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Researcher profile

Qilin Deng

Qilin Deng contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
physics.opticsComputer Visionhep-phInformation Retrieval

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Published work

4 published item(s)

preprint2026arXiv

DeepFilters: Scattering-Aware Pupil Engineering with Learned Digital Filter Reconstruction for Extended Depth of Field Microscopy

Extended depth of field microscopy encodes axial information into a single acquisition through engineered point spread functions, but conventional and deep optics approaches are subject to degradation in scattering tissue. We introduce DeepFilters, a scattering-aware deep optics framework that jointly optimizes a parameterized pupil filter and a digital-filter-based reconstruction network through a calibrated differentiable forward model to achieve broad generalization without retraining. Incorporating empirical scattering kernels, physics-guided regularization, and a hybrid genetic-gradient initialization strategy, DeepFilters extends the PSF from 16 micron to >400 micron in clear media and enables signal recovery beyond 120 micron deep in biological tissues, validated across fixed brain slices and sea urchin embryos.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Efficient reference-less transmission matrix retrieval for a multimode fiber using fast Fourier transform

Transmission matrix (TM) linearly maps the incident and transmitted complex fields, and has been used widely due to its ability to characterize scattering media. It is computationally demanding to reconstruct the TM from intensity images measured by a reference-less experimental setup. Removing reference beam for interference gains the advantage of simple experimental setup. However, the long computational time still limits its practical application. We propose an efficient reference-less TM retrieval method for multimode fiber (MMF). Our method adopts a data acquisition scheme which employs Fourier transform matrix in the design of the incident fields. We develop a nonlinear optimization algorithm to solve the TM retrieval problem in a parallel manner. The data acquisition scheme allows the algorithm to be implemented with fast Fourier transform (FFT), and hence achieves great efficiency improvement. Further, our method acquires intensity images at a defocus plane and correct the error of relative phase offset of TM recovered from the intensity images measured at one fixed plane. We validate the proposed TM retrieval method with both simulations and experiments. By using FFT, our TM retrieval algorithm achieves 1200x speed-up in computational time, and recovers $2286 \times 8192$ TM of a 0.22 NA and $50 \ μm$ diameter MMF with 124.9 seconds by a computer of 32 CPU cores. With the advantages of efficiency and the correction of phase offset, our method paves the way for the application of reference-less TM retrieval in real practice.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering

Recent years have witnessed the great accuracy performance of graph-based Collaborative Filtering (CF) models for recommender systems. By taking the user-item interaction behavior as a graph, these graph-based CF models borrow the success of Graph Neural Networks (GNN), and iteratively perform neighborhood aggregation to propagate the collaborative signals. While conventional CF models are known for facing the challenges of the popularity bias that favors popular items, one may wonder "Whether the existing graph-based CF models alleviate or exacerbate popularity bias of recommender systems?" To answer this question, we first investigate the two-fold performances w.r.t. accuracy and novelty for existing graph-based CF methods. The empirical results show that symmetric neighborhood aggregation adopted by most existing graph-based CF models exacerbate the popularity bias and this phenomenon becomes more serious as the depth of graph propagation increases. Further, we theoretically analyze the cause of popularity bias for graph-based CF. Then, we propose a simple yet effective plugin, namely r-AdjNorm, to achieve an accuracy-novelty trade-off by controlling the normalization strength in the neighborhood aggregation process. Meanwhile, r-AdjNorm can be smoothly applied to the existing graph-based CF backbones without additional computation. Finally, experimental results on three benchmark datasets show that our proposed method can improve novelty without sacrificing accuracy under various graph-based CF backbones.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Impact of LHCb 13 TeV $W$ and $Z$ pseudo-data on the Parton Distribution Functions

We study the potential of the LHCb 13 TeV single $W^{\pm}$ and $Z$ boson pseudo-data for constraining the parton distribution functions (PDFs) of the proton. As an example, we demonstrate the sensitivity of the LHCb 13 TeV data, collected with integrated luminosities of 5 fb$^{-1}$ and 300 fb$^{-1}$, to reducing the PDF uncertainty bands of the CT14HERA2 PDFs, using the error PDF updating package {\sc ePump}. The sensitivities of various experimental observables are compared. Generally, sizable reductions in PDF uncertainties can be observed in the 300 fb$^{-1}$ data sample, particularly in the small-$x$ region. The double-differential cross section measurement of $Z$ boson $p_T$ and rapidity can greatly reduce the uncertainty bands of $u$ and $d$ quarks in almost the whole $x$ range, as compared to various single observable measurements.

0 citations0 reviewsNo code linkedOpen access