为了提高模型透明度并允许用户形成训练有素的ML模型的心理模型，解释对AI和机器学习（ML）社区的兴趣越来越高。但是，解释可以超越这种方式通信作为引起用户控制的机制，因为一旦用户理解，他们就可以提供反馈。本文的目的是介绍研究概述，其中解释与交互式功能相结合，是从头开始学习新模型并编辑和调试现有模型的手段。为此，我们绘制了最先进的概念图，根据其预期目的以及它们如何构建相互作用，突出它们之间的相似性和差异来分组相关方法。我们还讨论开放研究问题并概述可能的方向，希望促使人们对这个开花研究主题进行进一步的研究。

自动劝说系统（APS）旨在说服用户通过进入交换参数和反向的对话来相信某事。为了最大化APS成功地说服用户的概率，它可以识别一个全局策略，该策略将允许它选择对话的每个阶段的最佳参数，无论用户提供的任何参数是什么参数。然而，在真实的应用程序中，例如医疗保健，对话结果的效用将是相同的，或者对AP和用户的完全相同。为了处理这种情况，在双党决策理论中采用了扩展表格的奥运会。这将打开我们在本文中地址的新问题：（1）我们如何使用机器学习（ML）方法来预测用于用户不同群体的实用功能？ （2）我们如何识别新用户，从我们学到的那些中获得最佳实用程序功能？在这种程度上，我们开发了两种ML方法，EAI和EDS，利用来自用户来预测其实用程序的信息。 EAI仅限于固定数量的信息，而EDS可以选择最能检测到用户的子步骤的信息。我们在模拟环境中评估EAI和EDS，并在有关健康饮食习惯的实际案例研究中。结果在这两种情况下都具有很大，但EDS在预测有用的实用功能方面更有效。

我们在标签噪声下解决序列学习，在可以查询人类主管的应用程序中，以抢购可疑的例子。现有方法存在缺陷，因为它们只重新标记向模型看起来“可疑”的传入示例。因此，那些误标定的例子，即躲避（或不经历）这种清洁步骤最终污染训练数据和模型，没有进一步清洁的机会。我们提出辛凯，一种新的方法，通过识别相互不相容的例子对新的和过去数据进行清洁。每当它检测到可疑示例时，CINCER在训练集中识别 - 根据模型的训练集中 - 与可疑示例最大限度地不兼容，并询问注释器以重新标记或两个示例，解决这些可能的不一致。选择反例是最大不兼容的，以便用作模型的怀疑和高度影响力的解释，从而在重新标记时尽可能多地传达任何信息。 CINCER通过利用基于FISHER信息矩阵（FIM）利用影响功能的高效和强大的近似来实现这一点。我们广泛的经验评估表明，通过清洁反击示例，阐明了模型背后的原因，有助于获得基本更好的数据和模型，特别是在与我们的FIM近似配对时。

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.