arXiv:2606. 02434v1 Announce Type: new Abstract: Precise parametric control over circuit geometry is essential for semiconductor inspection, yet obtaining sufficient real training data remains costly.
arXiv:2609.36639v1 Announce Type: new Abstract: Scanning Tunneling Microscopy (STM) is a widely used tool for characterizing surfaces of materials at the atomic scale, playing a crucial role in disco...
arXiv:2606. 09419v1 Announce Type: cross Abstract: Artificial intelligence is rapidly advancing materials characterization, yet most applications in electron microscopy rely solely on image contrast, overlooking the chemical and experimental context that shapes image formation.
SciFusion is a multimodal framework that uses a latent diffusion backbone to generate unified 3D scientific images. It introduces a compositional conditioning scheme that separates each modality into domain, object, and contrast representations, allowing knowledge transfer and zero‑shot synthesis of unseen compositions. The model also incorporates physics‑informed objectives specific to MRI, CT, and electron microscopy to ensure generated images respect modality‑dependent image formation processes, and demonstrates improved fidelity and cross‑domain generalization across 15 imaging modalities.
Existing 3D generative approaches are typically developed for individual imaging modalities, requiring substantial modality-specific data and limiting their ability to exploit shared structure across...
A central premise of autonomous scientific imaging is that smarter navigation, whether Bayesian, RL-based, or otherwise adaptive, is the principal lever for sample-efficient acquisition. We present evidence to the contrary in scanning transmission electron microscopy (STEM), an atomic-resolution imaging modality whose every measurement deposits damaging electron dose.
The paper introduces D3S2, a diffusion‑guided dataset distillation framework tailored for semantic segmentation. It tackles long‑tailed class imbalance, pixel‑wise alignment, and high computational cost by first selecting class‑balanced masks and then synthesizing images with a pretrained diffusion model conditioned on those masks. Guided diffusion sampling further refines the data with segmentation‑consistency and class‑wise feature matching losses, achieving superior performance at a 1% compression rate on ADE20K and COCO‑Stuff.
arXiv:2608. 03218v1 Announce Type: cross Abstract: Dataset distillation compresses a large training set into a compact synthetic set while retaining its downstream utility.
The paper introduces a spatial-frequency learning framework for enhancing Scanning Transmission Electron Microscopy (STEM) images. It employs a spatial-frequency fusion network that separately enhances features in the spatial and frequency domains before fusing them, leveraging periodic atomic arrangements in the frequency domain and local atomic details in the spatial domain. Additionally, the authors present a STEM noise calibration method to synthesize realistic images, creating a diverse dataset that improves performance on synthetic and real advanced material images, including better atomic localization accuracy.
arXiv:2606. 29592v1 Announce Type: new Abstract: A central premise of autonomous scientific imaging is that smarter navigation, whether Bayesian, RL-based, or otherwise adaptive, is the principal lever for sample-efficient acquisition.
Scanning Transmission Electron Microscopy (STEM) enables the observation of atomic arrangements at sub-angstrom resolution and plays a key role in the discovery and characterization of advanced materi...
arXiv:2607. 19332v1 Announce Type: new Abstract: Generative models have undergone many generations of evolution, from VAEs/GANs to diffusion/flow matching.