title: Lake Rotoiti catfish incursion: Something new or the Emergence of an old problem?

authors: Hicks, Brendan J.

abstract: Kia ora everybody. I have been involved in pest fish research, control and generally understanding their life history and what we might do about them. For a number of years I have been called in by Bay of Plenty Regional Council to help with the catfish problem and I ask, ‘Is this something new or the emergence of an old problem?’ I suggest that maybe we have been waiting for this in Rotoiti but hoping that it would not happen.

text: Lake Rotoiti catfish incursion: Something new or the Emergence of an old problem? Kia ora everybody. I have been involved in pest fish research, control and generally understanding their life history and what we might do about them. For a number of years I have been called in by Bay of Plenty Regional Council to help with the catfish problem and I ask, ‘Is this something new or the emergence of an old problem?’ I suggest that maybe we have been waiting for this in Rotoiti but hoping that it would not happen.

year: 2017

title: A Comparison of Multi-instance Learning Algorithms

authors: Dong, Lin

subjects: machine learning

abstract: Motivated by various challenging real-world applications, such as drug activity prediction and image retrieval, multi-instance (MI) learning has attracted considerable interest in recent years.  Compared with standard supervised learning, the MI learning task is more difficult as the label information of each training example is incomplete. Many MI algorithms have been proposed. Some of them are specifically designed for MI problems whereas others have been upgraded or adapted from standard single-instance learning algorithms.  Most algorithms have been evaluated on only one or two benchmark datasets, and there is a lack of systematic comparisons of MI learning algorithms.

This thesis presents a comprehensive study of MI learning algorithms that aims to compare their performance and find a suitable way to properly address different MI problems. First, it briefly reviews the history of research on MI learning. Then it discusses five general classes of MI approaches that cover a total of 16 MI algorithms. After that, it presents empirical results for these algorithms that were obtained from 15 datasets which involve five different real-world application domains.  Finally, some conclusions are drawn from these results: (1) applying suitable standard single-instance learners to MI problems can often generate the best result on the datasets that were tested, (2) algorithms exploiting the standard asymmetric MI assumption do not show significant advantages over approaches using the so-called collective assumption, and (3) different MI approaches are suitable for different application domains, and no MI algorithm works best on all MI problems.

text: A Comparison of Multi-instance Learning Algorithms Motivated by various challenging real-world applications, such as drug activity prediction and image retrieval, multi-instance (MI) learning has attracted considerable interest in recent years.  Compared with standard supervised learning, the MI learning task is more difficult as the label information of each training example is incomplete. Many MI algorithms have been proposed. Some of them are specifically designed for MI problems whereas others have been upgraded or adapted from standard single-instance learning algorithms.  Most algorithms have been evaluated on only one or two benchmark datasets, and there is a lack of systematic comparisons of MI learning algorithms.

This thesis presents a comprehensive study of MI learning algorithms that aims to compare their performance and find a suitable way to properly address different MI problems. First, it briefly reviews the history of research on MI learning. Then it discusses five general classes of MI approaches that cover a total of 16 MI algorithms. After that, it presents empirical results for these algorithms that were obtained from 15 datasets which involve five different real-world application domains.  Finally, some conclusions are drawn from these results: (1) applying suitable standard single-instance learners to MI problems can often generate the best result on the datasets that were tested, (2) algorithms exploiting the standard asymmetric MI assumption do not show significant advantages over approaches using the so-called collective assumption, and (3) different MI approaches are suitable for different application domains, and no MI algorithm works best on all MI problems.

year: 2006

title: Developing an integrated ocean observing system for New Zealand

authors: O'Callaghan, J

subjects: Science & Technology

abstract: New Zealand (NZ) is an island nation with stewardship of an ocean twenty times larger than its land area. While the challenges facing NZ's ocean are similar to other maritime countries, no coherent national plan exists that meets the needs of scientists, stakeholders or kaitiakitanga (guardianship) of NZ's ocean in a changing climate. The NZ marine science community used the OceanObs'19 white paper to establish a framework and implementation plan for a collaborative NZ ocean observing system (NZ-OOS). Co-production of ocean knowledge with Māori will be embedded in this national strategy for growing a sustainable, blue economy for NZ. The strengths of an observing system for a relatively small nation come from direct connections between the science impetus through to users and stakeholders of an NZ-OOS. The community will leverage off existing ocean observations to optimize effort and resources in a system that has historically made limited investment in ocean observing. The goal of the community paper will be achieved by bringing together oceanographers, data scientists and marine stakeholders to develop an NZ-OOS that provides best knowledge and tools to the sectors of society that use or are influenced by the ocean.

text: Developing an integrated ocean observing system for New Zealand New Zealand (NZ) is an island nation with stewardship of an ocean twenty times larger than its land area. While the challenges facing NZ's ocean are similar to other maritime countries, no coherent national plan exists that meets the needs of scientists, stakeholders or kaitiakitanga (guardianship) of NZ's ocean in a changing climate. The NZ marine science community used the OceanObs'19 white paper to establish a framework and implementation plan for a collaborative NZ ocean observing system (NZ-OOS). Co-production of ocean knowledge with Māori will be embedded in this national strategy for growing a sustainable, blue economy for NZ. The strengths of an observing system for a relatively small nation come from direct connections between the science impetus through to users and stakeholders of an NZ-OOS. The community will leverage off existing ocean observations to optimize effort and resources in a system that has historically made limited investment in ocean observing. The goal of the community paper will be achieved by bringing together oceanographers, data scientists and marine stakeholders to develop an NZ-OOS that provides best knowledge and tools to the sectors of society that use or are influenced by the ocean.

year: 2019-03-26