arXiv subjects

Hao Liu

Publications and source records attributed to Hao Liu.

At least 289 records · Page 16Linked to original sources

arXiv2025

Sign reversal of Berry curvature triple driven by magnetic phase transition in a ferromagnetic polar metal

While the time-reversal-even (T-even) nonlinear Hall effect has been extensively discussed in nonmagnetic materials, the impact of magnetic phase transition on it remains largely overlooked. Here, we report an abrupt enhancement of the T-even nonlinear Hall effect in non-centrosymmetric SrRuO3(111) thin films during the paramagnetic-ferromagnetic transition. Scaling analysis reveals a sign reversal of both the skew scattering and side jump contributions upon the emergence of magnetism, which we attribute to the sign change of Berry curvature triple on the Fermi surface. Density functional theory calculations support this interpretation, ascribing this behavior to distinctive origins of Berry curvature hot spots in paramagnetic and ferromagnetic phases. Our findings unveil the exchange-induced dramatic nonperturbative change of nonlinear Hall effect, and establish SrRuO3(111) thin films as a promising platform for exploring magnetically tunable nonlinear transport effects.

cond-mat.mtrl-sci↗

arXiv2025

Optimized Path Planning for Logistics Robots Using Ant Colony Algorithm under Multiple Constraints

With the rapid development of the logistics industry, the path planning of logistics vehicles has become increasingly complex, requiring consideration of multiple constraints such as time windows, task sequencing, and motion smoothness. Traditional path planning methods often struggle to balance these competing demands efficiently. In this paper, we propose a path planning technique based on the Ant Colony Optimization (ACO) algorithm to address these challenges. The proposed method optimizes key performance metrics, including path length, task completion time, turning counts, and motion smoothness, to ensure efficient and practical route planning for logistics vehicles. Experimental results demonstrate that the ACO-based approach outperforms traditional methods in terms of both efficiency and adaptability. This study provides a robust solution for logistics vehicle path planning, offering significant potential for real-world applications in dynamic and constrained environments.

cs.RO↗

arXiv2025

Scene Splatter: Momentum 3D Scene Generation from Single Image with Video Diffusion Model

In this paper, we propose Scene Splatter, a momentum-based paradigm for video diffusion to generate generic scenes from single image. Existing methods, which employ video generation models to synthesize novel views, suffer from limited video length and scene inconsistency, leading to artifacts and distortions during further reconstruction. To address this issue, we construct noisy samples from original features as momentum to enhance video details and maintain scene consistency. However, for latent features with the perception field that spans both known and unknown regions, such latent-level momentum restricts the generative ability of video diffusion in unknown regions. Therefore, we further introduce the aforementioned consistent video as a pixel-level momentum to a directly generated video without momentum for better recovery of unseen regions. Our cascaded momentum enables video diffusion models to generate both high-fidelity and consistent novel views. We further finetune the global Gaussian representations with enhanced frames and render new frames for momentum update in the next step. In this manner, we can iteratively recover a 3D scene, avoiding the limitation of video length. Extensive experiments demonstrate the generalization capability and superior performance of our method in high-fidelity and consistent scene generation.

cs.CV↗

arXiv2025

Multi-User MISO with Stacked Intelligent Metasurfaces: A DRL-Based Sum-Rate Optimization Approach

Stacked intelligent metasurfaces (SIMs) represent a novel signal processing paradigm that enables over-the-air processing of electromagnetic waves at the speed of light. Their multi-layer architecture exhibits customizable computational capabilities compared to conventional single-layer reconfigurable intelligent surfaces and metasurface lenses. In this paper, we deploy SIM to improve the performance of multi-user multiple-input single-output (MISO) wireless systems through a low complexity manner with reduced numbers of transmit radio frequency chains. In particular, an optimization formulation for the joint design of the SIM phase shifts and the transmit power allocation is presented, which is efficiently tackled via a customized deep reinforcement learning (DRL) approach that systematically explores pre-designed states of the SIM-parametrized smart wireless environment. The presented performance evaluation results demonstrate the proposed method's capability to effectively learn from the wireless environment, while consistently outperforming conventional precoding schemes under low transmit power conditions. Furthermore, the implementation of hyperparameter tuning and whitening process significantly enhance the robustness of the proposed DRL framework.

cs.IT↗

arXiv2025

Visual Self-paced Iterative Learning for Unsupervised Temporal Action Localization

Recently, temporal action localization (TAL) has garnered significant interest in information retrieval community. However, existing supervised/weakly supervised methods are heavily dependent on extensive labeled temporal boundaries and action categories, which is labor-intensive and time-consuming. Although some unsupervised methods have utilized the ``iteratively clustering and localization'' paradigm for TAL, they still suffer from two pivotal impediments: 1) unsatisfactory video clustering confidence, and 2) unreliable video pseudolabels for model training. To address these limitations, we present a novel self-paced iterative learning model to enhance clustering and localization training simultaneously, thereby facilitating more effective unsupervised TAL. Concretely, we improve the clustering confidence through exploring the contextual feature-robust visual information. Thereafter, we design two (constant- and variable- speed) incremental instance learning strategies for easy-to-hard model training, thus ensuring the reliability of these video pseudolabels and further improving overall localization performance. Extensive experiments on two public datasets have substantiated the superiority of our model over several state-of-the-art competitors.

cs.CV↗

arXiv2025

Unstable molecular state with unstable constituent

Based on the developed Bethe-Salpeter theory for dealing with unstable state, we investigate unstable meson-meson molecular state in which at least one of the constituents is an unstable meson and provide a reasonable and feasible scheme to deal with this interesting problem in the framework of relativistic quantum field theory. The developed Bethe-Salpeter theory is applied to investigate the unstable meson composed of a quark and an antiquark, and we obtain the Green's function for unstable meson in the framework of relativistic quantum field theory, which is used to deal with unstable constituent of molecular state. We can obtain the physical mass and width of unstable molecular state composed of two heavy mesons, which contain the contribution from at least one unstable constituent of molecular state.

hep-ph↗

arXiv2025

Image Decomposition with G-norm Weighted by Total Symmetric Variation

In this paper, we propose a novel variational model for decomposing images into their respective cartoon and texture parts. Our model characterizes certain non-local features of any Bounded Variation (BV) image by its Total Symmetric Variation (TSV). We demonstrate that TSV is effective in identifying regional boundaries. Based on this property, we introduce a weighted Meyer's $G$-norm to identify texture interiors without including contour edges. For BV images with bounded TSV, we show that the proposed model admits a solution. Additionally, we design a fast algorithm based on operator-splitting to tackle the associated non-convex optimization problem. The performance of our method is validated by a series of numerical experiments.

cs.CV↗

arXiv2025

UGen: Unified Autoregressive Multimodal Model with Progressive Vocabulary Learning

We introduce UGen, a unified autoregressive multimodal model that demonstrates strong performance across text processing, image understanding, and image generation tasks simultaneously. UGen converts both texts and images into discrete token sequences and utilizes a single transformer to generate them uniformly in an autoregressive manner. To address the challenges associated with unified multimodal learning, UGen is trained using a novel mechanism, namely progressive vocabulary learning. In this process, visual token IDs are incrementally activated and integrated into the training phase, ultimately enhancing the effectiveness of unified multimodal learning. Experiments on comprehensive text and image tasks show that UGen achieves a significant overall performance improvement of 13.3% compared to the vanilla unified autoregressive method, and it also delivers competitive results across all tasks against several task-specific models.

cs.CL↗

arXiv2025

Ultrafast photoinduced phase transition in the antiferromagnetic Dirac semimetal EuAgAs

We report the observation of a light-induced subpicosecond phase transition in the antiferromagnetic Dirac semimetal EuAgAs, achieved through ultrafast optical excitation. Using ultrafast optical spectroscopy, we probe the nonequilibrium carrier dynamics, discovering distinct fluence-dependent responses in the antiferromagnetic and paramagnetic states, and revealing a possible magnetic order-driven transition between different topological states. Our results demonstrate that EuAgAs, with its highly tunable magnetic structure, possibly offers a unique platform for exploring topological phase transitions. These results underscore the potential of ultrashort optical pulses as powerful tools for the real-time control of topological phases, opening pathways for advances in spintronics, quantum computing, and energy-efficient information technologies.

cond-mat.str-el↗

arXiv2025

High Efficiency Wiener Filter-based Point Cloud Quality Enhancement for MPEG G-PCC

Point clouds, which directly record the geometry and attributes of scenes or objects by a large number of points, are widely used in various applications such as virtual reality and immersive communication. However, due to the huge data volume and unstructured geometry, efficient compression of point clouds is very crucial. The Moving Picture Expert Group is establishing a geometry-based point cloud compression (G-PCC) standard for both static and dynamic point clouds in recent years. Although lossy compression of G-PCC can achieve a very high compression ratio, the reconstruction quality is relatively low, especially at low bitrates. To mitigate this problem, we propose a high efficiency Wiener filter that can be integrated into the encoder and decoder pipeline of G-PCC to improve the reconstruction quality as well as the rate-distortion performance for dynamic point clouds. Specifically, we first propose a basic Wiener filter, and then improve it by introducing coefficients inheritance and variance-based point classification for the Luma component. Besides, to reduce the complexity of the nearest neighbor search during the application of the Wiener filter, we also propose a Morton code-based fast nearest neighbor search algorithm for efficient calculation of filter coefficients. Experimental results demonstrate that the proposed method can achieve average Bjøntegaard delta rates of -6.1%, -7.3%, and -8.0% for Luma, Chroma Cb, and Chroma Cr components, respectively, under the condition of lossless-geometry-lossy-attributes configuration compared to the latest G-PCC encoding platform (i.e., geometry-based solid content test model version 7.0 release candidate 2) by consuming affordable computational complexity.

cs.CV↗

arXiv2025

SePer: Measure Retrieval Utility Through The Lens Of Semantic Perplexity Reduction

Large Language Models (LLMs) have demonstrated improved generation performance by incorporating externally retrieved knowledge, a process known as retrieval-augmented generation (RAG). Despite the potential of this approach, existing studies evaluate RAG effectiveness by 1) assessing retrieval and generation components jointly, which obscures retrieval's distinct contribution, or 2) examining retrievers using traditional metrics such as NDCG, which creates a gap in understanding retrieval's true utility in the overall generation process. To address the above limitations, in this work, we introduce an automatic evaluation method that measures retrieval quality through the lens of information gain within the RAG framework. Specifically, we propose Semantic Perplexity (SePer), a metric that captures the LLM's internal belief about the correctness of the retrieved information. We quantify the utility of retrieval by the extent to which it reduces semantic perplexity post-retrieval. Extensive experiments demonstrate that SePer not only aligns closely with human preferences but also offers a more precise and efficient evaluation of retrieval utility across diverse RAG scenarios.

cs.CL↗

arXiv2025

Minute-cadence observations on Galactic plane with Wide Field Survey Telescope (WFST): Overview, methodology and early results

As the time-domain survey telescope of the highest survey power in the northern hemisphere currently, Wide Field Survey Telescope (WFST) is scheduled to hourly/daily/semi-weekly scan northern sky up to ~23 mag in four optical (ugri) bands. Unlike the observation cadences in the forthcoming regular survey missions, WFST performed "staring" observations toward Galactic plane in a cadence of $\approx$1 minute for a total on-source time of about 13 hours, during the commissioning and pilot observation phases. Such an observation cadence is well applied in producing densely sampling light curves and hunting for stars exhibiting fast stellar variabilities. Here we introduce the primary methodologies in detecting variability, periodicity, and stellar flares among a half million sources from the minute-cadence observations, and present the WFST g-/r-band light curves generated from periodic variable stars and flaring stars. Benefit from high photometric precisions and deep detection limits of WFST, the observations have captured several rare variable stars, such as a variable hot white dwarf (WD) and an ellipsoidal WD binary candidate. By surveying the almost unexplored parameter spaces for variables, WFST will lead to new opportunities in discovering unique variable stars in the northern sky.

astro-ph.SR↗

arXiv2025

Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph Learning

Graph Anomaly Detection (GAD) plays a vital role in various data mining applications such as e-commerce fraud prevention and malicious user detection. Recently, Graph Neural Network (GNN) based approach has demonstrated great effectiveness in GAD by first encoding graph data into low-dimensional representations and then identifying anomalies under the guidance of supervised or unsupervised signals. However, existing GNN-based approaches implicitly follow the homophily principle (i.e., the "like attracts like" phenomenon) and fail to learn discriminative embedding for anomalies that connect vast normal nodes. Moreover, such approaches identify anomalies in a unified global perspective but overlook diversified abnormal patterns conditioned on local graph context, leading to suboptimal performance. To overcome the aforementioned limitations, in this paper, we propose a Multi-hypersphere Heterophilic Graph Learning (MHetGL) framework for unsupervised GAD. Specifically, we first devise a Heterophilic Graph Encoding (HGE) module to learn distinguishable representations for potential anomalies by purifying and augmenting their neighborhood in a fully unsupervised manner. Then, we propose a Multi-Hypersphere Learning (MHL) module to enhance the detection capability for context-dependent anomalies by jointly incorporating critical patterns from both global and local perspectives. Extensive experiments on ten real-world datasets show that MHetGL outperforms 14 baselines. Our code is publicly available at https://github.com/KennyNH/MHetGL.

cs.LG↗

arXiv2025

SpaceSeg: A High-Precision Intelligent Perception Segmentation Method for Multi-Spacecraft On-Orbit Targets

With the continuous advancement of human exploration into deep space, intelligent perception and high-precision segmentation technology for on-orbit multi-spacecraft targets have become critical factors for ensuring the success of modern space missions. However, the complex deep space environment, diverse imaging conditions, and high variability in spacecraft morphology pose significant challenges to traditional segmentation methods. This paper proposes SpaceSeg, an innovative vision foundation model-based segmentation framework with four core technical innovations: First, the Multi-Scale Hierarchical Attention Refinement Decoder (MSHARD) achieves high-precision feature decoding through cross-resolution feature fusion via hierarchical attention. Second, the Multi-spacecraft Connected Component Analysis (MS-CCA) effectively resolves topological structure confusion in dense targets. Third, the Spatial Domain Adaptation Transform framework (SDAT) eliminates cross-domain disparities and resist spatial sensor perturbations through composite enhancement strategies. Finally, a custom Multi-Spacecraft Segmentation Task Loss Function is created to significantly improve segmentation robustness in deep space scenarios. To support algorithm validation, we construct the first multi-scale on-orbit multi-spacecraft semantic segmentation dataset SpaceES, which covers four types of spatial backgrounds and 17 typical spacecraft targets. In testing, SpaceSeg achieves state-of-the-art performance with 89.87$\%$ mIoU and 99.98$\%$ mAcc, surpassing existing best methods by 5.71 percentage points. The dataset and code are open-sourced at https://github.com/Akibaru/SpaceSeg to provide critical technical support for next-generation space situational awareness systems.

cs.CV↗

arXiv2025

CoLLMLight: Cooperative Large Language Model Agents for Network-Wide Traffic Signal Control

Traffic Signal Control (TSC) plays a critical role in urban traffic management by optimizing traffic flow and mitigating congestion. While Large Language Models (LLMs) have recently emerged as promising tools for TSC due to their exceptional problem-solving and generalization capabilities, existing approaches fail to address the essential need for inter-agent coordination, limiting their effectiveness in achieving network-wide optimization. To bridge this gap, we propose CoLLMLight, a cooperative LLM agent framework for TSC. Specifically, we first construct a structured spatiotemporal graph to capture real-time traffic dynamics and spatial relationships among neighboring intersections, enabling the LLM to reason about complex traffic interactions. Moreover, we introduce a complexity-aware reasoning mechanism that dynamically adapts reasoning depth based on real-time traffic conditions, ensuring optimal computational efficiency without sacrificing decision quality. Besides, we propose a fine-tuning strategy that leverages iterative simulation-driven data collection and environmental feedback to build a lightweight LLM tailored for cooperative TSC. Extensive experiments on both synthetic and real-world datasets demonstrate that CoLLMLight outperforms state-of-the-art methods in diverse traffic scenarios, showcasing its effectiveness, scalability, and robustness.

cs.LG↗

arXiv2025

Anisotropic Hybridization Dynamics in the Quasi-One-Dimensional Kondo Lattice CeCo$_2$Ga$_8$ Revealed by Ultrafast Optical Spectroscopy

We investigate the ultrafast dynamics of the quasi-one-dimensional Kondo lattice CeCo$_2$Ga$_8$ using optical pump-probe spectroscopy. Time-resolved pump-probe reflectivity measurements reveal a strong anisotropy in the photoinduced response, which is a direct consequence of the material's unique electronic structure. The temperature dependence of the relaxation dynamics provides evidence for the formation of two distinct hybridization gaps that appear at different temperatures in the heavy fermion state. A direct gap of 2$Δ_{dir}$ $\approx$ 50 meV that persists up to $T^†$ $\approx$ 90 K, well above the coherence temperature $T^*$ $\approx$ 20 K. We attribute this higher-temperature gap to the hybridization fluctuations. An indirect gap of 2$Δ_{ind}$ $\approx$ 10 meV opens closer to $T^*$, signifying the development of long-range coherence in the heavy fermion state. Furthermore, we find that the hybridization gap can be suppressed with increasing pump fluence, indicating a delicate interplay between photoexcitation and the coherent heavy fermion state. Our results provide insights into the interplay of Kondo physics and low dimensionality in CeCo$_2$Ga$_8$, and establish ultrafast optical spectroscopy as a sensitive probe of anisotropic hybridization in heavy fermion materials.

cond-mat.str-el↗

arXiv2025

Interplay of electron-phonon coupling, pseudogap, and superconductivity in CsCa$_2$Fe$_4$As$_4$F$_2$ studied using ultrafast optical spectroscopy

The quasiparticle relaxation dynamics of the iron-based superconductor CsCa$_2$Fe$_4$As$_4$F$_2$ ($T_c$ $\sim$ 29 K) were investigated using ultrafast optical spectroscopy. A pseudogap ($Δ_{PG}$ $\approx$ 3.3 meV) was observed to open below $T^{\ast}$ $\approx$ 60 K, prior to the emergence of a superconducting gap ($Δ$ $\approx$ 6.6 $\pm$ 0.4 meV). At high excitation fluence, a coherent A1g phonon mode at 5.49 THz was identified, exhibiting deviations from anharmonic behavior below $T_c$. The electron-phonon coupling constant for this mode was estimated to be $λ_{A_{1g}}$ $\approx$ 0.23. These results provide insights into the interplay between the electron-phonon interactions, pseudogap, and the superconducting pairing mechanism in CsCa$_2$Fe$_4$As$_4$F$_2$.

cond-mat.supr-con↗

arXiv2025

Coefficient-to-Basis Network: A Fine-Tunable Operator Learning Framework for Inverse Problems with Adaptive Discretizations and Theoretical Guarantees

We propose a Coefficient-to-Basis Network (C2BNet), a novel framework for solving inverse problems within the operator learning paradigm. C2BNet efficiently adapts to different discretizations through fine-tuning, using a pre-trained model to significantly reduce computational cost while maintaining high accuracy. Unlike traditional approaches that require retraining from scratch for new discretizations, our method enables seamless adaptation without sacrificing predictive performance. Furthermore, we establish theoretical approximation and generalization error bounds for C2BNet by exploiting low-dimensional structures in the underlying datasets. Our analysis demonstrates that C2BNet adapts to low-dimensional structures without relying on explicit encoding mechanisms, highlighting its robustness and efficiency. To validate our theoretical findings, we conducted extensive numerical experiments that showcase the superior performance of C2BNet on several inverse problems. The results confirm that C2BNet effectively balances computational efficiency and accuracy, making it a promising tool to solve inverse problems in scientific computing and engineering applications.

cs.LG↗