1,053,959 research outputs found
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
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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
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