L. Nunes | Searcharxiv

arXiv2020

Effects of anisotropic correlations in fermionic zero-energy bound states of topological phases

Topological phases of matter have been used as a fertile realm of intensive discussions about fermionic fractionalization. In this work, we study the effects of anisotropic superconducting correlations in the fermionic fractionalization on the topological phases. We consider a hybrid version of the SSH and Kitaev models with an anisotropic superconducting order parameter to investigate the unusual states with zero energy that emerges in a finite chain. To obtain these zero energy solutions, we built a chain with a well-defined domain wall at the middle of the chain. Our solutions indicate an interesting dynamic between the zero-energy state around the domain wall and the superconducting correlation parameters. Finally, we find that the presence of an isolated Majorana at the ends of the chain is strongly dependent on the existence of the solitonic excitation at the middle of the chain.

cond-mat.mes-hall↗

arXiv2002

On Learning by Exchanging Advice

One of the main questions concerning learning in Multi-Agent Systems is: (How) can agents benefit from mutual interaction during the learning process?. This paper describes the study of an interactive advice-exchange mechanism as a possible way to improve agents' learning performance. The advice-exchange technique, discussed here, uses supervised learning (backpropagation), where reinforcement is not directly coming from the environment but is based on advice given by peers with better performance score (higher confidence), to enhance the performance of a heterogeneous group of Learning Agents (LAs). The LAs are facing similar problems, in an environment where only reinforcement information is available. Each LA applies a different, well known, learning technique: Random Walk (hill-climbing), Simulated Annealing, Evolutionary Algorithms and Q-Learning. The problem used for evaluation is a simplified traffic-control simulation. Initial results indicate that advice-exchange can improve learning speed, although bad advice and/or blind reliance can disturb the learning performance.

cs.LG↗