arXiv subjects

Hao Wu

Publications and source records attributed to Hao Wu.

At least 325 records · Page 18Linked to original sources

arXiv2025

Decomposition of global 2-SLE for $κ\in (4,8)$ and an application for critical FK-Ising model

We consider global 2-SLE$_κ$ $(η_1, η_2)$ in a topological rectangle with $κ\in (4,8)$. We derive the law of a random hitting point of the curves and show that, conditional on this random hitting point, the pair of two curves has the same law as Gaussian free field flow lines with proper boundary data. Using a similar idea, we derive the asymptotic of the probability for $η_1\capη_2=\emptyset$. As an application, we derive the asymptotic of the probability for the existence of two disjoint open paths in critical FK-Ising model.

math.PR↗

arXiv2025

Unlocking Smarter Device Control: Foresighted Planning with a World Model-Driven Code Execution Approach

The automatic control of mobile devices is essential for efficiently performing complex tasks that involve multiple sequential steps. However, these tasks pose significant challenges due to the limited environmental information available at each step, primarily through visual observations. As a result, current approaches, which typically rely on reactive policies, focus solely on immediate observations and often lead to suboptimal decision-making. To address this problem, we propose \textbf{Foresighted Planning with World Model-Driven Code Execution (FPWC)},a framework that prioritizes natural language understanding and structured reasoning to enhance the agent's global understanding of the environment by developing a task-oriented, refinable \emph{world model} at the outset of the task. Foresighted actions are subsequently generated through iterative planning within this world model, executed in the form of executable code. Extensive experiments conducted in simulated environments and on real mobile devices demonstrate that our method outperforms previous approaches, particularly achieving a 44.4\% relative improvement in task success rate compared to the state-of-the-art in the simulated environment. Code and demo are provided in the supplementary material.

cs.CV↗

arXiv2025

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples

We propose a general model-agnostic Contrastive learning framework with Counterfactual Samples Synthesizing (CCSS) for modeling the monotonicity between the neural network output and numerical features which is critical for interpretability and effectiveness of recommender systems. CCSS models the monotonicity via a two-stage process: synthesizing counterfactual samples and contrasting the counterfactual samples. The two techniques are naturally integrated into a model-agnostic framework, forming an end-to-end training process. Abundant empirical tests are conducted on a publicly available dataset and a real industrial dataset, and the results well demonstrate the effectiveness of our proposed CCSS. Besides, CCSS has been deployed in our real large-scale industrial recommender, successfully serving over hundreds of millions users.

cs.IR↗

arXiv2025

Deep Tangent Bundle (DTB) method: a Deep Neural Network approach to compute solutions of PDES

We develop a numerical framework, the Deep Tangent Bundle (DTB) method, that is suitable for computing solutions of evolutionary partial differential equations (PDEs) in high dimensions. The main idea is to use the tangent bundle of an adaptively updated deep neural network (DNN) to approximate the vector field in the spatial variables while applying the traditional schemes for time discretization. The DTB method takes advantage of the expression power of DNNs and the simplicity of the tangent bundle approximation. It does not involve nonconvex optimization. Several numerical examples demonstrate that the DTB is simple, flexible, and efficient for various PDEs of higher dimensions.

math.NA↗

arXiv2025

Metastability-Induced Solid-State Quantum Batteries for Powering Microwave Quantum Electronics

Metastability is ubiquitous in diverse complex systems. In open quantum systems, metastability offers protection against dissipation and decoherence, yet its application in quantum batteries remains unexplored. We propose a solid-state open quantum battery where metastable states enable stable superextensive charging without complicated protocols and energy storage with extended lifetime. Using a realistic organic maser platform, we show the controllable manner of the work extraction from the quantum battery, which can be exploited for on-demand coherent microwave emission at room temperature. These results not only demonstrate the usefulness of metastability for developing the quantum batteries robust against energy losses, but also provide a paradigm of the practical quantum device powered up by quantum batteries.

quant-ph↗

arXiv2025

Revisiting the Proper Motions of M31 and M33 Using Massive Supergiant Stars with Gaia DR3

The proper motions (PMs) of M31 and M33 are key to understanding the Local Group's dynamical evolution. However, measurement discrepancies between Gaia blue and red samples, regarding whether the transverse velocity is remarkable, introduce significant ambiguity. In this work, we remeasure the systemic PMs of M31 and M33 using massive supergiant stars from Gaia Data Release 3. Clean disk tracers are selected via color-color diagrams, with foreground contaminants removed through kinematic and astrometric cuts. We identify the discrepancy in M31's blue and red samples as arising from systematic differences between Gaia's 5-parameter (5p) and 6-parameter (6p) astrometric solutions. The 6p solution, applied to sources lacking accurate color information, relies on a pseudo-color approximation, leading to lower precision and larger uncertainties. Two key limitations of the 6p solution are: 1) degraded astrometric accuracy for very red sources (GBP - GRP > 2.6); 2) significant PM zero-point offsets. In our sample, red sources are dominated by the 6p solution, while blue sources include a substantial fraction of 5p sources; this mismatch drives the observed discrepancy. By excluding extreme red sources and calibrating PM zero-points separately for 5p and 6p sources using background quasars, we reduce the discrepancy, bringing blue and red measurements into agreement within 1 sigma. We ultimately report the most robust Gaia-based PMs using high-quality 5p sources. For M31, we obtain (μ_α*, μ_δ)_M31 = (45.9 +/- 8.1, -20.5 +/- 6.6) μas/yr, consistent with, but more precise than, the HST result. For M33, we find (μ_α*, μ_δ)_M33 = (45.3 +/- 9.7, 26.3 +/- 7.3) μas/yr, agreeing with VLBA measurement within 1.5 sigma. These results support a first infall scenario for M33.

astro-ph.GA↗

arXiv2025

LLM-based Human-like Traffic Simulation for Self-driving Tests

Ensuring realistic traffic dynamics is a prerequisite for simulation platforms to evaluate the reliability of self-driving systems before deployment in the real world. Because most road users are human drivers, reproducing their diverse behaviors within simulators is vital. Existing solutions, however, typically rely on either handcrafted heuristics or narrow data-driven models, which capture only fragments of real driving behaviors and offer limited driving style diversity and interpretability. To address this gap, we introduce HDSim, an HD traffic generation framework that combines cognitive theory with large language model (LLM) assistance to produce scalable and realistic traffic scenarios within simulation platforms. The framework advances the state of the art in two ways: (i) it introduces a hierarchical driver model that represents diverse driving style traits, and (ii) it develops a Perception-Mediated Behavior Influence strategy, where LLMs guide perception to indirectly shape driver actions. Experiments reveal that embedding HDSim into simulation improves detection of safety-critical failures in self-driving systems by up to 68% and yields realism-consistent accident interpretability.

cs.RO↗

arXiv2025

Ultrasensitive Label-free Detection of Human CEACAM5 using a WGM Resonator Based on Thin-walled Capillary

Whispering gallery mode (WGM) laser sensors, utilizing the interaction between the in-plane evanescent field and the surface vicinity, provide enhanced sensitivity in label-free sensing for bioanalysis and disease screening. However, the unavoidably excited spiral modes resulting from the weak axial confinement and their sensing potential were overlooked. In this study, a microfluidic biosensor using the localized conical modes of an active resonator based on the thin-walled capillaries was developed, demonstrating ultrasensitive refractive index and biomolecule detection capabilities. This sensor provides nonspecific detection of bovine serum albumin (BSA) and specific detection of Carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) with ultra-low detection limits, large sensing range, rapid response, and a cost-effective design, making it a promising candidate for industrial-scale production. The theoretical detection limit for CEACAM5 is as low as 0.38ag/mL (5zM), and the sensitivity in the linear region reaches 0.25nm/(ag/mL). These results are approximately an order of magnitude higher in sensitivity than currently reported active WGM biosensors, demonstrating enormous detection potential for biomarkers.

physics.optics↗

arXiv2025

A Convergent Primal-Dual Algorithm for Computing Rate-Distortion-Perception Functions

Recent advances in Rate-Distortion-Perception (RDP) theory highlight the importance of balancing compression level, reconstruction quality, and perceptual fidelity. While previous work has explored numerical approaches to approximate the information RDP function, the lack of theoretical guarantees remains a major limitation, especially in the presence of complex perceptual constraints that introduce non-convexity and computational intractability. Inspired by our previous constrained Blahut-Arimoto algorithm for solving the rate-distortion function, in this paper, we present a new theoretical framework for computing the information RDP function by relaxing the constraint on the reconstruction distribution and replacing it with an alternative optimization approach over the reconstruction distribution itself. This reformulation significantly simplifies the optimization and enables a rigorous proof of convergence. Based on this formulation, we develop a novel primal-dual algorithm with provable convergence guarantees. Our analysis establishes, for the first time, a rigorous convergence rate of $O(1/n)$ for the computation of RDP functions. The proposed method not only bridges a key theoretical gap in the existing literature but also achieves competitive empirical performance in representative settings. These results lay the groundwork for more reliable and interpretable optimization in RDP-constrained compression systems. Experimental results demonstrate the efficiency and accuracy of the proposed algorithm.

cs.IT↗

arXiv2025

Multi-objective Optimization in CPU Design Space Exploration: Attention is All You Need

Design Space Exploration (DSE) is essential to modern CPU design, yet current frameworks struggle to scale and generalize in high-dimensional architectural spaces. As the dimensionality of design spaces continues to grow, existing DSE frameworks face three fundamental challenges: (1) reduced accuracy and poor scalability of surrogate models in large design spaces; (2) inefficient acquisition guided by hand-crafted heuristics or exhaustive search; (3) limited interpretability, making it hard to pinpoint architectural bottlenecks. In this work, we present \textbf{AttentionDSE}, the first end-to-end DSE framework that \emph{natively integrates} performance prediction and design guidance through an attention-based neural architecture. Unlike traditional DSE workflows that separate surrogate modeling from acquisition and rely heavily on hand-crafted heuristics, AttentionDSE establishes a unified, learning-driven optimization loop, in which attention weights serve a dual role: enabling accurate performance estimation and simultaneously exposing the performance bottleneck. This paradigm shift elevates attention from a passive representation mechanism to an active, interpretable driver of design decision-making. Key innovations include: (1) a \textbf{Perception-Driven Attention} mechanism that exploits architectural hierarchy and locality, scaling attention complexity from $\mathcal{O}(n^2)$ to $\mathcal{O}(n)$ via sliding windows; (2) an \textbf{Attention-aware Bottleneck Analysis} that automatically surfaces critical parameters for targeted optimization, eliminating the need for domain-specific heuristics. Evaluated on high-dimensional CPU design space using the SPEC CPU2017 benchmark suite, AttentionDSE achieves up to \textbf{3.9\% higher Pareto Hypervolume} and over \textbf{80\% reduction in exploration time} compared to state-of-the-art baselines.

cs.LG↗

arXiv2025

Task Offloading and Resource Allocation for MEC-assisted Consumer Internet of Vehicle Systems

Mobile edge computing (MEC)-assisted internet of vehicle (IoV) is emerging as a promising paradigm to provide computing services for vehicles. However, meeting the computing-sensitive and computation-intensive demands of vehicles poses several challenges, including the discrepancy between the limited resource provision and stringent computing requirement, the difficulty in capturing and integrating the intricate features of the MEC-assisted IoV system into the problem formulation, and the need for real-time processing and efficient resource management in the dynamic environment. In this work, we explore the AI-enabled task offloading and resource allocation for MEC-assisted consumer IoV systems. Specifically, we first present a multi-MEC-assisted consumer IoV architecture that leverages the computational resources of MEC servers to provide offloading services close to vehicles. Subsequently, we formulate a system cost minimization optimization problem (SCMOP) by integrating the service delay and energy consumption. To efficiently solve this problem, we design a joint task offloading and computing resource allocation approach (JTOCRA) by applying the multi-agent deep deterministic policy gradient (MADDPG) algorithm. Finally, simulation results demonstrate that the proposed JTOCRA can achieve superior system performances and exhibits better scalability compared to other alternative approaches.

cs.NI↗

arXiv2025

A parameterized Wasserstein Hamiltonian flow approach for solving the Schrödinger equation

In this paper, we propose a new method to compute the solution of time-dependent Schrödinger equation (TDSE). Using push-forward maps and Wasserstein Hamiltonian flow, we reformulate the TDSE as a Hamiltonian system in terms of push-forward maps. The new formulation can be viewed as a generative model in the Wasserstein space, which is a manifold of probability density functions. Then we parameterize the push-forward maps by reduce-order models such as neural networks. This induces a new metric in the parameter space by pulling back the Wasserstein metric on density manifold, which further results in a system of ordinary differential equations (ODEs) for the parameters of the reduce-order model. Leveraging the computational techniques from deep learning, such as Neural ODE, we design an algorithm to solve the TDSE in the parameterized push-forward map space, which provides an alternative approach with the potential to scale up to high-dimensional problems. Several numerical examples are presented to demonstrate the performance of this algorithm.

math.NA↗

arXiv2025

WMAS: A Multi-Agent System Towards Intelligent and Customized Wireless Networks

The fast development of Artificial Intelligence (AI) agents provides a promising way for the realization of intelligent and customized wireless networks. In this paper, we propose a Wireless Multi-Agent System (WMAS), which can provide intelligent and customized services for different user equipment (UEs). Note that orchestrating multiple agents carries the risk of malfunction, and multi-agent conversations may fall into infinite loops. It is thus crucial to design a conversation topology for WMAS that enables agents to complete UE task requests with high accuracy and low conversation overhead. To address this issue, we model the multi-agent conversation topology as a directed acyclic graph and propose a reinforcement learning-based algorithm to optimize the adjacency matrix of this graph. As such, WMAS is capable of generating and self-optimizing multi-agent conversation topologies, enabling agents to effectively and collaboratively handle a variety of task requests from UEs. Simulation results across various task types demonstrate that WMAS can achieve higher task performance and lower conversation overhead compared to existing multi-agent systems. These results validate the potential of WMAS to enhance the intelligence of future wireless networks.

cs.MA↗

arXiv2025

LVM-GP: Uncertainty-Aware PDE Solver via coupling latent variable model and Gaussian process

We propose a novel probabilistic framework, termed LVM-GP, for uncertainty quantification in solving forward and inverse partial differential equations (PDEs) with noisy data. The core idea is to construct a stochastic mapping from the input to a high-dimensional latent representation, enabling uncertainty-aware prediction of the solution. Specifically, the architecture consists of a confidence-aware encoder and a probabilistic decoder. The encoder implements a high-dimensional latent variable model based on a Gaussian process (LVM-GP), where the latent representation is constructed by interpolating between a learnable deterministic feature and a Gaussian process prior, with the interpolation strength adaptively controlled by a confidence function learned from data. The decoder defines a conditional Gaussian distribution over the solution field, where the mean is predicted by a neural operator applied to the latent representation, allowing the model to learn flexible function-to-function mapping. Moreover, physical laws are enforced as soft constraints in the loss function to ensure consistency with the underlying PDE structure. Compared to existing approaches such as Bayesian physics-informed neural networks (B-PINNs) and deep ensembles, the proposed framework can efficiently capture functional dependencies via merging a latent Gaussian process and neural operator, resulting in competitive predictive accuracy and robust uncertainty quantification. Numerical experiments demonstrate the effectiveness and reliability of the method.

stat.ML↗

arXiv2025

ETrace:Event-Driven Vulnerability Detection in Smart Contracts via LLM-Based Trace Analysis

With the advance application of blockchain technology in various fields, ensuring the security and stability of smart contracts has emerged as a critical challenge. Current security analysis methodologies in vulnerability detection can be categorized into static analysis and dynamic analysis methods.However, these existing traditional vulnerability detection methods predominantly rely on analyzing original contract code, not all smart contracts provide accessible code.We present ETrace, a novel event-driven vulnerability detection framework for smart contracts, which uniquely identifies potential vulnerabilities through LLM-powered trace analysis without requiring source code access. By extracting fine-grained event sequences from transaction logs, the framework leverages Large Language Models (LLMs) as adaptive semantic interpreters to reconstruct event analysis through chain-of-thought reasoning. ETrace implements pattern-matching to establish causal links between transaction behavior patterns and known attack behaviors. Furthermore, we validate the effectiveness of ETrace through preliminary experimental results.

cs.CR↗

arXiv2025

Real-Time Distributed Optical Fiber Vibration Recognition via Extreme Lightweight Model and Cross-Domain Distillation

Distributed optical fiber vibration sensing (DVS) systems offer a promising solution for large-scale monitoring and intrusion event recognition. However, their practical deployment remains hindered by two major challenges: degradation of recognition accuracy in dynamic conditions, and the computational bottleneck of real-time processing for mass sensing data. This paper presents a new solution to these challenges, through a FPGA-accelerated extreme lightweight model along with a newly proposed knowledge distillation framework. The proposed three-layer depthwise separable convolution network contains only 4141 parameters, which is the most compact architecture in this field to date, and achieves a maximum processing speed of 0.019 ms for each sample covering a 12.5 m fiber length over 0.256 s. This performance corresponds to real-time processing capabilities for sensing fibers extending up to 168.68 km. To improve generalizability under changing environments, the proposed cross-domain distillation framework guided by physical priors is used here to embed frequency-domain insights into the time-domain model. This allows for time-frequency representation learning without increasing complexity and boosts recognition accuracy from 51.93% to 95.72% under unseen environmental conditions. The proposed methodology provides key advancements including a framework combining interpretable signal processing technique with deep learning and a reference architecture for real-time processing and edge-computing in DVS systems, and more general distributed optical fiber sensing (DOFS) area. It mitigates the trade-off between sensing range and real-time capability, bridging the gap between theoretical capabilities and practical deployment requirements. Furthermore, this work reveals a new direction for building more efficient, robust and explainable artificial intelligence systems for DOFS technologies.

eess.SP↗

arXiv2025

An Optimal Transport-Based Method for Computing LM Rate and Its Convergence Analysis

The mismatch capacity characterizes the highest information rate of the channel under a prescribed decoding metric and serves as a critical performance indicator in numerous practical communication scenarios. Compared to the commonly used Generalized Mutual Information (GMI), the Lower bound on the Mismatch capacity (LM rate) generally provides a tighter lower bound on the mismatch capacity. However, the efficient computation of the LM rate is significantly more challenging than that of the GMI, particularly as the size of the channel input alphabet increases. This growth in complexity renders standard numerical methods (e.g., interior point methods) computationally intensive and, in some cases, impractical. In this work, we reformulate the computation of the LM rate as a special instance of the optimal transport (OT) problem with an additional constraint. Building on this formulation, we develop a novel numerical algorithm based on the Sinkhorn algorithm, which is well known for its efficiency in solving entropy regularized optimization problems. We further provide the convergence analysis of the proposed algorithm, revealing that the algorithm has a sub-linear convergence rate. Numerical experiments demonstrate the feasibility and efficiency of the proposed algorithm for the computation of the LM rate.

cs.IT↗

arXiv2025

Efficient Computation of Marton's Error Exponent via Constraint Decoupling

The error exponent in lossy source coding characterizes the asymptotic decay rate of error probability with respect to blocklength. The Marton's error exponent provides the theoretically optimal bound on this rate. However, computation methods of the Marton's error exponent remain underdeveloped due to its formulation as a non-convex optimization problem with limited efficient solvers. While a recent grid search algorithm can compute its inverse function, it incurs prohibitive computational costs from two-dimensional brute-force parameter grid searches. This paper proposes a composite maximization approach that effectively handles both Marton's error exponent and its inverse function. Through a constraint decoupling technique, the resulting problem formulations admit efficient solvers driven by an alternating maximization algorithm. By fixing one parameter via a one-dimensional line search, the remaining subproblem becomes convex and can be efficiently solved by alternating variable updates, thereby significantly reducing search complexity. Therefore, the global convergence of the algorithm can be guaranteed. Numerical experiments for simple sources and the Ahlswede's counterexample, demonstrates the superior efficiency of our algorithm in contrast to existing methods.

cs.IT↗