title: E Kui Rāwinia

authors: Maxwell, Te Kahautu

abstract: E Kui Rāwinia; this composition is a lament to a Te Whakatōhea chieftainess who married the paramount Ngāti Porou rangatira Tokorua (Toko) Te Kani. This composition recites the genealogical ties between the East Coast iwi to Te Whakatōhea. The composition paints a spiritual journey for Rāwinia to return to her birth place Ōpōtiki. The journey names geographical features of importance to map her journey home. This composition gained first place for composition. Performed by Ōpōtiki Mai Tawhiti at the Te Matatini World Championship Kapa Haka competition.

text: E Kui Rāwinia E Kui Rāwinia; this composition is a lament to a Te Whakatōhea chieftainess who married the paramount Ngāti Porou rangatira Tokorua (Toko) Te Kani. This composition recites the genealogical ties between the East Coast iwi to Te Whakatōhea. The composition paints a spiritual journey for Rāwinia to return to her birth place Ōpōtiki. The journey names geographical features of importance to map her journey home. This composition gained first place for composition. Performed by Ōpōtiki Mai Tawhiti at the Te Matatini World Championship Kapa Haka competition.

year: 2015

title: A case study of using the He Pikinga Waiora Implementation Framework: Challenges and successes in implementing a twelve-week lifestyle intervention to reduce weight in Māori men at risk of diabetes, cardiovascular disease and obesity

authors: Oetzel, John G.

subjects: Science & Technology

abstract: BACKGROUND: Māori men have stark health inequities around non-communicable diseases. This study describes the case of a partnership attempting to develop and implement a culturally centred intervention through a collaborative partnership to potentially address the inequities. In particular, the partnership followed a participatory, co-design approach using the He Pikinga Waiora (HPW) Implementation Framework; the study presents lessons learnt in addressing health inequities following this framework. METHODS: The partnership involved a university research team and a Māori community health provider. They engaged with other stakeholders and several cohorts of Māori men through a co-design process to adapt a 12-week lifestyle intervention. The co-design process was documented through meeting notes and interviews with partners. Two cohorts participated in separate single group pre-intervention/post-intervention designs with multi-method data collection. Key outcome measures included weight loss, self-reported health, physical activity, and nutrition. Post-intervention data collection included qualitative data. RESULTS: The co-design process resulted in a strong and engaged partnership between the university team and the provider. There were significant challenges in implementing the intervention including having two additional partner organisations dropping out of the partnership just after the initial implementation phase. However, a flexible and adaptable partnership resulted in developing two distinct lifestyle interventions run with 32 Māori men (in two different cohorts of 8 and 24). All but one in the first cohort completed the programme. The first cohort had a modest although statistically insignificant improvement in weight loss (d = 1.04) and body mass index (BMI; d = 1.08). The second cohort had a significant reduction in weight loss (d = 1.16) and BMI (d = 1.15). They also had a significant increase in health-related quality of life (d = 1.7) and self-rated health (d = 2.0). CONCLUSION: The HPW Framework appears to be well suited to advance implementation science for Indigenous communities in general and Māori in particular. The framework has promise as a policy and planning tool to evaluate and design interventions for chronic disease prevention in Indigenous communities. Despite this promise, there are structural challenges in developing and implementing interventions to address health inequities. TRIAL REGISTRATION: Retrospectively registered, Australia New Zealand Clinical Trials Registry, ACTRN12619001783112.

text: A case study of using the He Pikinga Waiora Implementation Framework: Challenges and successes in implementing a twelve-week lifestyle intervention to reduce weight in Māori men at risk of diabetes, cardiovascular disease and obesity BACKGROUND: Māori men have stark health inequities around non-communicable diseases. This study describes the case of a partnership attempting to develop and implement a culturally centred intervention through a collaborative partnership to potentially address the inequities. In particular, the partnership followed a participatory, co-design approach using the He Pikinga Waiora (HPW) Implementation Framework; the study presents lessons learnt in addressing health inequities following this framework. METHODS: The partnership involved a university research team and a Māori community health provider. They engaged with other stakeholders and several cohorts of Māori men through a co-design process to adapt a 12-week lifestyle intervention. The co-design process was documented through meeting notes and interviews with partners. Two cohorts participated in separate single group pre-intervention/post-intervention designs with multi-method data collection. Key outcome measures included weight loss, self-reported health, physical activity, and nutrition. Post-intervention data collection included qualitative data. RESULTS: The co-design process resulted in a strong and engaged partnership between the university team and the provider. There were significant challenges in implementing the intervention including having two additional partner organisations dropping out of the partnership just after the initial implementation phase. However, a flexible and adaptable partnership resulted in developing two distinct lifestyle interventions run with 32 Māori men (in two different cohorts of 8 and 24). All but one in the first cohort completed the programme. The first cohort had a modest although statistically insignificant improvement in weight loss (d = 1.04) and body mass index (BMI; d = 1.08). The second cohort had a significant reduction in weight loss (d = 1.16) and BMI (d = 1.15). They also had a significant increase in health-related quality of life (d = 1.7) and self-rated health (d = 2.0). CONCLUSION: The HPW Framework appears to be well suited to advance implementation science for Indigenous communities in general and Māori in particular. The framework has promise as a policy and planning tool to evaluate and design interventions for chronic disease prevention in Indigenous communities. Despite this promise, there are structural challenges in developing and implementing interventions to address health inequities. TRIAL REGISTRATION: Retrospectively registered, Australia New Zealand Clinical Trials Registry, ACTRN12619001783112.

year: 2020

title: Feature extractor stacking for cross-domain few-shot learning

authors: Wang, Hongyu

subjects: cross-domain few-shot learning

abstract: Cross-domain few-shot learning (CDFSL) addresses learning problems where knowledge needs to be transferred from one or more source domains into an instance-scarce target domain with an explicitly different distribution. Recently published CDFSL methods generally construct a universal model that combines knowledge of multiple source domains into one feature extractor. This enables efficient inference but necessitates re-computation of the extractor whenever a new source domain is added. Some of these methods are also incompatible with heterogeneous source domain extractor architectures.

The first part of this thesis proposes feature extractor stacking (FES), a new CDFSL method for combining information from a collection of extractors, that can utilise heterogeneous pretrained extractors out of the box and does not maintain a universal model that needs to be re-computed when its extractor collection is updated. We present the basic FES algorithm, which is inspired by the classic stacked generalisation approach, and also introduce two variants: convolutional FES (ConFES) and regularised FES (ReFES). Given a target-domain task, these algorithms fine-tune each extractor independently, use cross-validation to extract training data for stacked generalisation from the support set, and learn a simple linear stacking classifier from this data. We evaluate our FES methods on the well-known Meta-Dataset benchmark, targeting image classification with convolutional neural networks, and show that they can achieve state-of-the-art performance.

The second part of this thesis proposes an efficient semi-supervised learning method that applies self-training to the classification head only and show that it can yield very consistent improvements in average performance in the Meta-Dataset benchmark for cross-domain few-shot learning when applied with FES and other contemporary methods utilising centroid-based classification.

The third part of this thesis proposes a bidirectional snapshot selection strategy for FES, leveraging its cross-validation process and the ordered nature of its snapshots, and demonstrates that a 95% snapshot reduction can be achieved while retaining the same level of accuracy.

text: Feature extractor stacking for cross-domain few-shot learning Cross-domain few-shot learning (CDFSL) addresses learning problems where knowledge needs to be transferred from one or more source domains into an instance-scarce target domain with an explicitly different distribution. Recently published CDFSL methods generally construct a universal model that combines knowledge of multiple source domains into one feature extractor. This enables efficient inference but necessitates re-computation of the extractor whenever a new source domain is added. Some of these methods are also incompatible with heterogeneous source domain extractor architectures.

The first part of this thesis proposes feature extractor stacking (FES), a new CDFSL method for combining information from a collection of extractors, that can utilise heterogeneous pretrained extractors out of the box and does not maintain a universal model that needs to be re-computed when its extractor collection is updated. We present the basic FES algorithm, which is inspired by the classic stacked generalisation approach, and also introduce two variants: convolutional FES (ConFES) and regularised FES (ReFES). Given a target-domain task, these algorithms fine-tune each extractor independently, use cross-validation to extract training data for stacked generalisation from the support set, and learn a simple linear stacking classifier from this data. We evaluate our FES methods on the well-known Meta-Dataset benchmark, targeting image classification with convolutional neural networks, and show that they can achieve state-of-the-art performance.

The second part of this thesis proposes an efficient semi-supervised learning method that applies self-training to the classification head only and show that it can yield very consistent improvements in average performance in the Meta-Dataset benchmark for cross-domain few-shot learning when applied with FES and other contemporary methods utilising centroid-based classification.

The third part of this thesis proposes a bidirectional snapshot selection strategy for FES, leveraging its cross-validation process and the ordered nature of its snapshots, and demonstrates that a 95% snapshot reduction can be achieved while retaining the same level of accuracy.

year: 2024