38 research outputs found

    A Novel Predictive-Coding-Inspired Variational RNN Model for Online Prediction and Recognition

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    This study introduces PV-RNN, a novel variational RNN inspired by the predictive-coding ideas. The model learns to extract the probabilistic structures hidden in fluctuating temporal patterns by dynamically changing the stochasticity of its latent states. Its architecture attempts to address two major concerns of variational Bayes RNNs: how can latent variables learn meaningful representations and how can the inference model transfer future observations to the latent variables. PV-RNN does both by introducing adaptive vectors mirroring the training data, whose values can then be adapted differently during evaluation. Moreover, prediction errors during backpropagation, rather than external inputs during the forward computation, are used to convey information to the network about the external data. For testing, we introduce error regression for predicting unseen sequences as inspired by predictive coding that leverages those mechanisms. The model introduces a weighting parameter, the meta-prior, to balance the optimization pressure placed on two terms of a lower bound on the marginal likelihood of the sequential data. We test the model on two datasets with probabilistic structures and show that with high values of the meta-prior the network develops deterministic chaos through which the data's randomness is imitated. For low values, the model behaves as a random process. The network performs best on intermediate values, and is able to capture the latent probabilistic structure with good generalization. Analyzing the meta-prior's impact on the network allows to precisely study the theoretical value and practical benefits of incorporating stochastic dynamics in our model. We demonstrate better prediction performance on a robot imitation task with our model using error regression compared to a standard variational Bayes model lacking such a procedure.Comment: The paper is accepted in Neural Computatio

    Polyurethane Flexible Foam Fire Behavior

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    Towards hybrid primary intersubjectivity: a neural robotics library for human science

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    Human-robot interaction is becoming an interesting area of research in cognitive science, notably, for the study of social cognition. Interaction theorists consider primary intersubjectivity a non-mentalist, pre-theoretical, non-conceptual sort of processes that ground a certain level of communication and understanding, and provide support to higher-level cognitive skills. We argue this sort of low level cognitive interaction, where control is shared in dyadic encounters, is susceptible of study with neural robots. Hence, in this work we pursue three main objectives. Firstly, from the concept of active inference we study primary intersubjectivity as a second person perspective experience characterized by predictive engagement, where perception, cognition, and action are accounted for an hermeneutic circle in dyadic interaction. Secondly, we propose an open-source methodology named \textit{neural robotics library} (NRL) for experimental human-robot interaction, and a demonstration program for interacting in real-time with a virtual Cartesian robot (VCBot). Lastly, through a study case, we discuss some ways human-robot (hybrid) intersubjectivity can contribute to human science research, such as to the fields of developmental psychology, educational technology, and cognitive rehabilitation

    How can a recurrent neurodynamic predictive coding model cope with fluctuation in temporal patterns? Robotic experiments on imitative interaction

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    The current paper examines how a recurrent neural network (RNN) model using a dynamic predictive coding scheme can cope with fluctuations in temporal patterns through generalization in learning. The conjecture driving this present inquiry is that a RNN model with multiple timescales (MTRNN) learns by extracting patterns of change from observed temporal patterns, developing an internal dynamic structure such that variance in initial internal states account for modulations in corresponding observed patterns. We trained a MTRNN with low-dimensional temporal patterns, and assessed performance on an imitation task employing these patterns. Analysis reveals that imitating fluctuated patterns consists in inferring optimal internal states by error regression. The model was then tested through humanoid robotic experiments requiring imitative interaction with human subjects. Results show that spontaneous and lively interaction can be achieved as the model successfully copes with fluctuations naturally occurring in human movement patterns

    Simulating developmental diversity: Impact of neural stochasticity on atypical flexibility and hierarchy

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    Introduction: Investigating the pathological mechanisms of developmental disorders is a challenge because the symptoms are a result of complex and dynamic factors such as neural networks, cognitive behavior, environment, and developmental learning. Recently, computational methods have started to provide a unified framework for understanding developmental disorders, enabling us to describe the interactions among those multiple factors underlying symptoms. However, this approach is still limited because most studies to date have focused on cross-sectional task performance and lacked the perspectives of developmental learning. Here, we proposed a new research method for understanding the mechanisms of the acquisition and its failures in hierarchical Bayesian representations using a state-of-the-art computational model, referred to as in silico neurodevelopment framework for atypical representation learning. Methods: Simple simulation experiments were conducted using the proposed framework to examine whether manipulating the neural stochasticity and noise levels in external environments during the learning process can lead to the altered acquisition of hierarchical Bayesian representation and reduced flexibility. Results: Networks with normal neural stochasticity acquired hierarchical representations that reflected the underlying probabilistic structures in the environment, including higher-order representation, and exhibited good behavioral and cognitive flexibility. When the neural stochasticity was high during learning, top-down generation using higher-order representation became atypical, although the flexibility did not differ from that of the normal stochasticity settings. However, when the neural stochasticity was low in the learning process, the networks demonstrated reduced flexibility and altered hierarchical representation. Notably, this altered acquisition of higher-order representation and flexibility was ameliorated by increasing the level of noises in external stimuli. Discussion: These results demonstrated that the proposed method assists in modeling developmental disorders by bridging between multiple factors, such as the inherent characteristics of neural dynamics, acquisitions of hierarchical representation, flexible behavior, and external environment.journal articl

    Dimitri: an Open-Source Humanoid Robot with Compliant Joint

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    We introduce Dimitri, an open-software & open-hardware humanoid robot with 31 DOFs, fitted with cost-effective modular compliant joints and parallel link legs, designed for advanced human-robot interaction research, force-informed object handling and intelligent environment discovery. Our main innovation is in the design of a robust full-body biped humanoid robot equipped with very low-cost polyurethane torsional spring fixed to traditional servo motors and a circuit to measure angular displacement, transforming the system into a series elastic actuator (SEA). In order to illustrate the robot\u27s qualities in the field of machine learning applied to robotics and manipulation, a multiple timescale recurrent neural network (MTRNN) is implemented, allowing the robot to replicate combined movement sequences earlier taught via interactive demonstration

    The Efficiency of Locking Compression Plates versus Dynamic Compression Plates in the Treatment of Low Distal Fibula Fracture: A Randomized Clinical Trial

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    Background: Uncertainties remain as to which type of plate [locking compression plate (LCP) or dynamic compression plate (DCP)] is more efficient and cost-effective in fixing and stabilizing the fractures. We aimed to compare the clinical utility of the two types of plates including LCPs and 3.5-mm DCPs in the treatment of low distal fibula fracture (distal lateral malleolus fractures).   Methods: This randomized single-blinded clinical trial was performed on 54 patients with distal fibula fractures who were candidates for surgical treatment using compression plate fixation. The patients were randomly assigned into two groups scheduled for treatment with fixation of LCPs or with 3.5-mm T-plates (DCPs). The patients were finally followed-up for two years to assess the clinical outcome of the procedures.   Results: No difference was revealed between the two groups in the prevalence of postoperative infection, nonunion, wound dehiscence, skin reactions, and local surgical pain. The mean functional score [Olerud-Molander Ankle Score (OMAS)] in the DCP and LCP groups was 85.33 ± 4.92 and 84.85 ± 5.12, respectively, indicating no difference between the groups (P = 0.726).   Conclusion: In the treatment of low distal fibula fractures, the use of LCPs and 3.5mm DCPs can similarly result in improving functional status with minimal postoperative complications. Due to the similarity of the consequences of using both plates and the fact that the DCP type is more cost-effective and available in remote and deprived areas, this type seems to be preferred.

    Monkeypox: a systematic review of epidemiology, pathogenesis, manifestations, and outcomes

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    Introduction. Since May 2022, an unusually large number of new monkeypox infections-a previously rare viral zoonotic disease, mainly reported from central and western Africa has been reported globally, and the World Health Organization (WHO) declared a global health emergency in July 2022. We aimed to systematically review the monkeypox virus epidemiology, pathogenesis, transmission, presentations, and outcomes. Materials and methods. Our aim is to systematically review the epidemiology, pathogenesis, manifestations, and outcomes of Monkeypox disease. We searched the keywords in the online databases of PubMed, Embase, Scopus, and Web of Science and investigated all English articles until December 2022. In order to ascertain the findings, this study adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. In order to optimize the quality, this review study benefits from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. To minimize any probable bias risk, we utilized the Newcastle-Ottawa Scale (NOS) risk assessment tool. Results. The most prevalent symptoms were rash and fever. The infection was accompanied by different complications such as, but not limited to, encephalitis (mainly in children), septicemia, bacterial cellulitis, retropharyngeal and parapharyngeal abscesses, etc. A wide range of hospitalization from 3.7% to 100% has been reported. The mortality rate ranged from 0% to 23%, which mainly occurred in infants and children. High mortality of the monkeypox rate was reported among pregnant women. The mortality rate of monkeypox is lower among women and those who received the smallpox vaccine compared to men and those who did not receive the vaccine. A wide range of the overall second-rate attack was reported, which is more pronounced in unvaccinated patients. Conclusion. In our systematic review of 35 studies on monkeypox, we cast light on the existing evidence on its epidemiology, pathogenesis, manifestation, and outcomes. Further studies are needed to elucidate the natural history of the disease in various patients’ population, as well as detailing the monkeypox attack rate
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