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Yong Zhang

Publications and source records attributed to Yong Zhang.

At least 181 records · Page 10Linked to original sources
arXiv2025

An efficient Fourier spectral algorithm for the Bogoliubov-de Gennes excitation eigenvalue problem

In this paper, we propose an efficient Fourier spectral algorithm for an eigenvalue problem, that is, the Bogoliubov-de Gennes (BdG) equation arsing from spin-1 Bose-Einstein condensates (BEC) to describe the elementary/collective excitations around the mean-field ground state. The BdG equation is essentially a constrained eigenvalue/eigenfunction system. Firstly, we investigate its analytical properties, including exact eigenpairs, generalized nullspace, and bi-orthogonality of eigenspaces. Secondly, by combining the standard Fourier spectral method for spatial discretization and a stable Gram-Schmidt bi-orthogonal algorithm, we develop a subspace iterative solver for such a large-scale dense eigenvalue problem, and it proves to be numerically stable, efficient, and accurate. Our solver is matrix-free and the operator-function evaluation is accelerated by discrete Fast Fourier Transform (FFT) with almost optimal efficiency. Therefore, it is memory-friendly and efficient for large-scale problems. Furthermore, we give a rigorous and detailed numerical analysis on the stability and spectral convergence. Finally, we present extensive numerical results to illustrate the spectral accuracy and efficiency, and investigate the excitation spectrum and Bogoliubov amplitudes around the ground state in 1-3 spatial dimensions.

math.NA↗
arXiv2025

A Bi-Orthogonal Structure-Preserving eigensolver for large-scale linear response eigenvalue problem

The linear response eigenvalue problem, which arises from many scientific and engineering fields, is quite challenging numerically for large-scale sparse/dense system, especially when it has zero eigenvalues. Based on a direct sum decomposition of biorthogonal invariant subspaces and the minimization principles in the biorthogonal complement, using the structure of generalized nullspace, we propose a Bi-Orthogonal Structure-Preserving subspace iterative solver, which is stable, efficient, and of excellent parallel scalability. The biorthogonality is of essential importance and created by a modified Gram-Schmidt biorthogonalization (MGS-Biorth) algorithm. We naturally deflate out converged eigenvectors by computing the rest eigenpairs in the biorthogonal complementary subspace without introducing any artificial parameters. When the number of requested eigenpairs is large, we propose a moving mechanism to compute them batch by batch such that the projection matrix size is small and independent of the requested eigenpair number. For large-scale problems, one only needs to provide the matrix-vector product, thus waiving explicit matrix storage. The numerical performance is further improved when the matrix-vector product is implemented using parallel computing. Ample numerical examples are provided to demonstrate the stability, efficiency, and parallel scalability.

math.NA↗
arXiv2025

Squeezing effect on three-dimensional Hanbury Brown-Twiss radii

This paper examines the impacts of the squeezing effect caused by the particle's in-medium mass modification on the three-dimensional Hanbury Brown-Twiss (HBT) radii. An analysis is conducted on how the squeezing effect impacts the three-dimensional HBT radii of $ϕϕ$, $D^0$$D^0$, and $K^+$$K^+$. The squeezing effect suppresses the impacts of transverse flow on the transverse source distribution and broadens the space-time rapidity distribution of the particle-emitting source, leading to an increase in the HBT radii, notably in out and longitudinal direction. This phenomenon becomes more significant for higher transverse pair momentum, resulting in a non-monotonic decrease in the HBT radii with increasing transverse pair momentum. The impact of the squeezing effect on the HBT radii is more pronounced for $D^0$$D^0$ than for $ϕϕ$. Furthermore, this effect is also more significant for $ϕ$$ϕ$ than for $K^+$$K^+$. The findings presented in this paper could offer fresh perspectives on investigating the squeezing effect.

hep-ph↗
arXiv2025

Thermalization in asymmetric harmonic chains

The symmetry of the interparticle interaction potential (IIP) plays a critical role in determining the thermodynamic and transport properties of solids. This study investigates the isolated effect of IIP asymmetry on thermalization. Asymmetry and nonlinearity are typically intertwined. To isolate the effect of asymmetry, we introduce a one-dimensional asymmetric harmonic (AH) model whose IIP possesses asymmetry but no nonlinearity, evidenced by energy-independent vibrational frequencies. Extensive numerical simulations confirm a power-law relationship between thermalization time ($T_{\rm eq}$) and perturbation strength for the AH chain, revealing an exponent larger than the previously observed inverse-square law in the thermodynamic limit. Upon adding symmetric quartic nonlinearity into the AH model, we systematically study thermalization under combined asymmetry and nonlinearity. Matthiessen's rule provides a good estimate of $T_{\rm eq}$ in this case. Our results demonstrate that asymmetry plays a distinct role in enhancing higher-order effects and governing relaxation dynamics.

cond-mat.stat-mech↗
arXiv2025

Spatiotemporal evolution of PM2.5 diffusion in Cheng-Yu urban agglomeration in response to COVID-19 lockdown using complex network

As the decrease in human activities resulting from the COVID-19 control measures had a significant impact on air quality, the epidemic provided an opportunity to investigate the extent to which air pollution is influenced by human activities and review existing measures. However, the corresponding diffusion pattern on a city scale is seldom mentioned at present stage, therefore, we chose the Cheng-Yu urban agglomeration, which is the largest city cluster in Southwest China, as our study area during the COVID-19 period, and attempted to investigate the process of PM2.5 diffusion using a complex network method. The results displayed that there was an evident external spillover effect of PM2.5 across all regions, and the PM2.5 spillovers were concentrated in several cities in the Cheng-Yu urban agglomeration during the lockdown period, whereas they are more dispersed during the recovery period. The overall decline in the impact of PM2.5 pollution source areas on receptor areas from a normal year to the pandemic year, and the intensity of PM2.5 spillover decreases gradually as the distance from the center increases. The implementation of the lockdown measures had an impact on both the input and output patterns of PM2.5 pollution in the region, the input pattern of PM2.5 pollution exhibited higher vulnerability, while the output pattern showed higher resilience. Additionally, the spillover relationship of PM2.5 pollution varies between different blocks, with relatively simple spillover relationships observed during the lockdown period and more complex dynamics during the recovery period. These findings have highlighted the importance of joint controls in combating regional air pollution.

math.NA↗
arXiv2025

Search for binarity in Asymptotic Giant Branch stars utilizing the future Chinese Space Station Telescope (CSST)

Binary systems in the Asymptotic Giant Branch (AGB) phase are widely recognized as a leading theoretical framework underpinning the observed asymmetric morphologies of planetary nebulae. However, the detection of binary companions in AGB systems is severely hampered by the overwhelming brightness and variability of the evolved primary star, which dominate the photo-metric and spectroscopic signatures. Ultraviolet (UV) excess emission has been proposed as a candidate diagnostic for the presence of binary companions in AGB systems. This paper evaluates the Chinese Space Station Telescope's (CSST) ability to detect UV excess emission in AGB stars, leveraging its unprecedented UV sensitivity and wide-field survey capabilities. We employed synthetic spectral libraries of M0-M8 type giants for primary stars and the ATLAS 9 atmospheric model grid for companion stars spanning a temperature range of 6500 K to 12000 K. By convolving these model spectra with the CSST multi-band filter system, we computed color-color diagrams (g-y versus NUV-u) to construct a diagnostic grid. This grid incorporates interstellar extinction corrections and establishes a framework for identifying AGB binary candidates through direct comparison between observed photometry and theoretical predictions. Furthermore, we discuss the physical origins of UV excess in AGB stars. This study pioneers a diagnostic framework leveraging CSST's unique multi-band UV-visible synergy to construct color-color grids for binary candidate identification, overcoming limitations of non-simultaneous multi-instrument observations.

astro-ph.SR↗
arXiv2025

LaWa: Using Latent Space for In-Generation Image Watermarking

With generative models producing high quality images that are indistinguishable from real ones, there is growing concern regarding the malicious usage of AI-generated images. Imperceptible image watermarking is one viable solution towards such concerns. Prior watermarking methods map the image to a latent space for adding the watermark. Moreover, Latent Diffusion Models (LDM) generate the image in the latent space of a pre-trained autoencoder. We argue that this latent space can be used to integrate watermarking into the generation process. To this end, we present LaWa, an in-generation image watermarking method designed for LDMs. By using coarse-to-fine watermark embedding modules, LaWa modifies the latent space of pre-trained autoencoders and achieves high robustness against a wide range of image transformations while preserving perceptual quality of the image. We show that LaWa can also be used as a general image watermarking method. Through extensive experiments, we demonstrate that LaWa outperforms previous works in perceptual quality, robustness against attacks, and computational complexity, while having very low false positive rate. Code is available here.

cs.CV↗
arXiv2025

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems

In complex physical systems, conventional differential equations often fall short in capturing non-local and memory effects, as they are limited to local dynamics and integer-order interactions. This study introduces a stepwise data-driven framework for discovering fractional differential equations (FDEs) directly from data. FDEs, known for their capacity to model non-local dynamics with fewer parameters than integer-order derivatives, can represent complex systems with long-range interactions. Our framework applies deep neural networks as surrogate models for denoising and reconstructing sparse and noisy observations while using Gaussian-Jacobi quadrature to handle the challenges posed by singularities in fractional derivatives. To optimize both the sparse coefficients and fractional order, we employ an alternating optimization approach that combines sparse regression with global optimization techniques. We validate the framework across various datasets, including synthetic anomalous diffusion data, experimental data on the creep behavior of frozen soils, and single-particle trajectories modeled by Lévy motion. Results demonstrate the framework's robustness in identifying the structure of FDEs across diverse noise levels and its capacity to capture integer-order dynamics, offering a flexible approach for modeling memory effects in complex systems.

physics.comp-ph↗
arXiv2025

Let Them Talk: Audio-Driven Multi-Person Conversational Video Generation

Audio-driven human animation methods, such as talking head and talking body generation, have made remarkable progress in generating synchronized facial movements and appealing visual quality videos. However, existing methods primarily focus on single human animation and struggle with multi-stream audio inputs, facing incorrect binding problems between audio and persons. Additionally, they exhibit limitations in instruction-following capabilities. To solve this problem, in this paper, we propose a novel task: Multi-Person Conversational Video Generation, and introduce a new framework, MultiTalk, to address the challenges during multi-person generation. Specifically, for audio injection, we investigate several schemes and propose the Label Rotary Position Embedding (L-RoPE) method to resolve the audio and person binding problem. Furthermore, during training, we observe that partial parameter training and multi-task training are crucial for preserving the instruction-following ability of the base model. MultiTalk achieves superior performance compared to other methods on several datasets, including talking head, talking body, and multi-person datasets, demonstrating the powerful generation capabilities of our approach.

cs.CV↗
arXiv2025

Synthesis of intrinsic magnetic topological insulator MnBi2nTe3n+1 family by chemical vapor transport method with feedback regulation

MnBi2nTe3n+1 (MBT) is a representative family of intrinsic magnetic topological insulators, in which numerous exotic phenomena such as the quantum anomalous Hall effect are expected. The high-quality crystal growth and magnetism manipulation are the most essential processes. Here we develop a modified chemical vapor transport method using a feedback-regulated strategy, which provides the closed-loop control of growth temperature within +/- 0.1 degree Celsius. Single crystals of MnBi2Te4, MnBi4Te7, and MnBi6Te10 are obtained under different temperature intervals respectively, and show variable tunability on magnetism by finely tuning the growth temperatures. Specifically, the cold-end temperatures not only vary the strength of antiferromagnetic coupling in MnBi2Te4, but also induce magnetic ground state transitions from antiferromagnetism to ferromagnetism in MnBi4Te7 and MnBi6Te10. In MnBi2Te4 with optimized magnetism, quantized transport with Chern insulator state is also realized at the low field of 3.7 T. Our results provide a systematic picture for the crystal growth and the rich magnetic tunability of MBT family, providing richer platforms for the related researches combining magnetism and topological physics.

cond-mat.mtrl-sci↗
arXiv2025

Enhancing Learned Knowledge in LoRA Adapters Through Efficient Contrastive Decoding on Ascend NPUs

Huawei Cloud users leverage LoRA (Low-Rank Adaptation) as an efficient and scalable method to fine-tune and customize large language models (LLMs) for application-specific needs. However, tasks that require complex reasoning or deep contextual understanding are often hindered by biases or interference from the base model when using typical decoding methods like greedy or beam search. These biases can lead to generic or task-agnostic responses from the base model instead of leveraging the LoRA-specific adaptations. In this paper, we introduce Contrastive LoRA Decoding (CoLD), a novel decoding framework designed to maximize the use of task-specific knowledge in LoRA-adapted models, resulting in better downstream performance. CoLD uses contrastive decoding by scoring candidate tokens based on the divergence between the probability distributions of a LoRA-adapted expert model and the corresponding base model. This approach prioritizes tokens that better align with the LoRA's learned representations, enhancing performance for specialized tasks. While effective, a naive implementation of CoLD is computationally expensive because each decoding step requires evaluating multiple token candidates across both models. To address this, we developed an optimized kernel for Huawei's Ascend NPU. CoLD achieves up to a 5.54% increase in task accuracy while reducing end-to-end latency by 28% compared to greedy decoding. This work provides practical and efficient decoding strategies for fine-tuned LLMs in resource-constrained environments and has broad implications for applied data science in both cloud and on-premises settings.

cs.LG↗
arXiv2025

Shubnikov-de Haas quantum oscillations with large spin splitting in highmobility Al0.8Ga0.2Sb/InAs/ Al0.8Ga0.2Sb quantum-well heterostructures

We report the epitaxial growth of high-quality Al0.8Ga0.2Sb-InAs-Al0.8Ga0.2Sb quantum well films featured by high carrier mobility and strong spin-orbit coupling. By appropriately optimizing the Al-to-Ga ratio in the AlGaSb barrier layer, the quantum confinement of the heterostructure is significantlyenhanced, which results in both an ultra-high electron mobility of 924000 cm2/Vs and a giant magnetoresistance ratio of 365000 at low temperatures. Meanwhile, pronounced Shubnikov-deHaas quantum oscillations persist up to 30 K, and their single-frequency feature indicates a well defined Fermi surface without subband mixing in the two-dimensional electron gas channel. Moreover, the large effective g-factor of 12.93 leads to the observation of Zeeman splitting at large magnetic fields. Our results validate the AlGaSb/InAs quantum well heterostructures as a suitable candidate for constructing energy-efficient topological spintronic devices.

cond-mat.mes-hall↗
arXiv2025

DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generation

Domain-specific QA systems require not just generative fluency but high factual accuracy grounded in structured expert knowledge. While recent Retrieval-Augmented Generation (RAG) frameworks improve context recall, they struggle with integrating heterogeneous data and maintaining reasoning consistency. To address these challenges, we propose DO-RAG, a scalable and customizable hybrid QA framework that integrates multi-level knowledge graph construction with semantic vector retrieval. Our system employs a novel agentic chain-of-thought architecture to extract structured relationships from unstructured, multimodal documents, constructing dynamic knowledge graphs that enhance retrieval precision. At query time, DO-RAG fuses graph and vector retrieval results to generate context-aware responses, followed by hallucination mitigation via grounded refinement. Experimental evaluations in the database and electrical domains show near-perfect recall and over 94% answer relevancy, with DO-RAG outperforming baseline frameworks by up to 33.38%. By combining traceability, adaptability, and performance efficiency, DO-RAG offers a reliable foundation for multi-domain, high-precision QA at scale.

cs.CL↗
arXiv2025

The ALMA-ATOMS Survey: Exploring Protostellar Outflows in HC$_3$N

We present the first systematic study of bipolar outflows using HC$_3$N as a tracer in a sample of 146 massive star-forming regions from ALMA-ATOMS survey. Protostellar outflows arise at the initial stage of star formation as a consequence of active accretion. In general, these outflows play a pivotal role in regulating the star formation processes by injecting energetic material in the parent molecular clouds. In such process, lower velocity components of outflows contain a significant portion of the energy. However, extraction of those component is difficult as the corresponding gas is often mixed with that of the ambient cloud. In our sample, we identified 44 bipolar outflows and one explosive outflow in HC$_3$N (J=11--10). The host clumps of these outflows are found to be at different evolutionary stages, suggesting that outflows in HC$_3$N are detectable in different stages of star formation. Also, the non-correlation of HC$_3$N outflows with clump evolutionary stages suggests that HC$_3$N is an unbiased tracer of outflows. Analyses revealed that HC$_3$N performs slightly better in detecting low-velocity components of outflows than traditionally employed tracers like SiO. The derived outflow parameters (i.e outflow mass, momentum, and energy) show moderate correlations with clump mass and luminosity. Our analysis of outflow opening angles and position-velocity diagrams across the outflow lobes show that, HC$_3$N is not only a good tracer of low-velocity outflows, but can also detect high-velocity collimated outflows. Overall, this study indicates that HC$_3$N can be used as a complementary outflow tracer along with the traditionally known outflow tracers, particularly in the detection of the low-velocity components of outflows.

astro-ph.GA↗
arXiv2025

Fast convolution solver based on far-field smooth approximation

The convolution potential arises in a wide variety of application areas, and its efficient and accurate evaluation encounters three challenges: singularity, nonlocality and anisotropy. We introduce a fast algorithm based on a far-field smooth approximation of the kernel, where the bounded domain Fourier transform, one of the most essential difficulties, is well approximated by the whole space Fourier transform which usually admits explicit formula. The convolution is split into a regular and singular integral, and they are well resolved by trapezoidal rule and Fourier spectral method respectively. The scheme is simplified to a discrete convolution and is implemented efficiently with Fast Fourier Transform (FFT). Importantly, the tensor generation procedure is quite simple, highly efficient and independent of the anisotropy strength. It is easy to implement and achieves spectral accuracy with nearly optimal efficiency and minimum memory requirement. Rigorous error estimates and extensive numerical investigations, together with a comprehensive comparison, showcase its superiorities for different kernels.

math.NA↗
arXiv2025

Hanbury Brown-Twiss correlation of $ϕϕ$ with in-medium mass modification

We study the impacts of the squeezing effect caused by in-medium mass modification on the Hanbury Brown-Twiss (HBT) correlation of $ϕϕ$ using a hydrodynamical source. The squeezing effect reduces the influence of transverse flow on the transverse distribution of $ϕ$ emitting source while expanding the longitudinal source distribution. This trend becomes more noticeable as the transverse momentum increases, resulting in an enlargement of the HBT radii for $ϕϕ$ . Notably, this enlargement is particularly evident with higher transverse pair momentum. As a result, the HBT radii of $ϕϕ$ increase as transverse pair momentum increases instead of showing a consistent crease, showcasing non-flow behavior of HBT radii in hydrodynamical sources. Both Gaussian fitting and Levy fitting support the conclusion.

hep-ph↗
arXiv2025

DRAMA: A Dynamic Packet Routing Algorithm using Multi-Agent Reinforcement Learning with Emergent Communication

The continuous expansion of network data presents a pressing challenge for conventional routing algorithms. As the demand escalates, these algorithms are struggling to cope. In this context, reinforcement learning (RL) and multi-agent reinforcement learning (MARL) algorithms emerge as promising solutions. However, the urgency and importance of the problem are clear, as existing RL/MARL-based routing approaches lack effective communication in run time among routers, making it challenging for individual routers to adapt to complex and dynamic changing networks. More importantly, they lack the ability to deal with dynamically changing network topology, especially the addition of the router, due to the non-scalability of their neural networks. This paper proposes a novel dynamic routing algorithm, DRAMA, incorporating emergent communication in multi-agent reinforcement learning. Through emergent communication, routers could learn how to communicate effectively to maximize the optimization objectives. Meanwhile, a new Q-network and graph-based emergent communication are introduced to dynamically adapt to the changing network topology without retraining while ensuring robust performance. Experimental results showcase DRAMA's superior performance over the traditional routing algorithm and other RL/MARL-based algorithms, achieving a higher delivery rate and lower latency in diverse network scenarios, including dynamic network load and topology. Moreover, an ablation experiment validates the prospect of emergent communication in facilitating packet routing.

cs.NI↗