title: On the precipitability of binary Ti alloys bearing 4-period d-metal eutectoid stabilisers

authors: Bolzoni, Leandro

subjects: Titanium alloys

abstract: There is no specific, simple approach for predicting whether the addition of an eutectoid beta stabiliser to Ti leads to an ‘active’ eutectoid transformation upon primary processing (e.g., sintering). Here, we demonstrate that, among theoretical/empirical models, phase diagram features, and electronic structure parameters, the hypoeutectoid area is the best predictor, followed by the molybdenum equivalent parameter (MoE), of the precipitability for 4-period d-metals. As the area increases, which corresponds to the addition of progressively stronger β-eutectoid stabilisers, the less active the eutectoid phase transformation, changing from pearlitic to bainitic (i.e., need for an ageing heat treatment). This occurs if the MoE weighted coefficient is, respectively, lower than or higher than 1. This is because molybdenum is taken as reference, and the higher the coefficient, the larger the drop of the β transus temperature (i.e., higher stabilisation). Valid for 4-period d-metals, it remains to be proven for 5-period and 6-period d-metals.

text: On the precipitability of binary Ti alloys bearing 4-period d-metal eutectoid stabilisers There is no specific, simple approach for predicting whether the addition of an eutectoid beta stabiliser to Ti leads to an ‘active’ eutectoid transformation upon primary processing (e.g., sintering). Here, we demonstrate that, among theoretical/empirical models, phase diagram features, and electronic structure parameters, the hypoeutectoid area is the best predictor, followed by the molybdenum equivalent parameter (MoE), of the precipitability for 4-period d-metals. As the area increases, which corresponds to the addition of progressively stronger β-eutectoid stabilisers, the less active the eutectoid phase transformation, changing from pearlitic to bainitic (i.e., need for an ageing heat treatment). This occurs if the MoE weighted coefficient is, respectively, lower than or higher than 1. This is because molybdenum is taken as reference, and the higher the coefficient, the larger the drop of the β transus temperature (i.e., higher stabilisation). Valid for 4-period d-metals, it remains to be proven for 5-period and 6-period d-metals.

year: 2026-06

title: Beyond trees: Adopting MITI to learn rules and ensemble classifiers for multi-instance data

authors: Bjerring, Luke

subjects: computer science

abstract: MITI is a simple and elegant decision tree learner designed for multi-instance classification problems, where examples for learning consist of bags of instances. MITI grows a tree in best-first manner by maintaining a priority queue containing the unexpanded nodes in the fringe of the tree. When the head node contains instances from positive examples only, it is made into a leaf, and any bag of data that is associated with this leaf is removed. In this paper we first revisit the basic algorithm and consider the effect of parameter settings on classification accuracy, using several benchmark datasets. We show that the chosen splitting criterion in particular can have a significant effect on accuracy. We identify a potential weakness of the algorithm—subtrees can contain structure that has been created using data that is subsequently removed—and show that a simple modification turns the algorithm into a rule learner that avoids this problem. This rule learner produces more compact classifiers with comparable accuracy on the benchmark datasets we consider. Finally, we present randomized algorithm variants that enable us to generate ensemble classifiers. We show that these can yield substantially improved classification accuracy.

text: Beyond trees: Adopting MITI to learn rules and ensemble classifiers for multi-instance data MITI is a simple and elegant decision tree learner designed for multi-instance classification problems, where examples for learning consist of bags of instances. MITI grows a tree in best-first manner by maintaining a priority queue containing the unexpanded nodes in the fringe of the tree. When the head node contains instances from positive examples only, it is made into a leaf, and any bag of data that is associated with this leaf is removed. In this paper we first revisit the basic algorithm and consider the effect of parameter settings on classification accuracy, using several benchmark datasets. We show that the chosen splitting criterion in particular can have a significant effect on accuracy. We identify a potential weakness of the algorithm—subtrees can contain structure that has been created using data that is subsequently removed—and show that a simple modification turns the algorithm into a rule learner that avoids this problem. This rule learner produces more compact classifiers with comparable accuracy on the benchmark datasets we consider. Finally, we present randomized algorithm variants that enable us to generate ensemble classifiers. We show that these can yield substantially improved classification accuracy.

year: 2011

title: Mihi (Foreword)

authors: Tuaupiki, Jackie Whetumarama

abstract: Mihi of Te Kōtihitihi - Ngā Tuhinga Reo Māori Putanga 3

text: Mihi (Foreword) Mihi of Te Kōtihitihi - Ngā Tuhinga Reo Māori Putanga 3

year: 2016