Using autonomous, decentralised control of unmanned aerial vehicles (UAVs) for critical scenarios such as bushfire firefighting provides clear safety and cost benefits. In this paper, we introduce a novel framework for autonomous, decentralised control based on multi-agent reinforcement learning (MARL) and a planning heuristic, designed to support learning a search-and-interact task in a multi-particle environment, which models a range of multi-UAV control applications. Our approach hybridises a planning heuristic with generic MARL methods under a discrete multi-action paradigm. The planning heuristic extracts only focused information from the environment, while also augmenting agent-centric observation data via exploiting observation-action symmetry to improve training. We employ this heuristic within a Centralised and Training Decentralised Execution (CTDE) paradigm to train autonomous control that is agnostic to the complexity of the environment, and effective under a range of action dynamics. The controller is encoded in a lean policy with reduced footprint, and can be deployed within resource-limited devices. Experimental results show that using even simple Q-learners within the framework provides highly effective agents for accurate firefighting in complex scenarios. Our approach is applicable to decision problems with similar search-and-interact characteristics.
The frequentist two-sample t-test for comparison of two means is one of the most widely used hypothesis tests in practice. Recently, several Bayesian alternatives to the t-test were derived and shown to have some advantages, such as incorporation of prior expert knowledge, over the standard methodology. This manuscript introduces a new Bayesian t-test based on the minimum message length (MML) principle of inductive inference. The aim of the manuscript is to introduce the general reader to MML and demonstrate how MML can be applied to a problem of central importance in statistics.
Physical literacy, encompassing movement skills, coordination, and confidence, is a critical yet underexplored domain in both sports and computer vision. In this paper, we introduce PLNet-12, a novel video dataset consisting of 12 fundamental movement skills designed to support the evaluation of physical literacy through multi-task vision-language models (VLMs). Unlike existing datasets, PLNet-12 includes fine-grained annotations for temporal action detection, repetition counting, displacement and distance measurement, duration estimation, and physical literacy scoring. We focus on zero-shot evaluation using large-scale VLMs without task-specific fine-tuning to investigate their generalization ability in real-world settings. To ensure annotation quality, we employ a semi-automated, rule-based pipeline verified by motion capture data and domain experts. We benchmark several state-of-the-art VLMs under different temporal and spatial settings and reveal challenges including fine-grained temporal understanding, action confusion, and temporal hallucination. Our findings highlight critical limitations of current VLMs in structured physical tasks and suggest directions for future multi-modal reasoning research. PLNet-12 offers a lightweight yet challenging platform for advancing temporal video understanding in human movement analysis.
Federated Learning (FL) is a decentralized paradigm that enables collaborative model training without sharing raw data. However, traditional FL methods often struggle with slow and unstable convergence, especially under non-Independent and non-Identically Distributed (non-IID) data, which degrades model performance and training stability. Most existing aggregation strategies rely on static, uniform, or heuristic-based weighting schemes that fail to adapt dynamic client behavior and overlook variations in local training quality. Although some recent approaches explore client selection or value-based reinforcement processes (e.g., Q-learning), they fall short of directly optimizing the aggregation process in an end-to-end fashion. To address these limitations, we propose Federated Reinforcement Weighted Aggregation (FedRWA), a policy gradient-based aggregation approach that dynamically assigns client-specific weights based on local training characteristics on the server side. Each client generates a feature vector consisting of training loss and accuracy, which is fed into a central policy network. This network outputs aggregation weights and is trained via a reinforcement process, using the global model’s validation performance as the reward. Extensive experiments on the UNSW-NB15 and NSL-KDD datasets, under both IID and non-IID conditions, show that FedRWA consistently outperforms state-of-the-art baselines in terms of classification metrics and communication efficiency. In addition, the learning curves demonstrate the superior convergence speed and training stability of FedRWA, validating its effectiveness as a robust solution for adaptive aggregation in heterogeneous FL environments.
Remote Sensing Object Detection (RSOD) presents unique challenges due to arbitrary object orientations, complex backgrounds, and data scarcity. Existing methods often rely on complex network architectures or auxiliary tasks for multi-task optimization, which increase model complexity and require additional data. In this work, we proposed an Adapter-based Few-shot Fine-Tuning framework for RSOD, termed AFFT. Specifically, to effectively capture global context and extract high-quality features, we adopted a pre-trained hierarchical Swin Transformer based on shifted windows attention mechanism as the backbone. This enables robust feature representation by efficiently modeling both local and global dependencies. Moreover, recognizing the limited availability of annotated remote sensing data, we designed a Group Equivariant Convolutions Multi-cognitive Visual Adapter (GCMONA) for few-shot fine-tuning. This approach significantly enhances model adaptability to new classes with minimal labeled samples, making it particularly well-suited for remote sensing scenarios. It is important that AFFT requires fine-tuning only 5
