Our lab researches machine learning and its application to cheminformatics, bioinformatics, and computational sustainability. We are always interested in interesting new research areas both for applied and fundamental machine learning. Currently, we are particularly interested in reliability of machine learning models, adversarial machine learning, and bias, with applications in chemistry, epidemiology, and environmental research.
To learn more about our lab, check out our publications or read more about our research and projects.
You can join us as PhD student, Honours student, or other postgraduate student. You can also visit our lab as visiting researcher or student.
News
-
Artificial Intelligence and Freshwater Modelling
To protect our freshwater for future generations, we develop a framework enabling an understanding of how environmental factors impact our water quality and how mitigation strategies can help. Our project […]
Social
- enviPath was first published in 2016. Since then, we have introduced substantial functionality enhancements and updates. We’ve summarized these advancements in our latest update paper. Check it out here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11304562/
- 🔁 Repeated @AI and Freshwater Modelling's Note
- 🔁 Repeated @AI and Freshwater Modelling's Note
- Wickerlab likes AI and Freshwater Modelling's status
- Likes @AI and Freshwater Modelling's NoteExciting News from the #AI and #freshwater Modelling project at the University of Auckland! 🎓Master’s student Nicolas Samelson, supervised by Dr. Jörg Wicker and Dr. Katharina Dost, is breaking new ground in #environmentalmodeling. His thesis leverages #machinelearning to enhance process-based models, especially in scenarios with limited data.Learn more about this […]
Recent Publications
Journal Articles
Brydon, Liam; Zhang, Kunyang; Dobbie, Gillian; Taskova, Katerina; Wicker, Jörg
Predictive Modeling of Biodegradation Pathways Using Transformer Architectures Journal Article
In: Journal of Cheminformatics, vol. 17, no. 1, pp. 21, 2025, ISSN: 1758-2946.
Abstract | Links | BibTeX | Altmetric | PlumX
@article{Brydon2024b,
title = {Predictive Modeling of Biodegradation Pathways Using Transformer Architectures},
author = {Liam Brydon and Kunyang Zhang and Gillian Dobbie and Katerina Taskova and J\"{o}rg Wicker},
url = { https://doi.org/10.21203/rs.3.rs-5200860/v3},
doi = {10.1186/s13321-025-00969-7},
issn = {1758-2946},
year = {2025},
date = {2025-02-17},
urldate = {2024-10-24},
journal = {Journal of Cheminformatics},
volume = {17},
number = {1},
pages = {21},
abstract = {In recent years, the integration of machine learning techniques into chemical reaction product prediction has opened new avenues for understanding and predicting the behaviour of chemical substances. The necessity for such predictive methods stems from the growing regulatory and social awareness of the environmental consequences associated with the persistence and accumulation of chemical residues. Traditional biodegradation prediction methods rely on expert knowledge to perform predictions. However, creating this expert knowledge is becoming increasingly prohibitive due to the complexity and diversity of newer datasets, leaving existing methods unable to perform predictions on these datasets. We formulate the product prediction problem as a sequence-to-sequence generation task and take inspiration from natural language processing and other reaction prediction tasks. In doing so, we reduce the need for the expensive manual creation of expert-based rules. },
howpublished = {ResearchSquare},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In recent years, the integration of machine learning techniques into chemical reaction product prediction has opened new avenues for understanding and predicting the behaviour of chemical substances. The necessity for such predictive methods stems from the growing regulatory and social awareness of the environmental consequences associated with the persistence and accumulation of chemical residues. Traditional biodegradation prediction methods rely on expert knowledge to perform predictions. However, creating this expert knowledge is becoming increasingly prohibitive due to the complexity and diversity of newer datasets, leaving existing methods unable to perform predictions on these datasets. We formulate the product prediction problem as a sequence-to-sequence generation task and take inspiration from natural language processing and other reaction prediction tasks. In doing so, we reduce the need for the expensive manual creation of expert-based rules.
Albrecht, Steffen; Broderick, David; Dost, Katharina; Cheung, Isabella; Nghiem, Nhung; Wu, Milton; Zhu, Johnny; Poonawala-Lohani, Nooriyan; Jamison, Sarah; Rasanathan, Damayanthi; Huang, Sue; Trenholme, Adrian; Stanley, Alicia; Lawrence, Shirley; Marsh, Samantha; Castelino, Lorraine; Paynter, Janine; Turner, Nikki; McIntyre, Peter; Riddle, Pat; Grant, Cameron; Dobbie, Gillian; Wicker, Jörg
Forecasting severe respiratory disease hospitalizations using machine learning algorithms Journal Article
In: BMC Medical Informatics and Decision Making, vol. 24, iss. 1, pp. 293, 2024, ISSN: 1472-6947.
Abstract | Links | BibTeX | Altmetric | PlumX
@article{Albrecht2024forecasting,
title = {Forecasting severe respiratory disease hospitalizations using machine learning algorithms},
author = {Steffen Albrecht and David Broderick and Katharina Dost and Isabella Cheung and Nhung Nghiem and Milton Wu and Johnny Zhu and Nooriyan Poonawala-Lohani and Sarah Jamison and Damayanthi Rasanathan and Sue Huang and Adrian Trenholme and Alicia Stanley and Shirley Lawrence and Samantha Marsh and Lorraine Castelino and Janine Paynter and Nikki Turner and Peter McIntyre and Pat Riddle and Cameron Grant and Gillian Dobbie and J\"{o}rg Wicker},
url = {https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-024-02702-0},
doi = {10.1186/s12911-024-02702-0},
issn = {1472-6947},
year = {2024},
date = {2024-10-09},
urldate = {2024-10-07},
journal = {BMC Medical Informatics and Decision Making},
volume = {24},
issue = {1},
pages = {293},
abstract = {Forecasting models predicting trends in hospitalization rates have the potential to inform hospital management during seasonal epidemics of respiratory diseases and the associated surges caused by acute hospital admissions. Hospital bed requirements for elective surgery could be better planned if it were possible to foresee upcoming peaks in severe respiratory illness admissions. Forecasting models can also guide the use of intervention strategies to decrease the spread of respiratory pathogens and thus prevent local health system overload. In this study, we explore the capability of forecasting models to predict the number of hospital admissions in Auckland, New Zealand, within a three-week time horizon. Furthermore, we evaluate probabilistic forecasts and the impact on model performance when integrating laboratory data describing the circulation of respiratory viruses.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Forecasting models predicting trends in hospitalization rates have the potential to inform hospital management during seasonal epidemics of respiratory diseases and the associated surges caused by acute hospital admissions. Hospital bed requirements for elective surgery could be better planned if it were possible to foresee upcoming peaks in severe respiratory illness admissions. Forecasting models can also guide the use of intervention strategies to decrease the spread of respiratory pathogens and thus prevent local health system overload. In this study, we explore the capability of forecasting models to predict the number of hospital admissions in Auckland, New Zealand, within a three-week time horizon. Furthermore, we evaluate probabilistic forecasts and the impact on model performance when integrating laboratory data describing the circulation of respiratory viruses.
Hua, Yan Cathy; Denny, Paul; Wicker, Jörg; Taskova, Katerina
A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trends Journal Article
In: Artificial Intelligence Review, vol. 57, no. 11, pp. 296, 2024, ISSN: 1573-7462.
Abstract | Links | BibTeX | Altmetric | PlumX
@article{hua2023systematic,
title = {A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trends},
author = {Yan Cathy Hua and Paul Denny and J\"{o}rg Wicker and Katerina Taskova},
url = {https://link.springer.com/article/10.1007/s10462-024-10906-z
https://arxiv.org/abs/2311.10777},
doi = {10.1007/s10462-024-10906-z},
issn = {1573-7462},
year = {2024},
date = {2024-09-17},
urldate = {2023-11-17},
journal = {Artificial Intelligence Review},
volume = {57},
number = {11},
pages = {296},
abstract = {Aspect-based sentiment analysis (ABSA) is a fine-grained type of sentiment analysis that identifies aspects and their associated opinions from a given text. With the surge of digital opinionated text data, ABSA gained increasing popularity for its ability to mine more detailed and targeted insights. Many review papers on ABSA subtasks and solution methodologies exist, however, few focus on trends over time or systemic issues relating to research application domains, datasets, and solution approaches. To fill the gap, this paper presents a systematic literature review (SLR) of ABSA studies with a focus on trends and high-level relationships among these fundamental components. This review is one of the largest SLRs on ABSA. To our knowledge, it is also the first to systematically examine the interrelations among ABSA research and data distribution across domains, as well as trends in solution paradigms and approaches. Our sample includes 727 primary studies screened from 8550 search results without time constraints via an innovative automatic filtering process. Our quantitative analysis not only identifies trends in nearly two decades of ABSA research development but also unveils a systemic lack of dataset and domain diversity as well as domain mismatch that may hinder the development of future ABSA research. We discuss these findings and their implications and propose suggestions for future research.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Aspect-based sentiment analysis (ABSA) is a fine-grained type of sentiment analysis that identifies aspects and their associated opinions from a given text. With the surge of digital opinionated text data, ABSA gained increasing popularity for its ability to mine more detailed and targeted insights. Many review papers on ABSA subtasks and solution methodologies exist, however, few focus on trends over time or systemic issues relating to research application domains, datasets, and solution approaches. To fill the gap, this paper presents a systematic literature review (SLR) of ABSA studies with a focus on trends and high-level relationships among these fundamental components. This review is one of the largest SLRs on ABSA. To our knowledge, it is also the first to systematically examine the interrelations among ABSA research and data distribution across domains, as well as trends in solution paradigms and approaches. Our sample includes 727 primary studies screened from 8550 search results without time constraints via an innovative automatic filtering process. Our quantitative analysis not only identifies trends in nearly two decades of ABSA research development but also unveils a systemic lack of dataset and domain diversity as well as domain mismatch that may hinder the development of future ABSA research. We discuss these findings and their implications and propose suggestions for future research.
Hafner, Jasmin; Lorsbach, Tim; Schmidt, Sebastian; Brydon, Liam; Dost, Katharina; Zhang, Kunyang; Fenner, Kathrin; Wicker, Jörg
Advancements in Biotransformation Pathway Prediction: Enhancements, Datasets, and Novel Functionalities in enviPath Journal Article
In: Journal of Cheminformatics, vol. 16, no. 1, pp. 93, 2024, ISSN: 1758-2946.
Abstract | Links | BibTeX | Altmetric | PlumX
@article{hafner2023advancements,
title = {Advancements in Biotransformation Pathway Prediction: Enhancements, Datasets, and Novel Functionalities in enviPath},
author = {Jasmin Hafner and Tim Lorsbach and Sebastian Schmidt and Liam Brydon and Katharina Dost and Kunyang Zhang and Kathrin Fenner and J\"{o}rg Wicker},
url = {https://jcheminf.biomedcentral.com/articles/10.1186/s13321-024-00881-6
https://envipath.org},
doi = {10.1186/s13321-024-00881-6},
issn = {1758-2946},
year = {2024},
date = {2024-08-06},
urldate = {2024-08-06},
journal = {Journal of Cheminformatics},
volume = {16},
number = {1},
pages = {93},
abstract = {enviPath is a widely used database and prediction system for microbial biotransformation pathways of primarily xenobiotic compounds. Data and prediction system are freely available both via a web interface and a public REST API. Since its initial release in 2016, we extended the data available in enviPath and improved the performance of the prediction system and usability of the overall system. We now provide three diverse data sets, covering microbial biotransformation in different environments and under different experimental conditions. This also enabled developing a pathway prediction model that is applicable to a more diverse set of chemicals. In the prediction engine, we implemented a new evaluation tailored towards pathway prediction, which returns a more honest and holistic view on the performance. We also implemented a novel applicability domain algorithm, which allows the user to estimate how well the model will perform on their data. Finally, we improved the implementation to speed up the overall system and provide new functionality via a plugin system.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
enviPath is a widely used database and prediction system for microbial biotransformation pathways of primarily xenobiotic compounds. Data and prediction system are freely available both via a web interface and a public REST API. Since its initial release in 2016, we extended the data available in enviPath and improved the performance of the prediction system and usability of the overall system. We now provide three diverse data sets, covering microbial biotransformation in different environments and under different experimental conditions. This also enabled developing a pathway prediction model that is applicable to a more diverse set of chemicals. In the prediction engine, we implemented a new evaluation tailored towards pathway prediction, which returns a more honest and holistic view on the performance. We also implemented a novel applicability domain algorithm, which allows the user to estimate how well the model will perform on their data. Finally, we improved the implementation to speed up the overall system and provide new functionality via a plugin system.
Lyu, Jiachen; Dost, Katharina; Koh, Yun Sing; Wicker, Jörg
Regional Bias in Monolingual English Language Models Journal Article
In: Machine Learning, 2024, ISSN: 1573-0565.
Abstract | Links | BibTeX | Altmetric | PlumX
@article{lyu2023regional,
title = {Regional Bias in Monolingual English Language Models},
author = {Jiachen Lyu and Katharina Dost and Yun Sing Koh and J\"{o}rg Wicker},
url = {https://link.springer.com/article/10.1007/s10994-024-06555-6
https://dx.doi.org/10.21203/rs.3.rs-3713494/v1},
doi = {10.1007/s10994-024-06555-6},
issn = {1573-0565},
year = {2024},
date = {2024-07-09},
urldate = {2024-07-09},
journal = {Machine Learning},
abstract = { In Natural Language Processing (NLP), pre-trained language models (LLMs) are widely employed and refined for various tasks. These models have shown considerable social and geographic biases creating skewed or even unfair representations of certain groups. Research focuses on biases toward L2 (English as a second language) regions but neglects bias within L1 (first language) regions. In this work, we ask if there is regional bias within L1 regions already inherent in pre-trained LLMs and, if so, what the consequences are in terms of downstream model performance. We contribute an investigation framework specifically tailored for low-resource regions, offering a method to identify bias without imposing strict requirements for labeled datasets. Our research reveals subtle geographic variations in the word embeddings of BERT, even in cultures traditionally perceived as similar. These nuanced features, once captured, have the potential to significantly impact downstream tasks. Generally, models exhibit comparable performance on datasets that share similarities, and conversely, performance may diverge when datasets differ in their nuanced features embedded within the language. It is crucial to note that estimating model performance solely based on standard benchmark datasets may not necessarily apply to the datasets with distinct features from the benchmark datasets. Our proposed framework plays a pivotal role in identifying and addressing biases detected in word embeddings, particularly evident in low-resource regions such as New Zealand.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In Natural Language Processing (NLP), pre-trained language models (LLMs) are widely employed and refined for various tasks. These models have shown considerable social and geographic biases creating skewed or even unfair representations of certain groups. Research focuses on biases toward L2 (English as a second language) regions but neglects bias within L1 (first language) regions. In this work, we ask if there is regional bias within L1 regions already inherent in pre-trained LLMs and, if so, what the consequences are in terms of downstream model performance. We contribute an investigation framework specifically tailored for low-resource regions, offering a method to identify bias without imposing strict requirements for labeled datasets. Our research reveals subtle geographic variations in the word embeddings of BERT, even in cultures traditionally perceived as similar. These nuanced features, once captured, have the potential to significantly impact downstream tasks. Generally, models exhibit comparable performance on datasets that share similarities, and conversely, performance may diverge when datasets differ in their nuanced features embedded within the language. It is crucial to note that estimating model performance solely based on standard benchmark datasets may not necessarily apply to the datasets with distinct features from the benchmark datasets. Our proposed framework plays a pivotal role in identifying and addressing biases detected in word embeddings, particularly evident in low-resource regions such as New Zealand.
Long, Derek; Eade, Liam; Dost, Katharina; Meier-Menches, Samuel M; Goldstone, David C; Sullivan, Matthew P; Hartinger, Christian; Wicker, Jörg; Taskova, Katerina
AdductHunter: Identifying Protein-Metal Complex Adducts in Mass Spectra Journal Article
In: Journal of Cheminformatics, vol. 16, iss. 1, 2024, ISSN: 1758-2946.
Abstract | Links | BibTeX | Altmetric | PlumX
@article{Long2023adducthunter,
title = {AdductHunter: Identifying Protein-Metal Complex Adducts in Mass Spectra},
author = {Derek Long and Liam Eade and Katharina Dost and Samuel M Meier-Menches and David C Goldstone and Matthew P Sullivan and Christian Hartinger and J\"{o}rg Wicker and Katerina Taskova},
url = {https://adducthunter.wickerlab.org
https://doi.org/10.21203/rs.3.rs-3322854/v1},
doi = {10.1186/s13321-023-00797-7},
issn = {1758-2946},
year = {2024},
date = {2024-02-06},
urldate = {2024-02-06},
journal = {Journal of Cheminformatics},
volume = {16},
issue = {1},
abstract = {Mass spectrometry (MS) is an analytical technique for molecule identification that can be used for investigating protein-metal complex interactions. Once the MS data is collected, the mass spectra are usually interpreted manually to identify the adducts formed as a result of the interactions between proteins and metal-based species. However, with increasing resolution, dataset size, and species complexity, the time required to identify adducts and the error-prone nature of manual assignment have become limiting factors in MS analysis. AdductHunter is a open-source web-based analysis tool that automates the peak identification process using constraint integer optimization to find feasible combinations of protein and fragments, and dynamic time warping to calculate the dissimilarity between the theoretical isotope pattern of a species and its experimental isotope peak distribution. Empirical evaluation on a collection of 22 unique MS datasetsshows fast and accurate identification of protein-metal complex adducts in deconvoluted mass spectra.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Mass spectrometry (MS) is an analytical technique for molecule identification that can be used for investigating protein-metal complex interactions. Once the MS data is collected, the mass spectra are usually interpreted manually to identify the adducts formed as a result of the interactions between proteins and metal-based species. However, with increasing resolution, dataset size, and species complexity, the time required to identify adducts and the error-prone nature of manual assignment have become limiting factors in MS analysis. AdductHunter is a open-source web-based analysis tool that automates the peak identification process using constraint integer optimization to find feasible combinations of protein and fragments, and dynamic time warping to calculate the dissimilarity between the theoretical isotope pattern of a species and its experimental isotope peak distribution. Empirical evaluation on a collection of 22 unique MS datasetsshows fast and accurate identification of protein-metal complex adducts in deconvoluted mass spectra.
Proceedings Articles
Park, Sean; Wicker, Jörg; Dost, Katharina
Resource-Constrained Binary Image Classification Proceedings Article
In: Pedreschi, Dino; Monreale, Anna; Pellungrini, Roberto; Naretto, Francesca (Ed.): Discovery Science, pp. 215-230, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-78980-9.
Abstract | Links | BibTeX | Altmetric | PlumX
@inproceedings{park2024resource,
title = {Resource-Constrained Binary Image Classification},
author = {Sean Park and J\"{o}rg Wicker and Katharina Dost },
editor = {Dino Pedreschi and Anna Monreale and Roberto Pellungrini and Francesca Naretto},
doi = {10.1007/978-3-031-78980-9_14},
isbn = {978-3-031-78980-9},
year = {2025},
date = {2025-01-28},
urldate = {2024-09-30},
booktitle = {Discovery Science},
pages = {215-230},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Deep convolutional neural networks (CNNs) have achieved state-of-the-art performance in image classification tasks by automatically learning discriminative features from raw pixel data. However, their success often relies on large labeled training datasets and substantial computational resources, which can be limiting in resource-constrained scenarios. This study explores alternative, lightweight approaches. In particular, we compare a lightweight CNN with a combination of randomly initialized convolutional layers with an ensemble of weak learners in a stacking framework for binary image classification. This method aims to leverage the feature extraction capabilities of convolutional layers while mitigating the need for large datasets and intensive computations. Extensive experiments on seven datasets show that under resource constraints, the decision as to which model to use is not straightforward and depends on a practitioner\'s prioritization of predictive performance vs. training and prediction time vs. memory requirements.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Deep convolutional neural networks (CNNs) have achieved state-of-the-art performance in image classification tasks by automatically learning discriminative features from raw pixel data. However, their success often relies on large labeled training datasets and substantial computational resources, which can be limiting in resource-constrained scenarios. This study explores alternative, lightweight approaches. In particular, we compare a lightweight CNN with a combination of randomly initialized convolutional layers with an ensemble of weak learners in a stacking framework for binary image classification. This method aims to leverage the feature extraction capabilities of convolutional layers while mitigating the need for large datasets and intensive computations. Extensive experiments on seven datasets show that under resource constraints, the decision as to which model to use is not straightforward and depends on a practitioner's prioritization of predictive performance vs. training and prediction time vs. memory requirements.
Graffeuille, Olivier; Koh, Yun Sing; Wicker, Jörg; Lehmann, Moritz
Remote Sensing for Water Quality: A Multi-Task, Metadata-Driven Hypernetwork Approach Proceedings Article
In: Larson, Kate (Ed.): Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI-24), pp. Pages 7287-7295, 2024, (AI for Good).
Abstract | Links | BibTeX | Altmetric | PlumX
@inproceedings{graffeuille2024remote,
title = {Remote Sensing for Water Quality: A Multi-Task, Metadata-Driven Hypernetwork Approach},
author = {Olivier Graffeuille and Yun Sing Koh and J\"{o}rg Wicker and Moritz Lehmann },
editor = {Kate Larson},
doi = {10.24963/ijcai.2024/806},
year = {2024},
date = {2024-08-05},
urldate = {2024-08-05},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI-24)},
pages = {Pages 7287-7295},
abstract = {Inland water quality monitoring is vital for clean water access and aquatic ecosystem management. Remote sensing machine learning models enable large-scale observations, but are difficult to train due to data scarcity and variability across many lakes. Multi-task learning approaches enable learning of lake differences by learning multiple lake functions simultaneously. However, they suffer from a trade-off between parameter efficiency and the ability to model task differences flexibly, and struggle to model many diverse lakes with few samples per task. We propose Multi-Task Hypernetworks, a novel multi-task learning architecture which circumvents this trade-off using a shared hypernetwork to generate different network weights for each task from small task-specific embeddings. Our approach stands out from existing works by providing the added capacity to leverage task-level metadata, such as lake depth and temperature, explicitly. We show empirically that Multi-Task Hypernetworks outperform existing multi-task learning architectures for water quality remote sensing and other tabular data problems, and leverages metadata more effectively than existing methods. },
note = {AI for Good},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Inland water quality monitoring is vital for clean water access and aquatic ecosystem management. Remote sensing machine learning models enable large-scale observations, but are difficult to train due to data scarcity and variability across many lakes. Multi-task learning approaches enable learning of lake differences by learning multiple lake functions simultaneously. However, they suffer from a trade-off between parameter efficiency and the ability to model task differences flexibly, and struggle to model many diverse lakes with few samples per task. We propose Multi-Task Hypernetworks, a novel multi-task learning architecture which circumvents this trade-off using a shared hypernetwork to generate different network weights for each task from small task-specific embeddings. Our approach stands out from existing works by providing the added capacity to leverage task-level metadata, such as lake depth and temperature, explicitly. We show empirically that Multi-Task Hypernetworks outperform existing multi-task learning architectures for water quality remote sensing and other tabular data problems, and leverages metadata more effectively than existing methods.
Kim, Jonathan; Urschler, Martin; Riddle, Pat; Wicker, Jörg
Attacking the Loop: Adversarial Attacks on Graph-based Loop Closure Detection Proceedings Article
In: Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp. 90-97, 2024.
Abstract | Links | BibTeX | Altmetric | PlumX
@inproceedings{kim2024attacking,
title = {Attacking the Loop: Adversarial Attacks on Graph-based Loop Closure Detection},
author = {Jonathan Kim and Martin Urschler and Pat Riddle and J\"{o}rg Wicker },
url = {http://arxiv.org/abs/2312.06991
https://doi.org/10.48550/arxiv.2312.06991},
doi = {10.5220/0012313100003660},
year = {2024},
date = {2024-02-27},
urldate = {2024-02-27},
booktitle = {Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications},
volume = {4},
pages = {90-97},
abstract = {With the advancement in robotics, it is becoming increasingly common for large factories and warehouses to incorporate visual SLAM (vSLAM) enabled automated robots that operate closely next to humans. This makes any adversarial attacks on vSLAM components potentially detrimental to humans working alongside them. Loop Closure Detection (LCD) is a crucial component in vSLAM that minimizes the accumulation of drift in mapping, since even a small drift can accumulate into a significant drift over time. Previous work by Kim et al. , unified visual features and semantic objects into a single graph structure for finding loop closure candidates. While this provided a performance improvement over visual feature-based LCD, it also created a single point of vulnerability for potential graph-based adversarial attacks. Unlike previously reported visual-patch based attacks, small graph perturbations are far more challenging to detect, making them a more significant threat. In this paper, we present Adversarial-LCD, a novel black-box evasion attack framework that employs an eigencentrality-based perturbation method and an SVM-RBF surrogate model with a Weisfeiler-Lehman feature extractor for attacking graph-based LCD. Our evaluation shows that the attack performance of Adversarial-LCD was superior to that of other machine learning surrogate algorithms, including SVM-linear, SVM-polynomial, and Bayesian classifier, demonstrating the effectiveness of our attack framework. Furthermore, we show that our eigencentrality-based perturbation method outperforms other algorithms, such as Random-walk and Shortest-path, highlighting the efficiency of Adversarial-LCD’s perturbation selection method.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
With the advancement in robotics, it is becoming increasingly common for large factories and warehouses to incorporate visual SLAM (vSLAM) enabled automated robots that operate closely next to humans. This makes any adversarial attacks on vSLAM components potentially detrimental to humans working alongside them. Loop Closure Detection (LCD) is a crucial component in vSLAM that minimizes the accumulation of drift in mapping, since even a small drift can accumulate into a significant drift over time. Previous work by Kim et al. , unified visual features and semantic objects into a single graph structure for finding loop closure candidates. While this provided a performance improvement over visual feature-based LCD, it also created a single point of vulnerability for potential graph-based adversarial attacks. Unlike previously reported visual-patch based attacks, small graph perturbations are far more challenging to detect, making them a more significant threat. In this paper, we present Adversarial-LCD, a novel black-box evasion attack framework that employs an eigencentrality-based perturbation method and an SVM-RBF surrogate model with a Weisfeiler-Lehman feature extractor for attacking graph-based LCD. Our evaluation shows that the attack performance of Adversarial-LCD was superior to that of other machine learning surrogate algorithms, including SVM-linear, SVM-polynomial, and Bayesian classifier, demonstrating the effectiveness of our attack framework. Furthermore, we show that our eigencentrality-based perturbation method outperforms other algorithms, such as Random-walk and Shortest-path, highlighting the efficiency of Adversarial-LCD’s perturbation selection method.
Miscellaneous
Lorsbach, Tim; Wicker, Jörg
enviPath-python: v0.2.3 Miscellaneous
Zenedo, 2024.
Links | BibTeX | Altmetric | PlumX
@misc{lorsbach2024envipath,
title = {enviPath-python: v0.2.3},
author = {Tim Lorsbach and J\"{o}rg Wicker},
url = {https://github.com/enviPath/enviPath-python/tree/v0.2.3},
doi = {10.5281/zenodo.10929408},
year = {2024},
date = {2024-04-05},
urldate = {2024-04-05},
howpublished = {Zenedo},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Chang, Xinglong; Brydon, Liam; Wicker, Jörg
Memento: v1.1.1 Miscellaneous
Zenedo, 2024.
Links | BibTeX | Altmetric | PlumX
@misc{chang2024memento,
title = {Memento: v1.1.1},
author = {Xinglong Chang and Liam Brydon and J\"{o}rg Wicker},
url = {https://github.com/wickerlab/memento/tree/v1.1.1},
doi = {10.5281/zenodo.10929406},
year = {2024},
date = {2024-04-05},
urldate = {2024-04-05},
howpublished = {Zenedo},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Unpublished
Dost, Katharina; Muraoka, Kohji; Ausseil, Anne-Gaelle; Benavidez, Rubianca; Blue, Brendan; Coland, Nic; Daughney, Chris; Semadeni-Davies, Annette; Hoang, Linh; Hooper, Anna; Kpodonu, Theodore Alfred; Marapara, Tapuwa; McDowell, Richard W.; Nguyen, Trung; Nguyet, Dang Anh; Norton, Ned; Özkundakci, Deniz; Pearson, Lisa; Rolinson, James; Smith, Ra; Stephens, Tom; Tamepo, Reina; Taylor, Ken; van Uitregt, Vincent; Jackson, Bethanna; Sarris, Theo; Elliott, Alexander; Wicker, Jörg
Freshwater Quality Modeling in Aotearoa New Zealand: Current Practice and Future Directions Unpublished Forthcoming
SSRN, Forthcoming.
Links | BibTeX | Altmetric | PlumX
@unpublished{dost2025freshwater,
title = {Freshwater Quality Modeling in Aotearoa New Zealand: Current Practice and Future Directions},
author = {Katharina Dost and Kohji Muraoka and Anne-Gaelle Ausseil and Rubianca Benavidez and Brendan Blue and Nic Coland and Chris Daughney and Annette Semadeni-Davies and Linh Hoang and Anna Hooper and Theodore Alfred Kpodonu and Tapuwa Marapara and Richard W. McDowell and Trung Nguyen and Dang Anh Nguyet and Ned Norton and Deniz \"{O}zkundakci and Lisa Pearson and James Rolinson and Ra Smith and Tom Stephens and Reina Tamepo and Ken Taylor and Vincent van Uitregt and Bethanna Jackson and Theo Sarris and Alexander Elliott and J\"{o}rg Wicker },
doi = {10.2139/ssrn.5105393},
year = {2025},
date = {2025-01-21},
urldate = {2025-01-21},
journal = {SSRN},
howpublished = {SSRN},
keywords = {},
pubstate = {forthcoming},
tppubtype = {unpublished}
}
Graffeuille, Olivier; Koh, Yun Sing; Wicker, Jörg; Lehmann, Moritz
Enabling Asymmetric Knowledge Transfer in Multi-Task Learning with Self-Auxiliaries Unpublished Forthcoming
Arxiv, Forthcoming.
@unpublished{graffeuille2024enabling,
title = {Enabling Asymmetric Knowledge Transfer in Multi-Task Learning with Self-Auxiliaries},
author = {Olivier Graffeuille and Yun Sing Koh and J\"{o}rg Wicker and Moritz Lehmann},
url = {https://arxiv.org/abs/2410.15875},
year = {2024},
date = {2024-10-21},
urldate = {2024-10-21},
abstract = {Knowledge transfer in multi-task learning is typically viewed as a dichotomy; positive transfer, which improves the performance of all tasks, or negative transfer, which hinders the performance of all tasks. In this paper, we investigate the understudied problem of asymmetric task relationships, where knowledge transfer aids the learning of certain tasks while hindering the learning of others. We propose an optimisation strategy that includes additional cloned tasks named self-auxiliaries into the learning process to flexibly transfer knowledge between tasks asymmetrically. Our method can exploit asymmetric task relationships, benefiting from the positive transfer component while avoiding the negative transfer component. We demonstrate that asymmetric knowledge transfer provides substantial improvements in performance compared to existing multi-task optimisation strategies on benchmark computer vision problems.},
howpublished = {Arxiv},
keywords = {},
pubstate = {forthcoming},
tppubtype = {unpublished}
}
Knowledge transfer in multi-task learning is typically viewed as a dichotomy; positive transfer, which improves the performance of all tasks, or negative transfer, which hinders the performance of all tasks. In this paper, we investigate the understudied problem of asymmetric task relationships, where knowledge transfer aids the learning of certain tasks while hindering the learning of others. We propose an optimisation strategy that includes additional cloned tasks named self-auxiliaries into the learning process to flexibly transfer knowledge between tasks asymmetrically. Our method can exploit asymmetric task relationships, benefiting from the positive transfer component while avoiding the negative transfer component. We demonstrate that asymmetric knowledge transfer provides substantial improvements in performance compared to existing multi-task optimisation strategies on benchmark computer vision problems.
Graffeuille, Olivier; Lehmann, Moritz; Allan, Matthew; Wicker, Jörg; Koh, Yun Sing
Lake by Lake, Globally: Enhancing Water Quality Remote Sensing with Multi-Task Learning Models Unpublished Forthcoming
Forthcoming, ISSN: 1556-5068.
Abstract | Links | BibTeX | Altmetric | PlumX
@unpublished{graffeuille2024lake,
title = {Lake by Lake, Globally: Enhancing Water Quality Remote Sensing with Multi-Task Learning Models},
author = {Olivier Graffeuille and Moritz Lehmann and Matthew Allan and J\"{o}rg Wicker and Yun Sing Koh },
doi = {10.2139/ssrn.4762429},
issn = {1556-5068},
year = {2024},
date = {2024-03-17},
urldate = {2024-03-17},
abstract = {The estimation of water quality from satellite remote sensing data in inland and coastal waters is an important yet challenging problem. Recent collaborative efforts have produced large global datasets with sufficient data to train machine learning models with high accuracy. In this work, we investigate global water quality remote sensing models at the granularity of individual water bodies. We introduce Multi-Task Learning (MTL), a machine learning technique that learns a distinct model for each water body in the dataset from few data points by sharing knowledge between models. This approach allows MTL to learn water body differences, leading to more accurate predictions. We train and validate our model on the GLORIA dataset of in situ measured remote sensing reflectance and three water quality indicators: chlorophyll$a$, total suspended solids and coloured dissolved organic matter. MTL outperforms other machine learning models by 8-31% in Root Mean Squared Error (RMSE) and 12-34% in Mean Absolute Percentage Error (MAPE). Training on a smaller dataset of chlorophyll$a$ measurements from New Zealand lakes with simultaneous Sentinel-3 OLCI remote sensing reflectance further demonstrates the effectiveness of our model when applied regionally. Additionally, we investigate the performance of machine learning models at estimating the variation in water quality indicators within individual water bodies. Our results reveal that overall performance metrics overestimate the quality of model fit of models trained on a large number of water bodies due to the large between-water body variability of water quality indicators. In our experiments, when estimating TSS or CDOM, all models excluding multi-task learning fail to learn within-water body variability, and fail to outperform a naive baseline approach, suggesting that these models may be of limited usefulness to practitioners monitoring water quality. Overall, our research highlights the importance of considering water body differences in water quality remote sensing research for both model design and evaluation. },
keywords = {},
pubstate = {forthcoming},
tppubtype = {unpublished}
}
The estimation of water quality from satellite remote sensing data in inland and coastal waters is an important yet challenging problem. Recent collaborative efforts have produced large global datasets with sufficient data to train machine learning models with high accuracy. In this work, we investigate global water quality remote sensing models at the granularity of individual water bodies. We introduce Multi-Task Learning (MTL), a machine learning technique that learns a distinct model for each water body in the dataset from few data points by sharing knowledge between models. This approach allows MTL to learn water body differences, leading to more accurate predictions. We train and validate our model on the GLORIA dataset of in situ measured remote sensing reflectance and three water quality indicators: chlorophyll$a$, total suspended solids and coloured dissolved organic matter. MTL outperforms other machine learning models by 8-31% in Root Mean Squared Error (RMSE) and 12-34% in Mean Absolute Percentage Error (MAPE). Training on a smaller dataset of chlorophyll$a$ measurements from New Zealand lakes with simultaneous Sentinel-3 OLCI remote sensing reflectance further demonstrates the effectiveness of our model when applied regionally. Additionally, we investigate the performance of machine learning models at estimating the variation in water quality indicators within individual water bodies. Our results reveal that overall performance metrics overestimate the quality of model fit of models trained on a large number of water bodies due to the large between-water body variability of water quality indicators. In our experiments, when estimating TSS or CDOM, all models excluding multi-task learning fail to learn within-water body variability, and fail to outperform a naive baseline approach, suggesting that these models may be of limited usefulness to practitioners monitoring water quality. Overall, our research highlights the importance of considering water body differences in water quality remote sensing research for both model design and evaluation.