We consider the general class of power series where the terms may be expressed as the Laplace transforms of known functions. The sum of the series can then be evaluated efficiently and accurately by means of quadratur...
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We consider the general class of power series where the terms may be expressed as the Laplace transforms of known functions. The sum of the series can then be evaluated efficiently and accurately by means of quadrature schemes, recently published by Frank Stenger. The method works also far outside the region of convergence as will be illustrated by numerical examples.
Some versions of the maximum common subgraph problem are studied and approximation algorithms are given. The maximum bounded common induced subgraph problem is shown to be Max SNP-hard and the maximum unbounded common...
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A query-reply system based on a Bayesian neural network is described. Strategies for generating questions which make the system both efficient and highly fault tolerant are presented. This involves having one phase of...
A query-reply system based on a Bayesian neural network is described. Strategies for generating questions which make the system both efficient and highly fault tolerant are presented. This involves having one phase of question generation intended to quickly reach a hypothesis followed by a phase where verification of the hypothesis is attempted. In addition, both phases have strategies for detecting and removing inconsistencies in the replies from the user. Also described is an explanatory mechanism which gives information related to why a certain hypotheses is reached or question asked. Specific examples of the systems behavior as well as the results of a statistical evaluation are presented.
Estimates concerning the spectrum of a graded matrix and other information useful for a reliable and efficient handling of certain complications in the numerical treatment of some stiff ODE's, can be inexpensively...
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Estimates concerning the spectrum of a graded matrix and other information useful for a reliable and efficient handling of certain complications in the numerical treatment of some stiff ODE's, can be inexpensively obtained from the factorized Jacobian. The validity of the estimates is studied by considering them as the first step in a block LR algorithm, which may be of interest in its own right. Its convergence properties are examined.
This paper presents an interview study in which 11 academics as interviewees participated for the purpose of revealing common collaborative writing practices, with particular focus on reviewing documents. First, we pr...
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This paper presents an interview study in which 11 academics as interviewees participated for the purpose of revealing common collaborative writing practices, with particular focus on reviewing documents. First, we present the findings obtained concerning the issues of co-operating strategies underlying the reviewing process, how people revise their documents and comment on them, what they use the previous revision history for, and to what extent current technology is used in the reviewing process. Second, we also discuss aspects of the design of collaborative writing tools.
This article shows how discrete derivative approximations can be defined so thatscale-space properties hold exactly also in the discrete domain. Starting from a set of natural requirements on the first processing stag...
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This article shows how discrete derivative approximations can be defined so thatscale-space properties hold exactly also in the discrete domain. Starting from a set of natural requirements on the first processing stages of a visual system,the visual front end, it gives an axiomatic derivation of how a multiscale representation of derivative approximations can be constructed from a discrete signal, so that it possesses analgebraic structure similar to that possessed by the derivatives of the traditional scale-space representation in the continuous domain. A family of kernels is derived that constitutediscrete analogues to the continuous Gaussian derivatives. The representation has theoretical advantages over other discretizations of the scale-space theory in the sense that operators that commute before discretizationcommute after discretization. Some computational implications of this are that derivative approximations can be computeddirectly from smoothed data and that this will giveexactly the same result as convolution with the corresponding derivative approximation kernel. Moreover, a number ofnormalization conditions are automatically satisfied. The proposed methodology leads to a scheme of computations of multiscale low-level feature extraction that is conceptually very simple and consists of four basic steps: (i)large support convolution smoothing, (ii)small support difference computations, (iii)point operations for computing differential geometric entities, and (iv)nearest-neighbour operations for feature detection. Applications demonstrate how the proposed scheme can be used for edge detection and junction detection based on derivatives up to order three.
Previous investigations of the storage capacity of associative nets have not explicitly considered quantitative aspects of the tradeoff between storage capacity and reconstructive power in these systems. Furthermore, ...
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Previous investigations of the storage capacity of associative nets have not explicitly considered quantitative aspects of the tradeoff between storage capacity and reconstructive power in these systems. Furthermore, few comparisons have been made between theoretical estimates and experimental results (simulations). In this correspondence, we describe some results recently obtained and relevant to these issues. It is shown that a high storage capacity is possible, without sacrificing reliability in the recall process. Furthermore, an efficient algorithm for retrieval of the information stored is presented, and the speed of recall employing various degrees of parallelism is discussed.","doi":"10.1109/TPAMI.1985.4767688","publicationTitle":"IEEE Transactions on Pattern analysis and Machine Intelligence","startPage":"490","endPage":"498","rightsLink":"http://***/AppDispatchServlet?publisherName=ieee&publication=0162-8828&title=Reliability+and+Speed+of+Recall+in+an+Associative+Network&isbn=&publicationDate=July+1985&author=Anders+Lansner&ContentID=10.1109/TPAMI.1985.4767688&orderBeanReset=true&startPage=490&endPage=498&volumeNum=PAMI-7&issueNum=4","displayPublicationTitle":"IEEE Transactions on Pattern analysis and Machine Intelligence","pdfPath":"/iel5/34/4767672/***","keywords":[{"type":"IEEE Keywords","kwd":["Power system reliability","Pattern recognition","Neuroscience","Associative memory","Estimation theory","Information retrieval","Parallel processing","Telecommunication network reliability","Biological neural networks","Computer science"]},{"type":"Author Keywords ","kwd":["storage capacity","Associative network","pattern completion","pattern recognition","reliability of recall"]}],"allowComments":false,"pubLink":"/xpl/***?punumber=34","issueLink":"/xpl/***?isnumber=4767672","standardTitle":"Reliability and Speed of Recall in an Associative Network
Focus-of-attention is extremely important in human visual perception. If computer vision systems are to perform tasks in a complex, dynamic world they will have to be able to control processing in a way that is analog...
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Focus-of-attention is extremely important in human visual perception. If computer vision systems are to perform tasks in a complex, dynamic world they will have to be able to control processing in a way that is analogous to visual attention in humans. Problems connected to foveation (examination of selected regions of the world at high resolution) are examined. In particular, the problem of finding and classifying junctions from this aspect is considered. It is shown that foveation as simulated by controlled, active zooming in conjunction with scale-space techniques allows for robust detection and classification of junctions.
Elementary techniques from real analysis, and singularity theory are applied to derive analytical results for the behaviour in scale-space of critical points and related entities. The main results of the treatment com...
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Elementary techniques from real analysis, and singularity theory are applied to derive analytical results for the behaviour in scale-space of critical points and related entities. The main results of the treatment comprise
In this paper we consider initial-boundary value problems for systems with a small parameter epsilon. The problems are mixed hyperbolic-parabolic when epsilon > 0 and hyperbolic when epsilon = 0. Often the solution...
In this paper we consider initial-boundary value problems for systems with a small parameter epsilon. The problems are mixed hyperbolic-parabolic when epsilon > 0 and hyperbolic when epsilon = 0. Often the solution can be expanded asymptotically in epsilon and to first approximation it consists of the solution of the corresponding hyperbolic problem and a boundary layer part. We prove sufficient conditions for the expansion to exist and give estimates of the remainder. We also examine how the boundary conditions should be choosen to avoid 0(1) boundary layers.
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