title: Eco-physiological adaptations that favour freshwater cyanobacteria in a changing climate

authors: Carey, Cayelan C.

subjects: algal blooms

abstract: Climate change scenarios predict that rivers, lakes, and reservoirs will experience increased temperatures, more intense and longer periods of thermal stratification, modified hydrology, and altered nutrient loading. These environmental drivers will have substantial effects on freshwater phytoplankton species composition and biomass, potentially favouring cyanobacteria over other phytoplankton. In this Review, we examine how several cyanobacterial eco-physiological traits, specifically, the ability to grow in warmer temperatures; buoyancy; high affinity for, and ability to store, phosphorus; nitrogen-fixation; akinete production; and efficient light harvesting, vary amongst cyanobacteria genera and may enable them to dominate in future climate scenarios. We predict that spatial variation in climate change will interact with physiological variation in cyanobacteria to create differences in the dominant cyanobacterial taxa among regions. Finally, we suggest that physiological traits specific to different cyanobacterial taxa may favour certain taxa over others in different regions, but overall, cyanobacteria as a group are likely to increase in most regions in the future.

text: Eco-physiological adaptations that favour freshwater cyanobacteria in a changing climate Climate change scenarios predict that rivers, lakes, and reservoirs will experience increased temperatures, more intense and longer periods of thermal stratification, modified hydrology, and altered nutrient loading. These environmental drivers will have substantial effects on freshwater phytoplankton species composition and biomass, potentially favouring cyanobacteria over other phytoplankton. In this Review, we examine how several cyanobacterial eco-physiological traits, specifically, the ability to grow in warmer temperatures; buoyancy; high affinity for, and ability to store, phosphorus; nitrogen-fixation; akinete production; and efficient light harvesting, vary amongst cyanobacteria genera and may enable them to dominate in future climate scenarios. We predict that spatial variation in climate change will interact with physiological variation in cyanobacteria to create differences in the dominant cyanobacterial taxa among regions. Finally, we suggest that physiological traits specific to different cyanobacterial taxa may favour certain taxa over others in different regions, but overall, cyanobacteria as a group are likely to increase in most regions in the future.

year: 2012-04

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: When your maunga is a mountain but your moana is not a sea; Insights from the perception of synonymy of te reo Māori loanwords and New Zealand English lexical equivalents

authors: Pohl, Alexander

abstract: Languages and their speakers are in ongoing contact with one another, which inevitably leads to a flow of words being borrowed from one language into another (Haspelmath 2009; Winford 2010). One perspective that has comparatively seen less attention in language contact research concerns how loanword relate to existing lexical alternatives. In recent years, language contact research has thus seen a paradigm shift towards an onomasiological perspective (see Anderson et al. 2017; Crombez et al. 2022; Zenner et al. 2023). Within this shift, most studies looking at lexical variation between loanwords and their near synonyms equivalents employ corpus linguistics methods (e.g. Onysko & WinterFroemel 2011; Soares da Silva 2013). 

This project presents a large-scale experimental approach from a case-study involving Māori loanwords borrowed into New Zealand English (NZE). Māori loanwords are ubiquitous, salient and probably still increasing within the NZE lexicon (Macalister 2007; Trye et al. 2019). Regarding onomasiology, Māori loanwords have been glossed and explained in different ways, and to date, there has been to our knowledge almost no research focused specifically on the relationship between loanwords and their near synonyms. 

Here, we report on a Qualtrics experiment devised to probe the perceived relationship between 60 Māori loanwords (spanning established semantic categories: flora and fauna, material culture, and social culture) and their NZE equivalents, as ascertained from published sources. The synonymy judgments were elicited for pairs (e.g. whānau – family), using an analogue slider (ranging from 0 = non-synonymous to 100 = fully synonymous; see Figure 1). Because synonymy judgements are subjective, we also included ten fillers, to help us calibrate and interpret participant judgements. The 60 words were divided into two-word lists in order avoid fatigue, resulting in a within participant design. The target demographic for our population of interest consisted of young adults (18-24 years). 

Following cleaning and exclusions (e.g. participants who ignored filler items), the final dataset contained 260 participants. Inspecting the dataset showed a skew towards female participants, and for this reason, we focus our statistical analysis on this core sample, while keeping an eye on the full dataset containing all participants. Preliminary findings suggest that young adults think Māori loanwords are (highly) synonymous with NZE lexical alternatives. These findings will be further scrutinised with regression techniques and qualitative analyses of the comments provided in an open answer section of the questionnaire.

text: When your maunga is a mountain but your moana is not a sea; Insights from the perception of synonymy of te reo Māori loanwords and New Zealand English lexical equivalents Languages and their speakers are in ongoing contact with one another, which inevitably leads to a flow of words being borrowed from one language into another (Haspelmath 2009; Winford 2010). One perspective that has comparatively seen less attention in language contact research concerns how loanword relate to existing lexical alternatives. In recent years, language contact research has thus seen a paradigm shift towards an onomasiological perspective (see Anderson et al. 2017; Crombez et al. 2022; Zenner et al. 2023). Within this shift, most studies looking at lexical variation between loanwords and their near synonyms equivalents employ corpus linguistics methods (e.g. Onysko & WinterFroemel 2011; Soares da Silva 2013). 

This project presents a large-scale experimental approach from a case-study involving Māori loanwords borrowed into New Zealand English (NZE). Māori loanwords are ubiquitous, salient and probably still increasing within the NZE lexicon (Macalister 2007; Trye et al. 2019). Regarding onomasiology, Māori loanwords have been glossed and explained in different ways, and to date, there has been to our knowledge almost no research focused specifically on the relationship between loanwords and their near synonyms. 

Here, we report on a Qualtrics experiment devised to probe the perceived relationship between 60 Māori loanwords (spanning established semantic categories: flora and fauna, material culture, and social culture) and their NZE equivalents, as ascertained from published sources. The synonymy judgments were elicited for pairs (e.g. whānau – family), using an analogue slider (ranging from 0 = non-synonymous to 100 = fully synonymous; see Figure 1). Because synonymy judgements are subjective, we also included ten fillers, to help us calibrate and interpret participant judgements. The 60 words were divided into two-word lists in order avoid fatigue, resulting in a within participant design. The target demographic for our population of interest consisted of young adults (18-24 years). 

Following cleaning and exclusions (e.g. participants who ignored filler items), the final dataset contained 260 participants. Inspecting the dataset showed a skew towards female participants, and for this reason, we focus our statistical analysis on this core sample, while keeping an eye on the full dataset containing all participants. Preliminary findings suggest that young adults think Māori loanwords are (highly) synonymous with NZE lexical alternatives. These findings will be further scrutinised with regression techniques and qualitative analyses of the comments provided in an open answer section of the questionnaire.

year: 2024