Towards Deep Learning with Competing Generalisation Objectives

The unreasonable effectiveness of Deep Learning continues to deliver unprecedented Artificial Intelligence capabilities to billions of people. Growing datasets and technological advances keep extending the reach of expressive model architectures trained through efficient optimisations. Thus, deep learning approaches continue to provide increasingly proficient subroutines for, among others, computer vision and natural interaction through speech and text. Due to their scalable learning and inference priors, higher performance is often gained cost-effectively through largely automatic training. As a result, new and improved capabilities empower more people while the costs of access drop. The arising opportunities and challenges have profoundly influenced research. Quality attributes of scalable software became central desiderata of deep learning paradigms, including reusability, efficiency, robustness and safety. Ongoing research into continual, meta- and robust learning aims to maximise such scalability metrics in addition to multiple generalisation criteria, despite possible conflicts. A significant challenge is to satisfy competing criteria automatically and cost-effectively. In this thesis, we introduce a unifying perspective on learning with competing generalisation objectives and make three additional contributions. When autonomous learning through multi-criteria optimisation is impractical, it is reasonable to ask whether knowledge of appropriate trade-offs could make it simultaneously effective and efficient. Informed by explicit trade-offs of interest to particular applications, we developed and evaluated bespoke model architecture priors. We introduced a novel architecture for sim-to-real transfer of robotic control policies by learning progressively to generalise anew. Competing desiderata of continual learning were balanced through disjoint capacity and hierarchical reuse of previously learnt representations. A new state-of-the-art meta-learning approach is then proposed. We showed that meta-trained hypernetworks efficiently store and flexibly reuse knowledge for new generalisation criteria through few-shot gradient-based optimisation. Finally, we characterised empirical trade-offs between the many desiderata of adversarial robustness and demonstrated a novel defensive capability of implicit neural networks to hinder many attacks simultaneously

Interactive optimisation for high-lift design.

Interactivity always involves two entities; one of them by default is a human user. The specialised subject of human factors is introduced in the context of computational aerodynamics and optimisation, specifically a high-lift aerofoil. The trial and error nature of a design process hinges on designer’s knowledge, skill and intuition. A basic, important assumption of a man-machine system is that in solving a problem, there are some steps in which the computer has an advantageous edge while in other steps a human has dominance. Computational technologies are now an indispensable part of aerospace technology; algorithms involving significant user interaction, either during the process of generating solutions or as a component of post-optimisation evaluation where human decision making is involved are increasingly becoming popular, multi-objective particle swarm is one such optimiser. Several design optimisation problems in engineering are by nature multi-objective; the interest of a designer lies in simultaneous optimisation against two or more objectives which are usually in conflict. Interactive optimisation allows the designer to understand trade-offs between various objectives, and is generally used as a tool for decision making. The solution to a multi-objective problem, one where betterment in one objective occurs over the deterioration of at least one other objective is called a Pareto set. There are multiple solutions to a problem and multiple betterment ideas to an already existing design. The final responsibility of identifying an optimal solution or idea rests on the design engineers and decision making is done based on quantitative metrics, displayed as numbers or graphs. However, visualisation, ergonomics and human factors influence and impact this decision making process. A visual, graphical depiction of the Pareto front is oftentimes used as a design aid tool for purposes of decision making with chances of errors and fallacies fundamentally existing in engineering design. An effective visualisation tool benefits complex engineering analyses by providing the decision-maker with a good imagery of the most important information. Two high-lift aerofoil data-sets have been used as test-case examples; a multi-element solver, an optimiser based on swarm intelligence technique, and visual techniques which include parallel co-ordinates, heat map, scatter plot, self-organising map and radial coordinate visualisation comprise the module. Factors that affect optima and various evaluation criteria have been studied in light of the human user. This research enquires into interactive optimisation by adapting three interactive approaches: information trade-off, reference point and classification, and investigates selected visualisation techniques which act as chief aids in the context of high-lift design trade studies. Human-in-the-loop engineering, man-machine interaction & interface along with influencing factors, reliability, validation and verification in the presence of design uncertainty are considered. The research structure, choice of optimiser and visual aids adapted in this work are influenced by and streamlined to fit with the parallel on-going development work on Airbus’ Python based tool. Results, analysis, together with literature survey are presented in this report. The words human, user, engineer, aerodynamicist, designer, analyst and decision-maker/ DM are synonymous, and are used interchangeably in this research. In a virtual engineering setting, for an efficient interactive optimisation task, a suitable visualisation tool is a crucial prerequisite. Various optimisation design tools & methods are most useful when combined with a human engineer's insight is the underlying premise of this work; questions such as why, what, how might help aid aeronautical technical innovation.PhD in Aerospac

Alternative feed sources: Effect on gut microbiota, immunity and health of rainbow trout

Shifting from conventional fishmeal- and soymeal-based aquafeed to low-cost, sustainable dietary alternatives is essential for expansion and resilience in aquaculture. Possible alternative feed resources include under-utilised organic wastes from agriculture and households and by-products from forestry. These resources usually have high nutrient content and may contain bioactive compounds (β-glucans, mannans, lignocellulose etc.) that elicit immunomodulatory responses, modulate gut microbiota and thus improve overall wellbeing in cultured fish. This thesis investigated the dietary potential of Neurospora intermedia, Yarrowia lipolytica and cello-oligosaccharides obtained from industrial by-products, municipal side-streams and forest by-products in diets for rainbow trout.  In a 30-day fish trial, N. intermedia was included in the diet by replacing 30% of the fishmeal-based control diet and diets containing fungi were processed with and without pre-conditioning (heat-treatment). The results showed high apparent digestibility coefficient of the N. intermedia diets and a gradual shift in overall gut microbiota, with increased abundance of Lactococcus from day 0 to 30. Preconditioning had no effect on digestibility or gut microbiota. Pre-treated Y. lipolytica yeast biomass in whole (WY) or autolysed (AY) form was incorporated in rainbow trout diets at 2% or 5% level in a 45-day trial. The 5% WY diet resulted in elevated expression of immune-related genes of the complement pathway, membrane receptor pathway, cytokines and adaptive immune pathway. There was a small impact of dietary Y. lipolytica on faecal microbiota in rainbow trout. The bioactivity of cello-oligosaccharides (COS) was examined by feeding rainbow trout diets containing 0-1.5% graded COS in a 45-day trial. Inclusion of 0.5-1.5% COS slightly increased lactic acid bacteria in faeces and marginally modulated gut immunity with respect to expression of complement and toll-like receptors. The COS diets also increased oxidative stress-reducing capacity in the gut and serum of the fish. These results indicate, N. intermedia, Y. lipolytica and COS can be used successfully as potential functional feed or additive for rainbow trout

Characterising food web responses to climate change using a combination of traditional and molecular tools

Freshwater ecosystems are considered hot spots for biodiversity and provide a wide range of ecosystem services for human beings. A variety of natural and anthropogenic stressors are now threatening the stability and prosperity of these ecosystems. In particular, climate change and pollution are the main stressors impacting all freshwater ecosystems on Earth. In this mix of multiple stressors, climate change is already having profound impacts, and is predicted to result in large-scale population collapses, species range shifts, and local species extinctions, as well as altered ecosystem properties. Scientists have spent considerable efforts in recent years investigating how these perturbations might impact food web structures and dynamics to predict potential future scenarios, but much of this work has been hindered by the slow pace of data generation using traditional techniques. Thus, there is a need to adopt and develop new approaches that can answer questions and generate data at a much higher pace. Molecular tools can address this issue by generating millions of DNA sequences in a short period of time with the potential to build food webs in a very reliable way. Therefore, a detailed understanding of food webs and their interactions is critical to predict what effects climate change will have in the near future, and we need to find faster and cheaper ways of building the necessary evidence base. This will ultimately improve our ability to forecast how communities and ecosystems will respond to global change and anticipate which species (and systems) are more likely to deteriorate under these new conditions. In this thesis, I have used a combination of traditional and molecular tools to characterize the diet of a widely distributed generalist predator. DNA sequencing revealed a higher number of links compared to traditional microscopy, but protocols need to be refined to accurately quantify each link. In addition to this, I carried out two sets of laboratory experiments to quantify warming impacts on freshwater invertebrate interactions. Functional response experiments showed increased feeding rates with warming, while qPCR was not able to detect changes in DNA retention time in predator gut contents.Open Acces

Kunapipi 15 (1) 1993 Full Version

Kunapipi 15 (1) 1993 Full Version

Security Technologies and Methods for Advanced Cyber Threat Intelligence, Detection and Mitigation

The rapid growth of the Internet interconnectivity and complexity of communication systems has led us to a significant growth of cyberattacks globally often with severe and disastrous consequences. The swift development of more innovative and effective (cyber)security solutions and approaches are vital which can detect, mitigate and prevent from these serious consequences. Cybersecurity is gaining momentum and is scaling up in very many areas. This book builds on the experience of the Cyber-Trust EU project’s methods, use cases, technology development, testing and validation and extends into a broader science, lead IT industry market and applied research with practical cases. It offers new perspectives on advanced (cyber) security innovation (eco) systems covering key different perspectives. The book provides insights on new security technologies and methods for advanced cyber threat intelligence, detection and mitigation. We cover topics such as cyber-security and AI, cyber-threat intelligence, digital forensics, moving target defense, intrusion detection systems, post-quantum security, privacy and data protection, security visualization, smart contracts security, software security, blockchain, security architectures, system and data integrity, trust management systems, distributed systems security, dynamic risk management, privacy and ethics