arXiv subjects

Boyuan Li

Publications and source records attributed to Boyuan Li.

28 records · Page 2Linked to original sources

arXiv2025

Experimental Evidence of Vortex $γ$ Photons in All-Optical Inverse Compton Scattering

Vortex $γ$ photons carrying orbital angular momenta (OAM) hold great potential for various applications. However, their generation remains a great challenge. Here, we successfully generate sub-MeV vortex $γ$ photons via all-optical inverse Compton scattering of relativistic electrons colliding with a sub-relativistic Laguerre-Gaussian laser. In principle, directly measuring the OAM of $γ$ photons is challenging due to their incoherence and extremely short wavelength. Therein, we put forward a novel method to determine the OAM properties by revealing the quantum opening angle of vortex $γ$ photons, since vortex particles exhibit not only a spiral phase but also transverse momentum according to the quantum electrodynamics theory. Thus,$γ$ photons carrying OAM anifest a much larger angular distribution than those without OAM, which has been clearly observed in our experiments. This angular expansion is considered as an overall effect lying beyond classical theory. Our method provides the first experimental evidence for detecting vortex $γ$ photons and opens a new perspective for investigating OAM-induced quantum phenomena in broad fields.

physics.plasm-ph↗

arXiv2025

Think-Then-React: Towards Unconstrained Human Action-to-Reaction Generation

Modeling human-like action-to-reaction generation has significant real-world applications, like human-robot interaction and games. Despite recent advancements in single-person motion generation, it is still challenging to well handle action-to-reaction generation, due to the difficulty of directly predicting reaction from action sequence without prompts, and the absence of a unified representation that effectively encodes multi-person motion. To address these challenges, we introduce Think-Then-React (TTR), a large language-model-based framework designed to generate human-like reactions. First, with our fine-grained multimodal training strategy, TTR is capable to unify two processes during inference: a thinking process that explicitly infers action intentions and reasons corresponding reaction description, which serve as semantic prompts, and a reacting process that predicts reactions based on input action and the inferred semantic prompts. Second, to effectively represent multi-person motion in language models, we propose a unified motion tokenizer by decoupling egocentric pose and absolute space features, which effectively represents action and reaction motion with same encoding. Extensive experiments demonstrate that TTR outperforms existing baselines, achieving significant improvements in evaluation metrics, such as reducing FID from 3.988 to 1.942.

cs.HC↗

arXiv2024

Two-in-One: Unified Multi-Person Interactive Motion Generation by Latent Diffusion Transformer

Multi-person interactive motion generation, a critical yet under-explored domain in computer character animation, poses significant challenges such as intricate modeling of inter-human interactions beyond individual motions and generating two motions with huge differences from one text condition. Current research often employs separate module branches for individual motions, leading to a loss of interaction information and increased computational demands. To address these challenges, we propose a novel, unified approach that models multi-person motions and their interactions within a single latent space. Our approach streamlines the process by treating interactive motions as an integrated data point, utilizing a Variational AutoEncoder (VAE) for compression into a unified latent space, and performing a diffusion process within this space, guided by the natural language conditions. Experimental results demonstrate our method's superiority over existing approaches in generation quality, performing text condition in particular when motions have significant asymmetry, and accelerating the generation efficiency while preserving high quality.

cs.CV↗

arXiv2024

A liquid crystal geometric phase hyperbolic lens with a positive focal length for arbitrary circularly polarized incidence

This paper presents the design and experimental validation of a liquid crystal geometric phase hyperbolic lens (LCHL) with positive focal lengths for arbitrary circularly polarized light. Utilizing Pancharatnam-Berry phase modulation, the lens enables isotropic focusing for both left- and right-handed circular polarization. We fabricated and characterized two lenses with different focal lengths, and their diffraction patterns were analyzed experimentally. The results exhibit strong agreement with theoretical simulations, highlighting the lens's capability for precise optical field modulation. The proposed LCHL demonstrates significant potential for detecting weak polarization states, making it a promising tool for advanced applications in biomedical imaging, optical vortex generation, and multifocal optical systems.

physics.optics↗

arXiv2024

Neighborhood and Global Perturbations Supported SAM in Federated Learning: From Local Tweaks To Global Awareness

Federated Learning (FL) can be coordinated under the orchestration of a central server to collaboratively build a privacy-preserving model without the need for data exchange. However, participant data heterogeneity leads to local optima divergence, subsequently affecting convergence outcomes. Recent research has focused on global sharpness-aware minimization (SAM) and dynamic regularization techniques to enhance consistency between global and local generalization and optimization objectives. Nonetheless, the estimation of global SAM introduces additional computational and memory overhead, while dynamic regularization suffers from bias in the local and global dual variables due to training isolation. In this paper, we propose a novel FL algorithm, FedTOGA, designed to consider optimization and generalization objectives while maintaining minimal uplink communication overhead. By linking local perturbations to global updates, global generalization consistency is improved. Additionally, global updates are used to correct local dynamic regularizers, reducing dual variables bias and enhancing optimization consistency. Global updates are passively received by clients, reducing overhead. We also propose neighborhood perturbation to approximate local perturbation, analyzing its strengths and limitations. Theoretical analysis shows FedTOGA achieves faster convergence $O(1/T)$ under non-convex functions. Empirical studies demonstrate that FedTOGA outperforms state-of-the-art algorithms, with a 1\% accuracy increase and 30\% faster convergence, achieving state-of-the-art.

cs.LG↗

arXiv2024

Report on the Advanced Linear Collider Study Group (ALEGRO) Workshop 2024

The workshop focused on the application of ANAs to particle physics keeping in mind the ultimate goal of a collider at the energy frontier (10\,TeV, e$^+$/e$^-$, e$^-$/e$^-$, or $γγ$). The development of ANAs is conducted at universities and national laboratories worldwide. The community is thematically broad and diverse, in particular since lasers suitable for ANA research (multi-hundred-terawatt peak power, a few tens of femtosecond-long pulses) and acceleration of electrons to hundreds of mega electron volts to multi giga electron volts became commercially available. The community spans several continents (Europe, America, Asia), including more than 62 laboratories in more than 20 countries. It is among the missions of the ICFA-ANA panel to feature the amazing progress made with ANAs, to provide international coordination and to foster international collaborations towards a future HEP collider. The scope of this edition of the workshop was to discuss the recent progress and necessary steps towards realizing a linear collider for particle physics based on novel-accelerator technologies (laser or beam driven in plasma or structures). Updates on the relevant aspects of the European Strategy for Particle Physics (ESPP) Roadmap Process as well as of the P5 (in the US) were presented, and ample time was dedicated to discussions. The major outcome of the workshop is the decision for ALEGRO to coordinate efforts in Europe, in the US, and in Asia towards a pre-CDR for an ANA-based, 10\,TeV CM collider. This goal of this coordination is to lead to a funding proposal to be submitted to both EU and EU/US funding agencies. This document presents a summary of the workshop, as seen by the co-chairs, as well as short 'one-pagers' written by the presenters at the workshop.

physics.acc-ph↗

arXiv2024

A Platform for All-optical Thomson/ Compton Scattering with Versatile Parameters

A dual-beam platform for all-optical electron-photon scattering, or Thomson/Compton scattering, with adjustable collision-angle and parameter tuning ability has been developed, which, in principle, can be used for the verification of strong-field quantum electrodynamics effects. Combining this platform with a 200 TW Ti:Sapphire laser system, we demonstrated the generation of inverse Compton scattering X/gamma-rays with tunable energies from tens of keV to MeV. The polarization of X/gamma radiation was manipulated by controlling the polarization of scattering laser. In the near future, by combining this experimental platform with multi-PW laser facilities, it is proposed to experimentally generate X/gamma radiation with orbital angular momentum for the nuclear isomer excitation, and more importantly, to explore the regime transition from nonlinear Thomson scattering to nonlinear Compton scattering, eventually to demonstrate the verification of theories on extremely strong field quantum electrodynamics effects.

hep-ex↗

arXiv2023

TeViS:Translating Text Synopses to Video Storyboards

A video storyboard is a roadmap for video creation which consists of shot-by-shot images to visualize key plots in a text synopsis. Creating video storyboards, however, remains challenging which not only requires cross-modal association between high-level texts and images but also demands long-term reasoning to make transitions smooth across shots. In this paper, we propose a new task called Text synopsis to Video Storyboard (TeViS) which aims to retrieve an ordered sequence of images as the video storyboard to visualize the text synopsis. We construct a MovieNet-TeViS dataset based on the public MovieNet dataset. It contains 10K text synopses each paired with keyframes manually selected from corresponding movies by considering both relevance and cinematic coherence. To benchmark the task, we present strong CLIP-based baselines and a novel VQ-Trans. VQ-Trans first encodes text synopsis and images into a joint embedding space and uses vector quantization (VQ) to improve the visual representation. Then, it auto-regressively generates a sequence of visual features for retrieval and ordering. Experimental results demonstrate that VQ-Trans significantly outperforms prior methods and the CLIP-based baselines. Nevertheless, there is still a large gap compared to human performance suggesting room for promising future work. The code and data are available at: \url{https://ruc-aimind.github.io/projects/TeViS/}

cs.CV↗

arXiv2023

Scene-Driven Exploration and GUI Modeling for Android Apps

Due to the competitive environment, mobile apps are usually produced under pressure with lots of complicated functionality and UI pages. Therefore, it is challenging for various roles to design, understand, test, and maintain these apps. The extracted transition graphs for apps such as ATG, WTG, and STG have a low transition coverage and coarse-grained granularity, which limits the existing methods of graphical user interface (GUI) modeling by UI exploration. To solve these problems, in this paper, we propose SceneDroid, a scene-driven exploration approach to extracting the GUI scenes dynamically by integrating a series of novel techniques including smart exploration, state fuzzing, and indirect launching strategies. We present the GUI scenes as a scene transition graph (SceneTG) to model the GUI of apps with high transition coverage and fine? grained granularity. Compared with the existing GUI modeling tools, SceneDroid has improved by 168.74% in the coverage of transition pairs and 162.42% in scene extraction. Apart from the effectiveness evaluation of SceneDroid, we also illustrate the future potential of SceneDroid as a fundamental capability to support app development, reverse engineering, and GUI regression testing.

cs.SE↗

arXiv2019

Model predictive approach to integrated path planning and tracking for autonomous vehicles

In the path planning problem of autonomous application, the existing studies separately consider the path planning and trajectory tracking control of the autonomous vehicle and few of them have integrated the trajectory planning and trajectory control together. To fill in this research gap, this study proposes an integrated trajectory planning and trajectory control method. This paper also studies the collision avoidance problem of autonomous by considering static and dynamic obstacles. Simulation results have been presented to show the effectiveness of the proposed control method.

cs.RO↗