João Medina : pensar e sentir a História

Livro de homenagem ao Professor João Medina, com textos de: António Ventura, Sérgio Campos Matos, Ernesto Castro Leal, Joaquim Cerqueira Gonçalves, Manuel Clemente, Norberto Ferreira da Cunha e Onésimo Teotónio de Almeida. Inclui texto autobiográfico de João Medina e uma bibliografia da sua obra, compilada por José Brissos.info:eu-repo/semantics/publishedVersio

OpenGraphGym: A Parallel Reinforcement Learning Framework for Graph Optimization Problems

This paper presents an open-source, parallel AI environment (named OpenGraphGym) to facilitate the application of reinforcement learning (RL) algorithms to address combinatorial graph optimization problems. This environment incorporates a basic deep reinforcement learning method, and several graph embeddings to capture graph features, it also allows users to rapidly plug in and test new RL algorithms and graph embeddings for graph optimization problems. This new open-source RL framework is targeted at achieving both high performance and high quality of the computed graph solutions. This RL framework forms the foundation of several ongoing research directions, including 1) benchmark works on different RL algorithms and embedding methods for classic graph problems; 2) advanced parallel strategies for extreme-scale graph computations, as well as 3) performance evaluation on real-world graph solutions

Intrusive polynomial chaos for CFD using OpenFOAM

We present the formulation and implementation of a stochas- tic Computational Fluid Dynamics (CFD) solver based on the widely used finite volume library - OpenFOAM. The solver employs General- ized Polynomial Chaos (gPC) expansion to (a) quantify the uncertainties associated with the fluid flow simulations, and (b) study the non-linear propagation of these uncertainties. The aim is to accurately estimate the uncertainty in the result of a CFD simulation at a lower computational cost than the standard Monte Carlo (MC) method. The gPC approach is based on the spectral decomposition of the random variables in terms of basis polynomials containing randomness and the unknown determin- istic expansion coefficients. As opposed to the mostly used non-intrusive approach, in this work, we use the intrusive variant of the gPC method in the sense that the deterministic equations are modified to directly solve for the (coupled) expansion coefficients. To this end, we have tested the intrusive gPC implementation for both the laminar and the turbulent flow problems in CFD. The results are in accordance with the analytical and the non-intrusive approaches. The stochastic solver thus developed, can serve as an alternative to perform uncertainty quantification, espe- cially when the non-intrusive methods are significantly expensive, which is mostly true for a lot of stochastic CFD problems

|Lib>: A cross-platform programming framework for quantum-accelerated scientific computing

This paper introduces a new cross-platform programming framework for developing quantum-accelerated scientific computing applications and executing them on most of today’s cloud-based quantum computers and simulators. It makes use of C++ template meta-programming techniques to implement quantum algorithms as generic, platform-independent expressions, which get automatically synthesized into device-specific compute kernels upon execution. Our software framework supports concurrent and asynchronous execution of multiple quantum kernels via a CUDA-inspired stream concept.</p

An adaptive network model for burnout and dreaming

As burnouts grow increasingly common, the necessity for a model describing burnout dynamics becomes increasingly apparent. The model discussed in this paper builds on previous research by adding dreams, a component that has been shown to have an adaptive regulating effect on emotions. The proposed model is a first-order adaptive temporal-causal network model, incorporating emotions, exercise, sleep, and dreams. The model was validated against given patterns found in empirical literature and it may be used to gain a better understanding of burnout dynamics

Strategic Use of Data Assimilation for Dynamic Data-Driven Simulation

Dynamic data-driven simulation (DDDS) incorporates real-time measurement data to improve simulation models during model run-time. Data assimilation (DA) methods aim to best approximate model states with imperfect measurements, where particle Filters (PFs) are commonly used with discrete-event simulations. In this paper, we study three critical conditions of DA using PFs: (1) the time interval of iterations, (2) the number of particles and (3) the level of actual and perceived measurement errors (or noises), and provide recommendations on how to strategically use data assimilation for DDDS considering these conditions. The results show that the estimation accuracy in DA is more constrained by the choice of time intervals than the number of particles. Good accuracy can be achieved without many particles if the time interval is sufficiently short. An over estimation of the level of measurement errors has advantages over an under estimation. Moreover, a slight over estimation has better estimation accuracy and is more responsive to system changes than an accurate perceived level of measurement errors

An adaptive computational network model for strange loops in political evolution in society

In this paper a multi-order adaptive temporal-causal network model is introduced to model political evolution. The computational network model makes use of Hofstadter’s notion of a Strange Loop and was tested and validated successfully to reflect political oscillations seen in presidential elections in the USA over time

Computational analysis of the adaptive causal relationships between cannabis, anxiety and sleep

In this paper an adaptive computational temporal-causal network model is presented to analyse the dynamic and adaptive relationships between cannabis usage, anxiety and sleep. The model has been used to simulate different well-known scenarios varying from intermittent usage to longer periods of usage interrupted by attempts to quit and to constant usage based on full addiction. It is described how the model has been verified and validated by empirical information from the literature