arXiv subjects

Zhen Liu

Publications and source records attributed to Zhen Liu.

At least 91 records · Page 5Linked to original sources

arXiv2025

Optical multistability in a compact microcavity enabled by near-exceptional coupling

Multistability -- the emergence of multiple stable states under identical conditions -- is a hallmark of nonlinear complexity and an enabling mechanism for multilevel optical memory and photonic computing. Its realization in a compact footprint, however, is limited by intrinsically weak optical nonlinearities and the enlarged free spectral range that raises the multistability threshold. Here, we overcome this constraint by engineering a pair of spectrally close, ultra-high-Q resonances in a photonic crystal microcavity. Leveraging structural perturbations that deliberately introduce non-Hermitian coupling through a shared radiation channel, we drive the resonances toward an exceptional point with nearly degenerate wavelengths and balanced quality factors approaching $10^6$. This configuration substantially enhances thermo-optical nonlinearity and produces pronounced tristability and hysteresis loops within a footprint of 20 μm at input powers below 240 μW. We further demonstrate proof-of-concept optical random-access memory through controlled switching among multistable states. These results establish a general strategy for nonlinear microcavities to achieve energy-efficient multistability for reconfigurable all-optical memories, logic, and neuromorphic processors.

physics.optics↗

arXiv2025

The continuous accumulation of civilization core in the cycle of elements-creature, benefits and weapons

The comprehensive strength of a country varies from strong to weak, divided into three condition: descending, periodicity destruction or rapidly rising, Exploring the differences can solve the development crisis. the most important things for a country are interests, weapons and creature, corresponding to money, technology and people. The ship industry has two attribute of financial benefits and technological weapons. Commercial ships can transport massive commodity and warships carry updating of massive technological weapons; But a new core: equity incentives have emerged, and it has helped the rapid development of the computer industry. This article uses comparative analysis and comparative historical analysis to observe the changes in the United States and China after the mutual circulation of two elements and the double circulation of three elements in history, such as the growth rates of GDP and patent applications. Then, it summarizes the changes brought by the core of civilization to the country.Through this article, it can be concluded that the core of civilization consists of ships, equity incentives, record-wisdom method; Through the circulation of new elements, a country can transform into civilizations with three cycles, achieving mutual circulation among the three and enhancing endogenous power; The core of civilization can enhance the stability of economic development, prevent economic crises, and achieve a more balanced civilization.

cs.OH↗

arXiv2025

Fusion Meets Diverse Conditions: A High-diversity Benchmark and Baseline for UAV-based Multimodal Object Detection with Condition Cues

Unmanned aerial vehicles (UAV)-based object detection with visible (RGB) and infrared (IR) images facilitates robust around-the-clock detection, driven by advancements in deep learning techniques and the availability of high-quality dataset. However, the existing dataset struggles to fully capture real-world complexity for limited imaging conditions. To this end, we introduce a high-diversity dataset ATR-UMOD covering varying scenarios, spanning altitudes from 80m to 300m, angles from 0° to 75°, and all-day, all-year time variations in rich weather and illumination conditions. Moreover, each RGB-IR image pair is annotated with 6 condition attributes, offering valuable high-level contextual information. To meet the challenge raised by such diverse conditions, we propose a novel prompt-guided condition-aware dynamic fusion (PCDF) to adaptively reassign multimodal contributions by leveraging annotated condition cues. By encoding imaging conditions as text prompts, PCDF effectively models the relationship between conditions and multimodal contributions through a task-specific soft-gating transformation. A prompt-guided condition-decoupling module further ensures the availability in practice without condition annotations. Experiments on ATR-UMOD dataset reveal the effectiveness of PCDF.

cs.CV↗

arXiv2025

A Beamdump Facility at Jefferson Lab

This White Paper is exploring the potential of intense secondary muon, neutrino, and (hypothetical) light dark matter beams produced in interactions of high-intensity electron beams with beam dumps. Light dark matter searches with the approved Beam Dump eXperiment (BDX) are driving the realization of a new underground vault at Jefferson Lab that could be extended to a Beamdump Facility with minimal additional installations. The paper summarizes contributions and discussions from the International Workshop on Secondary Beams at Jefferson Lab (BDX & Beyond). Several possible muon physics applications and neutrino detector technologies for Jefferson Lab are highlighted. The potential of a secondary neutron beam will be addressed in a future edition.

physics.acc-ph↗

arXiv2025

Structure-Attribute Transformations with Markov Chain Boost Graph Domain Adaptation

Graph domain adaptation has gained significant attention in label-scarce scenarios across different graph domains. Traditional approaches to graph domain adaptation primarily focus on transforming node attributes over raw graph structures and aligning the distributions of the transformed node features across networks. However, these methods often struggle with the underlying structural heterogeneity between distinct graph domains, which leads to suboptimal distribution alignment. To address this limitation, we propose Structure-Attribute Transformation with Markov Chain (SATMC), a novel framework that sequentially aligns distributions across networks via both graph structure and attribute transformations. To mitigate the negative influence of domain-private information and further enhance the model's generalization, SATMC introduces a private domain information reduction mechanism and an empirical Wasserstein distance. Theoretical proofs suggest that SATMC can achieve a tighter error bound for cross-network node classification compared to existing graph domain adaptation methods. Extensive experiments on nine pairs of publicly available cross-domain datasets show that SATMC outperforms state-of-the-art methods in the cross-network node classification task. The code is available at https://github.com/GiantZhangYT/SATMC.

cs.LG↗

arXiv2025

AIM 2025 challenge on Inverse Tone Mapping Report: Methods and Results

This paper presents a comprehensive review of the AIM 2025 Challenge on Inverse Tone Mapping (ITM). The challenge aimed to push forward the development of effective ITM algorithms for HDR image reconstruction from single LDR inputs, focusing on perceptual fidelity and numerical consistency. A total of \textbf{67} participants submitted \textbf{319} valid results, from which the best five teams were selected for detailed analysis. This report consolidates their methodologies and performance, with the lowest PU21-PSNR among the top entries reaching 29.22 dB. The analysis highlights innovative strategies for enhancing HDR reconstruction quality and establishes strong benchmarks to guide future research in inverse tone mapping.

cs.CV↗

arXiv2025

The coupling of mixed and primal finite element methods for the coupled body-plate problem

This paper considers the coupled problem of a three-dimensional elastic body and a two-dimensional plate, which are rigidly connected at their interface. The plate consists of a plane elasticity model along the longitudinal direction and a plate bending model with Kirchhoff assumptions along the transverse direction. The Hellinger-Reissner formulation is adopted for the body by introducing the stress as an auxiliary variable, while the primal formulation is employed for the plate. The well-posedness of the weak formulation is established. This approach enables direct stress approximations and allows for non-matching meshes at the interface since the continuity condition of displacements acts as a natural boundary condition for the body. Under certain assumptions, discrete stability and error estimates are derived for both conforming and nonconforming finite element methods. Two specific pairs of conforming and nonconforming finite elements are shown to satisfy the required assumptions, respectively. Furthermore, the problem is reduced to an interface problem based on the domain decomposition, which can be solved effectively by a conjugate gradient iteration. Numerical experiments are conducted to validate the theoretical results.

math.NA↗

arXiv2025

Heavy QCD Axions at High-Energy Muon Colliders

We study the physics potential of heavy QCD axions at high-energy muon colliders. Unlike typical axion-like particles, heavy QCD axions solve the strong CP problem with phenomenology driven by the anomalous gluon ($aG\widetilde G$) couplings. Several ultraviolet scenarios are presented in which QCD axions with TeV-scale masses and decay constants arise consistently with a solution to both the strong CP problem and the axion quality problem. We perform a detailed collider analysis for both a 3 and 10~TeV muon collider, focusing on hadronic axion decays that gives rise to a dijet-resonance signature. Our projections for the axion discovery reach in the multi-TeV mass range demonstrate that a muon collider can significantly extend sensitivity to heavy QCD axions compared to existing experiments.

hep-ph↗

arXiv2025

Numerical modelling of a partially loaded intermodal container freight train passing through a tunnel

The bluff nature of a freight train locomotive, coupled with large gaps created between different wagon formations and loaded goods, influence the overall pressure wave pattern generated as the train passes through a tunnel. Typically, 1D models are used to predict the patterns and properties of tunnel pressure wave formations. However, accurate modelling of regions of separation at the head of the blunted containers and at unloaded gap sections is essential for precise predictions of pressure magnitudes. This has traditionally been difficult to capture with 1D models. Furthermore, achieving this accuracy through 3D computational methods demands exceptional mesh quality, significant computational resources, and the careful selection of numerical models. This paper evaluates various numerical models to capture these complexities within regions of flow separation. Findings have supported the development of a new 1D programme to calculate the pressure wave generated by a freight locomotive entering a tunnel, and is here further extended to consider the discontinuities of the train body created by intermodal container loading patterns, by implementing new mesh system and boundary conditions into the 1D programme. A parameterisation study for different loading configurations is also presented to improve the overall programme adaptability, and the relationship between predetermined parameters and gap length is investigated. We validate the effectiveness of the improved 1D model through comprehensive Large Eddy Simulation (LES) results and conduct an extensive parameterisation study to enhance its applicability across various loading configurations. Consequently, this research bridges the gap in freight train tunnel aerodynamics, offering a versatile 1D numerical tool for accurate pressure wave prediction.

physics.flu-dyn↗

arXiv2025

Data Skeleton Learning: Scalable Active Clustering with Sparse Graph Structures

In this work, we focus on the efficiency and scalability of pairwise constraint-based active clustering, crucial for processing large-scale data in applications such as data mining, knowledge annotation, and AI model pre-training. Our goals are threefold: (1) to reduce computational costs for iterative clustering updates; (2) to enhance the impact of user-provided constraints to minimize annotation requirements for precise clustering; and (3) to cut down memory usage in practical deployments. To achieve these aims, we propose a graph-based active clustering algorithm that utilizes two sparse graphs: one for representing relationships between data (our proposed data skeleton) and another for updating this data skeleton. These two graphs work in concert, enabling the refinement of connected subgraphs within the data skeleton to create nested clusters. Our empirical analysis confirms that the proposed algorithm consistently facilitates more accurate clustering with dramatically less input of user-provided constraints, and outperforms its counterparts in terms of computational performance and scalability, while maintaining robustness across various distance metrics.

cs.LG↗

arXiv2025

AMCR: A Framework for Assessing and Mitigating Copyright Risks in Generative Models

Generative models have achieved impressive results in text to image tasks, significantly advancing visual content creation. However, this progress comes at a cost, as such models rely heavily on large-scale training data and may unintentionally replicate copyrighted elements, creating serious legal and ethical challenges for real-world deployment. To address these concerns, researchers have proposed various strategies to mitigate copyright risks, most of which are prompt based methods that filter or rewrite user inputs to prevent explicit infringement. While effective in handling obvious cases, these approaches often fall short in more subtle situations, where seemingly benign prompts can still lead to infringing outputs. To address these limitations, this paper introduces Assessing and Mitigating Copyright Risks (AMCR), a comprehensive framework which i) builds upon prompt-based strategies by systematically restructuring risky prompts into safe and non-sensitive forms, ii) detects partial infringements through attention-based similarity analysis, and iii) adaptively mitigates risks during generation to reduce copyright violations without compromising image quality. Extensive experiments validate the effectiveness of AMCR in revealing and mitigating latent copyright risks, offering practical insights and benchmarks for the safer deployment of generative models.

cs.LG↗

arXiv2025

Constraints on Symmetric Dark Matter from Neutron Star Capture and Collapse

Dark matter (DM) models with a conserved particle$-$antiparticle number, $n_χ-n_{\tilde χ}$, and the asymmetry in the cosmological abundance $n_χ\neq n_{\tilde χ}$, are known to be challenged by the existence of old neutron stars (NSs), as the sufficient accumulation of DM will lead to the collapse of NSs into black holes. We demonstrate that the applicability of these constraints is much wider and covers models with symmetric populations of DM, $n_χ= n_{\tilde χ}$, as the process of DM capture regulated by a nucleon-DM scattering can be inherently asymmetric, $σ_{χn}\neq σ_{\tildeχn}$. The asymmetry is induced by the interference of different types of $χ$-$n$ interactions, provided that their combination is odd under charge conjugation in the DM sector, $C_χ$, and even under combined parity $P_{χ+ n}$. We provide a complete analysis of DM-nucleon bilinear $χ$-$n$ interactions and find that this asymmetry is very generic. Using canonical NS parameters and local DM halo inputs, we exclude spin-averaged scattering cross sections down to $σ_{nχ}\!\gtrsim\!10^{-46}\,{\rm cm}^{2}$ at DM mass $m_χ\!\lesssim\!10^{10}\,{\rm GeV}$ for the maximally asymmetric capture rate, and show that the constraints persist down to very small values of the cross-section asymmetry, ${\cal A}=(σ_{χn}- σ_{\tildeχn})/(σ_{χn}+ σ_{\tildeχn})\gtrsim 10^{-5}$.

hep-ph↗

arXiv2025

Bridging Simulation and Experiment: A Self-Supervised Domain Adaptation Framework for Concrete Damage Classification

Reliable assessment of concrete degradation is critical for ensuring structural safety and longevity of engineering structures. This study proposes a self-supervised domain adaptation framework for robust concrete damage classification using coda wave signals. To support this framework, an advanced virtual testing platform is developed, combining multiscale modeling of concrete degradation with ultrasonic wave propagation simulations. This setup enables the generation of large-scale labeled synthetic data under controlled conditions, reducing the dependency on costly and time-consuming experimental labeling. However, neural networks trained solely on synthetic data often suffer from degraded performance when applied to experimental data due to domain shifts. To bridge this domain gap, the proposed framework integrates domain adversarial training, minimum class confusion loss, and the Bootstrap Your Own Latent (BYOL) strategy. These components work jointly to facilitate effective knowledge transfer from the labeled simulation domain to the unlabeled experimental domain, achieving accurate and reliable damage classification in concrete. Extensive experiments demonstrate that the proposed method achieves notable performance improvements, reaching an accuracy of 0.7762 and a macro F1 score of 0.7713, outperforming both the plain 1D CNN baseline and six representative domain adaptation techniques. Moreover, the method exhibits high robustness across training runs and introduces only minimal additional computational cost. These findings highlight the practical potential of the proposed simulation-driven and label-efficient framework for real-world applications in structural health monitoring.

cs.CE↗

arXiv2025

A complement of the Erdős-Hajnal problem on paths with equal-degree endpoints

Answering a question of Erdős and Hajnal, Chen and Ma proved that for all $n\geq600$ every graph with $2n + 1$ vertices and at least $n^2 + n+1$ edges contains two vertices of equal degree connected by a path of length three. The complete bipartite graph $K_{n,n+1}$ shows that this edge bound is sharp. In this paper, we develop a novel approach to handle graphs with large equal degrees, which enables us to establish the result for all $n\ge2$, thereby fully resolving the problem posed by Erdős and Hajnal.

math.CO↗

arXiv2025

The Neutrino Slice at Muon Colliders

Muon colliders provide an exciting new direction to expand the energy frontier of particle physics. We point out a new use of these facilities for neutrino and beyond the Standard Model physics using their main detectors. Muon decays along the accelerator rings create an intense and highly collimated neutrino beam that crosses a thin slice of the kt-scale detector. As a result, it would induce an unprecedented number of neutrino interactions, with $\mathcal{O}(10^4)$ events per second for a 10 TeV $μ^+μ^-$ collider. These interactions are highly energetic and possess a distinct timing signature and a large transverse displacement. We discuss promising applications of these events for instrumentation, electroweak, and beyond-the-Standard Model physics. For instance, a sub-percent measurement of the neutrino-electron scattering rate enables new precision measurements of the Weak angle and a novel detection of the neutrino charge radius.

hep-ph↗

arXiv2025

Learning to See in the Extremely Dark

Learning-based methods have made promising advances in low-light RAW image enhancement, while their capability to extremely dark scenes where the environmental illuminance drops as low as 0.0001 lux remains to be explored due to the lack of corresponding datasets. To this end, we propose a paired-to-paired data synthesis pipeline capable of generating well-calibrated extremely low-light RAW images at three precise illuminance ranges of 0.01-0.1 lux, 0.001-0.01 lux, and 0.0001-0.001 lux, together with high-quality sRGB references to comprise a large-scale paired dataset named See-in-the-Extremely-Dark (SIED) to benchmark low-light RAW image enhancement approaches. Furthermore, we propose a diffusion-based framework that leverages the generative ability and intrinsic denoising property of diffusion models to restore visually pleasing results from extremely low-SNR RAW inputs, in which an Adaptive Illumination Correction Module (AICM) and a color consistency loss are introduced to ensure accurate exposure correction and color restoration. Extensive experiments on the proposed SIED and publicly available benchmarks demonstrate the effectiveness of our method. The code and dataset are available at https://github.com/JianghaiSCU/SIED.

cs.CV↗

arXiv2025

Flavor Physics at the CEPC: a General Perspective

We discuss the landscape of flavor physics at the Circular Electron-Positron Collider (CEPC), based on the nominal luminosity outlined in its Technical Design Report. The CEPC is designed to operate in multiple modes to address a variety of tasks. At the $Z$ pole, the expected production of 4 Tera $Z$ bosons will provide unique and highly precise measurements of $Z$ boson couplings, while the substantial number of boosted heavy-flavored quarks and leptons produced in clean $Z$ decays will facilitate investigations into their flavor physics with unprecedented precision. We investigate the prospects of measuring various physics benchmarks and discuss their implications for particle theories and phenomenological models. Our studies indicate that, with its highlighted advantages and anticipated excellent detector performance, the CEPC can explore beauty and $τ$ physics in ways that are superior to or complementary with the Belle II and Large-Hadron-Collider-beauty experiments, potentially enabling the detection of new physics at energy scales of 10 TeV and above. This potential also extends to the observation of yet-to-be-discovered rare and exotic processes, as well as testing fundamental principles such as lepton flavor universality, lepton and baryon number conservation, etc., making the CEPC a vibrant platform for flavor physics research. The $WW$ threshold scan, Higgs-factory operation and top-pair productions of the CEPC further enhance its merits in this regard, especially for measuring the Cabibbo-Kobayashi-Maskawa matrix elements, and Flavor-Changing-Neutral-Current physics of Higgs boson and top quarks. We outline the requirements for detector performance and considerations for future development to achieve the anticipated scientific goals.

hep-ex↗

arXiv2025

Depth Gives a False Sense of Privacy: LLM Internal States Inversion

Large Language Models (LLMs) are increasingly integrated into daily routines, yet they raise significant privacy and safety concerns. Recent research proposes collaborative inference, which outsources the early-layer inference to ensure data locality, and introduces model safety auditing based on inner neuron patterns. Both techniques expose the LLM's Internal States (ISs), which are traditionally considered irreversible to inputs due to optimization challenges and the highly abstract representations in deep layers. In this work, we challenge this assumption by proposing four inversion attacks that significantly improve the semantic similarity and token matching rate of inverted inputs. Specifically, we first develop two white-box optimization-based attacks tailored for low-depth and high-depth ISs. These attacks avoid local minima convergence, a limitation observed in prior work, through a two-phase inversion process. Then, we extend our optimization attack under more practical black-box weight access by leveraging the transferability between the source and the derived LLMs. Additionally, we introduce a generation-based attack that treats inversion as a translation task, employing an inversion model to reconstruct inputs. Extensive evaluation of short and long prompts from medical consulting and coding assistance datasets and 6 LLMs validates the effectiveness of our inversion attacks. Notably, a 4,112-token long medical consulting prompt can be nearly perfectly inverted with 86.88 F1 token matching from the middle layer of Llama-3 model. Finally, we evaluate four practical defenses that we found cannot perfectly prevent ISs inversion and draw conclusions for future mitigation design.

cs.CR↗