8 resultados para Adaptive Learning Systems

em Universidade do Minho


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The paper reflects the work of COST Action TU1403 Workgroup 3/Task group 1. The aim is to identify research needs from a review of the state of the art of three aspects related to adaptive façade systems: (1) dynamic performance requirements; (2) façade design under stochastic boundary conditions and (3) experiences with adaptive façade systems and market needs.

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Objective: Immunosenescence and cognitive decline are common markers of the aging process. Taking into consideration the heterogeneity observed in aging processes and the recently described link between lymphocytes and cognition, we herein explored the possibility of an association between alterations in lymphocytic populations and cognitive performance. Methods: In a cohort of cognitively healthy adults (n = 114), previously characterized by diverse neurocognitive/psychological performance patterns, detailed peripheral blood immunophenotyping of both the innate and adaptive immune systems was performed by flow cytometry. Results: Better cognitive performance was associated with lower numbers of effector memory CD4(+) T cells and higher numbers of naive CD8(+) T cells and B cells. Furthermore, effector memory CD4(+) T cells were found to be predictors of general and executive function and memory, even when factors known to influence cognitive performance in older individuals (e.g., age, sex, education, and mood) were taken into account. Conclusions: This is the first study in humans associating specific phenotypes of the immune system with distinct cognitive performance in healthy aging.

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It is a difficult task to avoid the “smart systems” topic when discussing smart prevention and, similarly, it is a difficult task to address smart systems without focusing their ability to learn. Following the same line of thought, in the current reality, it seems a Herculean task (or an irreparable omission) to approach the topic of certified occupational health and safety management systems (OHSMS) without discussing the integrated management systems (IMSs). The available data suggest that seldom are the OHSMS operating as the single management system (MS) in a company so, any statement concerning OHSMS should mainly be interpreted from an integrated perspective. A major distinction between generic systems can be drawn between those that learn, i.e., those systems that have “memory” and those that have not. These former systems are often depicted as adaptive since they take into account past events to deal with novel, similar and future events modifying their structure to enable success in its environment. Often, these systems, present a nonlinear behavior and a huge uncertainty related to the forecasting of some events. This paper seeks to portray, for the first time as we were able to find out, the IMSs as complex adaptive systems (CASs) by listing their properties and dissecting the features that enable them to evolve and self-organize in order to, holistically, fulfil the requirements from different stakeholders and thus thrive by assuring the successful sustainability of a company. Based on the revision of literature carried out, this is the first time that IMSs are pointed out as CASs which may develop fruitful synergies both for the MSs and for CASs communities. By performing a thorough revision of literature and based on some concepts embedded in the “DNA” of the subsystems implementation standards it is intended, specifically, to identify, determine and discuss the properties of a generic IMS that should be considered to classify it as a CAS.

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"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

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Series: "Advances in intelligent systems and computing , ISSN 2194-5357, vol. 417"

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Tese de Doutoramento em Tecnologias e Sistemas de Informação

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There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.

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Tese de Doutoramento em Engenharia de Eletrónica e de Computadores