arXiv subjects

Ting Qu

Publications and source records attributed to Ting Qu.

6 recordsLinked to original sources

arXiv2026

Relaxation of quenched structural glasses: descent in a stiffening caging potential over inflection-point 'speed bumps'

The slow energy relaxation in quenched glasses is a ubiquitous yet poorly understood phenomenon. Despite extensive study, the microscopic origin of the observed power-law decay remains debated, with proposed mechanisms ranging from saddle-point slowdown and marginal stability to coarsening of localized excitations and phonon dynamics. Here, by simulating gradient descent in archetypal structural glass formers, we show that none of these scenarios can account for our data. Instead, the power-law behavior emerges from a remarkably simple caging effect: each particle experiences an effective stiffening potential that arises from many-body confinement and diverges at a characteristic cage size. This mechanism analytically yields the observed power-law decay and is quantitatively reproduced by a minimal single-particle cage model with fixed neighbours, demonstrating that collective relaxation modes are not essential. The dynamics is punctuated by fluctuations as the system rolls through inflection points on the energy landscape, which act as `speed bumps' but do not affect the overall power-law behaviour. In contrast to mean-field spin glass theory, we find no characteristic temperature that separates distinct dynamical regimes; state following within a given glass basin occurs universally for all initial temperatures whenever the system is sufficiently close to the inherent structure. Our results establish a complete physical picture of gradient descent dynamics in typical structural glasses.

cond-mat.dis-nn↗

arXiv2025

Distributed optimization: designed for federated learning

Federated learning (FL), as a distributed collaborative machine learning (ML) framework under privacy-preserving constraints, has garnered increasing research attention in cross-organizational data collaboration scenarios. This paper proposes a class of distributed optimization algorithms based on the augmented Lagrangian technique, designed to accommodate diverse communication topologies in both centralized and decentralized FL settings. Furthermore, we develop multiple termination criteria and parameter update mechanisms to enhance computational efficiency, accompanied by rigorous theoretical guarantees of convergence. By generalizing the augmented Lagrangian relaxation through the incorporation of proximal relaxation and quadratic approximation, our framework systematically recovers a broad of classical unconstrained optimization methods, including proximal algorithm, classic gradient descent, and stochastic gradient descent, among others. Notably, the convergence properties of these methods can be naturally derived within the proposed theoretical framework. Numerical experiments demonstrate that the proposed algorithm exhibits strong performance in large-scale settings with significant statistical heterogeneity across clients.

cs.LG↗

arXiv2025

A distributed augmented Lagrangian decomposition algorithm for constrained optimization

Within the framework of the augmented Lagrangian (AL), we propose a novel distributed optimization method, termed Distributed Augmented Lagrangian Decomposition (DALD), and provide a rigorous convergence proof for its standard version. To address the high iteration costs in early stages, we propose several accelerated variants of DALD that enhances efficiency without compromising theoretical guarantees, supported by a comprehensive convergence analysis. To facilitate the description of the distributed optimization process, the concept of hierarchical coordination networks is introduced, integrating hierarchical matrix concepts to aid in this explanation. We further explore and expand the applicability of the DALD method and demonstrate how it unifies existing distributed optimization theories within the AL framework. The effectiveness and applicability of the proposed distributed optimization method and its variants are further validated through numerical experiments.

math.OC↗

arXiv2025

From Failures to Fixes: LLM-Driven Scenario Repair for Self-Evolving Autonomous Driving

Ensuring robust and generalizable autonomous driving requires not only broad scenario coverage but also efficient repair of failure cases, particularly those related to challenging and safety-critical scenarios. However, existing scenario generation and selection methods often lack adaptivity and semantic relevance, limiting their impact on performance improvement. In this paper, we propose \textbf{SERA}, an LLM-powered framework that enables autonomous driving systems to self-evolve by repairing failure cases through targeted scenario recommendation. By analyzing performance logs, SERA identifies failure patterns and dynamically retrieves semantically aligned scenarios from a structured bank. An LLM-based reflection mechanism further refines these recommendations to maximize relevance and diversity. The selected scenarios are used for few-shot fine-tuning, enabling targeted adaptation with minimal data. Experiments on the benchmark show that SERA consistently improves key metrics across multiple autonomous driving baselines, demonstrating its effectiveness and generalizability under safety-critical conditions.

cs.CV↗

arXiv2024

Universal activated aging and weak ergodicity breaking in spin and structural glasses

Glasses possess complex energy landscapes and exhibit non-equilibrium aging dynamics. Here, we propose a generalized trap model for activated aging based on a key static property of the energy landscape: the distribution of energy barriers. Our theory predicts that, upon cooling, weak ergodicity breaking (WEB) in quenching dynamics occurs prior to strong ergodicity breaking in equilibrium dynamics. Furthermore, the theory indicates that the characteristic size of activation clusters can be deduced from the logarithmic decay of the time-correlation function. We rigorously test the model's assumptions and predictions using the simplest spin glass model - the random energy model. The predicted aging behavior is also universally observed in paradigmatic structural glasses, including the Weeks-Chandler-Andersen (WCA) model and amorphous silica. Remarkably, applying our framework to the WCA model allows us to extract a static length from the non equilibrium dynamics, extending its observable growth range from a mere factor of 2-3 to a full order of magnitude and providing supportive evidence for the random first-order transition scenario. Finally, we propose a unified ergodic-WEB phase diagram for aging dynamics in general glassy systems.

cond-mat.dis-nn↗

arXiv2023

Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluation

Autonomous vehicle (AV) evaluation has been the subject of increased interest in recent years both in industry and in academia. This paper focuses on the development of a novel framework for generating adversarial driving behavior of background vehicle interfering against the AV to expose effective and rational risky events. Specifically, the adversarial behavior is learned by a reinforcement learning (RL) approach incorporated with the cumulative prospect theory (CPT) which allows representation of human risk cognition. Then, the extended version of deep deterministic policy gradient (DDPG) technique is proposed for training the adversarial policy while ensuring training stability as the CPT action-value function is leveraged. A comparative case study regarding the cut-in scenario is conducted on a high fidelity Hardware-in-the-Loop (HiL) platform and the results demonstrate the adversarial effectiveness to infer the weakness of the tested AV.

cs.AI↗