通过时间（BPTT）的反向传播是训练复发性神经网络（RNN）的事实上的标准，但它是非毒性和非局部性的。实时复发性学习是一种因果替代方法，但效率很低。最近，E-Prop被提出为这些算法的因果，局部和有效的实用替代方法，通过从根本上修剪随时间携带的经常性依赖性来提供确切梯度的近似值。在这里，我们使用详细的符号从BPTT得出RTRL，从而为它们的连接方式带来了直觉和澄清。此外，我们在图片中内部构图E-Prop，使其近似。最后，我们得出了一种特殊案例的算法系列。

由于神经网络在关键领域起着越来越重要的作用，因此解释网络预测已成为关键研究主题。反事实解释可以帮助理解为什么分类器模型决定特定类分配的原因，此外，还必须如何修改各自的输入样本，以使类预测发生变化。先前的方法主要关注图像和表格数据。在这项工作中，我们提出了Sparce，这是一种生成对抗网络（GAN）体系结构，为多元时间序列生成稀疏的反事实解释。我们的方法提供了一个自定义的稀疏层，并根据相似性，稀疏性和轨迹的平滑性来规范反事实损失函数。我们评估了现实世界人类运动数据集的方法以及合成时间序列的可解释性基准。尽管我们比其他方法进行了明显的稀疏修改，但我们在所有指标上实现了可比或更好的性能。此外，我们证明我们的方法主要会修改显着的时间步骤和功能，从而使非征收输入未被触及。

灵活的目标指导行为是人类生活的一个基本方面。基于自由能最小化原理，主动推断理论从计算神经科学的角度正式产生了这种行为。基于该理论，我们介绍了一个输出型，时间预测的，模块化的人工神经网络体系结构，该建筑处理感觉运动信息，渗透到世界上与行为相关的方面，并引起高度灵活的，目标定向的行为。我们表明，我们的建筑经过端对端训练，以最大程度地减少自由能的近似值，它会发展出可以将其解释为负担能力地图的潜在状态。也就是说，新兴的潜在状态表明哪种行动导致哪些效果取决于局部环境。结合主动推断，我们表明可以调用灵活的目标指导行为，并结合新兴的负担能力图。结果，我们的模拟代理会在连续的空间中灵活地转向，避免与障碍物发生碰撞，并且更喜欢高确定性地导致目标的途径。此外，我们表明，学识渊博的代理非常适合跨环境的零拍概括：在训练少数固定环境中的代理商在具有障碍和其他影响其行为的固定环境中，它在程序生成的环境中表现出色，其中包含不同量的环境不同位置的各种尺寸的障碍和地形。

我们介绍了一种用于学习时空平流扩散过程的组成物理学意识的神经网络（FINN）。 FINN实现了一种新的方式，通过以组成方式模拟部分微分方程（PDE）的成分来实现与数值模拟的物理和结构知识结合人工神经网络的学习能力。导致单维和二维PDE（汉堡，扩散，扩散反应，Allen-Cahn）展示了FinN的卓越的建模精度和超出初始和边界条件的优异分配概率。只有十分之一的参数数量平均，Finn在所有情况下占纯机学习和其他最先进的物理知识模型 - 通常甚至通过多个数量级。此外，在扩散吸附场景中近似稀疏的实际数据时，Finn优于校准的物理模型，通过揭示观察过程的未知延迟因子来确认其泛化能力并显示出说明潜力。

We present a dynamic path planning algorithm to navigate an amphibious rotor craft through a concave time-invariant obstacle field while attempting to minimize energy usage. We create a nonlinear quaternion state model that represents the rotor craft dynamics above and below the water. The 6 degree of freedom dynamics used within a layered architecture to generate motion paths for the vehicle to follow and the required control inputs. The rotor craft has a 3 dimensional map of its surroundings that is updated via limited range onboard sensor readings within the current medium (air or water). Path planning is done via PRM and D* Lite.

Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains.

The analysis of network structure is essential to many scientific areas, ranging from biology to sociology. As the computational task of clustering these networks into partitions, i.e., solving the community detection problem, is generally NP-hard, heuristic solutions are indispensable. The exploration of expedient heuristics has led to the development of particularly promising approaches in the emerging technology of quantum computing. Motivated by the substantial hardware demands for all established quantum community detection approaches, we introduce a novel QUBO based approach that only needs number-of-nodes many qubits and is represented by a QUBO-matrix as sparse as the input graph's adjacency matrix. The substantial improvement on the sparsity of the QUBO-matrix, which is typically very dense in related work, is achieved through the novel concept of separation-nodes. Instead of assigning every node to a community directly, this approach relies on the identification of a separation-node set, which -- upon its removal from the graph -- yields a set of connected components, representing the core components of the communities. Employing a greedy heuristic to assign the nodes from the separation-node sets to the identified community cores, subsequent experimental results yield a proof of concept. This work hence displays a promising approach to NISQ ready quantum community detection, catalyzing the application of quantum computers for the network structure analysis of large scale, real world problem instances.

The following article presents a memetic algorithm with applying deep reinforcement learning (DRL) for solving practically oriented dual resource constrained flexible job shop scheduling problems (DRC-FJSSP). In recent years, there has been extensive research on DRL techniques, but without considering realistic, flexible and human-centered shopfloors. A research gap can be identified in the context of make-to-order oriented discontinuous manufacturing as it is often represented in medium-size companies with high service levels. From practical industry projects in this domain, we recognize requirements to depict flexible machines, human workers and capabilities, setup and processing operations, material arrival times, complex job paths with parallel tasks for bill of material (BOM) manufacturing, sequence-depended setup times and (partially) automated tasks. On the other hand, intensive research has been done on metaheuristics in the context of DRC-FJSSP. However, there is a lack of suitable and generic scheduling methods that can be holistically applied in sociotechnical production and assembly processes. In this paper, we first formulate an extended DRC-FJSSP induced by the practical requirements mentioned. Then we present our proposed hybrid framework with parallel computing for multicriteria optimization. Through numerical experiments with real-world data, we confirm that the framework generates feasible schedules efficiently and reliably. Utilizing DRL instead of random operations leads to better results and outperforms traditional approaches.

The acquisition of high-quality human annotations through crowdsourcing platforms like Amazon Mechanical Turk (MTurk) is more challenging than expected. The annotation quality might be affected by various aspects like annotation instructions, Human Intelligence Task (HIT) design, and wages paid to annotators, etc. To avoid potentially low-quality annotations which could mislead the evaluation of automatic summarization system outputs, we investigate the recruitment of high-quality MTurk workers via a three-step qualification pipeline. We show that we can successfully filter out bad workers before they carry out the evaluations and obtain high-quality annotations while optimizing the use of resources. This paper can serve as basis for the recruitment of qualified annotators in other challenging annotation tasks.

We present NusaCrowd, a collaborative initiative to collect and unite existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have has brought together 137 datasets and 117 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their effectiveness has been demonstrated in multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and its local languages. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and its local languages. Our work is intended to help advance natural language processing research in under-represented languages.