我们提出了一个新的框架，以解决对风险敏感的增强学习（RL）问题，在该问题中，代理优化了时间一致的动态光谱风险度量。基于有条件诱因的概念，我们的方法构建（严格一致）评分函数在估计程序中用作惩罚者。我们的贡献是三重的：我们（i）设计了一种有效的方法来估计具有深层神经网络的动态频谱风险度量，（ii）证明，使用深层神经网络和任何任意精度，这些动态光谱风险度量可能近似于（iii）开发一种使用完整发作的风险敏感的参与者批评算法，不需要任何其他嵌套过渡。我们将概念上改进的增强学习算法与嵌套模拟方法进行了比较，并在两个设置中说明了其性能：统计套利和模拟和真实数据上的统计套利和投资组合分配。

我们开发了一种利用无模型增强学习（RL）解决时间一致风险敏感随机优化问题的方法。具体地，我们假设代理商使用动态凸面风险措施评估一系列随机变量的风险。我们采用时间一致的动态编程原则来确定特定策略的值，并开发策略渐变更新规则。我们进一步开发了一个使用神经网络的演员批评风格算法，以优化策略。最后，我们通过将其应用于统计套利交易和障碍避免机器人控制中的优化问题来证明我们的方法的性能和灵活性。

我们提出了一种强化学习（RL）方法，用于稳健优化风险感知性能标准。要允许代理表达各种风险奖励简档，我们使用Rank Inceredent预期实用程序（RDEU）评估策略的值。RDEU允许代理人寻求收益，同时保护自己免受下行风险。为了强调对模型不确定性的最佳政策，我们通过分布来评估一个政策，而是通过围绕Wassersein球中的最严重的可能分布来评估一项政策。因此，我们的问题制定可以被视为选择策略（外部问题）的演员/代理人，并且对手作用以恶化该策略的性能（内部问题）。我们为内部和外部问题制定明确的政策渐变公式，并在三种原型财务问题上显示出效力：强大的投资组合分配，优化基准和统计套利。

我们在连续时间内研究一般的熵正规的多变量LQG平均场比赛（MFGS），以k $ Distint的代理商。我们将动作的概念扩展到行动分发（探索性行为），并明确地导出了限制MFG中各个代理的最佳动作分布。我们证明，最佳的动作分布集产生了$ \ epsilon $ -Nash均衡，为有限群体熵定期的MFG。此外，我们将由此产生的解决方案与古典LQG MFG的结果进行比较，并建立其存在的等价。

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.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.