沿规定的任务空间路径的冗余机器人的轨迹的离线最佳规划通常分为两个连续的过程：首先，任务空间路径倒置以获得一个联合空间路径，然后，后者通过时间定律进行参数化。如果两个过程分开，它们将无法优化相同的目标函数，最终提供了次优的结果。在本文中，提出了一种统一的方法，而动态编程是基础优化技术。它的灵活性允许安装任意约束和客观功能，从而为真实系统的最佳计划提供了一个通用框架。为了证明其适用于现实世界情景，该框架是实例化的，以进行时间优势。与数值求解器相比，所提出的方法提供了基础分辨率过程的可见性，从而超出了最佳轨迹的计算以外的进一步分析。该框架的有效性已在真正的7度自由串行链上证明。还讨论和解决了与实际控制器上执行最佳轨迹相关的问题。实验表明，所提出的框架能够有效利用运动学冗余，以优化计划级别定义的性能索引，并生成可行的轨迹，这些轨迹可以在真实硬件上执行，并具有令人满意的结果。

在这项工作中，我们考虑了需要通过电缆或机器人臂操纵/运输物体的移动机器人的问题。我们考虑一种操纵机器人的数量冗余的场景，即，可以通过机器人的不同配置获得所需的对象配置。这项工作的目的是表明，可以使用通信来实现机器人中的协同局部反馈控制器，以改善扰动抑制并降低对象中的结构应力。特别地，我们考虑采样测量并通过无线传输测量的现实场景，并且采样周期与系统动态时间常数相当。我们首先提出了一种运动模型，该模型与高增益控制下的整体系统动态一致，然后我们为不同规范下的配置误差提供了足够的指数稳定性和单调减少。最后，我们在完整的动态系统上测试所提出的控制器，显示出局部通信的益处。

在本文中，我们为采样通信场景中的一类多机器人系统提出了一种反向运动控制器。目标是使一组机器人执行轨迹跟踪{以协调的方式}当通信的采样时间是不可忽略的，破坏标准控制设计的理论收敛保证。鉴于配置空间中可行的期望轨迹，所提出的控制器从采样时间瞬间从系统接收测量，并计算由低级控制器跟踪的机器人的速度引用。我们提出了一个共同设计的反馈加上馈电控制器，具有可提供的稳定性和误差会聚保证，并进一步表明所获得的控制器是可分散的实现的可供选择。我们使用现实模拟器（飞行起重机）的电缆悬挂负荷的协同空中操纵方案中的数值模拟来测试所提出的控制策略。最后，我们将建议的分散控制器与集中式方法进行比较，可通过智能启发式调整反馈增益，并表明它实现了可比性。

Process monitoring and control are essential in modern industries for ensuring high quality standards and optimizing production performance. These technologies have a long history of application in production and have had numerous positive impacts, but also hold great potential when integrated with Industry 4.0 and advanced machine learning, particularly deep learning, solutions. However, in order to implement these solutions in production and enable widespread adoption, the scalability and transferability of deep learning methods have become a focus of research. While transfer learning has proven successful in many cases, particularly with computer vision and homogenous data inputs, it can be challenging to apply to heterogeneous data. Motivated by the need to transfer and standardize established processes to different, non-identical environments and by the challenge of adapting to heterogeneous data representations, this work introduces the Domain Adaptation Neural Network with Cyclic Supervision (DBACS) approach. DBACS addresses the issue of model generalization through domain adaptation, specifically for heterogeneous data, and enables the transfer and scalability of deep learning-based statistical control methods in a general manner. Additionally, the cyclic interactions between the different parts of the model enable DBACS to not only adapt to the domains, but also match them. To the best of our knowledge, DBACS is the first deep learning approach to combine adaptation and matching for heterogeneous data settings. For comparison, this work also includes subspace alignment and a multi-view learning that deals with heterogeneous representations by mapping data into correlated latent feature spaces. Finally, DBACS with its ability to adapt and match, is applied to a virtual metrology use case for an etching process run on different machine types in semiconductor manufacturing.

An Anomaly Detection (AD) System for Self-diagnosis has been developed for Multiphase Flow Meter (MPFM). The system relies on machine learning algorithms for time series forecasting, historical data have been used to train a model and to predict the behavior of a sensor and, thus, to detect anomalies.

Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing. A key issue is how to address the inherent non-linearity of classical deep learning, a problem in the quantum domain due to the fact that the composition of an arbitrary number of quantum gates, consisting of a series of sequential unitary transformations, is intrinsically linear. This problem has been variously approached in the literature, principally via the introduction of measurements between layers of unitary transformations. In this paper, we introduce the Quantum Path Kernel, a formulation of quantum machine learning capable of replicating those aspects of deep machine learning typically associated with superior generalization performance in the classical domain, specifically, hierarchical feature learning. Our approach generalizes the notion of Quantum Neural Tangent Kernel, which has been used to study the dynamics of classical and quantum machine learning models. The Quantum Path Kernel exploits the parameter trajectory, i.e. the curve delineated by model parameters as they evolve during training, enabling the representation of differential layer-wise convergence behaviors, or the formation of hierarchical parametric dependencies, in terms of their manifestation in the gradient space of the predictor function. We evaluate our approach with respect to variants of the classification of Gaussian XOR mixtures - an artificial but emblematic problem that intrinsically requires multilevel learning in order to achieve optimal class separation.

Aliasing is a highly important concept in signal processing, as careful consideration of resolution changes is essential in ensuring transmission and processing quality of audio, image, and video. Despite this, up until recently aliasing has received very little consideration in Deep Learning, with all common architectures carelessly sub-sampling without considering aliasing effects. In this work, we investigate the hypothesis that the existence of adversarial perturbations is due in part to aliasing in neural networks. Our ultimate goal is to increase robustness against adversarial attacks using explainable, non-trained, structural changes only, derived from aliasing first principles. Our contributions are the following. First, we establish a sufficient condition for no aliasing for general image transformations. Next, we study sources of aliasing in common neural network layers, and derive simple modifications from first principles to eliminate or reduce it. Lastly, our experimental results show a solid link between anti-aliasing and adversarial attacks. Simply reducing aliasing already results in more robust classifiers, and combining anti-aliasing with robust training out-performs solo robust training on $L_2$ attacks with none or minimal losses in performance on $L_{\infty}$ attacks.

The problem of generating an optimal coalition structure for a given coalition game of rational agents is to find a partition that maximizes their social welfare and is known to be NP-hard. This paper proposes GCS-Q, a novel quantum-supported solution for Induced Subgraph Games (ISGs) in coalition structure generation. GCS-Q starts by considering the grand coalition as initial coalition structure and proceeds by iteratively splitting the coalitions into two nonempty subsets to obtain a coalition structure with a higher coalition value. In particular, given an $n$-agent ISG, the GCS-Q solves the optimal split problem $\mathcal{O} (n)$ times using a quantum annealing device, exploring $\mathcal{O}(2^n)$ partitions at each step. We show that GCS-Q outperforms the currently best classical solvers with its runtime in the order of $n^2$ and an expected worst-case approximation ratio of $93\%$ on standard benchmark datasets.

Anomaly Detection is a relevant problem that arises in numerous real-world applications, especially when dealing with images. However, there has been little research for this task in the Continual Learning setting. In this work, we introduce a novel approach called SCALE (SCALing is Enough) to perform Compressed Replay in a framework for Anomaly Detection in Continual Learning setting. The proposed technique scales and compresses the original images using a Super Resolution model which, to the best of our knowledge, is studied for the first time in the Continual Learning setting. SCALE can achieve a high level of compression while maintaining a high level of image reconstruction quality. In conjunction with other Anomaly Detection approaches, it can achieve optimal results. To validate the proposed approach, we use a real-world dataset of images with pixel-based anomalies, with the scope to provide a reliable benchmark for Anomaly Detection in the context of Continual Learning, serving as a foundation for further advancements in the field.

Digital media have enabled the access to unprecedented literary knowledge. Authors, readers, and scholars are now able to discover and share an increasing amount of information about books and their authors. Notwithstanding, digital archives are still unbalanced: writers from non-Western countries are less represented, and such a condition leads to the perpetration of old forms of discrimination. In this paper, we present the Under-Represented Writers Knowledge Graph (URW-KG), a resource designed to explore and possibly amend this lack of representation by gathering and mapping information about works and authors from Wikidata and three other sources: Open Library, Goodreads, and Google Books. The experiments based on KG embeddings showed that the integrated information encoded in the graph allows scholars and users to be more easily exposed to non-Western literary works and authors with respect to Wikidata alone. This opens to the development of fairer and effective tools for author discovery and exploration.