由于技术成本的降低和卫星发射的增加，卫星图像变得越来越流行和更容易获得。除了提供仁慈的目的外，还可以出于恶意原因（例如错误信息）使用卫星数据。事实上，可以依靠一般图像编辑工具来轻松操纵卫星图像。此外，随着深层神经网络（DNN）的激增，可以生成属于各种领域的现实合成图像，与合成生成的卫星图像的扩散有关的其他威胁正在出现。在本文中，我们回顾了关于卫星图像的产生和操纵的最新技术（SOTA）。特别是，我们既关注从头开始的合成卫星图像的产生，又要通过图像转移技术对卫星图像进行语义操纵，包括从一种类型的传感器到另一种传感器获得的图像的转换。我们还描述了迄今已研究的法医检测技术，以对合成图像伪造进行分类和检测。虽然我们主要集中在法医技术上明确定制的，该技术是针对AI生成的合成内容物的检测，但我们还审查了一些用于一般剪接检测的方法，这些方法原则上也可以用于发现AI操纵图像

合成孔径雷达（SAR）图像是各种任务的有价值资产。在过去的几年里，许多网站以易于管理产品的形式免费提供它们，倾向于在S​​AR领域的广泛扩散和研究工作。这些机会的缺点是，这些图像可能会被恶意用户暴露于伪造和操纵，提高对他们的诚信和可信度的新担忧。到目前为止，多媒体取证文献提出了各种技术来定位自然照片中的操纵，但从未调查过SAR图像的完整性评估。此任务构成了新的挑战，因为SAR图像是由处理链完全不同于自然照片的图像。这意味着对于自然图像开发的许多取证方法不保证成功。在本文中，我们研究了SAR图像拼接定位问题的问题。我们的目标是本地化已经复制和粘贴了从另一个图像复制和粘贴的幅度SAR图像的区域，可能正在进行该过程中的某种编辑。为此，我们利用卷积神经网络（CNN）来提取在分析的输入的处理迹线中突出的指纹突出显示。然后，我们检查该指纹以产生二进制篡改掩模，指示拼接攻击下的像素区域。结果表明，我们提出的方法，针对SAR信号的性质量身定制，提供比为自然图像开发的最先进的法医工具更好的表现。

综合产生的内容的广泛扩散是一种需要紧急对策的严重威胁。合成含量的产生不限于多媒体数据，如视频，照片或音频序列，但涵盖了可以包括生物图像的显着大面积，例如西幕和微观图像。在本文中，我们专注于检测综合生成的西幕图像。生物医学文献在很大程度上探讨了西部污染图像，已经表明了如何通过目视检查或标准取证检测器轻松地伪造这些图像。为了克服缺乏公开可用的数据集，我们创建了一个包含超过14k原始的西幕图像和18K合成的Western-Blot图像的新数据集，由三种不同的最先进的生成方法产生。然后，我们调查不同的策略来检测合成的Western印迹，探索二进制分类方法以及单级探测器。在这两种情况下，我们从不利用培训阶段的合成纤维图像。所达到的结果表明，即使在这些科学图像的合成版本未优化利用检测器，综合生成的西幕图像也可以具有良好的精度。

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.

The paper addresses the problem of time offset synchronization in the presence of temperature variations, which lead to a non-Gaussian environment. In this context, regular Kalman filtering reveals to be suboptimal. A functional optimization approach is developed in order to approximate optimal estimation of the clock offset between master and slave. A numerical approximation is provided to this aim, based on regular neural network training. Other heuristics are provided as well, based on spline regression. An extensive performance evaluation highlights the benefits of the proposed techniques, which can be easily generalized to several clock synchronization protocols and operating environments.