Vision-Based Production of Personalized Video

In this paper we present a novel vision-based system for the automated production of personalised video souvenirs for visitors in leisure and cultural heritage venues. Visitors are visually identified and tracked through a camera network. The system produces a personalized DVD souvenir at the end of a visitor’s stay allowing visitors to relive their experiences. We analyze how we identify visitors by fusing facial and body features, how we track visitors, how the tracker recovers from failures due to occlusions, as well as how we annotate and compile the final product. Our experiments demonstrate the feasibility of the proposed approach

HARMONIA: strategy of an integrated resilience assessment platform (IRAP) with available tools and geospatial services

The huge amount of the available data nowadays has raised some major challenges which are related to the storage, fusion, structure, streaming and processing of these data. In this paper, we present the development of a holistic framework, entitled HARMONIA, that encompasses State-of-The-Art solutions for the emerging issues related to Climate Change, natural and/or man-made hazards and urban/peri-urban risks. The Horizon 2020 HARMONIA project is developing an Integrated Resilience Assessment Platform (IRAP) which plans to provide targeted services for different groups of end-users. In particular, it will actively support urban decision-makers in strategic decisions and planning and citizens in facing daily effects and risks of Climate Change. Additionally, the platform will be a place to interconnect cities which end up facing similar Climate Change effects. HARMONIA IRAP leverages cuttingedge technologies (i.e., explainable Artificial Intelligence, Data Mining, multi-criteria analysis, dynamic programming) and services (ie., Virtual Machines, Containers) in order to provide solutions considering the complexity and diversity of extreme earth and non-earth data. In addition, this platform includes a Decision Support System providing early-warning feedback and recommendations to the end-users. In this way the HARMONIA IRAP design tends to address these challenges by offering the corresponding dynamic, scalable and robust mechanisms with the aim to provide useful integrated tools for the related users. Datacubes architecture, which is a major part of the IRAP, offers the opportunity to investigate more sophisticated correlations among the data and provide a more tangible representation of the extracted information

Pacemaker Implantation following Heart Transplantation: Analysis of a Nation-Wide Database.

BACKGROUND: The 2018 United-Network-for-Organ-Sharing (UNOS) allocation-system changes resulted in greater recognition of mechanical circulatory support (MCS), leading to more heart transplantations (HTx) in patients with MCS. We aimed to investigate the effect of the new UNOS allocation system on the need for a permanent pacemaker and associated complications following HTx. METHODS: The UNOS Registry was questioned, to identify patients that received HTx in the US between 2000 and 2021. The primary objectives were to identify risk factors for the need for a pacemaker implantation following HTx. RESULTS: 49,529 HTx patients were identified, 1421 (2.9%) requiring a pacemaker post-HTx. Patients who required a pacemaker were older (53.9 ± 11.5 vs. 52.6 ± 12.8 years, p < 0.001), more frequently white (73% vs. 67%; p < 0.001) and less frequently black (18% vs. 20%; p < 0.001). In the pacemaker group, UNOS status 1A (46% vs. 41%; p < 0.001) and 1B (31% vs. 27%; p < 0.001) were more prevalent, and donor age was higher (34.4 ± 12.4 vs. 31.8 ± 11.5 years; p < 0.001). One-year survival was no different between the groups (HR: 1.08; 95% CI: 0.85, 1.37; p = 0.515). An era effect was observed (per year: OR: 0.97; 95% CI: 0.96, 0.98; p = 0.003), while ECMO pre-transplant was associated with lower risk of a pacemaker (OR: 0.41; 95% CI: 0.19, 0.86; p < 0.001). CONCLUSIONS: While associated with various patient and transplant characteristics, pacemaker implantation does not seem to impact one-year survival after HTx. The need for pacemaker implantation was lower in the more recent era and in patients who required ECMO pre-transplant, a finding explained by recent advances in perioperative care

Unsupervised Activity Extraction on Long-Term Video Recordings employing Soft Computing Relations

International audienceIn this work we present a novel approach for activity extraction and knowledge discovery from video employing fuzzy relations. Spatial and temporal properties from detected mobile objects are modeled with fuzzy relations. These can then be aggregated employing typical soft-computing algebra. A clustering algorithm based on the transitive closure calculation of the fuzzy relations allows finding spatio-temporal patterns of activity. We present results obtained on videos corresponding to different sequences of apron monitoring in the Toulouse airport in France

STAMINA: Bioinformatics Platform for Monitoring and Mitigating Pandemic Outbreaks

Data Availability Statement: All data driven applications used the our world in data COVID-19 datasets, complimented by proprietary datasets share by the STAMINA consortium.Copyright © 2022 by the authors. This paper presents the components and integrated outcome of a system that aims to achieve early detection, monitoring and mitigation of pandemic outbreaks. The architecture of the platform aims at providing a number of pandemic-response-related services, on a modular basis, that allows for the easy customization of the platform to address user’s needs per case. This customization is achieved through its ability to deploy only the necessary, loosely coupled services and tools for each case, and by providing a common authentication, data storage and data exchange infrastructure. This way, the platform can provide the necessary services without the burden of additional services that are not of use in the current deployment (e.g., predictive models for pathogens that are not endemic to the deployment area). All the decisions taken for the communication and integration of the tools that compose the platform adhere to this basic principle. The tools presented here as well as their integration is part of the project STAMINA.The paper presented is based on research undertaken as part of the European Commission-funded project STAMINA (Grant Agreement 883441)

Deep neural architectures for prediction in healthcare

This paper presents a novel class of systems assisting diagnosis and personalised assessment of diseases in healthcare. The targeted systems are end-to-end deep neural architectures that are designed (trained and tested) and subsequently used as whole systems, accepting raw input data and producing the desired outputs. Such architectures are state-of-the-art in image analysis and computer vision, speech recognition and language processing. Their application in healthcare for prediction and diagnosis purposes can produce high accuracy results and can be combined with medical knowledge to improve effectiveness, adaptation and transparency of decision making. The paper focuses on neurodegenerative diseases, particularly Parkinson’s, as the development model, by creating a new database and using it for training, evaluating and validating the proposed systems. Experimental results are presented which illustrate the ability of the systems to detect and predict Parkinson’s based on medical imaging information