arXiv subjects

Jiang Chen

Publications and source records attributed to Jiang Chen.

13 recordsLinked to original sources

arXiv2026

Movable-Antenna Assisted Energy Minimization in UAV-Enabled Mobile Edge Computing Systems

Driven by the exponential growth of latency-sensitive applications, mobile edge computing (MEC) has emerged as a pivotal paradigm, yet mitigating its substantial energy consumption remains critical. This paper explores a movable-antenna (MA) assisted energy minimization scheme in an uncrewed aerial vehicle (UAV)-enabled MEC system, where a UAV equipped with an MA array serves as an edge server to process tasks offloaded from terrestrial consumer electronics (CE) devices. To minimize the total system energy consumption, we jointly optimize computation resource allocation, CE transmit power, receive beamforming, and MA positions. To tackle the resulting non-convex problem with coupled variables, a robust alternating optimization algorithm based on the block coordinate descent method is developed. The problem is iteratively decomposed into three subproblems. In particular, the subproblem of transmit power and receive beamforming is reformulated and optimized using the quadratic transform technique, while the MA array positions are optimized via the particle swarm optimization algorithm. Numerical simulations verify that the proposed scheme achieves substantial energy savings over conventional benchmarks.

cs.IT↗

arXiv2026

Robust High-Precision Time Transfer over 91-km Hollow-Core Fiber: Immunity to Dispersion and Nonlinearity

To address the fundamental limitations imposed by chromatic dispersion and environmental susceptibility in standard single-mode fiber (SMF) for long-haul high-precision time transfer, we systematically explore the application potential of hollow-core fiber (HCF) through comparative experiments. We designed a bidirectional time transfer platform enabling direct comparison between HCF and SMF links across distances of 91 km, 68 km, and 54 km. We quantitatively characterize the impact of critical non-reciprocal error sources, specifically the optical Kerr effect and chromatic dispersion, under varying laser power, wavelength drift, and environmental perturbations. Our results show that HCF exhibits significantly suppressed dispersion, with a mean coefficient of 3.4 ps per nm per km, and reduced environmental sensitivity compared with SMF. Notably, over the 91 km link, the HCF yields a signal-to-noise ratio (SNR) enhancement of more than 24 dB and confines the time deviation to less than 80 ps, which is nearly an order-of-magnitude improvement over SMF, where the time deviation exceeds 600 ps, while remaining nearly immune to power and wavelength fluctuations. Under 24 hour diurnal monitoring, the 68 km HCF link demonstrates strong robustness, with environment-induced time delay fluctuations of 776 ps, corresponding to only 24.5% of those in SMF, which reach 3166 ps. Consequently, the time transfer stability, evaluated by time deviation (TDEV), reaches 0.2 ps at an integration time of 1000 s, representing a twofold improvement over SMF. These findings validate HCF as a superior transmission medium with low latency, low nonlinearity, and high thermal stability, paving the way for next-generation ultra-stable, long-haul time-frequency distribution networks.

physics.optics↗

arXiv2026

Counterfactual identifiability beyond global monotonicity: non-monotone triangular structural causal models

Structural causal models provide a unified semantics for interventions and counterfactuals, but most identifiability results rely on restrictive assumptions like global monotonicity, which are often violated in embodied interaction, where the same exogenous perturbation can induce opposite responses under different contact contexts. We ask what structure still suffices once global monotonicity is dropped. We introduce non-monotone triangular structural causal models (NM-TM-SCM), which retain triangular recursion but replace global monotonicity with mechanism-wise invertibility and context-independent inverse transport. We prove that these conditions are equivalent to exogenous isomorphism and imply complete counterfactual identifiability, and we give a counterexample showing that local invertibility alone is insufficient. We instantiate the theory in CausalInverter, with triangular invertible layers, orientation gates, and transport-stability regularization. On synthetic non-monotonic mechanisms, the structural bias yields systematic counterfactual gains as non-monotonicity increases. On MuJoCo Door, our model achieves perfect event-level counterfactual recovery, lowers continuous angle error relative to a Transformer baseline, and delivers substantially more stable recovery than Transformer and conditional-flow predictors. On MuJoCo Push, where non-monotonicity is weaker, the same low-data predictors remain competitive or better, consistent with a bias-variance boundary. These results identify a broader identifiable regime between globally monotone triangular models and unconstrained black-box world models.

cs.LG↗

arXiv2025

QoS-Aware Integrated Sensing, Communication, and Control with Movable Antenna

Integrated sensing, communication, and control (ISCC) has emerged as a key enabler for low-altitude wireless networks with enhanced adaptability through resource allocation co-design and intelligent environment awareness. However, dynamic interference and channel attenuation constrain the potential of the ISCC system. To address this challenge, we propose a novel movable antenna-empowered ISCC system. An achievable data rate maximization problem is formulated while guaranteeing the sensing and control quality-of-service (QoS) by optimizing the positions of the antennas and the beamforming strategy for communication, sensing, and control co-design. An efficient alternating optimization (AO)-based algorithm is proposed to solve the highly coupled non-convex problem. Numerical results demonstrate that the proposed AO-based algorithm achieves substantial gains in the achievable data rate and the control QoS compared with benchmark schemes.

cs.IT↗

arXiv2025

Generation of 95-qubit genuine entanglement and verification of symmetry-protected topological phases

Symmetry-protected topological (SPT) phases are fundamental features of cluster states, serving as key resources for measurement-based quantum computation (MBQC). Generating large-scale cluster states and verifying their SPT phases are essential steps toward practical MBQC, which however still presents significant experimental challenges. In this work, we address these challenges by utilizing advanced superconducting hardware with optimized gate operations, enhanced readout fidelity, and error mitigation techniques. We successfully generate and verify 95-qubit one-dimensional and 72-qubit two-dimensional genuine entangled cluster states, achieving fidelities of $0.5603 \pm 0.0084$ and $0.5519 \pm 0.0054$, respectively. Leveraging these high-fidelity cluster states, we investigate SPT phases through quantum teleportation across all 95 qubits and demonstrate input-state-dependent robustness against symmetry-breaking perturbations, highlighting the practicality and intrinsic robustness of MBQC enabled by the SPT order. Our results represent a significant advancement in large-scale entanglement generation and topological phase simulation, laying the foundation for scalable and practical MBQC using superconducting quantum systems.

quant-ph↗

arXiv2025

NM-SpMM: Accelerating Matrix Multiplication Using N:M Sparsity with GPGPU

Deep learning demonstrates effectiveness across a wide range of tasks. However, the dense and over-parameterized nature of these models results in significant resource consumption during deployment. In response to this issue, weight pruning, particularly through N:M sparsity matrix multiplication, offers an efficient solution by transforming dense operations into semi-sparse ones. N:M sparsity provides an option for balancing performance and model accuracy, but introduces more complex programming and optimization challenges. To address these issues, we design a systematic top-down performance analysis model for N:M sparsity. Meanwhile, NM-SpMM is proposed as an efficient general N:M sparsity implementation. Based on our performance analysis, NM-SpMM employs a hierarchical blocking mechanism as a general optimization to enhance data locality, while memory access optimization and pipeline design are introduced as sparsity-aware optimization, allowing it to achieve close-to-theoretical peak performance across different sparsity levels. Experimental results show that NM-SpMM is 2.1x faster than nmSPARSE (the state-of-the-art for general N:M sparsity) and 1.4x to 6.3x faster than cuBLAS's dense GEMM operations, closely approaching the theoretical maximum speedup resulting from the reduction in computation due to sparsity. NM-SpMM is open source and publicly available at https://github.com/M-H482/NM-SpMM.

cs.DC↗

arXiv2024

Establishing a New Benchmark in Quantum Computational Advantage with 105-qubit Zuchongzhi 3.0 Processor

In the relentless pursuit of quantum computational advantage, we present a significant advancement with the development of Zuchongzhi 3.0. This superconducting quantum computer prototype, comprising 105 qubits, achieves high operational fidelities, with single-qubit gates, two-qubit gates, and readout fidelity at 99.90%, 99.62% and 99.18%, respectively. Our experiments with an 83-qubit, 32-cycle random circuit sampling on Zuchongzhi 3.0 highlight its superior performance, achieving one million samples in just a few hundred seconds. This task is estimated to be infeasible on the most powerful classical supercomputers, Frontier, which would require approximately $6.4\times 10^9$ years to replicate the task. This leap in processing power places the classical simulation cost six orders of magnitude beyond Google's SYC-67 and SYC-70 experiments [Nature 634, 328(2024)], firmly establishing a new benchmark in quantum computational advantage. Our work not only advances the frontiers of quantum computing but also lays the groundwork for a new era where quantum processors play an essential role in tackling sophisticated real-world challenges.

quant-ph↗

arXiv2023

Asteroseismic investigation on KIC 10526294 to probe convective core overshoot mixing

In the overshoot mixing model with an exponentially decreasing diffusion coefficient, the initial value of the diffusion coefficient plays a crucial role. According to the turbulent convective mixing model, the characteristic length of convection in the convection zone differs from that in the overshoot region, resulting in a rapid decrease of the diffusion coefficient near the convective boundary. To investigate this quick decrease, we conducted an asteroseismic study on the intermediate-mass SPB star KIC 10526294. We generated stellar models with varied input parameters, including the overshoot parameters, and compared the resulting stellar oscillation periods with observations. To mitigate the potential issue arising from large steps in the stellar parameters and stellar age, we employed a comprehensive interpolation scheme for the stellar oscillatory frequencies, considering all stellar parameters and stellar age. Our analysis revealed that the quick decreasing of the diffusion coefficient has discernible effects on the stellar oscillations and a quick decrease with 4 magnitude orders shows the best oscillatory frequencies compared with the observations. This provides weak evidence in support of the prediction made by the turbulent convective mixing model. Furthermore, we examined the residuals of the oscillation periods and discovered a potential association between abundance anomalies in the buoyancy frequency profile and the oscillation-like patterns observed in the residuals.

astro-ph.SR↗

arXiv2021

Study on S2 Flow Path Design and Three-dimensional Numerical Simulation Parameter Calibration in Axial Compressor

Aerodynamic design process of multi - stage axial flow compressor usually uses the way that combines the S2 flow design and three-dimensional CFD numerical simulation analysis. Based on Mr. Wu Zhonghua's " Three-dimensional flow theory ", aiming at the S2 flow design matching parameters and the three-dimensional CFD numerical simulation data, through autonomous programming, the S2 design parameter distribution and the corresponding CGNS format CFD calculation results are extracted. Then make the comparative analysis of the two and provide the modification suggestion of the design. The examples have been tested by the comparison and correction of the eight-stage axial flow compressor calculation and finally improve the design performance of the compressor design. The design adiabatic efficiency increases by 0.5%. The surge margin increases by more than 5%. The validity and feasibility of the method are verified.

math.OC↗

arXiv2018

ARPA Whitepaper

We propose a secure computation solution for blockchain networks. The correctness of computation is verifiable even under malicious majority condition using information-theoretic Message Authentication Code (MAC), and the privacy is preserved using Secret-Sharing. With state-of-the-art multiparty computation protocol and a layer2 solution, our privacy-preserving computation guarantees data security on blockchain, cryptographically, while reducing the heavy-lifting computation job to a few nodes. This breakthrough has several implications on the future of decentralized networks. First, secure computation can be used to support Private Smart Contracts, where consensus is reached without exposing the information in the public contract. Second, it enables data to be shared and used in trustless network, without disclosing the raw data during data-at-use, where data ownership and data usage is safely separated. Last but not least, computation and verification processes are separated, which can be perceived as computational sharding, this effectively makes the transaction processing speed linear to the number of participating nodes. Our objective is to deploy our secure computation network as an layer2 solution to any blockchain system. Smart Contracts\cite{smartcontract} will be used as bridge to link the blockchain and computation networks. Additionally, they will be used as verifier to ensure that outsourced computation is completed correctly. In order to achieve this, we first develop a general MPC network with advanced features, such as: 1) Secure Computation, 2) Off-chain Computation, 3) Verifiable Computation, and 4)Support dApps' needs like privacy-preserving data exchange.

cs.CR↗

arXiv2012

Large Deviations for Stochastic Partial Differential Equations Driven by a Poisson Random Measure

Stochastic partial differential equations driven by Poisson random measures (PRM) have been proposed as models for many different physical systems, where they are viewed as a refinement of a corresponding noiseless partial differential equations (PDE). A systematic framework for the study of probabilities of deviations of the stochastic PDE from the deterministic PDE is through the theory of large deviations. The goal of this work is to develop the large deviation theory for small Poisson noise perturbations of a general class of deterministic infinite dimensional models. Although the analogous questions for finite dimensional systems have been well studied, there are currently no general results in the infinite dimensional setting. This is in part due to the fact that in this setting solutions may have little spatial regularity, and thus classical approximation methods for large deviation analysis become intractable. The approach taken here, which is based on a variational representation for nonnegative functionals of general PRM, reduces the proof of the large deviation principle to establishing basic qualitative properties for controlled analogues of the underlying stochastic system. As an illustration of the general theory, we consider a particular system that models the spread of a pollutant in a waterway.

math.PR↗

arXiv2012

A Numerical Scheme for Invariant Distributions of Constrained Diffusions

Reflected diffusions in polyhedral domains are commonly used as approximate models for stochastic processing networks in heavy traffic. Stationary distributions of such models give useful information on the steady state performance of the corresponding stochastic networks and thus it is important to develop reliable and efficient algorithms for numerical computation of such distributions. In this work we propose and analyze a Monte-Carlo scheme based on an Euler type discretization of the reflected stochastic differential equation using a single sequence of time discretization steps which decrease to zero as time approaches infinity. Appropriately weighted empirical measures constructed from the simulated discretized reflected diffusion are proposed as approximations for the invariant probability measure of the true diffusion model. Almost sure consistency results are established that in particular show that weighted averages of polynomially growing continuous functionals evaluated on the discretized simulated system converge a.s. to the corresponding integrals with respect to the invariant measure. Proofs rely on constructing suitable Lyapunov functions for tightness and uniform integrability and characterizing almost sure limit points through an extension of Echeverria's criteria for reflected diffusions. Regularity properties of the underlying Skorohod problems play a key role in the proofs. Rates of convergence for suitable families of test functions are also obtained. A key advantage of Monte-Carlo methods is the ease of implementation, particularly for high dimensional problems. A numerical example of a eight dimensional Skorohod problem is presented to illustrate the applicability of the approach.

math.PR↗

arXiv2010

Learning to Blend by Relevance

Emergence of various vertical search engines highlights the fact that a single ranking technology cannot deal with the complexity and scale of search problems. For example, technology behind video and image search is very different from general web search. Their ranking functions share few features. Question answering websites (e.g., Yahoo! Answer) can make use of text matching and click features developed for general web, but they have unique page structures and rich user feedback, e.g., thumbs up and thumbs down ratings in Yahoo! answer, which greatly benefit their own ranking. Even for those features shared by answer and general web, the correlation between features and relevance could be very different. Therefore, dedicated functions are needed in order to better rank documents within individual domains. These dedicated functions are defined on distinct feature spaces. However, having one search box for each domain, is neither efficient nor scalable. Rather than typing the same query two times into both Yahoo! Search and Yahoo! Answer and retrieving two ranking lists, we would prefer putting it only once but receiving a comprehensive list of documents from both domains on the subject. This situation calls for new technology that blends documents from different sources into a single ranking list. Despite the content richness of the blended list, it has to be sorted by relevance none the less. We call such technology blending, which is the main subject of this paper.

cs.IR↗