Han Gu | Searcharxiv

arXiv2026

Physics-Aligned Deep Learning Enables SERS Resolving and Sequencing of Dynamic Single-Molecule DNA Oligomers in Plasmonic Nanocavity

Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) captures dynamic molecular behavior with ultrahigh sensitivity, but its biopolymer analysis is hindered by strong spectral heterogeneity, transient hotspot sampling, and background interference. Here, we develop a physics-aligned deep learning framework integrating contrastive attention-based multiple-instance learning (CAMIL), a tri-channel multi-kernel CNN classifier, and trajectory-level transition-guided sequence reconstruction to decode single-molecule DNA oligomer dynamics in a plasmonic nanocavity. In this work, CAMIL mines informative spectra enriched with chain-embedded nucleotide and dinucleotide signatures from contrastive positive and negative DNA trajectory bags, generating a single-molecule DNA-segment spectral-states library. This library trains a 12-class CNN to assign query time-resolved DNA SM-SERS frames to high-confidence DNA-segment states, enabling trajectory-level analysis of state composition, dwell length, entropy, switching frequency, and transition matrix. The transition matrix is further converted into state-transition-edge evidence for candidate sequence scoring, enabling asymmetric DNA sequences inference from confined stochastic sampling dynamics. This framework transforms SM-SERS heterogeneity into quantitative analytical information, advancing dynamic single-molecule decoding.

physics.optics↗

arXiv2025

FOAM: A General Frequency-Optimized Anti-Overlapping Framework for Overlapping Object Perception

Overlapping object perception aims to decouple the randomly overlapping foreground-background features, extracting foreground features while suppressing background features, which holds significant application value in fields such as security screening and medical auxiliary diagnosis. Despite some research efforts to tackle the challenge of overlapping object perception, most solutions are confined to the spatial domain. Through frequency domain analysis, we observe that the degradation of contours and textures due to the overlapping phenomenon can be intuitively reflected in the magnitude spectrum. Based on this observation, we propose a general Frequency-Optimized Anti-Overlapping Framework (FOAM) to assist the model in extracting more texture and contour information, thereby enhancing the ability for anti-overlapping object perception. Specifically, we design the Frequency Spatial Transformer Block (FSTB), which can simultaneously extract features from both the frequency and spatial domains, helping the network capture more texture features from the foreground. In addition, we introduce the Hierarchical De-Corrupting (HDC) mechanism, which aligns adjacent features in the separately constructed base branch and corruption branch using a specially designed consistent loss during the training phase. This mechanism suppresses the response to irrelevant background features of FSTBs, thereby improving the perception of foreground contour. We conduct extensive experiments to validate the effectiveness and generalization of the proposed FOAM, which further improves the accuracy of state-of-the-art models on four datasets, specifically for the three overlapping object perception tasks: Prohibited Item Detection, Prohibited Item Segmentation, and Pneumonia Detection. The code will be open source once the paper is accepted.

cs.CV↗