scholarly journals Engineering the LOUDS Succinct Tree Representation

2006 ◽  
pp. 134-145 ◽  
Author(s):  
O’Neil Delpratt ◽  
Naila Rahman ◽  
Rajeev Raman
Keyword(s):  
Tree Representation

Fault Tree Representation and Evaluation

2003 ◽  
Author(s):  
Richard Saucier
Keyword(s):  
Fault Tree ◽  
Tree Representation

Optimally Routable Networks

1998 ◽  
Vol 08 (04) ◽  
pp. 567-575
Author(s):  
Arthur M. Farley
Keyword(s):  
Communication Network ◽  
Decision Tree ◽  
General Model ◽  
Maximum Size ◽  
Tree Representation ◽  
Routing Tree

Routing is the process, performed at each site of a communication network upon receipt of a message, that decides to which neighbor the message is to be sent or that the message is to be received locally. We present a general model of routing based on a decision tree representation of routing information. We describe several classes of optimally routable networks, i.e., networks for which the maximum size of a routing tree over all sites of the network is ⌈log2 n⌉.

The role of constituent order and level of embedding in cross-linguistic structural priming

2016 ◽  
Vol 20 (2) ◽  
pp. 269-282 ◽  
Author(s):  
GUNNAR JACOB ◽  
KALLIOPI KATSIKA ◽  
NEILOUFAR FAMILY ◽  
SHANLEY E. M. ALLEN
Keyword(s):  
Priming Effect ◽  
Target Sentence ◽  
Structural Features ◽  
Structural Priming ◽  
Tree Representation ◽  
Priming Effects ◽  
Hierarchical Tree ◽  
Constituent Order

In two cross-linguistic priming experiments with native German speakers of L2 English, we investigated the role of constituent order and level of embedding in cross-linguistic structural priming. In both experiments, significant priming effects emerged only if prime and target were similar with regard to constituent order and also situated on the same level of embedding. We discuss our results on the basis of two current theoretical accounts of cross-linguistic priming, and conclude that neither an account based on combinatorial nodes nor an account assuming that constituent order is directly responsible for the priming effect can fully explain our data pattern. We suggest an account that explains cross-linguistic priming through a hierarchical tree representation. This representation is computed during processing of the prime, and can influence the formulation of a target sentence only when the structural features specified in it are grammatically correct in the target sentence.

Automatically Evolving Texture Image Descriptors using the Multi-tree Representation in Genetic Programming using Few Instances

2020 ◽  
pp. 1-34
Author(s):  
Harith Al-Sahaf ◽  
Ausama Al-Sahaf ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Feature Vector ◽  
Local Binary Patterns ◽  
Texture Image ◽  
Tree Representation ◽  
Line Segments ◽  
Image Descriptor ◽  
Image Descriptors ◽  
The Cost

The performance of image classification is highly dependent on the quality of the extracted features that are used to build a model. Designing such features usually requires prior knowledge of the domain and is often undertaken by a domain expert who, if available, is very costly to employ. Automating the process of designing such features can largely reduce the cost and efforts associated with this task. Image descriptors, such as local binary patterns, have emerged in computer vision, and aim at detecting keypoints, e.g., corners, line-segments and shapes, in an image and extracting features from those keypoints. In this paper, genetic programming (GP) is used to automatically evolve an image descriptor using only two instances per class by utilising a multi-tree program representation. The automatically evolved descriptor operates directly on the raw pixel values of an image and generates the corresponding feature vector. Seven well-known datasets were adapted to the few-shot setting and used to assess the performance of the proposed method and compared against six hand-crafted and one evolutionary computation-based image descriptor as well as three convolutional neural network (CNN) based methods. The experimental results show that the new method has significantly outperformed the competitor image descriptors and CNN-based methods. Furthermore, different patterns have been identified from analysing the evolved programs.

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