Data-driven spectroscopic dictionaries and detector-calibrated inference for photon-limited Raman hyperspectral imaging of living cells

Fast and compact temporal convolutional network for non-resonant background removal in broadband CARS.

Comparative experiments demonstrate that the TCN model outperforms state-of-the-art methods including SpecNet, VECTOR, LSTM, Bi-LSTM, GAN, and CNN + GRU in identifying authentic Raman peaks, and significantly reduces computational cost.

Hadamard-encoded compressive principal component regression for rapid wavelength-scanning surface plasmon resonance imaging.

A Spectral-Temporal Decoupling Framework for the High-Fidelity Reconstruction of Low-SNR Transient Absorption Spectra.

Transient absorption spectroscopy (TAS) is a cornerstone for investigating dynamical mechanisms in quantum dots, photovoltaics, and photosynthesis. A primary challenge in the field is the development of high-sensitivity techniques capable of probing species in low-signal regimes, such as single-molecule detection, spat...

High-order spectral deconvolution of complex X-ray distributions using machine learning

This work presents a machine learning-based reconstruction framework that enables rapid spectral inference under realistic detector conditions, without a priori assumptions on spectral shape during inference, and demonstrates robust reconstruction across diverse spectral morphologies and flux levels.

MolDeTr: A Chemistry-Informed Deep Learning Model for Next-Generation Automated Analysis of 1H NMR Spectra

MolDeTr addresses the spectrum-conditioned inverse problem and extracts spin-system parameters directly from measured 1D 1H NMR spectra, thereby substantially improving chemical-shift prediction precision by one to 2 orders of magnitude compared to existing structure-conditioned approaches.

Adaptive mixture-of-experts denoising for deep learning of optical ellipsometry spectra

Spectral noise limits the reliability of deep-learning ellipsometry (DLE), particularly under short measurement times required for high-throughput materials characterization. Here, we propose an adaptive denoising framework that integrates a Mixture-of-Experts (MoE) module into a U-Net architecture for spectroscopic el...