arXiv subjects

Jiaxiang Wang

Publications and source records attributed to Jiaxiang Wang.

At least 19 recordsLinked to original sources

arXiv2026

Decay-Resolved Charge Changes from Radioactive Decays in Levitated Microparticles

We measure event-by-event discrete changes in the net electric charge of an optically levitated silica microsphere arising from individual radioactive decays within the sphere, in coincidence with energy depositions in a nearby scintillation detector. The net charge of the levitated sphere is continuously monitored by measuring its driven response to an oscillating electric field, allowing individual charge-change events to be resolved on millisecond timescales with precision below an elementary charge. Simultaneously, $α$ and $β$ particles emitted during decays of implanted $^{212}$Pb and its daughters are detected using a scintillator read out with an array of silicon photomultipliers. By correlating reconstructed charge-change times with the scintillator response, we can directly attribute abrupt changes in the sphere's net charge to individual nuclear decays, and identify differences in the distribution of charges ejected for $α$ and $β$ decays. These results establish a new approach for studying low energy charged particles emitted by radioactive decays at the single-decay level, and identify showers of radiogenically produced low-energy electrons emitted by $α$-decaying radon daughters implanted near solid surfaces.

physics.ins-det↗

arXiv2026

CRB-Guided Sensing and Resource Allocation for Human Pose Prediction in Integrated Sensing, Communication, and Computation Systems

Integrated sensing, communication, and computation (ISCC) provides a promising framework for indoor human-centric applications. In these applications, short-term human pose prediction facilitates continuous human pose tracking and proactive resource allocation. This paper proposes a Cramer-Rao bound (CRB)-guided sensing framework and investigates a problem of minimizing prediction error in resource-constrained ISCC systems. Specifically, a pose prediction model (ET-Mamba) is first developed to predict human joint positions for continuous tracking. To account for computation-resource limitations, lightweight prediction heads are attached to different inference layers, enabling adaptive-depth pose prediction. A CRB-guided perturbation strategy is then introduced to translate sensing uncertainty at different sensing SNR levels into point-cloud perturbations. Based on that, an empirical relationship among pose prediction error, sensing SNR, and model inference depth is established. Furthermore, to improve prediction accuracy under limited resources, this paper formulates a resource allocation optimization problem that minimizes the pose prediction error by jointly optimizing the beamforming matrix, model inference depth, and computation frequency. To solve this mixed-integer non-convex optimization problem, we propose an alternating optimization (AO)-based algorithm, where closed-form updates and semidefinite programming (SDP) are integrated into the iterative solution process. Simulation results show that the proposed method effectively improves pose prediction performance by up to 35 percent under resource constraints, verifying the effectiveness of conducting joint sensing, communication, and computation design in ISCC systems.

cs.IT↗

arXiv2026

Energy Efficiency Maximization for FAS-Assisted Downlink Communication in Mobile Embodied AI Networks (MEAN) over Interference Channels

In this paper, we investigate a fluid antenna system (FAS)-assisted downlink mobile embodied AI network (MEAN) over interference channels, where multiple base station (BS)-agent pairs reuse the same spectrum. The BSs employ FASs to improve the communication quality, while the mobile embodied artificial intelligence (AI) agents can adjust their positions according to environment-aware channel information, such as a channel-to-interference-plus-noise map (CINM). Considering both co-channel interference and the energy consumption caused by communication and agent movement, we formulate an energy efficiency (EE) maximization problem by jointly optimizing the agent positions, FAS port selections, and transmit powers. To solve this mixed-integer non-convex problem, we first derive the optimal transmit power in closed form for given agent positions and FAS ports. We then develop an iterative algorithm with adaptive FAS-port optimization and sequential agent-position optimization, together with a low-complexity power-update method. Simulation results demonstrate that the proposed design outperforms the considered benchmark schemes and provides improved feasibility under severe noise conditions.

eess.SP↗

arXiv2026

A broadband, individually addressing two- and three-dimensional photonic integrated circuit for trapped-ion qubit control

Trapped ions provide a high-fidelity platform for quantum information processing, yet delivery of multiple, distinct wavelengths across large networks of interaction zones remains a bottleneck. Conventional free-space light delivery lacks scalability, while on-chip grating couplers suffer from narrow operational bandwidth that increases circuit footprint and optical interfacing complexity. Here we show a broadband photonic integrated circuit capable of addressing individual ions. The circuit combines a planar waveguide lens with a micromirror fabricated using two-photon polymerization at wafer scale. This implementation can address three individual ions from $λ$ = 405 - 880 nm with -27 dB average intensity crosstalk at $5\,μ\mathrm{m}$ pitch. We trap $^{40}\mathrm{Ca}^{+}$ and $^{138}\mathrm{Ba}^{+}$ ions above such devices, characterize optical crosstalk with barium ions, and demonstrate individual repumping of calcium ions. This monolithic photonic architecture brings broadband addressing in an on-chip modality to trapped-ion technology. More generally, integrating additive manufacturing into quantum devices is poised to unlock expanded design space for implementing novel quantum architectures.

quant-ph↗

arXiv2026

Mixture of Enhanced-View Experts for Multi-Query Vehicle ReID and A Large-Scale Benchmark

Multi-query vehicle ReID aims to leverage complementary information from diverse views for robust feature learning. However, current methods suffer from simplistic feature fusion and thus easily ignores some important view information and cross-view relationships. To handle these problems, this work presents a novel approach called Mixture of Enhanced-View Experts (EV-MoE), which enhances the feature representation of each view and efficiently integrate the view-specific enhanced features by MoE, for robust multi-query ReID. In particular, we design a mixture of enhanced-view experts module, which consists of two parts including view-specific feature enhancement sub-Module (VFEM) and dynamic multi-view fusion sub-Module (DMFM). Moreover, we further introduce Multi-view Alignment Loss (MAL), which aligns features through bidirectional crossview contrastive learning and reconstruction constraints, addressing the challenges of consistency between multi-query features and single-image features. In addition, to evaluate multi-query ReID in real-world environments, we collect LCRI-1K, a largescale vehicle ReID dataset with 1,090 identities, 107,805 images, across 23,637 cameras, where each vehicle appears in an average of 67.5 cameras, providing a comprehensive benchmark to test the robustness in complex environments. Extensive experiments demonstrate the robustness of CAFNet in addressing the multiquery vehicle ReID problem. The code is available at https: //github.com/xiaozhen28/CAFNet.

cs.CV↗

arXiv2026

Digital Twin Synchronization Over Mobile Embodied AI Network With Agentic Intelligence

Efficient digital twin (DT) synchronization relies on maintaining high-fidelity virtual representations with minimal age of information (AoI). However, the synergistic potential of cooperative sensing and autonomous mobility of the sensing agent remains underexplored in existing DT synchronization frameworks. In this paper, we propose an agentic AI-empowered mobile embodied AI network (MEAN) framework for DT synchronization. In the proposed hybrid architecture, the base station (BS) conducts global orchestration, while the agents autonomously execute a five-stage closed-loop workflow: move-to-sense, cooperative sensing, onboard semantic processing, channel-aware mobility, and uplink transmission. To optimize synchronization performance, we formulate a joint topology dispatching and multidimensional resource allocation problem aimed at minimizing the maximum twin deviation across regions, subject to heterogeneous sensing fidelity and energy budget constraints. To tackle this, we develop a hierarchical two-layer optimization algorithm, where the outer-layer refines multi-agent assignment via a dynamic matching game, and the inner-layer iteratively optimizes the continuous resources. Extensive simulation results verify the convergence of the proposed algorithm and demonstrate its substantial superiority over multiple baseline schemes in reducing synchronization deviation. Furthermore, the results reveal that semantic compression serves as a vital substitute for channel resources in latency reduction under constrained bandwidth, while autonomous velocity adaptation provides an essential degree of freedom for the system to navigate the fundamental energy-time trade-off.

cs.IT↗

arXiv2026

Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks

In this paper, we investigate a problem of minimizing total energy consumption for secure federated learning (FL) over wireless edge networks. To address the high computational cost and privacy challenges in conventional FL with neural networks (NN) for resource-constrained users, we propose a novel FL with hyperdimensional computing and differential privacy (FL-HDC-DP) framework. In the considered model, each edge user employs hyperdimensional computing (HDC) for local training, which replaces complex neural updates with simple hypervector operations, and applies differential privacy (DP) noise to protect transmitted model information. We optimize the total energy of computation and communication under both latency and privacy constraints. We formulate the problem as an optimization that minimizes the total energy of all users by jointly allocating HDC dimension, transmission time, system bandwidth, transmit power, and CPU frequency. To solve this problem, a sigmoid-variant function is proposed to characterize the relationship between the HDC dimension and the convergence rounds required to reach a target accuracy. Based on this model, we develop two alternating optimization algorithms, where closed-form expressions for time, frequency, bandwidth, and power allocations are derived at each iteration. Since the iterative algorithm requires a feasible initialization, we construct a feasibility problem and obtain feasible initial resource parameters by solving a per round transmission time minimization problem. Simulation results demonstrate that the proposed FL-HDC-DP framework achieves up to 83.3% total energy reduction compared with the baseline, while attaining about 90% accuracy in approximately 3.5X fewer communication rounds than the NN baseline.

cs.DC↗

arXiv2026

TED: Related Party Transaction guided Tax Evasion Detection on Heterogeneous Graph

Tax evasion causes severe losses of government revenues and disturbs the economic order of fair competition. To help alleviate this problem, the latest tax evasion detection solutions utilize expert knowledge to extract features and then train classifiers to determine whether a company is suspected of tax evasion. However, existing solutions mainly focus on the statistical features of the company, but fail to exploit the rich interactive information in tax scenarios, which affect the detection performance. In this paper, we first model the tax scenario as a heterogeneous graph and study the tax evasion detection problem under the heterogeneous graph model. To improve the performance of tax evasion detection, a novel graph neural network model is proposed to extract the comprehensive information of heterogeneous graphs. Specifically, we use heterogeneous and complex related party transaction groups to filter low-level noise information. Moreover, a hierarchical attention mechanism is designed to capture the deeper structure and semantic information hidden in the related party transaction group. We apply our method to the real risk management system of the tax bureau, and evaluate it on two human-labeled real-world tax datasets. The results demonstrate that our method significantly outperforms the state-of-the-art in the tax evasion detection task.

cs.LG↗

arXiv2026

Joint Communication and Computation Design for Mobile Embodied AI Network (MEAN)

This letter investigates the problem of energy efficient collaborative strategy for mobile embodied artificial intelligence network (MEAN) over wireless communication. In the considered model, the agents execute the tasks through collaboration, and they can switch between two operating modes based on the signal-to-noise ratio (SNR) and global collaboration. The dual-mode comprises the base station (BS)-assisted collaborative mode, in which agents make decisions through semantic communication with BS and then collaborate on tasks, and the local computing mode, in which the agents make decisions and execute tasks independently. Due to the dynamic wireless communication and flexible collaboration strategy, we jointly consider computation energy, communication energy, and task-execution energy with specific collaborative gains into a mixed-integer nonlinear programming (MINLP) optimization problem whose goal is to minimize the total system energy consumption. To solve it, we propose a lower-complexity enumeration algorithm: first, we get the optimal closed-form solution for semantic compression ratio and transmit power by proving the strict convexity. Second, we determine the scale of collaboration and the operating mode of each agent by a greedy sorting algorithm based on individual energy-saving potentials. Simulation results show that the proposed algorithm can significantly reduce the total energy consumption compared to benchmark schemes.

eess.SP↗

arXiv2026

TOIB: Task-Oriented Orthogonalised Information Bottleneck for Distributed Semantic Communication

Task-oriented semantic communication emerges as a crucial paradigm for next-generation wireless networks, aiming to efficiently transmit task-relevant information while reducing interference and redundancy across multiple users. Existing information bottleneck (IB)-based frameworks predominantly focus on single-user scenarios, neglecting cross-user semantic interference in distributed semantic communications. To overcome this limitation, we propose a task-oriented orthogonalised information bottleneck (TOIB) approach, explicitly designed for distributed semantic communication systems. By introducing task-conditioned latent variables, TOIB adaptively balances semantic sufficiency, semantic compression, and inter-user semantic orthogonality. Extensive simulations conducted on classification tasks demonstrate that TOIB consistently achieves superior classification accuracy across various signal-to-noise ratio (SNR) regimes compared to traditional IB and deep joint source-channel coding (JSCC) methods. Specifically, the proposed method significantly enhances robustness under harsh low-SNR conditions and effectively suppresses cross-user semantic interference, as validated by cross-decoding accuracy metrics.

eess.SP↗

arXiv2026

Semantic Feature Multiple Access Empowered Integrated Learning and Communication Networks

Integrated learning and communication (ILAC) unifies learned transceivers with radio resource management, where semantic feature multiple access (SFMA) enables paired users to superpose their learned representations over shared time-frequency resources. Unlike conventional multiple access schemes, SFMA interference arises in the learned feature space and depends jointly on the user pair, the transmit power, and the compression ratio. This coupling ties binary pairing decisions to continuous resource variables, yielding a mixed-integer non-convex optimization problem. To address this problem, we first propose similarity-conditioned SFMA (SC-SFMA), a Swin Transformer-based transceiver whose dual-conditioned similarity modulator (DC-SimM) gates cross-user feature fusion according to the inter-user semantic similarity. We then characterize the resulting pair-dependent interference by a bivariate logistic function parameterized by transmit power and compression ratio, thereby bridging the learned transceiver with network-level optimization. On this basis, we formulate a sum-rate maximization problem subject to per-user distortion, latency, energy, power, and bandwidth constraints. To solve this problem, we develop a three-block alternating optimization algorithm that integrates dual-decomposition-assisted compression ratio allocation, trust-region successive convex approximation (SCA) for joint power-bandwidth optimization, and dynamic feasible graph-based user pairing. Simulation results show that SC-SFMA achieves considerable peak signal-to-noise ratio (PSNR) and multi-scale structural similarity index measure (MS-SSIM) gains over deep joint source-channel coding (JSCC) and separation-based baselines. The proposed optimization framework attains significant sum rate improvements over conventional multiple access baselines.

eess.SP↗

arXiv2026

Joint Device Pairing and Bandwidth Allocation Optimisation for Semantic Feature Multiple Access Networks

This paper presents a Semantic Feature Multiple Access (SFMA) framework for multi-user semantic communication in downlink wireless systems. By extending SwinJSCC to a two-user superimposition paradigm, SFMA enables simultaneous semantic transmission to multiple users over shared time-frequency resources. A key innovation is the Cross-User Attention (CUA) module, which facilitates controlled semantic feature exchange between paired users by leveraging inter-image similarity while mitigating interference. We formulate a joint user pairing and resource allocation problem to minimize global semantic distortion under constraints on bandwidth, end-to-end latency, and energy. This mixed-integer non-convex problem is decomposed into a Minimum-Weight Perfect Matching (MWPM) sub-problem and a convex bandwidth allocation feasibility check, with semi-closed-form bandwidth bounds derived from a strictly concave rate expression. A polynomial-time algorithm based on Blossom matching and bisection search is proposed. Extensive simulations on ImageNet-100 show that SFMA significantly improves reconstruction quality across pairing modes, and the proposed optimization effectively reduces overall distortion while satisfying physical-layer constraints.

eess.SP↗

arXiv2026

Near-Field Integrated Sensing, Computing and Semantic Communication in Digital Twin-Assisted Vehicular Networks

Digital twin (DT) technology offers transformative potential for vehicular networks, enabling high-fidelity virtual representations for enhanced safety and automation. However, seamless DT synchronization in dynamic environments faces challenges such as massive data transmission, precision sensing, and strict computational constraints. This paper proposes an integrated sensing, computing, and semantic communication (ISCSC) framework tailored for DT-assisted vehicular networks in the near-field (NF) regime. Leveraging a multi-user multiple-input multiple-output (MU-MIMO) configuration, each roadside unit (RSU) employs semantic communication to serve vehicles while simultaneously utilizing millimeter-wave (mmWave) radar for environmental mapping. We implement particle filtering at RSUs to achieve high-precision vehicle tracking. To optimize performance, we formulate a joint optimization problem balancing semantic communication rates and sensing accuracy under limited computational resources and power budget. Our solution includes a hybrid heuristic algorithm for vehicle-to-RSU assignment and an alternating optimization approach for determining semantic extraction ratios and beamforming matrices. Performance is extensively evaluated via the Cramér-Rao bound (CRB) for angle and distance estimation, semantic transmission rates, and resource utilization. Numerical results demonstrate that the proposed ISCSC framework achieves a 20% improvement in transmission rate while maintaining the sensing accuracy of existing integrated sensing and communication (ISAC) schemes under constrained resource conditions.

cs.IT↗

arXiv2026

Agentic AI-Empowered Wireless Agent Networks With Semantic-Aware Collaboration via ILAC

The rapid development of agentic artificial intelligence (AI) is driving future wireless networks to evolve from passive data pipes into intelligent collaborative ecosystems under the emerging paradigm of integrated learning and communication (ILAC). However, realizing efficient agentic collaboration faces challenges not only in handling semantic redundancy but also in the lack of an integrated mechanism for communication, computation, and control. To address this, we propose a wireless agent network (WAN) framework that orchestrates a progressive knowledge aggregation mechanism. Specifically, we formulate the aggregation process as a joint energy minimization problem where the agents perform semantic compression to eliminate redundancy, optimize transmission power to deliver semantic payloads, and adjust physical trajectories to proactively enhance channel qualities. To solve this problem, we develop a hierarchical algorithm that integrates inner-level resource optimization with outer-level topology evolution. Theoretically, we reveal that incorporating a potential field into the topology evolution effectively overcomes the short-sightedness of greedy matching, providing a mathematically rigorous heuristic for long-term energy minimization. Simulation results demonstrate that the proposed framework achieves superior energy efficiency and scalability compared to conventional benchmarks, validating the efficacy of semantic-aware collaboration in dynamic environments.

cs.NI↗

arXiv2026

Sparse-Dense Mixture of Experts Adapter for Multi-Modal Tracking

Parameter-efficient fine-tuning (PEFT) techniques, such as prompts and adapters, are widely used in multi-modal tracking because they alleviate issues of full-model fine-tuning, including time inefficiency, high resource consumption, parameter storage burden, and catastrophic forgetting. However, due to cross-modal heterogeneity, most existing PEFT-based methods struggle to effectively represent multi-modal features within a unified framework with shared parameters. To address this problem, we propose a novel Sparse-Dense Mixture of Experts Adapter (SDMoEA) framework for PEFT-based multi-modal tracking under a unified model structure. Specifically, we design an SDMoE module as the multi-modal adapter to model modality-specific and shared information efficiently. SDMoE consists of a sparse MoE and a dense-shared MoE: the former captures modality-specific information, while the latter models shared cross-modal information. Furthermore, to overcome limitations of existing tracking methods in modeling high-order correlations during multi-level multi-modal fusion, we introduce a Gram-based Semantic Alignment Hypergraph Fusion (GSAHF) module. It first employs Gram matrices for cross-modal semantic alignment, ensuring that the constructed hypergraph accurately reflects semantic similarity and high-order dependencies between modalities. The aligned features are then integrated into the hypergraph structure to exploit its ability to model high-order relationships, enabling deep fusion of multi-level multi-modal information. Extensive experiments demonstrate that the proposed method achieves superior performance compared with other PEFT approaches on several multi-modal tracking benchmarks, including LasHeR, RGBT234, VTUAV, VisEvent, COESOT, DepthTrack, and VOT-RGBD2022.

cs.CV↗

arXiv2026

Energy Efficient Federated Learning with Hyperdimensional Computing (HDC)

This paper investigates the problem of minimizing total energy consumption for secure federated learning (FL) in wireless edge networks, a key paradigm for decentralized big data analytics. To tackle the high computational cost and privacy challenges of processing large-scale distributed data with conventional neural networks, we propose an FL with hyperdimensional computing and differential privacy (FL-HDC-DP) framework. Each edge device employs hyperdimensional computing (HDC) for lightweight local training and applies differential privacy (DP) noise to protect transmitted model updates. The total energy consumption is minimized through a joint optimization of the HDC dimension, transmit power, and CPU frequency. An efficient hybrid algorithm is developed, combining an outer enumeration search for HDC dimensions with an inner one-dimensional search for resource allocation. Simulation results show that the proposed framework achieves up to 83.3% energy reduction compared with baseline schemes, while maintaining high accuracy and faster convergence.

cs.DC↗

arXiv2026

MedAD-R1: Eliciting Consistent Reasoning in Interpretible Medical Anomaly Detection via Consistency-Reinforced Policy Optimization

Medical Anomaly Detection (MedAD) presents a significant opportunity to enhance diagnostic accuracy using Large Multimodal Models (LMMs) to interpret and answer questions based on medical images. However, the reliance on Supervised Fine-Tuning (SFT) on simplistic and fragmented datasets has hindered the development of models capable of plausible reasoning and robust multimodal generalization. To overcome this, we introduce MedAD-38K, the first large-scale, multi-modal, and multi-center benchmark for MedAD featuring diagnostic Chain-of-Thought (CoT) annotations alongside structured Visual Question-Answering (VQA) pairs. On this foundation, we propose a two-stage training framework. The first stage, Cognitive Injection, uses SFT to instill foundational medical knowledge and align the model with a structured think-then-answer paradigm. Given that standard policy optimization can produce reasoning that is disconnected from the final answer, the second stage incorporates Consistency Group Relative Policy Optimization (Con-GRPO). This novel algorithm incorporates a crucial consistency reward to ensure the generated reasoning process is relevant and logically coherent with the final diagnosis. Our proposed model, MedAD-R1, achieves state-of-the-art (SOTA) performance on the MedAD-38K benchmark, outperforming strong baselines by more than 10\%. This superior performance stems from its ability to generate transparent and logically consistent reasoning pathways, offering a promising approach to enhancing the trustworthiness and interpretability of AI for clinical decision support.

cs.CV↗

arXiv2025

Search for Dark Matter Scattering from Optically Levitated Nanoparticles

The development of levitated optomechanics has enabled precise force sensors that operate in the quantum measurement regime, opening up unique opportunities to search for new physics whose weak interactions may have evaded existing sensors. We demonstrate the detection of impulsive forces acting on optically levitated nanoparticles, where the dominant noise source is provided by measurement backaction. Using these sensors, we search for momentum transfers that may originate from scattering of passing particlelike dark matter. For dark matter that couples to Standard Model neutrons via a generic long-range interaction, this search constrains a range of models in the mass range $1$-$10^7~\mathrm{GeV/}c^2$, placing upper limits on single neutron coupling strength as low as $\leq 1 \times 10^{-7}$ at the 95% confidence level. We also demonstrate the ability of using the inherent directional sensitivity of these sensors to separate possible dark matter signals from backgrounds. Future extensions of the techniques developed here can enable searches for light dark matter and massive neutrinos that can reach sensitivity several orders of magnitude beyond existing searches.

hep-ex↗