Social Mapping of Human-Populated Environments by Implicit Function Learning

International audienceWith robots technology shifting towards entering human populated environments, the need for augmented perceptual and planning robotic skills emerges that complement to human presence. In this integration, perception and adaptation to the implicit human social conventions plays a fundamental role. Toward this goal, we propose a novel framework that can model context-dependent human spatial interactions, encoded in the form of a social map. The core idea of our approach resides in modelling human personal spaces as non-linearly scaled probability functions within the robotic state space and devise the structure and shape of a social map by solving a learning problem in kernel space. The social borders are subsequently obtained as isocontours of the learned implicit function that can realistically model arbitrarily complex social interactions of varying shape and size. We present our experiments using a rich dataset of human interactions, demonstrating the feasibility and utility of the proposed approach and promoting its application to social mapping of human-populated environments

Spatiotemporal graph queries on geographic databases under a conceptual abstraction scale

Visual queries assist non-expert users to extract information from spatial databases in an intuitive and natural approach, making Geographic information systems comprehensive and efficient for a wide range of applications. A common visual means of querying takes the form of drawings or graphs, under which many spatial ambiguity and translation errors rise. In this study, common query attributes extracted from user graphs such as spatial topology, size, cardinality, and proximity are regarded under a conceptual moderation scheme. Thus, the system/user may concentrate on various conceptual combinations of information. Furthermore, time is incorporated to support spatiotemporal queries for changing scenes and moving objects. Arbitrary, relative, and absolute scaling is possible according to the data-set and application at hand. The theoretic approach is implemented under a prototype user interface system, called ShapeController. Under this prototype, a user may extract scene-based relations in an automatically inferred fashion, or include single object-oriented relations when all possible relations seem redundant. Finally, a natural language description of the query is extracted upon which the user may select the desired query relations. Experimentation on a spatial database demonstrates the concepts of predefined draw objects, scaling relaxation, conceptual abstraction, and scene, object- and textual-oriented transitions that promote query expressiveness and restrain ambiguities.peer-reviewe

Social Mapping of Human-Populated Environments by Implicit Function Learning

International audienceWith robots technology shifting towards entering human populated environments, the need for augmented perceptual and planning robotic skills emerges that complement to human presence. In this integration, perception and adaptation to the implicit human social conventions plays a fundamental role. Toward this goal, we propose a novel framework that can model context-dependent human spatial interactions, encoded in the form of a social map. The core idea of our approach resides in modelling human personal spaces as non-linearly scaled probability functions within the robotic state space and devise the structure and shape of a social map by solving a learning problem in kernel space. The social borders are subsequently obtained as isocontours of the learned implicit function that can realistically model arbitrarily complex social interactions of varying shape and size. We present our experiments using a rich dataset of human interactions, demonstrating the feasibility and utility of the proposed approach and promoting its application to social mapping of human-populated environments

Scalable monitoring for multiple virtualized infrastructures for 5G services

This paper presents a high level architecture and functionality details of the monitoring framework that has been implemented and integrated within the SONATA project, in order to support the management of 5G services under the Software Defined Networking / Network Function Virtualization (SDN/NFV) paradigm. The innovative framework, extending the functionality of Prometheus.io, is unique in its support for multiple Points of Presence (PoP), its extensibility using Websockets, and its availability as opensource

Can athletes be tough yet compassionate to themselves? Practical implications for NCAA mental health best practice no. 4

Recent tragic events and data from official NCAA reports suggest student-athletes’ wellbeing is compromised by symptoms of mental health (MH) disorders. Self-compassion (SC) and mental toughness (MT) are two psychological constructs that have been shown effective against stressors associated with sports. The purpose of this study was to investigate SC, MT, and MH in a NCAA environment for the first time and provide practical suggestions for MH best practice No.4. In total, 542 student-athletes participated across Divisions (Mage = 19.84, SD = 1.7). Data were collected through Mental Toughness Index, Self-Compassion Scale, and Mental Health Continuum–Short Form. MT, SC (including mindfulness), and MH were positively correlated. Males scored higher than females on all three scales. No differences were found between divisions. SC partially mediated the MT-MH relationship, but moderation was not significant. Working towards NCAA MH best practice should include training athletes in both MT and SC skills (via mindfulness)

Interfacility transfers in a non-trauma system setting: an assessment of the Greek reality

<p>Abstract</p> <p>Background</p> <p>Quality assessment of any trauma system involves the evaluation of the transferring patterns. This study aims to assess interfacility transfers in the absence of a formal trauma system setting and to estimate the benefits from implementing a more organized structure.</p> <p>Methods</p> <p>The 'Report of the Epidemiology and Management of Trauma in Greece' is a one year project of trauma patient reporting throughout the country. It provided data concerning the patterns of interfacility transfers. We compared the transferred patient group to the non transferred patient group. Information reviewed included patient and injury characteristics, need for an operation, Intensive Care Unit (ICU) admittance and mortality. Analysis employed descriptive statistics and Chi-square test. Interfacility transfers were then assessed according to each health care facility's availability of five requirements; Computed Tomography scanner, ICU, neurosurgeon, orthopedic and vascular surgeon.</p> <p>Results</p> <p>Data on 8,524 patients were analyzed; 86.3% were treated at the same facility, whereas 13.7% were transferred. Transferred patients tended to be younger, male, and more severely injured than non transferred patients. Moreover, they were admitted to ICU more often, had a higher mortality rate but were less operated on compared to non transferred patients. The 34.3% of transfers was from facilities with none of the five requirements, whereas the 12.4% was from those with one requirement. Low level facilities, with up to three requirements transferred 43.2% of their transfer volume to units of equal resources.</p> <p>Conclusion</p> <p>Trauma management in Greece results in a high number of transfers. Patients are frequently transferred between low level facilities. Better coordination could lead to improved outcomes and less cost.</p

Hydrotherapy (Project Hydriades)

Natural resources are being used for the maintenance of health. According to the Law 3498/2006 of the Greek Parliament the natural health spas must be validated for their therapeutic properties. The Association of Municipalities and Communities of Health Springs of Greece signed a contract with the Research Committee of the Aristotle University of Thessaloniki, Greece, in order to conduct the research programme: ‘Study for the documentation of the therapeutic properties of the thermomineral waters’. The main aim of the project is: (1) the study of biological and therapeutic parameters of the natural health sources, (2) the identification of the indications and contraindications of hydrotherapy. Aims parallel to the main ones have been also set

Convolutional neural networks for hydrothermal vents substratum classification: An introspective study

The increasing availability of seabed images has created new opportunities and challenges for monitoring and better understanding the spatial distribution of fauna and substrata. To date, however, deep-sea substratum classification relies mostly on visual interpretation, which is costly, time-consuming, and prone to human bias or error. Motivated by the success of convolutional neural networks in learning semantically rich representations directly from images, this work investigates the application of state-of-the-art network architectures, originally employed in the classification of non-seabed images, for the task of hydrothermal vent substrata image classification. In assessing their potential, we conduct a study on the generalization, complementarity and human interpretability aspects of those architectures. Specifically, we independently trained deep learning models with the selected architectures using images obtained from three distinct sites within the Lucky-Strike vent field and assessed the models' performances on-site as well as off-site. To investigate complementarity, we evaluated a classification decision committee (CDC) built as an ensemble of networks in which individual predictions were fused through a majority voting scheme. The experimental results demonstrated the suitability of the deep learning models for deep-sea substratum classification, attaining accuracies reaching up to 80% in terms of F1-score. Finally, by further investigating the classification uncertainty computed from the set of individual predictions of the CDC, we describe a semiautomatic framework for human annotation, which prescribes visual inspection of only the images with high uncertainty. Overall, the results demonstrated that high accuracy values of over 90% F1-score can be obtained with the framework, with a small amount of human intervention

Current practices and perceived barriers to tobacco-treatment delivery among healthcare professionals from 15 European countries. The EPACTT Plus project

Introduction: The latest evidence-based Guidelines for Treating Tobacco Dependence highlight the significant role of healthcare professionals in supporting smokers interested to quit. This study aimed to identify the current practices of healthcare professionals in Europe and perceived barriers in delivering tobacco treatment to their patients who smoke. Methods: In the context of EPACTT-Plus, collaborating institutions from 15 countries (Albania, Armenia, Belgium, Italy, France, Georgia, Greece, Kosovo, Romania, North Macedonia, Russia, Serbia, Slovenia, Spain, Ukraine) worked for the development of an accredited eLearning course on Tobacco Treatment Delivery available at http://elearning-ensp.eu/. In total, 444 healthcare professionals from the wider European region successfully completed the course from December 2018 to July 2019. Cross-sectional data were collected online on healthcare professionals' current practices and perceived barriers in introducing tobacco-dependence treatment into their daily clinical life. Results: At registration, 41.2% of the participants reported having asked their patients if they smoked. Advise to quit smoking was offered by 47.1% of the participants, while 29.5% reported offering assistance to their patients who smoked in order to quit. From the total number of participants, 39.9% regarded the lack of patient compliance as a significant barrier. Other key barriers were lack of: interest from the patients (37.4%), healthcare professionals training (33.1%), community resources to refer patients (31.5%), and adequate time during their everyday clinical life (29.7%). Conclusions: The identification of current practices and significant barriers is important to build evidence-based guidelines and training programs (online and/or live) that will improve the performance of healthcare professionals in offering tobacco-dependence treatment for their patients who smoke

Impact of the ENSP eLearning platform on improving knowledge, attitudes and self-efficacy for treating tobacco dependence: An assessment across 15 European countries

Introduction: In 2018, the European Network for Smoking Cessation and Prevention (ENSP) released an update to its Tobacco Treatment Guidelines for healthcare professionals, which was the scientific base for the development of an accredited eLearning curriculum to train healthcare professionals, available in 14 languages. The aim of this study was to evaluate the effectiveness of ENSP eLearning curriculum in increasing healthcare professionals' knowledge, attitudes, self-efficacy (perceived behavioral control) and intentions in delivering tobacco treatment interventions in their daily clinical routines. Methods: We conducted a quasi-experimental pre-post design study with 444 healthcare professionals, invited by 20 collaborating institutions from 15 countries (Albania, Armenia, Belgium, Italy, France, Georgia, Greece, Kosovo, Romania, North Macedonia, Russia, Serbia, Slovenia, Spain, Ukraine), which completed the eLearning course between December 2018 and July 2019. Results: Healthcare professionals' self-reported knowledge improved after the completion of each module of the eLearning program. Increases in healthcare professionals' self-efficacy in delivering tobacco treatment interventions (p<0.001) were also documented. Significant improvements were documented in intentions to address tobacco use as a priority, document tobacco use, offer support, provide brief counselling, give written material, discuss available medication, prescribe medication, schedule dedicated appointment to develop a quit plan, and be persistent in addressing tobacco use with the patients (all p<0.001). Conclusions: An evidence-based digital intervention can be effective in improving knowledge, attitudes, self-efficacy and intentions on future delivery of tobacco-treatment interventions