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Tong Lin

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Biological Physics physics.app-ph physics.optics Artificial Intelligence Computational Engineering, Finance, and Science Computer Vision cond-mat.mes-hall cond-mat.soft eess.IV Machine Learning physics.med-ph

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preprint2026arXiv

A framework for modeling and inferring tracer diffusion in crowded environments

Tracer diffusion in crowded environments is central to many biological and soft matter systems, but quantitative frameworks for linking tracer motion to environmental structure remain limited. Here, we study the transport of rigid tracers in suspensions of soft particles and within living cells. Experiments reveal a transition from diffusive to confined motion as the matrix area fraction increases. We develop a minimal simulation that incorporates steric exclusion and hydrodynamic hindrance to reproduce the observed mean-squared displacements (MSDs). Using simulation outputs, we train a parallel partial Gaussian process (PPGP) model that rapidly predicts MSDs from matrix geometric variables, including area fraction, particle size, and polydispersity. The PPGP model accelerates predictions by several orders of magnitude relative to simulation and experiments. Analysis reveals that tracer transport is primarily governed by accessible pore sizes and that distinct global structures can produce indistinguishable MSDs. We find that the minimal model can also capture the MSDs of internalized tracer particles in cells. The framework enables rapid inference of structural properties in crowded environments, including transport in the intracellular environment.

preprint2020arXiv

Broadband Ultrahigh-Resolution chip-scale Scanning Soliton Dual-Comb Spectroscopy

We present a chip-scale scanning dual-comb spectroscopy (SDCS) approach for broadband ultrahigh-resolution spectral acquisition. SDCS uses Si3N4 microring resonators that generate two single soliton micro-combs spanning 37 THz (300 nm) on the same chip from a single 1550-nm laser, forming a high-mutual-coherence dual-comb. We realize continuous tuning of the dual-comb system over the entire optical span of 37.5 THz with high precision using integrated microheater-based wavelength trackers. This continuous wavelength tuning is enabled by simultaneous tuning of the laser frequency and the two single soliton micro-combs over a full free spectral range of the microrings. We measure the SDCS resolution to be 319+-4.6 kHz. Using this SDCS system, we perform the molecular absorption spectroscopy of H13C14N over its 2.3 THz (18 nm)-wide overtone band, and show that the massively parallel heterodyning offered by the dual-comb expands the effective spectroscopic tuning speed of the laser by one order of magnitude. Our chip-scale SDCS opens the door to broadband spectrometry and massively parallel sensing with ultrahigh spectral resolution.

preprint2020arXiv

TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain

In topology optimization using deep learning, load and boundary conditions represented as vectors or sparse matrices often miss the opportunity to encode a rich view of the design problem, leading to less than ideal generalization results. We propose a new data-driven topology optimization model called TopologyGAN that takes advantage of various physical fields computed on the original, unoptimized material domain, as inputs to the generator of a conditional generative adversarial network (cGAN). Compared to a baseline cGAN, TopologyGAN achieves a nearly $3\times$ reduction in the mean squared error and a $2.5\times$ reduction in the mean absolute error on test problems involving previously unseen boundary conditions. Built on several existing network models, we also introduce a hybrid network called U-SE(Squeeze-and-Excitation)-ResNet for the generator that further increases the overall accuracy. We publicly share our full implementation and trained network.

preprint2019arXiv

Integrated near-field thermo-photovoltaics for on-demand heat recycling

The energy transferred via thermal radiation between two surfaces separated by nanometers distances (near-field) can be much larger than the blackbody limit. However, realizing a reconfigurable platform that utilizes this energy exchange mechanism to generate electricity in industrial and space applications on-demand, remains a challenge. The challenge lies in designing a platform that can separate two surfaces by a small and tunable gap while simultaneously maintaining a large temperature differential. Here, we present a fully integrated, reconfigurable and scalable platform operating in near-field regime that performs controlled heat extraction and energy recycling. Our platform relies on an integrated nano-electromechanical system (NEMS) that enables precise positioning of a large area thermal emitter within nanometers distances from a room-temperature germanium photodetector to form a thermo-photovoltaic (TPV) cell. We show over an order of magnitude higher power generation $\mathrm{P_{gen} \sim 1.25 \, μW \cdot cm^{-2}}$ from our TPV cell by tuning the gap between a hot emitter ($\mathrm{T_E \sim 880 \, K}$) and the cold photodetector ($\mathrm{T_D \sim 300 \, K}$) from $\mathrm{\sim 500 \, nm}$ to $\mathrm{\sim 100 \, nm}$. The significant enhancement in $\mathrm{P_{gen}}$ at such small distances is a clear indication of near-field heat transfer effect. Our electrostatically controlled NEMS switch consumes negligible tuning power ($\mathrm{P_{gen}/P_{NEMS} \sim 10^4}$) and relies on conventional silicon-based process technologies.

preprint2012arXiv

Total Variation and Euler's Elastica for Supervised Learning

In recent years, total variation (TV) and Euler's elastica (EE) have been successfully applied to image processing tasks such as denoising and inpainting. This paper investigates how to extend TV and EE to the supervised learning settings on high dimensional data. The supervised learning problem can be formulated as an energy functional minimization under Tikhonov regularization scheme, where the energy is composed of a squared loss and a total variation smoothing (or Euler's elastica smoothing). Its solution via variational principles leads to an Euler-Lagrange PDE. However, the PDE is always high-dimensional and cannot be directly solved by common methods. Instead, radial basis functions are utilized to approximate the target function, reducing the problem to finding the linear coefficients of basis functions. We apply the proposed methods to supervised learning tasks (including binary classification, multi-class classification, and regression) on benchmark data sets. Extensive experiments have demonstrated promising results of the proposed methods.

preprint2009arXiv

A feasibility study of treatment verification using EPID cine images for hypofractionated lung radiotherapy

We propose a novel approach for potential on-line treatment verification using cine EPID (Electronic Portal Imaging Device) images for hypofractionated lung radiotherapy based on a machine learning algorithm. Hypofractionated radiotherapy requires high precision. It is essential to effectively monitor the target to ensure that the tumor is within the beam aperture. We modeled the treatment verification problem as a two-class classification problem and applied an Artificial Neural Network (ANN) to classify the cine EPID images acquired during the treatment into corresponding classes with the tumor inside or outside of the beam aperture. Training samples were generated for the ANN using digitally reconstructed radiographs (DRRs) with artificially added shifts in tumor location to simulate cine EPID images with different tumor locations. Principal Component Analysis (PCA) was used to reduce the dimensionality of the training samples and cine EPID images acquired during the treatment. The proposed treatment verification algorithm was tested on five hypofractionated lung patients in a retrospective fashion. On average, our proposed algorithm achieved a 98.0% classification accuracy, a 97.6% recall rate, and a 99.7% precision rate.