预示着在不同时间尺度上作用的软件化，可编程网络控制和使用作用的全包装控制器的使用，作为下一代蜂窝网络发展的关键驱动力。这些技术已经培养了新设计的智能数据驱动的解决方案，用于管理大量各种蜂窝功能，基本上不可能在传统上闭合的蜂窝体系结构中实施。尽管行业对人工智能（AI）和机器学习（ML）解决方案具有明显的兴趣，该解决方案是对无线电访问网络（RAN）的闭环控制，并且该领域的几项研究工作远非主流，但仍然是一个复杂的操作，而且经常被忽略。在本文中，我们讨论了如何为开放式RAN的智能闭环控制设计AI/ML解决方案，从而根据具有高性能记录的示例解决方案提供指南和见解。然后，我们展示如何通过OpenRan Gym在O-RAN近实时RAN智能控制器（RIC）上实例化这些解决方案，Openran Gym是第一个用于数据驱动的O-RAN实验的公共可用工具箱。我们展示了一个由OpenRan Gym开发的XAPP的用例，并在蜂窝网络上进行了测试，其中有7个基站和42位用户部署在Colosseum Wireless网络模拟器上。我们的演示表明，位于Openran的XAPP开发环境的高度灵活性，该环境与部署方案和交通需求无关。

开放式无线电访问网络（RAN）体系结构将在下一代蜂窝网络中启用互操作性，开放性和可编程数据驱动控制。但是，开发和测试有效的解决方案，这些解决方案跨越了异质的细胞部署和量表，并在如此多样化的环境中优化网络性能是一项复杂的任务，这是一项复杂的任务，仍然在很大程度上没有探索。在本文中，我们介绍了OpenRan Gym，这是一个统一，开放和O-Ran符合的实验工具箱，用于数据收集，设计，原型设计和测试下一代Open RAN Systems的端到端数据驱动的控制解决方案。 OpenRan Gym扩展并结合了一个独特的解决方案，几个软件框架用于数据收集统计和控制控制，以及轻巧的O-Ran近实时RAN智能控制器（RIC）量身定制，可在实验性无线平台上运行。我们首先概述了OpenRan Gym的各种建筑组件，并描述了如何按大规模收集数据和设计，训练和测试人工智能和机器学习O-Ran-Commiate应用程序（XAPP）。然后，我们详细描述了如何在SoftWarized Rans上测试开发的XAPP，并提供了一个使用OpenRan Gym开发的两个XAPP的示例，这些XAPP用于控制一个具有7个基站的网络，并在奥马斗马会测试中部署了42个用户。最后，我们展示了如何通过罗马竞技场上的Openran Gym开发的解决方案，可以将其导出到现实世界中的异质无线平台，例如Arena Testbed以及PAWR计划的粉末和宇宙平台。 OpenRan Gym及其软件组件是开源的，并且对研究社区公开可用。

尽管开放式运输所带来的新机遇，但基于ML的网络自动化的进步已经缓慢，主要是因为大规模数据集和实验测试基础设施的不可用。这减缓了实际网络上的深度加强学习（DRL）代理的开发和广泛采用，延迟了智能和自主运行控制的进展。在本文中，我们通过提出用于开放式RAN基于DRL基闭环控制的设计，培训，测试和实验评估的实用解决方案和软件管道来解决这些挑战。我们介绍了Colo-RAN，这是一个具有软件定义的无线电循环的第一个公开的大型O-RAN测试框架。在ColoSseum无线网络仿真器的规模和计算能力上，Colo-RAN使用O-RAN组件，可编程基站和“无线数据厂”来实现ML研究。具体而言，我们设计并开发三种示例性XApp，用于基于DRL的RAN切片，调度和在线模型培训，并评估其在具有7个软化基站和42个用户的蜂窝网络上的性能。最后，我们通过在竞技场上部署一个室内可编程测试平台来展示Colo-RAN到不同平台的可移植性。我们的一类大型评估的广泛结果突出了基于DRL的自适应控制的益处和挑战。他们还提供关于无线DRL管道的开发的见解，从数据分析到DRL代理商的设计，以及与现场训练相关的权衡。 Colo-RAN和收集的大型数据集将公开向研究界公开提供。

Colorsseum是一种开放式和公开可用的大型无线无线测试，可通过虚拟化和软载波形和协议堆栈进行实验研究，在完全可编程的“白盒子”平台上。通过256最先进的软件定义的无线电和巨大的通道仿真器核心，罗马斗兽场几乎可以模拟任何方案，在各种部署和渠道条件下，可以在规模上进行设计，开发和测试解决方案。通过有限脉冲响应滤波器通过高保真FPGA的仿真再现这些罗马孔射频场景。过滤器模拟所需的无线通道的抽头，并将它们应用于无线电节点生成的信号，忠实地模拟现实世界无线环境的条件。在本文中，我们将罗马斗兽场介绍为测试楼，这是第一次向研究界开放。我们描述了罗马斗兽场的建筑及其实验和仿真能力。然后，我们通过示例性用例证明了罗马斗兽场对实验研究的有效性，包括频谱共享和无人空中车辆场景的普遍用途用例，包括普遍的无线技术（例如，蜂窝和Wi-Fi）。斗兽索斗兽场未来更新的路线图总结了这篇论文。

It is well known that conservative mechanical systems exhibit local oscillatory behaviours due to their elastic and gravitational potentials, which completely characterise these periodic motions together with the inertial properties of the system. The classification of these periodic behaviours and their geometric characterisation are in an on-going secular debate, which recently led to the so-called eigenmanifold theory. The eigenmanifold characterises nonlinear oscillations as a generalisation of linear eigenspaces. With the motivation of performing periodic tasks efficiently, we use tools coming from this theory to construct an optimization problem aimed at inducing desired closed-loop oscillations through a state feedback law. We solve the constructed optimization problem via gradient-descent methods involving neural networks. Extensive simulations show the validity of the approach.

Detecting anomalous data within time series is a very relevant task in pattern recognition and machine learning, with many possible applications that range from disease prevention in medicine, e.g., detecting early alterations of the health status before it can clearly be defined as "illness" up to monitoring industrial plants. Regarding this latter application, detecting anomalies in an industrial plant's status firstly prevents serious damages that would require a long interruption of the production process. Secondly, it permits optimal scheduling of maintenance interventions by limiting them to urgent situations. At the same time, they typically follow a fixed prudential schedule according to which components are substituted well before the end of their expected lifetime. This paper describes a case study regarding the monitoring of the status of Laser-guided Vehicles (LGVs) batteries, on which we worked as our contribution to project SUPER (Supercomputing Unified Platform, Emilia Romagna) aimed at establishing and demonstrating a regional High-Performance Computing platform that is going to represent the main Italian supercomputing environment for both computing power and data volume.

Methods based on ordinary differential equations (ODEs) are widely used to build generative models of time-series. In addition to high computational overhead due to explicitly computing hidden states recurrence, existing ODE-based models fall short in learning sequence data with sharp transitions - common in many real-world systems - due to numerical challenges during optimization. In this work, we propose LS4, a generative model for sequences with latent variables evolving according to a state space ODE to increase modeling capacity. Inspired by recent deep state space models (S4), we achieve speedups by leveraging a convolutional representation of LS4 which bypasses the explicit evaluation of hidden states. We show that LS4 significantly outperforms previous continuous-time generative models in terms of marginal distribution, classification, and prediction scores on real-world datasets in the Monash Forecasting Repository, and is capable of modeling highly stochastic data with sharp temporal transitions. LS4 sets state-of-the-art for continuous-time latent generative models, with significant improvement of mean squared error and tighter variational lower bounds on irregularly-sampled datasets, while also being x100 faster than other baselines on long sequences.

This project leverages advances in multi-agent reinforcement learning (MARL) to improve the efficiency and flexibility of order-picking systems for commercial warehouses. We envision a warehouse of the future in which dozens of mobile robots and human pickers work together to collect and deliver items within the warehouse. The fundamental problem we tackle, called the order-picking problem, is how these worker agents must coordinate their movement and actions in the warehouse to maximise performance (e.g. order throughput) under given resource constraints. Established industry methods using heuristic approaches require large engineering efforts to optimise for innately variable warehouse configurations. In contrast, the MARL framework can be flexibly applied to any warehouse configuration (e.g. size, layout, number/types of workers, item replenishment frequency) and the agents learn via a process of trial-and-error how to optimally cooperate with one another. This paper details the current status of the R&D effort initiated by Dematic and the University of Edinburgh towards a general-purpose and scalable MARL solution for the order-picking problem in realistic warehouses.

With the rise in high resolution remote sensing technologies there has been an explosion in the amount of data available for forest monitoring, and an accompanying growth in artificial intelligence applications to automatically derive forest properties of interest from these datasets. Many studies use their own data at small spatio-temporal scales, and demonstrate an application of an existing or adapted data science method for a particular task. This approach often involves intensive and time-consuming data collection and processing, but generates results restricted to specific ecosystems and sensor types. There is a lack of widespread acknowledgement of how the types and structures of data used affects performance and accuracy of analysis algorithms. To accelerate progress in the field more efficiently, benchmarking datasets upon which methods can be tested and compared are sorely needed. Here, we discuss how lack of standardisation impacts confidence in estimation of key forest properties, and how considerations of data collection need to be accounted for in assessing method performance. We present pragmatic requirements and considerations for the creation of rigorous, useful benchmarking datasets for forest monitoring applications, and discuss how tools from modern data science can improve use of existing data. We list a set of example large-scale datasets that could contribute to benchmarking, and present a vision for how community-driven, representative benchmarking initiatives could benefit the field.

In this work, we devise robust and efficient learning protocols for orchestrating a Federated Learning (FL) process for the Federated Tumor Segmentation Challenge (FeTS 2022). Enabling FL for FeTS setup is challenging mainly due to data heterogeneity among collaborators and communication cost of training. To tackle these challenges, we propose Robust Learning Protocol (RoLePRO) which is a combination of server-side adaptive optimisation (e.g., server-side Adam) and judicious parameter (weights) aggregation schemes (e.g., adaptive weighted aggregation). RoLePRO takes a two-phase approach, where the first phase consists of vanilla Federated Averaging, while the second phase consists of a judicious aggregation scheme that uses a sophisticated reweighting, all in the presence of an adaptive optimisation algorithm at the server. We draw insights from extensive experimentation to tune learning rates for the two phases.