The Cyprus Institute

About: The Cyprus Institute is a other organization based out in Nicosia, Cyprus. It is known for research contribution in the topics: Aerosol & Environmental science. The organization has 418 authors who have published 1252 publications receiving 32586 citations.

GRAVITATE: geometric and semantic matching for cultural heritage artefacts

Abstract: The GRAVITATE project is developing techniques that bring together geometric and semantic data analysis to provide a new and more effective method of re-associating, reassembling or reunifying cultural objects that have been broken or dispersed over time. The project is driven by the needs of archaeological institutes, and the techniques are exemplified by their application to a collection of several hundred 3D-scanned fragments of large-scale terracotta statues from Salamis, Cyprus. The integration of geometrical feature extraction and matching with semantic annotation and matching into a single decision support platform will lead to more accurate reconstructions of artefacts and greater insights into history. In this paper we describe the project and its objectives, then we describe the progress made to date towards achieving those objectives: describing the datasets, requirements and analysing the state of the art. We follow this with an overview of the architecture of the integrated decision support platform and the first realisation of the user dashboard. The paper concludes with a description of the continuing work being undertaken to deliver a workable system to cultural heritage curators and researchers.

Gaze-to-Object Mapping during Visual Search in 3D Virtual Environments

Abstract: Stimuli obtained from highly dynamic 3D virtual environments and synchronous eye-tracking data are commonly used by algorithms that strive to correlate gaze to scene objects, a process referred to as gaze-to-object mapping (GTOM). We propose to address this problem with a probabilistic approach using Bayesian inference. The desired result of the inference is a predicted probability density function (PDF) specifying for each object in the scene a probability to be attended by the user. To evaluate the quality of a predicted attention PDF, we present a methodology to assess the information value (i.e., likelihood) in the predictions of different approaches that can be used to infer object attention. To this end, we propose an experiment based on a visual search task, which allows us to determine the object of attention at a certain point in time under controlled conditions. We perform this experiment with a wide range of static and dynamic visual scenes to obtain a ground-truth evaluation dataset, allowing us to assess GTOM techniques in a set of 30 particularly challenging cases.

Ruling Out the Massless Up-Quark Solution to the Strong C P Problem by Computing the Topological Mass Contribution with Lattice QCD

Abstract: The infamous strong $CP$ problem in particle physics can in principle be solved by a massless up quark. In particular, it was hypothesized that topological effects could substantially contribute to the observed nonzero up-quark mass without reintroducing $CP$ violation. Alternatively to previous work using fits to chiral perturbation theory, in this Letter, we bound the strength of the topological mass contribution with direct lattice QCD simulations, by computing the dependence of the pion mass on the dynamical strange-quark mass. We find that the size of the topological mass contribution is inconsistent with the massless up-quark solution to the strong $CP$ problem.

Advanced procedures for skull sex estimation using sexually dimorphic morphometric features.

Abstract: This paper introduces an automated method for estimating sex from cranial sex diagnostic traits by extracting and evaluating specialized morphometric features from the glabella, the supraorbital ridge, the occipital protuberance, and the mastoid process. The proposed method was developed and evaluated using two European population samples, a Czech sample comprising 170 crania reconstructed from anonymized CT scans and a Greek sample of 156 crania from the Athens Collection. It is based on a fully automatic algorithm applied on 3D models for extracting sex diagnostic morphometric features which are further processed by computer vision and machine learning algorithms. Classification accuracy was evaluated in a population specific and a population generic 2-way cross-validation scheme. Population-specific accuracy for individual morphometric features ranged from 78.5 to 96.7%, whereas population generic correct classification ranged from 71.7 to 90.8%. Combining all sex diagnostic traits in multi-feature sex estimation yielded correct classification performance in excess of 91% for the entire sample, whereas the sex of about three fourths of the sample could be determined with 100% accuracy according to posterior probability estimates. The proposed method provides an efficient and reliable way to estimate sex from cranial remains, and it offers significant advantages over existing methods. The proposed method can be readily implemented with the skullanalyzer computer program and the estimate_sex.m GNU Octave function, which are freely available under a suitable license.