Doped ceria nanostructures for the oxidation of pollutants: investigations into the role of defect sites

L'abstract è presente nell'allegato / the abstract is in the attachmen

Fully automated deep learning powered calcium scoring in patients undergoing myocardial perfusion imaging

BACKGROUND To assess the accuracy of fully automated deep learning (DL) based coronary artery calcium scoring (CACS) from non-contrast computed tomography (CT) as acquired for attenuation correction (AC) of cardiac single-photon-emission computed tomography myocardial perfusion imaging (SPECT-MPI). METHODS AND RESULTS Patients were enrolled in this study as part of a larger prospective study (NCT03637231). In this study, 56 Patients who underwent cardiac SPECT-MPI due to suspected coronary artery disease (CAD) were prospectively enrolled. All patients underwent non-contrast CT for AC of SPECT-MPI twice. CACS was manually assessed (serving as standard of reference) on both CT datasets (n = 112) and by a cloud-based DL tool. The agreement in CAC scores and CAC score risk categories was quantified. For the 112 scans included in the analysis, interscore agreement between the CAC scores of the standard of reference and the DL tool was 0.986. The agreement in risk categories was 0.977 with a reclassification rate of 3.6%. Heart rate, image noise, body mass index (BMI), and scan did not significantly impact (p=0.09 - p=0.76) absolute percentage difference in CAC scores. CONCLUSION A DL tool enables a fully automated and accurate estimation of CAC scores in patients undergoing non-contrast CT for AC of SPECT-MPI

Intent-based Deep Reinforcement Learning for Multi-agent Informative Path Planning

In multi-agent informative path planning (MAIPP), agents must collectively construct a global belief map of an underlying distribution of interest (e.g., gas concentration, light intensity, or pollution levels) over a given domain, based on measurements taken along their trajectory. They must frequently replan their path to balance the exploration of new areas with the exploitation of known high-interest areas, to maximize information gain within a predefined budget. Traditional approaches rely on reactive path planning conditioned on other agents' predicted future actions. However, as the belief is continuously updated, the predicted actions may not match the executed actions, introducing noise and reducing performance. We propose a decentralized, deep reinforcement learning (DRL) approach using an attention-based neural network, where agents optimize long-term individual and cooperative objectives by sharing their intent, represented as a distribution of medium-/long-term future positions obtained from their own policy. Intent sharing enables agents to learn to claim or avoid broader areas, while the use of attention mechanisms allows them to identify useful portions of imperfect predictions, maximizing cooperation even based on imperfect information. Our experiments compare the performance of our approach, its variants, and high-quality baselines across various MAIPP scenarios. We finally demonstrate the effectiveness of our approach under limited communication ranges, towards deployments under realistic communication constraints.Comment: \c{opyright} 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

À la recherche d’une « nouvelle patrie ». La collection Új Pátria de Hongrie

En Hongrie, la maison de disques Fonó s’est lancée dans un vaste travail de collection de musiques traditionnelles de Transylvanie. Echo lointain des travaux de Béla Bartók, 18 CD sont parus à ce jour. Une petite légende avant d’arpenter les chemins poussiéreux de Transylvanie en quête d’orchestres de villages éparpillés entre torrents et montagnes. Piéton cabochard, tireur d’archet, bavard impénitent, mais aussi professeur au Trinity College de Dublin et président de la renommée Gypsy Lore S..

Determination of the size, mass, and density of "exomoons" from photometric transit timing variations

Precise photometric measurements of the upcoming space missions allow the size, mass, and density of satellites of exoplanets to be determined. Here we present such an analysis using the photometric transit timing variation ($TTV_p$). We examined the light curve effects of both the transiting planet and its satellite. We define the photometric central time of the transit that is equivalent to the transit of a fixed photocenter. This point orbits the barycenter, and leads to the photometric transit timing variations. The exact value of $TTV_p$ depends on the ratio of the density, the mass, and the size of the satellite and the planet. Since two of those parameters are independent, a reliable estimation of the density ratio leads to an estimation of the size and the mass of the exomoon. Upper estimations of the parameters are possible in the case when an upper limit of $TTV_p$ is known. In case the density ratio cannot be estimated reliably, we propose an approximation with assuming equal densities. The presented photocenter $TTV_p$ analysis predicts the size of the satellite better than the mass. We simulated transits of the Earth-Moon system in front of the Sun. The estimated size and mass of the Moon are 0.020 Earth-mass and 0.274 Earth-size if equal densities are assumed. This result is comparable to the real values within a factor of 2. If we include the real density ratio (about 0.6), the results are 0.010 Earth-Mass and 0.253 Earth-size, which agree with the real values within 20%.Comment: 6 pages, 5 figures, to appear in Astronomy and Astrophysic