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: Origin and distribution of mineral species in limestone caves

authors: Broughton, Paul L.

abstract: More than 80 mineral species are known to result from secondary chemical reactions in solutional limestone caverns. These minerals are transitional and end-products of carbonate wall rock interactions with ground water and organic residue. Bacteria are often critical in fixing various ions in these reactions. Ultimate source of metallic ions is usually the sulphide minerals weathering in overlying strata, and brought to the cave environment by circulating ground waters. Hydrothermal solutions may bring heavy metallic ion concentrations from deeper strata. About one-third of the known cave mineral species occur in stalactitic form.

text: Origin and distribution of mineral species in limestone caves More than 80 mineral species are known to result from secondary chemical reactions in solutional limestone caverns. These minerals are transitional and end-products of carbonate wall rock interactions with ground water and organic residue. Bacteria are often critical in fixing various ions in these reactions. Ultimate source of metallic ions is usually the sulphide minerals weathering in overlying strata, and brought to the cave environment by circulating ground waters. Hydrothermal solutions may bring heavy metallic ion concentrations from deeper strata. About one-third of the known cave mineral species occur in stalactitic form.

year: 1971

title: Akuhata Mokena: Eldest son of Mokena Hou

authors: Hart, Philip

abstract: Akuhata Mokena was recorded as being the first child of Mokena Hou and Rina. By the early 1860s he was living at the northern boundary of the Aroha Block as well as at Puriri, where he dug kauri gum. From 1857 to 1868 he also cultivated close to the southern edge of the future Thames township. Having run cattle on the Aroha block from 1867 onwards, he settled there in 1878, operating the first hotel to be erected at the hot springs. 
In the land court he tried to obtain interests in as many land blocks as possible, and obtained income by selling some of these. After a brief involvement in Te Aroha mining, when he was the only member of his family to delay ceding their land for mining, he returned to live at Puriri. He also invested, very modestly, in mining at Thames and Puriri. 
A rangatira who was steadfastly loyal to the Crownt, when he died without issue his estate became a cause of conflict between Maori and Pakeha perceptions of how it should be shared amongst his wife and his family.

text: Akuhata Mokena: Eldest son of Mokena Hou Akuhata Mokena was recorded as being the first child of Mokena Hou and Rina. By the early 1860s he was living at the northern boundary of the Aroha Block as well as at Puriri, where he dug kauri gum. From 1857 to 1868 he also cultivated close to the southern edge of the future Thames township. Having run cattle on the Aroha block from 1867 onwards, he settled there in 1878, operating the first hotel to be erected at the hot springs. 
In the land court he tried to obtain interests in as many land blocks as possible, and obtained income by selling some of these. After a brief involvement in Te Aroha mining, when he was the only member of his family to delay ceding their land for mining, he returned to live at Puriri. He also invested, very modestly, in mining at Thames and Puriri. 
A rangatira who was steadfastly loyal to the Crownt, when he died without issue his estate became a cause of conflict between Maori and Pakeha perceptions of how it should be shared amongst his wife and his family.

year: 2016