arXiv subjects

Kunzan Liu

Publications and source records attributed to Kunzan Liu.

10 recordsLinked to original sources

arXiv2026

Tunable supercontinuum in multimode fiber via bending-induced dispersion modification

Nonlinear pulse propagation in multimode fibers (MMFs) offers a compact, low-cost route to broadband, tunable femtosecond light, but most control schemes act by changing the spatial mode composition, typically resulting in irregular or speckled beams in exchange for maximal spectral tunability. Here we introduce a complementary mechanism: bending-induced local dispersion modification of a high-order mode (HOM) to steer the spectrum while keeping the spatial mode fixed. We launch an LP0,7 mode into a step-index MMF and apply programmable macrobends near the input. With a standard Yb pump at 1030 nm, this yields spatially clean, continuous spectral tuning across 700-1350 nm, while the output profile remains Bessel-like and robust to reconfiguration of controlled bends. A perturbative model explains the observed spatial-spectral decorrelation, showing that moderate curvature produces first- and second-order shifts in group delay and group-velocity dispersion of the HOM with minimal change in its modal composition; these dispersion shifts control soliton fission, dispersive-wave emission, and the soliton self-frequency shift. We further validate application utility by driving multicolor, extended-depth-of-focus multiphoton microscopy directly from this all-fiber source. To our knowledge, this is the first demonstration of bending-induced dispersion modification, rather than mode mixing, used to tune MMF supercontinuum spectra without sacrificing beam quality, laying the foundation for an alternative pathway to tunable femtosecond illumination for imaging and spectroscopy.

physics.optics↗

arXiv2026

MAD: Microenvironment-Aware Distillation -- A Pretraining Strategy for Virtual Spatial Omics from Microscopy

Bridging microscopy and omics would allow us to read molecular states from images-at single-cell resolution and tissue scale-without the cost and throughput limits of omics technologies. Self-supervised pretraining offers a scalable approach with minimal labels, yet how to encode single-cell identity within tissue environments-and the extent of biological information such models can capture-remains an open question. Here, we introduce MAD (microenvironment-aware distillation), a pretraining strategy that learns cell-centric embeddings by jointly self-distilling the morphology view and the microenvironment view of the same indexed cell into a unified embedding space. Across diverse tissues and imaging modalities, MAD achieves state-of-the-art prediction performance on downstream tasks including cell subtyping, transcriptomic prediction, and bioinformatic inference. MAD even outperforms foundation models with a similar number of model parameters that have been trained on substantially larger datasets. These results demonstrate that MAD's dual-view joint self-distillation effectively captures the complexity and diversity of cells within tissues. Together, this establishes MAD as a general tool for representation learning in microscopy, enabling virtual spatial omics and biological insights from vast microscopy datasets.

cs.CV↗

arXiv2025

Self-localized ultrafast pencil beam for volumetric multiphoton imaging

The formation of organized optical states in multidimensional systems is crucial for understanding light-matter interaction and advancing light-shaping technologies. Here, we report the observation of a self-localized, ultrafast pencil beam near the critical power in a standard multimode fiber (MMF) and demonstrate its application in volumetric multiphoton imaging. We show that self-focusing in step-index MMFs, traditionally considered detrimental, can facilitate the formation of a nonlinear spatiotemporal localized state with a sidelobe-suppressed Bessel-like beam profile, exhibiting markedly improved stability and noise characteristics. By simply launching an overfilled on-axis Gaussian beam into a standard MMF, a high-quality ultrafast pencil beam can be generated through a self-localized process and readily integrated into an existing multiphoton point-scanning microscope. We apply this self-localized pencil beam to two-photon imaging of intact mouse enteric nervous systems, benchmarking with diffraction-limited Gaussian beams and outperforming conventional Bessel beams with reduced sidelobes and enhanced resilience to tissue-induced aberration. Finally, we monitor the transferrin uptake dynamics in a live human blood-brain barrier model by combining NAD(P)H-FAD-based metabolic phenotyping with minute-resolved 3D scans, revealing spatially and temporally resolved inter- and intra-cell heterogeneity. Our findings provide new insights into nonlinear dynamics of multidimensional optical systems and offer a promising approach for generating robust ultrafast pencil beams, enabling high-throughput 3D biosystem imaging to elucidate biological transport pathways and guide the design of therapeutics requiring cell-specific delivery.

physics.optics↗

arXiv2025

Learned, uncertainty-driven adaptive acquisition for photon-efficient scanning microscopy

Scanning microscopy systems, such as confocal and multiphoton microscopy, are powerful imaging tools for probing deep into biological tissue. However, scanning systems have an inherent trade-off between acquisition time, field of view, phototoxicity, and image quality, often resulting in noisy measurements when fast, large field of view, and/or gentle imaging is needed. Deep learning could be used to denoise noisy microscopy measurements, but these algorithms can be prone to hallucination, which can be disastrous for medical and scientific applications. We propose a method to simultaneously denoise and predict pixel-wise uncertainty for scanning microscopy systems, improving algorithm trustworthiness and providing statistical guarantees for deep learning predictions. Furthermore, we propose to leverage this learned, pixel-wise uncertainty to drive an adaptive acquisition technique that rescans only the most uncertain regions of a sample, saving time and reducing the total light dose to the sample. We demonstrate our method on experimental confocal and multiphoton microscopy systems, showing that our uncertainty maps can pinpoint hallucinations in the deep learned predictions. Finally, with our adaptive acquisition technique, we demonstrate up to 16X reduction in acquisition time and total light dose while successfully recovering fine features in the sample and reducing hallucinations. We are the first to demonstrate distribution-free uncertainty quantification for a denoising task with real experimental data and the first to propose adaptive acquisition based on reconstruction uncertainty.

eess.IV↗

arXiv2024

System- and Sample-agnostic Isotropic 3D Microscopy by Weakly Physics-informed, Domain-shift-resistant Axial Deblurring

Three-dimensional (3D) subcellular imaging is essential for biomedical research, but the diffraction limit of optical microscopy compromises axial resolution, hindering accurate 3D structural analysis. This challenge is particularly pronounced in label-free imaging of thick, heterogeneous tissues, where assumptions about data distribution (e.g. sparsity, label-specific distribution, and lateral-axial similarity) and system priors (e.g. independent and identically distributed (i.i.d.) noise and linear shift-invariant (LSI) point-spread functions (PSFs)) are often invalid. Here, we introduce SSAI-3D, a weakly physics-informed, domain-shift-resistant framework for robust isotropic 3D imaging. SSAI-3D enables robust axial deblurring by generating a PSF-flexible, noise-resilient, sample-informed training dataset and sparsely fine-tuning a large pre-trained blind deblurring network. SSAI-3D was applied to label-free nonlinear imaging of living organoids, freshly excised human endometrium tissue, and mouse whisker pads, and further validated in publicly available ground-truth-paired experimental datasets of 3D heterogeneous biological tissues with unknown blurring and noise across different microscopy systems.

physics.optics↗

arXiv2024

Deep and Dynamic Metabolic and Structural Imaging in Living Tissues

Label-free imaging through two-photon autofluorescence (2PAF) of NAD(P)H allows for non-destructive and high-resolution visualization of cellular activities in living systems. However, its application to thick tissues and organoids has been restricted by its limited penetration depth within 300 $μ$m, largely due to tissue scattering at the typical excitation wavelength (~750 nm) required for NAD(P)H. Here, we demonstrate that the imaging depth for NAD(P)H can be extended to over 700 $μ$m in living engineered human multicellular microtissues by adopting multimode fiber (MMF)-based low-repetition-rate high-peak-power three-photon (3P) excitation of NAD(P)H at 1100 nm. This is achieved by having over 0.5 MW peak power at the band of 1100$\pm$25 nm through adaptively modulating multimodal nonlinear pulse propagation with a compact fiber shaper. Moreover, the 8-fold increase in pulse energy at 1100 nm enables faster imaging of monocyte behaviors in the living multicellular models. These results represent a significant advance for deep and dynamic metabolic and structural imaging of intact living biosystems. The modular design (MMF with a slip-on fiber shaper) is anticipated to allow wide adoption of this methodology for demanding in vivo and in vitro imaging applications, including cancer research, autoimmune diseases, and tissue engineering.

physics.optics↗

arXiv2023

Spectral-temporal-spatial customization via modulating multimodal nonlinear pulse propagation

Multimode fibers (MMFs) have recently reemerged as attractive avenues for nonlinear effects due to their high-dimensional spatiotemporal nonlinear dynamics and scalability for high power. High-brightness MMF sources with effective control of the nonlinear processes would offer new possibilities for a wide range of applications from high-power fiber lasers, to bioimaging and chemical sensing, and to novel physics phenomena. Here we present a simple yet effective way of controlling nonlinear effects at high peak power levels: by leveraging not only the spatial but also the temporal degrees of freedom of the multimodal nonlinear pulse propagation in step-index MMFs using a programmable fiber shaper. This method represents the first method that enables modulation and optimization of multimodal nonlinear pulse propagation, achieving high tunability and broadband high peak power. Its potential as a nonlinear imaging source is further demonstrated by applying the MMF source to multiphoton microscopy, where widely tunable two-photon and three-photon imaging is achieved with adaptive optimization. These demonstrations highlight the effectiveness of directly modulating multimodal nonlinear pulse propagation to enhance the high-dimensional customization and optimize the high spectral brightness of MMF output. These advancements provide new possibilities for technology advances in nonlinear optics, bioimaging, spectroscopy, optical computing, and material processing.

physics.optics↗

arXiv2023

Sensing RISs: Enabling Dimension-Independent CSI Acquisition for Beamforming

Reconfigurable intelligent surfaces (RISs) are envisioned as a potentially transformative technology for future wireless communications. However, RISs' inability to process signals and the attendant increased channel dimension have brought new challenges to RIS-assisted systems, including significantly increased pilot overhead required for channel estimation. To address these problems, several prior contributions that enhance the hardware architecture of RISs or develop algorithms to exploit the channels' mathematical properties have been made, where the required pilot overhead is reduced to be proportional to the number of RIS elements. In this paper, we propose a dimension-independent channel state information (CSI) acquisition approach in which the required pilot overhead is independent of the number of RIS elements. Specifically, in contrast to traditional signal transmission methods, where signals from the base station (BS) and the users are transmitted in different time slots, we propose a novel method in which signals are transmitted from the BS and the user simultaneously during CSI acquisition. With this method, an electromagnetic interference random field (IRF) will be induced on the RIS, and we propose the structure of sensing RIS to capture its features. Moreover, we develop three algorithms for parameter estimation in this system, in which one of the proposed vM-EM algorithm is analyzed with the fixed-point perturbation method to obtain an asymptotic achievable bound. In addition, we also derive the Cramér-Rao lower bound (CRLB) and an asymptotic expression for characterizing the best possible performance of the proposed algorithms. Simulation results verify that our proposed signal transmission method and the corresponding algorithms can achieve dimension-independent CSI acquisition for beamforming.

eess.SP↗

arXiv2022

Multi-Sample Training for Neural Image Compression

This paper considers the problem of lossy neural image compression (NIC). Current state-of-the-art (sota) methods adopt uniform posterior to approximate quantization noise, and single-sample pathwise estimator to approximate the gradient of evidence lower bound (ELBO). In this paper, we propose to train NIC with multiple-sample importance weighted autoencoder (IWAE) target, which is tighter than ELBO and converges to log likelihood as sample size increases. First, we identify that the uniform posterior of NIC has special properties, which affect the variance and bias of pathwise and score function estimators of the IWAE target. Moreover, we provide insights on a commonly adopted trick in NIC from gradient variance perspective. Based on those analysis, we further propose multiple-sample NIC (MS-NIC), an enhanced IWAE target for NIC. Experimental results demonstrate that it improves sota NIC methods. Our MS-NIC is plug-and-play, and can be easily extended to other neural compression tasks.

cs.CV↗

arXiv2021

Compact User-Specific Reconfigurable Intelligent Surfaces for Uplink Transmission

Large-scale antenna arrays employed by the base station (BS) constitute an essential next-generation communications technique. However, due to the constraints of size, cost, and power consumption, it is usually considered unrealistic to use a large-scale antenna array at the user side. Inspired by the emerging technique of reconfigurable intelligent surfaces (RIS), we firstly propose the concept of user-side RIS (US-RIS) for facilitating the employment of a large-scale antenna array at the user side in a cost- and energy-efficient way. In contrast to the existing employments of RIS, which belong to the family of base-station-side RISs (BSS-RISs), the US-RIS concept by definition facilitates the employment of RIS at the user side for the first time. This is achieved by conceiving a multi-layer structure to realize a compact form-factor. Furthermore, our theoretical results demonstrate that, in contrast to the existing single-layer structure, where only the phase of the signal reflected from RIS can be adjusted, the amplitude of the signal penetrating multi-layer US-RIS can also be partially controlled, which brings about a new degree of freedom (DoF) for beamformer design that can be beneficially exploited for performance enhancement. In addition, based on the proposed multi-layer US-RIS, we formulate the signal-to-noise ratio (SNR) maximization problem of US-RIS-aided communications. Due to the non-convexity of the problem introduced by this multi-layer structure, we propose a multi-layer transmit beamformer design relying on an iterative algorithm for finding the optimal solution by alternately updating each variable. Finally, our simulation results verify the superiority of the proposed multi-layer US-RIS as a compact realization of a large-scale antenna array at the user side for uplink transmission.

cs.IT↗