title: Planning in the post-pandemic era

authors: Haughton, Graham

abstract: The COVID-19 lock-down has exposed shortcomings in how we have been planning our towns and cities, and in the new political space that this is opening up planners should look for a ‘new normal’ that reduces inequalities and risks.

text: Planning in the post-pandemic era The COVID-19 lock-down has exposed shortcomings in how we have been planning our towns and cities, and in the new political space that this is opening up planners should look for a ‘new normal’ that reduces inequalities and risks.

year: 2020

title: The Weibull distribution as an extreme value model for transformed annual maxima

authors: Bardsley, W. Earl

subjects: Weibull distribution

abstract: There is no mathematical difference between theoretical extreme value models of maxima and minima because they link to each other by a simple sign reversal transformation. However, other transformations that change sample maxima to sample minima raise the possibility of alternatives to the generalised extreme value (GEV) distribution for annual maxima, while still maintaining extreme value justification. A general class of transformation is proposed that converts positive annual maxima to lower-bounded minima, then amenable to Weibull extreme value analysis for sufficiently large sample sizes. That is, the Weibull distribution of smallest extremes provides a theoretical extreme value model for the transformed maxima, which holds irrespective of any GEV form of the original maxima. This would apply, for example, to the analysis of reciprocals of discharge or rainfall annual maxima.
A useful feature of the transformation approach is that alternative prediction expressions with extreme value justification can arise for defining event magnitude as a function of return period. These expressions may result in different hydrological conclusions than from a GEV analysis of maxima. A double exponential transformation is introduced and its prediction function for maxima is noted to have capability to mimic equivalent Type 3 extreme value (EV3) distribution expressions, but without having to introduce an upper bound parameter. The new function gives a good fit to apparent EV3 annual flood maxima recorded from two very different catchments: the Yangtze River in China and the upper Whanganui River in New Zealand.

text: The Weibull distribution as an extreme value model for transformed annual maxima There is no mathematical difference between theoretical extreme value models of maxima and minima because they link to each other by a simple sign reversal transformation. However, other transformations that change sample maxima to sample minima raise the possibility of alternatives to the generalised extreme value (GEV) distribution for annual maxima, while still maintaining extreme value justification. A general class of transformation is proposed that converts positive annual maxima to lower-bounded minima, then amenable to Weibull extreme value analysis for sufficiently large sample sizes. That is, the Weibull distribution of smallest extremes provides a theoretical extreme value model for the transformed maxima, which holds irrespective of any GEV form of the original maxima. This would apply, for example, to the analysis of reciprocals of discharge or rainfall annual maxima.
A useful feature of the transformation approach is that alternative prediction expressions with extreme value justification can arise for defining event magnitude as a function of return period. These expressions may result in different hydrological conclusions than from a GEV analysis of maxima. A double exponential transformation is introduced and its prediction function for maxima is noted to have capability to mimic equivalent Type 3 extreme value (EV3) distribution expressions, but without having to introduce an upper bound parameter. The new function gives a good fit to apparent EV3 annual flood maxima recorded from two very different catchments: the Yangtze River in China and the upper Whanganui River in New Zealand.

year: 2019

title: Microsoft Translator Hub for Māori language

authors: Keegan, Te Taka Adrian Gregory

subjects: computer science

abstract: Recent improvements in Machine Translation (MT) software has opened new possibilities for applications of automatic language translation. But can these opportunities exist for the smaller, minority languages of the world? Training data was collected for the language pair of Māori and English which was used to build an MT system using Microsoft’s Translator Hub software. A comparative analysis was undertaken with this system and Google Translate. Various MT metrics and analysis software was considered before deciding to use the Asiya toolkit to undertake the comparative analysis. Māori language experts were also used to provide a human perspective on the output translations. The overall results showed no significant difference in the translation quality produced by the two systems. Despite some misgivings around the accuracy of the translations these results do show promise for MT usage by minority languages.

text: Microsoft Translator Hub for Māori language Recent improvements in Machine Translation (MT) software has opened new possibilities for applications of automatic language translation. But can these opportunities exist for the smaller, minority languages of the world? Training data was collected for the language pair of Māori and English which was used to build an MT system using Microsoft’s Translator Hub software. A comparative analysis was undertaken with this system and Google Translate. Various MT metrics and analysis software was considered before deciding to use the Asiya toolkit to undertake the comparative analysis. Māori language experts were also used to provide a human perspective on the output translations. The overall results showed no significant difference in the translation quality produced by the two systems. Despite some misgivings around the accuracy of the translations these results do show promise for MT usage by minority languages.

year: 2018