CORTEXA
← Browse
arxiveess.IVcs.CVcs.LGcs.MMphysics.ins-det2026-06-27

Complete virtual unwrapping and reading of a rolled Herculaneum papyrus

Giorgio Angelotti, Stephen Parsons, Federica Nicolardi, Youssef Nader, Sean Johnson, David Josey, Paul Henderson, Hendrik Schilling, Johannes Rudolph, Forrest McDonald, Elian Rafael Dal Prá, Paul Tafforeau, Alessandro Mirone, Clifford Seth Parker, Jan Paul Posma, Benjamin Kyles, Claudio Vergara, Alessia Lavorante, Rossella Villa, Maria Chiara Robustelli, Marzia D'Angelo, Gianluca Del Mastro, Michael McOsker, Kilian Fleischer, Christy Chapman, Nat Friedman, William Brent Seales

The carbonized papyri from Herculaneum preserve the only large-scale library to survive from classical antiquity, but many unopened rolls remain unread because physical opening risks irreversible damage. X-ray computed microtomography ($μ$CT) and virtual unwrapping offer a non-invasive route to their texts, yet previous work on sealed Herculaneum scrolls has recovered only localized readings or limited surface regions. Here, using high-resolution phase-contrast $μ$CT acquired on the BM18 beamline at the European Synchrotron Radiation Facility (ESRF), together with improved computational unrolling and machine learning, we achieve the complete virtual unwrapping and reading of PHerc. 1667 under explicit coverage and papyrological-review criteria. This makes PHerc. 1667 the first Herculaneum papyrus to be fully digitally unrolled and read for extended scholarly study without physical opening. In PHerc. Paris 4, the optimized scan protocol makes ink directly visible in the tomographic volume, allowing three-dimensional ink segmentation and independent validation of surface-conditioned ink recovery. In PHerc. 139, we recover title and author-attribution evidence identifying the scroll as Philodemus, On Gods, Book 8. These results move virtual unwrapping of the Herculaneum scrolls beyond isolated demonstrations towards a scalable framework for systematic recovery of the still-unopened library.

View free PDFSource page

Related papers

arxivcs.CVcs.LGeess.IV2026-07-24

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement

Mohammadreza Narimani, Vikram Anand, Parastoo Farajpoor

Agricultural field maps are often proprietary, incomplete, or outdated, yet they provide the spatial framework for crop monitoring, production accounting, and land-conversion analysis. This study presents a reproducible workflow for mapping farmland extent and visible boundaries…

View free PDFSource page
arxiveess.IVcs.CVcs.LG2026-07-31

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification

Sebastian Doerrich, Daniel Würtinger, Francesco Di Salvo, Shyam Nandan Rai, Christian Ledig

Adapting deep learning models to profound clinical heterogeneity typically relies on parameter-efficient fine-tuning (PEFT) to avoid the severe overfitting associated with full end-to-end network updates. Although PEFT successfully navigates limited data scenarios, it inherently…

View free PDFSource page
arxivcs.LGcs.AIcs.CV2026-07-31

A Human-Centered Validation of the Explainability-Performance Coefficient

Christian Oliva, Luis F. Lago-Fernández

The rapid adoption of deep learning models in high-risk domains has intensified the need for trustworthy Explainable Artificial Intelligence (XAI). However, objectively evaluating explanation fidelity and aligning XAI metrics with human-centered understanding remain critical open…

View free PDFSource page
arxivcs.CVcs.LGcs.PF2026-07-31

Lightweight Neural Networks for Affordance Segmentation: Enhancement of the Decoder Module

Simone Lugani, Edoardo Ragusa, Rodolfo Zunino, Paolo Gastaldo

The deployment of deep neural networks for visual affordance segmentation on wearable robots poses may prove critical, due to some conflicting aspects of the problem. On one hand, affordance segmentation requires high-level abstraction capabilities, that typically involve large-s…

View free PDFSource page
arxivcs.LGcs.AIcs.CV2026-07-31

SERUM: State Extraction and Refinement for User Modeling

Andy J. Phu, James Mooney, Karin de Langis, Khanh Chi Le, Dongyeop Kang

Agentic assistants capable of proactive, personalized interactions require structured models of user intent and workflow. However, building these models from raw, unstructured screen activity remains an open challenge. We present SERUM, a multi-pass framework that extracts finite…

View free PDFSource page
arxivcs.CVcs.LG2026-07-31

TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners

Mostafa ElAraby, Samer B. Nashed, Liam Paull

The primary challenge of continual learning (CL) systems is to learn new tasks while remaining performant on previously learned tasks. A similarly important though less well-studied aspect of CL systems is their ability to distinguish inputs that are unlikely to come from within…

View free PDFSource page