arXiv subjects

Bin Zhou

Publications and source records attributed to Bin Zhou.

At least 55 records · Page 3Linked to original sources

arXiv2025

DAMSDAN: Distribution-Aware Multi-Source Domain Adaptation Network for Cross-Domain EEG-based Emotion Recognition

Significant inter-individual variability limits the generalization of EEG-based emotion recognition under cross-domain settings. We address two core challenges in multi-source adaptation: (1) dynamically modeling distributional heterogeneity across sources and quantifying their relevance to a target to reduce negative transfer; and (2) achieving fine-grained semantic consistency to strengthen class discrimination. We propose a distribution-aware multi-source domain adaptation network (DAMSDAN). DAMSDAN integrates prototype-based constraints with adversarial learning to drive the encoder toward discriminative, domain-invariant emotion representations. A domain-aware source weighting strategy based on maximum mean discrepancy (MMD) dynamically estimates inter-domain shifts and reweights source contributions. In addition, a prototype-guided conditional alignment module with dual pseudo-label interaction enhances pseudo-label reliability and enables category-level, fine-grained alignment, mitigating noise propagation and semantic drift. Experiments on SEED and SEED-IV show average accuracies of 94.86\% and 79.78\% for cross-subject, and 95.12\% and 83.15\% for cross-session protocols. On the large-scale FACED dataset, DAMSDAN achieves 82.88\% (cross-subject). Extensive ablations and interpretability analyses corroborate the effectiveness of the proposed framework for cross-domain EEG-based emotion recognition.

cs.LG↗

arXiv2025

Exemplar-Guided Planing: Enhanced LLM Agent for KGQA

Large Language Models (LLMs) as interactive agents show significant promise in Knowledge Graph Question Answering (KGQA) but often struggle with the semantic gap between natural language queries and structured knowledge graph (KG) representations. This leads to suboptimal planning and inefficient exploration on KG, while training-free approaches often underutilize valuable reasoning patterns in training data. To address these limitations, we propose a novel framework, Exemplar-Guided Planning (EGP), which enhances the planning capabilities of LLM agents for KGQA. EGP first preprocesses the training set questions via entity templating to normalize semantic variations. It then retrieves highly similar exemplary questions and their successful reasoning paths from this preprocessed set using semantic embeddings and an efficient FAISS index. These retrieved exemplars dynamically guide the LLM's planning process in two key phases: (1) Task Decomposition, by aligning generated sub-objectives with proven reasoning steps, and (2) Relation Exploration, by providing high-quality auxiliary information to improve relation pruning accuracy. Additionally, we introduce a Smart Lookahead mechanism during relation exploration to improve efficiency by preemptively exploring promising paths and potentially terminating exploration earlier. We apply EGP to the Plan-on-Graph (PoG) framework, termed PoG-EGP. Extensive experiments on two real-world KGQA datasets, WebQSP and CWQ, demonstrate that PoG-EGP significantly improves over the baseline PoG system and other compared methods.

cs.CL↗

arXiv2025

A decomposition for transverse spins in structured vector fields

Classical vector waves can possess intricate spin angular momenta (SAM), which are \emph{perpendicular} to the propagation direction, as revealed by the recent recognition of surprisingly transverse SAM in electromagnetic (EM) fields. In this paper, we employ the Hertz potential method to define structured vector fields and analytically decompose the SAM of the wave fields in two parts. Our novel approach of decomposition not only confirms that transverse SAM may originate from the first-order spatial inhomogeneity of the Poynting momentum, but also points out that for \emph{non-planar vector waves with near fields}, an extraordinary spin appears as a distinct part out of transverse spin. By four examples of vector beams, we further demonstrate that the proposed transverse spins prevail universally in both propagating and evanescent waves. This work renews our fundamental understanding of the decomposition of SAM for classical vector waves.

physics.optics↗

arXiv2025

Over-The-Air Phase Calibration of Spaceborne Phased Array for LEO Satellite Communications

To avoid the unpredictable phase deviations of the spaceborne phased array (SPA), this paper considers the over-the-air (OTA) phase calibration of the SPA for the low earth orbit (LEO) satellite communications, where the phase deviations of the SPA and the unknown channel are jointly estimated with multiple transmissions of the pilots. Moreover, the Cramer Rao Bound (CRB) is derived, and the optimization of beam patterns is also presented to lower the root mean squared error (RMSE) of the OTA calibration. The simulation results verify the effectiveness of the proposed OTA phase calibration algorithm as the RMSEs of the phase estimates closely approach the corresponding CRB, and the beam pattern optimization scheme is also validated for more than 4dB gain of SNR over the randomly generated beam patterns.

eess.SP↗

arXiv2025

Terahertz frequency conversion at plasma-induced time boundary

We report on the frequency conversions of terahertz (THz) waves at ultrafast time boundaries created via femtosecond laser-induced air-to-plasma phase transitions. Our combined experimental and theoretical approach reveals that the abrupt change in refractive index at the ultrafast time boundaries drives both the red and blue shifts over the broadband THz spectrum due to the dispersive plasma, with distinctive amplitude variations. The present study contrasts these effects with those from spatial boundaries, highlighting the superior efficacy of temporal manipulations for spectral engineering. These findings not only deepen the understanding of light-matter interactions in time-varying media but also pave the way for innovative applications in THz technology and lay the groundwork for the observation of temporal reflection effects, photonic time crystals, and spatio-temporally modulated matter.

physics.optics↗

arXiv2025

Fragmentation Functions of Charged Hadrons at Next-to-Next-to-Leading Order and Constraints on the Proton Parton Distribution Functions

We present the first global analysis of fragmentation functions (FFs) for light charged hadrons ($π^{\pm}$, $K^{\pm}$) at full next-to-next-to-leading order in Quantum Chromodynamics (QCD), incorporating world data from both single-inclusive electron-positron annihilation and semi-inclusive deep-inelastic scattering. The collinear factorization has been tested with low-momentum-transfer data and has demonstrated success at high hadron momenta. Additionally, we study the impact of current global data on hadron production to the parton distribution functions (PDFs), and find they favor a reduced asymmetry in the strange (anti-) quark PDFs, as compared to the asymmetry predicted by state-of-the-art PDFs derived from inclusive data.

hep-ph↗

arXiv2025

A topological rigidity theorem on noncompact Hessian manifolds

In this work, we obtain a short time solution for a geometric flow on noncompact affine Riemannian manifolds. Using this result, we can construct a Hessian metric with nonnegative bounded Hessian sectional curvature on some Hessian manifolds with nonnegative Hessian sectional curvature. Our results can be regarded as a real version of Lee-Tam \cite{LT20}. As an application, we prove that a complete noncompact Hessian manifold with nonnegative Hessian sectional curvature is diffeomorphic to $\mathbb{R}^n$ if its tangent bundle has maximal volume growth. This is an improvement of Theorem 1.3 in Jiao-Yin \cite{JY25}.

math.DG↗

arXiv2025

Self-Discharging Mitigated Quantum Battery

As a quantum thermodynamic device that utilizes quantum systems for energy storage and delivery, the quantum battery (QB) is expected to offer revolutionary advantages in terms of increasing the charging power and the extractable work by using quantum resources. However, the ubiquitous decoherence in the microscopic world inevitably forces the QB to spontaneously lose its stored energy. This is called the self-discharging of the QB and severely limits its realization. We propose a QB scheme based on the nitrogen-vacancy center in diamond, where the electronic spin serves as the QB. Inspired by our finding that the coherent ergotropy decays more slowly than the incoherent ergotropy, we reveal a mechanism to enhance the inherent robustness of the QB to the self-discharging by improving the ratio of coherent ergotropy to total ergotropy. The unique hyperfine interaction between the electron and the native $^{14}$N nucleus in our scheme allows one to coherently optimize this ratio. Mitigating the self-discharging and optimizing the extractable work simultaneously, our results pave the way for the practical realization of the QB.

quant-ph↗

arXiv2025

Drive Any Mesh: 4D Latent Diffusion for Mesh Deformation from Video

We propose DriveAnyMesh, a method for driving mesh guided by monocular video. Current 4D generation techniques encounter challenges with modern rendering engines. Implicit methods have low rendering efficiency and are unfriendly to rasterization-based engines, while skeletal methods demand significant manual effort and lack cross-category generalization. Animating existing 3D assets, instead of creating 4D assets from scratch, demands a deep understanding of the input's 3D structure. To tackle these challenges, we present a 4D diffusion model that denoises sequences of latent sets, which are then decoded to produce mesh animations from point cloud trajectory sequences. These latent sets leverage a transformer-based variational autoencoder, simultaneously capturing 3D shape and motion information. By employing a spatiotemporal, transformer-based diffusion model, information is exchanged across multiple latent frames, enhancing the efficiency and generalization of the generated results. Our experimental results demonstrate that DriveAnyMesh can rapidly produce high-quality animations for complex motions and is compatible with modern rendering engines. This method holds potential for applications in both the gaming and filming industries.

cs.CV↗

arXiv2025

Empathic network learning for multi-expert emergency decision-making under incomplete and inconsistent information

Challenges, such as a lack of information for emergency decision-making, time pressure, and limited knowledge of experts acting as decision-makers (DMs), can result in the generation of poor or inconsistent indirect information regarding DMs' preferences. Simultaneously, the empathic relationship represents a tangible social connection within the context of actual emergency decision-making, with the structure of the empathic network being a significant factor influencing the outcomes of the decision-making process. To deduce the empathic network underpinning the decision behaviors of DMs from incomplete and inconsistent preference information, we introduce an empathic network learning methodology rooted in the concept of robust ordinal regression via preference disaggregation. Firstly, we complete incomplete fuzzy judgment matrices including holistic preference information given in terms of decision examples on some reference alternatives, independently by each DM, and we calculate the intrinsic utilities of DMs. Secondly, we establish constraints for empathic network learning models based on empathic preference information and information about relations between some reference nodes. Then, the necessary and possible empathic relationships between any two DMs are calculated. Lastly, tailored to the specific requirements of different emergency scenarios, we design six target networks and construct models to derive the most representative empathic network.

math.OC↗

arXiv2025

MGFF-TDNN: A Multi-Granularity Feature Fusion TDNN Model with Depth-Wise Separable Module for Speaker Verification

In speaker verification, traditional models often emphasize modeling long-term contextual features to capture global speaker characteristics. However, this approach can neglect fine-grained voiceprint information, which contains highly discriminative features essential for robust speaker embeddings. This paper introduces a novel model architecture, termed MGFF-TDNN, based on multi-granularity feature fusion. The MGFF-TDNN leverages a two-dimensional depth-wise separable convolution module, enhanced with local feature modeling, as a front-end feature extractor to effectively capture time-frequency domain features. To achieve comprehensive multi-granularity feature fusion, we propose the M-TDNN structure, which integrates global contextual modeling with fine-grained feature extraction by combining time-delay neural networks and phoneme-level feature pooling. Experiments on the VoxCeleb dataset demonstrate that the MGFF-TDNN achieves outstanding performance in speaker verification while remaining efficient in terms of parameters and computational resources.

cs.SD↗

arXiv2025

Stabilization of linear systems with multiple unknown time-varying input delays by linear time-varying feedback

This paper addresses the stabilization of linear systems with multiple time-varying input delays. In scenarios where neither the exact delays information nor their bound is known, we propose a class of linear time-varying state feedback controllers by using the solution to a parametric Lyapunov equation (PLE). By leveraging the properties of the solution to the PLE and constructing a time-varying Lyapunov-Krasovskii-like functional, we prove that (the zero solution of) the closed-loop system is asymptotically stable. Furthermore, this result is extended to the observer-based output feedback case. The notable characteristic of these controllers is their utilization of linear time-varying gains. Furthermore, they are designed entirely independent of any knowledge of the time delays, resulting in controllers that are exceedingly easy to implement. Finally, a numerical example demonstrates the effectiveness of the proposed approaches.

math.DS↗

arXiv2025

LPA3D: 3D Room-Level Scene Generation from In-the-Wild Images

Generating realistic, room-level indoor scenes with semantically plausible and detailed appearances from in-the-wild images is crucial for various applications in VR, AR, and robotics. The success of NeRF-based generative methods indicates a promising direction to address this challenge. However, unlike their success at the object level, existing scene-level generative methods require additional information, such as multiple views, depth images, or semantic guidance, rather than relying solely on RGB images. This is because NeRF-based methods necessitate prior knowledge of camera poses, which is challenging to approximate for indoor scenes due to the complexity of defining alignment and the difficulty of globally estimating poses from a single image, given the unseen parts behind the camera. To address this challenge, we redefine global poses within the framework of Local-Pose-Alignment (LPA) -- an anchor-based multi-local-coordinate system that uses a selected number of anchors as the roots of these coordinates. Building on this foundation, we introduce LPA-GAN, a novel NeRF-based generative approach that incorporates specific modifications to estimate the priors of camera poses under LPA. It also co-optimizes the pose predictor and scene generation processes. Our ablation study and comparisons with straightforward extensions of NeRF-based object generative methods demonstrate the effectiveness of our approach. Furthermore, visual comparisons with other techniques reveal that our method achieves superior view-to-view consistency and semantic normality.

cs.CV↗

arXiv2025

VGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learning

Graph Neural Networks (GNNs) have gained attention for their ability to learn representations from graph data. Due to privacy concerns and conflicts of interest that prevent clients from directly sharing graph data with one another, Vertical Graph Federated Learning (VGFL) frameworks have been developed. Recent studies have shown that VGFL is vulnerable to adversarial attacks that degrade performance. However, it is a common problem that client nodes are often unlabeled in the realm of VGFL. Consequently, the existing attacks, which rely on the availability of labeling information to obtain gradients, are inherently constrained in their applicability. This limitation precludes their deployment in practical, real-world environments. To address the above problems, we propose a novel graph adversarial attack against VGFL, referred to as VGFL-SA, to degrade the performance of VGFL by modifying the local clients structure without using labels. Specifically, VGFL-SA uses a contrastive learning method to complete the attack before the local clients are trained. VGFL-SA first accesses the graph structure and node feature information of the poisoned clients, and generates the contrastive views by node-degree-based edge augmentation and feature shuffling augmentation. Then, VGFL-SA uses the shared graph encoder to get the embedding of each view, and the gradients of the adjacency matrices are obtained by the contrastive function. Finally, perturbed edges are generated using gradient modification rules. We validated the performance of VGFL-SA by performing a node classification task on real-world datasets, and the results show that VGFL-SA achieves good attack effectiveness and transferability.

cs.LG↗

arXiv2025

GSV3D: Gaussian Splatting-based Geometric Distillation with Stable Video Diffusion for Single-Image 3D Object Generation

Image-based 3D generation has vast applications in robotics and gaming, where high-quality, diverse outputs and consistent 3D representations are crucial. However, existing methods have limitations: 3D diffusion models are limited by dataset scarcity and the absence of strong pre-trained priors, while 2D diffusion-based approaches struggle with geometric consistency. We propose a method that leverages 2D diffusion models' implicit 3D reasoning ability while ensuring 3D consistency via Gaussian-splatting-based geometric distillation. Specifically, the proposed Gaussian Splatting Decoder enforces 3D consistency by transforming SV3D latent outputs into an explicit 3D representation. Unlike SV3D, which only relies on implicit 2D representations for video generation, Gaussian Splatting explicitly encodes spatial and appearance attributes, enabling multi-view consistency through geometric constraints. These constraints correct view inconsistencies, ensuring robust geometric consistency. As a result, our approach simultaneously generates high-quality, multi-view-consistent images and accurate 3D models, providing a scalable solution for single-image-based 3D generation and bridging the gap between 2D Diffusion diversity and 3D structural coherence. Experimental results demonstrate state-of-the-art multi-view consistency and strong generalization across diverse datasets. The code will be made publicly available upon acceptance.

cs.CV↗

arXiv2025

The impact of data from future lepton colliders on light hadrons fragmentation functions

In this work, we study the constraining power of future lepton colliders on fragmentation functions (FFs) to light charged hadrons from quarks and gluon in the framework of QCD collinear factorization. We perform analyses of FFs at NLO by including a wide range of pseudo--data from future lepton colliders, such as measurements on hadron multiplicities in the production of two jets and $W$ boson pairs, at various center of mass energies, and from hadronic decays of the Higgs boson, including both to heavy quarks and to gluons. The high luminosity and high energies of future lepton colliders allow for quark flavor separations and ensure a precise determination of FFs based solely on data from electron-positron collisions. We find that either the CEPC, FCC-$ee$ or ILC can significantly reduce the uncertainties of FFs in a wide kinematic range, compared to the NPC23 set obtained with a global analysis to current world data. We also discuss the impact of higher-order QCD corrections, and the potential constraints from measurements of three-jet production. Furthermore, we describe an update of the FMNLO program allowing for calculating hadron production cross sections at next-to-next-to-leading order in QCD, which is used in this study.

hep-ph↗

arXiv2025

Probing $k$-Space Alternating Spin Polarization via the Anomalous Hall Effect

Altermagnets represent a recently discovered class of collinear magnets, characterized by antiparallel neighboring magnetic moments and alternating-sign spin polarization in momentum-space($k$-space). However, experimental methods for probing the $k$-space spin polarization in altermagnets remain limited. In this work, we propose an approach to address this challenge by interfacing an altermagnet with the surface of a topological insulator. The massless Dirac fermions on the topological insulator surface acquire a mass due to the time-reversal symmetry breaking. The local $k$-space magnetic moment at the Dirac point directly determines both the sign and magnitude of this Dirac mass, resulting in an anomalous Hall effect. By measuring the Hall conductance, we can extract the local $k$-space magnetic moment. Moreover, we can map the global magnetic moment distribution by tuning the Dirac point position using an in-plane magnetic field, thereby revealing the $k$-space spin density of the altermagnet. This work establishes the Dirac fermion on the topological insulator surface as a sensitive probe for unveiling spin characters of altermagnets and those of other unconventional antiferromagnets.

cond-mat.mes-hall↗

arXiv2025

Five-dimensional Floquet topological semimetals with emergent Yang monopoles and linked Weyl surfaces

Recently, Floquet topological matter has attracted significant attention for its potential to reveal novel topological phases inaccessible in static systems. In this paper, we investigate the effect of a time-periodic driving on the five-dimensional (5D) normal insulators. We show that the time-periodic driving can induce a topological phase transition from a \textbf{TP} (time reversal combined with space inversion) symmetry-preserving normal insulator to a 5D Floquet topological semimetal with emergent Yang monopoles characterized by the second Chern number. Additionally, we show that this time-periodic driving can also lead to a topological phase transition from a \textbf{TP} symmetry-breaking normal insulator to a 5D Floquet topological semimetal with a Hopf link formed by Weyl surfaces. Besides, further increasing the strength of the time-periodic driving, the two 5D Floquet topological semimetal phases are transformed into the 5D Floquet Chern insulators. Our paper is helpful for future studies in higher dimensional Floquet systems.

cond-mat.mes-hall↗