afspm: A Framework for Manufacturer-Agnostic Automation in Scanning Probe Microscopy

• URL: http://arxiv.org/abs/2509.00113v1
• Date: Thu, 28 Aug 2025 14:51:09 GMT
• Title: afspm: A Framework for Manufacturer-Agnostic Automation in Scanning Probe Microscopy
• Authors: Nicholas J. Sullivan, Julio J. Valdés, Kirk H. Bevan, Peter Grutter,
• Abstract summary: We propose an automation framework for scanning probe microscopy (SPM)<n>Our framework defines generic control and data structure schemas which are passed among independent software processes (components)<n>Our mediation logic limits access to the SPM to a single component at a time, with a simple override mechanism in order to correct detected experiment problems.
• Score: 0.4999814847776097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scanning probe microscopy (SPM) is a valuable technique by which one can investigate the physical characteristics of the surfaces of materials. However, its widespread use is hampered by the time-consuming nature of running an experiment and the significant domain knowledge required. Recent studies have shown the value of multiple forms of automation in improving this, but their use is limited due to the difficulty of integrating them with SPMs other than the one it was developed for. With this in mind, we propose an automation framework for SPMs aimed toward facilitating code sharing and reusability of developed components. Our framework defines generic control and data structure schemas which are passed among independent software processes (components), with the final SPM commands sent after passing through an SPM-specific translator. This approach permits multi-language support and allows for experimental components to be decoupled among multiple computers. Our mediation logic limits access to the SPM to a single component at a time, with a simple override mechanism in order to correct detected experiment problems. To validate our proposal, we integrated and tested it with two SPMs from separate manufacturers, and ran an experiment involving a thermal drift correction component.

Related papers

• PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation [16.043307024789062]
  PGP-SAM is a prototype-based few-shot tuning approach that uses limited samples to replace tedious manual prompts.<n>Our key idea is to leverage inter- and intra-class prototypes to capture class-specific knowledge and relationships.<n>Experiments on a public multi-organ dataset and a private ventricle dataset demonstrate that PGP-SAM achieves superior mean Dice scores compared with existing prompt-free SAM variants.
  arXiv  Detail & Related papers  (2025-01-12T02:57:04Z)
• PCoTTA: Continual Test-Time Adaptation for Multi-Task Point Cloud Understanding [40.42904797189929]
  We present PCoTTA, an innovative framework for Continual Test-Time Adaptation (CoTTA) in multi-task point cloud understanding. Our PCoTTA involves three key components: automatic prototype mixture (APM), Gaussian Splatted feature shifting (GSFS), and contrastive prototype repulsion (CPR) CPR is proposed to pull the nearest learnable prototype close to the testing feature and push it away from other prototypes, making each prototype distinguishable during the adaptation.
  arXiv  Detail & Related papers  (2024-11-01T14:41:36Z)
• Mixture-of-Noises Enhanced Forgery-Aware Predictor for Multi-Face Manipulation Detection and Localization [52.87635234206178]
  This paper proposes a new framework, namely MoNFAP, specifically tailored for multi-face manipulation detection and localization. The framework incorporates two novel modules: the Forgery-aware Unified Predictor (FUP) Module and the Mixture-of-Noises Module (MNM)
  arXiv  Detail & Related papers  (2024-08-05T08:35:59Z)
• MacLaSa: Multi-Aspect Controllable Text Generation via Efficient Sampling from Compact Latent Space [110.85888003111653]
  Multi-aspect controllable text generation aims to generate fluent sentences that possess multiple desired attributes simultaneously. We introduce a novel approach for multi-aspect control, namely MacLaSa, that estimates compact latent space for multiple aspects. We show that MacLaSa outperforms several strong baselines on attribute relevance and textual quality while maintaining a high inference speed.
  arXiv  Detail & Related papers  (2023-05-22T07:30:35Z)
• Hard-normal Example-aware Template Mutual Matching for Industrial Anomaly Detection [78.734927709231]
  Anomaly detectors are widely used in industrial manufacturing to detect and localize unknown defects in query images.<n>These detectors are trained on anomaly-free samples and have successfully distinguished anomalies from most normal samples.<n>However, hard-normal examples are scattered and far apart from most normal samples, and thus they are often mistaken for anomalies by existing methods.
  arXiv  Detail & Related papers  (2023-03-28T17:54:56Z)
• UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models [92.43617471204963]
  Diffusion probabilistic models (DPMs) have demonstrated a very promising ability in high-resolution image synthesis. We develop a unified corrector (UniC) that can be applied after any existing DPM sampler to increase the order of accuracy. We propose a unified predictor-corrector framework called UniPC for the fast sampling of DPMs.
  arXiv  Detail & Related papers  (2023-02-09T18:59:48Z)
• Towards Semantic Communication Protocols: A Probabilistic Logic Perspective [69.68769942563812]
  We propose a semantic protocol model (SPM) constructed by transforming an NPM into an interpretable symbolic graph written in the probabilistic logic programming language (ProbLog) By leveraging its interpretability and memory-efficiency, we demonstrate several applications such as SPM reconfiguration for collision-avoidance.
  arXiv  Detail & Related papers  (2022-07-08T14:19:36Z)
• Object Detection Made Simpler by Eliminating Heuristic NMS [70.93004137521946]
  We show a simple NMS-free, end-to-end object detection framework. We attain on par or even improved detection accuracy compared with the original one-stage detector.
  arXiv  Detail & Related papers  (2021-01-28T02:38:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.