in the continuous spatial domain, thus limiting the system's spatial multiplexing performance and diversity. To address these limitations, movable antennas (MAs) have been introduced, offering a breakthrough in signal processing and spatial multiplexing by overcoming the constraints of FPA-based systems. Furthermore, this paper extends the functionality of MAs by introducing movable rotatable antennas (MRAs), which enhance the system's ability to optimize performance in the spatial domain by adding rotational degrees of freedom. By incorporating a dynamic precoding framework based on both antenna position and rotation angle optimization, and employing the zero-forcing (ZF) precoding method, this paper proposes an efficient optimization approach aimed at improving signal quality, mitigating interference, and solving the non-linear, constrained optimization problem using the sequential quadratic programming (SQP) algorithm. This approach effectively enhances the communication system's performance.

eess.SP↗

arXiv2025

Generative Artificial Intelligence in Robotic Manipulation: A Survey

This survey provides a comprehensive review on recent advancements of generative learning models in robotic manipulation, addressing key challenges in the field. Robotic manipulation faces critical bottlenecks, including significant challenges in insufficient data and inefficient data acquisition, long-horizon and complex task planning, and the multi-modality reasoning ability for robust policy learning performance across diverse environments. To tackle these challenges, this survey introduces several generative model paradigms, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), diffusion models, probabilistic flow models, and autoregressive models, highlighting their strengths and limitations. The applications of these models are categorized into three hierarchical layers: the Foundation Layer, focusing on data generation and reward generation; the Intermediate Layer, covering language, code, visual, and state generation; and the Policy Layer, emphasizing grasp generation and trajectory generation. Each layer is explored in detail, along with notable works that have advanced the state of the art. Finally, the survey outlines future research directions and challenges, emphasizing the need for improved efficiency in data utilization, better handling of long-horizon tasks, and enhanced generalization across diverse robotic scenarios. All the related resources, including research papers, open-source data, and projects, are collected for the community in https://github.com/GAI4Manipulation/AwesomeGAIManipulation

cs.RO↗

arXiv2025

Vibration Analysis and Mitigation in Semiconductor Motion Stages Using DMAIC Methodology- A Case Study

Motion stages are critical in semiconductor manufacturing equipment for processes like die bonding, wafer loading, and chip packaging, as their performance must meet the industry's stringent precision requirements. Vibration, a significant yet often overlooked adversary to precision motion stages, is challenging to identify and mitigate due to its subtle nature. This study, conducted at a motion stage manufacturer facing frequent vibration-related complaints, proposes a novel approach to resolving vibration issues. By leveraging the DMAIC methodology, it introduces VIBGUARD, an active vibration monitoring and mitigation solution, instead of solely focusing on traditional hardware vibration control. This comprehensive strategy enhances value and competitiveness, increasing UPH (units per hour) by 15.3% from 8,500 to 9,800 and reducing downtime by 68.2% from 2.2 to 0.7 occurrences per month. This case study and the DMAIC methodology offer valuable resources for quality control and problem analysis in the semiconductor industry.

eess.SY↗

arXiv2025

Bounds and anomalies of inhomogeneous anomalous Hall effects

It is well recognized that interpreting transport experiment results can be challenging when the samples being measured are spatially nonuniform. However, quantitative understanding on the differences between measured and actual transport coefficients, especially the Hall effects, in inhomogeneous systems is lacking. In this work we use homogenization theory to find exact bounds of the measured or homogenized anomalous Hall conductivity (AHC) in inhomogeneous conductors under minimal assumptions. In particular, we prove that the homogenized AHC cannot exceed the bounds of the local AHC. However, in common experimental setups, anomalies that appear to violate the above bounds can occur, with a popular example being the "humps" or "dips" of the Hall hysteresis curves usually ascribed to the topological Hall effect (THE). We give two examples showing how such apparent anomalies could be caused by different types of inhomogeneities and discuss their relevance in experiments.

cond-mat.mes-hall↗

arXiv2025

Fermi surface origin of the low-temperature magnetoresistance anomaly

A magnetoresistance (MR) anomaly at low temperatures has been observed in a variety of systems, ranging from low-dimensional chalcogenides to spin and charge density wave (SDW/CDW) metals and, most recently, topological semimetals. In some systems parabolic magnetoresistance can rise to hundreds of thousands of times its low-temperature, zero-field value. While the origin of such a dramatic effect remains unresolved, these systems are often low-carrier-density compensated metals, and the physics is expected to be quasi-classical. Here we demonstrate that this MR anomaly in temperature also exists in high conductivity good metals with large Fermi surfaces, namely Cr, Mo, and W, for both linear and quadratic field-dependent regimes with their non-saturation attributed to open orbit and electron-hole compensation, respectively. We provide evidence that quantum transport across sharp Fermi surface arcs, but not necessarily the full cyclotron orbit, governs this low-temperature MR anomaly. In Cr, extremely sharp curvatures are induced by superposed lattice and SDW band structures. One observes an overlay of the temperature dependence of three phenomena: namely, MR at a constant high field, linear MR in the low-field limit, and Shubnikov-de Haas (SdH) oscillations of the lightest orbit. In Mo, the temperature dependence of low-T MR anomaly extends beyond those of its SdH oscillations but disappears at temperatures where Kohler's scaling reemerges. In the low-temperature and high-field limit, large magnetoresistance from carriers circling quantum orbits is the three-dimensional analogy to the zero-conductance state of carrier localization in the integer quantum Hall effect, especially with regard to the adverse effect of disorder.

cond-mat.mtrl-sci↗

arXiv2025

MRG: A Multi-Robot Manufacturing Digital Scene Generation Method Using Multi-Instance Point Cloud Registration

A high-fidelity digital simulation environment is crucial for accurately replicating physical operational processes. However, inconsistencies between simulation and physical environments result in low confidence in simulation outcomes, limiting their effectiveness in guiding real-world production. Unlike the traditional step-by-step point cloud "segmentation-registration" generation method, this paper introduces, for the first time, a novel Multi-Robot Manufacturing Digital Scene Generation (MRG) method that leverages multi-instance point cloud registration, specifically within manufacturing scenes. Tailored to the characteristics of industrial robots and manufacturing settings, an instance-focused transformer module is developed to delineate instance boundaries and capture correlations between local regions. Additionally, a hypothesis generation module is proposed to extract target instances while preserving key features. Finally, an efficient screening and optimization algorithm is designed to refine the final registration results. Experimental evaluations on the Scan2CAD and Welding-Station datasets demonstrate that: (1) the proposed method outperforms existing multi-instance point cloud registration techniques; (2) compared to state-of-the-art methods, the Scan2CAD dataset achieves improvements in MR and MP by 12.15% and 17.79%, respectively; and (3) on the Welding-Station dataset, MR and MP are enhanced by 16.95% and 24.15%, respectively. This work marks the first application of multi-instance point cloud registration in manufacturing scenes, significantly advancing the precision and reliability of digital simulation environments for industrial applications.

cs.CV↗

arXiv2024

Hall mass and transverse Noether spin currents in noncollinear antiferromagnets

Noncollinear antiferromagnets (AFMs) have recently attracted attention in the emerging field of antiferromagnetic spintronics because of their various interesting properties. Due to the noncollinear magnetic order, the localized electron spins on different magnetic sublattices are not conserved even when spin-orbit coupling is neglected, making it difficult to understand the transport of spin angular momentum. Here we study the conserved Noether current due to spin-rotation symmetry of the local spins in noncollinear AFMs. Interestingly, we find that a Hall component of the spin current can be generically created by a longitudinal driving force associated with a propagating spin wave, inherently distinguishing noncollinear AFMs from collinear ones. We coin the corresponding Hall coefficient, an isotropic rank-4 tensor, as the Hall (inverse) mass, which generally exists in noncollinear AFMs and their polycrystals. The resulting Hall spin current can be realized by spin pumping in a ferromagnet (FM)-noncollinear AFM bilayer structure as we demonstrate numerically, for which we also give the criteria of ideal boundary conditions. Our results shed light on the potential of noncollinear AFMs in manipulating the polarization and flow of spin currents in general spintronic devices.

cond-mat.mes-hall↗

arXiv2024

Liouville Theorem for Lane Emden Equation of Baouendi Grushin operators

In this paper, we establish a Liouville theorem for solutions to the Lane Emden equation involving Baouendi Grushin operators. We focus on solutions that are stable outside a compact set. Specifically, we prove that when p is smaller than the Joseph Lundgren exponent and differs from the Sobolev exponent, 0 is the unique solution stable outside a compact set. This work extends the results obtained by Farina (J. Math. Pures Appl., 87 (5) (2007)).

math.AP↗

arXiv2024

Liouville Theorem for Lane-Emden Equation on the Heisenberg Group

This paper establishes some Liouville type results for solutions to the Lane Emden equation on the entire Heisenberg group, both in the stable and stable outside a compact set scenarios.Specifically, we prove that when p is smaller than the Joseph Lundgren exponent and does not equal the Sobolev exponent, 0 is the unique solution that is stable outside a compact set.

math.AP↗

arXiv2024

Tunneling current-controlled spin states in few-layer van der Waals magnets

Effective control of magnetic phases in two-dimensional magnets would constitute crucial progress in spintronics, holding great potential for future computing technologies. Here, we report a new approach of leveraging tunneling current as a tool for controlling spin states in CrI3. We reveal that a tunneling current can deterministically switch between spin-parallel and spin-antiparallel states in few-layer CrI3, depending on the polarity and amplitude of the current. We propose a mechanism involving nonequilibrium spin accumulation in the graphene electrodes in contact with the CrI3 layers. We further demonstrate tunneling current-tunable stochastic switching between multiple spin states of the CrI3 tunnel devices, which goes beyond conventional bi-stable stochastic magnetic tunnel junctions and has not been documented in two-dimensional magnets. Our findings not only address the existing knowledge gap concerning the influence of tunneling currents in controlling the magnetism in two-dimensional magnets, but also unlock possibilities for energy-efficient probabilistic and neuromorphic computing.

cond-mat.mes-hall↗

arXiv2024

RSTAR4D: Rotational Streak Artifact Reduction in 4D CBCT using a Separable 4D CNN

Four-dimensional cone-beam computed tomography (4D CBCT) provides respiration-resolved images and can be used for image-guided radiation therapy. However, the ability to reveal respiratory motion comes at the cost of image artifacts. As raw projection data are sorted into multiple respiratory phases, the cone-beam projections become much sparser and the reconstructed 4D CBCT images will be covered by severe streak artifacts. Although several deep learning-based methods have been proposed to address this issue, most algorithms employ 2D network models as backbones, neglecting the intrinsic structural priors within 4D CBCT images. In this paper, we first explore the origin and appearance of streak artifacts in 4D CBCT images. We find that streak artifacts exhibit a unique rotational motion along with the patient's respiration, distinguishable from diaphragm-driven respiratory motion in the spatiotemporal domain. Therefore, we propose a novel 4D neural network model, RSTAR4D-Net, designed to address Rotational STreak Artifact Reduction by integrating the spatial and temporal information within 4D CBCT images. Specifically, we overcome the computational and training difficulties of a 4D neural network. The specially designed model adopts an efficient implementation of 4D convolutions to reduce computational costs and thus can process the whole 4D image in one pass. Additionally, a Tetris training strategy pertinent to the separable 4D convolutions is proposed to effectively train the model using limited 4D training samples. Extensive experiments substantiate the effectiveness of our proposed method, and the RSTAR4D-Net shows superior performance compared to other methods. The source code and dynamic demos are available at https://github.com/ivy9092111111/RSTAR.

eess.IV↗

arXiv2024

Existence and uniqueness for cone degenerate p-Laplace equation

In this paper, we study the cone degenerate p-Laplace equation. We provide the existence of the viscosity solutions by proving Alexandrov-Bakelman-Pucci and Hölder estimates. Further more, we give the comparison principle by an equivalent transformation. Finally, we obtain the existence of weak solutions by analyzing the relationship between weak solutions and viscosity solutions.

math.AP↗

arXiv2024

CTS: Concurrent Teacher-Student Reinforcement Learning for Legged Locomotion

Thanks to recent explosive developments of data-driven learning methodologies, reinforcement learning (RL) emerges as a promising solution to address the legged locomotion problem in robotics. In this paper, we propose CTS, a novel Concurrent Teacher-Student reinforcement learning architecture for legged locomotion over uneven terrains. Different from conventional teacher-student architecture that trains the teacher policy via RL first and then transfers the knowledge to the student policy through supervised learning, our proposed architecture trains teacher and student policy networks concurrently under the reinforcement learning paradigm. To this end, we develop a new training scheme based on a modified proximal policy gradient (PPO) method that exploits data samples collected from the interactions between both the teacher and the student policies with the environment. The effectiveness of the proposed architecture and the new training scheme is demonstrated through substantial quantitative simulation comparisons with the state-of-the-art approaches and extensive indoor and outdoor experiments with quadrupedal and point-foot bipedal robot platforms, showcasing robust and agile locomotion capability. Quantitative simulation comparisons show that our approach reduces the average velocity tracking error by up to 20% compared to the two-stage teacher-student, demonstrating significant superiority in addressing blind locomotion tasks. Videos are available at https://clearlab-sustech.github.io/concurrentTS.

cs.RO↗

arXiv2024

Valuing an Engagement Surface using a Large Scale Dynamic Causal Model

With recent rapid growth in online shopping, AI-powered Engagement Surfaces (ES) have become ubiquitous across retail services. These engagement surfaces perform an increasing range of functions, including recommending new products for purchase, reminding customers of their orders and providing delivery notifications. Understanding the causal effect of engagement surfaces on value driven for customers and businesses remains an open scientific question. In this paper, we develop a dynamic causal model at scale to disentangle value attributable to an ES, and to assess its effectiveness. We demonstrate the application of this model to inform business decision-making by understanding returns on investment in the ES, and identifying product lines and features where the ES adds the most value.

cs.LG↗