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591,767
Maximal antichains of minimum size
, 2013
"... Let n � 4 be a natural number, and let K be a set K ⊆ [n]: = {1, 2,..., n}. We study the problem to find the smallest possible size of a maximal family A of subsets of [n] such that A contains only sets whose size is in K, and A ⊆ B for all {A, B} ⊆ A, i.e. A is an antichain. We present a general ..."
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Cited by 1 (1 self)
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Let n � 4 be a natural number, and let K be a set K ⊆ [n]: = {1, 2,..., n}. We study the problem to find the smallest possible size of a maximal family A of subsets of [n] such that A contains only sets whose size is in K, and A ⊆ B for all {A, B} ⊆ A, i.e. A is an antichain. We present a general
Simultaneous Multithreading: Maximizing OnChip Parallelism
, 1995
"... This paper examines simultaneous multithreading, a technique permitting several independent threads to issue instructions to a superscalar’s multiple functional units in a single cycle. We present several models of simultaneous multithreading and compare them with alternative organizations: a wide s ..."
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Cited by 802 (48 self)
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This paper examines simultaneous multithreading, a technique permitting several independent threads to issue instructions to a superscalar’s multiple functional units in a single cycle. We present several models of simultaneous multithreading and compare them with alternative organizations: a wide superscalar, a finegrain multithreaded processor, and singlechip, multipleissue multiprocessing architectures. Our results show that both (singlethreaded) superscalar and finegrain multithreaded architectures are limited in their ability to utilize the resources of a wideissue processor. Simultaneous multithreading has the potential to achieve 4 times the throughput of a superscalar, and double that of finegrain multithreading. We evaluate several cache configurations made possible by this type of organization and evaluate tradeoffs between them. We also show that simultaneous multithreading is an attractive alternative to singlechip multiprocessors; simultaneous multithreaded processors with a variety of organizations outperform corresponding conventional multiprocessors with similar execution resources. While simultaneous multithreading has excellent potential to increase processor utilization, it can add substantial complexity to the design. We examine many of these complexities and evaluate alternative organizations in the design space.
Minimum Error Rate Training in Statistical Machine Translation
, 2003
"... Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training cri ..."
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Cited by 663 (7 self)
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Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training criteria which directly optimize translation quality.
Maximal flat antichains of minimum weight
"... We study maximal families A of subsets of [n] = {1,2,...,n} such that A contains only pairs and triples and A ̸ ⊆ B for all {A,B} ⊆ A, i.e. A is an antichain. For any n, all such families A of minimum size are determined. This is equivalent to finding all graphs G = (V,E) with V  = n and with t ..."
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Cited by 4 (3 self)
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We study maximal families A of subsets of [n] = {1,2,...,n} such that A contains only pairs and triples and A ̸ ⊆ B for all {A,B} ⊆ A, i.e. A is an antichain. For any n, all such families A of minimum size are determined. This is equivalent to finding all graphs G = (V,E) with V  = n
Segmentation of brain MR images through a hidden Markov random field model and the expectationmaximization algorithm
 IEEE TRANSACTIONS ON MEDICAL. IMAGING
, 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogrambased model, the FM has an intrinsic limi ..."
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Cited by 619 (14 self)
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The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogrambased model, the FM has an intrinsic limitation—no spatial information is taken into account. This causes the FM model to work only on welldefined images with low levels of noise; unfortunately, this is often not the the case due to artifacts such as partial volume effect and bias field distortion. Under these conditions, FM modelbased methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown that the FM model is a degenerate version of the HMRF model. The advantage of the HMRF model derives from the way in which the spatial information is encoded through the mutual influences of neighboring sites. Although MRF modeling has been employed in MR image segmentation by other researchers, most reported methods are limited to using MRF as a general prior in an FM modelbased approach. To fit the HMRF model, an EM algorithm is used. We show that by incorporating both the HMRF model and the EM algorithm into a HMRFEM framework, an accurate and robust segmentation can be achieved. More importantly, the HMRFEM framework can easily be combined with other techniques. As an example, we show how the bias field correction algorithm of Guillemaud and Brady (1997) can be incorporated into this framework to achieve a threedimensional fully automated approach for brain MR image segmentation.
MultiModal Volume Registration by Maximization of Mutual Information
, 1996
"... A new informationtheoretic approach is presented for finding the registration of volumetric medical images of differing modalities. Registration is achieved by adjustment of the relative pose until the mutual information between images is maximized. In our derivation of the registration procedure, ..."
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Cited by 459 (23 self)
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A new informationtheoretic approach is presented for finding the registration of volumetric medical images of differing modalities. Registration is achieved by adjustment of the relative pose until the mutual information between images is maximized. In our derivation of the registration procedure
A HighThroughput Path Metric for MultiHop Wireless Routing
, 2003
"... This paper presents the expected transmission count metric (ETX), which finds highthroughput paths on multihop wireless networks. ETX minimizes the expected total number of packet transmissions (including retransmissions) required to successfully deliver a packet to the ultimate destination. The E ..."
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Cited by 1078 (5 self)
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. The ETX metric incorporates the effects of link loss ratios, asymmetry in the loss ratios between the two directions of each link, and interference among the successive links of a path. In contrast, the minimum hopcount metric chooses arbitrarily among the different paths of the same minimum length
CostAware WWW Proxy Caching Algorithms
 IN PROCEEDINGS OF THE 1997 USENIX SYMPOSIUM ON INTERNET TECHNOLOGY AND SYSTEMS
, 1997
"... Web caches can not only reduce network traffic and downloading latency, but can also affect the distribution of web traffic over the network through costaware caching. This paper introduces GreedyDualSize, which incorporates locality with cost and size concerns in a simple and nonparameterized fash ..."
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Cited by 544 (6 self)
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Web caches can not only reduce network traffic and downloading latency, but can also affect the distribution of web traffic over the network through costaware caching. This paper introduces GreedyDualSize, which incorporates locality with cost and size concerns in a simple and non
Finding community structure in networks using the eigenvectors of matrices
, 2006
"... We consider the problem of detecting communities or modules in networks, groups of vertices with a higherthanaverage density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as “modularity ” over possible div ..."
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Cited by 500 (0 self)
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We consider the problem of detecting communities or modules in networks, groups of vertices with a higherthanaverage density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as “modularity ” over possible
Results 1  10
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591,767