Decision making under abnormal conditions is a critical process that involves evaluating the current state and determining the optimal action to restore the system to a normal state at an acceptable cost. However, in such scenarios, existing decision-making frameworks highly rely on reinforcement learning or root cause analysis, resulting in them frequently neglecting the cost of the actions or failing to incorporate causal mechanisms adequately. By relaxing the existing causal decision framework to solve the necessary cause, we propose a minimum-cost causal decision (MiCCD) framework via counterfactual reasoning to address the above challenges. Emphasis is placed on making counterfactual reasoning processes identifiable in the presence of a large amount of mixed anomaly data, as well as finding the optimal intervention state in a continuous decision space. Specifically, it formulates a surrogate model based on causal graphs, using abnormal pattern clustering labels as supervisory signals. This enables the approximation of the structural causal model among the variables and lays a foundation for identifiable counterfactual reasoning. With the causal structure approximated, we then established an optimization model based on counterfactual estimation. The Sequential Least Squares Programming (SLSQP) algorithm is further employed to optimize intervention strategies while taking costs into account. Experimental evaluations on both synthetic and real-world datasets reveal that MiCCD outperforms conventional methods across multiple metrics, including F1-score, cost efficiency, and ranking quality(nDCG@k values), thus validating its efficacy and broad applicability.

--Almost all existing hierarchical federated learning (FL) models are limited to two aggregation layers, restricting scalability and flexibility in complex, large-scale networks. In this work, we propose a Multi-Layer Hierarchical Federated Learning framework ( QMLHFL), which appears to be the first study that generalizes hierarchical FL to arbitrary numbers of layers and network architectures through nested aggregation, while employing a layer-specific quantization scheme to meet communication constraints. We develop a comprehensive convergence analysis for QMLHFL and derive a general convergence condition and rate that reveal the effects of key factors, including quantization parameters, hierarchical architecture, and intra-layer iteration counts. Furthermore, we determine the optimal number of intra-layer iterations to maximize the convergence rate while meeting a deadline constraint that accounts for both communication and computation times. Our results show that QMLHFL consistently achieves high learning accuracy, even under high data heterogeneity, and delivers notably improved performance when optimized, compared to using randomly selected values. Federated Learning (FL) has gained significant attention as an approach that enables distributed model training without requiring centralized access to raw data [1]. This method is especially valuable in situations where data is costly to gather or difficult to consolidate. Additionally, FL supports improved computational efficiency by enabling simultaneous model training across multiple devices.

The vision of sixth-generation (6G) wireless networks paves the way for the seamless integration of digital twins into vehicular networks, giving rise to a Vehicular Digital Twin Network (VDTN). The large amount of computing resources as well as the massive amount of spatial-temporal data in Digital Twin (DT) domain can be utilized to enhance the communication and control performance of Internet of Vehicle (IoV) systems. In this article, we first propose the architecture of VDTN, emphasizing key modules that center on functions related to the joint optimization of control and communication. We then delve into the intricacies of the multitimescale decision process inherent in joint optimization in VDTN, specifically investigating the dynamic interplay between control and communication. To facilitate the joint optimization, we define two Value of Information (VoI) concepts rooted in control performance. Subsequently, utilizing VoI as a bridge between control and communication, we introduce a novel joint optimization framework, which involves iterative processing of two Deep Reinforcement Learning (DRL) modules corresponding to control and communication to derive the optimal policy. Finally, we conduct simulations of the proposed framework applied to a platoon scenario to demonstrate its effectiveness in ensu

This paper introduces MACH, a novel approach for optimizing task handover in vehicular computing scenarios. To ensure fast and latency-aware placement of tasks, the decision-making -- where and when should tasks be offloaded -- is carried out decentralized at the Road Side Units (RSUs) who also execute the tasks. By shifting control to the network edge, MACH moves away from the traditional centralized or vehicle-based handover method. Still, it focuses on contextual factors, such as the current RSU load and vehicle trajectories. Thus, MACH improves the overall Quality of Service (QoS) while fairly balancing computational loads between RSUs. To evaluate the effectiveness of our approach, we develop a robust simulation environment composed of real-world traffic data, dynamic network conditions, and different infrastructure capacities. For scenarios that demand low latency and high reliability, our experimental results demonstrate how MACH significantly improves the adaptability and efficiency of vehicular computations. By decentralizing control to the network edge, MACH effectively reduces communication overhead and optimizes resource utilization, offering a robust framework for task handover management.

Microgrids have emerged as a pivotal solution in the quest for a sustainable and energy-efficient future. While microgrids offer numerous advantages, they are also prone to issues related to reliably forecasting renewable energy demand and production, protecting against cyberattacks, controlling operational costs, optimizing power flow, and regulating the performance of energy management systems (EMS). Tackling these energy management challenges is essential to facilitate microgrid applications and seamlessly incorporate renewable energy resources. Artificial intelligence (AI) has recently demonstrated immense potential for optimizing energy management in microgrids, providing efficient and reliable solutions. This paper highlights the combined benefits of enabling AI-based methodologies in the energy management systems of microgrids by examining the applicability and efficiency of AI-based EMS in achieving specific technical and economic objectives. The paper also points out several future research directions that promise to spearhead AI-driven EMS, namely the development of self-healing microgrids, integration with blockchain technology, use of Internet of things (IoT), and addressing interpretability, data privacy, scalability, and the prospects to generative AI in the context of future AI-based EMS.

We present the first scene-update aerial path planning algorithm specifically designed for detecting and updating change areas in urban environments. While existing methods for large-scale 3D urban scene reconstruction focus on achieving high accuracy and completeness, they are inefficient for scenarios requiring periodic updates, as they often re-explore and reconstruct entire scenes, wasting significant time and resources on unchanged areas. To address this limitation, our method leverages prior reconstructions and change probability statistics to guide UAVs in detecting and focusing on areas likely to have changed. Our approach introduces a novel changeability heuristic to evaluate the likelihood of changes, driving the planning of two flight paths: a prior path informed by static priors and a dynamic real-time path that adapts to newly detected changes. The framework integrates surface sampling and candidate view generation strategies, ensuring efficient coverage of change areas with minimal redundancy. Extensive experiments on real-world urban datasets demonstrate that our method significantly reduces flight time and computational overhead, while maintaining high-quality updates comparable to full-scene re-exploration and reconstruction. These contributions pave the way for efficient, scalable, and adaptive UAV-based scene updates in complex urban environments.

Synthesizability in small molecule generative design remains a bottleneck. Existing works that do consider synthesizability can output predicted synthesis routes for generated molecules. However, there has been minimal attention in addressing the ease of synthesis and enabling flexibility to incorporate desired reaction constraints. In this work, we propose a small molecule generative design framework that enables steerable and granular synthesizability control. Generated molecules satisfy arbitrary multi-parameter optimization objectives with predicted synthesis routes containing pre-defined allowed reactions, while optionally avoiding others. One can also enforce that all reactions belong to a pre-defined set. We show the capability to mix-and-match these reaction constraints across the most common medicinal chemistry transformations. Next, we show how our framework can be used to valorize industrial byproducts towards de novo optimized molecules. Going further, we demonstrate how granular control over synthesizability constraints can loosely mimic virtual screening of ultra-large make-on-demand libraries. Using only a single GPU, we generate and dock 15k molecules to identify promising candidates in Freedom 4.0 constituting 142B make-on-demand molecules (assessing only 0.00001% of the library). Generated molecules satisfying the reaction constraints have > 90% exact match rate. Lastly, we benchmark our framework against recent synthesizability-constrained generative models and demonstrate the highest sample efficiency even when imposing the additional constraint that all molecules must be synthesizable from a single reaction type. The main theme is demonstrating that a pre-trained generalist molecular generative model can be incentivized to generate property-optimized small molecules under challenging synthesizability constraints through reinforcement learning.

Federated Learning (FL) enables distributed machine learning training while preserving privacy, representing a paradigm shift for data-sensitive and decentralized environments. Despite its rapid advancements, FL remains a complex and multifaceted field, requiring a structured understanding of its methodologies, challenges, and applications. In this survey, we introduce a meta-framework perspective, conceptualising FL as a composition of modular components that systematically address core aspects such as communication, optimisation, security, and privacy. We provide a historical contextualisation of FL, tracing its evolution from distributed optimisation to modern distributed learning paradigms. Additionally, we propose a novel taxonomy distinguishing Aggregation from Alignment, introducing the concept of alignment as a fundamental operator alongside aggregation. To bridge theory with practice, we explore available FL frameworks in Python, facilitating real-world implementation. Finally, we systematise key challenges across FL sub-fields, providing insights into open research questions throughout the meta-framework modules. By structuring FL within a meta-framework of modular components and emphasising the dual role of Aggregation and Alignment, this survey provides a holistic and adaptable foundation for understanding and advancing FL research and deployment.

Algorithm selection, aiming to identify the best algorithm for a given problem, plays a pivotal role in continuous black-box optimization. A common approach involves representing optimization functions using a set of features, which are then used to train a machine learning meta-model for selecting suitable algorithms. Various approaches have demonstrated the effectiveness of these algorithm selection meta-models. However, not all evaluation approaches are equally valid for assessing the performance of meta-models. We highlight methodological issues that frequently occur in the community and should be addressed when evaluating algorithm selection approaches. First, we identify flaws with the "leave-instance-out" evaluation technique. We show that non-informative features and meta-models can achieve high accuracy, which should not be the case with a well-designed evaluation framework. Second, we demonstrate that measuring the performance of optimization algorithms with metrics sensitive to the scale of the objective function requires careful consideration of how this impacts the construction of the meta-model, its predictions, and the model's error. Such metrics can falsely present overly optimistic performance assessments of the meta-models. This paper emphasizes the importance of careful evaluation, as loosely defined methodologies can mislead researchers, divert efforts, and introduce noise into the field

--Data imputation is a critical step in data pre-processing, particularly for datasets with missing or unreliable values. This study introduces a novel quantum-inspired imputation framework evaluated on the UCI Diabetes dataset, which contains biologically implausible missing values across several clinical features. The method integrates Principal Component Analysis (PCA) with quantum-assisted rotations, optimized through gradient-free classical optimizers--COBYLA, Simulated Annealing, and Differential Evolution--to reconstruct missing values while preserving statistical fidelity. Reconstructed values are constrained within 2 standard deviations of original feature distributions, avoiding unrealistic clustering around central tendencies. This approach achieves a substantial and statistically significant improvement, including an average reduction of over 85% in Wasserstein distance and Kolmogorov-Smirnov test p-values between 0.18 and 0.22, compared to p-values > 0.99 in classical methods such as Mean, KNN, and MICE. The method also eliminates zero-value artifacts and enhances the realism and variability of imputed data. By combining quantum-inspired transformations with a scalable classical framework, this methodology provides a robust solution for imputation tasks in domains such as healthcare and AI pipelines, where data quality and integrity are crucial. I NTRODUCTION Data imputation is a statistical technique for addressing missing or partial data values within a dataset. Missing data may arise from various sources, including sensor faults, human errors, system failures, or privacy constraints [1]. The imputation process replaces missing values with estimates derived from the available data while preserving the dataset's integrity and minimizing bias [2]. Imputation plays a vital role in numerous sectors and scenarios where data completeness is essential for analysis and decision-making.