物理知识的神经网络（PINN）在解决涉及部分微分方程的前进和反问题方面表现出了希望。尽管最近在扩展PINN可以解决的问题类别方面取得了进展，但大多数现有用例都涉及简单的几何域。迄今为止，还没有明确的方法来告知Pinns有关解决问题的域拓扑。在这项工作中，我们提出了一种基于拉普拉斯 - 贝特拉米操作员的特征函数的PINN的新型位置编码机制。该技术允许为代表给定对象几何形状的神经网络创建一个输入空间。我们近似具有有限元素的偏微分方程的特征函数以及涉及的操作员。我们对所提出的方法进行了广泛的测试和比较，以复杂形状（例如线圈，散热器和兔子），具有不同的物理学，例如二基核方程和传热。我们还研究了我们方法对所使用的本征函数数量的敏感性，以及用于本征函数和基础操作员的离散化。我们的结果表明，在传统的PINN无法产生有意义的解决方案的情况下，与地面真相数据非常吻合。我们设想这种新技术将扩大PINNS的有效性，以更现实的应用。

我们提出了Fibernet，一种估计\ emph {in-Vivo}的方法，从电动激活的多个导管记录中，人心房的心脏纤维结构。心脏纤维在心脏的电力功能中起着核心作用，但是它们很难确定体内，因此在现有心脏模型中很少有特定于患者的特定于患者。 Fibernet通过解决物理知识的神经网络的逆问题来学习纤维布置。逆问题等于从一组稀疏激活图中识别心脏传播模型的传导速度张量。多个地图的使用可以同时识别传导速度张量（包括局部纤维角）的所有组件。我们对合成2-D和3-D示例，扩散张量纤维和患者特异性病例进行广泛测试。我们表明，在存在噪声的情况下，也足以准确捕获纤维。随着地图的较少，正则化的作用变得突出。此外，我们表明拟合的模型可以稳健地重现看不见的激活图。我们设想，纤维网将帮助创建特定于患者的个性化医学模型。完整代码可在http://github.com/fsahli/fibernet上找到。

心房颤动的计算模型已成功地用于预测最佳消融部位。评估消融模式的效果的关键步骤是从不同，潜在的随机的位置加速模型以确定是否可以在ATRIA中诱导心律失常。在这项工作中，我们建议使用黎曼歧管的多保真高斯过程分类，以有效地确定心律失常是诱导性诱导的区域内的区域。我们构建一个直接在心房表面上运行的概率分类器。我们利用较低的分辨率模型来探索心房表面，并与高分辨率模型无缝结合，以识别诱导区域。当用40个样本培训时，我们的多保真性分级器显示了比使用作为基线心房颤动模型的最近邻分类器的均衡精度，并且在心房颤动的情况下具有9％。我们希望这种新技术将允许更快，更精确地对心房颤动的计算模型临床应用。

Numerous works use word embedding-based metrics to quantify societal biases and stereotypes in texts. Recent studies have found that word embeddings can capture semantic similarity but may be affected by word frequency. In this work we study the effect of frequency when measuring female vs. male gender bias with word embedding-based bias quantification methods. We find that Skip-gram with negative sampling and GloVe tend to detect male bias in high frequency words, while GloVe tends to return female bias in low frequency words. We show these behaviors still exist when words are randomly shuffled. This proves that the frequency-based effect observed in unshuffled corpora stems from properties of the metric rather than from word associations. The effect is spurious and problematic since bias metrics should depend exclusively on word co-occurrences and not individual word frequencies. Finally, we compare these results with the ones obtained with an alternative metric based on Pointwise Mutual Information. We find that this metric does not show a clear dependence on frequency, even though it is slightly skewed towards male bias across all frequencies.

Reinforcement learning is a machine learning approach based on behavioral psychology. It is focused on learning agents that can acquire knowledge and learn to carry out new tasks by interacting with the environment. However, a problem occurs when reinforcement learning is used in critical contexts where the users of the system need to have more information and reliability for the actions executed by an agent. In this regard, explainable reinforcement learning seeks to provide to an agent in training with methods in order to explain its behavior in such a way that users with no experience in machine learning could understand the agent's behavior. One of these is the memory-based explainable reinforcement learning method that is used to compute probabilities of success for each state-action pair using an episodic memory. In this work, we propose to make use of the memory-based explainable reinforcement learning method in a hierarchical environment composed of sub-tasks that need to be first addressed to solve a more complex task. The end goal is to verify if it is possible to provide to the agent the ability to explain its actions in the global task as well as in the sub-tasks. The results obtained showed that it is possible to use the memory-based method in hierarchical environments with high-level tasks and compute the probabilities of success to be used as a basis for explaining the agent's behavior.

In recent years, unmanned aerial vehicle (UAV) related technology has expanded knowledge in the area, bringing to light new problems and challenges that require solutions. Furthermore, because the technology allows processes usually carried out by people to be automated, it is in great demand in industrial sectors. The automation of these vehicles has been addressed in the literature, applying different machine learning strategies. Reinforcement learning (RL) is an automation framework that is frequently used to train autonomous agents. RL is a machine learning paradigm wherein an agent interacts with an environment to solve a given task. However, learning autonomously can be time consuming, computationally expensive, and may not be practical in highly-complex scenarios. Interactive reinforcement learning allows an external trainer to provide advice to an agent while it is learning a task. In this study, we set out to teach an RL agent to control a drone using reward-shaping and policy-shaping techniques simultaneously. Two simulated scenarios were proposed for the training; one without obstacles and one with obstacles. We also studied the influence of each technique. The results show that an agent trained simultaneously with both techniques obtains a lower reward than an agent trained using only a policy-based approach. Nevertheless, the agent achieves lower execution times and less dispersion during training.

The widespread use of information and communication technology (ICT) over the course of the last decades has been a primary catalyst behind the digitalization of power systems. Meanwhile, as the utilization rate of the Internet of Things (IoT) continues to rise along with recent advancements in ICT, the need for secure and computationally efficient monitoring of critical infrastructures like the electrical grid and the agents that participate in it is growing. A cyber-physical system, such as the electrical grid, may experience anomalies for a number of different reasons. These may include physical defects, mistakes in measurement and communication, cyberattacks, and other similar occurrences. The goal of this study is to emphasize what the most common incidents are with power systems and to give an overview and classification of the most common ways to find problems, starting with the consumer/prosumer end working up to the primary power producers. In addition, this article aimed to discuss the methods and techniques, such as artificial intelligence (AI) that are used to identify anomalies in the power systems and markets.

Vision Transformers (ViTs) have become a dominant paradigm for visual representation learning with self-attention operators. Although these operators provide flexibility to the model with their adjustable attention kernels, they suffer from inherent limitations: (1) the attention kernel is not discriminative enough, resulting in high redundancy of the ViT layers, and (2) the complexity in computation and memory is quadratic in the sequence length. In this paper, we propose a novel attention operator, called lightweight structure-aware attention (LiSA), which has a better representation power with log-linear complexity. Our operator learns structural patterns by using a set of relative position embeddings (RPEs). To achieve log-linear complexity, the RPEs are approximated with fast Fourier transforms. Our experiments and ablation studies demonstrate that ViTs based on the proposed operator outperform self-attention and other existing operators, achieving state-of-the-art results on ImageNet, and competitive results on other visual understanding benchmarks such as COCO and Something-Something-V2. The source code of our approach will be released online.

Explainable artificial intelligence is proposed to provide explanations for reasoning performed by an Artificial Intelligence. There is no consensus on how to evaluate the quality of these explanations, since even the definition of explanation itself is not clear in the literature. In particular, for the widely known Local Linear Explanations, there are qualitative proposals for the evaluation of explanations, although they suffer from theoretical inconsistencies. The case of image is even more problematic, where a visual explanation seems to explain a decision while detecting edges is what it really does. There are a large number of metrics in the literature specialized in quantitatively measuring different qualitative aspects so we should be able to develop metrics capable of measuring in a robust and correct way the desirable aspects of the explanations. In this paper, we propose a procedure called REVEL to evaluate different aspects concerning the quality of explanations with a theoretically coherent development. This procedure has several advances in the state of the art: it standardizes the concepts of explanation and develops a series of metrics not only to be able to compare between them but also to obtain absolute information regarding the explanation itself. The experiments have been carried out on image four datasets as benchmark where we show REVEL's descriptive and analytical power.

In this work, we estimate the depth in which domestic waste are located in space from a mobile robot in outdoor scenarios. As we are doing this calculus on a broad range of space (0.3 - 6.0 m), we use RGB-D camera and LiDAR fusion. With this aim and range, we compare several methods such as average, nearest, median and center point, applied to those which are inside a reduced or non-reduced Bounding Box (BB). These BB are obtained from segmentation and detection methods which are representative of these techniques like Yolact, SOLO, You Only Look Once (YOLO)v5, YOLOv6 and YOLOv7. Results shown that, applying a detection method with the average technique and a reduction of BB of 40%, returns the same output as segmenting the object and applying the average method. Indeed, the detection method is faster and lighter in comparison with the segmentation one. The committed median error in the conducted experiments was 0.0298 ${\pm}$ 0.0544 m.