Late Pleistocene arctic ground squirrel middens as palaeoecological archives of east Beringia

This thesis explores Pleistocene-aged palaeoecological records from Yukon Territory in the unglaciated landscape of east Beringia. This region was home to the mammoth steppe, a loess-fed cold and arid grassland-forb ecosystem that supported diverse megafauna. Although a focus has remained on Beringia’s megafauna, this thesis focuses on the palaeoecological importance of arctic ground squirrel (Sciuridae: Urocitellus parryii Richardson) middens, and the role they continue to play in understanding the steppe-tundra ecosystems of Beringia. This thesis uses plant, invertebrate, and vertebrate remains from arctic ground squirrel middens, pore-ice water isotopes, and sedimentary ancient DNA (sedaDNA) from late Pleistocene sites in easternmost Beringia to investigate records of small mammals, the occurrence of previously undocumented invertebrate taxa, and the persistence of steppe-tundra in a landscape experiencing the expansion of shrubs. The late Pleistocene record of small mammals from Yukon Territory has predominantly been reported from sites in the Old Crow basin that are limited by complex stratigraphy and mixed faunal assemblages. Chapter 2 presents a chronologically constrained record of the late Pleistocene small mammal community preserved in arctic ground squirrel middens from the Klondike goldfields. This chapter recovers the remains of arctic ground squirrels (U. parryii), lemmings (Dicrostonyx and Lemmus) and several species of vole (Microtus) that provide insight into the fossorial behaviour of small mammals during the late Pleistocene. By the continued analysis of these Pleistocene middens this thesis further emphasises that they are unique and valuable archives for the preservation of invertebrate life. Previous work has shown the remains of beetles (Coleoptera) and non-biting midges (Chironomidae) from Pleistocene deposits across Beringia, however, the remains of fleas (Siphonaptera), mites (Astigmata and Mesostigmata), thrips (Thysanoptera) and grasshoppers (Orthoptera) are either rare (fleas and mites) or not reported (thrips and grasshoppers). Chapter 3 explores these underrepresented taxa showing an 80,000-year long association between the flea Oropsylla alaskensis and its host, the arctic ground squirrel, and the first Quaternary records of the glycyphagid mite Fusacarus sp., thrips, and three individuals of gomphocerine grasshoppers. In the case of the glycyphagid mite, Fuscacarus sp. we document the first record of this genus from both the fossil and present-day record in Yukon Territory. The preservation of these invertebrate taxa provides data on the biogeographic and phylogenetic histories of Beringian invertebrates, particularly when considering this region as a refugium during the Last Glacial Maximum. Lastly, this thesis explores several latest Pleistocene sites to track the persistence of steppe-tundra in easternmost Beringia. The regional expansion of shrubs around 14,000 cal yr BP is recorded from sites in interior Alaska, but local records of steppe persistence is limited to only a few sites in Yukon Territory. Plant and invertebrate assemblages from latest Pleistocene arctic ground squirrel middens record the persistence of steppe-tundra until at least 13,680 cal yr BP by the presence of taxa including the plants Penstemon cf. gormanii and Silene cf. involucrate subsp. tenella, and the weevil Connatichela artemisiae. These species remain components of grasslands and south-facing azonal steppe communities in present-day Yukon. This thesis further explores steppe persistence by analysing sedaDNA and pore-ice isotopes from Mint Gulch, a site spanning from ~16,000 to 13,180 cal yr BP. The Mint Gulch site indicates a co-occurrence of steppe and shrub-associated taxa by ~13,910 cal yr BP. The persistence of steppe-tundra, despite a shifting regional hydroclimate, is further supported by macrofossil records that include steppe-tundra taxa like Artemisia sp. (likely A. frigida), Potentilla sp., and Penstemon cf. gormanii, the dry-tundra weevil Lepidophorus lineaticollis, and the steppe-tundra weevil C. artemisiae. A multiproxy approach to the steppe-tundra to shrub-tundra transition captures the co-occurrence of steppe tundra taxa like mammoth and horse in addition to the arrival of browsers and shrub dependent taxa like moose, elk, and willow ptarmigan. Collectively, this thesis contributes to an understanding of the biodiversity, resilience and ultimately the collapse of Beringia’s steppe-tundra ecosystem at the end of the Pleistocene

An Investigation on Explainable Graph Neural Networks and Large Language Models for Malware Analysis

As the cybersecurity field is evolving, improvements in malware detection systems are urgently needed as cyber threats become sophisticated. Considering the predictions of an increase in software supply chain attacks, it is necessary to deploy strong cybersecurity solutions to protect national and community interests. Although Large Language Models (LLMs) have been shown to work well in cybersecurity, particularly in analyzing threat reports, their application to directly analyze Portable Executable (PE) malware files has yet to be explored. This study pioneers the use of LLMs to assist malware analysts in classifying malware types and predicting behavioral information contained in PE files. We propose GraPE, a graph-based framework that leverages Large Language Models (LLMs) to analyze malicious PE files. GraPE enables the classification of malware types as well as analysis of suspicious behavior to justify its predictions. To remove noise and overcome the token limits of LLMs, we integrate an Explainable Graph Neural Network (XGNN) method for critical subgraph extraction from a large hierarchical graph representation of disassembled PE files. Furthermore, GraPE employs Retrieval-Augmented Generation (RAG) to incorporate relevant supplementary behavior knowledge based on graph embeddings, substantially enhancing the quality and reliability of LLM-generated analysis. Comprehensive experiments on selected subsets of the BODMAS PE malware dataset show that our method outperforms traditional machine learning-based methods in both classifying malware type and ATT&CK techniques

Degradation Assessment and Maintenance Decision-making for Mechanical and Electrical Assets Driven by Condition Data

Condition-based maintenance (CBM) is a proactive maintenance strategy that utilizes condition data to inform maintenance decisions, aiming to prevent critical asset failures, reduce maintenance costs, and enhance system safety. Despite significant advancements in prognostic techniques and the widespread adoption of sensors for continuous data collection, integrating these heterogeneous data into effective maintenance decision-making remains challenging. In terms of methodology, developing degradation models that accurately reflect system health is both critical and complex. This complexity arises from the diverse data types and intricate failure mechanisms associated with various assets, such as electrical distribution systems (EDS), wind turbines incorporating load and supervisory control and data acquisition (SCADA) data, and rotating machinery with multiple failure modes. From the application aspect, practical implementation faces obstacles including stakeholders’ specific asset requirements, cost-effective monitoring decisions, and the necessity for targeted maintenance policies rather than generic strategies. For example, stakeholders may have differing benefits and priorities, as seen between maintenance contractors and wind farm owners. Making cost-effective monitoring decisions involves determining which assets require sensor deployment. Furthermore, the implementation of targeted maintenance policies is crucial, such as the adoption of contractor-oriented maintenance strategies for wind farms and opportunistic maintenance (OM) strategies for large-scale electrical distribution systems. Addressing these challenges is essential for optimizing maintenance practices in mechanical and electrical assets. To address these gaps, the overarching objective of this thesis is structured around four key topics, focusing on integrating precise degradation modeling with practical maintenance decision-making frameworks for targeted mechanical and electrical assets. In the first topic, an opportunistic CBM is proposed for EDS to address the complexity of large-scale asset management. Maintenance actions are triggered based on the health status observed during inspections. In the second topic, a contractor-oriented maintenance strategy is developed for both onshore and offshore wind farms, aiming to maximize the maintenance contractor profits. This strategy utilizes prognostic information from both monitorable and non-monitorable components, constructing a degradation-related efficiency model that quantifies wind turbine efficiency losses due to component degradation and integrates this into the maintenance decision-making process. Moreover, the third topic introduces an asset-criticality-guided maintenance strategy, which incorporates machine criticality and sensor deployment into the decision-making framework. This approach provides practical insights for identifying asset-specific criteria and aims to maximize the expected revenue of the mechanical systems. Finally, the fourth topic explores the feasibility of implementing additional load monitoring for wind turbine degradation assessment. A cost-effective load sensor system is designed to collect the load data, and a novel degradation assessment method is proposed to incorporate the load data with a nonlinear dynamic state-space neural network model to extract the degradation information of a wind turbine more efficiently. This thesis advances the field of CBM by offering innovative, data-driven, and actionable strategies tailored to the specific needs of different stakeholders in mechanical and electrical asset management. The developed methods will contribute to significantly reducing operation and maintenance expenses while enhancing net revenue for mechanical and electrical assets across diverse engineering applications

The Hill Times, Monday, July 7, 2025

The newspaper of Parliament

Learning Instance-Specific Parameters of Black-Box Models Using Differentiable Surrogates

Tuning parameters of a non-differentiable or black-box compute is challenging. Existing methods rely mostly on random sampling or grid sampling from the parameter space. Further, with all the current methods, it is not possible to supply any input specific parameters to the black-box. To the best of our knowledge, for the first time, we are able to learn input-specific parameters for a black box in this work. As a test application, we choose a popular image denoising method BM3D as our black-box compute. Then, we use a differentiable surrogate model (a neural network) to approximate the black-box behaviour. Next, another neural network is used in an end-to-end fashion to learn input instance-specific parameters for the black-box. Motivated by prior advances in surrogate-based optimization, we applied our method to the Smartphone Image Denoising Dataset (SIDD) and the Color Berkeley Segmentation Dataset (CBSD68) for image denoising. The results are compelling, demonstrating a significant increase in PSNR and a notable improvement in SSIM nearing 0.93. Experimental results underscore the effectiveness of our approach in achieving substantial improvements in both model performance and optimization efficiency. For code and implementation details, please refer to our GitHub repository: https://github.com/arnisha-k/instance-specific-param

Deep Visual Anomaly Detection and Localization

Unsupervised visual anomaly detection and localization are fundamental challenges in computer vision, aimed at identifying unknown anomalous patterns. We focus on novel deep learning technologies for visual anomaly detection, addressing the challenges of limited and unknown data in applications such as industrial inspection, medical diagnosis, and video surveillance. These application scenarios have stringent requirements on the performance of anomaly detection algorithms, especially in terms of accuracy, scalability, and generalization ability. With the development of deep learning techniques uncovering great potential in visual anomaly detection, we propose a series of specific deep visual anomaly detection frameworks for this problem. We first propose reverse teacher-student networks for anomaly detection, where the student network learns to predict normal patterns from a teacher network. By reversing the teacher-student comparative flow, we enhance feature discrepancies between the networks for unknown abnormalities, improving the one-class anomaly detection performance. Additionally, we propose structural normality learning to overcome the cross-class interference issue, allowing a single teacher-student model to detect anomalies across multiple categories, improving scalability and generalization of our proposed models. We then investigate zero-shot anomaly detection, where the goal is to identify anomalies without prior training on normal data. By leveraging contrastive vision-language networks, we introduce domain-aware textual prompts and anomaly-oriented test-time adaptation strategy to guide anomaly detection and localization. Furthermore, we propose an open-world anomaly detection framework, enabling detection and localization of anomalies in both known and unknown objects via the adaptive fusion of the prompting vision-language and reverse teacher-student networks. In addition to natural image objects, we also explore the application of visual anomaly detection in video surveillance and medical imaging, two domains with unique challenges. For video anomaly detection, we address the spatiotemporal dependencies in video sequences, improving anomaly detection by modeling temporal continuity. In medical imaging, we focus on the specialized imaging structures to improve anomaly detection methods in challenging medical diagnostic settings. Since previous anomaly detection methods have limitations in scalability, we propose novel methods tailored to the specific scenarios of video and medical applications. In summary, this thesis advances visual anomaly detection by proposing novel algorithms for one-class, multi-class, zero-shot, and open-world anomaly detection and localization. We also explore the expanding applications of anomaly detection in industrial, video and medical domains. Our study paves the way for more robust and scalable anomaly detection systems in real-world applications