Accelerating Reinforcement Learning with Prioritized Experience Replay for Maze Game

In this paper we implemented two ways of improving the performance of reinforcement learning algorithms. We proposed a new equation to prioritize transition samples to improve model accuracy, and by deploying a generalized solver of randomly-generated two-dimensional mazes on a distributed computing platform, our dual-network model is available to others for further research and development. Reinforcement Learning is concerned with identifying the optimal sequence of actions for an agent to take in order to reach an objective to achieve the highest score in the future. Complex situations can lead to computational challenges in terms of both finding the best answer and the training time required to do so. Our prioritization algorithm increased model accuracy by 7% versus a baseline model with no prioritization, and using five workers on the RAY platform using RLlib achieved a 4.5X acceleration in training time versus using one worker

Ambient Assisted Living Systems - The Conflicts between Technology, Acceptance, Ethics and Privacy

Installing and using AAL Smart Home-systems in the homes of older people not only offers a tremendous potential for increasing safety and quality of life but may also evoke reluctance and anxiety. Will such a system become a "Big Brother" watching the steps and the behaviour of the inhabitants and betray them to their outside world? In several field-trials of an AAL Smart Home-system with inhabitants of senior residences we were able to learn about the issues concerning acceptance, ethics and privacy when senior citizens and their care persons are confronted with this kind of technology for the first time

Making video communication mobile by using a small humanoid social assistive robot

There is a high need among older persons to maintain their social contacts and to stay involved in social life. In this area of social communication ICT and assistive technology can bring a significant support provided that the actual needs and preferences of the user groups are actually met. The paper describes an innovative solution consisting of a mobile video communication facility using a LED projector which is integrated in a social assistive robot system developed in the framework of the KSERA project

Advances in Engine Test Capabilities at the NASA Glenn Research Center's Propulsion Systems Laboratory

The Propulsion Systems Laboratory at the National Aeronautics and Space Administration (NASA) Glenn Research Center is one of the premier U.S. facilities for research on advanced aeropropulsion systems. The facility can simulate a wide range of altitude and Mach number conditions while supplying the aeropropulsion system with all the support services necessary to operate at those conditions. Test data are recorded on a combination of steady-state and highspeed data-acquisition systems. Recently a number of upgrades were made to the facility to meet demanding new requirements for the latest aeropropulsion concepts and to improve operational efficiency. Improvements were made to data-acquisition systems, facility and engine-control systems, test-condition simulation systems, video capture and display capabilities, and personnel training procedures. This paper discusses the facility s capabilities, recent upgrades, and planned future improvements

T1 based oxygen-enhanced MRI in tumours; a scoping review of current research

Objective: Oxygen-enhanced MRI (OE-MRI) or tissue oxygen-level dependent (TOLD) MRI is an imaging technique under investigation for its ability to quantify and map oxygen distributions within tumours. The aim of this study was to identify and characterise the research into OE-MRI for characterising hypoxia in solid tumours. Methods: A scoping review of published literature was performed on the PubMed and Web of Science databases for articles published before 27th May 2022. Studies imaging solid tumours using proton-MRI to measure oxygen induced T1/R1 relaxation time/rate changes were included. Grey literature was searched from conference abstracts and active clinical trials. Results: 49 unique records met the inclusion criteria consisting of 34 journal articles and 15 conference abstracts. The majority of articles were pre-clinical studies (31 articles) with 15 human only studies. Pre-clinical studies in a range of tumour types demonstrated consistent correlation of OE-MRI with alternative hypoxia measurements. No clear consensus on optimal acquisition technique or analysis methodology was found. No prospective, adequately powered, multicentre clinical studies relating OE-MRI hypoxia markers to patient outcomes were identified. Conclusions: There is good preclinical evidence of the utility of OE-MRI in tumour hypoxia assessment however there are significant gaps in clinical research that need to be addressed to develop OE-MRI into a clinically applicable tumour hypoxia imaging technique. Advances in knowledge: The evidence base of OE-MRI in tumour hypoxia assessment is presented along with a summary of the research gaps to be addressed to transform OE-MRI derived parameters into tumour hypoxia biomarkers

Comprehensive overview of the structure and regulation of the glucocorticoid receptor

Glucocorticoids are among the most prescribed drugs worldwide for the treatment of numerous immune and inflammatory disorders. They exert their actions by binding to the glucocorticoid receptor (GR), a member of the nuclear receptor superfamily. There are several GR isoforms resulting from alternative RNA splicing and translation initiation of the GR transcript. Additionally, these isoforms are all subject to several transcriptional, post-transcriptional, and post-translational modifications, all of which affect the protein's stability and/or function. In this review, we summarize recent knowledge on the distinct GR isoforms and the processes that generate them. We also review the importance of all known transcriptional, post-transcriptional, and post-translational modifications, including the regulation of GR by microRNAs. Moreover, we discuss the crucial role of the putative GR-bound DNA sequence as an allosteric ligand influencing GR structure and activity. Finally, we describe how the differential composition and distinct regulation at multiple levels of different GR species could account for the wide and diverse effects of glucocorticoids

Notes for genera: basal clades of Fungi (including Aphelidiomycota, Basidiobolomycota, Blastocladiomycota, Calcarisporiellomycota, Caulochytriomycota, Chytridiomycota, Entomophthoromycota, Glomeromycota, Kickxellomycota, Monoblepharomycota, Mortierellomycota, Mucoromycota, Neocallimastigomycota, Olpidiomycota, Rozellomycota and Zoopagomycota)

Compared to the higher fungi (Dikarya), taxonomic and evolutionary studies on the basal clades of fungi are fewer in number. Thus, the generic boundaries and higher ranks in the basal clades of fungi are poorly known. Recent DNA based taxonomic studies have provided reliable and accurate information. It is therefore necessary to compile all available information since basal clades genera lack updated checklists or outlines. Recently, Tedersoo et al. (MycoKeys 13:1--20, 2016) accepted Aphelidiomycota and Rozellomycota in Fungal clade. Thus, we regard both these phyla as members in Kingdom Fungi. We accept 16 phyla in basal clades viz. Aphelidiomycota, Basidiobolomycota, Blastocladiomycota, Calcarisporiellomycota, Caulochytriomycota, Chytridiomycota, Entomophthoromycota, Glomeromycota, Kickxellomycota, Monoblepharomycota, Mortierellomycota, Mucoromycota, Neocallimastigomycota, Olpidiomycota, Rozellomycota and Zoopagomycota. Thus, 611 genera in 153 families, 43 orders and 18 classes are provided with details of classification, synonyms, life modes, distribution, recent literature and genomic data. Moreover, Catenariaceae Couch is proposed to be conserved, Cladochytriales Mozl.-Standr. is emended and the family Nephridiophagaceae is introduced

Sollten Assistenzroboter eine „Persönlichkeit“ haben?

The final publication is available at Springer via https://doi.org/10.1007/s00391-016-1068-3.HintergrundDas Forschungsprojekt „HOBBIT“ der Europäischen Union (EU) hat Prototypen von assistiven Robotern zur Unterstützung eines sicheren und selbstständigen Lebens entwickelt und bei älteren Personen in der häuslichen Umgebung Hause getestet.Ziel der ArbeitIm Projekt „personAAL“ wurde untersucht, ob verschiedene Verhaltensweisen eines solchen Roboterprototyps („Persönlichkeiten“) bei ansonsten gleicher Funktion (Überbringen einer Nachricht) von Testpersonen (TP) als unterschiedliche „Persönlichkeiten“ wahrgenommen werden.Material und MethodenDer Roboter wurde mit 2 Verhaltensweisen ausgestattet: einmal introvertiert (rein funktionsorientiert, nüchtern sachlich) und das andere Mal extrovertiert (lebendig, angereichert) und 13 Testpersonen (großteils älteren Personen bzw. Experten und Expertinnen aus dem Pflegebereich) vorgeführt.ErgebnisseZwölf der 13 TP nahmen den Unterschied im Verhalten wahr. Eine TP präferierte den introvertierten Roboter, 9 den extrovertierten, und 3 hatten keine Präferenz oder machten diese von der konkreten Situation abhängig. Die Auswertung ergab bei den Teilfragen „Anthropomorphismus“ und „Belebtheit“ sowie in der Gesamtbewertung signifikante Wahrnehmungsunterschiede. Bemerkenswert ist, dass für die Teilfragen „Sympathie“ und „Sicherheit“ keine signifikanten Unterschiede festgestellt wurden. Es gab keinerlei signifikante Korrelation zwischen dem bevorzugten Verhalten des Roboters und der Selbsteinschätzung der TP als intro- oder extrovertiert.SchlussfolgerungAus dem Ergebnis kann vermutet werden, dass die Akzeptanz eines Roboters nicht rein von der nüchternen Funktion, die natürlich Voraussetzung ist, abhängt. Daher wird es sinnvoll sein, für eine höhere Akzeptanz verschiedene Verhaltensweisen zur Wahl zu stellen.BackgroundIn the European Union (EU) research project “HOBBIT” prototypes of assistive robots were developed in order to support a more secure and independent life style and trials were run at the homes of elderly users.ObjectiveThe project “personAAL” aimed at investigating whether distinct behavioral forms of a robot (“personality”), despite an identical basic function (e.g. bringing a message) are perceived as different personalities by the test persons.Material and methodsFor this study a robot was equipped with two types of behavior, one more introverted (purely function-oriented, impersonal and factual) and the other more extroverted (lively and enriched). The two types of behavior were demonstrated to 13 test persons (mostly older persons and experts from the care domain).ResultsNearly all of the test persons (12 out of 13) perceived a difference with respect to the behavior of the robot. Of the test persons 1 preferred the introverted robot, 9 the extroverted behavior of the robot and 3 had no preferences or stated that the preference depended on the context. The evaluation of answers to the Godspeed questionnaire resulted in significantly different perceptions of “anthropomorphism” and “animated” as well as in the total evaluation. It is also remarkable that no significant differences could be found for “likeability” and “perceived security”. There was also no significant correlation between the preferred behavior of the robot and the self-assessment of the test person as being more introverted or extroverted.ConclusionFrom these results it can be concluded that acceptance of a robot does not solely depend on the mere function, which of course is a precondition but that it is advisable to offer a choice between different types of behavior in order to optimize acceptance.Austrian Research Promotion Agency (FFG) BenefitEuropean Commission 7th framewor

Accelerating Reinforcement Learning with Prioritized Experience Replay for Maze Game

In this paper we implemented two ways of improving the performance of reinforcement learning algorithms. We proposed a new equation to prioritize transition samples to improve model accuracy, and by deploying a generalized solver of randomly-generated two-dimensional mazes on a distributed computing platform, our dual-network model is available to others for further research and development. Reinforcement Learning is concerned with identifying the optimal sequence of actions for an agent to take in order to reach an objective to achieve the highest score in the future. Complex situations can lead to computational challenges in terms of both finding the best answer and the training time required to do so. Our prioritization algorithm increased model accuracy by 7% versus a baseline model with no prioritization, and using five workers on the RAY platform using RLlib achieved a 4.5X acceleration in training time versus using one worker