116,586 research outputs found
Analytic machines
In this paper we present some results about it analytic machines regarding th power of computations over sf bf Q and sf bf R, solutions of differential equations and the stability problem of dynamical systems. We first explain the machine model, wich is a kind of sc Blum-Shub-Smale machine enhanced by infinite convergent computiations. Next, we compare the computional power of such machinesofer the fields sf bf Q and sf bf R showing that finite computations with real numbers can be simulated by infinite converging computations on rational numbers, but the precision of the approximation is not known during the process. Our attention is then shifted to it ordinary differential equations (ODEs), dynamical systems described by ODEs and the undecidability of a class of stability problems for dynamical syste
Investigating the generalizability of EEG-based Cognitive Load Estimation Across Visualizations
We examine if EEG-based cognitive load (CL) estimation is generalizable
across the character, spatial pattern, bar graph and pie chart-based
visualizations for the nback~task. CL is estimated via two recent approaches:
(a) Deep convolutional neural network, and (b) Proximal support vector
machines. Experiments reveal that CL estimation suffers across visualizations
motivating the need for effective machine learning techniques to benchmark
visual interface usability for a given analytic task
A Boltzmann machine for the organization of intelligent machines
In the present technological society, there is a major need to build machines that would execute intelligent tasks operating in uncertain environments with minimum interaction with a human operator. Although some designers have built smart robots, utilizing heuristic ideas, there is no systematic approach to design such machines in an engineering manner. Recently, cross-disciplinary research from the fields of computers, systems AI and information theory has served to set the foundations of the emerging area of the design of intelligent machines. Since 1977 Saridis has been developing an approach, defined as Hierarchical Intelligent Control, designed to organize, coordinate and execute anthropomorphic tasks by a machine with minimum interaction with a human operator. This approach utilizes analytical (probabilistic) models to describe and control the various functions of the intelligent machine structured by the intuitively defined principle of Increasing Precision with Decreasing Intelligence (IPDI) (Saridis 1979). This principle, even though resembles the managerial structure of organizational systems (Levis 1988), has been derived on an analytic basis by Saridis (1988). The purpose is to derive analytically a Boltzmann machine suitable for optimal connection of nodes in a neural net (Fahlman, Hinton, Sejnowski, 1985). Then this machine will serve to search for the optimal design of the organization level of an intelligent machine. In order to accomplish this, some mathematical theory of the intelligent machines will be first outlined. Then some definitions of the variables associated with the principle, like machine intelligence, machine knowledge, and precision will be made (Saridis, Valavanis 1988). Then a procedure to establish the Boltzmann machine on an analytic basis will be presented and illustrated by an example in designing the organization level of an Intelligent Machine. A new search technique, the Modified Genetic Algorithm, is presented and proved to converge to the minimum of a cost function. Finally, simulations will show the effectiveness of a variety of search techniques for the intelligent machine
GLAMER Part II: Multiple Plane Gravitational Lensing
We present an extension to multiple planes of the gravitational lensing code
{\small GLAMER}. The method entails projecting the mass in the observed
light-cone onto a discrete number of lens planes and inverse ray-shooting from
the image to the source plane. The mass on each plane can be represented as
halos, simulation particles, a projected mass map extracted form a numerical
simulation or any combination of these. The image finding is done in a source
oriented fashion, where only regions of interest are iteratively refined on an
initially coarse image plane grid. The calculations are performed in parallel
on shared memory machines. The code is able to handle different types of
analytic halos (NFW, NSIE, power-law, etc.), haloes extracted from numerical
simulations and clusters constructed from semi-analytic models ({\small MOKA}).
Likewise, there are several different options for modeling the source(s) which
can be distributed throughout the light-cone. The distribution of matter in the
light-cone can be either taken from a pre-existing N-body numerical
simulations, from halo catalogs, or are generated from an analytic mass
function. We present several tests of the code and demonstrate some of its
applications such as generating mock images of galaxy and galaxy cluster
lenses.Comment: 14 pages, 10 figures, submitted to MNRA
Throughput enhancement with parallel redundancy in multi-product flow line system
We develop a new analytic approximation method to replace a set of parallel machines by an equivalent machine in a series-parallel flow line with finite buffer. We develop our method based on discrete state Markov chain. The proposed technique replaces a set of parallel machines at a work centre by an equivalent machine in order to obtain a traditional flow line with machines in series separated by intermediate buffers. We derive equations for the parameters of the equivalent machine when it operates in isolation as well as in flow line. The existing analytic methods for series-parallel systems can tract only lines with a maximum of two machines in series and a buffer in-between them. The method we propose in this thesis can be used in conjunction with an approximation method or simulation to solve flow lines of any length. We also model and evaluate the performance of series-parallel systems manufacturing more than one product types with predefined sequence and lot size. We address this issue for a considerable longer flow line system with finite buffer which is common in industry. We consider the set-up time of the machines as the product type changes, deterministic processing times and operation dependent failures of the machines. We analyze the effects of buffer and number of machines in parallel on the performance of series-parallel systems
Bayesian Structural Inference for Hidden Processes
We introduce a Bayesian approach to discovering patterns in structurally
complex processes. The proposed method of Bayesian Structural Inference (BSI)
relies on a set of candidate unifilar HMM (uHMM) topologies for inference of
process structure from a data series. We employ a recently developed exact
enumeration of topological epsilon-machines. (A sequel then removes the
topological restriction.) This subset of the uHMM topologies has the added
benefit that inferred models are guaranteed to be epsilon-machines,
irrespective of estimated transition probabilities. Properties of
epsilon-machines and uHMMs allow for the derivation of analytic expressions for
estimating transition probabilities, inferring start states, and comparing the
posterior probability of candidate model topologies, despite process internal
structure being only indirectly present in data. We demonstrate BSI's
effectiveness in estimating a process's randomness, as reflected by the Shannon
entropy rate, and its structure, as quantified by the statistical complexity.
We also compare using the posterior distribution over candidate models and the
single, maximum a posteriori model for point estimation and show that the
former more accurately reflects uncertainty in estimated values. We apply BSI
to in-class examples of finite- and infinite-order Markov processes, as well to
an out-of-class, infinite-state hidden process.Comment: 20 pages, 11 figures, 1 table; supplementary materials, 15 pages, 11
figures, 6 tables; http://csc.ucdavis.edu/~cmg/compmech/pubs/bsihp.ht
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