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

Cloud service purchasedecision process

This article analyzes questions arising from a choice of available cloud computing services. Precisely what type of services should an organization choose? The article aims to introduce decision-making process according to the best practices. In order to describe this decision-making process we divide organizations into classes and for each class we recommend one or more available types of suitable cloud computing services. Moreover, there are introduced interesting cloud computing provider practices and service aspects including service operation, service parameters and service costs There are also introduced steps for customers how to deal with mentioned aspects

Leveraging simulation practice in industry through use of desktop grid middleware

This chapter focuses on the collaborative use of computing resources to support decision making in industry. Through the use of middleware for desktop grid computing, the idle CPU cycles available on existing computing resources can be harvested and used for speeding-up the execution of applications that have “non-trivial” processing requirements. This chapter focuses on the desktop grid middleware BOINC and Condor, and discusses the integration of commercial simulation software together with free-to-download grid middleware so as to offer competitive advantage to organizations that opt for this technology. It is expected that the low-intervention integration approach presented in this chapter (meaning no changes to source code required) will appeal to both simulation practitioners (as simulations can be executed faster, which in turn would mean that more replications and optimization is possible in the same amount of time) and the management (as it can potentially increase the return on investment on existing resources)

Towards hydrodynamics without an entropy current

We present a generating functional which describes the equilibrium thermodynamic response of a relativistic system to external sources. A variational principle gives rise to constraints on the response parameters of relativistic hydrodynamics without making use of an entropy current. Our method reproduces and extends results available in the literature. It also provides a technique for efficiently computing n-point zero-frequency hydrodynamic correlation functions without the need to solve the equations of hydrodynamics.Comment: 4+epsilon pages, v2: comments and references adde

An Algorithmic Framework for Computing Validation Performance Bounds by Using Suboptimal Models

Practical model building processes are often time-consuming because many different models must be trained and validated. In this paper, we introduce a novel algorithm that can be used for computing the lower and the upper bounds of model validation errors without actually training the model itself. A key idea behind our algorithm is using a side information available from a suboptimal model. If a reasonably good suboptimal model is available, our algorithm can compute lower and upper bounds of many useful quantities for making inferences on the unknown target model. We demonstrate the advantage of our algorithm in the context of model selection for regularized learning problems

Arbitrarily Large Continuous-Variable Cluster States from a Single Quantum Nondemolition Gate

We present a compact experimental design for producing an arbitrarily large optical continuous-variable cluster state using just one single-mode vacuum squeezer and one quantum nondemolition gate. Generating the cluster state and computing with it happen simultaneously: more entangled modes become available as previous modes are measured, thereby making finite the requirements for coherence and stability even as the computation length increases indefinitely.Comment: (v2) 5 pages, 4 color figures, added brief mention of fault tolerance, version accepted for publication (note: actual published version is edited slightly for space); (v1) 4 pages, 4 color figure