title: Balanced cross-section and crustal shortening analysis in the Tanggula-Tuotuohe Area, Northern Tibet

authors: Li, Yalin

subjects: Tibetan plateau

abstract: The Tanggula (唐古拉) thrust system and the Tuotuohe (沱沱河) foreland basin, which represent major Cenozoic tectonic units of the central Tibetan plateau, have been recently studied. Field investigation, analyses of deformation and construction of two restored balanced structural sections suggest 75–100 km (51%–64%) of N-S shortening in the Tanggula thrust system and 55–114 km (42%–47%) of N-S shortening in the Tuotuohe basin. The shortening ratios indicate that the Tanggula-Tuotuohe area has undergone intensive deformation and crustal shortening during the Early Tertiary, resulting not only in crustal thickening, but also in large scale volcanism and in rapid uplift of the Tanggula Mountains.

text: Balanced cross-section and crustal shortening analysis in the Tanggula-Tuotuohe Area, Northern Tibet The Tanggula (唐古拉) thrust system and the Tuotuohe (沱沱河) foreland basin, which represent major Cenozoic tectonic units of the central Tibetan plateau, have been recently studied. Field investigation, analyses of deformation and construction of two restored balanced structural sections suggest 75–100 km (51%–64%) of N-S shortening in the Tanggula thrust system and 55–114 km (42%–47%) of N-S shortening in the Tuotuohe basin. The shortening ratios indicate that the Tanggula-Tuotuohe area has undergone intensive deformation and crustal shortening during the Early Tertiary, resulting not only in crustal thickening, but also in large scale volcanism and in rapid uplift of the Tanggula Mountains.

year: 2011

title: Random Relational Rules

authors: Anderson, Grant

subjects: relational data mining

abstract: In the field of machine learning, methods for learning from single-table data have received much more attention than those for learning from multi-table, or relational data, which are generally more computationally complex. However, a significant amount of the world's data is relational. This indicates a need for algorithms that can operate efficiently on relational data and exploit the larger body of work produced in the area of single-table techniques.

This thesis presents algorithms for learning from relational data that mitigate, to some extent, the complexity normally associated with such learning. All algorithms in this thesis are based on the generation of random relational rules. The assumption is that random rules enable efficient and effective relational learning, and this thesis presents evidence that this is indeed the case. To this end, a system for generating random relational rules is described, and algorithms using these rules are evaluated. These algorithms include direct classification, classification by propositionalisation, clustering, semi-supervised learning and generating random forests.

The experimental results show that these algorithms perform competitively with previously published results for the datasets used, while often exhibiting lower runtime than other tested systems. This demonstrates that sufficient information for classification and clustering is retained in the rule generation process and that learning with random rules is efficient.

Further applications of random rules are investigated. Propositionalisation allows single-table algorithms for classification and clustering to be applied to the resulting data, reducing the amount of relational processing required. Further results show that techniques for utilising additional unlabeled training data improve accuracy of classification in the semi-supervised setting. The thesis also develops a novel algorithm for building random forests by makingefficient use of random rules to generate trees and leaves in parallel.

text: Random Relational Rules In the field of machine learning, methods for learning from single-table data have received much more attention than those for learning from multi-table, or relational data, which are generally more computationally complex. However, a significant amount of the world's data is relational. This indicates a need for algorithms that can operate efficiently on relational data and exploit the larger body of work produced in the area of single-table techniques.

This thesis presents algorithms for learning from relational data that mitigate, to some extent, the complexity normally associated with such learning. All algorithms in this thesis are based on the generation of random relational rules. The assumption is that random rules enable efficient and effective relational learning, and this thesis presents evidence that this is indeed the case. To this end, a system for generating random relational rules is described, and algorithms using these rules are evaluated. These algorithms include direct classification, classification by propositionalisation, clustering, semi-supervised learning and generating random forests.

The experimental results show that these algorithms perform competitively with previously published results for the datasets used, while often exhibiting lower runtime than other tested systems. This demonstrates that sufficient information for classification and clustering is retained in the rule generation process and that learning with random rules is efficient.

Further applications of random rules are investigated. Propositionalisation allows single-table algorithms for classification and clustering to be applied to the resulting data, reducing the amount of relational processing required. Further results show that techniques for utilising additional unlabeled training data improve accuracy of classification in the semi-supervised setting. The thesis also develops a novel algorithm for building random forests by makingefficient use of random rules to generate trees and leaves in parallel.

year: 2008

title: The state of the Rotorua Lakes in 2017

authors: Hamilton, David P.

abstract: I have been asked to give an overview of the Rotorua Te Arawa Lakes and then focus on just four of the lakes. Chris McBride will pick up other lakes.  I would like to acknowledge Warwick Silvester who set up the Lakes Chair, Simon Stuart who is working outside the rohe, on Lake Taupō, but has a great body of knowledge on the food chain in that lake, with potential applications to the Te Arawa lakes. Also to Kohji Muraoka who helped me over the last 24 hours, putting data together which enabled me to synthesise my thoughts on these lakes.

text: The state of the Rotorua Lakes in 2017 I have been asked to give an overview of the Rotorua Te Arawa Lakes and then focus on just four of the lakes. Chris McBride will pick up other lakes.  I would like to acknowledge Warwick Silvester who set up the Lakes Chair, Simon Stuart who is working outside the rohe, on Lake Taupō, but has a great body of knowledge on the food chain in that lake, with potential applications to the Te Arawa lakes. Also to Kohji Muraoka who helped me over the last 24 hours, putting data together which enabled me to synthesise my thoughts on these lakes.

year: 2017