arXiv subjects

Yu Chen

Publications and source records attributed to Yu Chen.

At least 163 records · Page 9Linked to original sources

arXiv2025

Lower Bounds on Tree Covers

Given an $n$-point metric space $(X,d_X)$, a tree cover $\mathcal{T}$ is a set of $|\mathcal{T}|=k$ trees on $X$ such that every pair of vertices in $X$ has a low-distortion path in one of the trees in $\mathcal{T}$. Tree covers have been playing a crucial role in graph algorithms for decades, and the research focus is the construction of tree covers with small size $k$ and distortion. When $k=1$, the best distortion is known to be $Θ(n)$. For a constant $k\ge 2$, the best distortion upper bound is $\tilde O(n^{\frac 1 k})$ and the strongest lower bound is $Ω(\log_k n)$, leaving a gap to be closed. In this paper, we improve the lower bound to $Ω(n^{\frac{1}{2^{k-1}}})$. Our proof is a novel analysis on a structurally simple grid-like graph, which utilizes some combinatorial fixed-point theorems. We believe that they will prove useful for analyzing other tree-like data structures as well.

cs.DS↗

arXiv2025

Scalable data-driven modeling of microstructure evolution by learning local dependency and spatiotemporal translation invariance rules in phase field simulation

Phase-field (PF) simulation provides a powerful framework for predicting microstructural evolution but suffers from prohibitive computational costs that severely limit accessible spatiotemporal scales in practical applications. While data-driven methods have emerged as promising approaches for accelerating PF simulations, existing methods require extensive training data from numerous evolution trajectories, and their inherent black-box nature raises concerns about long-term prediction reliability. This work demonstrates, through examples of grain growth and spinodal decomposition, that a minimalist Convolutional Neural Network (CNN) trained with a remarkably small dataset even from a single small-scale simulation can achieve seamless scalability to larger systems and reliable long-term predictions far beyond the temporal range of the training data. The key insight of this work lies in revealing that the success of CNN-based models stems from the alignment between their inductive biases and the physical priors of phase-field simulations specifically, locality and spatiotemporal translation invariance. Through effective receptive field analysis, we verify that the model captures these essential properties during training. Therefore, from a reductionist perspective, the surrogate model essentially establishes a spatiotemporally invariant regression mapping between a grid point's local environment and its subsequent state. Further analysis of the model's feature space demonstrates that microstructural evolution effectively represents a continuous redistribution of a finite set of local environments. When the model has already encountered nearly all possible local environments in the early-stage training data, it can reliably generalize to much longer evolution timescales, regardless of the dramatic changes in global microstructural morphology.

cond-mat.mtrl-sci↗

arXiv2025

PixCLIP: Achieving Fine-grained Visual Language Understanding via Any-granularity Pixel-Text Alignment Learning

While the Contrastive Language-Image Pretraining(CLIP) model has achieved remarkable success in a variety of downstream vison language understanding tasks, enhancing its capability for fine-grained image-text alignment remains an active research focus. To this end, most existing works adopt the strategy of explicitly increasing the granularity of visual information processing, e.g., incorporating visual prompts to guide the model focus on specific local regions within the image. Meanwhile, researches on Multimodal Large Language Models(MLLMs) have demonstrated that training with long and detailed textual descriptions can effectively improve the model's fine-grained vision-language alignment. However, the inherent token length limitation of CLIP's text encoder fundamentally limits CLIP to process more granular textual information embedded in long text sequences. To synergistically leverage the advantages of enhancing both visual and textual content processing granularity, we propose PixCLIP, a novel framework designed to concurrently accommodate visual prompt inputs and process lengthy textual descriptions. Specifically, we first establish an automated annotation pipeline capable of generating pixel-level localized, long-form textual descriptions for images. Utilizing this pipeline, we construct LongGRIT, a high-quality dataset comprising nearly 1.5 million samples. Secondly, we replace CLIP's original text encoder with the LLM and propose a three-branch pixel-text alignment learning framework, facilitating fine-grained alignment between image regions and corresponding textual descriptions at arbitrary granularity. Experiments demonstrate that PixCLIP showcases breakthroughs in pixel-level interaction and handling long-form texts, achieving state-of-the-art performance.

cs.CV↗

arXiv2025

Distributionally Robust Control Synthesis for Stochastic Systems with Safety and Reach-Avoid Specifications

We investigate the problem of synthesizing distributionally robust control policies for stochastic systems under safety and reach-avoid specifications. Using a game-theoretical framework, we consider the setting where the probability distribution of the disturbance at each time step is selected from an ambiguity set defined by the Wasserstein distance. The goal is to synthesize a distributionally robust control policy that ensures the satisfaction probability exceeds a specified threshold under any distribution within the ambiguity set. First, for both safety and reach-avoid specifications, we establish the existence of optimal policies by leveraging the dynamic programming principles. Then we demonstrate how the associated optimization problem can be efficiently solved using the dual representation of Wasserstein distributionally robust optimization. Furthermore, for safety specifications in particular, we introduce a novel concept of distributionally robust control barrier certificates and show how these enable the efficient synthesis of controllers through sum-of-squares programming techniques. Finally, our experimental results reveal that incorporating distributional robustness during the synthesis phase significantly improves the satisfaction probability during online execution, even with limited statistical knowledge of the disturbance distribution.

eess.SY↗

arXiv2025

Robustness for expander graphs

We study robust versions of properties of $(n,d,λ)$-graphs, namely, the property of a random sparsification of an $(n,d,λ)$-graph, where each edge is retained with probability $p$ independently. We prove such results for the containment problem of perfect matchings, Hamiltonian cycles, and triangle factors. These results address a series of problems posed by Frieze and Krivelevich. First we prove that given $γ>0$, for sufficient large $n$, any $(n,d,λ)$-graph $G$ with $λ=o(d)$, $d=Ω(\log n)$ and $p\ge\frac{(1+γ)\log n}{d}$, $G\cap G(n,p)$ contains a Hamiltonian cycle (and thus a perfect matching if $n$ is even) with high probability. This result is asymptotically optimal. Moreover, we show that for sufficient large $n$, any $(n,d,λ)$-graph $G$ with $λ=o(\frac{d^2}{n})$, $d=Ω(n^{\frac{5}{6}}\log^{\frac{1}{2}}n)$ and $p\gg d^{-1}n^{\frac{1}{3}}\log^{\frac{1}{3}} n$, $G\cap G(n,p)$ contains a triangle factor with high probability. Here, the restrictions on $p$ and $λ$ are asymptotically optimal. Our proof for the triangle factor problem uses the iterative absorption approach to build a spread measure on the triangle factors, and we also prove and use a coupling result for triangles in the random subgraph of an expander $G$ and the hyperedges in the random subgraph of the triangle-hypergraph of $G$.

math.CO↗

arXiv2025

AIM: A User-friendly GUI Workflow program for Isotherm Fitting, Mixture Prediction, Isosteric Heat of Adsorption Estimation, and Breakthrough Simulation

Adsorption breakthrough modeling often requires complex software environments and scripting, limiting accessibility for many practitioners. We present AIM, a MATLAB-based graphical user interface (GUI) application that streamlines fixed-bed adsorption modeling and analysis through an integrated workflow, which includes isotherm fitting, estimation of the enthalpy of adsorption, prediction of mixture behavior, and multicomponent breakthrough simulations. AIM supports 13 isotherm models for isotherm fitting and includes the implementation of Ideal Adsorbed Solution Theory (IAST) (FastIAS) and extended Langmuir models for predicting mixture isotherms. Moreover, the isotherm models can be used to run non-isothermal breakthrough simulations along with isosteric enthalpies of adsorption from the Clausius-Clapeyron and Virial equations. Users can export detailed column and outlet profiles (e.g., composition, temperature) in multiple formats, enhancing reproducibility and data sharing among practitioners. We compared the breakthrough simulation results from the AIM workflow and compared that with the experimental data in the literature for a ternary gas mixture (CO2/H2/N2) and found excellent agreement for outlet compositions and temperature profiles.

cond-mat.mtrl-sci↗

arXiv2025

PrediQL: Automated Testing of GraphQL APIs with LLMs

GraphQL's flexible query model and nested data dependencies expose APIs to complex, context-dependent vulnerabilities that are difficult to uncover using conventional testing tools. Existing fuzzers either rely on random payload generation or rigid mutation heuristics, failing to adapt to the dynamic structures of GraphQL schemas and responses. We present PrediQL, the first retrieval-augmented, LLM-guided fuzzer for GraphQL APIs. PrediQL combines large language model reasoning with adaptive feedback loops to generate semantically valid and diverse queries. It models the choice of fuzzing strategy as a multi-armed bandit problem, balancing exploration of new query structures with exploitation of past successes. To enhance efficiency, PrediQL retrieves and reuses execution traces, schema fragments, and prior errors, enabling self-correction and progressive learning across test iterations. Beyond input generation, PrediQL integrates a context-aware vulnerability detector that uses LLM reasoning to analyze responses, interpreting data values, error messages, and status codes to identify issues such as injection flaws, access-control bypasses, and information disclosure. Our evaluation across open-source and benchmark GraphQL APIs shows that PrediQL achieves significantly higher coverage and vulnerability discovery rates compared to state-of-the-art baselines. These results demonstrate that combining retrieval-augmented reasoning with adaptive fuzzing can transform API security testing from reactive enumeration to intelligent exploration.

cs.CR↗

arXiv2025

MirrorFuzz: Leveraging LLM and Shared Bugs for Deep Learning Framework APIs Fuzzing

Deep learning (DL) frameworks serve as the backbone for a wide range of artificial intelligence applications. However, bugs within DL frameworks can cascade into critical issues in higher-level applications, jeopardizing reliability and security. While numerous techniques have been proposed to detect bugs in DL frameworks, research exploring common API patterns across frameworks and the potential risks they entail remains limited. Notably, many DL frameworks expose similar APIs with overlapping input parameters and functionalities, rendering them vulnerable to shared bugs, where a flaw in one API may extend to analogous APIs in other frameworks. To address this challenge, we propose MirrorFuzz, an automated API fuzzing solution to discover shared bugs in DL frameworks. MirrorFuzz operates in three stages: First, MirrorFuzz collects historical bug data for each API within a DL framework to identify potentially buggy APIs. Second, it matches each buggy API in a specific framework with similar APIs within and across other DL frameworks. Third, it employs large language models (LLMs) to synthesize code for the API under test, leveraging the historical bug data of similar APIs to trigger analogous bugs across APIs. We implement MirrorFuzz and evaluate it on four popular DL frameworks (TensorFlow, PyTorch, OneFlow, and Jittor). Extensive evaluation demonstrates that MirrorFuzz improves code coverage by 39.92\% and 98.20\% compared to state-of-the-art methods on TensorFlow and PyTorch, respectively. Moreover, MirrorFuzz discovers 315 bugs, 262 of which are newly found, and 80 bugs are fixed, with 52 of these bugs assigned CNVD IDs.

cs.SE↗

arXiv2025

Online Continual Learning via Spiking Neural Networks with Sleep Enhanced Latent Replay

Edge computing scenarios necessitate the development of hardware-efficient online continual learning algorithms to be adaptive to dynamic environment. However, existing algorithms always suffer from high memory overhead and bias towards recently trained tasks. To tackle these issues, this paper proposes a novel online continual learning approach termed as SESLR, which incorporates a sleep enhanced latent replay scheme with spiking neural networks (SNNs). SESLR leverages SNNs' binary spike characteristics to store replay features in single bits, significantly reducing memory overhead. Furthermore, inspired by biological sleep-wake cycles, SESLR introduces a noise-enhanced sleep phase where the model exclusively trains on replay samples with controlled noise injection, effectively mitigating classification bias towards new classes. Extensive experiments on both conventional (MNIST, CIFAR10) and neuromorphic (NMNIST, CIFAR10-DVS) datasets demonstrate SESLR's effectiveness. On Split CIFAR10, SESLR achieves nearly 30% improvement in average accuracy with only one-third of the memory consumption compared to baseline methods. On Split CIFAR10-DVS, it improves accuracy by approximately 10% while reducing memory overhead by a factor of 32. These results validate SESLR as a promising solution for online continual learning in resource-constrained edge computing scenarios.

cs.NE↗

arXiv2025

Finite-time Convergence Analysis of Actor-Critic with Evolving Reward

Many popular practical reinforcement learning (RL) algorithms employ evolving reward functions-through techniques such as reward shaping, entropy regularization, or curriculum learning-yet their theoretical foundations remain underdeveloped. This paper provides the first finite-time convergence analysis of a single-timescale actor-critic algorithm in the presence of an evolving reward function under Markovian sampling. We consider a setting where the reward parameters may change at each time step, affecting both policy optimization and value estimation. Under standard assumptions, we derive non-asymptotic bounds for both actor and critic errors. Our result shows that an $O(1/\sqrt{T})$ convergence rate is achievable, matching the best-known rate for static rewards, provided the reward parameters evolve slowly enough. This rate is preserved when the reward is updated via a gradient-based rule with bounded gradient and on the same timescale as the actor and critic, offering a theoretical foundation for many popular RL techniques. As a secondary contribution, we introduce a novel analysis of distribution mismatch under Markovian sampling, improving the best-known rate by a factor of $\log^2T$ in the static-reward case.

cs.LG↗

arXiv2025

In-plane polar domains enhanced energy storage

Relaxor ferroelectric thin films are recognized for their ultrahigh power density, rendering them highly promising for energy storage applications in electrical and electronic systems. However, achieving high energy storage performance with chemically homogeneous, environmentally friendly and compositionally stable materials remains challenging. In this work, we present a design of dielectrics with high energy storage performance via an in-plane polar domains incorporating polar nanoregions mechanism. Guided by phase-field simulations, we synthesized La/Si co-doping BaTiO3 solid-solution thin films with high chemical homogeneity to realize high energy storage performance. Given that, we achieve a high energy density of 203.7J/cm3 and an energy efficiency of approximately 80% at an electric field of 6.15MV/cm. This mechanism holds significant promise for the design of next-generation high-performance dielectric materials for energy storage and other advanced functional materials.

cond-mat.mtrl-sci↗

arXiv2025

On Prediction-Based Properties of Discrete-Event Systems: Notions, Applications and Supervisor Synthesis

In this work, we investigate the problem of synthesizing property-enforcing supervisors for partially-observed discrete-event systems (DES). Unlike most existing approaches, where the enforced property depends solely on the executed behavior of the system, here we consider a more challenging scenario in which the property relies on predicted future behaviors that have not yet occurred. This problem arises naturally in applications involving future information, such as active prediction or intention protection. To formalize the problem, we introduce the notion of prediction-based properties, a new class of observational properties tied to the system's future information. We demonstrate that this notion is very generic and can model various practical properties, including predictability in fault prognosis and pre-opacity in intention security. We then present an effective approach for synthesizing supervisors that enforce prediction-based properties. Our method relies on a novel information structure that addresses the fundamental challenge arising from the dependency between current predictions and the control policy. The key idea is to first borrow information from future instants and then ensure information consistency. This reduces the supervisor synthesis problem to a safety game in the information space. We prove that the proposed algorithm is both sound and complete, and the resulting supervisor is maximally permissive.

eess.SY↗

arXiv2025

ATLAS: Autoformalizing Theorems through Lifting, Augmentation, and Synthesis of Data

Autoformalization, the automatic translation of mathematical content from natural language into machine-verifiable formal languages, has seen significant progress driven by advances in large language models (LLMs). Nonetheless, a primary barrier to further improvements is the limited availability of parallel corpora that map informal mathematical text to its formal counterpart. To address this limitation, we propose ATLAS (Autoformalizing Theorems through Lifting, Augmentation, and Synthesis of Data), a novel data generation framework designed to produce large-scale, high-quality parallel corpora of theorem statements. Distinct from prior approaches, ATLAS begins with a concept repository, accelerates the improvement of the student model through expert iteration combined with knowledge distillation, and introduces two novel augmentation strategies that exploit the structural characteristics of formal languages. Running the proposed ATLAS framework for 10 iterations, we construct an undergraduate-level dataset of 117k theorem statements and develop the ATLAS Translator by fine-tuning Llama3.1-8B-Instruct with LoRA. This model establishes a new state of the art, demonstrating statistically significant improvements over both the Herald Translator and the Kimina-Autoformalizer across all benchmarks (p<0.05, two-sided t-test). Furthermore, we demonstrate that the full-parameter fine-tuning of a stronger base model on the ATLAS dataset leads to superior performance. The datasets, model, and code are available at https://github.com/XiaoyangLiu-sjtu/ATLAS.

cs.CL↗

arXiv2025

A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing

Event cameras are bio-inspired vision sensor that encode visual information with high dynamic range, high temporal resolution, and low latency.Current state-of-the-art event stream processing methods rely on end-to-end deep learning techniques. However, these models are heavily dependent on data structures, limiting their stability and generalization capabilities across tasks, thereby hindering their deployment in real-world scenarios. To address this issue, we propose a chaotic dynamics event signal processing framework inspired by the dorsal visual pathway of the brain. Specifically, we utilize Continuous-coupled Neural Network (CCNN) to encode the event stream. CCNN encodes polarity-invariant event sequences as periodic signals and polarity=changing event sequences as chaotic signals. We then use continuous wavelet transforms to analyze the dynamical states of CCNN neurons and establish the high-order mappings of the event stream. The effectiveness of our method is validated through integration with conventional classification networks, achieving state-of-the-art classification accuracy on the N-Caltech101 and N-CARS datasets, with results of 84.3% and 99.9%, respectively. Our method improves the accuracy of event camera-based object classification while significantly enhancing the generalization and stability of event representation. Our code is available in https://github.com/chenyu0193/ACDF.

q-bio.NC↗

arXiv2025

CSF: Fixed-outline Floorplanning Based on the Conjugate Subgradient Algorithm Assisted by Q-Learning

The state-of-the-art researches indicate that analytic algorithms are promising in handling complex floorplanning scenarios. However, it is challenging to generate compact floorplans with excellent wirelength optimization effect due to the local convergence of gradient-based optimization algorithms designed for constructed smooth optimization models. Accordingly, we propose to construct a nonsmooth analytic floorplanning model addressed by the conjugate subgradient algorithm (CSA), which is accelerated by a population-based scheme adaptively regulating the stepsize with the assistance of Q-learning. In this way, the proposed CSA assisted by Q-learning (CSAQ) can strike a good balance on exploration and exploitation. Experimental results on the MCNC and GSRC benchmarks demonstrate that the proposed fixed-outline floorplanning algorithm based on CSAQ (CSF) not only address global floorplanning effectively, but also get legal floorplans more efficiently than the constraint graph-based legalization algorithm as well as its improved variants. It is also demonstrated that the CSF is competitive to the state-of-the-art algorithms on floorplanning scenarios only containing hard modules.

cs.LG↗

arXiv2025

TsqLoRA: Towards Sensitivity and Quality Low-Rank Adaptation for Efficient Fine-Tuning

Fine-tuning large pre-trained models for downstream tasks has become a fundamental approach in natural language processing. Fully fine-tuning all model parameters is computationally expensive and memory-intensive, especially in resource-constrained environments. Existing parameter-efficient fine-tuning methods reduce the number of trainable parameters but typically overlook the varying sensitivity of different model layers and the importance of training data. In this work, we propose TsqLoRA, a novel method that integrates data-quality-driven selection with sensitivity-aware low-rank adaptation, consisted of two main components: a quality-aware sampling mechanism for selecting the most informative training data, and a dynamic rank allocation module that adjusts the rank of each layer based on its sensitivity to parameter updates. The experimental results demonstrate that TsqLoRA improves fine-tuning efficiency while maintaining or even improving performance on a variety of NLP tasks. Our code will be available at https://github.com/Benjamin-Ricky/TsqLoRA.

cs.CL↗

arXiv2025

CLASS: A Controller-Centric Layout Synthesizer for Dynamic Quantum Circuits

Layout Synthesis for Quantum Computing (LSQC) is a critical component of quantum design tools. Traditional LSQC studies primarily focus on optimizing for reduced circuit depth by adopting a device-centric design methodology. However, these approaches overlook the impact of classical processing and communication time, thereby being insufficient for Dynamic Quantum Circuits (DQC). To address this, we introduce CLASS, a controller-centric layout synthesizer designed to reduce inter-controller communication latency in a distributed control system. It consists of a two-stage framework featuring a hypergraph-based modeling and a heuristic-based graph partitioning algorithm. Evaluations demonstrate that CLASS effectively reduces communication latency by up to 100% with only a 2.10% average increase in the number of additional operations.

quant-ph↗

arXiv2025

Optimal Control of Markov Decision Processes for Efficiency with Linear Temporal Logic Tasks

We investigate the problem of optimal control synthesis for Markov Decision Processes (MDPs), addressing both qualitative and quantitative objectives. Specifically, we require the system to satisfy a qualitative task specified by a Linear Temporal Logic (LTL) formula with probability one. Additionally, to quantify the system's performance, we introduce the concept of efficiency, defined as the ratio between rewards and costs. This measure is more general than the standard long-run average reward metric, as it seeks to maximize the reward obtained per unit cost. Our objective is to synthesize a control policy that not only ensures the LTL task is satisfied but also maximizes efficiency. We present an effective approach for synthesizing a stationary control policy that achieves $ε$-optimality by integrating state classifications of MDPs with perturbation analysis in a novel manner. Our results extend existing work on efficiency-optimal control synthesis for MDPs by incorporating qualitative LTL tasks. Case studies in robot task planning are provided to illustrate the proposed algorithm.

eess.SY↗