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The rapid growth of non-terrestrial communication necessitates its integration with existing terrestrial networks, as highlighted in 3GPP Releases 16 and 17. This paper analyses the concept of functional splits in 3D-Networks. To manage this complex structure effectively, the adoption of a Radio Access Network (RAN) architecture with Functional Split (FS) offers advantages in flexibility, scalability, and cost-efficiency. RAN achieves this by disaggregating functionalities into three separate units. Analogous to the terrestrial network approach, 3GPP is extending this concept to non-terrestrial platforms as well. This work presents a general analysis of the requested Fronthaul (FH) data rate on feeder link between a non-terrestrial platform and the ground-station. Each split option is a trade-of between FH data rate and the respected complexity. Since flying nodes face more limitations regarding power consumption and complexity on board in comparison to terrestrial ones, we are investigating the split options between lower and higher physical layer.

The application of deep neural network models in various security-critical applications has raised significant security concerns, particularly the risk of backdoor attacks. Neural backdoors pose a serious security threat as they allow attackers to maliciously alter model behavior. While many defenses have been explored, existing approaches are often bounded by model-specific constraints, or necessitate complex alterations to the training process, or fall short against diverse backdoor attacks. In this work, we introduce a novel method for comprehensive and effective elimination of backdoors, called ULRL (short for UnLearn and ReLearn for backdoor removal). ULRL requires only a small set of clean samples and works effectively against all kinds of backdoors. It first applies unlearning for identifying suspicious neurons and then targeted neural weight tuning for backdoor mitigation (i.e., by promoting significant weight deviation on the suspicious neurons). Evaluated against 12 different types of backdoors, ULRL is shown to significantly outperform state-of-the-art methods in eliminating backdoors whilst preserving the model utility.

Motivated by the need to solve open-loop optimal control problems with computational efficiency and reliable constraint satisfaction, we introduce a general framework that combines diffusion models and numerical optimization solvers. Optimal control problems are rarely solvable in closed form, hence they are often transcribed into numerical trajectory optimization problems, which then require initial guesses. These initial guesses are supplied in our framework by diffusion models. To mitigate the effect of samples that violate the problem constraints, we develop a novel constrained diffusion model to approximate the true distribution of locally optimal solutions with an additional constraint violation loss in training. To further enhance the robustness, the diffusion samples as initial guesses are fed to the numerical solver to refine and derive final optimal (and hence feasible) solutions. Experimental evaluations on three tasks verify the improved constraint satisfaction and computational efficiency with 4$\times$ to 30$\times$ acceleration using our proposed framework, which generalizes across trajectory optimization problems and scales well with problem complexity.

While the benefits of reconfigurable manufacturing systems (RMS) are well-known, there are still challenges to their development, including, among others, a modular software architecture that enables rapid reconfiguration without much reprogramming effort. Skill-based engineering improves software modularity and increases the reconfiguration potential of RMS. Nevertheless, a skills' composition framework with a focus on frequent and rapid software changes is still missing. The Behavior trees (BTs) framework is a novel approach, which enables intuitive design of modular hierarchical control structures. BTs have been mostly explored from the AI and robotics perspectives, and little work has been done in investigating their potential for composing skills in the manufacturing domain. This paper proposes a framework for skills' composition and execution in skill-based reconfigurable cyber-physical production modules (RCPPMs). It is based on distributed BTs and provides good integration between low-level devices' specific code and AI-based task-oriented frameworks. We have implemented the provided models for the IEC 61499-based distributed automation controllers to show the instantiation of the proposed framework with the specific industrial technology and enable its evaluation by the automation community.

Computer systems that have been successfully deployed for dense regular workloads fall short of achieving scalability and efficiency when applied to irregular and dynamic graph applications. Conventional computing systems rely heavily on static, regular, numeric intensive computations while High Performance Computing systems executing parallel graph applications exhibit little locality, spatial or temporal, and are fine-grained and memory intensive. With the strong interest in AI which depend on these very different use cases combined with the end of Moore's Law at nanoscale, dramatic alternatives in architecture and underlying execution models are required. This paper identifies an innovative non-von Neumann architecture, Continuum Computer Architecture (CCA), that redefines the nature of computing structures to yield powerful innovations in computational methods to deliver a new generation of highly parallel hardware architecture. CCA reflects a genus of highly parallel architectures that while varying in specific quantities (e.g., memory blocks), share a multiple of attributes not found in typical von Neumann machines. Among these are memory-centric components, message-driven asynchronous flow control, and lightweight out-of-order execution across a global name space. Together these innovative non-von Neumann architectural properties guided by a new original execution model will deliver the new future path for extending beyond the von Neumann model. This paper documents a series of interrelated experiments that together establish future directions for next generation non-von Neumann architectures, especially for graph processing.

Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. One common way to compute high-dimensional MVN probabilities is the Separation-of-Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n^3) and space complexity of O(n^2), mainly due to a Cholesky factorization operation for an n X n covariance matrix, where $n$ represents the dimensionality of the MVN problem. This work proposes a high-performance computing framework that allows scaling the SOV algorithm and, subsequently, the confidence region detection algorithm. The framework leverages parallel linear algebra algorithms with a task-based programming model to achieve performance scalability in computing process probabilities, especially on large-scale systems. In addition, we enhance our implementation by incorporating Tile Low-Rank (TLR) approximation techniques to reduce algorithmic complexity without compromising the necessary accuracy. To evaluate the performance and accuracy of our framework, we conduct assessments using simulated data and a wind speed dataset. Our proposed implementation effectively handles high-dimensional multivariate normal (MVN) probability computations on shared and distributed-memory systems using finite precision arithmetics and TLR approximation computation. Performance results show a significant speedup of up to 20X in solving the MVN problem using TLR approximation compared to the reference dense solution without sacrificing the application's accuracy. The qualitative results on synthetic and real datasets demonstrate how we maintain high accuracy in detecting confidence regions even when relying on TLR approximation to perform the underlying linear algebra operations.

Edge computing facilitates low-latency services at the network's edge by distributing computation, communication, and storage resources within the geographic proximity of mobile and Internet-of-Things (IoT) devices. The recent advancement in Unmanned Aerial Vehicles (UAVs) technologies has opened new opportunities for edge computing in military operations, disaster response, or remote areas where traditional terrestrial networks are limited or unavailable. In such environments, UAVs can be deployed as aerial edge servers or relays to facilitate edge computing services. This form of computing is also known as UAV-enabled Edge Computing (UEC), which offers several unique benefits such as mobility, line-of-sight, flexibility, computational capability, and cost-efficiency. However, the resources on UAVs, edge servers, and IoT devices are typically very limited in the context of UEC. Efficient resource management is, therefore, a critical research challenge in UEC. In this article, we present a survey on the existing research in UEC from the resource management perspective. We identify a conceptual architecture, different types of collaborations, wireless communication models, research directions, key techniques and performance indicators for resource management in UEC. We also present a taxonomy of resource management in UEC. Finally, we identify and discuss some open research challenges that can stimulate future research directions for resource management in UEC.

The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning. Towards this goal, the Autonomous Agents Research Group develops novel machine learning algorithms for autonomous systems control, with a specific focus on deep reinforcement learning and multi-agent reinforcement learning. Research problems include scalable learning of coordinated agent policies and inter-agent communication; reasoning about the behaviours, goals, and composition of other agents from limited observations; and sample-efficient learning based on intrinsic motivation, curriculum learning, causal inference, and representation learning. This article provides a broad overview of the ongoing research portfolio of the group and discusses open problems for future directions.

Autonomic computing investigates how systems can achieve (user) specified control outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.

Current models for event causality identification (ECI) mainly adopt a supervised framework, which heavily rely on labeled data for training. Unfortunately, the scale of current annotated datasets is relatively limited, which cannot provide sufficient support for models to capture useful indicators from causal statements, especially for handing those new, unseen cases. To alleviate this problem, we propose a novel approach, shortly named CauSeRL, which leverages external causal statements for event causality identification. First of all, we design a self-supervised framework to learn context-specific causal patterns from external causal statements. Then, we adopt a contrastive transfer strategy to incorporate the learned context-specific causal patterns into the target ECI model. Experimental results show that our method significantly outperforms previous methods on EventStoryLine and Causal-TimeBank (+2.0 and +3.4 points on F1 value respectively).