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Hui Chen

Publications and source records attributed to Hui Chen.

At least 55 records · Page 3Linked to original sources
arXiv2026

Adaptive Subspace Signal Detection and Performance Analysis in Nonzero-Mean Clutter

To solve the problem of detecting subspace signals in nonzero-mean clutter, we propose adaptive detectors, based on the strategies of generalized likelihood ratio test (GLRT), Rao test, Wald test, gradient test, and Durbin test. The results show that the detectors based on GLRT, Rao and Wald are structurally consistent with the subspace detectors in zero-means clutter. The analytic expressions for the probability of detection (PD) and probability of false alarm (PFA) of each detector are derived, and two major performance differences in the nonzero-mean clutter scenario are revealed. One is the loss of degree of freedom (DOF), which is reduced by 1 compared with the zero-mean clutter scenario. The second is the loss of signal-to-clutter (SCR) ratio. Simulation and measured data verify the effectiveness of the proposed detectors and demonstrate their practical value in real-world radar systems.

stat.OT↗
arXiv2026

From Pilot to Precoding Design: Blind Angular Spoofing For Location Privacy in MIMO Systems

This paper studies location privacy in uplink MIMO systems, where a user equipment seeks to spoof the angular signature observed by a single base station performing localization. We propose a blind analog precoder design that manipulates the perceived angle-of-arrival and angle-of-departure configuration without requiring channel-gain knowledge. The method enforces consistency between the received signal and a desired spoofed angular subspace, and is solved using an alternating optimization algorithm under practical amplitude constraints. Simulations in a multipath scenario show that the proposed approach achieves near-perfect angular spoofing and clearly outperforms pilot-only blind spoofing, which exhibits an error floor. The results also show a trade-off between spoofing accuracy and communication rate, depending on the chosen virtual geometry.

eess.SP↗
arXiv2026

Observation antibunching with classical light in a linear interferometer

Understanding the boundary between classical and nonclassical phenomena is important for both fundamental researches in quantum optics and applications in quantum information. One of the most interesting research directions in this field is exploring nonclassical effects with classical light. In this paper, we will show that it is possible to observe antibunching with thermal light in a Hanbury Brown-Twiss interferometer by treating single-photon detectors as photon-number-resolving detectors to perform photon-number projection measurements. Both temporal and spatial antibunching is observed via the correlation of two detectors detecting one and zero photon, respectively. By comparing the measured results of thermal and laser light, it is found that the observed antibunching arises from the combined effect of photon statistics of thermal light and photon-number projection measurement.The classical and nonclassical nature of the observed antibunching is analyzed. The results are helpful to understand the connection between classical and nonclassical correlation and may find applications in multiphoton interference and quantum imaging.

quant-ph↗
arXiv2026

Spatially anisotropic Kondo resonance coupled with the superconducting gap in a kagome metal

The chromium-based kagome metal CsCr3Sb5 has garnered significant interest due to its strong electron correlations, intertwined orders and potential for unconventional superconductivity under high pressure. The evolution of magnetic and superconducting interactions as the more frequently studied CsV3Sb5 is doped to CsCr3Sb5 remains poorly understood. Here, we demonstrate the emergence of a spatially anisotropic Kondo resonance intertwined with the superconducting gap, enabled by introducing magnetic Cr impurities into the kagome superconductor CsV3Sb5. The addition of dilute Cr impurities not only weakens long range charge density wave order but also produces local magnetic moments that leads to Kondo resonances. We show that the Kondo resonance forms anisotropic, ripple like spatial patterns around individual Cr atoms, breaking all local mirror symmetries. We further reveal that with the emergence of Kondo screening, the coherence peak and depth of superconducting gap with finite zero-energy conductance are enhanced. This suggests that non superconducting carriers at the Fermi surface in the parent compound participate in the Kondo effect, simultaneously screening Cr magnetic moments and increasing the superfluid density. Our findings offer an opportunity to study the interplay between superconductivity and local magnetism in kagome materials.

cond-mat.supr-con↗
arXiv2026

Spin-mediated hysteretic switching of unidirectional charge density waves by rotating magnetic fields

Charge density waves (CDWs) are a widespread collective electronic order in quantum materials, furnishing key insights into symmetry breaking and competing phases. However, their dynamic control with external fields remains a pivotal challenge. Here, we report deterministic and hysteretic switching of unidirectional CDW orientation via in-plane magnetic field rotation in magnetic kagome metal GdTi3Bi4. Atomically resolved spectroscopy shows two types of 3a0*1a0 CDW domains, Q1 and Q2 oriented 60 degree apart along two distinct crystallographic directions and separated by atomically sharp domain walls. Rotating the magnetic field drives reversible transitions between these CDW configurations, exhibiting a robust C2-symmetric phase diagram with pronounced hysteresis. This hysteretic switching is mediated by a field-dependent reorientation of underlying antiferromagnetic spins, revealing a tunable energy landscape with stable and metastable states and modulates the electronic charge order via spin-lattice coupling. Our findings not only demonstrate the switching of CDW configurations by in-plane magnetic field but also reveal the mechanism of coupling between CDW and magnetic fields, offering new insights into CDW manipulation and versatile platform for developing a spin-mediated multistate spin-charge coupling memory and programmable quantum devices.

cond-mat.str-el↗
arXiv2026

StaMo: Unsupervised Learning of Generalizable Robot Motion from Compact State Representation

A fundamental challenge in embodied intelligence is developing expressive and compact state representations for efficient world modeling and decision making. However, existing methods often fail to achieve this balance, yielding representations that are either overly redundant or lacking in task-critical information. We propose an unsupervised approach that learns a highly compressed two-token state representation using a lightweight encoder and a pre-trained Diffusion Transformer (DiT) decoder, capitalizing on its strong generative prior. Our representation is efficient, interpretable, and integrates seamlessly into existing VLA-based models, improving performance by 14.3% on LIBERO and 30% in real-world task success with minimal inference overhead. More importantly, we find that the difference between these tokens, obtained via latent interpolation, naturally serves as a highly effective latent action, which can be further decoded into executable robot actions. This emergent capability reveals that our representation captures structured dynamics without explicit supervision. We name our method StaMo for its ability to learn generalizable robotic Motion from compact State representation, which is encoded from static images, challenging the prevalent dependence to learning latent action on complex architectures and video data. The resulting latent actions also enhance policy co-training, outperforming prior methods by 10.4% with improved interpretability. Moreover, our approach scales effectively across diverse data sources, including real-world robot data, simulation, and human egocentric video.

cs.RO↗
arXiv2026

CommUNext: Deep Learning-Based Cross-Band and Multi-Directional Signal Prediction

Sixth-generation (6G) networks are envisioned to achieve full-band cognition by jointly utilizing spectrum resources from Frequency Range 1 (FR1) to Frequency Range 3 (FR3, 7-24 GHz). Realizing this vision faces two challenges. First, physicsbased ray tracing (RT), the standard tool for network planning and coverage modeling, becomes computationally prohibitive for multi-band and multi-directional analysis over large areas. Second, current 5G systems rely on inter-frequency measurement gaps for carrier aggregation and beam management, which reduce throughput, increase latency, and scale poorly as bands and beams proliferate. These limitations motivate a datadriven approach to infer high-frequency characteristics from low-frequency observations. This work proposes CommUNext, a unified deep learning framework for cross-band, multi-directional signal strength (SS) prediction. The framework leverages lowfrequency coverage data and crowd-aided partial measurements at the target band to generate high-fidelity FR3 predictions. Two complementary architectures are introduced: Full CommUNext, which substitutes costly RT simulations for large-scale offline modeling, and Partial CommUNext, which reconstructs incomplete low-frequency maps to mitigate measurement gaps in real-time operation. Experimental results show that CommUNext delivers accurate and robust high-frequency SS prediction even with sparse supervision, substantially reducing both simulation and measurement overhead.

cs.IT↗
arXiv2026

Ghost imaging with zero photons

Ghost imaging was first demonstrated with entangled photon pairs and well-known for its peculiar properties. The signal beam that illuminates the object possesses no spatial resolution, whereas the reference beam, which never interacts with the object, is spatially resolved. Either beam alone cannot retrieve the image, which can only be obtained when the signal and reference beams are correlated. Here we will report a ghost imaging experiment with even more peculiar properties, in which the image can be reconstructed when no photon interacts with the object or even no photon in neither signal nor reference beam. All the photons interacted with the object are discarded. Only the time bins with zero photon are employed to retrieve the image, a process referred to as "ghost imaging with zero photons" hereafter. The reason why ghost image can be retrieved with zero photons is jointly determined by photon-number projection measurement and photon statistics of thermal light. The results are helpful to resolve the debate on the physics of ghost imaging and understand the relation between quantum and classical correlations.

quant-ph↗
arXiv2026

AgentSwing: Adaptive Parallel Context Management Routing for Long-Horizon Web Agents

As large language models (LLMs) evolve into autonomous agents for long-horizon information-seeking, managing finite context capacity has become a critical bottleneck. Existing context management methods typically commit to a single fixed strategy throughout the entire trajectory. Such static designs may work well in some states, but they cannot adapt as the usefulness and reliability of the accumulated context evolve during long-horizon search. To formalize this challenge, we introduce a probabilistic framework that characterizes long-horizon success through two complementary dimensions: search efficiency and terminal precision. Building on this perspective, we propose AgentSwing, a state-aware adaptive parallel context management routing framework. At each trigger point, AgentSwing expands multiple context-managed branches in parallel and uses lookahead routing to select the most promising continuation. Experiments across diverse benchmarks and agent backbones show that AgentSwing consistently outperforms strong static context management methods, often matching or exceeding their performance with up to $3\times$ fewer interaction turns while also improving the ultimate performance ceiling of long-horizon web agents. Beyond the empirical gains, the proposed probabilistic framework provides a principled lens for analyzing and designing future context management strategies for long-horizon agents.

cs.CL↗
arXiv2026

Bayesian estimation of optical constants using mixtures of Gaussian process experts

We propose modeling absorption spectrum measurements as mixtures of Gaussian process experts. This enables us to construct a flexible statistical model for interpolating and extrapolating measurements, facilitating statistical integration of Kramers-Kronig relations to estimate the whole complex refractive index. Additionally, we statistically model the anchoring points used in subtractive Kramers-Kronig relations to account for possible measurement errors of the anchor point. In addition to flexible statistical modeling, the mixtures of Gaussian process formulation enables automatic selection of measurement points to use for extrapolation. We apply the method to experimental absorption spectrum measurements of gallium arsenide, potassium chloride, and transparent wood.

stat.AP↗
arXiv2026

Near-Field Localization via Reconfigurable Antennas

Reconfigurable antennas (RAs) utilize the electromagnetic (EM) domain to provide dynamic control over antenna radiation patterns, which offers an effective way to enhance power efficiency in wireless links. Unlike conventional arrays with fixed element patterns, RAs enable on-demand beam-pattern synthesis by directly controlling each antenna's EM characteristics. While existing research on RAs has primarily focused on improving spectral efficiency, this paper explores their application for downlink localization. Moreover, the majority of existing works focus on far-field scenarios with little attention on near-field (NF). Motivated by these gaps, we consider a synthesis model in which each antenna generates desired beampatterns from a finite set of EM basis functions. We then formulate a joint optimization problem for the baseband (BB) and EM precoders with the objective of minimizing the user equipment (UE) position error bound (PEB) in NF conditions. Our analytical derivations and extensive simulation results demonstrate that the proposed hybrid precoder design for RAs significantly improves UE positioning accuracy compared to traditional non-reconfigurable arrays.

eess.SP↗
arXiv2026

Pair-loaded electron-only magnetic reconnection using laser-driven capacitor coils

We propose and simulate a laboratory platform to study the effects of positrons in magnetic reconnection using laser-driven capacitor coils. Using particle-in-cell simulations, we show that externally injected MeV electron-positron pairs are trapped in the coil current sheet, significantly modifying the reconnection dynamics and particle acceleration. These pairs increase the reconnection rate by a factor of approximately 8, which Ohm's law decomposition reveals to be driven by the divergence of the generalized pressure tensor. Based on their high energy and magnetization, the pairs also substantially broaden the diffusion region. Particle tracking simulations in realistic coil magnetic fields further demonstrate that injected pairs can remain confined for several picoseconds, providing conditions for sustained interaction with the reconnection region. These results establish a near-term pathway to laboratory studies of positron-influenced reconnection, bridging high-energy-density experiments with pair-dominated astrophysical environments.

physics.plasm-ph↗
arXiv2026

xLLM Technical Report

We introduce xLLM, an intelligent and efficient Large Language Model (LLM) inference framework designed for high-performance, large-scale enterprise-grade serving, with deep optimizations for diverse AI accelerators. To address these challenges, xLLM builds a novel decoupled service-engine architecture. At the service layer, xLLM-Service features an intelligent scheduling module that efficiently processes multimodal requests and co-locates online and offline tasks through unified elastic scheduling to maximize cluster utilization. This module also relies on a workload-adaptive dynamic Prefill-Decode (PD) disaggregation policy and a novel Encode-Prefill-Decode (EPD) disaggregation policy designed for multimodal inputs. Furthermore, it incorporates a distributed architecture to provide global KV Cache management and robust fault-tolerant capabilities for high availability. At the engine layer, xLLM-Engine co-optimizes system and algorithm designs to fully saturate computing resources. This is achieved through comprehensive multi-layer execution pipeline optimizations, an adaptive graph mode and an xTensor memory management. xLLM-Engine also further integrates algorithmic enhancements such as optimized speculative decoding and dynamic EPLB, collectively serving to substantially boost throughput and inference efficiency. Extensive evaluations demonstrate that xLLM delivers significantly superior performance and resource efficiency. Under identical TPOT constraints, xLLM achieves throughput up to 1.7x that of MindIE and 2.2x that of vLLM-Ascend with Qwen-series models, while maintaining an average throughput of 1.7x that of MindIE with Deepseek-series models. xLLM framework is publicly available at https://github.com/jd-opensource/xllm and https://github.com/jd-opensource/xllm-service.

cs.DC↗
arXiv2026

Electric Vehicle User Charging Behavior Analysis Integrating Psychological and Environmental Factors: A Statistical-Driven LLM based Agent Approach

With the growing adoption of electric vehicles (EVs), understanding user charging behavior has become critical for grid stability and transportation planning. This study investigates the behavioral heterogeneity of EV taxi drivers by analyzing the interaction between psychological traits and situational triggers within dynamic travel contexts. Leveraging large language models (LLMs) as a core simulation tool, a novel framework with statistical enhancement is developed to replicate and analyze the charging behaviors of taxi drivers. LLMs simulate personalized decision-making processes by leveraging natural language reasoning and role-playing capabilities, accounting for factors such as time sensitivity, price awareness, and range anxiety. Simulation results indicate that the framework reliably reproduces real-world charging behaviors across multiple urban environments. his fidelity arises from integrating statistical priors into the reasoning process, allowing the model to anchor its decisions in empirical behavioral patterns. Further analysis highlights the joint influence of environmental and psychological variables on charging decisions and reveals the heterogeneity of different user groups. The findings provide new insights into EV user behavior, offering a foundation for optimizing charging infrastructure, informing energy policy, and advancing the integration of EV behavioral models into smart transportation and energy management systems.

cs.AI↗
arXiv2026

FedUAF: Uncertainty-Aware Fusion with Reliability-Guided Aggregation for Multimodal Federated Sentiment Analysis

Multimodal sentiment analysis in federated learning environments faces significant challenges due to missing modalities, heterogeneous data distributions, and unreliable client updates. Existing federated approaches often struggle to maintain robust performance under these practical conditions. In this paper, we propose FedUAF, a unified multimodal federated learning framework that addresses these challenges through uncertainty-aware fusion and reliability-guided aggregation. FedUAF explicitly models modality-level uncertainty during local training and leverages client reliability to guide global aggregation, enabling effective learning under incomplete and noisy multimodal data. Extensive experiments on CMU-MOSI and CMU-MOSEI demonstrate that FedUAF consistently outperforms state-of-the-art federated baselines across various missing-modality patterns and Non-IID settings. Moreover, FedUAF exhibits superior robustness against noisy clients, highlighting its potential for real-world multimodal federated applications.

cs.LG↗
arXiv2026

A Financial Brain Scan of the LLM

Emerging techniques in computer science make it possible to "brain scan" large language models (LLMs), identify the plain-English concepts that guide their reasoning, and steer them while holding other factors constant. We show that this approach can map LLM-generated economic forecasts to concepts such as sentiment, technical analysis, and timing, and compute their relative importance without reducing performance. We also show that models can be steered to be more or less risk-averse, optimistic, or pessimistic, which allows researchers to correct or simulate biases. The method is transparent, lightweight, and replicable for empirical research in the social sciences.

q-fin.GN↗
arXiv2026

Mismatch Analysis and Cooperative Calibration of Array Beam Patterns for ISAC Systems

Integrated sensing and communication (ISAC) is a key technology for enabling a wide range of applications in future wireless systems. However, the sensing performance is often degraded by model mismatches caused by geometric errors (e.g., position and orientation) and hardware impairments (e.g., mutual coupling and amplifier non-linearity). This paper focuses on the angle estimation performance with antenna arrays and tackles the critical challenge of array beam pattern calibration for ISAC systems. To assess calibration quality from a sensing perspective, a novel performance metric that accounts for angle estimation error, rather than beam pattern similarity, is proposed and incorporated into a differentiable loss function. Additionally, a cooperative calibration framework is introduced, allowing multiple user equipments to iteratively optimize the beam pattern based on the proposed loss functions and local data, and collaboratively update global calibration parameters. The proposed models and algorithms are validated using real-world beam pattern measurements collected in an anechoic chamber. Experimental results show that the angle estimation error can be reduced from {$\textbf{1.01}^\circ$} to $\textbf{0.11}^\circ$ in 2D calibration scenarios, and from $\textbf{5.19}^\circ$ to $\textbf{0.86}^\circ$ in 3D calibration ones.

eess.SP↗