The main problem in information storage has previously been how large amounts of data can be stored. However, the technological development over the years has been able to give rather satisfactory answers to this prob...
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The main problem in information storage has previously been how large amounts of data can be stored. However, the technological development over the years has been able to give rather satisfactory answers to this problem. Recently, the focus has shifted towards determining how stored information can be efficiently retrieved. This problem is addressed in an article by E. Yaakobi and J. Bruck (2012), where information retrieval in associative memories is studied. In this paper, we focus on the case where the retrieved information unit is unambiguous. In particular, we present characterizations and study various extremal properties of such associative memories. Moreover, the algorithmic complexity of certain naturally rising problems is considered. (C) 2015 Elsevier Inc. All rights reserved.
Non-local means filtering (NLM), has garnered a large amount of interest in the image processing community due to its capability to exploit image patch self-similarity in order to effectively filter noisy images. Howe...
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ISBN:
(纸本)9781467399623
Non-local means filtering (NLM), has garnered a large amount of interest in the image processing community due to its capability to exploit image patch self-similarity in order to effectively filter noisy images. However, the computational complexity of non-local means filtering is the product of three different factors;namely, O(NDK), where K is the number of filter kernel taps (e.g. search window size), D is the number of patch taps, and N is number of pixels. We propose a fast approximation of non-local means filtering using the multiscale methodology of the pull-push scattered data interpolation method. By using NLM with a small filter kernel to selectively propagate filtering results and noise variance estimates from fine to coarse scales and back, the process can be used to provide comparable filtering capability to brute force NLM but with algorithmic complexity that is linear in the number of image pixels and the patch comparison taps, O(ND). In practical application, we demonstrate its denoising capability is comparable to NLM with much larger filter kernels, but at a fraction of the computational cost.
A flat cover is a collection of flats identifying the non-bases of a matroid. We introduce the notion of cover complexity, the minimal size of such a flat cover, as a measure for the complexity of a matroid, and prese...
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A flat cover is a collection of flats identifying the non-bases of a matroid. We introduce the notion of cover complexity, the minimal size of such a flat cover, as a measure for the complexity of a matroid, and present bounds on the number of matroids on n elements whose cover complexity is bounded. We apply cover complexity to show that the class of matroids without an N-minor is asymptotically small in case N is one of the sparse paving matroids U-2,U-k, U-3,U-6, P-6, Q(6) or R-6, thus confirming a few special cases of a conjecture due to Mayhew, Newman, Welsh, and Whittle. On the other hand, we show a lower bound on the number of matroids without an M(K-4)-minor which asymptotically matches the best known lower bound on the number of all matroids, due to Knuth. (C) 2014 Elsevier Inc. All rights reserved.
The skeleton is an efficient and complete shape descriptor often used for matching. However, existing skeleton-based shape matching methods are computationally intensive. To reduce the algorithmic complexity, we propo...
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ISBN:
(纸本)9781467399623
The skeleton is an efficient and complete shape descriptor often used for matching. However, existing skeleton-based shape matching methods are computationally intensive. To reduce the algorithmic complexity, we propose to exploit the natural hierarchy of the skeleton. The aim is to quantify the importance of skeleton branches to guide the shape matching algorithm, in order to match branches having the same order of importance. Our method is based on successive shape smoothing operations and on the deformability of the skeleton to adapt it to each smoothed shape. Moreover, we show that our method is independent from the initial skeleton.
The use of Bayesian Networks (BNs) as classifiers in different fields of application has recently witnessed a noticeable growth. Yet, the Naive Bayes application, and even the augmented Naive Bayes, to classifier-stru...
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The use of Bayesian Networks (BNs) as classifiers in different fields of application has recently witnessed a noticeable growth. Yet, the Naive Bayes application, and even the augmented Naive Bayes, to classifier-structure learning, has been vulnerable to certain limits, which explains the practitioners resort to other more sophisticated types of algorithms. Consequently, the use of such algorithms has paved the way for raising the problem of super-exponential increase in computational complexity of the Bayesian classifier learning structure, with the increasing number of descriptive variables. In this context, the present work's major objective lies in setting up a further solution whereby a remedy can be conceived for the intricate algorithmic complexity imposed during the learning of Bayesian classifiers structure with the use of sophisticated algorithms. (C) 2015 The Authors. Published by Elsevier B.V.
How does children's limited processing capacity affect cultural transmission of complex information? We show that over the course of iterated reproduction of two-dimensional random dot patterns transmission accura...
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How does children's limited processing capacity affect cultural transmission of complex information? We show that over the course of iterated reproduction of two-dimensional random dot patterns transmission accuracy increased to a similar extent in 5- to 8-year-old children and adults whereas algorithmic complexity decreased faster in children. Thus, children require more structure to render complex inputs learnable. In line with the Less-Is-More hypothesis, we interpret this as evidence that children's processing limitations affecting working memory capacity and executive control constrain the ability to represent and generate complexity, which, in turn, facilitates emergence of structure. This underscores the importance of investigating the role of children in the transmission of complex cultural traits. (C) 2014 Elsevier B.V. All rights reserved.
The use of Bayesian Networks (BNs) as classifiers in different application fields has recently witnessed a noticeable growth. Yet, using the Naïve Bayes application, and even the augmented Naïve Bayes, to cl...
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Different measures derived from linear and non linear fields are used in order to discriminate between normal fetuses and those exposed to cigarette smoking produced substances during pregnancy as well as fetuses with...
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ISBN:
(纸本)9783319111278
Different measures derived from linear and non linear fields are used in order to discriminate between normal fetuses and those exposed to cigarette smoking produced substances during pregnancy as well as fetuses with intra-uterine growth restriction (IUGR). There are computed parameters that indicate correlation or measures of complexity of the Fetal Heart Rate (FHR), in order to get closer to the core of information that the Cardiotocography (CTG) signal can convey and thus increase our understanding of FHR fluctuations. We analyzed signals recorded from 84 low risk pregnant women without any risk factor in a singleton pregnancy, 15 pregnant women with IUGR and 15 pregnant women that smoked during pregnancy. The analysis of FHR has shown that some parameters, such as Pearson Autocorrelation, Kurtosis and algorithmic complexity in cigarette-exposed fetuses as well as Hjorth Mobility in fetuses with IUGR could be used for early recognition of these potentially dangerous conditions and thus form biomarkers for risk stratification.
Various existing performance metrics within the parallel systems domain are analyzed. These include the different flavors of speedup, efficiency, and isoefficiency. Execution time still remains the most widely used me...
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ISBN:
(纸本)9781467369619
Various existing performance metrics within the parallel systems domain are analyzed. These include the different flavors of speedup, efficiency, and isoefficiency. Execution time still remains the most widely used metric. A new method to automatically estimate algorithmic cost is provided.
We undertake an extensive numerical investigation of the graph spectra of thousands regular graphs, a set of random Erdos-Renyi graphs, the two most popular types of complex networks and an evolving genetic network by...
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ISBN:
(纸本)9783319164809;9783319164793
We undertake an extensive numerical investigation of the graph spectra of thousands regular graphs, a set of random Erdos-Renyi graphs, the two most popular types of complex networks and an evolving genetic network by using novel conceptual and experimental tools. Our objective in so doing is to contribute to an understanding of the meaning of the Eigenvalues of a graph relative to its topological and information-theoretic properties. We introduce a technique for identifying the most informative Eigenvalues of evolving networks by comparing graph spectra behavior to their algorithmic complexity. We suggest that extending techniques can be used to further investigate the behavior of evolving biological networks. In the extended version of this paper we apply these techniques to seven tissue specific regulatory networks as static example and network of a naive pluripotent immune cell in the process of differentiating towards a Th17 cell as evolving example, finding the most and least informative Eigenvalues at every stage.
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