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Researcher profile

Adrian Martin

Adrian Martin contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial Intelligenceastro-ph.GAastro-ph.IMComputation and LanguageComputer VisionLogic in Computer ScienceMachine Learning

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Published work

3 published item(s)

preprint2026arXiv

Inflight performance and future improvements for the INtegral Field Ultraviolet Spectrographic Experiment, the first far ultraviolet integral field spectrograph

Integral field spectroscopy allows for spectral mapping of extended sources in a time efficient manner. An integral field unit (IFU) in the ultraviolet on Habitable Worlds Observatory (HWO) could be used to quickly map extended objects like supernova remnants or galaxies and their surroundings, but there are technical challenges to an ultraviolet IFU. The INtegral Field Ultraviolet Spectrographic Experiment (INFUSE), a sounding rocket project, is the first static configuration far ultraviolet integral field spectrograph. INFUSE features an f/16, 0.49m Cassegrain telescope and a 26-element image slicer feeding 26 replica holographic gratings, with spectra imaged by the largest cross-strip microchannel plate detector flown in space. The first launch of INFUSE occurred from White Sands Missile Range on October 29th, 2023, and demonstrated spectral multiplexing, successfully detecting ionizing gas emission in the XA region of the Cygnus Loop. INFUSE will launch again in fall 2025 to observe NGC 2366, a local analog for Green Pea type galaxies, with several enhancements including a xenon-enhanced lithium fluoride + aluminum coated grating, testing the leading flight coating for HWO for the first time. The INFUSE IFU is designed as a pathfinder for a potential IFU mode on HWO, enabling rapid 3D spectroscopy of extended sources.

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preprint2026arXiv

Ontology for Policing: Conceptual Knowledge Learning for Semantic Understanding and Reasoning in Law Enforcement Reports

Law enforcement reports contain structured fields and written narratives. However, many incident facts that are needed for review, police training, and investigations are in natural language and require manual reading. We propose a framework using symbolic methods for converting narratives into evidence-linked facts. Our objective is to measure the value of narratives to recover incident details only from the unstructured text and build temporal graphs with time cues and domain axioms. We achieve this by redacting personal identifiers, semantic parsing, predicate mapping to ontology, and reasoning. We evaluate the symbolic approach on 450 property crime reports and a short human review. Of the extracted events from the system, 54.1% had a confidence score of at least 0.80 and 93.7% were mapped through the PropBank--VerbNet--WordNet semantic path. 100% agreement was reached on incident initiation, stolen items, and temporal cues and lower agreement for forced entry interpretation.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Visual Timelines of Police Encounters in Body-Worn Camera Footage: Operational Context and Activity Cataloging for Training and Analysis in OpenBWC

Law enforcement agencies are accumulating vast amounts of body-worn camera (BWC) footage. However, this remains operationally opaque. That is, analysts and trainers still have to invest considerable time watching full-length videos to pinpoint the start of key encounters and identify the points where activity shifts to something more physically intense. We present an approach to process BWC video into a time-aligned sequence of fixed-length 10-second windows, processed and labeled using a privacy-conscious protocol. Each window is labeled with two dimensions of information: (i) the operational context of the window and (ii) the level of motion intensity within the window, with low-evidence labels for windows for which insufficient evidence exists due to darkness, blur or occlusion. We train models to classify windows based on these two axes using frames sampled from each window encoded using CLIP model and aggregated into a window-level representation. We extract dense optical flow statistics for each window to capture motion intensity. On test windows the best context model achieves 78.75% accuracy, and the best-accuracy activity model achieves 88.33%. We also included integrity audits to show the results and how the visual timeline representations support faster incident review and make the officer training workflow more practical.

0 citations0 reviewsNo code linkedOpen access