arXiv subjects

Tingting Zheng

Publications and source records attributed to Tingting Zheng.

At least 19 recordsLinked to original sources

arXiv2026

Mid-Infrared Single-Photon Edge Enhanced Imaging based on Nonlinear Vortex Filtering

Edge enhanced imaging via the spiral phase contrast enables to reveal the phase or amplitude gradients of a target, which has been proved useful in feature recognition, machine vision, and object identification. A long quest is to extend the operation wavelength into the mid-infrared (MIR) region, as highly demanded in various fields including infrared sensing, astronomic observation, and biomedical diagnosis. Here, we demonstrated ultra-sensitive MIR imaging at the single-photon level based on nonlinear frequency upconversion, where the spectrally converted replica of the MIR object image at 3070 nm was captured by a silicon electron multiplying charged coupled device. The imaging sensitivity was significantly improved by the coincidence pulsed pumping with a spectro-temporal optimization. Furthermore, the edge enhancement has been realized by imprinting the spiral phase pattern of the pump onto the upconverted field at the Fourier plane within the nonlinear crystal. Such a nonlinear spatial filter not only provided an effective way to implement the required high-fidelity vortex screening in the edge enhanced detection, but also rendered the MIR illumination into a visible image in an efficient and low-noise fashion. The presented system for MIR edge enhanced imaging might facilitate immediate applications in label-free histopathological diagnosis and non-destructive defect inspection.

physics.optics↗

arXiv2026

High-speed mid-infrared single-photon upconversion spectrometer

Sensitive and fast mid-infrared (MIR) spectroscopy is highly attractive in a variety of applications including astronomical observation, pharmaceutical synthesis, and environmental monitoring. However, the performance of conventional MIR spectrometers has long been hindered by the limited sensitivity of narrow-bandgap detectors and/or the deficient brightness of broadband light sources. Here, we devise and implement an ultra-sensitive and broadband MIR upconversion spectrometer, which integrates a supercontinuum source covering 1.5-4.2 $μ$m based on a silicon nitride nanophotonic waveguide. High-efficiency and low-noise nonlinear frequency upconversion is realized based on coincidence pulsed pumping with spectro-temporal optimization, which enables to leverage silicon detectors for facilitating MIR single-photon spectroscopy at 0.2 photons/nm/pulse. Furthermore, the upconversion-based array spectrometer is manifested with high-speed spectral acquisition rates beyond 200 kHz, which is about ten-fold faster than the state-of-the-art scan rates for FTIR-based spectrometers at a comparable spectral resolution. The achieved features of broadband spectral coverage, single-photon sensitivity, and sub-MHz refreshing rate might open up new possibilities in infrared transient spectral measurements in combustion analysis, high-throughput sorting and reaction tracking, among others.

physics.optics↗

arXiv2026

Single-photon time-stretch infrared spectroscopy

Sensitive mid-infrared (MIR) spectroscopy is highly demanded in various fields ranging from industrial inspection, biomedical diagnosis to astronomical observation. However, the detection sensitivity of conventional MIR spectrometers has been severely limited by excessive noises for existing infrared sensors, which hinders widespread use in photon-scarce scenarios. Here, we devise and implement a broadband MIR single-photon time-stretch spectrometer based on high-fidelity spectral upconversion and time-correlated coincidence counting. Specifically, a nanophotonic supercontinuum illumination covering 2.4-4.2 $μ$m is nonlinearly converted to the near-infrared band, where low-loss single-mode fiber and high-performance silicon detector can be leveraged to facilitate dispersive operation and sensitive detection, respectively. The arrival time for the dispersed upconversion photons is precisely registered with a low-timing-jitter photon counter, which enables us to obtain a high spectral resolution about 0.5 cm$^{-1}$ under a low-light-level illumination down to 0.14 photons/nm/pulse. In comparison to previous MIR upconversion spectrometers, the presented time-stretch architecture favors single-pixel simplicity and high-throughput acquisition for the single-photon spectral measurement. The achieved MIR spectroscopic features of broadband spectral coverage, sub-wavenumber resolution, single-photon sensitivity, and room-temperature operation would stimulate immediate applications in material and life sciences.

physics.optics↗

arXiv2026

Mid-Infrared Single-Photon Compressive Spectroscopy

Sensitive mid-infrared (MIR) spectroscopy plays an indispensable role in various photon-starved conditions. However, the detection sensitivity of conventional MIR spectrometers is severely limited by excessive noises of the involved infrared sensors, especially for multi-pixel arrays in parallel spectral acquisition. Here, we devise and implement an ultra-sensitive MIR single-pixel spectrometer, which relies on high-fidelity spectral upconversion and wavelength-encoding compressive measurement. Specifically, a MIR nanophotonic supercontinuum from 3.1 to 3.9 $μ$m is nonlinearly converted to the near-infrared band via synchronous chirped-pulse pumping, which facilitates both the precise spectral mapping and sensitive upconversion detection. The upconverted signal is then spatially dispersed onto a programmable digital micromirror device, before being registered by a single-element silicon detector. Consequently, the spectral information can be deciphered from the correlation between encoded patterns and recorded measurements, which results in a spectral resolution of 0.5 cm$^{-1}$ under an illumination flux down to 0.01 photons/nm/pulse. Moreover, we demonstrate faithful reconstructions at sub-Nyquist sampling rates by using the compressive sensing algorithm, which leads to a 95\% reduction in data acquisition time. The presented single-pixel computational spectrometer features wavelength multiplexing, high throughput, and efficient sampling, which thus paves a new way for sensitive and fast spectroscopic analysis at the single-photon level.

physics.optics↗

arXiv2026

Mid-infrared Fourier ptychographic upconversion imaging

Frequency upconversion technique offers an appealing approach for sensitive mid-infrared (MIR) imaging at room temperature. However, the spatial resolution of the upconversion imager has been notoriously restricted by the limited transverse section of the involved nonlinear crystal at the Fourier plane. Here, we implement a wide-field and high-resolution MIR upconversion imaging system based on elliptical pumping and Fourier ptychography. Specifically, an elliptical pump beam is engineered to accommodate the narrow aperture of chirped-poling crystals, thus facilitating the acquisition of high spatial frequency components in the lateral direction. Such an elliptical passband in the Fourier space is then discretely rotated to generate a sequence of upconversion images, which allows computational recovery of a high-resolution object image through a combination of synthetic aperture and phase retrieval operations. Consequently, an enhanced spatial resolution of 39 $μ$m is achieved within a field of view about 25 mm, which corresponds to a space-bandwidth product of 3.2$\times$10$^5$, over tenfold larger than previously demonstrated values. Moreover, the MIR upconversion imager can operate under a low-light illumination of 1 photon/pulse/pixel. Therefore, the presented paradigm of nonlinear Fourier ptychography paves the way toward high-throughput infrared imaging with massive resolvable elements and single-photon sensitivity, which would stimulate a variety of applications such as industry inspection and biomedical diagnosis.

physics.optics↗

arXiv2026

Wide-field mid-infrared edge-enhanced upconversion imaging

Edge-enhanced imaging is critical for visualizing weakly absorbing and transparent objects. Extending this functionality into the mid-infrared (MIR) region enables chemical sensitivity and improved imaging performance for biomedical, material, and remote-sensing applications. Here, we present a wide-field MIR edge-enhanced upconversion imaging system that integrates vortex-pump complex-amplitude engineering with aperiodic quasi-phase matching. In contrast to the bright-field modality, the wide-field edge-enhanced operation shows sensitive dependence on the crystal position relative to the Fourier plane. The system achieves single-shot operation with a 25-mm field of view and 79-$μ$m spatial resolution, yielding a record-high space-bandwidth product of $7.9 \times 10^4$. We show that this capability enables direct visualization of phase gradients in transparent optical elements and enhances structural contrast in biological specimens. The demonstrated architecture combines high sensitivity, spectral specificity, and robust edge detection, offering a promising route toward advanced MIR imaging in industrial inspection and biomedical diagnostics.

physics.optics↗

arXiv2026

AINet: Anchor Instances Learning for Regional Heterogeneity in Whole Slide Image

Recent advances in multi-instance learning (MIL) have witnessed impressive performance in whole slide image (WSI) analysis. However, the inherent sparsity of tumors and their morphological diversity lead to obvious heterogeneity across regions, posing significant challenges in aggregating high-quality and discriminative representations. To address this, we introduce a novel concept of anchor instance (AI), a compact subset of instances that are representative within their regions (local) and discriminative at the bag (global) level. These AIs act as semantic references to guide interactions across regions, correcting non-discriminative patterns while preserving regional diversity. Specifically, we propose a dual-level anchor mining (DAM) module to \textbf{select} AIs from massive instances, where the most informative AI in each region is extracted by assessing its similarity to both local and global embeddings. Furthermore, to ensure completeness and diversity, we devise an anchor-guided region correction (ARC) module that explores the complementary information from all regions to \textbf{correct} each regional representation. Building upon DAM and ARC, we develop a concise yet effective framework, AINet, which employs a simple predictor and surpasses state-of-the-art methods with substantially fewer FLOPs and parameters. Moreover, both DAM and ARC are modular and can be seamlessly integrated into existing MIL frameworks, consistently improving their performance.

eess.IV↗

arXiv2025

When Semantics Regulate: Rethinking Patch Shuffle and Internal Bias for Generated Image Detection with CLIP

The rapid progress of GANs and Diffusion Models poses new challenges for detecting AI-generated images. Although CLIP-based detectors exhibit promising generalization, they often rely on semantic cues rather than generator artifacts, leading to brittle performance under distribution shifts. In this work, we revisit the nature of semantic bias and uncover that Patch Shuffle provides an unusually strong benefit for CLIP, that disrupts global semantic continuity while preserving local artifact cues, which reduces semantic entropy and homogenizes feature distributions between natural and synthetic images. Through a detailed layer-wise analysis, we further show that CLIP's deep semantic structure functions as a regulator that stabilizes cross-domain representations once semantic bias is suppressed. Guided by these findings, we propose SemAnti, a semantic-antagonistic fine-tuning paradigm that freezes the semantic subspace and adapts only artifact-sensitive layers under shuffled semantics. Despite its simplicity, SemAnti achieves state-of-the-art cross-domain generalization on AIGCDetectBenchmark and GenImage, demonstrating that regulating semantics is key to unlocking CLIP's full potential for robust AI-generated image detection.

cs.CV↗

arXiv2025

Spiking Meets Attention: Efficient Remote Sensing Image Super-Resolution with Attention Spiking Neural Networks

Spiking neural networks (SNNs) are emerging as a promising alternative to traditional artificial neural networks (ANNs), offering biological plausibility and energy efficiency. Despite these merits, SNNs are frequently hampered by limited capacity and insufficient representation power, yet remain underexplored in remote sensing super-resolution (SR) tasks. In this paper, we first observe that spiking signals exhibit drastic intensity variations across diverse textures, highlighting an active learning state of the neurons. This observation motivates us to apply SNNs for efficient SR of RSIs. Inspired by the success of attention mechanisms in representing salient information, we devise the spiking attention block (SAB), a concise yet effective component that optimizes membrane potentials through inferred attention weights, which, in turn, regulates spiking activity for superior feature representation. Our key contributions include: 1) we bridge the independent modulation between temporal and channel dimensions, facilitating joint feature correlation learning, and 2) we access the global self-similar patterns in large-scale remote sensing imagery to infer spatial attention weights, incorporating effective priors for realistic and faithful reconstruction. Building upon SAB, we proposed SpikeSR, which achieves state-of-the-art performance across various remote sensing benchmarks such as AID, DOTA, and DIOR, while maintaining high computational efficiency. Code of SpikeSR will be available at https://github.com/XY-boy/SpikeSR.

cs.CV↗

arXiv2024

Lower Bounds for Adaptive Relaxation-Based Algorithms for Single-Source Shortest Paths

We consider the classical single-source shortest path problem in directed weighted graphs. D.~Eppstein proved recently an $Ω(n^3)$ lower bound for oblivious algorithms that use relaxation operations to update the tentative distances from the source vertex. We generalize this result by extending this $Ω(n^3)$ lower bound to \emph{adaptive} algorithms that, in addition to relaxations, can perform queries involving some simple types of linear inequalities between edge weights and tentative distances. Our model captures as a special case the operations on tentative distances used by Dijkstra's algorithm.

cs.DS↗

arXiv2024

Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification

Multi-Instance Learning (MIL) has shown impressive performance for histopathology whole slide image (WSI) analysis using bags or pseudo-bags. It involves instance sampling, feature representation, and decision-making. However, existing MIL-based technologies at least suffer from one or more of the following problems: 1) requiring high storage and intensive pre-processing for numerous instances (sampling); 2) potential over-fitting with limited knowledge to predict bag labels (feature representation); 3) pseudo-bag counts and prior biases affect model robustness and generalizability (decision-making). Inspired by clinical diagnostics, using the past sampling instances can facilitate the final WSI analysis, but it is barely explored in prior technologies. To break free these limitations, we integrate the dynamic instance sampling and reinforcement learning into a unified framework to improve the instance selection and feature aggregation, forming a novel Dynamic Policy Instance Selection (DPIS) scheme for better and more credible decision-making. Specifically, the measurement of feature distance and reward function are employed to boost continuous instance sampling. To alleviate the over-fitting, we explore the latent global relations among instances for more robust and discriminative feature representation while establishing reward and punishment mechanisms to correct biases in pseudo-bags using contrastive learning. These strategies form the final Dynamic Policy-Driven Adaptive Multi-Instance Learning (PAMIL) method for WSI tasks. Extensive experiments reveal that our PAMIL method outperforms the state-of-the-art by 3.8\% on CAMELYON16 and 4.4\% on TCGA lung cancer datasets.

cs.CV↗

arXiv2023

Global Contrast Masked Autoencoders Are Powerful Pathological Representation Learners

Based on digital pathology slice scanning technology, artificial intelligence algorithms represented by deep learning have achieved remarkable results in the field of computational pathology. Compared to other medical images, pathology images are more difficult to annotate, and thus, there is an extreme lack of available datasets for conducting supervised learning to train robust deep learning models. In this paper, we propose a self-supervised learning (SSL) model, the global contrast-masked autoencoder (GCMAE), which can train the encoder to have the ability to represent local-global features of pathological images, also significantly improve the performance of transfer learning across data sets. In this study, the ability of the GCMAE to learn migratable representations was demonstrated through extensive experiments using a total of three different disease-specific hematoxylin and eosin (HE)-stained pathology datasets: Camelyon16, NCTCRC and BreakHis. In addition, this study designed an effective automated pathology diagnosis process based on the GCMAE for clinical applications. The source code of this paper is publicly available at https://github.com/StarUniversus/gcmae.

eess.IV↗

arXiv2022

A Deep Reinforcement Learning Framework for Rapid Diagnosis of Whole Slide Pathological Images

The deep neural network is a research hotspot for histopathological image analysis, which can improve the efficiency and accuracy of diagnosis for pathologists or be used for disease screening. The whole slide pathological image can reach one gigapixel and contains abundant tissue feature information, which needs to be divided into a lot of patches in the training and inference stages. This will lead to a long convergence time and large memory consumption. Furthermore, well-annotated data sets are also in short supply in the field of digital pathology. Inspired by the pathologist's clinical diagnosis process, we propose a weakly supervised deep reinforcement learning framework, which can greatly reduce the time required for network inference. We use neural network to construct the search model and decision model of reinforcement learning agent respectively. The search model predicts the next action through the image features of different magnifications in the current field of view, and the decision model is used to return the predicted probability of the current field of view image. In addition, an expert-guided model is constructed by multi-instance learning, which not only provides rewards for search model, but also guides decision model learning by the knowledge distillation method. Experimental results show that our proposed method can achieve fast inference and accurate prediction of whole slide images without any pixel-level annotations.

eess.IV↗

arXiv2021

Stacking faults in $α$-RuCl$_3$ revealed by local electric polarization

We present out-of-plane dielectric and magnetodielectric measurements of single crystallines $α$-RuCl$_3$ with various degrees of stack faults. A frequency dependent, but field independent, dielectric anomaly appears at $T_{A}\:(f=100\:\mathrm{kHz})\sim$ 4 K once both magnetic transitions at $T_{N1}\sim$ 7 K and $T_{N2}\sim$ 14 K set in. The observed dielectric anomaly is attributed to the emergency of possible local electric polarizations whose inversion symmetry is broken by inhomogeneously distributed stacking faults. A field-induced intermediate phase is only observed when a magnetic field is applied perpendicular to the Ru-Ru bonds for samples with minimal stacking faults. Less pronounced in-plane anisotropy is found in samples with sizable contribution from stacking imperfections. Our findings suggest that dielectric measurement is a sensitive probe in detecting the structural and magnetic properties, which may be a promising tool especially in studying $α$-RuCl$_3$ thin film devices. Moreover, the stacking details of RuCl$_3$ layers strongly affect the ground state both in the magnetic and electric channels. Such a fragile ground state against stacking faults needs to be overcome for realistic applications utilizing the magnetic and/or electric properties of Kitaev based physics in $α$-RuCl$_3$.

cond-mat.str-el↗

arXiv2020

Asymptotic behavior of solutions toward the strong contact discontinuity for compressible Navier-Stokes equations with Cauchy problem

In this paper, we consider the nonisentropic ideal polytropic Navier-Stokes equations to the Cauchy problem. The asymptotic stability of contact discontinuity is established under the condition that the initial perturbations are partly small but the strength of contact discontinuity can be suitably large. With this conditions, the bounds of density and temperature can be obtained from the complicated structure of Navier-Stokes equations. The proofs are given by the elementary energy method.

math.AP↗

arXiv2018

Is the Spiral Galaxy a Cosmic Hurricane?

It is discussed that the formation of the spiral galaxies is driven by the cosmic background rotation, not a result of an isolated evolution proposed by the density wave theory. To analyze the motions of the galaxies, a simple double particle galaxy model is considered and the Coriolis force formed by the rotational background is introduced. The numerical analysis shows that not only the trajectory of the particle is the spiral shape, but also the relationship between the velocity and the radius reveals both the existence of spiral arm and the change of the arm number. In addition, the results of the three-dimensional simulation also give the warped structure of the spiral galaxies, and shows that the disc surface of the warped galaxy, like a spinning coins on the table, exists a whole overturning movement. Through the analysis, it can be concluded that the background environment of the spiral galaxies have a large-scale rotation, and both the formation and evolution of hurricane-like spiral galaxies are driven by this background rotation.

astro-ph.GA↗

arXiv2016

Asymptotic stability of strong contact discontinuity for full compressible Navier-Stokes equations with initial boundary value problem

This paper is concerned with Dirichlet problem $u(0,t)=0$, $θ(0,t)=θ_-$ for one-dimensional full compressible Navier-Stokes equations in the half space $\R_+=(0,+\infty)$. Because the boundary decay rate is hard to control, stability of contact discontinuity result is very difficult. In this paper, we raise the decay rate and establish that for a certain class of large perturbation, the asymptotic stability result is contact discontinuity. Also, we ask the strength of contact discontinuity not small. The proofs are given by the elementary energy method.

math.AP↗

arXiv2014

The stability of strong viscous contact discontinuity to a free boundary problem for compressible Navier-Stokes equations

This paper is concerned with nonlinear stability of viscous contact discontinuity to a free boundary problem for the one-dimensional full compressible Navier-Stokes equations in half space $[0,\infty)$. For the case when the local stability of the contact discontinuities was first studied by [1],later generalized by [2], local stability of weak viscous contact discontinuity is well-established by [4-8], but for the global stability of the impermeable gas with big oscillation ends $(|θ_+-θ_-|>1) $, fewer results have been obtained excluding zero dissipation [9] or $γ\to 1$ gas see [10]. Our main purpose is to deduce the corresponding nonlinear stability result with the two different ends to temperature by exploiting the elementary energy method. As a first step towards this goal, we will show in this paper that with a certain class of big perturbation which can allow $|θ_--θ_+|>1$, the global stability result holds.

math.AP↗