Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning

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.The work was funded by FCT - Fundacao para a Ciencia e Tecnologia, through the PhD Grants SFRH/BD/48529/2008 and SFRH/BD/41179/2007 and Project NETT: Neural Engineering Transformative Technologies, EU-FP7 ITN (nr. 289146) and the FCT-Research Center CMAT (PEst-OE/MAT/UI0013/2014)

MUVTIME: a Multivariate time series visualizer for behavioral science

As behavioral science becomes progressively more data driven, the need is increasing for appropriate tools for visual exploration and analysis of large datasets, often formed by multivariate time series. This paper describes MUVTIME, a multimodal time series visualization tool, developed in Matlab that allows a user to load a time series collection (a multivariate time series dataset) and an associated video. The user can plot several time series on MUVTIME and use one of them to do brushing on the displayed data, i.e. select a time range dynamically and have it updated on the display. The tool also features a categorical visualization of two binary time series that works as a high-level descriptor of the coordination between two interacting partners. The paper reports the successful use of MUVTIME under the scope of project TURNTAKE, which was intended to contribute to the improvement of human-robot interaction systems by studying turn- taking dynamics (role interchange) in parent-child dyads during joint action.Marie Curie International Incoming Fellowship PIIF-GA-2011- 301155; Portuguese Foundation for Science and Technology (FCT) project PTDC/PSI- PCO/121494/2010; AFP was also partially funded by the FCT project (IF/00217/2013)This research was supported by: Marie Curie International Incoming Fellowship PIIF-GA-2011301155; Portuguese Foundation for Science and Technology (FCT) Strategic program FCT UID/EEA/00066/2013; FCT project PTDC/PSIPCO/121494/2010. AFP was also partially funded by the FCT project (IF/00217/2013). REFERENCE

Efeito do ruído de tráfego na decisão de atravessamento dos peões

Para atravessar uma rua, os peões devem detetar o tráfego, combinar dados provenientes de diferentes fontes, auditivas e visuais, avaliar o tempo para atravessar com segurança e monitorizar a posição dos veículos que se aproximam. Este trabalho teve como objetivo avaliar a influência do ruído emitido pelos veículos na tomada de decisão de atravessamento dos peões. Realizou-se uma experiência em ambiente virtual que reproduziu condições reais de travessia, no que diz principalmente respeito aos estímulos dos veículos em aproximação com diferentes padrões de movimento e velocidades. Foi considerado o ruído de um veículo elétrico, um veículo de combustão a gasolina, e uma condição de referência sem pistas auditivas. Vários participantes indicaram o momento da sua decisão de atravessamento. A tomada de decisão dos participantes baseou-se sobretudo nas características do movimento de aproximação do veículo, indicando que, a baixa velocidade, o efeito do ruído do veículo na decisão dos peões é reduzido.To cross a street, pedestrians must detect traffic, combine data from different sources, auditory and visual, assess the time to cross safely and monitor the position of approaching vehicles. This work aimed to evaluate the influence of noise emitted by vehicles on pedestrians’ crossing decision. An experiment was carried out in a virtual environment that reproduced real crossing conditions, mainly regarding the presentation of stimuli concerning the approach of a vehicle with different movement patterns and speeds. The noise of an electric vehicle, a gasoline combustion vehicle, and a reference condition without auditory cues were considered. Several participants indicated the moment of their crossing decision. The participants’ decision-making was based mainly on the characteristics of the vehicle's movement, indicating that, at low speed, the effect of vehicle noise on pedestrians’ decision is reduced.Este trabalho enquadra-se nas atividades do projeto de investigação AnPeB – Análise do comportamento de peões com base em ambientes simulados e sua incorporação na modelação de risco (PTDC/ECM-TRA/3568/2014), foi financiado no âmbito do projeto Promover a Produção Científica e Desenvolvimento Tecnológico e a Constituição de Redes Temáticas (3599-PPCDT) e comparticipado pelo Fundo Comunitário Europeu FEDER. De referir ainda a Fundação para a Ciência e a Tecnologia devido ao financiamento da bolsa de doutoramento SFRH/BD/131638/2017

Visual-vestibular and postural analysis of motion sickness, panic, and acrophobia

Trigger motion sickness and can also have a role in some anxiety disorders. We explore a method to detect individual sensitivity to visual-vestibular unusual patterns, which can signal a vulnerability to develop motion sickness and possibly anxiety disorders such as a fear of heights and panic. 65 undergraduate students were recruited for the purposes of this study as voluntary participants (44 females); average age 21.65 years (SD=2.84) with normal or corrected to normal vision, without vestibular or postural deficiencies. Panic was assessed with the Albany Panic and Phobia Questionnaire, Motion Sickness with the Motion Sickness Susceptibility Questionnaire and Acrophobia was assessed by means of the Acrophobia Questionnaire. The Sharpened Romberg Test was used to test participant’s postural balance. The Rod and Frame Test (RFT) measures the participant’s ability to align a rod to the vertical within a titled frame providing a measure of error in the perception of verticality by degrees. This test was changed to measure the error offered when a participant’s head was tilted, and to trace the error caused by manipulating the vestibular system input. The main findings show only motion sickness to be correlated with significant errors while performing a visual-vestibular challenging situation, and fear of heights is the only anxiety disorder connected with postural stability, although all disorders (fear of heights, panic and motion sickness) are correlated between each other in the self- report questionnaires. All disorders are correlated to each other in the surveys, and might have some common visual-vestibular origin, in theory. The rod and frame test was exclusively correlated with motion sickness whereas the postural stability test only displayed sensibility to acrophobia. Panic disorder was correlated to neither the RFT nor the Romberg. Although this method was initially employed to increase sensibility in order to detect anxiety disorders, it ended up showing its value in the detection of motion sickness.National funds and co-financed by FEDER through COMPETE2020 under the PT2020 Partnership Agreement (POCI-01-0145-FEDER-007653). FCT/3599-PPCDT/121494/PTGrant IF/00217/ 2013info:eu-repo/semantics/publishedVersio

Impactos do tratamento com liraglutida na progressão da doença renal crônica em pacientes com diabetes mellitus tipo 2 / Impacts of treatment with liraglutide on the progression of chronic kidney disease in patients with type 2 diabetes mellitus

INTRODUÇÃO: A doença renal diabética é uma importante complicação decorrente da diabetes mellitus tipo 2. O estudo visa analisar o impacto da utilização da liraglutida, um fármaco agonista de GLP-1, no controle desse agravo. METODOLOGIA: Foi realizada uma revisão narrativa baseada em artigos encontrados entre os anos 2016 e 2020, nas bases de dados LILACS e PubMed, por meio de consulta ao DeCs, através dos descritores: GLP-1, liraglutide, diabetes mellitus e kidney chronic disease. RESULTADOS: Foram selecionadas 9 publicações para serem utilizadas como referencial para este artigo. DISCUSSÃO: Estudos estão sendo realizados a fim de identificar medicamentos que controlam a glicemia e sejam eficazes na preservação da função renal, assim evidenciou-se a Liraglutida. O fármaco apresentou bons resultados sobre o controle glicêmico, mostrando-se mais eficaz em relação a outros medicamentos. Além disso, foram evidenciados resultados positivos sobre o controle da obesidade e da pressão arterial, sendo esses fatores contribuintes para diminuir a progressão da doença renal diabética. Importante ressaltar também a atuação da liraglutida no sistema renal, reduzindo o declínio da taxa de filtração glomerular e os valores de albuminúria. CONCLUSÃO: A liraglutida se mostrou eficiente na redução da progressão da doença renal crônica em pacientes diabéticos tipo 2

Genomic epidemiology unveils the dynamics and spatial corridor behind the Yellow Fever virus outbreak in Southern Brazil

Despite the considerable morbidity and mortality of yellow fever virus (YFV) infections in Brazil, our understanding of disease outbreaks is hampered by limited viral genomic data. Here, through a combination of phylogenetic and epidemiological models, we reconstructed the recent transmission history of YFV within different epidemic seasons in Brazil. A suitability index based on the highly domesticated Aedes aegypti was able to capture the seasonality of reported human infections. Spatial modeling revealed spatial hotspots with both past reporting and low vaccination coverage, which coincided with many of the largest urban centers in the Southeast. Phylodynamic analysis unraveled the circulation of three distinct lineages and provided proof of the directionality of a known spatial corridor that connects the endemic North with the extra-Amazonian basin. This study illustrates that genomics linked with eco-epidemiology can provide new insights into the landscape of YFV transmission, augmenting traditional approaches to infectious disease surveillance and control

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

Aprendizagem e generalização de representações numa arquitetura baseada em campos dinâmicos para interação humano-robô

Programa Doutoral em Engenharia Eletrónica e de ComputadoresDue to the increasing demand for adaptive robots able to assist humans in their everyday tasks, furnishing robots with learning abilities is one of the most important goals of current robotics research. The work reported in this thesis is focused on the integration of learning capacities in an existing Dynamic Field based control architecture developed for natural human-robot collaboration. Specifically, it addresses two important serial order problems that appear in the architecture at distinct but closely coupled levels of abstraction: 1) the learning of the sequential order of sub-goals that has to be followed to accomplish a certain task, and 2) the learning of representations of motor primitives that can be chained to achieve a certain sub-goal. A model based on the theoretical framework of Dynamic Neural Fields (DNFs) is developed that allows the robot to acquire a multi-order sequential plan of a task from demonstration by human tutors. The model is inspired by known processing principles of human serial order learning. Specifically, it implements the idea of two complementary learning systems. A fast system encodes the sequential order of a single demonstration. During periods of internal rehearsal, it acts as a teacher for a slow system that is responsible for extracting generalized task knowledge from memorized demonstrations of different users. The efficiency of the learning model is tested in a real world experiment in which the humanoid robot ARoS learns the plan of an assembly task by observing human tutors executing possible sequential orders of sub-goals. An extension of the basic model is also proposed and tested in a real-world experiment. It addresses the fundamental problem of a hierarchical encoding of complex sequential tasks. It is shown how verbal feedback by the tutor about a serial order error may lead to the autonomous development of a neural representation of a group of sub-goals forming a sub-task. The second serial order problem of learning goal-directed chains of motor primitives is addressed by combining the associative learning mechanism of the dynamic field model with self-organizing properties. Inspired by the basic idea of the Kohonen's map algorithm, it is shown how self-organizing principles can be exploited to develop field representations of motor primitives, like for instance, a specific grasping behaviour, from observed motion trajectories. Moreover, the integration of additional contextual cues (e.g. object properties) in the learning process may cause the splitting of an existing motor primitive representation into two new representations that are context sensitive. In model simulations, it is shown that the learning mechanisms for representing sequential task knowledge in the DNF model can be also applied to establish chains of motor primitives directed towards a final goal (e.g. reach-grasp-place). Such a chained organization has been discussed in the neurophysiological literature to support not only a fluent execution of known action sequences but also the cognitive capacity of inferring the goal of observed motor behaviour of another individual.Devido à procura crescente por robôs adaptativos capazes de auxiliar humanos nas suas tarefas diárias, um dos mais importantes objetivos da investigação em robótica atual é o de dotar robôs com a capacidade de aprender. O trabalho apresentado nesta tese foca-se na integração de capacidades de aprendizagem numa arquitetura de controlo, baseada em campos dinâmicos, para colaboração fluente entre Humano e Robô. Especificamente são abordados dois problemas importantes relacionados com ordem sequencial que surgem na arquitetura em níveis de abstração distintos embora relacionados: 1) a aprendizagem da ordem sequencial de sub-objetivos que tem de ser seguida para completar uma determinada tarefa e 2) a aprendizagem de representações de primitivas motoras que podem ser encadeadas para atingir um certo sub-objetivo. Foi desenvolvido um modelo baseado em teoria de Campos Dinâmicos Neuronais (CDNs) que permite ao robô adquirir um plano sequencial multi-ordem de uma tarefa a partir de demonstrações de tutores humanos. O modelo é inspirado em princípios conhecidos da aprendizagem da ordem sequencial por humanos. Especificamente, implementa a ideia de dois sistemas de aprendizagem complementares. Um mecanismo rápido codifica a ordem sequencial de uma demonstração única. Durante períodos de repetição interna, este mecanismo age como professor de um sistema mais lento responsável por extrair conhecimento generalizado da tarefa a partir das demonstrações de diferentes utilizadores. A eficiência do modelo de aprendizagem é testada numa experiência em cenário real na qual o robô humanoide ARoS aprende o plano de uma tarefa de montagem através da observação de tutores humanos que executam possíveis ordens sequenciais de execução dos sub-objetivos. Uma extensão do modelo básico é também proposta e testada em ambiente real. A extensão aborda o problema fundamental da codificação hierárquica de tarefas sequenciais complexas. É mostrado como feedback verbal fornecido pelo tutor acerca de erros na sequência pode levar ao desenvolvimento autónomo de uma representação neuronal de um grupo de sub-objetivos que formam uma sub-tarefa. O segundo problema de ordem sequencial, que consiste na aprendizagem de cadeias de primitivas motoras direcionadas a um objetivo, é abordado através da combinação do mecanismo de aprendizagem associativa do modelo baseado em Campos Dinâmicos com propriedades de auto-organização. Partindo da ideia fundamental do algoritmo do mapa de Kohonen, é mostrado como princípios de auto-organização podem ser explorados para desenvolver representações, em campos dinâmicos, de primitivas motoras, como por exemplo, um gesto especifico de agarrar, a partir de trajetórias de movimentos observados. Além disso, a integração de informação contextual adicional (e.g. propriedades do objeto) no processo de aprendizagem pode causar a divisão de uma representação de uma primitiva motora em duas novas representações específicas de cada contexto. É mostrado em simulação que os modelos de aprendizagem para representação do conhecimento da tarefa sequencial no modelo baseado em CDNs podem ser também aplicados na formação de cadeias de primitivas motoras direcionadas a um objetivo final (e.g. aproximar-agarrar-colocar). A organização em cadeia tem sido discutida na literatura sobre neurofisiologia, como sendo o suporte não só da execução fluente de ações sequenciais conhecidas, mas também da capacidade cognitiva de inferir o objetivo do comportamento motor observado num outro individuo.Fundação para a Ciência e a Tecnologia SFRH/BD/48529/200

Transport noise and health

Transportation noise is considered today a major environmental risk factor and an important public health issue, especially in urban settings. Road, railway, and aircraft transports can produce high levels of continuous or intermittent noise, which, upon continued exposure, may have both short- and long-term impacts. Relevant short-term impacts are the experience of annoyance (a general feeling of nuisance caused by noise) and sleep disturbance, which affect wellbeing and impair both physical and mental performance. As for long-term impacts, a growing body of evidence points to a relation between long-term exposure to transportation noise and increasing incidence of cardiovascular and metabolic diseases, effects on cognitive development (in children), and effects on mental health

Implementing auralized CPB sounds on a pedestrian simulator

Recently, several studies on pedestrian safety and particularly those addressing pedestrian crossing behaviour and decision-making have been performed using virtual reality systems. The use of simulators to assess pedestrian behaviour is conditioned by the feeling of presence and immersion, for which the sound is a determining factor. This paper presents an implementation procedure in which tyre-road noise samples are auralized and presented as auditory stimuli in a virtual environment, for assessing pedestrian crossing decision-making. The auditory samples obtained through the Close Proximity (CPX) method and subsequently auralized to represent Controlled Pass-By (CPB) recordings reproduce the sounds of a vehicle approaching a crosswalk. The auralized sounds together with the presentation of visual stimuli were used in an experiment carried out with 15 participants. Safety indicators, such as the time-to-passage at the moment that participants decided to cross a virtual crosswalk and the minimum time-to-collision were registered and compared with data obtained in real-world road crossings. The comparison of the results obtained in virtual and real environments, indicated a good suitability of the approach for studying pedestrian crossing behaviour.A preliminary indication that the simulation approach used in the experiments is able to realistically depict road crossing situations, where the CPB auralized sounds certainly played an important role. However, full validation will require more in-depth studies of multiple conditions, to assess the suitability of the implementation of the approach as a valid tool for simulation-based safety analysis of car-pedestrian interactions. FCT – Fundação para a Ciência e a Tecnologia, under grant agreement [SFRH/BD/131638/2017] attributed to the first author; by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Engineering Structures (ISISE) [UIDB/04029/2020]; and by the project Promoting Scientific Production and Technological Development and Thematic Networking (3599-PPCDT), supported by the European Community Fund FEDER, through the grant attributed to the project AnPeB – Analysis of pedestrians behaviour based on simulated urban environments and its incorporation in risk modelling [PTDC/ECM-TRA/3568/2014