arXiv subjects

Yucheng Wang

Publications and source records attributed to Yucheng Wang.

At least 91 records · Page 5Linked to original sources

arXiv2025

Inducing a transition between thermal and many-body localized states and detecting many-body mobility edges through dissipation

The many-body mobility edge (MBME) in energy, which separates thermal states from many-body localization (MBL) states, is a critical yet controversial concept in MBL physics. Here we examine the quasiperiodic $t_1-t_2$ model that features a mobility edge. With the addition of nearest-neighbor interactions, we suggest the potential existence of a MBME. Then we investigate the impact of a type of bond dissipation on the many-body system by calculating the steady-state density matrix and analyzing the transport behavior, and demonstrate that dissipation can cause the system to predominantly occupy either the thermal region or the MBL region, irrespective of the initial state. Finally, we discuss the effects of increasing system size. Our results indicate that dissipation can induce transitions between thermal and MBL states, providing a new approach for experimentally determining the existence of the MBME.

cond-mat.dis-nn↗

arXiv2025

Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation

Considering the inherent limitations of parametric knowledge in large language models (LLMs), retrieval-augmented generation (RAG) is widely employed to expand their knowledge scope. Since RAG has shown promise in knowledge-intensive tasks like open-domain question answering, its broader application to complex tasks and intelligent assistants has further advanced its utility. Despite this progress, the underlying knowledge utilization mechanisms of LLM-based RAG remain underexplored. In this paper, we present a systematic investigation of the intrinsic mechanisms by which LLMs integrate internal (parametric) and external (retrieved) knowledge in RAG scenarios. Specially, we employ knowledge stream analysis at the macroscopic level, and investigate the function of individual modules at the microscopic level. Drawing on knowledge streaming analyses, we decompose the knowledge utilization process into four distinct stages within LLM layers: knowledge refinement, knowledge elicitation, knowledge expression, and knowledge contestation. We further demonstrate that the relevance of passages guides the streaming of knowledge through these stages. At the module level, we introduce a new method, knowledge activation probability entropy (KAPE) for neuron identification associated with either internal or external knowledge. By selectively deactivating these neurons, we achieve targeted shifts in the LLM's reliance on one knowledge source over the other. Moreover, we discern complementary roles for multi-head attention and multi-layer perceptron layers during knowledge formation. These insights offer a foundation for improving interpretability and reliability in retrieval-augmented LLMs, paving the way for more robust and transparent generative solutions in knowledge-intensive domains.

cs.CL↗

arXiv2025

Optimal Control for Kuramoto Model: from Many-Particle Liouville Equation to Diffusive Mean-Field Problem

In this paper, we investigate the mean-field optimal control problem of a swarm of Kuramoto oscillators. Using the notion of wrapped distribution, we explain the connection between the stochastic particle system and the mean-field PDE on the periodic domain. In the limit of an infinite number of oscillators the collective dynamics of the agents' density is described by a diffusive mean-field model in the form of a non-local PDE, where the non-locality arises from the synchronization mechanism. We prove the existence of the optimal control of the mean-field model by using $Γ$-convergence strategy of the cost functional corresponding to the Liouville equation on the particle level. In the discussion of propagation of chaos for fixed control functions we complete the relative entropy estimate by using large deviation estimate given by \cite{MR3858403}.

math.AP↗

arXiv2025

Dissipation induced transition between delocalization and localization in the three-dimensional Anderson model

We investigate the probable delocalization-localization transition in open quantum systems with disorder. The disorder can induce localization in isolated quantum systems and it is generally recognized that localization is fragile under the action of dissipations from the external environment due to its interfering nature. Recent work [Y. Liu, et al, Phys. Rev. Lett. 132, 216301 (2024).] found that a one-dimensional quasiperiodic system can be driven into the localization phase by a tailored local dissipation where a dissipation-induced delocalized-localized transition is proposed. Based on this, we consider a more realistic system and show that a dissipation-induced transition between delocalization and localization appears in the three-dimensional (3D) Anderson model. By tuning local dissipative operators acting on nearest neighboring sites, we find that the system can relax to localized states dominated steady state instead of the choice of initial conditions and dissipation strengths. Moreover, we can also realize a delocalized states predominated steady-state from a localized initial state by using a kind of dissipation operators acting on next nearest neighboring sites. Our results enrich the applicability of dissipation-induced localization and identify the transition between delocalized and localized phases in 3D disordered systems.

cond-mat.dis-nn↗

arXiv2025

LongWriter-V: Enabling Ultra-Long and High-Fidelity Generation in Vision-Language Models

Existing Large Vision-Language Models (LVLMs) can process inputs with context lengths up to 128k visual and text tokens, yet they struggle to generate coherent outputs beyond 1,000 words. We find that the primary limitation is the absence of long output examples during supervised fine-tuning (SFT). To tackle this issue, we introduce LongWriter-V-22k, a SFT dataset comprising 22,158 examples, each with multiple input images, an instruction, and corresponding outputs ranging from 0 to 10,000 words. Moreover, to achieve long outputs that maintain high-fidelity to the input images, we employ Direct Preference Optimization (DPO) to the SFT model. Given the high cost of collecting human feedback for lengthy outputs (e.g., 3,000 words), we propose IterDPO, which breaks long outputs into segments and uses iterative corrections to form preference pairs with the original outputs. Additionally, we develop MMLongBench-Write, a benchmark featuring six tasks to evaluate the long-generation capabilities of VLMs. Our 7B parameter model, trained with LongWriter-V-22k and IterDPO, achieves impressive performance on this benchmark, outperforming larger proprietary models like GPT-4o. Code and data: https://github.com/THU-KEG/LongWriter-V

cs.CV↗

arXiv2025

MDE: Modality Discrimination Enhancement for Multi-modal Recommendation

Multi-modal recommendation systems aim to enhance performance by integrating an item's content features across various modalities with user behavior data. Effective utilization of features from different modalities requires addressing two challenges: preserving semantic commonality across modalities (modality-shared) and capturing unique characteristics for each modality (modality-specific). Most existing approaches focus on aligning feature spaces across modalities, which helps represent modality-shared features. However, modality-specific distinctions are often neglected, especially when there are significant semantic variations between modalities. To address this, we propose a Modality Distinctiveness Enhancement (MDE) framework that prioritizes extracting modality-specific information to improve recommendation accuracy while maintaining shared features. MDE enhances differences across modalities through a novel multi-modal fusion module and introduces a node-level trade-off mechanism to balance cross-modal alignment and differentiation. Extensive experiments on three public datasets show that our approach significantly outperforms other state-of-the-art methods, demonstrating the effectiveness of jointly considering modality-shared and modality-specific features.

cs.IR↗

arXiv2025

Robustness of quantum many-body scars in the presence of Markovian bath

A generic closed quantum many-body system will inevitably tend to thermalization, whose local information encoded in the initial state eventually scrambles into the full space, known as quantum ergodicity. A paradigmatic exception in closed quantum systems for strong ergodicity breaking is known as many-body localization, where strong disorder-induced localization prevents the occurrence of thermalization. It is generally recognized that a localized quantum system would be delocalized under dissipation induced by the environment. However, this consequence recently has received challenges where an exotic dissipation-induced localization mechanism is proposed, and transitions between localized and extended phases are found. In this Letter, we promote this mechanism to systems for weak ergodicity breaking hosting quantum many-body scars (QMBS). We find that the system relaxes to a steady state dominated by QMBS, and the dissipative dynamics exhibit dynamic revivals by suitably preparing an initial state. We point out an experimental realization of the controlled dissipation with a cold atomic setup. This makes the signature of ergodicity breaking visible over dissipative dynamics and offers potential possibilities for experimentally preparing stable QMBS with associated coherent dynamics.

cond-mat.dis-nn↗

arXiv2024

TEGEE: Task dEfinition Guided Expert Ensembling for Generalizable and Few-shot Learning

Large Language Models (LLMs) exhibit the ability to perform in-context learning (ICL), where they acquire new tasks directly from examples provided in demonstrations. This process is thought to operate through an implicit task selection mechanism that involves extracting and processing task definitions from these demonstrations. However, critical questions remain: Which is more essential -- task extraction or definition? And how can these capabilities be further improved? To address these questions, we propose \textbf{TEGEE} (Task Definition Guided Expert Ensembling), a method that explicitly extracts task definitions and generates responses based on specific tasks. Our framework employs a dual 3B model approach, with each model assigned a distinct role: one focuses on task definition extraction, while the other handles learning from demonstrations. This modular approach supports the hypothesis that extracting task definitions is more vital than processing the task itself. Empirical evaluations show that TEGEE performs comparably to the larger LLaMA2-13B model. By leveraging a modular design, our approach extends traditional ICL from few-shot to many-shot learning, supporting an unlimited number of demonstrations and enhancing continual learning capabilities.

cs.CL↗

arXiv2024

Dissipation-assisted preparation of topological boundary states

Robust states emerging at the boundaries of a system are an important hallmark of topological matter. Here, using the Su-Schrieffer-Heeger model and the Kitaev chain as examples, we study the impact of a type of experimentally realizable bond dissipation on topological systems by calculating the steady-state density matrix, and demonstrate that such dissipation applied near the system boundary can assist in preparing topological edge states of the parent Hamiltonian, irrespective of the initial state or filling. This effect stems from the matching between the phase distribution encoded in the topological edge states and the target state prepared through bond dissipation. This work provides new insights into the preparation of topological edge states, particularly in the context of Majorana zero modes.

cond-mat.mes-hall↗

arXiv2024

Influencing Factors of the FLASH Effect: Unveiling the Importance of Free Radicals

Purpose: Our aim was to elucidate the critical factors responsible for inducing the FLASH effect, focusing on the role of free radicals through simulation and experimental approaches. Methods and Materials: The whole abdomen of C57BL/6 mice was irradiated with 6 MeV electron beam. The endpoint was acute intestinal toxicity quantified by histological score. Total doses ranging from 6 to 15 Gy were evaluated. The impact of the mean dose rate (MDR) was assessed in the range of 40 to 900 Gy/s. Dose per pulse (DPP) of 0.5 Gy and 3 Gy were compared. The recombination of peroxyl radicals were simulated. Further comparisons were conducted by incorporating the antioxidant amifostine. Results: When varying total doses with a constant MDR of 900 Gy/s, the FLASH effect was not observed until the dose reached 15 Gy. For a total dose of 15 Gy and varying MDR, the FLASH effect was observed only when MDR reached 100 Gy/s. For a dose of 15 Gy and an MDR of 150 Gy/s, no significant difference in biological effect was observed between low DPP and high DPP. The simulation results indicated that the fraction of peroxyl radicals recombination remained nearly zero at conventional dose rates. For FLASH irradiation, the recombination fraction increased linearly with the dose. Notably, the dose delivery time corresponding to 50% change in the recombination fraction was approximately 300 ms. The addition of amifostine effectively eliminated the difference between FLASH group and CONV group. Conclusions: The critical requirement for observing the sparing effect at the biological endpoint is the administration of an adequate dose within the time window of the radical reaction. Additionally, the important role of free radical was verified after introducing antioxidants, suggesting that the generation and recombination of free radicals are pivotal factors influencing the FLASH sparing effect.

physics.med-ph↗

arXiv2024

Anderson transition and mobility edges on hyperbolic lattices with randomly connected boundaries

Hyperbolic lattices, formed by tessellating the hyperbolic plane with regular polygons, exhibit a diverse range of exotic physical phenomena beyond conventional Euclidean lattices. Here, we investigate the impact of disorder on hyperbolic lattices and reveal that the Anderson localization occurs at strong disorder strength, accompanied by the presence of mobility edges. Taking the hyperbolic $\{p,q\}=\{3,8\}$ and $\{p,q\}=\{4,8\}$ lattices as examples, we employ finite-size scaling of both spectral statistics and the inverse participation ratio to pinpoint the transition point and critical exponents. Our findings indicate that the transition points tend to increase with larger values of $\{p,q\}$ or curvature. In the limiting case of $\{\infty, q\}$, we further determine its Anderson transition using the cavity method, drawing parallels with the random regular graph. Our work lays the cornerstone for a comprehensive understanding of Anderson transition and mobility edges on hyperbolic lattices.

cond-mat.dis-nn↗

arXiv2024

Mean-Field Control for Diffusion Aggregation system with Coulomb Interaction

The mean-field control problem for a multi-dimensional diffusion-aggregation system with Coulomb interaction (the so called parabolic elliptic Keller-Segel system) is considered. The existence of optimal control is proved through the $Γ$-convergence of the corresponding control problem of the interacting particle system. There are three building blocks in the whole argument. Firstly, for the optimal control problem on the particle level, instead of using classical method for stochastic system, we study directly the control problem of high-dimensional parabolic equation, i.e. the Liouville equation of it. Secondly, we obtain a strong propagation of chaos result for the interacting particle system by combining the convergence in probability and relative entropy method. Due to this strong mean field limit result, we avoid giving compact support requirement for control functions, which has been often used in the literature. Thirdly, because of strong aggregation effect, additional difficulties arise from control function in obtaining the well-posedness theory of the diffusion-aggregation equation, so that the known method cannot be directly applied. Instead, we use a combination of local existence result and bootstrap argument to obtain the global solution in the sub-critical regime.

math.OC↗

arXiv2024

Exploring Multifractal Critical Phases in Two-Dimensional Quasiperiodic Systems

The multifractal critical phase (MCP) fundamentally differs from extended and localized phases, exhibiting delocalized distributions in both position and momentum spaces. The investigation on the MCP has largely focused on one-dimensional quasiperiodic systems. Here, we introduce a two-dimensional (2D) quasiperiodic model with a MCP. We present its phase diagram and investigate the characteristics of the 2D system's MCP in terms of wave packet diffusion and transport based on this model. We further investigate the movement of the phase boundary induced by the introduction of next-nearest-neighbor hopping by calculating the fidelity susceptibility. Finally, we consider how to realize our studied model in superconducting circuits. Our work opens the door to exploring MCP in 2D systems.

cond-mat.dis-nn↗

arXiv2024

A Survey on Graph Neural Networks for Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends

Remaining Useful Life (RUL) prediction is a critical aspect of Prognostics and Health Management (PHM), aimed at predicting the future state of a system to enable timely maintenance and prevent unexpected failures. While existing deep learning methods have shown promise, they often struggle to fully leverage the spatial information inherent in complex systems, limiting their effectiveness in RUL prediction. To address this challenge, recent research has explored the use of Graph Neural Networks (GNNs) to model spatial information for more accurate RUL prediction. This paper presents a comprehensive review of GNN techniques applied to RUL prediction, summarizing existing methods and offering guidance for future research. We first propose a novel taxonomy based on the stages of adapting GNNs to RUL prediction, systematically categorizing approaches into four key stages: graph construction, graph modeling, graph information processing, and graph readout. By organizing the field in this way, we highlight the unique challenges and considerations at each stage of the GNN pipeline. Additionally, we conduct a thorough evaluation of various state-of-the-art (SOTA) GNN methods, ensuring consistent experimental settings for fair comparisons. This rigorous analysis yields valuable insights into the strengths and weaknesses of different approaches, serving as an experimental guide for researchers and practitioners working in this area. Finally, we identify and discuss several promising research directions that could further advance the field, emphasizing the potential for GNNs to revolutionize RUL prediction and enhance the effectiveness of PHM strategies. The benchmarking codes are available in GitHub: https://github.com/Frank-Wang-oss/GNN\_RUL\_Benchmarking.

cs.LG↗

arXiv2024

Conditional Generative Denoiser for Nighttime UAV Tracking

State-of-the-art (SOTA) visual object tracking methods have significantly enhanced the autonomy of unmanned aerial vehicles (UAVs). However, in low-light conditions, the presence of irregular real noise from the environments severely degrades the performance of these SOTA methods. Moreover, existing SOTA denoising techniques often fail to meet the real-time processing requirements when deployed as plug-and-play denoisers for UAV tracking. To address this challenge, this work proposes a novel conditional generative denoiser (CGDenoiser), which breaks free from the limitations of traditional deterministic paradigms and generates the noise conditioning on the input, subsequently removing it. To better align the input dimensions and accelerate inference, a novel nested residual Transformer conditionalizer is developed. Furthermore, an innovative multi-kernel conditional refiner is designed to pertinently refine the denoised output. Extensive experiments show that CGDenoiser promotes the tracking precision of the SOTA tracker by 18.18\% on DarkTrack2021 whereas working 5.8 times faster than the second well-performed denoiser. Real-world tests with complex challenges also prove the effectiveness and practicality of CGDenoiser. Code, video demo and supplementary proof for CGDenoier are now available at: \url{https://github.com/vision4robotics/CGDenoiser}.

cs.RO↗

arXiv2024

Unveiling High Selectivity Origin of Pt-Bi Catalysts for Alkaline Methanol Electrooxidation via CO-free pathway

A long-standing puzzle for methanol electrooxidation is how to achieve a CO-free pathway and accurately understand the origin of electrocatalytic selectivity. Herein, we unequivocally demonstrate that the Bi-modified Pt/C follows a CO-free dominated pathway during alkaline methanol electrooxidation, and unveil the formaldehyde (HCHO) intermediate as a critical factor influencing pathway selectivity. These findings are substantiated by kinetic isotope effects, formate Faradaic efficiency, in situ spectroscopy, ab initio molecular dynamic simulations, and density functional theory calculations. Bi modification significantly increases the HCHO dehydrogenation barrier, which facilitates its desorption and subsequent conversion to the H2COOH- anion at the alkaline interface, intrinsically avoiding CO formation. More specifically, the formation of ensemble sites featuring V-shaped Bi-Pt-Bi configuration inhibits the cleavage of C-H bond, and the weak OH binding energy at Bi adatoms effectively prevents blockage of oxygenated species, allowing such ensemble sites to fulfill their functional role. Our study opens up a novel dimension for designing advanced CO-free catalysts.

physics.chem-ph↗

arXiv2024

Manipulating the Relaxation Time of Boundary-Dissipative Systems through Bond Dissipation

Relaxation time plays a crucial role in describing the relaxation processes of quantum systems. We study the effect of a type of bond dissipation on the relaxation time of boundary dissipative systems and find that it can change the scaling of the relaxation time $T_c\sim L^{z}$ from $z=3$ to a value significantly less than $3$. We further reveal that the reason such bond dissipation can significantly reduce the relaxation time is that it can selectively target specific states. For Anderson localized systems, the scaling behavior of the relaxation time changes from an exponential form to a power-law form as the system size varies. This is because the bond dissipation we consider can not only select specific states but also disrupt the localization properties. Our work reveals that in open systems, one type of dissipation can be used to regulate the effects produced by another type of dissipation.

cond-mat.dis-nn↗

arXiv2024

Anomalous universal quantum transport in 2D asymptotic quasiperiodic system

Quasiperiodic systems extend the concept of the Anderson transition to quasi-random and low-dimensional realms and have garnered widespread attention. Here, we propose the asymptotic quasiperiodic two-dimensional systems characterized by a sequence of rational magnetic fluxes, which have an irrational limit, and predict exotic universal wave-packet dynamics and transport phenomena associated with the asymptotic quasiperiodicity (AQP). The predictions unveil a class of multiple metal-insulator transitions driven by a novel interplay effect between AQP, relaxation, and finite temperature, which further reveals a unified and profound mechanism. Specifically, all the transport phenomena, including the wave-packet dynamics, the bulk and edge transport, are unified in the universal scaling laws unveiled in the asymptotic quasiperiodic regime, which demonstrate a nontrivial asymptotic connection to quantum phases in the quasiperiodic limit. Our work enriches the universal quantum transport phenomena, adds to the basic mechanisms underlying metal-insulator transitions, and opens up an avenue to study the exotic transport physics with AQP in high dimensions.

cond-mat.mes-hall↗