arXiv subjects

Rongxiang Zhang

Publications and source records attributed to Rongxiang Zhang.

8 recordsLinked to original sources

arXiv2026

All-optical reconstruction of valley polarization through helicity-resolved high-harmonic generation

We theoretically investigate valley-resolved high-order harmonic generation in gapped graphene driven by elliptically polarized laser fields. Using two-band density-matrix simulations and an electron-hole recombination trajectory model, we find that the two inequivalent valleys emit harmonics with opposite helicities. Under an elliptically polarized field, these emissions occur predominantly in different half cycles of the laser field. Time-dependent density functional theory calculations for monolayer MoS$_2$ show the same temporal separation of harmonic emissions with opposite helicities, supporting the generality of this valley-dependent chiral response. We further propose an all-optical scheme to reconstruct valley polarization from helicity-resolved harmonic signals. A circularly polarized pulse first prepares a valley population imbalance. A subsequent elliptically polarized laser induces different changes in the harmonic intensities of opposite helicities through Pauli blocking. The ratio of these intensity changes provides a direct measure of the valley polarization. Our results demonstrate that chiral high-harmonic emission can serve as an all-optical probe of ultrafast valley-dependent carrier dynamics.

physics.optics↗

arXiv2026

LeapTalk: Breaking the Latency-Quality Trade-off in Talking Head Generation

Long-form and real-time talking-head generation remains challenging due to a latency-quality trade-off: inefficient multi-step diffusion prohibits streaming generation, whereas real-time autoregressive approaches suffer from error accumulation and identity drift. To address this drawback, we propose LeapTalk, a novel framework that achieves stable and real-time talking-head generation with a single forward step, scaling to arbitrarily long videos. At the heart of our approach lies a single-step bridge distillation scheme. On the one hand, departing from the conventional noise-to-data paradigm, we introduce a data-to-data transport formulation based on a Brownian bridge. Anchored by a persistent reference, this strategy effectively mitigates identity drift and enhances long-term temporal stability. On the other hand, to enable smooth knowledge transfer from a pre-trained diffusion teacher to the student bridge model, we explore a heterogeneous distillation framework with an SNR-aligned time transformation $Φ(τ)$, which bridges the functional discrepancy between the two models. Moreover, we propose an audio-driven classifier-free guidance mechanism to maintain fine-grained lip synchronization under extreme step reduction. Extensive experiments demonstrate that our method achieves high-fidelity and temporally consistent video generation with only 1 step at up to 200 FPS, significantly outperforming existing approaches in both efficiency and stability. Project Page: https://zhangrongxiang.github.io/leaptalk-page/

cs.CV↗

arXiv2025

Cooperative Energy Scheduling of Multi-Microgrids Based on Risk-Sensitive Reinforcement Learning

With the rapid development of distributed renewable energy, multi-microgrids play an increasingly important role in improving the flexibility and reliability of energy supply. Reinforcement learning has shown great potential in coordination strategies due to its model-free nature. Current methods lack explicit quantification of the relationship between individual and joint risk values, resulting in obscured credit assignment. Moreover, they often depend on explicit communication, which becomes inefficient as system complexity grows. To address these challenges, this paper proposes a risk-sensitive reinforcement learning framework with shared memory (RRL-SM) for multi-microgrid scheduling. Specifically, a risk-sensitive value factorization scheme is proposed to quantify the relationship between individual and joint risk values by leveraging distributional modeling and attention-based representations, thereby aligning local decisions with global risk objectives. An implicit shared-memory coordination mechanism is implemented through a global memory space to enhance the overall efficiency of decentralized decision-making. Collectively, the integrated approach delivers more reliable cooperative scheduling under renewable energy uncertainty. Simulation results show that RRL-SM reduces load-shedding risk by 84.5%, demonstrating a favorable balance between reliability and economic performance.

eess.SY↗

arXiv2025

Attosecond transient absorption spectroscopy in monolayer hexagonal boron nitride

We simulate the attosecond transient absorption spectroscopy (ATAS) of monolayer hexagonal boron nitride (hBN) using the time-dependent density functional theory and two-band density-matrix equations within the tight-binding approximation. The simulation results from the two methods are qualitatively consistent. We focus on the fishbone structure around the gap energy of the M point, which exhibits a temporal period equal to that of the pump laser. To gain deeper insight into this structure, we simplify the two-band model to a single-electron model located at the M point, allowing us to derive an analytical expression that can qualitatively reproduce the numerical results. By isolating the influence of the Berry connection on the ATAS, our analytical results reveal that both the interband transition dipole moments and the Berry connection play important roles in the fishbone structure of the ATAS. Moreover, we also have investigated the dependence of ATAS on the gap energy based the tight-binding approximation. The results demonstrate that the ATAS intensity is enhanced as the gap energy increases, in agreement with our analytical prediction. Our study may shed light on the generation mechanism of the fishbone structure of the ATAS in hBN.

physics.atom-ph↗

arXiv2025

ManuSearch: Democratizing Deep Search in Large Language Models with a Transparent and Open Multi-Agent Framework

Recent advances in web-augmented large language models (LLMs) have exhibited strong performance in complex reasoning tasks, yet these capabilities are mostly locked in proprietary systems with opaque architectures. In this work, we propose \textbf{ManuSearch}, a transparent and modular multi-agent framework designed to democratize deep search for LLMs. ManuSearch decomposes the search and reasoning process into three collaborative agents: (1) a solution planning agent that iteratively formulates sub-queries, (2) an Internet search agent that retrieves relevant documents via real-time web search, and (3) a structured webpage reading agent that extracts key evidence from raw web content. To rigorously evaluate deep reasoning abilities, we introduce \textbf{ORION}, a challenging benchmark focused on open-web reasoning over long-tail entities, covering both English and Chinese. Experimental results show that ManuSearch substantially outperforms prior open-source baselines and even surpasses leading closed-source systems. Our work paves the way for reproducible, extensible research in open deep search systems. We release the data and code in https://github.com/RUCAIBox/ManuSearch

cs.CL↗

arXiv2025

Isolated elliptically-polarized attosecond pulse generation in gapped graphene driven by linearly polarized laser fields

We theoretically investigate high-order harmonic generation (HHG) and its ellipticity in gapped graphene, driven by a femtosecond short-pulse laser at various orientation angles, employing the two-band density-matrix equations within the tight-binding approximation. The orientation-dependent harmonic spectra exhibit pronounced enhancement of specific harmonics, which we attribute to the caustic effect. Using the recombination trajectory model, we reveal that the orientation dependence of these enhanced harmonics originates from the distinct band structures encountered by electrons ionized from the two inequivalent $\textrm{K}$ points. Moreover, we focus on the ellipticity of the enhanced harmonics at specific angles and demonstrate that it primarily depends on the phase difference between the parallel and perpendicular components, which can be accurately predicted by our recombination trajectory model. Based on these insights, we propose a two-color (fundamental plus second-harmonic) field scheme to generate isolated elliptically polarized attosecond pulses (IEAPs) in gapped graphene. Our findings may provide a promising pathway toward the generation of IEAPs in gapped graphene or transition metal dichalcogenides.

physics.atom-ph↗

arXiv2025

AdaMHF: Adaptive Multimodal Hierarchical Fusion for Survival Prediction

The integration of pathologic images and genomic data for survival analysis has gained increasing attention with advances in multimodal learning. However, current methods often ignore biological characteristics, such as heterogeneity and sparsity, both within and across modalities, ultimately limiting their adaptability to clinical practice. To address these challenges, we propose AdaMHF: Adaptive Multimodal Hierarchical Fusion, a framework designed for efficient, comprehensive, and tailored feature extraction and fusion. AdaMHF is specifically adapted to the uniqueness of medical data, enabling accurate predictions with minimal resource consumption, even under challenging scenarios with missing modalities. Initially, AdaMHF employs an experts expansion and residual structure to activate specialized experts for extracting heterogeneous and sparse features. Extracted tokens undergo refinement via selection and aggregation, reducing the weight of non-dominant features while preserving comprehensive information. Subsequently, the encoded features are hierarchically fused, allowing multi-grained interactions across modalities to be captured. Furthermore, we introduce a survival prediction benchmark designed to resolve scenarios with missing modalities, mirroring real-world clinical conditions. Extensive experiments on TCGA datasets demonstrate that AdaMHF surpasses current state-of-the-art (SOTA) methods, showcasing exceptional performance in both complete and incomplete modality settings.

cs.CV↗

arXiv2024

PatchScaler: An Efficient Patch-Independent Diffusion Model for Image Super-Resolution

While diffusion models significantly improve the perceptual quality of super-resolved images, they usually require a large number of sampling steps, resulting in high computational costs and long inference times. Recent efforts have explored reasonable acceleration schemes by reducing the number of sampling steps. However, these approaches treat all regions of the image equally, overlooking the fact that regions with varying levels of reconstruction difficulty require different sampling steps. To address this limitation, we propose PatchScaler, an efficient patch-independent diffusion pipeline for single image super-resolution. Specifically, PatchScaler introduces a Patch-adaptive Group Sampling (PGS) strategy that groups feature patches by quantifying their reconstruction difficulty and establishes shortcut paths with different sampling configurations for each group. To further optimize the patch-level reconstruction process of PGS, we propose a texture prompt that provides rich texture conditional information to the diffusion model. The texture prompt adaptively retrieves texture priors for the target patch from a common reference texture memory. Extensive experiments show that our PatchScaler achieves superior performance in both quantitative and qualitative evaluations, while significantly speeding up inference. Our code will be available at \url{https://github.com/yongliuy/PatchScaler}.

cs.CV↗