280 research outputs found
Perseus: Randomized Point-based Value Iteration for POMDPs
Partially observable Markov decision processes (POMDPs) form an attractive
and principled framework for agent planning under uncertainty. Point-based
approximate techniques for POMDPs compute a policy based on a finite set of
points collected in advance from the agents belief space. We present a
randomized point-based value iteration algorithm called Perseus. The algorithm
performs approximate value backup stages, ensuring that in each backup stage
the value of each point in the belief set is improved; the key observation is
that a single backup may improve the value of many belief points. Contrary to
other point-based methods, Perseus backs up only a (randomly selected) subset
of points in the belief set, sufficient for improving the value of each belief
point in the set. We show how the same idea can be extended to dealing with
continuous action spaces. Experimental results show the potential of Perseus in
large scale POMDP problems
Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties
In this paper, we introduce a denoising diffusion algorithm to discover
microstructures with nonlinear fine-tuned properties. Denoising diffusion
probabilistic models are generative models that use diffusion-based dynamics to
gradually denoise images and generate realistic synthetic samples. By learning
the reverse of a Markov diffusion process, we design an artificial intelligence
to efficiently manipulate the topology of microstructures to generate a massive
number of prototypes that exhibit constitutive responses sufficiently close to
designated nonlinear constitutive responses. To identify the subset of
microstructures with sufficiently precise fine-tuned properties, a
convolutional neural network surrogate is trained to replace high-fidelity
finite element simulations to filter out prototypes outside the admissible
range. The results of this study indicate that the denoising diffusion process
is capable of creating microstructures of fine-tuned nonlinear material
properties within the latent space of the training data. More importantly, the
resulting algorithm can be easily extended to incorporate additional
topological and geometric modifications by introducing high-dimensional
structures embedded in the latent space. The algorithm is tested on the
open-source mechanical MNIST data set. Consequently, this algorithm is not only
capable of performing inverse design of nonlinear effective media but also
learns the nonlinear structure-property map to quantitatively understand the
multiscale interplay among the geometry and topology and their effective
macroscopic properties.Comment: 21 pages, 11 figure
Gaussian Mixture Model of Heart Rate Variability
Heart rate variability (HRV) is an important measure of sympathetic and parasympathetic functions of the autonomic nervous system and a key indicator of cardiovascular condition. This paper proposes a novel method to investigate HRV, namely by modelling it as a linear combination of Gaussians. Results show that three Gaussians are enough to describe the stationary statistics of heart variability and to provide a straightforward interpretation of the HRV power spectrum. Comparisons have been made also with synthetic data generated from different physiologically based models showing the plausibility of the Gaussian mixture parameters
Troppo - A Python framework for the reconstruction of context-specific metabolic models
The surge in high-throughput technology availability for molecular biology has enabled the development of powerful predictive tools for use in many applications, including (but not limited to) the diagnosis and treatment of human diseases such as cancer. Genome-scale metabolic models have shown some promise in clearing a path towards precise and personalized medicine, although some challenges still persist. The integration of omics data and subsequent creation of context-specific models for specific cells/tissues still poses a significant hurdle, and most current tools for this purpose have been implemented using proprietary software. Here, we present a new software tool developed in Python, troppo - Tissue-specific RecOnstruction and Phenotype Prediction using Omics data, implementing a large variety of context-specific reconstruction algorithms. Our framework and workflow are modular, which facilitates the development of newer algorithms or omics data sources.This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2019 unit and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte. The authors also thank the PhD scholarships funded by national funds through Fundacao para a Ciencia e Tecnologia, with references: SFRH/BD/133248/2017 (J.F.), SFRH/BD/118657/2016 (V.V.).info:eu-repo/semantics/publishedVersio
Learning motions from demonstrations and rewards with time-invariant dynamical systems based policies
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