Pooling or sampling: Collective dynamics for electrical flow estimation

The computation of electrical flows is a crucial primitive for many recently proposed optimization algorithms on weighted networks. While typically implemented as a centralized subroutine, the ability to perform this task in a fully decentralized way is implicit in a number of biological systems. Thus, a natural question is whether this task can provably be accomplished in an efficient way by a network of agents executing a simple protocol. We provide a positive answer, proposing two distributed approaches to electrical flow computation on a weighted network: a deterministic process mimicking Jacobi's iterative method for solving linear systems, and a randomized token diffusion process, based on revisiting a classical random walk process on a graph with an absorbing node. We show that both processes converge to a solution of Kirchhoff's node potential equations, derive bounds on their convergence rates in terms of the weights of the network, and analyze their time and message complexity

The Changing Geometry of a Fitness Landscape Along an Adaptive Walk

It has recently been noted that the relative prevalence of the various kinds of epistasis varies along an adaptive walk. This has been explained as a result of mean regression in NK model fitness landscapes. Here we show that this phenomenon occurs quite generally in fitness landscapes. We propose a simple and general explanation for this phenomemon, confirming the role of mean regression. We provide support for this explanation with simulations, and discuss the empirical relevance of our findings.Comment: 29 pages, 7 figure

A Hybrid Monte Carlo Ant Colony Optimization Approach for Protein Structure Prediction in the HP Model

The hydrophobic-polar (HP) model has been widely studied in the field of protein structure prediction (PSP) both for theoretical purposes and as a benchmark for new optimization strategies. In this work we introduce a new heuristics based on Ant Colony Optimization (ACO) and Markov Chain Monte Carlo (MCMC) that we called Hybrid Monte Carlo Ant Colony Optimization (HMCACO). We describe this method and compare results obtained on well known HP instances in the 3 dimensional cubic lattice to those obtained with standard ACO and Simulated Annealing (SA). All methods were implemented using an unconstrained neighborhood and a modified objective function to prevent the creation of overlapping walks. Results show that our methods perform better than the other heuristics in all benchmark instances.Comment: In Proceedings Wivace 2013, arXiv:1309.712

How do field of view and resolution affect the information content of panoramic scenes for visual navigation? A computational investigation

The visual systems of animals have to provide information to guide behaviour and the informational requirements of an animal’s behavioural repertoire are often reflected in its sensory system. For insects, this is often evident in the optical array of the compound eye. One behaviour that insects share with many animals is the use of learnt visual information for navigation. As ants are expert visual navigators it may be that their vision is optimised for navigation. Here we take a computational approach in asking how the details of the optical array influence the informational content of scenes used in simple view matching strategies for orientation. We find that robust orientation is best achieved with low-resolution visual information and a large field of view, similar to the optical properties seen for many ant species. A lower resolution allows for a trade-off between specificity and generalisation for stored views. Additionally, our simulations show that orientation performance increases if different portions of the visual field are considered as discrete visual sensors, each giving an independent directional estimate. This suggests that ants might benefit by processing information from their two eyes independently