伦敦国王学院

AI-assisted cybersecurity systems are characterised by continuous adaptation between attackers and defenders, making evolutionary game theory a natural framework for studying their long-term behaviour. However, existing evolutionary cybersecurity models have primarily focused on homogeneous interactions, providing limited understanding of how population structure influences cyber attack–defence dynamics. In this paper, we develop a mixed-role evolutionary game in which adaptive cyber agents can exhibit both offensive and defensive behaviours, and investigate its dynamics in well-mixed and structured populations. The proposed framework combines stochastic evolutionary analysis with large-scale agent-based simulations to examine how interaction structure shapes long-run strategic behaviour. Our results reveal a fundamental difference between global and local interactions. While well-mixed populations exhibit broad coexistence between attacking and defensive strategies, structured populations generate well-defined evolutionary regimes in which secure defensive behaviour becomes dominant over a much larger region of the parameter space. Spatial analysis further shows that neighbouring defenders naturally form resilient clusters that suppress persistent attacks through network reciprocity. These findings demonstrate that interaction structure is a fundamental determinant of AI-assisted cybersecurity evolution and suggest that organising defensive agents through local networked interactions can substantially improve long-term cyber resilience without requiring additional defensive incentives.

We extend earlier international efforts to optimise hexahedral-based spectral element methods on GPUs and vectorised CPUs to mixed element meshes additionally involving prismatic, pyramidic, and tetrahedral shapes using tensorial expansions. We demonstrate that common finite element operators (such as the mass and Helmholtz matrices) benefit from alternative implementation strategies depending on the element shape, choice of polynomial order, and system architecture in order to achieve optimal performance. In addition, we introduce a new approach/interpretation to efficiently evaluate more complex operations involving inner products with the derivative of the expansions as part of the integrand such as the stiffness matrix. This approach seeks to maximise operations using the collocation properties of the nodal tensorial expansion associated with classical quadrature rules. Our GPU performance tests demonstrate that the throughput of the Helmholtz operator on tetrahedral elements is at most 2.5 times slower than on hexahedral elements, despite tetrahedra having a factor of six greater quadrature points per degrees of freedom.

Understanding resilience dynamics remains a central challenge in social-ecological systems research, particularly in marginalised and rapidly transforming local contexts. Although resilience theory offers a compelling framework for how systems absorb disturbances, adapt, and transform, managing system resilience remains difficult without explicitly foregrounding human agency. This paper introduces an enriched model for analysing resilience dynamics at local scales by integrating the adaptive cycle framework with human responses conceptualised as coping, adaptation, and transformation. Applied to one of Ethiopia’s most marginalised local contexts—the agropastoral Lower Omo Valley, which bears the burden of state-led mega-developments such as hydroelectric dams and sugar plantations—the model examines how local actors navigate the externally imposed changes and either sustain or lose resilience. Findings reveal that while national narratives frame these developments as transformative, local responses are predominantly characterised by coping. The study documents declining agricultural, livestock, and decision-making potential, weakened social and economic connectedness, and limited adaptive capacity. It argues that resilience at local scales is undermined by large-scale interventions that exclude community agency, pushing systems toward maladaptive responses and poverty traps. By foregrounding local agency and integrating resilience responses into adaptive cycle analysis, the study provides a nuanced understanding of resilience dynamics in marginalised social-ecological systems highly vulnerable to regime shifts. Our findings have broader implications for resilience-building in marginalised regions, particularly where external interventions reshape local systems without community participation.

Chaos in infinite-dimensional linear systems can arise from a simple combination of transport and spatially heterogeneous amplification. We study this mechanism on C0(R), the Banach space of continuous localized profiles, and connect it to finite ReLU approximation of selected orbit states. For a positive weight that contracts the far-left tail and expands the far-right tail, we prove that the weighted translation Twf(x)=w(x)f(x+1) is hypercyclic and has dense periodic points; hence it is chaotic in the standard sense of linear dynamics. The same transport-amplification argument yields chaotic weighted composition operators for increasing, proper, fixed-point-free input deformations, while injectivity, properness, and absence of fixed points are shown to be necessary. A new qualitative obstruction appears when macroscopic endpoint coordinates are retained: their geometric evolution prevents hypercyclicity on the full endpoint spaces. We restore chaotic dynamics on the equal-endpoint space by an explicit rank-one feedback from point evaluation to the endpoint coordinate. On the approximation side, we prove that bounded one-dimensional ReLU networks are uniformly dense in the space of continuous functions with finite endpoint limits and that compactly supported ReLU splines are dense in C0(R). Consequently, each selected element of a single chaotic orbit can be approximated uniformly by a finite ReLU network. Reproducible computations illustrate spline closure, finite orbit block encoding, and endpoint steering. These experiments verify the finite-stage mechanisms used in the proofs without being presented as numerical evidence for an infinite-dimensional topological property.

Transposable elements (TEs) have made important contributions to the evolution of the placenta, and are argued to have played a role in the wide inter-species diversification of this critical developmental organ. Co-option of TEs by host genomes has led to the genesis of important placental genes, as well as trophoblast-specific gene regulatory elements. In mice, past work has demonstrated how multiple species-specific TE subfamilies are used as transcriptional enhancers in trophoblast stem cells. However, the involvement of TEs in the regulation of mouse placental gene expression in vivo remains unclear. Here, we characterised the TE regulatory and transcriptional landscape in mouse placenta and gauged their evolutionary dynamics through a comparative approach. We found that overall, TE cis-regulatory activity is greatly diminished in differentiated mouse trophoblast when compared to their stem cell counterpart. On the other hand, evolutionarily young intracisternal A particle (IAP) elements are highly expressed in the placenta and create several alternative, placenta-specific transcriptional start sites for protein-coding genes. Placenta-expressed IAP elements are genetically polymorphic between mouse strains and drive species-specific expression of associated genes. These putative co-option events are therefore recent and may represent a prime example of how TE activity can drive fast placental evolution. ### Competing Interest Statement The authors have declared no competing interest. Biotechnology and Biological Sciences Research Council, BB/T000031/1, BB/X007758/1 Medical Research Council, MR/X008487/1