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444,214
SeriesParallel Automata and Short Regular Expressions
, 2009
"... Computing short regular expressions equivalent to a given finite automaton is a hard task. In this work we present a class of acyclic automata for which it is possible to obtain in time O(n² log n) an equivalent regular expression of size O(n). A characterisation of this class is made using propert ..."
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Cited by 3 (1 self)
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properties of the underlying digraphs that correspond to the seriesparallel digraphs class. Using this characterisation we present an algorithm for the generation of automata of this class and an enumerative formula for the underlying digraphs with a given number of vertices.
A Framework for Dynamic Graph Drawing
 CONGRESSUS NUMERANTIUM
, 1992
"... Drawing graphs is an important problem that combines flavors of computational geometry and graph theory. Applications can be found in a variety of areas including circuit layout, network management, software engineering, and graphics. The main contributions of this paper can be summarized as follows ..."
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Cited by 627 (44 self)
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as follows: ffl We devise a model for dynamic graph algorithms, based on performing queries and updates on an implicit representation of the drawing, and we show its applications. ffl We present several efficient dynamic drawing algorithms for trees, seriesparallel digraphs, planar stdigraphs, and planar
On Recognition of AndOr SeriesParallel Digraphs
"... The computation task of a distributed processing system usually can be partitioned into a set of modules and then modeled as a directed graph, called the task digraph. In the task digraph, vertices represent modules and arcs represent message passing links between two modules. Particularly, accordin ..."
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Or SeriesParallel (AOSP) digraphs. There is a certain probability, called the task reliability , associated with the event that a task completes successfully. This measure accurately models the reliability of a task running in the system. The task reliability problem is known to be NPhard for general
Digraphs  Theory, Algorithms and Applications
, 2007
"... Graph theory is a very popular area of discrete mathematics with not only numerous theoretical developments, but also countless applications to practical problems. As a research area, graph theory is still relatively young, but it is maturing rapidly with many deep results having been discovered o ..."
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Cited by 382 (54 self)
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than directed graphs. One of the reasons is that undirected graphs form in a sense a special class of directed graphs (symmetric digraphs) and hence problems that can be formulated
The 2005 pascal visual object classes challenge
, 2006
"... Abstract. The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not presegmented objects). Four object classes were selected: motorbikes, bicycles, cars and peop ..."
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Cited by 633 (24 self)
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Abstract. The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not presegmented objects). Four object classes were selected: motorbikes, bicycles, cars
The PASCAL Visual Object Classes (VOC) challenge
, 2009
"... ... is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has be ..."
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Cited by 624 (20 self)
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... is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection. This paper describes the dataset and evaluation procedure. We review the stateoftheart in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.
Upgrading Vertices In Trees, SeriesParallel Digraphs And General SeriesParallel Digraphs To Bound Path Length+
, 1991
"... We consider trees, seriesparallel digraphs, and general seriesparallel digraphs that have vertex weights and delays. The length/delay of a path is the sum of the delays on the path. We show that minimal weight vertex subsets X such that the length of the longest path is bounded by a given value d ..."
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We consider trees, seriesparallel digraphs, and general seriesparallel digraphs that have vertex weights and delays. The length/delay of a path is the sum of the delays on the path. We show that minimal weight vertex subsets X such that the length of the longest path is bounded by a given value d
GromovWitten classes, quantum cohomology, and enumerative geometry
 Commun. Math. Phys
, 1994
"... The paper is devoted to the mathematical aspects of topological quantum field theory and its applications to enumerative problems of algebraic geometry. In particular, it contains an axiomatic treatment of Gromov–Witten classes, and a discussion of their properties for Fano varieties. Cohomological ..."
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Cited by 484 (3 self)
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The paper is devoted to the mathematical aspects of topological quantum field theory and its applications to enumerative problems of algebraic geometry. In particular, it contains an axiomatic treatment of Gromov–Witten classes, and a discussion of their properties for Fano varieties. Cohomological
Additive Logistic Regression: a Statistical View of Boosting
 Annals of Statistics
, 1998
"... Boosting (Freund & Schapire 1996, Schapire & Singer 1998) is one of the most important recent developments in classification methodology. The performance of many classification algorithms can often be dramatically improved by sequentially applying them to reweighted versions of the input dat ..."
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Cited by 1719 (25 self)
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data, and taking a weighted majority vote of the sequence of classifiers thereby produced. We show that this seemingly mysterious phenomenon can be understood in terms of well known statistical principles, namely additive modeling and maximum likelihood. For the twoclass problem, boosting can
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