title: Effect of Coriaria arborea on seed banks during primary succession on Mt Tarawera, New Zealand

authors: Clarkson, Beverley R.

subjects: Coriaria arborea

abstract: An experiment was conducted over two years to investigate the effect of Coriaria arborea, a native nitrogen-fixing shrub, on soil seed banks at sites representing a post-volcanic successional sequence on Mt Tarawera, New Zealand. The sites ranged from bare volcanic ash and lapilli substrate, through low-growing pre-Coriaria vegetation, to dense stands of Coriaria scrub. Soils (to a depth of 50 mm) under recently established Coriaria and older stands had more seedlings (1096 and 1585 seedlings 0.4 m-2, respectively) and species (37 and 45 species 0.4 m-2, respectively) emerge than where there was no Coriaria (243-320 seedlings 0.4 m-2, 14-25 species 0.4 m-2) and were the only soils with Coriaria seedlings. In total, 3488 seedlings representing 63 taxa were recorded. Seeds were still germinating after 24 months but rates declined markedly in the second year. For example, Coriaria reached a germination peak at 8 weeks but continued to germinate sporadically over the 2-year period. Tree species present in young forest within 0.5 km of the sites were absent. Establishment of Coriaria greatly accelerated an underlying trend of gradually increasing abundance and diversity of seeds in the soil with vegetation age. Adventive, wind-dispersed, and annual species were over-represented in the seed banks compared with the regional evergreen forest-dominated flora. These proportions are expected to decline as succession to forest gradually occurs.

text: Effect of Coriaria arborea on seed banks during primary succession on Mt Tarawera, New Zealand An experiment was conducted over two years to investigate the effect of Coriaria arborea, a native nitrogen-fixing shrub, on soil seed banks at sites representing a post-volcanic successional sequence on Mt Tarawera, New Zealand. The sites ranged from bare volcanic ash and lapilli substrate, through low-growing pre-Coriaria vegetation, to dense stands of Coriaria scrub. Soils (to a depth of 50 mm) under recently established Coriaria and older stands had more seedlings (1096 and 1585 seedlings 0.4 m-2, respectively) and species (37 and 45 species 0.4 m-2, respectively) emerge than where there was no Coriaria (243-320 seedlings 0.4 m-2, 14-25 species 0.4 m-2) and were the only soils with Coriaria seedlings. In total, 3488 seedlings representing 63 taxa were recorded. Seeds were still germinating after 24 months but rates declined markedly in the second year. For example, Coriaria reached a germination peak at 8 weeks but continued to germinate sporadically over the 2-year period. Tree species present in young forest within 0.5 km of the sites were absent. Establishment of Coriaria greatly accelerated an underlying trend of gradually increasing abundance and diversity of seeds in the soil with vegetation age. Adventive, wind-dispersed, and annual species were over-represented in the seed banks compared with the regional evergreen forest-dominated flora. These proportions are expected to decline as succession to forest gradually occurs.

year: 2002-12-01

title: Electromyography based gesture decoding employing few-shot learning, transfer learning, and training from scratch

authors: Godoy, Richardo V.

subjects: artificial intelligence

abstract: Over the last decade several machine learning (ML) based data-driven approaches have been used for Electromyography (EMG) based control of prosthetic hands. However, the performance of EMG-based frameworks can be affected by: i) the onset of fatigue due to long data collection sessions, ii) musculoskeletal differences between individuals, and iii) sensor position drifting between different sessions with the same user. To evaluate these aspects, in this work, we compare the performance of EMG-based hand gesture decoding models developed using three approaches. This comparison allows for future works in EMG-based Human-Machine Interfaces development to make more informed ML decisions. First, we trained from scratch a Transformer-based architecture, called Temporal Multi-Channel Vision Transformer (TMC-ViT). For our second approach, we utilized a pre-trained and fine-tuned TMC-ViT model (a transfer learning approach). Finally, for our third approach, we developed a Prototypical Network (a few-shot learning approach). The models are trained in a subject-specific and subject-generic manner for eight subjects and validated employing the 10-fold cross-validation procedure. This study shows that training a deep learning decoding model from scratch in a subject-specific manner leads to higher decoding accuracies when a larger dataset is available. For smaller datasets, subject-generic models, or inter-session models, the few-shot learning approach produces more robust results with better performance, and is more suited to applications where long data collection scenarios are not possible, or where multiple users are intended for the interface. Our findings show that the few-shot learning approach can outperform training a model from scratch in different scenarios.

text: Electromyography based gesture decoding employing few-shot learning, transfer learning, and training from scratch Over the last decade several machine learning (ML) based data-driven approaches have been used for Electromyography (EMG) based control of prosthetic hands. However, the performance of EMG-based frameworks can be affected by: i) the onset of fatigue due to long data collection sessions, ii) musculoskeletal differences between individuals, and iii) sensor position drifting between different sessions with the same user. To evaluate these aspects, in this work, we compare the performance of EMG-based hand gesture decoding models developed using three approaches. This comparison allows for future works in EMG-based Human-Machine Interfaces development to make more informed ML decisions. First, we trained from scratch a Transformer-based architecture, called Temporal Multi-Channel Vision Transformer (TMC-ViT). For our second approach, we utilized a pre-trained and fine-tuned TMC-ViT model (a transfer learning approach). Finally, for our third approach, we developed a Prototypical Network (a few-shot learning approach). The models are trained in a subject-specific and subject-generic manner for eight subjects and validated employing the 10-fold cross-validation procedure. This study shows that training a deep learning decoding model from scratch in a subject-specific manner leads to higher decoding accuracies when a larger dataset is available. For smaller datasets, subject-generic models, or inter-session models, the few-shot learning approach produces more robust results with better performance, and is more suited to applications where long data collection scenarios are not possible, or where multiple users are intended for the interface. Our findings show that the few-shot learning approach can outperform training a model from scratch in different scenarios.

year: 2023-09-27

title: Early French views of the Waikato and its inhabitants (1840-54)

authors: Jennings, William

abstract: This paper will investigate how the cultural background of these Frenchmen informed their descriptions of Waikato Maori and the Waikato environment. It will use the newly-published ten-volume collected correspondence of the pioneer Marists in the Pacific,the Lettres refues d'Odanie, as its principal source.

text: Early French views of the Waikato and its inhabitants (1840-54) This paper will investigate how the cultural background of these Frenchmen informed their descriptions of Waikato Maori and the Waikato environment. It will use the newly-published ten-volume collected correspondence of the pioneer Marists in the Pacific,the Lettres refues d'Odanie, as its principal source.

year: 2011