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808,249
Obstructions for simple embeddings
"... Suppose that K ⊆ G is a graph embedded in some surface and F is a face of K with singular branches e and f such that F ∪ ∂F is homeomorphic to the torus minus an open disk. An embedding extension of K to G is a simple embedding if each Kbridge embedded in F is attached to at most one appearance of ..."
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of e and at most one appearance of f on ∂F. Combinatorial structure of minimal obstructions for existence of simple embedding extensions is described. Moreover, a linear time algorithm is presented that either finds a simple embedding, or returns an obstruction for existence of such embeddings. 1
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
 Advances in Neural Information Processing Systems 14
, 2001
"... Drawing on the correspondence between the graph Laplacian, the LaplaceBeltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low dimensional manifold embedded in a higher ..."
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Cited by 664 (8 self)
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Drawing on the correspondence between the graph Laplacian, the LaplaceBeltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low dimensional manifold embedded in a
RealTime Dynamic Voltage Scaling for LowPower Embedded Operating Systems
, 2001
"... In recent years, there has been a rapid and wide spread of nontraditional computing platforms, especially mobile and portable computing devices. As applications become increasingly sophisticated and processing power increases, the most serious limitation on these devices is the available battery lif ..."
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Cited by 498 (4 self)
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the necessary peak computation power in generalpurpose systems. However, for a large class of applications in embedded realtime systems like cellular phones and camcorders, the variable operating frequency interferes with their deadline guarantee mechanisms, and DVS in this context, despite its growing
Rapid object detection using a boosted cascade of simple features
 ACCEPTED CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION 2001
, 2001
"... This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the " ..."
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Cited by 3222 (9 self)
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This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the "Integral Image" which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers[6]. The third contribution is a method for combining increasingly more complex classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising objectlike regions. The cascade can be viewed as an object specific focusofattention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in realtime applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.
A simple method for displaying the hydropathic character of a protein
 Journal of Molecular Biology
, 1982
"... A computer program that progressively evaluates the hydrophilicity and hydrophobicity of a protein along its amino acid sequence has been devised. For this purpose, a hydropathy scale has been composed wherein the hydrophilic and hydrophobic properties of each of the 20 amino acid sidechains is tak ..."
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Cited by 2249 (2 self)
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A computer program that progressively evaluates the hydrophilicity and hydrophobicity of a protein along its amino acid sequence has been devised. For this purpose, a hydropathy scale has been composed wherein the hydrophilic and hydrophobic properties of each of the 20 amino acid sidechains is taken into consideration. The scale is based on an amalgam of experimental observations derived from the literature. The program uses a movingsegment approach that continuously determines the average hydropathy within a segment of predetermined length as it advances through the sequence. The consecutive scores are plotted from the amino to the carboxy terminus. At the same time, a midpoint line is printed that corresponds to the grand average of the hydropathy of the amino acid compositions found in most of the sequenced proteins. In the case of soluble, globular proteins there is a remarkable correspondence between the interior portions of their sequence and the regions appearing on the hydrophobic side of the midpoint line, as well as the exterior portions and the regions on the hydrophilic side. The correlation was demonstrated by comparisons between the plotted values and known structures determined by crystallography. In the case of membranebound proteins, the portions of their sequences that are located within the lipid bilayer are also clearly delineated by large uninterrupted areas on the hydrophobic side of the midpoint line. As such, the membranespanning segment’s of these proteins can be identified by this procedure. Although the method is not unique and embodies principles that have long been appreciated, its simplicity and its graphic nature make it a very useful tool for the evaluation of protein structures. 1.
A Simple Estimator of Cointegrating Vectors in Higher Order Cointegrated Systems
 ECONOMETRICA
, 1993
"... Efficient estimators of cointegrating vectors are presented for systems involving deterministic components and variables of differing, higher orders of integration. The estimators are computed using GLS or OLS, and Wald Statistics constructed from these estimators have asymptotic x2 distributions. T ..."
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Cited by 507 (3 self)
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Efficient estimators of cointegrating vectors are presented for systems involving deterministic components and variables of differing, higher orders of integration. The estimators are computed using GLS or OLS, and Wald Statistics constructed from these estimators have asymptotic x2 distributions. These and previously proposed estimators of cointegrating vectors are used to study longrun U.S. money (Ml) demand. Ml demand is found to be stable over 19001989; the 95 % confidence intervals for the income elasticity and interest rate semielasticity are (.88,1.06) and (.13,.08), respectively. Estimates based on the postwar data alone, however, are unstable, with variances which indicate substantial sampling uncertainty.
Quantization Index Modulation: A Class of Provably Good Methods for Digital Watermarking and Information Embedding
 IEEE TRANS. ON INFORMATION THEORY
, 1999
"... We consider the problem of embedding one signal (e.g., a digital watermark), within another "host" signal to form a third, "composite" signal. The embedding is designed to achieve efficient tradeoffs among the three conflicting goals of maximizing informationembedding rate, mini ..."
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Cited by 495 (15 self)
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We consider the problem of embedding one signal (e.g., a digital watermark), within another "host" signal to form a third, "composite" signal. The embedding is designed to achieve efficient tradeoffs among the three conflicting goals of maximizing informationembedding rate
Learning the Kernel Matrix with SemiDefinite Programming
, 2002
"... Kernelbased learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information ..."
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Cited by 780 (22 self)
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Kernelbased learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information
Primitives for the manipulation of general subdivisions and the computations of Voronoi diagrams
 ACM Tmns. Graph
, 1985
"... The following problem is discussed: Given n points in the plane (the sites) and an arbitrary query point 4, find the site that is closest to q. This problem can be solved by constructing the Voronoi diagram of the given sites and then locating the query point in one of its regions. Two algorithms ar ..."
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Cited by 543 (11 self)
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to the separation of the geometrical and topological aspects of the problem and to the use of two simple but powerful primitives, a geometric predicate and an operator for manipulating the topology of the diagram. The topology is represented by a new data structure for generalized diagrams, that is, embeddings
Localitysensitive hashing scheme based on pstable distributions
 In SCG ’04: Proceedings of the twentieth annual symposium on Computational geometry
, 2004
"... inÇÐÓ�Ò We present a novel LocalitySensitive Hashing scheme for the Approximate Nearest Neighbor Problem underÐÔnorm, based onÔstable distributions. Our scheme improves the running time of the earlier algorithm for the case of theÐnorm. It also yields the first known provably efficient approximate ..."
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Cited by 513 (10 self)
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NN algorithm for the caseÔ�. We also show that the algorithm finds the exact near neigbhor time for data satisfying certain “bounded growth ” condition. Unlike earlier schemes, our LSH scheme works directly on points in the Euclidean space without embeddings. Consequently, the resulting query time
Results 1  10
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