arXiv subjects

Yuquan Zhang

Publications and source records attributed to Yuquan Zhang.

10 recordsLinked to original sources

arXiv2026

Multi-mode fiber enabled multi-wavelength optical trapping and dynamic manipulation

Optical fiber tweezers offer distinct advantages for long-distance manipulation, compact integration, and minimally invasive operation in biological environments. However, most optical fiber tweezers rely on single-mode fibers (SMFs), which are constrained by limited optical mode diversity and reduced control flexibility. Although multi-mode fibers (MMFs) support a wider spectrum of propagation modes, their inherent mixed guided modes with low coherence become a long-standing limitation for the design of focused trapping configurations. To address these limitations, we propose and experimentally validate a fully MMF-based optical tweezer system integrated with a micro-lens structure fabricated on the fiber facet, enabling stable optical trapping across multiple wavelengths and dynamic manipulation of trapped cells. Employing 532 nm continuous-wave and 800 nm femtosecond lasers, we demonstrate that both light sources can generate tightly focused optical spots through the micro-lens with a high numerical aperture (NA>0.7), achieving robust trapping and axial dynamic manipulation of cells. Compared with conventional SMF-based tweezers, this approach leverages the broadband and multi-mode properties of MMFs, allows for wavelength-flexible and dynamically adjustable trapping of cells, and paves the way for lab-on-fiber biophotonic platforms with potential applications such as interventional manipulation, cell sorting, and cellular fluorescence analysis.

physics.optics↗

arXiv2026

UBEP: Re-architecting Expert Parallelism Communication Library for Production Superpods

The deployment of Mixture-of-Experts (MoE) models on production high-bandwidth superpods, such as NVIDIA's NVL72/576 and Huawei's CloudMatrix384, introduces critical challenges beyond raw interconnect bandwidth. While these systems provide unified global address spaces and high-bandwidth fabrics, their full potential for sparse MoE communication is hindered by three fundamental bottlenecks: (1) Strict execution serialization imposed by coarse-grained Bulk Synchronous Parallel (BSP) orchestration of interdependent communication phases; (2) Prohibitive synchronization overhead that fails to scale alongside high interconnect bandwidth; and (3) Severe load imbalance resulting from distance-agnostic scheduling of irregular token traffic. To eliminate these bottlenecks, we introduce UBEP (Unified-Bus Expert Parallelism), a production-ready communication library that rethinks MoE's All-to-All primitives for modern superpod architectures. Through large scale experiments, UBEP reduces All-to-All latency by up to 52.4% and MoE inference Time Per Output Token (TPOT) by up to 11.1%.

cs.DC↗

arXiv2026

Ultrafast wide-field 3D topography with extended depth of field

Ultrafast optical imaging has enabled direct observation of femtosecond-nanosecond dynamics, yet three-dimensional (3D) dynamic measurements at high numerical aperture (NA) remain hindered by the intrinsically shallow depth of field (DoF) of conventional microscopes. Here, we propose an ultrafast, wide-field pump-probe interferometric microscope on a telecentric platform that significantly extends the effective DoF to ~18 micrometer at a high NA of 0.9 while maintaining high spatial resolution (down to 235 nm) and temporal resolution (~170 fs). The system enables single-frame 3D topography reconstruction without axial scanning or multi-view acquisition. We demonstrate these capabilities by capturing axial material flow during laser-induced microsphere melting that remain unobservable with conventional narrow-DoF systems, and by tracking the azimuthal rotation of ablation lobes during axial propagation of temporal focused spatiotemporal optical vortex (TF-STOV) pulses, directly revealing the spatiotemporal evolution of STOV-matter interactions

physics.optics↗

arXiv2026

Temporal Focusing Enables Distortion-Resistant high-intensity Spatiotemporal Optical Vortices

Spatiotemporal optical vortices (STOVs) carry transverse orbital angular momentum and offer new degrees of freedom for light-matter interactions. Yet conventional focusing of STOVs introduces spatiotemporal astigmatism: the beam diffracts while the pulse duration stays constant, causing the vortex to deform away from focus. Here we overcome this limitation by introducing spectral phase modulation into a temporal focusing configuration, where angular dispersion forces the pulse to compress only at the geometric focus so that the spatial and temporal dimensions focus and defocus together. Our approach generates stable STOVs with self-similar, distortion-free evolution over an extended focal region. Besides, the orbital angular momentum vector can be continuously steered from purely longitudinal to strongly tilted orientations by adjusting the spatial dispersion, objective focal length, or input beam size. More importantly, our method offers full compatibility with high NA focusing geometry, allowing high-intensity and high-resolution applications. We validate these properties through femtosecond laser ablation under high-NA conditions and interferometric spatiotemporal field reconstruction under low-NA conditions.

physics.optics↗

arXiv2025

Synthetic Spatiotemporal Plasmonic Vortices On Chip

Spatiotemporal vortices are polychromatic modes that intertwine orbital angular momentum (OAM) in space and time. Here we introduce a new class of such vortices, spatiotemporal plasmonic vortices (STPVs), carrying nontrivial topological spin textures. They are generated by chronotopic interference of temporally delayed plasmonic eigen-vortices, where a $π$-phase dislocation in the space-frequency domain maps into a 2$π$ spiraling phase in space-time, with the resulting focus-defocus dynamics emulate U(1) gauge transitions. Using interferometric time-resolved photoemission electron microscopy (ITR-PEEM), we directly image their nanometer-attosecond (nano-atto) evolution and control vortex number and position. Quantum-path analysis of coherent two-photon photoemission (2PP) processes reveals the nonlinear plasmonic polarization fields and angular-momentum conservation, establishing STPVs as a platform for probing spatiotemporally structured quantum matter.

cond-mat.mes-hall↗

arXiv2025

Scalable and Programmable Topological Transitions in Plasmonic Moire Superlattices

Topological transitions are fundamental phenomena in electronics, photonics, and quantum technologies. However, the scalability and tunability of Topological transitions in these systems have still been constrained by their material properties or structural rigidities. Here, we demonstrate that plasmonic Moire superlattices offer a platform for large-range and programmable topological transitions via wavefront engineering. By tailoring the phases of elementary evanescent waves in hexagonal systems, we create Moire-structured optical skyrmion lattices whose topological invariants evolve programmably and scalably. Theoretical calculations indicate that the topological invariants span from -58 to +58 and are extendable by tuning the Moire angle. Remarkably, their values are constrained by symmetry to exclude integer multiples of 3/2, revealing an intrinsic link between symmetry and topological quantization. Our work establishes a versatile real-space topology control platform for exploring topological transitions mechanisms and studying topologically critical phenomena, and further promoting breakthroughs in structured light, photonic computing, and condensed matter physics.

physics.optics↗

arXiv2025

Tumor-associated CD19$^+$ macrophages induce immunosuppressive microenvironment in hepatocellular carcinoma

Tumor-associated macrophages are a key component that contributes to the immunosuppressive microenvironment in human cancers. However, therapeutic targeting of macrophages has been a challenge in clinic due to the limited understanding of their heterogeneous subpopulations and distinct functions. Here, we identify a unique and clinically relevant CD19$^+$ subpopulation of macrophages that is enriched in many types of cancer, particularly in hepatocellular carcinoma (HCC). The CD19$^+$ macrophages exhibit increased levels of PD-L1 and CD73, enhanced mitochondrial oxidation, and compromised phagocytosis, indicating their immunosuppressive functions. Targeting CD19$^+$ macrophages with anti-CD19 chimeric antigen receptor T (CAR-T) cells inhibited HCC tumor growth. We identify PAX5 as a primary driver of up-regulated mitochondrial biogenesis in CD19$^+$ macrophages, which depletes cytoplasmic Ca$^{2+}$, leading to lysosomal deficiency and consequent accumulation of CD73 and PD-L1. Inhibiting CD73 or mitochondrial oxidation enhanced the efficacy of immune checkpoint blockade therapy in treating HCC, suggesting great promise for CD19$^+$ macrophage-targeting therapeutics.

q-bio.CB↗

arXiv2025

Controllable perfect spatiotemporal optical vortices

Spatiotemporal optical vortices (STOVs), as a kind of structured light pulses carrying transverse orbital angular momentum (OAM), have recently attracted significant research interest due to their unique photonic properties. However, general STOV pulses typically exhibit an annular intensity profile in the spatiotemporal plane, with a radius that scales with the topological charge, limiting their potential in many applications. Here, to address this limitation, we introduce the concept of perfect spatiotemporal optical vortices (PSTOVs). Unlike STOV pulses, the intensity distribution of PSTOV wavepackets is nearly independent of the topological charge. We show that such wavepackets can be generated by applying the spatiotemporal Fourier transform to a Bessel-Gaussian mode in the spatiotemporal frequency domain. More importantly, the mode distribution of PSTOV wavepackets can be freely controlled by introducing azimuthal-dependent phase modulation, enabling conversion from a standard annular profile to arbitrary polygonal shapes. Finally, experimental results confirm the successful generation of these wavepackets. Our findings will expand the study of STOV pulses and explore their potential applications in optical communications, information processing, topological photonics, and ultrafast control of light-matter interactions.

physics.optics↗

arXiv2023

Scientific Preparation for CSST: Classification of Galaxy and Nebula/Star Cluster Based on Deep Learning

The Chinese Space Station Telescope (abbreviated as CSST) is a future advanced space telescope. Real-time identification of galaxy and nebula/star cluster (abbreviated as NSC) images is of great value during CSST survey. While recent research on celestial object recognition has progressed, the rapid and efficient identification of high-resolution local celestial images remains challenging. In this study, we conducted galaxy and NSC image classification research using deep learning methods based on data from the Hubble Space Telescope. We built a Local Celestial Image Dataset and designed a deep learning model named HR-CelestialNet for classifying images of the galaxy and NSC. HR-CelestialNet achieved an accuracy of 89.09% on the testing set, outperforming models such as AlexNet, VGGNet and ResNet, while demonstrating faster recognition speeds. Furthermore, we investigated the factors influencing CSST image quality and evaluated the generalization ability of HR-CelestialNet on the blurry image dataset, demonstrating its robustness to low image quality. The proposed method can enable real-time identification of celestial images during CSST survey mission.

cs.CV↗

arXiv2023

FedPDC:Federated Learning for Public Dataset Correction

As people pay more and more attention to privacy protection, Federated Learning (FL), as a promising distributed machine learning paradigm, is receiving more and more attention. However, due to the biased distribution of data on devices in real life, federated learning has lower classification accuracy than traditional machine learning in Non-IID scenarios. Although there are many optimization algorithms, the local model aggregation in the parameter server is still relatively traditional. In this paper, a new algorithm FedPDC is proposed to optimize the aggregation mode of local models and the loss function of local training by using the shared data sets in some industries. In many benchmark experiments, FedPDC can effectively improve the accuracy of the global model in the case of extremely unbalanced data distribution, while ensuring the privacy of the client data. At the same time, the accuracy improvement of FedPDC does not bring additional communication costs.

cs.LG↗