Optoelectronic Reservoir Computing

Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processing time dependent data. The basic scheme of reservoir computing consists of a non linear recurrent dynamical system coupled to a single input layer and a single output layer. Within these constraints many implementations are possible. Here we report an opto-electronic implementation of reservoir computing based on a recently proposed architecture consisting of a single non linear node and a delay line. Our implementation is sufficiently fast for real time information processing. We illustrate its performance on tasks of practical importance such as nonlinear channel equalization and speech recognition, and obtain results comparable to state of the art digital implementations.Comment: Contains main paper and two Supplementary Material

The Political Economy of Myanmar's Transition

This is an Author's Original Manuscript of an article whose final and definitive form, the Version of Record, has been published in the JOURNAL OF CONTEMPORARY ASIA, 07 Feb 2013, copyright Taylor & Francis, available online at: http://www.tandfonline.com/10.1080/00472336.2013.764143.Since holding elections in 2010, Myanmar has transitioned from a direct military dictatorship to a formally democratic system and has embarked on a period of rapid economic reform. After two decades of military rule, the pace of change has startled almost everyone and led to a great deal of cautious optimism. To make sense of the transition and assess the case for optimism, this article explores the political economy of Myanmar's dual transition from state socialism to capitalism and from dictatorship to democracy. It analyses changes within Myanmar society from a critical political economy perspective in order to both situate these developments within broader regional trends and to evaluate the country's current trajectory. In particular, the emergence of state-mediated capitalism and politico-business complexes in Myanmar's borderlands are emphasised. These dynamics, which have empowered a narrow oligarchy, are less likely to be undone by the reform process than to fundamentally shape the contours of reform. Consequently, Myanmar's future may not be unlike those of other Southeast Asian states that have experienced similar developmental trajectories

Reservoir Topology in Deep Echo State Networks

Deep Echo State Networks (DeepESNs) recently extended the applicability of Reservoir Computing (RC) methods towards the field of deep learning. In this paper we study the impact of constrained reservoir topologies in the architectural design of deep reservoirs, through numerical experiments on several RC benchmarks. The major outcome of our investigation is to show the remarkable effect, in terms of predictive performance gain, achieved by the synergy between a deep reservoir construction and a structured organization of the recurrent units in each layer. Our results also indicate that a particularly advantageous architectural setting is obtained in correspondence of DeepESNs where reservoir units are structured according to a permutation recurrent matrix.Comment: Preprint of the paper published in the proceedings of ICANN 201

Reservoir Computing Approach to Robust Computation using Unreliable Nanoscale Networks

As we approach the physical limits of CMOS technology, advances in materials science and nanotechnology are making available a variety of unconventional computing substrates that can potentially replace top-down-designed silicon-based computing devices. Inherent stochasticity in the fabrication process and nanometer scale of these substrates inevitably lead to design variations, defects, faults, and noise in the resulting devices. A key challenge is how to harness such devices to perform robust computation. We propose reservoir computing as a solution. In reservoir computing, computation takes place by translating the dynamics of an excited medium, called a reservoir, into a desired output. This approach eliminates the need for external control and redundancy, and the programming is done using a closed-form regression problem on the output, which also allows concurrent programming using a single device. Using a theoretical model, we show that both regular and irregular reservoirs are intrinsically robust to structural noise as they perform computation

Spin-orbit coupling control of anisotropy, ground state and frustration in 5d2 Sr2MgOsO6

The influence of spin-orbit coupling (SOC) on the physical properties of the 5d2 system Sr2MgOsO6 is probed via a combination of magnetometry, specific heat measurements, elastic and inelastic neutron scattering, and density functional theory calculations. Although a significant degree of frustration is expected, we find that Sr2MgOsO6 orders in a type I antiferromagnetic structure at the remarkably high temperature of 108 K. The measurements presented allow for the first accurate quantification of the size of the magnetic moment in a 5d2 system of 0.60(2) muB - a significantly reduced moment from the expected value for such a system. Furthermore, significant anisotropy is identified via a spin excitation gap, and we confirm by first principles calculations that SOC not only provides the magnetocrystalline anisotropy, but also plays a crucial role in determining both the ground state magnetic order and the size of the local moment in this compound. Through comparison to Sr2ScOsO6, it is demonstrated that SOC-induced anisotropy has the ability to relieve frustration in 5d2 systems relative to their 5d3 counterparts, providing an explanation of the high TN found in Sr2MgOsO6.Comment: Submitted to Scientific Report

The role of structure and complexity on Reservoir Computing quality

We explore the effect of structure and connection complexity on the dynamical behaviour of Reservoir Computers (RC). At present, considerable effort is taken to design and hand-craft physical reservoir computers. Both structure and physical complexity are often pivotal to task performance, however, assessing their overall importance is challenging. Using a recently proposed framework, we evaluate and compare the dynamical freedom (referring to quality) of neural network structures, as an analogy for physical systems. The results quantify how structure affects the range of behaviours exhibited by these networks. It highlights that high quality reached by more complex structures is often also achievable in simpler structures with greater network size. Alternatively, quality is often improved in smaller networks by adding greater connection complexity. This work demonstrates the benefits of using abstract behaviour representation, rather than evaluation through benchmark tasks, to assess the quality of computing substrates, as the latter typically has biases, and often provides little insight into the complete computing quality of physical systems

Social Cohesion, Structural Holes, and a Tale of Two Measures

EMBARGOED - author can archive pre-print or post-print on any open access repository after 12 months from publication. Publication date is May 2013 so embargoed until May 2014.This is an author’s accepted manuscript (deposited at arXiv arXiv:1211.0719v2 [physics.soc-ph] ), which was subsequently published in Journal of Statistical Physics May 2013, Volume 151, Issue 3-4, pp 745-764. The final publication is available at link.springer.com http://link.springer.com/article/10.1007/s10955-013-0722-

Are bisphosphonates effective in the treatment of osteoarthritis pain? A meta-analysis and systematic review.

Osteoarthritis (OA) is the most common form of arthritis worldwide. Pain and reduced function are the main symptoms in this prevalent disease. There are currently no treatments for OA that modify disease progression; therefore analgesic drugs and joint replacement for larger joints are the standard of care. In light of several recent studies reporting the use of bisphosphonates for OA treatment, our work aimed to evaluate published literature to assess the effectiveness of bisphosphonates in OA treatment

Information processing using a single dynamical node as complex system

Novel methods for information processing are highly desired in our information-driven society. Inspired by the brain's ability to process information, the recently introduced paradigm known as 'reservoir computing' shows that complex networks can efficiently perform computation. Here we introduce a novel architecture that reduces the usually required large number of elements to a single nonlinear node with delayed feedback. Through an electronic implementation, we experimentally and numerically demonstrate excellent performance in a speech recognition benchmark. Complementary numerical studies also show excellent performance for a time series prediction benchmark. These results prove that delay-dynamical systems, even in their simplest manifestation, can perform efficient information processing. This finding paves the way to feasible and resource-efficient technological implementations of reservoir computing