title: Experiments in cross-domain few-shot learning for image classification

authors: Wang, Hongyu

subjects: computer science

abstract: Cross-domain few-shot learning has many practical applications. This paper attempts to shed light on suitable configurations of feature exactors and ‘shallow’ classifiers in this machine learning setting. We apply ResNet-based feature extractors pretrained on two versions of the ImageNet dataset to five target domains with different degrees of similarity to ImageNet, varying the feature extractor size, the network stage at which features are extracted, and the learning algorithm applied to the extracted features. We evaluate standard learning algorithms such as logistic regression and linear discriminant analysis, as well as variants thereof, and additionally consider the effect of normalising the feature vectors using various p-norms. We also apply multi-instance learning to improve training image utilisation. In our experiments, the cosine similarity classifier and ℓ2-regularised 1-vs-rest logistic regression generally exhibit the best classification performance. We also find that algorithms such as linear discriminant analysis yield consistently higher accuracy using ℓ2-normalised feature vectors. Features extracted from the penultimate stage of a ResNet-101 model, and multi-instance learning techniques, produce the highest accuracy for most target domains. Our results will inform practitioners who are considering the application of pretrained ImageNet feature extractors in cross-domain few-shot settings.

text: Experiments in cross-domain few-shot learning for image classification Cross-domain few-shot learning has many practical applications. This paper attempts to shed light on suitable configurations of feature exactors and ‘shallow’ classifiers in this machine learning setting. We apply ResNet-based feature extractors pretrained on two versions of the ImageNet dataset to five target domains with different degrees of similarity to ImageNet, varying the feature extractor size, the network stage at which features are extracted, and the learning algorithm applied to the extracted features. We evaluate standard learning algorithms such as logistic regression and linear discriminant analysis, as well as variants thereof, and additionally consider the effect of normalising the feature vectors using various p-norms. We also apply multi-instance learning to improve training image utilisation. In our experiments, the cosine similarity classifier and ℓ2-regularised 1-vs-rest logistic regression generally exhibit the best classification performance. We also find that algorithms such as linear discriminant analysis yield consistently higher accuracy using ℓ2-normalised feature vectors. Features extracted from the penultimate stage of a ResNet-101 model, and multi-instance learning techniques, produce the highest accuracy for most target domains. Our results will inform practitioners who are considering the application of pretrained ImageNet feature extractors in cross-domain few-shot settings.

year: 2022

title: Complete genome sequence of a New Zealand Mycobacterium tuberculosis strain responsible for ongoing transmission over the past 30 years.

authors: Mulholland, Claire V.

abstract: We report here the complete genome sequence of Mycobacterium tuberculosis strain Colonial S-type 1 (CS1), which has been responsible for ongoing outbreaks of tuberculosis in New Zealand over the past 30 years. CS1 appears to be highly transmissible, with greater rates of progression to active disease, compared to other circulating M. tuberculosis strains; therefore, comparison of its genomic content is of interest.

text: Complete genome sequence of a New Zealand Mycobacterium tuberculosis strain responsible for ongoing transmission over the past 30 years. We report here the complete genome sequence of Mycobacterium tuberculosis strain Colonial S-type 1 (CS1), which has been responsible for ongoing outbreaks of tuberculosis in New Zealand over the past 30 years. CS1 appears to be highly transmissible, with greater rates of progression to active disease, compared to other circulating M. tuberculosis strains; therefore, comparison of its genomic content is of interest.

year: 2022

title: Mapping Māori knowledge from the past to inform marine management futures

authors: Paul-Burke, Kura

subjects: mātauranga Māori

abstract: This article provides an overview of research which used mātauranga moana (Māori marine knowledge systems) to inform and assist Western science field research methods and surveys. Place-based Māori marine knowledge identified the traditional distribution range and sizing of taonga (culturally important) species in traditional coastal areas which had been fished and managed by consecutive generations of Māori; kina, Evechinus chloroticus, sea urchin; koura, Jasus edwardsii, red rock lobster; kūtai, Perna canaliculus, green lipped mussel; and pāua, Haliotis iris, abalone. This knowledge was then mapped and used as the baseline for sub-tidal marine science field research surveys. Findings from the transdisciplinary marine research was used to develop management actions to assist Māori and Government entities for improving, enhancing and safeguarding marine taonga species into the future. This article critically discusses and demonstrates the relevance and complementarity of mātauranga Māori and Western science, and the importance of kaupapa Māori strategies for empowering Māori collaboration and voices in marine research co-development, implementation and communication.

text: Mapping Māori knowledge from the past to inform marine management futures This article provides an overview of research which used mātauranga moana (Māori marine knowledge systems) to inform and assist Western science field research methods and surveys. Place-based Māori marine knowledge identified the traditional distribution range and sizing of taonga (culturally important) species in traditional coastal areas which had been fished and managed by consecutive generations of Māori; kina, Evechinus chloroticus, sea urchin; koura, Jasus edwardsii, red rock lobster; kūtai, Perna canaliculus, green lipped mussel; and pāua, Haliotis iris, abalone. This knowledge was then mapped and used as the baseline for sub-tidal marine science field research surveys. Findings from the transdisciplinary marine research was used to develop management actions to assist Māori and Government entities for improving, enhancing and safeguarding marine taonga species into the future. This article critically discusses and demonstrates the relevance and complementarity of mātauranga Māori and Western science, and the importance of kaupapa Māori strategies for empowering Māori collaboration and voices in marine research co-development, implementation and communication.

year: 2020