Ultrametric tree representations of incomplete dissimilarity data

1984 ◽  
Vol 1 (1) ◽  
pp. 235-242 ◽  
Author(s):  
Geert De Soete
Keyword(s):  
Dissimilarity Data ◽  
Ultrametric Tree ◽  
Tree Representations

Additive-tree representations of incomplete dissimilarity data

1984 ◽  
Vol 18 (4) ◽  
pp. 387-393 ◽  
Author(s):  
Geert De Soete
Keyword(s):  
Dissimilarity Data ◽  
Additive Tree ◽  
Tree Representations

Compressed Tree Representations

2016 ◽  
pp. 397-401
Author(s):  
Gonzalo Navarro ◽  
Kunihiko Sadakane
Keyword(s):  
Tree Representations

scholarly journals Isomorphisms between determinantal point processes with translation-invariant kernels and Poisson point processes

2020 ◽  
pp. 1-14
Author(s):  
SHOTA OSADA
Keyword(s):  
Point Processes ◽  
Duke Math ◽  
Random Point ◽  
Determinantal Point Processes ◽  
Fredholm Determinants ◽  
Poisson Point Processes ◽  
Translation Invariant ◽  
Ergodic Properties ◽  
Tree Representations ◽  
Invariant Kernels

Abstract We prove the Bernoulli property for determinantal point processes on $ \mathbb{R}^d $ with translation-invariant kernels. For the determinantal point processes on $ \mathbb{Z}^d $ with translation-invariant kernels, the Bernoulli property was proved by Lyons and Steif [Stationary determinantal processes: phase multiplicity, bernoullicity, and domination. Duke Math. J.120 (2003), 515–575] and Shirai and Takahashi [Random point fields associated with certain Fredholm determinants II: fermion shifts and their ergodic properties. Ann. Probab.31 (2003), 1533–1564]. We prove its continuum version. For this purpose, we also prove the Bernoulli property for the tree representations of the determinantal point processes.

PoClustering: Lossless Clustering of Dissimilarity Data

2007 ◽  
Author(s):  
Jinze Liu ◽  
Qi Zhang ◽  
Wei Wang ◽  
Leonard McMillan ◽  
Jan Prins
Keyword(s):  
Dissimilarity Data

scholarly journals Visual Encoding of Dissimilarity Data via Topology-Preserving Map Deformation

2016 ◽  
Vol 22 (9) ◽  
pp. 2200-2213 ◽  
Author(s):  
Quirijn W. Bouts ◽  
Tim Dwyer ◽  
Jason Dykes ◽  
Bettina Speckmann ◽  
Sarah Goodwin ◽  
...  
Keyword(s):  
Dissimilarity Data ◽  
Visual Encoding

An Algorithm for the Enumeration of Serial and/or Parallel Combinations of Kinematic Building Blocks

2004 ◽  
Author(s):  
Hong-Sen Yan ◽  
Feng-Ming Ou ◽  
Ming-Feng Tang
Keyword(s):  
Graph Theory ◽  
Building Blocks ◽  
Power Source ◽  
Cam Follower ◽  
Tree Representations ◽  
The Given

An algorithm is presented, based on graph theory, for enumerating all feasible serial and/or parallel combined mechanisms from the given rotary or translational power source and specific kinematic building blocks. Through the labeled out-tree representations for the configurations of combined mechanisms, the enumeration procedure is developed by adapting the algorithm for the enumeration of trees. A rotary power source and four kinematic building blocks: a crank-rocker linkage, a rack-pinion, a double-slider mechanism, and a cam-follower mechanism, are chosen as the combination to illustrate the algorithm. And, two examples are provided to validate the algorithm.

scholarly journals Multi-purpose Syntax Definition with SDF3

2020 ◽  
pp. 1-23 ◽  
Author(s):  
Luís Eduardo de Souza Amorim ◽  
Eelco Visser
Keyword(s):  
Error Recovery ◽  
Abstract Syntax ◽  
Abstract Syntax Tree ◽  
Syntax Tree ◽  
Full Class ◽  
High Level ◽  
Modular Composition ◽  
Tree Representations ◽  
Context Free ◽  
Context Free Grammars

Abstract SDF3 is a syntax definition formalism that extends plain context-free grammars with features such as constructor declarations, declarative disambiguation rules, character-level grammars, permissive syntax, layout constraints, formatting templates, placeholder syntax, and modular composition. These features support the multi-purpose interpretation of syntax definitions, including derivation of type schemas for abstract syntax tree representations, scannerless generalized parsing of the full class of context-free grammars, error recovery, layout-sensitive parsing, parenthesization and formatting, and syntactic completion. This paper gives a high level overview of SDF3 by means of examples and provides a guide to the literature for further details.

scholarly journals Hierarchical correction of p-values via an ultrametric tree running Ornstein-Uhlenbeck process

2021 ◽  
Author(s):  
Antoine Bichat ◽  
Christophe Ambroise ◽  
Mahendra Mariadassou
Keyword(s):  
Multiple Testing ◽  
R Package ◽  
Statistical Testing ◽  
Uhlenbeck Process ◽  
Explanatory Variables ◽  
New Associations ◽  
Penalized Estimation ◽  
Ultrametric Tree ◽  
Ornstein Uhlenbeck Process ◽  
The Mean

AbstractStatistical testing is classically used as an exploratory tool to search for association between a phenotype and many possible explanatory variables. This approach often leads to multiple testing under dependence. We assume a hierarchical structure between tests via an Ornstein-Uhlenbeck process on a tree. The process correlation structure is used for smoothing the p-values. We design a penalized estimation of the mean of the Ornstein-Uhlenbeck process for p-value computation. The performances of the algorithm are assessed via simulations. Its ability to discover new associations is demonstrated on a metagenomic dataset. The corresponding R package is available from https://github.com/abichat/zazou.

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