On general regular simplicial partitions $\mathcal{T}$ of bounded polytopal domains $\Omega \subset \mathbb{R}^d$, $d\in\{2,3\}$, we construct \emph{exact neural network (NN) emulations} of all lowest order finite element spaces in the discrete de Rham complex. These include the spaces of piecewise constant functions, continuous piecewise linear (CPwL) functions, the classical ``Raviart-Thomas element'', and the ``N\'{e}d\'{e}lec edge element''. For all but the CPwL case, our network architectures employ both ReLU (rectified linear unit) and BiSU (binary step unit) activations to capture discontinuities. In the important case of CPwL functions, we prove that it suffices to work with pure ReLU nets. Our construction and DNN architecture generalizes previous results in that no geometric restrictions on the regular simplicial partitions $\mathcal{T}$ of $\Omega$ are required for DNN emulation. In addition, for CPwL functions our DNN construction is valid in any dimension $d\geq 2$. Our ``FE-Nets'' are required in the variationally correct, structure-preserving approximation of boundary value problems of electromagnetism in nonconvex polyhedra $\Omega \subset \mathbb{R}^3$. They are thus an essential ingredient in the application of e.g., the methodology of ``physics-informed NNs'' or ``deep Ritz methods'' to electromagnetic field simulation via deep learning techniques. We indicate generalizations of our constructions to higher-order compatible spaces and other, non-compatible classes of discretizations, in particular the ``Crouzeix-Raviart'' elements and Hybridized, Higher Order (HHO) methods.

Autoregressive processes naturally arise in a large variety of real-world scenarios, including e.g., stock markets, sell forecasting, weather prediction, advertising, and pricing. When addressing a sequential decision-making problem in such a context, the temporal dependence between consecutive observations should be properly accounted for converge to the optimal decision policy. In this work, we propose a novel online learning setting, named Autoregressive Bandits (ARBs), in which the observed reward follows an autoregressive process of order $k$, whose parameters depend on the action the agent chooses, within a finite set of $n$ actions. Then, we devise an optimistic regret minimization algorithm AutoRegressive Upper Confidence Bounds (AR-UCB) that suffers regret of order $\widetilde{\mathcal{O}} \left( \frac{(k+1)^{3/2}\sqrt{nT}}{(1-\Gamma)^2} \right)$, being $T$ the optimization horizon and $\Gamma < 1$ an index of the stability of the system. Finally, we present a numerical validation in several synthetic and one real-world setting, in comparison with general and specific purpose bandit baselines showing the advantages of the proposed approach.

Behavioral Cloning (BC) aims at learning a policy that mimics the behavior demonstrated by an expert. The current theoretical understanding of BC is limited to the case of finite actions. In this paper, we study BC with the goal of providing theoretical guarantees on the performance of the imitator policy in the case of continuous actions. We start by deriving a novel bound on the performance gap based on Wasserstein distance, applicable for continuous-action experts, holding under the assumption that the value function is Lipschitz continuous. Since this latter condition is hardy fulfilled in practice, even for Lipschitz Markov Decision Processes and policies, we propose a relaxed setting, proving that value function is always Holder continuous. This result is of independent interest and allows obtaining in BC a general bound for the performance of the imitator policy. Finally, we analyze noise injection, a common practice in which the expert action is executed in the environment after the application of a noise kernel. We show that this practice allows deriving stronger performance guarantees, at the price of a bias due to the noise addition.

This paper is in the field of stochastic Multi-Armed Bandits (MABs), i.e., those sequential selection techniques able to learn online using only the feedback given by the chosen option (a.k.a. arm). We study a particular case of the rested and restless bandits in which the arms' expected payoff is monotonically non-decreasing. This characteristic allows designing specifically crafted algorithms that exploit the regularity of the payoffs to provide tight regret bounds. We design an algorithm for the rested case (R-ed-UCB) and one for the restless case (R-less-UCB), providing a regret bound depending on the properties of the instance and, under certain circumstances, of $\widetilde{\mathcal{O}}(T^{\frac{2}{3}})$. We empirically compare our algorithms with state-of-the-art methods for non-stationary MABs over several synthetically generated tasks and an online model selection problem for a real-world dataset. Finally, using synthetic and real-world data, we illustrate the effectiveness of the proposed approaches compared with state-of-the-art algorithms for the non-stationary bandits.

Robot assistants are emerging as high-tech solutions to support people in everyday life. Following and assisting the user in the domestic environment requires flexible mobility to safely move in cluttered spaces. We introduce a new approach to person following for assistance and monitoring. Our methodology exploits an omnidirectional robotic platform to detach the computation of linear and angular velocities and navigate within the domestic environment without losing track of the assisted person. While linear velocities are managed by a conventional Dynamic Window Approach (DWA) local planner, we trained a Deep Reinforcement Learning (DRL) agent to predict optimized angular velocities commands and maintain the orientation of the robot towards the user. We evaluate our navigation system on a real omnidirectional platform in various indoor scenarios, demonstrating the competitive advantage of our solution compared to a standard differential steering following.

我们假设现有的句子级机器翻译（MT）指标在人类参考包含歧义时会效率降低。为了验证这一假设，我们提出了一种非常简单的方法，用于扩展预审计的指标以在文档级别合并上下文。我们将我们的方法应用于三个流行的指标，即Bertscore，Prism和Comet，以及无参考的公制Comet-QE。我们使用提供的MQM注释评估WMT 2021指标共享任务的扩展指标。我们的结果表明，扩展指标的表现在约85％的测试条件下优于其句子级别的级别，而在排除低质量人类参考的结果时。此外，我们表明我们的文档级扩展大大提高了其对话语现象任务的准确性，从而优于专用基线高达6.1％。我们的实验结果支持我们的初始假设，并表明对指标的简单扩展使他们能够利用上下文来解决参考中的歧义。

结构分解方法，例如普遍的高树木分解，已成功用于解决约束满意度问题（CSP）。由于可以重复使用分解以求解具有相同约束范围的CSP，因此即使计算本身很难，将资源投资于计算良好的分解是有益的。不幸的是，即使示波器仅略有变化，当前方法也需要计算全新的分解。在本文中，我们迈出了解决CSP $ P $分解的问题的第一步，以使其成为由$ P $修改产生的新CSP $ P'$的有效分解。即使从理论上讲问题很难，我们还是提出并实施了一个有效更新GHD的框架。我们算法的实验评估强烈提出了实际适用性。

衡量心理工作量的主要原因是量化执行任务以预测人类绩效的认知成本。不幸的是，一种评估具有一般适用性的心理工作量的方法。这项研究提出了一种新型的自我监督方法，用于从脑电图数据中使用深度学习和持续的大脑率，即认知激活的指标，而无需人类声明性知识，从而从脑电图数据进行了精神负荷建模。该方法是可培训的卷积复发性神经网络，该神经网络可通过空间保留脑电图数据的光谱地形图训练，以适合大脑速率变量。发现证明了卷积层从脑电图数据中学习有意义的高级表示的能力，因为受试者内模型的测试平均绝对百分比误差平均为11％。尽管确实提高了其准确性，但增加了用于处理高级表示序列的长期期内存储层并不重要。发现指出，认知激活的高级高水平表示存在准稳定的块，因为它们可以通过卷积诱导，并且似乎随着时间的流逝而彼此依赖，从而直观地与大脑反应的非平稳性质相匹配。跨主体模型，从越来越多的参与者的数据诱导，因此包含更多的可变性，获得了与受试者内模型相似的精度。这突出了人们在人们之间诱发的高级表示的潜在普遍性，这表明存在非依赖于受试者的认知激活模式。这项研究通过为学者提供一种用于心理工作负载建模的新型计算方法来促进知识的体系，该方法旨在通常适用，不依赖于支持可复制性和可复制性的临时人工制作的模型。

知识图（kgs）已被证明是构建数据的可靠方法。他们可以提供有关文化遗产收藏的丰富情境信息。但是，文化遗产库库远非完整。他们通常会缺少重要的属性，例如地理位置，尤其是对于雕塑，移动或室内实体，例如绘画。在本文中，我们首先提出了一个框架，用于从各种数据源及其连接的多跳知识中汲取有关有形文化遗产实体的知识。其次，我们提出了一个多视图学习模型，用于估计给定的文化遗产实体之间的相对距离，该模型基于实体的地理和知识联系。

单像超分辨率可以在需要可靠的视觉流以监视任务，处理远程操作或研究相关视觉细节的环境中支持机器人任务。在这项工作中，我们为实时超级分辨率提出了一个有效的生成对抗网络模型。我们采用了原始SRGAN的量身定制体系结构和模型量化，以提高CPU和Edge TPU设备上的执行，最多达到200 fps的推断。我们通过将其知识提炼成较小版本的网络，进一步优化我们的模型，并与标准培训方法相比获得显着的改进。我们的实验表明，与较重的最新模型相比，我们的快速和轻量级模型可保持相当令人满意的图像质量。最后，我们对图像传输进行带宽降解的实验，以突出提出的移动机器人应用系统的优势。