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: Arbitrary boolean advertisements: the final step in supporting the boolean publish/subscribe model

authors: Bittner, Sven

subjects: computer science

abstract: Publish/subscribe systems allow for an efficient filtering of incoming information. This filtering is based on the specifications of subscriber interests, which are registered with the system as subscriptions. Publishers conversely specify advertisements, describing the messages they will send later on. What is missing so far is the support of arbitrary Boolean advertisements in publish/subscribe systems. Introducing the opportunity to specify these richer Boolean advertisements increases the accuracy of publishers to state their future messages compared to currently supported conjunctive advertisements. Thus, the amount of subscriptions forwarded in the network is reduced. Additionally, the system can more time efficiently decide whether a subscription needs to be forwarded and more space efficiently store and index advertisements. 

In this paper, we introduce a publish/subscribe system that supports arbitrary Boolean advertisements and, symmetrically, arbitrary Boolean subscriptions. We show the advantages of supporting arbitrary Boolean advertisements and present an algorithm to calculate the practically required overlapping relationship among subscriptions and advertisements. Additionally, we develop the first optimization approach for arbitrary Boolean advertisements, advertisement pruning. Advertisement pruning is tailored to optimize advertisements, which is a strong contrast
to current optimizations for conjunctive advertisements. These recent proposals mainly apply subscription-based optimization ideas, which is leading to the same disadvantages.

In the second part of this paper, our evaluation of practical experiments, we analyze the efficiency properties of our approach to determine the overlapping relationship. We also compare conjunctive solutions for the overlapping problem to our calculation algorithm to show its benefits. Finally, we present a detailed evaluation of the optimization potential of advertisement pruning. This includes the analysis of the effects of additionally optimizing subscriptions on the advertisement pruning optimization.

text: Arbitrary boolean advertisements: the final step in supporting the boolean publish/subscribe model Publish/subscribe systems allow for an efficient filtering of incoming information. This filtering is based on the specifications of subscriber interests, which are registered with the system as subscriptions. Publishers conversely specify advertisements, describing the messages they will send later on. What is missing so far is the support of arbitrary Boolean advertisements in publish/subscribe systems. Introducing the opportunity to specify these richer Boolean advertisements increases the accuracy of publishers to state their future messages compared to currently supported conjunctive advertisements. Thus, the amount of subscriptions forwarded in the network is reduced. Additionally, the system can more time efficiently decide whether a subscription needs to be forwarded and more space efficiently store and index advertisements. 

In this paper, we introduce a publish/subscribe system that supports arbitrary Boolean advertisements and, symmetrically, arbitrary Boolean subscriptions. We show the advantages of supporting arbitrary Boolean advertisements and present an algorithm to calculate the practically required overlapping relationship among subscriptions and advertisements. Additionally, we develop the first optimization approach for arbitrary Boolean advertisements, advertisement pruning. Advertisement pruning is tailored to optimize advertisements, which is a strong contrast
to current optimizations for conjunctive advertisements. These recent proposals mainly apply subscription-based optimization ideas, which is leading to the same disadvantages.

In the second part of this paper, our evaluation of practical experiments, we analyze the efficiency properties of our approach to determine the overlapping relationship. We also compare conjunctive solutions for the overlapping problem to our calculation algorithm to show its benefits. Finally, we present a detailed evaluation of the optimization potential of advertisement pruning. This includes the analysis of the effects of additionally optimizing subscriptions on the advertisement pruning optimization.

year: 2006-06-01

title: Science-practice roadshow 1: Summary perspectives from centralised entities and agencies

authors: Serrao-Neumann, Silvia

abstract: Flooding is Aotearoa-New Zealand’s most frequent natural hazard, regularly damaging buildings and infrastructure networks, and causing months of disruption to communities and businesses (Hutchings et al. 2019). Also, floods have indirect, cascading, and more intangible impacts (Lawrence, Blackett, and Cradock-Henry 2020), such as the withdrawal of future investment or the emotional stress of recovery. flood risk is predicted to rise quickly due to the combination of climate change and rapid urban intensification (Ministry for the Environment and The Flood Risk Management and River Control Review Steering Group 2008). 

Currently, there is no consistent approach to accurately determine flood risk on a national scale, nor how this may be changing as the climate warms. Instead, flooding risk assessments are done for individual catchments or locations, with the goal of managing and mitigating on the local scale. Local and regional governments vary widely in their capability and capacity to undertake this work (Ford, Berrang-Ford, and Berrang Ford 2011) and, thus, significant differences in data availability and modelling approaches mean that integration of results to gain a pan-region or nationwide picture has not been possible. 

The ‘Mā te haumaru ō nga puna wai ō Rākaihautū ka ora mo ake tonu: Increasing flood resilience across Aotearoa’ research develop a detailed nationwide model that could inform strategic and co-ordinated nationwide decision-making and regulations to support improved resilience to flood risk across the country. Utilising new methods and advances in computer processing and automation, the programme is developing a dynamic risk modelling framework that will assess flood hazard for every catchment in the country and under differing climate scenarios. The model will account for the risks to both built infrastructure and communities so that a fuller understanding of societal costs and impacts can be established. The model’s utility for supporting decision-making will be investigated using an initial set of scenarios for environmental planning, policy settings and infrastructure investment options. Two overarching themes across all of the research - Mtauranga Mori and Uncertainty – are designed to utilise the diverse knowledge systems available to develop tools that are relevant for communities throughout Aotearoa-New Zealand, and to take into account and communicate more clearly flood risks. 

Improved resilience to flooding across Aotearoa-New Zealand is a key outcome sought by the research programme, however it cannot be achieved by research alone. Instead, knowledge and information developed within the programme must be utilised by practitioners in their work to assist communities to plan for, manage, mitigate and avoid flood risks. New knowledge from the programme must be incorporated by practitioners into further tools, policy and decision-making frameworks. This knowledge translation however is not a straight forward process (Dilling and Lemos 2011). Cultural, behavioural and cognitive differences between those who produce and those who utilise knowledge mean that research outputs may not fit the requirements of their intended users, and thus, uptake of research outputs may be compromised. Conversely, strong engagement of knowledge users in the knowledge production process is known to increase the likelihood, and impact, of knowledge implementation. 

Stakeholders are key members of the programme’s research team, representing views across central and local government, engineering, planning consultancies, emergency management, insurance and banking industries, and Te Rūnanga o Wairewa (Banks Peninsula). To broaden the range of stakeholder perspectives, and to facilitate relationships across the programme, we have established a boundary organisation – Te W Te W . Boundary organisations (Guston 2001; Kirchhoff, Esselman, and Brown 2015) are forums that bridge between the two relatively different social worlds of knowledge producers (researchers/scientists) and users (practitioners/policy makers). These forums create and sustain legitimate space and collaboration for knowledge/information to be co-designed, co-produced and co-disseminated (Serrao-Neumann, Di Giulio, and Low Choy 2020). Within our programme, Te W Te W is responsible for organising and facilitating communication and collaboration activities – events (Science-Practice oadshows), communications channels such as an electronic newsletter and website, and learning opportunities. Through this organisation, we are fostering trust, legitimacy and capacity for information use so that our research results will be usable, useful, and used. 

This report summarises the discussion and perspectives shared at the first Science-Practice Roadshow held in October 2021.

text: Science-practice roadshow 1: Summary perspectives from centralised entities and agencies Flooding is Aotearoa-New Zealand’s most frequent natural hazard, regularly damaging buildings and infrastructure networks, and causing months of disruption to communities and businesses (Hutchings et al. 2019). Also, floods have indirect, cascading, and more intangible impacts (Lawrence, Blackett, and Cradock-Henry 2020), such as the withdrawal of future investment or the emotional stress of recovery. flood risk is predicted to rise quickly due to the combination of climate change and rapid urban intensification (Ministry for the Environment and The Flood Risk Management and River Control Review Steering Group 2008). 

Currently, there is no consistent approach to accurately determine flood risk on a national scale, nor how this may be changing as the climate warms. Instead, flooding risk assessments are done for individual catchments or locations, with the goal of managing and mitigating on the local scale. Local and regional governments vary widely in their capability and capacity to undertake this work (Ford, Berrang-Ford, and Berrang Ford 2011) and, thus, significant differences in data availability and modelling approaches mean that integration of results to gain a pan-region or nationwide picture has not been possible. 

The ‘Mā te haumaru ō nga puna wai ō Rākaihautū ka ora mo ake tonu: Increasing flood resilience across Aotearoa’ research develop a detailed nationwide model that could inform strategic and co-ordinated nationwide decision-making and regulations to support improved resilience to flood risk across the country. Utilising new methods and advances in computer processing and automation, the programme is developing a dynamic risk modelling framework that will assess flood hazard for every catchment in the country and under differing climate scenarios. The model will account for the risks to both built infrastructure and communities so that a fuller understanding of societal costs and impacts can be established. The model’s utility for supporting decision-making will be investigated using an initial set of scenarios for environmental planning, policy settings and infrastructure investment options. Two overarching themes across all of the research - Mtauranga Mori and Uncertainty – are designed to utilise the diverse knowledge systems available to develop tools that are relevant for communities throughout Aotearoa-New Zealand, and to take into account and communicate more clearly flood risks. 

Improved resilience to flooding across Aotearoa-New Zealand is a key outcome sought by the research programme, however it cannot be achieved by research alone. Instead, knowledge and information developed within the programme must be utilised by practitioners in their work to assist communities to plan for, manage, mitigate and avoid flood risks. New knowledge from the programme must be incorporated by practitioners into further tools, policy and decision-making frameworks. This knowledge translation however is not a straight forward process (Dilling and Lemos 2011). Cultural, behavioural and cognitive differences between those who produce and those who utilise knowledge mean that research outputs may not fit the requirements of their intended users, and thus, uptake of research outputs may be compromised. Conversely, strong engagement of knowledge users in the knowledge production process is known to increase the likelihood, and impact, of knowledge implementation. 

Stakeholders are key members of the programme’s research team, representing views across central and local government, engineering, planning consultancies, emergency management, insurance and banking industries, and Te Rūnanga o Wairewa (Banks Peninsula). To broaden the range of stakeholder perspectives, and to facilitate relationships across the programme, we have established a boundary organisation – Te W Te W . Boundary organisations (Guston 2001; Kirchhoff, Esselman, and Brown 2015) are forums that bridge between the two relatively different social worlds of knowledge producers (researchers/scientists) and users (practitioners/policy makers). These forums create and sustain legitimate space and collaboration for knowledge/information to be co-designed, co-produced and co-disseminated (Serrao-Neumann, Di Giulio, and Low Choy 2020). Within our programme, Te W Te W is responsible for organising and facilitating communication and collaboration activities – events (Science-Practice oadshows), communications channels such as an electronic newsletter and website, and learning opportunities. Through this organisation, we are fostering trust, legitimacy and capacity for information use so that our research results will be usable, useful, and used. 

This report summarises the discussion and perspectives shared at the first Science-Practice Roadshow held in October 2021.

year: 2022-02