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In the first TABASCAL paper we showed how to calibrate in the presence of Radio Frequency Interference (RFI) sources by simultaneously isolating the trajectories and signals of the RFI sources. Here we show that we can accurately remove RFI from simulated MeerKAT radio interferometry target data, for a single frequency channel, corrupted by up to 9 simultaneous satellites with average RFI amplitudes varying from weak to very strong (1 - 1000 Jy). Additionally, TABASCAL also manages to leverage the RFI signal-to-noise to phase calibrate the recovered astronomical signal. TABASCAL effectively performs a suitably phased up fringe filter for each RFI source which allows essentially perfect removal of RFI across all strengths. As a result, TABASCAL reaches image noises equivalent to the uncorrupted, no-RFI, case. Consequently, point-source science with TABASCAL almost matches the no-RFI case with near perfect completeness for all RFI amplitudes. In contrast the completeness of AOFlagger and idealised 3$\sigma$ flagging drops below 40% for strong RFI amplitudes where recovered flux errors are $\sim$10x-100x worse than those from TABASCAL. Finally we highlight that TABASCAL works for both static and varying astronomical sources., Comment: 19 pages, 11 figures, 3 tables

Plasma kinetics, for both flat and curved spacetime, is conventionally performed on the mass shell, a 7--dimensional time-phase space with a Vlasov vector field, also known as the Liouville vector field. The choice of this time-phase space encodes the parameterisation of the underling 2nd order ordinary differential equations. By replacing the Vlasov vector on time-phase space with a bivector on an 8--dimensional sub-bundle of the tangent bundle, we create a parameterisation free version of Vlasov theory. This has a number of advantages, which include working for lightlike and ultra-relativistic particles, non metric connections, and metric-free and premetric theories. It also works for theories where no time-phase space can exist for topological topological reasons. An example of this is when we wish to consider all geodesics, including spacelike geodesics. We extend the particle density function to a 6--form on the subbundle of the tangent space, and define the transport equations, which correspond to the Vlasov equation. We then show how to define the corresponding 3--current on spacetime. We discuss the stress-energy tensor needed for the Einstein-Vlasov system. This theory can be generalised to create parameterisation invariant Vlasov theories for many 2nd order theories, on arbitrary manifolds. The relationship to sprays and semi-sprays is given and examples from Finsler geometry are also given., Comment: 28 pages 6 figures

The HyperText Markup Language 5 (HTML5) is useful for creating visual-centric web applications. However, unlike traditional web applications, HTML5 applications render objects onto the bitmap without representing them in the Document Object Model (DOM). Mismatches between the expected and actual visual output of the bitmap are termed visual bugs. Due to the visual-centric nature of applications, visual bugs are important to detect because such bugs can render a application useless. As we showed in prior work, Asset-Based graphics can provide the ground truth for a visual test oracle. However, many applications procedurally generate their graphics. In this paper, we investigate how to detect visual bugs in applications that use Procedural graphics as well. In particular, we explore the potential of Vision-Language Models (VLMs) to automatically detect visual bugs. Instead of defining an exact visual test oracle, information about the application's expected functionality (the context) can be provided with the screenshot as input to the VLM. To evaluate this approach, we constructed a dataset containing 80 bug-injected screenshots across four visual bug types (Layout, Rendering, Appearance, and State) plus 20 bug-free screenshots from 20 applications. We ran experiments with a state-of-the-art VLM using several combinations of text and image context to describe each application's expected functionality. Our results show that by providing the application README(s), a description of visual bug types, and a bug-free screenshot as context, VLMs can be leveraged to detect visual bugs with up to 100% per-application accuracy., Comment: Submitted to Empirical Software Engineering (EMSE) journal

Reinsurance optimization is critical for insurers to manage risk exposure, ensure financial stability, and maintain solvency. Traditional approaches often struggle with dynamic claim distributions, high-dimensional constraints, and evolving market conditions. This paper introduces a novel hybrid framework that integrates {Generative Models}, specifically Variational Autoencoders (VAEs), with {Reinforcement Learning (RL)} using Proximal Policy Optimization (PPO). The framework enables dynamic and scalable optimization of reinsurance strategies by combining the generative modeling of complex claim distributions with the adaptive decision-making capabilities of reinforcement learning. The VAE component generates synthetic claims, including rare and catastrophic events, addressing data scarcity and variability, while the PPO algorithm dynamically adjusts reinsurance parameters to maximize surplus and minimize ruin probability. The framework's performance is validated through extensive experiments, including out-of-sample testing, stress-testing scenarios (e.g., pandemic impacts, catastrophic events), and scalability analysis across portfolio sizes. Results demonstrate its superior adaptability, scalability, and robustness compared to traditional optimization techniques, achieving higher final surpluses and computational efficiency. Key contributions include the development of a hybrid approach for high-dimensional optimization, dynamic reinsurance parameterization, and validation against stochastic claim distributions. The proposed framework offers a transformative solution for modern reinsurance challenges, with potential applications in multi-line insurance operations, catastrophe modeling, and risk-sharing strategy design.

We present Track Anything Behind Everything (TABE), a novel dataset, pipeline, and evaluation framework for zero-shot amodal completion from visible masks. Unlike existing methods that require pretrained class labels, our approach uses a single query mask from the first frame where the object is visible, enabling flexible, zero-shot inference. Our dataset, TABE-51 provides highly accurate ground truth amodal segmentation masks without the need for human estimation or 3D reconstruction. Our TABE pipeline is specifically designed to handle amodal completion, even in scenarios where objects are completely occluded. We also introduce a specialised evaluation framework that isolates amodal completion performance, free from the influence of traditional visual segmentation metrics.

Satellite magnetic field observations have the potential to provide valuable information on dynamics, heat content and salinity throughout the ocean. Here we present the expected spatio-temporal characteristics of the ocean-induced magnetic field at satellite altitude on periods of months to decades. We compare these to the characteristics of other sources of Earth's magnetic field, and discuss whether it is feasible for the ocean-induced magnetic field to be retrieved and routinely monitored from space. We focus on large length scales (spherical harmonic degrees up to 30) and periods from one month up to five years. To characterize the expected ocean signal we make use of advanced numerical simulations taking high resolution oceanographic inputs and solve the magnetic induction equation in 3D including galvanic coupling and self induction effects. We find the time-varying ocean-induced signal dominates over the primary source of the internal field, the core dynamo, at high spherical harmonic degree with the cross-over taking place at degree 15 to 20 depending on the considered period. The ionospheric and magnetospheric fields (including their Earth induced counterparts) have most power on periods shorter than one month and are expected to be mostly zonal in magnetic coordinates at satellite altitude. Based on these findings we discuss future prospects for isolating and monitoring long period ocean induced magnetic field variations using data collected by present and upcoming magnetic survey satellites.

We introduce a dynamic benchmarking system for conversational agents that evaluates their performance through a single, simulated, and lengthy user$\leftrightarrow$agent interaction. The interaction is a conversation between the user and agent, where multiple tasks are introduced and then undertaken concurrently. We context switch regularly to interleave the tasks, which constructs a realistic testing scenario in which we assess the Long-Term Memory, Continual Learning, and Information Integration capabilities of the agents. Results from both proprietary and open-source Large-Language Models show that LLMs in general perform well on single-task interactions, but they struggle on the same tasks when they are interleaved. Notably, short-context LLMs supplemented with an LTM system perform as well as or better than those with larger contexts. Our benchmark suggests that there are other challenges for LLMs responding to more natural interactions that contemporary benchmarks have heretofore not been able to capture., Comment: Accepted as a poster at NeurIPS D&B Track 2024

The low-frequency component of the upcoming Square Kilometre Array Observatory (SKA-Low) will be sensitive enough to construct 3D tomographic images of the 21-cm signal distribution during reionization. However, foreground contamination poses challenges for detecting this signal, and image recovery will heavily rely on effective mitigation methods. We introduce \texttt{SERENEt}, a deep-learning framework designed to recover the 21-cm signal from SKA-Low's foreground-contaminated observations, enabling the detection of ionized (HII) and neutral (HI) regions during reionization. \texttt{SERENEt} can recover the signal distribution with an average accuracy of 75 per cent at the early stages ($\overline{x}_\mathrm{HI}\simeq0.9$) and up to 90 per cent at the late stages of reionization ($\overline{x}_\mathrm{HI}\simeq0.1$). Conversely, HI region detection starts at 92 per cent accuracy, decreasing to 73 per cent as reionization progresses. Beyond improving image recovery, \texttt{SERENEt} provides cylindrical power spectra with an average accuracy exceeding 93 per cent throughout the reionization period. We tested \texttt{SERENEt} on a 10-degree field-of-view simulation, consistently achieving better and more stable results when prior maps were provided. Notably, including prior information about HII region locations improved 21-cm signal recovery by approximately 10 per cent. This capability was demonstrated by supplying \texttt{SERENEt} with ionizing source distribution measurements, showing that high-redshift galaxy surveys of similar observation fields can optimize foreground mitigation and enhance 21-cm image construction., Comment: 16 pages, 14 figures, 1 table

A key problem in agent-based simulation is that integrating qualitative insights from multiple discipline experts is extremely hard. In most simulations, agent capabilities and corresponding behaviour needs to be programmed into the agent. We report on the architecture of a tool that disconnects the programmed functions of the agent, from the acquisition of capability and displayed behaviour. This allows multiple different domain experts to represent qualitative insights, without the need for code to be changed. It also allows a continuous integration (or even change) of qualitative behaviour processes, as more insights are gained. The consequent behaviour observed in the model is both, more faithful to the expert's insight as well as able to be contrasted against other models representing other insights., Comment: 14 pages, 6 figures, accepted at the 19th Social Simulation Conference 2024

This paper demonstrates a disconnected ABM architecture that enables domain experts, and non-programmers to add qualitative insights into the ABM model without the intervention of the programmer. This role separation within the architecture allows policy-makers to systematically experiment with multiple policy interventions, different starting conditions, and visualizations to interrogate their ABM, Comment: 6 pages, including references, 4 figures. Accepted for the demo track at EUMAS 2024