title: Impact of elevated levels of CO2 on animal mediated ecosystem function: The modification of sediment nutrient fluxes by burrowing urchins

authors: Widdicombe, S.

subjects: Bioturbation

abstract: A mesocosm experiment was conducted to quantify the relationships between the presence and body size of two burrowing heart urchins (Brissopsis lyrifera and Echinocardium cordatum) and rates of sediment nutrient flux. Furthermore, the impact of seawater acidification on these relationships was determined during this 40-day exposure experiment. Using carbon dioxide (CO ₂) gas, seawater was acidified to pH NBS 7.6, 7.2 or 6.8. Control treatments were maintained in natural seawater (pH ≈ 8.0). Under normocapnic conditions, burrowing urchins were seen to reduce the sediment uptake of nitrite or nitrate whilst enhancing the release of silicate and phosphate. In acidified (hypercapnic) treatments, the biological control of biogeochemical cycles by urchins was significantly affected, probably through the combined impacts of high CO ₂ on nitrifying bacteria, benthic algae and urchin behaviour. This study highlights the importance of considering biological interactions when predicting the consequences of seawater acidification on ecosystem function.

text: Impact of elevated levels of CO2 on animal mediated ecosystem function: The modification of sediment nutrient fluxes by burrowing urchins A mesocosm experiment was conducted to quantify the relationships between the presence and body size of two burrowing heart urchins (Brissopsis lyrifera and Echinocardium cordatum) and rates of sediment nutrient flux. Furthermore, the impact of seawater acidification on these relationships was determined during this 40-day exposure experiment. Using carbon dioxide (CO ₂) gas, seawater was acidified to pH NBS 7.6, 7.2 or 6.8. Control treatments were maintained in natural seawater (pH ≈ 8.0). Under normocapnic conditions, burrowing urchins were seen to reduce the sediment uptake of nitrite or nitrate whilst enhancing the release of silicate and phosphate. In acidified (hypercapnic) treatments, the biological control of biogeochemical cycles by urchins was significantly affected, probably through the combined impacts of high CO ₂ on nitrifying bacteria, benthic algae and urchin behaviour. This study highlights the importance of considering biological interactions when predicting the consequences of seawater acidification on ecosystem function.

year: 2012

title: Contextualised approaches to embedding word senses

authors: Ansell, Alan John

subjects: Natural Language Processing

abstract: Vector representations of text are an essential tool for modern Natural Language Processing (NLP), and there has been much work devoted to finding effective methods for obtaining such representations. Most previously proposed methods derive vector representations for individual words, known as word embeddings. While word embeddings have enabled considerable advances in NLP, they have a significant theoretical drawback: many words have several, often completely unrelated meanings; it seems dubious to conflate these multiple meanings into a single point in semantic space.

This drawback has inspired an alternative, "multi-sense" approach to representing words. In this approach, rather than learning a single vector for each word, multiple vectors, or "sense embeddings," are learned corresponding to the individual meanings of the word. While this approach has not in general surpassed the word embedding approach, it has proved beneficial for a number of tasks such as word similarity estimation and word sense induction.

One of the most significant recent advances in NLP has been the development of "contextualised" word embedding models. Whereas word embeddings model the semantic properties of words in isolation, contextualised models represent the meanings of words in context. This enables them to capture some of the vast array of linguistic phenomena that occur above the word level.

I propose a number of new methods for learning sense embeddings which exploit contextualised techniques, based on the underlying hypothesis that the probability of a word occurring in a given context is equal to the sum of the probabilities of its individual senses occurring in the context. I first validate this hypothesis by using it to derive a simple method for learning sense embeddings inspired by the Skip-gram model. I then present a method for extracting sense embeddings from a contextualised word embedding model. Finally I propose an end-to-end model for learning sense embeddings, and show that it comprehensively outperforms previous sense embedding models on the task of word sense induction, a standard task for evaluation of such models. To demonstrate the model's flexibility I apply it to some other word-sense related tasks with good results.

text: Contextualised approaches to embedding word senses Vector representations of text are an essential tool for modern Natural Language Processing (NLP), and there has been much work devoted to finding effective methods for obtaining such representations. Most previously proposed methods derive vector representations for individual words, known as word embeddings. While word embeddings have enabled considerable advances in NLP, they have a significant theoretical drawback: many words have several, often completely unrelated meanings; it seems dubious to conflate these multiple meanings into a single point in semantic space.

This drawback has inspired an alternative, "multi-sense" approach to representing words. In this approach, rather than learning a single vector for each word, multiple vectors, or "sense embeddings," are learned corresponding to the individual meanings of the word. While this approach has not in general surpassed the word embedding approach, it has proved beneficial for a number of tasks such as word similarity estimation and word sense induction.

One of the most significant recent advances in NLP has been the development of "contextualised" word embedding models. Whereas word embeddings model the semantic properties of words in isolation, contextualised models represent the meanings of words in context. This enables them to capture some of the vast array of linguistic phenomena that occur above the word level.

I propose a number of new methods for learning sense embeddings which exploit contextualised techniques, based on the underlying hypothesis that the probability of a word occurring in a given context is equal to the sum of the probabilities of its individual senses occurring in the context. I first validate this hypothesis by using it to derive a simple method for learning sense embeddings inspired by the Skip-gram model. I then present a method for extracting sense embeddings from a contextualised word embedding model. Finally I propose an end-to-end model for learning sense embeddings, and show that it comprehensively outperforms previous sense embedding models on the task of word sense induction, a standard task for evaluation of such models. To demonstrate the model's flexibility I apply it to some other word-sense related tasks with good results.

year: 2020

title: Revitalizing Te Ika-a-Maui: Māori migration and the nation

authors: Mahuika, Nepia

subjects: Māori

abstract: Maui-Tikitiki-A-Taranga is more than simply a ‘mythic’ hero. For many he is a prominent figure in a long family line. The whakapapa that binds Māori to our tupuna is significant to the present and future, and carries with it the ‘ultimate expression’ of who we are. Maui has been described as ‘the most important culture hero in Māori mythology’, whose exploits and archetype provide precedents that Māori respond to in the present. The status of Maui within my own tribal boundaries is clear: ‘In accordance with the traditions and tikanga of Ngati Porou, we as People of this Land have been here since the beginning of time, or more aptly in the context of Aotearoa, since Maui fished up Te Ika-a-Maui (North Island). Maui-Tikitiki-a-Taranga is attributed with fishing up the North Island, raising it out of the depths of the sea, for successive generations of Māori to populate and cultivate.’ Maui’s waka, as Ngāti Porou uphold, is Nukutaimemeha, believed to be ‘cradled’ upon our ancestral mountain Hikurangi. However, there are other stories that recognize the waka as the South Island, with Stewart Island its anchor. These variations aside, the history remains a well-rehearsed one throughout the Māori world and in other parts of Polynesia.

text: Revitalizing Te Ika-a-Maui: Māori migration and the nation Maui-Tikitiki-A-Taranga is more than simply a ‘mythic’ hero. For many he is a prominent figure in a long family line. The whakapapa that binds Māori to our tupuna is significant to the present and future, and carries with it the ‘ultimate expression’ of who we are. Maui has been described as ‘the most important culture hero in Māori mythology’, whose exploits and archetype provide precedents that Māori respond to in the present. The status of Maui within my own tribal boundaries is clear: ‘In accordance with the traditions and tikanga of Ngati Porou, we as People of this Land have been here since the beginning of time, or more aptly in the context of Aotearoa, since Maui fished up Te Ika-a-Maui (North Island). Maui-Tikitiki-a-Taranga is attributed with fishing up the North Island, raising it out of the depths of the sea, for successive generations of Māori to populate and cultivate.’ Maui’s waka, as Ngāti Porou uphold, is Nukutaimemeha, believed to be ‘cradled’ upon our ancestral mountain Hikurangi. However, there are other stories that recognize the waka as the South Island, with Stewart Island its anchor. These variations aside, the history remains a well-rehearsed one throughout the Māori world and in other parts of Polynesia.

year: 2009