title: Better Self-training for Image Classification Through Self-supervision

authors: Sahito, Attaullah

subjects: Science & Technology

abstract: Self-training is a simple semi-supervised learning approach: Unlabelled examples that attract high-confidence predictions are labelled with their predictions and added to the training set, with this process being repeated multiple times. Recently, self-supervision—learning without manual supervision by solving an automatically-generated pretext task—has gained prominence in deep learning. This paper investigates three different ways of incorporating self-supervision into self-training to improve accuracy in image classification: self-supervision as pretraining only, self-supervision performed exclusively in the first iteration of self-training, and self-supervision added to every iteration of self-training. Empirical results on the SVHN, CIFAR-10, and PlantVillage datasets, using both training from scratch, and Imagenet-pretrained weights, show that applying self-supervision only in the first iteration of self-training can greatly improve accuracy, for a modest increase in computation time.

text: Better Self-training for Image Classification Through Self-supervision Self-training is a simple semi-supervised learning approach: Unlabelled examples that attract high-confidence predictions are labelled with their predictions and added to the training set, with this process being repeated multiple times. Recently, self-supervision—learning without manual supervision by solving an automatically-generated pretext task—has gained prominence in deep learning. This paper investigates three different ways of incorporating self-supervision into self-training to improve accuracy in image classification: self-supervision as pretraining only, self-supervision performed exclusively in the first iteration of self-training, and self-supervision added to every iteration of self-training. Empirical results on the SVHN, CIFAR-10, and PlantVillage datasets, using both training from scratch, and Imagenet-pretrained weights, show that applying self-supervision only in the first iteration of self-training can greatly improve accuracy, for a modest increase in computation time.

year: 2022-01-01

title: Dynamic mud deposition along the fluvial–tidal transition zone in the Waihou River, Aotearoa New Zealand

authors: La Croix, Andrew D.

abstract: The fluvial–tidal transition zone (FTT) is a critical interface where complex interactions between river flow, tides, and sedimentation shape geomorphic systems and influence the dynamics of aquatic environments. However, few previous studies have integrated real‐time hydrodynamic data with sedimentary deposits. In particular, the range of depositional conditions over which mud accumulates remains poorly constrained, and little is understood about how these deposits are preserved in the stratigraphic record. To address this knowledge gap, we examined co‐located hydrodynamic instrument data and sedimentary deposits from the lower Waihou River, Aotearoa New Zealand. Results reveal that “dynamic mud” events, including fluid mud and rapidly deposited mud, dominate the spatial and temporal record, with few “static mud” events in which mud is deposited through gravitational settling. We suggest that dynamic mud conditions with the potential for deposition may occur throughout the tidal cycle, although cyclic tidal successions are never fully preserved. Many of the trends in sedimentation observed in studies of larger systems are not present in this small muddy river system, indicating the significance of climatic and river‐flow characteristics on the sedimentary record. This work underscores the importance of studying systems of multiple sizes across diverse climatic regimes to establish holistic facies models to reconstruct geological history accurately.

text: Dynamic mud deposition along the fluvial–tidal transition zone in the Waihou River, Aotearoa New Zealand The fluvial–tidal transition zone (FTT) is a critical interface where complex interactions between river flow, tides, and sedimentation shape geomorphic systems and influence the dynamics of aquatic environments. However, few previous studies have integrated real‐time hydrodynamic data with sedimentary deposits. In particular, the range of depositional conditions over which mud accumulates remains poorly constrained, and little is understood about how these deposits are preserved in the stratigraphic record. To address this knowledge gap, we examined co‐located hydrodynamic instrument data and sedimentary deposits from the lower Waihou River, Aotearoa New Zealand. Results reveal that “dynamic mud” events, including fluid mud and rapidly deposited mud, dominate the spatial and temporal record, with few “static mud” events in which mud is deposited through gravitational settling. We suggest that dynamic mud conditions with the potential for deposition may occur throughout the tidal cycle, although cyclic tidal successions are never fully preserved. Many of the trends in sedimentation observed in studies of larger systems are not present in this small muddy river system, indicating the significance of climatic and river‐flow characteristics on the sedimentary record. This work underscores the importance of studying systems of multiple sizes across diverse climatic regimes to establish holistic facies models to reconstruct geological history accurately.

year: 2025

title: "Kia Mauri Tau!" Narratives of recovery from disabling mental health problems

authors: Lapsley, Hilary

subjects: Maori

abstract: A bicultural research team at the University of Waikato successfully applied for funding from the Health Research Council to do a qualitative research project in the area of ‘pathways to wellness’. The Mental Health Narratives Project set out to gather forty narratives of mental ill health, recovery and life after recovery by interviewing Maori and non-Maori women and men who once had a disabling mental health problem. We aimed to describe journeys to mental health, identify key themes in accounts of the recovery process and describe the impact of the experience of mental ill health on life after recovery.

text: "Kia Mauri Tau!" Narratives of recovery from disabling mental health problems A bicultural research team at the University of Waikato successfully applied for funding from the Health Research Council to do a qualitative research project in the area of ‘pathways to wellness’. The Mental Health Narratives Project set out to gather forty narratives of mental ill health, recovery and life after recovery by interviewing Maori and non-Maori women and men who once had a disabling mental health problem. We aimed to describe journeys to mental health, identify key themes in accounts of the recovery process and describe the impact of the experience of mental ill health on life after recovery.

year: 2002