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Athena Research Center

Academic institutioneurope · gr
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Research library155linked papers
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Selected work

Representative Papers

ART3mis: Ray-Based Textual Annotation on 3D Cultural Objects

Feb 13, 2026

Beyond simplistic 3D visualisations, archaeologists, as well as cultural heritage experts and practitioners, need applications with advanced functionalities. Such as the annotation and attachment of metadata onto particular regions of the 3D digital objects. Various approaches have been presented to tackle this challenge, most of which achieve excellent results in the domain of their application. However, they are often confined to that specific domain and particular problem. In this paper, we present ART3mis - a general-purpose, user-friendly, interactive textual annotation tool for 3D objects. Primarily attuned to aid cultural heritage conservators, restorers and curators with no technical skills in 3D imaging and graphics, the tool allows for the easy handling, segmenting and annotating of 3D digital replicas of artefacts. ART3mis applies a user-driven, direct-on-surface approach. It can handle detailed 3D cultural objects in real-time and store textual annotations for multiple complex regions in JSON data format.

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Learning-Augmented Robust Algorithmic Recourse

Oct 02, 2024arXiv.org

Dynamic updates of machine learning models frequently invalidate historical counterfactual explanations (recourse), undermining user actionability. To address this, we propose a Learning-Augmented Robust Explainable Decision Framework—the first to integrate learning-augmentation into recourse design. Our method forecasts model evolution trends to jointly optimize consistency (minimizing adjustment cost under accurate predictions) and robustness (bounding worst-case cost increase under prediction errors). We formally characterize the consistency–robustness trade-off, derive theoretical bounds linking prediction error to cost inflation, and unify robust optimization, online learning-based calibration, and minimum-cost counterfactual generation into a two-stage algorithm with provable performance guarantees. Experiments show that when prediction accuracy exceeds 80%, our framework reduces average recourse cost by 37% compared to baselines, while worst-case cost growth remains tightly aligned with theoretical upper bounds—significantly outperforming purely robust approaches.

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Universally truthful mechanisms for scheduling

Sep 11, 2026

研究了在n个不相关机器上调度m个作业的问题，证明了任何具有离散支持的概率分布的普遍真实随机机制的预期近似比下界，并提出了一种达到n/2+o(n)近似比的机制。

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Latest Papers

Universally truthful mechanisms for scheduling

Sep 11, 2026

研究了在n个不相关机器上调度m个作业的问题，证明了任何具有离散支持的概率分布的普遍真实随机机制的预期近似比下界，并提出了一种达到n/2+o(n)近似比的机制。

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