1,074,288 research outputs found
On the capacity of information processing systems
We propose and analyze a family of information processing systems, where a
finite set of experts or servers are employed to extract information about a
stream of incoming jobs. Each job is associated with a hidden label drawn from
some prior distribution. An inspection by an expert produces a noisy outcome
that depends both on the job's hidden label and the type of the expert, and
occupies the expert for a finite time duration. A decision maker's task is to
dynamically assign inspections so that the resulting outcomes can be used to
accurately recover the labels of all jobs, while keeping the system stable.
Among our chief motivations are applications in crowd-sourcing, diagnostics,
and experiment designs, where one wishes to efficiently learn the nature of a
large number of items, using a finite pool of computational resources or human
agents.
We focus on the capacity of such an information processing system. Given a
level of accuracy guarantee, we ask how many experts are needed in order to
stabilize the system, and through what inspection architecture. Our main result
provides an adaptive inspection policy that is asymptotically optimal in the
following sense: the ratio between the required number of experts under our
policy and the theoretical optimal converges to one, as the probability of
error in label recovery tends to zero
An information-theoretic on-line update principle for perception-action coupling
Inspired by findings of sensorimotor coupling in humans and animals, there
has recently been a growing interest in the interaction between action and
perception in robotic systems [Bogh et al., 2016]. Here we consider perception
and action as two serial information channels with limited
information-processing capacity. We follow [Genewein et al., 2015] and
formulate a constrained optimization problem that maximizes utility under
limited information-processing capacity in the two channels. As a solution we
obtain an optimal perceptual channel and an optimal action channel that are
coupled such that perceptual information is optimized with respect to
downstream processing in the action module. The main novelty of this study is
that we propose an online optimization procedure to find bounded-optimal
perception and action channels in parameterized serial perception-action
systems. In particular, we implement the perceptual channel as a multi-layer
neural network and the action channel as a multinomial distribution. We
illustrate our method in a NAO robot simulator with a simplified cup lifting
task.Comment: 8 pages, 2017 IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS
A FIELD-BASED INVESTIGATION INTO THE EFFECT OF EXPERT SYSTEM USE ON THE INFORMATION PROCESSING CAPACITY OF THE FIRM
To date, there have been many reports on the use of decision support systems (DSS) and knowledgebased systems (KBS) in organizations. Little of this research evaluates the impact of commercial systems on the performance of the organization using the system. This study attempts to shed some light on this intellectual domain. This paper reports on a field-based investigation into the effects of the use of MUDMAN™ (a commercial expert systems product) on the information-processing capacity of N.L. Baroid, an oil services firm. The study uses the notions of organizational programs as defined by March and Simon and operationalizes the information processing capacity framework £ut forth by Galbraith to show how information-processing capacity was affected when the MUDMAN system was used at Baroid
Optical memory disks in optical information processing
We describe the use of optical memory disks as elements in optical information processing architectures. The optical disk is an optical memory devicew ith a storage capacity approaching 1010b its which is naturally suited to parallel access. We discuss optical disk characteristics which are important in optical computing systems such as contrast, diffraction efficiency, and phase uniformity. We describe techniques for holographic storage on optical disks and present reconstructions of several types of computer-generated holograms. Various optical information processing architectures are described for applications such as database retrieval, neural network implementation, and image correlation. Selected systems are experimentally demonstrated
Quantum channels and their entropic characteristics
One of the major achievements of the recently emerged quantum information
theory is the introduction and thorough investigation of the notion of quantum
channel which is a basic building block of any data-transmitting or
data-processing system. This development resulted in an elaborated structural
theory and was accompanied by the discovery of a whole spectrum of entropic
quantities, notably the channel capacities, characterizing
information-processing performance of the channels. This paper gives a survey
of the main properties of quantum channels and of their entropic
characterization, with a variety of examples for finite dimensional quantum
systems. We also touch upon the "continuous-variables" case, which provides an
arena for quantum Gaussian systems. Most of the practical realizations of
quantum information processing were implemented in such systems, in particular
based on principles of quantum optics. Several important entropic quantities
are introduced and used to describe the basic channel capacity formulas. The
remarkable role of the specific quantum correlations - entanglement - as a
novel communication resource, is stressed.Comment: review article, 60 pages, 5 figures, 194 references; Rep. Prog. Phys.
(in press
Information Processing view of Electricity Demand Response Systems: A Comparative Study Between India and Australia
Background: In recent years, demand response (DR) has gained increased attention from utilities, regulators, and market aggregators to meet the growing demands of electricity. The key aspect of a successful DR program is the effective processing of data and information to gain critical insights. This study aims to identify information processing needs and capacity that interact to improve energy DR effectiveness. To this end, organizational information processing theory (OIPT) is employed to understand the role of Information Systems (IS) resources in achieving desired DR program performance. This study also investigates how information processing for DR systems differ between developing (India) and developed (Australia) countries.
Method: This work adopts a case study methodology to propose a theoretical framework using OIPT for information processing in DR systems. The study further employs a comparative case data analyses between Australian and Indian DR initiatives.
Results: Our cross case analysis identifies variables of value creation in designing DR programs - pricing structure for demand side participation, renewable integration at supply side, reforms in the regulatory instruments, and emergent technology. This research posits that the degree of information processing capacity mediates the influence of information processing needs on energy DR effectiveness. Further, we develop five propositions on the interaction between task based information processing needs and capacity, and their influence on DR effectiveness.
Conclusions: The study generates insights on the role of IS resources that can help stakeholders in the electricity value chain to take informed and intelligent decisions for improved performance of DR programs
- …