阿拉伯联合酋长国阿布扎比技术创新研究所最近完成了一辆新的无人面车辆的生产和测试，称为Nukhada，专门用于自主调查，检查和对水下行动的支持。此稿件描述了Nukhada USV的主要特征，以及在开发期间进行的一些试验。

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

This paper focuses on the uncertainty estimation of white matter lesions (WML) segmentation in magnetic resonance imaging (MRI). On one side, voxel-scale segmentation errors cause the erroneous delineation of the lesions; on the other side, lesion-scale detection errors lead to wrong lesion counts. Both of these factors are clinically relevant for the assessment of multiple sclerosis patients. This work aims to compare the ability of different voxel- and lesion- scale uncertainty measures to capture errors related to segmentation and lesion detection respectively. Our main contributions are (i) proposing new measures of lesion-scale uncertainty that do not utilise voxel-scale uncertainties; (ii) extending an error retention curves analysis framework for evaluation of lesion-scale uncertainty measures. Our results obtained on the multi-center testing set of 58 patients demonstrate that the proposed lesion-scale measures achieves the best performance among the analysed measures. All code implementations are provided at https://github.com/NataliiaMolch/MS_WML_uncs

分配转移或培训数据和部署数据之间的不匹配是在高风险工业应用中使用机器学习的重要障碍，例如自动驾驶和医学。这需要能够评估ML模型的推广以及其不确定性估计的质量。标准ML基线数据集不允许评估这些属性，因为培训，验证和测试数据通常相同分布。最近，已经出现了一系列专用基准测试，其中包括分布匹配和转移的数据。在这些基准测试中，数据集在任务的多样性以及其功能的数据模式方面脱颖而出。虽然大多数基准测试由2D图像分类任务主导，但Shifts包含表格天气预测，机器翻译和车辆运动预测任务。这使得可以评估模型的鲁棒性属性，并可以得出多种工业规模的任务以及通用或直接适用的特定任务结论。在本文中，我们扩展了偏移数据集，其中两个数据集来自具有高社会重要性的工业高风险应用程序。具体而言，我们考虑了3D磁共振脑图像中白质多发性硬化病变的分割任务以及海洋货物容器中功耗的估计。两项任务均具有无处不在的分配变化和由于错误成本而构成严格的安全要求。这些新数据集将使研究人员能够进一步探索新情况下的强大概括和不确定性估计。在这项工作中，我们提供了两个任务的数据集和基线结果的描述。

分布式机器学习的传统方法是将学习算法调整到网络中，例如减少更新以遏制开销。相反，基于智能边缘的网络使得可以遵循相反的方法，即定义围绕要执行的学习任务的逻辑网络拓扑，以达到所需的学习表现。在本文中，我们提出了一个系统模型，该模型在监督机器学习的背景下捕获了此类方面，考虑了学习节点（执行计算）和信息节点（提供数据）。然后，我们制定了选择（i）的问题，哪些学习和信息节点应配合以完成学习任务，以及（ii）执行的迭代次数，以最大程度地减少学习成本，同时满足目标预测错误和执行时间。在证明了上述问题的重要属性之后，我们设计了一种名为DoubleClemb的算法，该算法可以找到1+1/| i | -competive解决方案（具有i是一组信息节点），具有分立最差的复杂性。我们的绩效评估，利用现实世界的网络拓扑并考虑分类和回归任务，还表明，双重攀登与最佳，优于最先进的替代方案非常匹配。

在现代建筑基础设施中，由于低成本传感器的大数据可用性以及深度学习等先进的建模工具，因此促进自适应和无监督的数据驱动的健康监测系统的机会正在受欢迎。本文的主要目的是将深度神经网络与双向短期内存结合和涉及瞬时频率和光谱峰度的先进统计分析，以开发出来自声发射事件（裂缝）的拉伸，剪切和混合模式的准确分类工具。我们调查了有效的事件描述符，以捕获不同类型模式的独特特征。实验结果的测试证实，该方法在不同的破解事件中实现了有希望的分类，并可能影响结构健康监测（SHM）技术的未来设计。这种方法有效地对初始损害进行分类，以92％的精度进行分类，这是有利的计划维护。

Continual Learning (CL) is a field dedicated to devise algorithms able to achieve lifelong learning. Overcoming the knowledge disruption of previously acquired concepts, a drawback affecting deep learning models and that goes by the name of catastrophic forgetting, is a hard challenge. Currently, deep learning methods can attain impressive results when the data modeled does not undergo a considerable distributional shift in subsequent learning sessions, but whenever we expose such systems to this incremental setting, performance drop very quickly. Overcoming this limitation is fundamental as it would allow us to build truly intelligent systems showing stability and plasticity. Secondly, it would allow us to overcome the onerous limitation of retraining these architectures from scratch with the new updated data. In this thesis, we tackle the problem from multiple directions. In a first study, we show that in rehearsal-based techniques (systems that use memory buffer), the quantity of data stored in the rehearsal buffer is a more important factor over the quality of the data. Secondly, we propose one of the early works of incremental learning on ViTs architectures, comparing functional, weight and attention regularization approaches and propose effective novel a novel asymmetric loss. At the end we conclude with a study on pretraining and how it affects the performance in Continual Learning, raising some questions about the effective progression of the field. We then conclude with some future directions and closing remarks.

Computational units in artificial neural networks follow a simplified model of biological neurons. In the biological model, the output signal of a neuron runs down the axon, splits following the many branches at its end, and passes identically to all the downward neurons of the network. Each of the downward neurons will use their copy of this signal as one of many inputs dendrites, integrate them all and fire an output, if above some threshold. In the artificial neural network, this translates to the fact that the nonlinear filtering of the signal is performed in the upward neuron, meaning that in practice the same activation is shared between all the downward neurons that use that signal as their input. Dendrites thus play a passive role. We propose a slightly more complex model for the biological neuron, where dendrites play an active role: the activation in the output of the upward neuron becomes optional, and instead the signals going through each dendrite undergo independent nonlinear filterings, before the linear combination. We implement this new model into a ReLU computational unit and discuss its biological plausibility. We compare this new computational unit with the standard one and describe it from a geometrical point of view. We provide a Keras implementation of this unit into fully connected and convolutional layers and estimate their FLOPs and weights change. We then use these layers in ResNet architectures on CIFAR-10, CIFAR-100, Imagenette, and Imagewoof, obtaining performance improvements over standard ResNets up to 1.73%. Finally, we prove a universal representation theorem for continuous functions on compact sets and show that this new unit has more representational power than its standard counterpart.

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.