Fernando C. Silva

Comparison of phytotherapy (Permixon®) with finasteride in the treatment of benign prostate hyperplasia: A randomized international study of 1,098 patients

Controversy regarding the relative efficacy of treatments for the relief of the symptoms of benign prostatic hyperplasia (BPH).

Weak majorization inequalities and convex functions

Abstract Let f be a convex function defined on an interval I, 0⩽α⩽1 and A,B n×n complex Hermitian matrices with spectrum in I. We prove that the eigenvalues of f(αA+(1−α)B) are weakly majorized by the eigenvalues of αf(A)+(1−α)f(B). Further if f is log convex we prove that the eigenvalues of f(αA+(1−α)B) are weakly majorized by the eigenvalues of f(A)αf(B)1−α. As applications we obtain generalizations of the famous Golden–Thomson trace inequality, a representation theorem and a harmonic–geometric mean inequality. Some related inequalities are discussed.

Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots.

Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.

odNEAT: An Algorithm for Distributed Online, Onboard Evolution of Robot Behaviours

We propose and evaluate a novel approach called Online Distributed NeuroEvolution of Augmenting Topologies (odNEAT). odNEAT is a completely distributed evolutionary algorithm for online learning in groups of embodied agents such as robots. While previous approaches to online distributed evolution of neural controllers have been limited to the optimisation of weights, odNEAT evolves both weights and network topology. We demonstrate odNEAT through a series of simulation-based experiments in which a group of e-puck-like robots must perform an aggregation task. Our results show that robots are capable of evolving effective aggregation strategies and that sustainable behaviours evolve quickly. We show that odNEAT approximates the performance of rtNEAT, a similar but centralised method. We also analyse the contribution of each algorithmic component on the performance through a series of ablation studies.

Matrices with prescribed eigenvalues and blocks

Abstract We solve the following problem proposed by Oliveira: Under what conditions does there exist a square matrix A = [ A ij ], i , j ∈ {1,2} ( A 11 is principal) over an arbitrary field, with prescribed eigenvalues and prescribed submatrices A 11 , A 12 , and A 22 ?

Odneat: An algorithm for decentralised online evolution of robotic controllers

Online evolution gives robots the capacity to learn new tasks and to adapt to changing environmental conditions during task execution. Previous approaches to online evolution of neural controllers are typically limited to the optimisation of weights in networks with a prespecified, fixed topology. In this article, we propose a novel approach to online learning in groups of autonomous robots called odNEAT. odNEAT is a distributed and decentralised neuroevolution algorithm that evolves both weights and network topology. We demonstrate odNEAT in three multirobot tasks: aggregation, integrated navigation and obstacle avoidance, and phototaxis. Results show that odNEAT approximates the performance of rtNEAT, an efficient centralised method, and outperforms IM-(), a decentralised neuroevolution algorithm. Compared with rtNEAT and IM-(), odNEAT’s evolutionary dynamics lead to the synthesis of less complex neural controllers with superior generalisation capabilities. We show that robots executing odNEAT can display a high degree of fault tolerance as they are able to adapt and learn new behaviours in the presence of faults. We conclude with a series of ablation studies to analyse the impact of each algorithmic component on performance.

Similarity invariants of completions of submatrices

Abstract We study the similarity invariants of a square matrix when we prescribe an arbitrary submatrix.

The eigenvalues of the product of matrices with prescribed similarity classes

Let A and B be n × n nonsingular matrices over a field F, and c 1,…,c n ∈ F. We give a necessary and sufficient condition for the existence of matrices A′ and B′ similar to A and B, respectively, such that A′ B′ has eigenvalues c 1,…,c n.

Unified theorems on completions of matrix pencils

Abstract We generalize and unify some theorems concerned with the existence of a pencil with a prescribed subpencil and prescribed strict equivalence class.

Eigenvalues of matrices with several prescribed blocks, II

Abstract A previous paper by D. Hershkowitz [Linear and Multilinear Algebra 14 (1983) 315–342] described the possible eigenvalues of an n×n matrix when 2n−3 entries are fixed and the others vary. This paper describes the possible eigenvalues of a pk×pk matrix, partitioned into k×k blocks of size p×p when 2k−3 blocks are fixed and the others vary.