title: A comparison of machine learning methods for cross-domain few-shot learning

authors: Wang, Hongyu

subjects: computer science

abstract: We present an empirical evaluation of machine learning algorithms in cross-domain few-shot learning based on a fixed pre-trained feature extractor. Experiments were performed in five target domains (CropDisease, EuroSAT, Food101, ISIC and ChestX) and using two feature extractors: a ResNet10 model trained on a subset of ImageNet known as miniImageNet and a ResNet152 model trained on the ILSVRC 2012 subset of ImageNet. Commonly used machine learning algorithms including logistic regression, support vector machines, random forests, nearest neighbour classification, naïve Bayes, and linear and quadratic discriminant analysis were evaluated on the extracted feature vectors. We also evaluated classification accuracy when subjecting the feature vectors to normalisation using p-norms. Algorithms originally developed for the classification of gene expression data—the nearest shrunken centroid algorithm and LDA ensembles obtained with random projections—were also included in the experiments, in addition to a cosine similarity classifier that has recently proved popular in few-shot learning. The results enable us to identify algorithms, normalisation methods and pre-trained feature extractors that perform well in cross-domain few-shot learning. We show that the cosine similarity classifier and   ℓ² -regularised 1-vs-rest logistic regression are generally the best-performing algorithms. We also show that algorithms such as LDA yield consistently higher accuracy when applied to   ℓ² -normalised feature vectors. In addition, all classifiers generally perform better when extracting feature vectors using the ResNet152 model instead of the ResNet10 model.

text: A comparison of machine learning methods for cross-domain few-shot learning We present an empirical evaluation of machine learning algorithms in cross-domain few-shot learning based on a fixed pre-trained feature extractor. Experiments were performed in five target domains (CropDisease, EuroSAT, Food101, ISIC and ChestX) and using two feature extractors: a ResNet10 model trained on a subset of ImageNet known as miniImageNet and a ResNet152 model trained on the ILSVRC 2012 subset of ImageNet. Commonly used machine learning algorithms including logistic regression, support vector machines, random forests, nearest neighbour classification, naïve Bayes, and linear and quadratic discriminant analysis were evaluated on the extracted feature vectors. We also evaluated classification accuracy when subjecting the feature vectors to normalisation using p-norms. Algorithms originally developed for the classification of gene expression data—the nearest shrunken centroid algorithm and LDA ensembles obtained with random projections—were also included in the experiments, in addition to a cosine similarity classifier that has recently proved popular in few-shot learning. The results enable us to identify algorithms, normalisation methods and pre-trained feature extractors that perform well in cross-domain few-shot learning. We show that the cosine similarity classifier and   ℓ² -regularised 1-vs-rest logistic regression are generally the best-performing algorithms. We also show that algorithms such as LDA yield consistently higher accuracy when applied to   ℓ² -normalised feature vectors. In addition, all classifiers generally perform better when extracting feature vectors using the ResNet152 model instead of the ResNet10 model.

year: 2020

title: Depth-differentiated, multivariate control of biopore number under different land-use practices

authors: Stolze, Katharina

subjects: earthworm communities

abstract: Earthworms and (tap-)roots impact the soil structure by creating large biopores, affecting infiltration capacity, seepage, nutrient cycling, and soil aeration. Despite the importance of biopores for the functions of soils and the fact that several hundreds of biopores >2 mm in diameter may occur on one square meter of soil, knowledge on the interdependence of soil properties, land-use intensity, and biopore number is still rudimentary. In this study, we investigate the linkage of the number of biopores (>2 mm i.d.) with the earthworm community, root biomass, and soil properties, including pH, water content, soil organic carbon (SOC), as well as the land-use intensity (pasture vs. cropland) as a function of the soil depth (15, 30 and 50 cm). Hypothesized causal relationships among these factors were analyzed by piecewise structural equation modelling (SEM). We found various and novel linkages between roots, earthworms, biopores, and soil properties depending on soil depth. In topsoil (at 15 cm depth), roots directly affected the number of small-sized biopores, and anecic earthworms were related to medium-sized biopores. These effects diminished with depth. We identified land-use intensity as the factor preponderating the relations between biopores, root biomass, and earthworm number in the topsoil horizons, thereby masking other interactions among variables. This appeared as high multicollinearity among variables in the SEM of the topsoil. Land-use intensity effects were found to impact the whole soil profile but decreased with soil depth. To further elucidate the single effects of soil properties on biopore-forming biota and number of biopores in the topsoil, we excluded land-use intensity as a variable in subsequent analyses. Biopores increased with soil pH and soil water content but decreased with increasing SOC. Based on our SEM analysis, we conclude that the occurrence, frequency, and persistence of biopores are the consequence of intricate interdependencies between earthworm communities, roots, and site-specific soil properties, governed by land-use intensity.

text: Depth-differentiated, multivariate control of biopore number under different land-use practices Earthworms and (tap-)roots impact the soil structure by creating large biopores, affecting infiltration capacity, seepage, nutrient cycling, and soil aeration. Despite the importance of biopores for the functions of soils and the fact that several hundreds of biopores >2 mm in diameter may occur on one square meter of soil, knowledge on the interdependence of soil properties, land-use intensity, and biopore number is still rudimentary. In this study, we investigate the linkage of the number of biopores (>2 mm i.d.) with the earthworm community, root biomass, and soil properties, including pH, water content, soil organic carbon (SOC), as well as the land-use intensity (pasture vs. cropland) as a function of the soil depth (15, 30 and 50 cm). Hypothesized causal relationships among these factors were analyzed by piecewise structural equation modelling (SEM). We found various and novel linkages between roots, earthworms, biopores, and soil properties depending on soil depth. In topsoil (at 15 cm depth), roots directly affected the number of small-sized biopores, and anecic earthworms were related to medium-sized biopores. These effects diminished with depth. We identified land-use intensity as the factor preponderating the relations between biopores, root biomass, and earthworm number in the topsoil horizons, thereby masking other interactions among variables. This appeared as high multicollinearity among variables in the SEM of the topsoil. Land-use intensity effects were found to impact the whole soil profile but decreased with soil depth. To further elucidate the single effects of soil properties on biopore-forming biota and number of biopores in the topsoil, we excluded land-use intensity as a variable in subsequent analyses. Biopores increased with soil pH and soil water content but decreased with increasing SOC. Based on our SEM analysis, we conclude that the occurrence, frequency, and persistence of biopores are the consequence of intricate interdependencies between earthworm communities, roots, and site-specific soil properties, governed by land-use intensity.

year: 2022

title: Ngā takenga o te riri

authors: Murphy, Ēnoka

subjects: Pakanga

abstract: Ko te kaupapa ake o tēnei tuhinga, ko te whakawhāriki i ētahi kōrero mō te takenga mai o te riri, o te pakanga ki tā rā ngā kōrero a te Māori. Ko aua take mai rā kei ngā kōrero o tua whakarere, e hokia ana ki a Ranginui rāua ko Papatūānuku me tā rāua hūnuku. Ko tāna e pātai ake nei, nō hea ake te riri Māori, otirā, te pakanga a te Māori? Nō hea anō hoki āna tikanga? He aha ā ērā kōrero tauiratanga mai ki te toa o te Māori ki te whawhai. Ka mutu, ko te wāhi uaua pea o ngā pātai nei, ko te āhua ki ngā kōrero a te Māori kāore nei i te tapatahi mō te oroko hīmatanga. Ahakoa rā, tērā ōna rite, ōna rerekē. Kua tuhia nuitia te kaupapa o te oroko tīmatanga mai o te ao e ngā momo o te kaituhi Pākehā, ā, tērā anō hoki ko ngā tuhinga a te Māori ake. Inā te rerekē o ngā kōrero mō te oroko hīmatanga mai o te ao, tērā noa ētahi kōrero kua tīkina hei mātaitanga mō tēnei rangahau, mō tēnei tuhinga. Ka mutu, ko tā tēnei tuhinga, he whakaatu i te takenga mai o te riri, o te matawhāura Māori.

text: Ngā takenga o te riri Ko te kaupapa ake o tēnei tuhinga, ko te whakawhāriki i ētahi kōrero mō te takenga mai o te riri, o te pakanga ki tā rā ngā kōrero a te Māori. Ko aua take mai rā kei ngā kōrero o tua whakarere, e hokia ana ki a Ranginui rāua ko Papatūānuku me tā rāua hūnuku. Ko tāna e pātai ake nei, nō hea ake te riri Māori, otirā, te pakanga a te Māori? Nō hea anō hoki āna tikanga? He aha ā ērā kōrero tauiratanga mai ki te toa o te Māori ki te whawhai. Ka mutu, ko te wāhi uaua pea o ngā pātai nei, ko te āhua ki ngā kōrero a te Māori kāore nei i te tapatahi mō te oroko hīmatanga. Ahakoa rā, tērā ōna rite, ōna rerekē. Kua tuhia nuitia te kaupapa o te oroko tīmatanga mai o te ao e ngā momo o te kaituhi Pākehā, ā, tērā anō hoki ko ngā tuhinga a te Māori ake. Inā te rerekē o ngā kōrero mō te oroko hīmatanga mai o te ao, tērā noa ētahi kōrero kua tīkina hei mātaitanga mō tēnei rangahau, mō tēnei tuhinga. Ka mutu, ko tā tēnei tuhinga, he whakaatu i te takenga mai o te riri, o te matawhāura Māori.

year: 2017