title: Image retrieval systems based on embeddings for trustworthy AI

authors: Neave, Zane

subjects: Artificial Intelligence

abstract: Embedding-based image retrieval systems have become essential for applications that require high accuracy, computational efficiency, and adaptability, particularly in fields like healthcare and agriculture. These systems utilize deep learning models to generate high-dimensional feature embeddings, enabling scalable and precise image retrieval. This study evaluates two distinct retrieval pipelines: the Domain-Specific Retrieval Pipeline (DSRP) and the Pre-trained Feature Extraction Pipeline (PMRP). The comparative analysis, conducted across multiple datasets including medical imaging, agricultural analysis, and general object retrieval, highlights the advantages and trade-offs between these approaches.

The results demonstrate that PMRP achieves consistently higher retrieval accuracy across all datasets, with near-perfect performance in tasks like leaf disease detection and general object retrieval. In contrast, DSRP, while benefiting from domain-specific adaptation, does not surpass PMRP in overall performance and exhibits variability depending on dataset characteristics. Despite its domain- tuned architecture, DSRP showed increased computational complexity without a proportional
increase in accuracy. Additionally, PMRP maintains more stable inference times, making it a preferable option for real-time applications.

Beyond retrieval accuracy, this study explores challenges related to dataset diversity, bias mitigation, explainability, and adversarial robustness. The findings underscore the importance of balancing retrieval precision with computational feasibility, particularly when deploying systems in high-stakes environments. Future research should focus on enhancing dataset representation, developing fairness-aware learning mechanisms, improving model transparency through explainable AI techniques, and strengthening adversarial defenses to ensure the security and reliability of embedding-based retrieval systems. By addressing these challenges, such systems can evolve into more efficient, interpretable, and ethically responsible solutions for real-world applications across various domains.

text: Image retrieval systems based on embeddings for trustworthy AI Embedding-based image retrieval systems have become essential for applications that require high accuracy, computational efficiency, and adaptability, particularly in fields like healthcare and agriculture. These systems utilize deep learning models to generate high-dimensional feature embeddings, enabling scalable and precise image retrieval. This study evaluates two distinct retrieval pipelines: the Domain-Specific Retrieval Pipeline (DSRP) and the Pre-trained Feature Extraction Pipeline (PMRP). The comparative analysis, conducted across multiple datasets including medical imaging, agricultural analysis, and general object retrieval, highlights the advantages and trade-offs between these approaches.

The results demonstrate that PMRP achieves consistently higher retrieval accuracy across all datasets, with near-perfect performance in tasks like leaf disease detection and general object retrieval. In contrast, DSRP, while benefiting from domain-specific adaptation, does not surpass PMRP in overall performance and exhibits variability depending on dataset characteristics. Despite its domain- tuned architecture, DSRP showed increased computational complexity without a proportional
increase in accuracy. Additionally, PMRP maintains more stable inference times, making it a preferable option for real-time applications.

Beyond retrieval accuracy, this study explores challenges related to dataset diversity, bias mitigation, explainability, and adversarial robustness. The findings underscore the importance of balancing retrieval precision with computational feasibility, particularly when deploying systems in high-stakes environments. Future research should focus on enhancing dataset representation, developing fairness-aware learning mechanisms, improving model transparency through explainable AI techniques, and strengthening adversarial defenses to ensure the security and reliability of embedding-based retrieval systems. By addressing these challenges, such systems can evolve into more efficient, interpretable, and ethically responsible solutions for real-world applications across various domains.

year: 2025

title: Reactions of isonitriles with [Fe₃(CO)₁₂] and [Ru₃(CO)₁₂] monitored by electrospray mass spectrometry: structural characterisation of [Fe₃(CO)₁₀(CNPh)₂] and [Ru₄(CO)₁₁(μ₃-η²-CNPh)₂(CNPh)]

authors: Decker, Corry

subjects: iron carbonyl

abstract: The reactions of [Fe₃(CO)₁₂] or [Ru₃(CO) ₁₂] with RNC (R=Ph, C₆H₄OMe-p or CH₂SO₂C₆H₄Me-p) have been investigated using electrospray mass spectrometry. Species arising from substitution of up to six ligands were detected for [Fe₃(CO)₁₂], but the higher-substituted compounds were too unstable to be isolated. The crystal structure of [Fe₃(CO)₁₀(CNPh)₂] was determined at 150 and 298 K to show that both isonitrile ligands were trans to each other on the same Fe atom. For [Ru₃(CO)₁₂] substitution of up to three COs was found, together with the formation of higher-nuclearity clusters. [Ru₄(CO)₁₁(CNPh)₃] was structurally characterised and has a spiked-triangular Ru₄ core with two of the CNPh ligands coordinated in an unusual μ₃-η² mode.
The substitution reactions of [M₃(CO)₁₂] by RNC have been investigated by electrospray mass spectrometry showing up to six COs can be replaced. [Fe₃(CO)₁₀(CNPh)₂] has both PhNC axially on the same Fe atom, and [Ru₄(CO)₁₁(μ₃-η²-CNPh)₂(CNPh)] has a spiked-triangular cluster core with two PhNC ligands in an unusual coordination mode.

text: Reactions of isonitriles with [Fe₃(CO)₁₂] and [Ru₃(CO)₁₂] monitored by electrospray mass spectrometry: structural characterisation of [Fe₃(CO)₁₀(CNPh)₂] and [Ru₄(CO)₁₁(μ₃-η²-CNPh)₂(CNPh)] The reactions of [Fe₃(CO)₁₂] or [Ru₃(CO) ₁₂] with RNC (R=Ph, C₆H₄OMe-p or CH₂SO₂C₆H₄Me-p) have been investigated using electrospray mass spectrometry. Species arising from substitution of up to six ligands were detected for [Fe₃(CO)₁₂], but the higher-substituted compounds were too unstable to be isolated. The crystal structure of [Fe₃(CO)₁₀(CNPh)₂] was determined at 150 and 298 K to show that both isonitrile ligands were trans to each other on the same Fe atom. For [Ru₃(CO)₁₂] substitution of up to three COs was found, together with the formation of higher-nuclearity clusters. [Ru₄(CO)₁₁(CNPh)₃] was structurally characterised and has a spiked-triangular Ru₄ core with two of the CNPh ligands coordinated in an unusual μ₃-η² mode.
The substitution reactions of [M₃(CO)₁₂] by RNC have been investigated by electrospray mass spectrometry showing up to six COs can be replaced. [Fe₃(CO)₁₀(CNPh)₂] has both PhNC axially on the same Fe atom, and [Ru₄(CO)₁₁(μ₃-η²-CNPh)₂(CNPh)] has a spiked-triangular cluster core with two PhNC ligands in an unusual coordination mode.

year: 2004-05-01

title: Maori & Psychology Research Unit annual report 2006

authors: Rua, Mohi

subjects: Maori and Psychology Research Unit

abstract: Annual report of the Maori and Psychology Research Unit (MPRU) 2006. The unit was established in August of 1997. The unit is designed to provide a catalyst and support network for enhancing research concerning the psychological needs, aspirations, and priorities of Maori people. The MPRU is well situated to draw together skilled and experienced interdisciplinary research groups by networking and establishing working relationships with staff and students within the Faculty of Arts and Social Sciences, the University, and the wider community.

text: Maori & Psychology Research Unit annual report 2006 Annual report of the Maori and Psychology Research Unit (MPRU) 2006. The unit was established in August of 1997. The unit is designed to provide a catalyst and support network for enhancing research concerning the psychological needs, aspirations, and priorities of Maori people. The MPRU is well situated to draw together skilled and experienced interdisciplinary research groups by networking and establishing working relationships with staff and students within the Faculty of Arts and Social Sciences, the University, and the wider community.

year: 2006