title: Evolution of the global carbon cycle and climate regulation on Earth

authors: Isson, Terry T.

subjects: Carbon cycle

abstract: The existence of stabilizing feedbacks within Earth's climate system is generally thought to be necessary for the persistence of liquid water and life. Over the course of Earth's history, Earth's atmospheric composition appears to have adjusted to the gradual increase in solar luminosity, resulting in persistently habitable surface temperatures. With limited exceptions, the Earth system has been observed to recover rapidly from pulsed climatic perturbations. Carbon dioxide (CO₂) regulation via negative feedbacks within the coupled global carbon‐silica cycles are classically viewed as the main processes giving rise to climate stability on Earth. Here we review the long‐term global carbon cycle budget, and how the processes modulating Earth's climate system have evolved over time. Specifically, we focus on the relative roles that shifts in carbon sources and sinks have played in driving long‐term changes in atmospheric pCO₂. We make the case that marine processes are an important component of the canonical silicate weathering feedback, and have played a much more important role in 𝘱CO₂ regulation than traditionally imagined. Notably, geochemical evidence indicate that the weathering of marine sediments and off‐axis basalt alteration act as major carbon sinks. However, this sink was potentially dampened during Earth's early history when oceans had higher levels of dissolved silicon (Si), iron (Fe), and magnesium (Mg), and instead likely fostered more extensive carbon recycling within the ocean‐atmosphere system via reverse weathering—that in turn acted to elevate ocean‐atmosphere CO₂ levels.

text: Evolution of the global carbon cycle and climate regulation on Earth The existence of stabilizing feedbacks within Earth's climate system is generally thought to be necessary for the persistence of liquid water and life. Over the course of Earth's history, Earth's atmospheric composition appears to have adjusted to the gradual increase in solar luminosity, resulting in persistently habitable surface temperatures. With limited exceptions, the Earth system has been observed to recover rapidly from pulsed climatic perturbations. Carbon dioxide (CO₂) regulation via negative feedbacks within the coupled global carbon‐silica cycles are classically viewed as the main processes giving rise to climate stability on Earth. Here we review the long‐term global carbon cycle budget, and how the processes modulating Earth's climate system have evolved over time. Specifically, we focus on the relative roles that shifts in carbon sources and sinks have played in driving long‐term changes in atmospheric pCO₂. We make the case that marine processes are an important component of the canonical silicate weathering feedback, and have played a much more important role in 𝘱CO₂ regulation than traditionally imagined. Notably, geochemical evidence indicate that the weathering of marine sediments and off‐axis basalt alteration act as major carbon sinks. However, this sink was potentially dampened during Earth's early history when oceans had higher levels of dissolved silicon (Si), iron (Fe), and magnesium (Mg), and instead likely fostered more extensive carbon recycling within the ocean‐atmosphere system via reverse weathering—that in turn acted to elevate ocean‐atmosphere CO₂ levels.

year: 2020

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: The Digital Divide in New Zealand: The position of Māori and Pacific peoples

authors: Gibson, John

subjects: Māori digital access

abstract: Information and communication technology plays an ever-growing role in economic, political and social life and those who are unable to access such technologies are increasingly disadvantaged. Hence, what has come to be known as the ‘digital divide’ is of considerable significance. Here, indicators of that divide as it affects Māori and Pacific Islands peoples in Aotearoa/New Zealand are presented and discussed.

text: The Digital Divide in New Zealand: The position of Māori and Pacific peoples Information and communication technology plays an ever-growing role in economic, political and social life and those who are unable to access such technologies are increasingly disadvantaged. Hence, what has come to be known as the ‘digital divide’ is of considerable significance. Here, indicators of that divide as it affects Māori and Pacific Islands peoples in Aotearoa/New Zealand are presented and discussed.

year: 2002-09