本文描述了可以用于控制上限假体的人机界面的新框架。目的是从嘈杂的表面肌电图信号中估算人类的电动机意图，并在存在以前看不见的扰动的情况下，对假体（即机器人）执行电动机意图。该框架包括每个自由度的肌肉弯曲模型，一种学习用于估计用户电机意图的模型的参数值的方法，以及使用从肌肉模型获得的刚度和阻尼值来适应的可变阻抗控制器假体运动轨迹和动力学。我们使用人机界面的模拟版本在强大的人类的背景下进行实验评估我们的框架，以执行主要在手腕中攻击一种自由的任务，并以统一力场的形式考虑外部扰动这将手腕从目标上推开。我们证明我们的框架提供了所需的自适应性能，并且与数据驱动的基线相比，可以大大提高性能。

最近被证明在强化学习（RL）设置中显示出的神经形式非常竞争，并且能够减轻基于梯度的方法的一些缺点。本文将专注于使用简单的遗传算法（GA）来应用神经发展，以找到产生最佳表现代理的神经网络的权重。此外，我们提出了两种新颖的修改，以提高与初始实施相比的数据效率和收敛速度。在Openai健身房提供的汇聚环境中评估了修改，并证明明显优于基线方法。

在这项工作中，我们提出了一种初步调查一种名为DYNA-T的新算法。在钢筋学习（RL）中，规划代理有自己的环境表示作为模型。要发现与环境互动的最佳政策，代理商会收集试验和错误时尚的经验。经验可用于学习更好的模型或直接改进价值函数和政策。通常是分离的，Dyna-Q是一种混合方法，在每次迭代，利用真实体验更新模型以及值函数，同时使用模拟数据从其模型中的应用程序进行行动。然而，规划过程是计算昂贵的并且强烈取决于国家行动空间的维度。我们建议在模拟体验上构建一个上置信树（UCT），并在在线学习过程中搜索要选择的最佳动作。我们证明了我们提出的方法对来自Open AI的三个测试平台环境的一系列初步测试的有效性。与Dyna-Q相比，Dyna-T通过选择更强大的动作选择策略来优于随机环境中的最先进的RL代理。

Graph-Slam是一种建立良好的算法，用于构建环境的拓扑图，同时尝试机器人的定位。它依赖于扫描匹配算法，以沿机器人的动作对齐嘈杂的观察，以计算当前机器人位置的估计。我们提出了一种基本上不同的方法来扫描匹配任务，以改善旋转转换位移的估计，从而提高完整的SLAM算法的性能。 Monte-Carlo方法用于生成两个扫描之间的几何位移的加权假设，然后我们纳入这些假设以计算导致最佳对准的位移。为了应对旋转转换的集群化，我们提出了一种新的聚类方法，通过将旋转翻译组件的内核密度分解内核密度来强大地扩展高斯平均转移到取向。我们在使用合成数据和英特尔研究实验室的基准数据集中展示了我们方法在广泛的实验中的有效性。结果证实，我们的方法在匹配的准确性和运行时计算方面具有卓越的性能，而不是基于最先进的基于迭代点的扫描匹配算法。

这项工作审查了水下环境中对象检测问题。我们在应用于这一具有挑战性环境时分析和量化计算机视觉社区中传统最新（SOTA）算法的缺点，以及为未来的研究努力提供见解和一般指导。首先，当对象检测器需要应用于可以应用于不同的特征分布的环境时，我们评估了对象检测器的预先估计是有益的。然后，在精度，联合（iou），每秒浮动操作（拖波）和推理时间的准确性方面，我们是否研究了两级检测器是否能够更好地产生更好的性能。最后，我们将每个模型的概括能力评估为较低质量的数据集，以模拟在实际情况下的性能，其中应该预期骚扰条件。我们的实验结果提供了证据，即水下对象检测需要搜索“ad-hoc”架构，而不是仅仅培训新数据上的SOTA架构，并且预先威胁并不有益。

There is a dramatic shortage of skilled labor for modern vineyards. The Vinum project is developing a mobile robotic solution to autonomously navigate through vineyards for winter grapevine pruning. This necessitates an autonomous navigation stack for the robot pruning a vineyard. The Vinum project is using the quadruped robot HyQReal. This paper introduces an architecture for a quadruped robot to autonomously move through a vineyard by identifying and approaching grapevines for pruning. The higher level control is a state machine switching between searching for destination positions, autonomously navigating towards those locations, and stopping for the robot to complete a task. The destination points are determined by identifying grapevine trunks using instance segmentation from a Mask Region-Based Convolutional Neural Network (Mask-RCNN). These detections are sent through a filter to avoid redundancy and remove noisy detections. The combination of these features is the basis for the proposed architecture.

Explainability is a vibrant research topic in the artificial intelligence community, with growing interest across methods and domains. Much has been written about the topic, yet explainability still lacks shared terminology and a framework capable of providing structural soundness to explanations. In our work, we address these issues by proposing a novel definition of explanation that is a synthesis of what can be found in the literature. We recognize that explanations are not atomic but the product of evidence stemming from the model and its input-output and the human interpretation of this evidence. Furthermore, we fit explanations into the properties of faithfulness (i.e., the explanation being a true description of the model's decision-making) and plausibility (i.e., how much the explanation looks convincing to the user). Using our proposed theoretical framework simplifies how these properties are ope rationalized and provide new insight into common explanation methods that we analyze as case studies.

In the era of digital healthcare, the huge volumes of textual information generated every day in hospitals constitute an essential but underused asset that could be exploited with task-specific, fine-tuned biomedical language representation models, improving patient care and management. For such specialized domains, previous research has shown that fine-tuning models stemming from broad-coverage checkpoints can largely benefit additional training rounds over large-scale in-domain resources. However, these resources are often unreachable for less-resourced languages like Italian, preventing local medical institutions to employ in-domain adaptation. In order to reduce this gap, our work investigates two accessible approaches to derive biomedical language models in languages other than English, taking Italian as a concrete use-case: one based on neural machine translation of English resources, favoring quantity over quality; the other based on a high-grade, narrow-scoped corpus natively written in Italian, thus preferring quality over quantity. Our study shows that data quantity is a harder constraint than data quality for biomedical adaptation, but the concatenation of high-quality data can improve model performance even when dealing with relatively size-limited corpora. The models published from our investigations have the potential to unlock important research opportunities for Italian hospitals and academia. Finally, the set of lessons learned from the study constitutes valuable insights towards a solution to build biomedical language models that are generalizable to other less-resourced languages and different domain settings.

Machine Learning algorithms have been extensively researched throughout the last decade, leading to unprecedented advances in a broad range of applications, such as image classification and reconstruction, object recognition, and text categorization. Nonetheless, most Machine Learning algorithms are trained via derivative-based optimizers, such as the Stochastic Gradient Descent, leading to possible local optimum entrapments and inhibiting them from achieving proper performances. A bio-inspired alternative to traditional optimization techniques, denoted as meta-heuristic, has received significant attention due to its simplicity and ability to avoid local optimums imprisonment. In this work, we propose to use meta-heuristic techniques to fine-tune pre-trained weights, exploring additional regions of the search space, and improving their effectiveness. The experimental evaluation comprises two classification tasks (image and text) and is assessed under four literature datasets. Experimental results show nature-inspired algorithms' capacity in exploring the neighborhood of pre-trained weights, achieving superior results than their counterpart pre-trained architectures. Additionally, a thorough analysis of distinct architectures, such as Multi-Layer Perceptron and Recurrent Neural Networks, attempts to visualize and provide more precise insights into the most critical weights to be fine-tuned in the learning process.

Efficient localization plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would contribute to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities for enhancing localization of UAVs and UGVs. In this paper, we review the radio frequency (RF) based approaches for localization. We review the RF features that can be utilized for localization and investigate the current methods suitable for Unmanned vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localization for both UAVs and UGVs is examined, and the envisioned 5G NR for localization enhancement, and the future research direction are explored.