分享自治是指使自治工人能够与人类合作的方法，以提高人类性能。然而，除了提高性能之外，它通常也可能是有益的，代理同时考虑保留用户的经验或合作满意度。为了解决这一额外目标，我们通过约束自主代理的干预次数来研究改进用户体验的方法。我们提出了两种无模型的加强学习方法，可以考虑到干预措施的艰难和软限制。我们表明，我们的方法不仅表现出现有的基线，而且还消除了手动调整黑匣子超参数，以控制援助水平。我们还提供了对干预情景的深入分析，以进一步照亮系统理解。

快速准确地检测该疾病可以大大帮助减少任何国家医疗机构对任何大流行期间死亡率降低死亡率的压力。这项工作的目的是使用新型的机器学习框架创建多模式系统，该框架同时使用胸部X射线（CXR）图像和临床数据来预测COVID-19患者的严重程度。此外，该研究还提出了一种基于nom图的评分技术，用于预测高危患者死亡的可能性。这项研究使用了25种生物标志物和CXR图像，以预测意大利第一波Covid-19（3月至6月2020年3月至6月）在930名Covid-19患者中的风险。提出的多模式堆叠技术分别产生了89.03％，90.44％和89.03％的精度，灵敏度和F1分数，以识别低风险或高危患者。与CXR图像或临床数据相比，这种多模式方法可提高准确性6％。最后，使用多元逻辑回归的列线图评分系统 - 用于对第一阶段确定的高风险患者的死亡风险进行分层。使用随机森林特征选择模型将乳酸脱氢酶（LDH），O2百分比，白细胞（WBC）计数，年龄和C反应蛋白（CRP）鉴定为有用的预测指标。开发了五个预测因素参数和基于CXR图像的列函数评分，以量化死亡的概率并将其分为两个风险组：分别存活（<50％）和死亡（> = 50％）。多模式技术能够预测F1评分为92.88％的高危患者的死亡概率。开发和验证队列曲线下的面积分别为0.981和0.939。

心血管疾病是世界各地最常见的死亡原因。为了检测和治疗心脏相关的疾病，需要连续血压（BP）监测以及许多其他参数。为此目的开发了几种侵入性和非侵入性方法。用于持续监测BP的医院中使用的大多数现有方法是侵入性的。相反，基于袖带的BP监测方法，可以预测收缩压（SBP）和舒张压（DBP），不能用于连续监测。几项研究试图从非侵​​入性可收集信号（例如光学肌谱（PPG）和心电图（ECG））预测BP，其可用于连续监测。在这项研究中，我们探讨了自动化器在PPG和ECG信号中预测BP的适用性。在12,000岁的MIMIC-II数据集中进行了调查，发现了一个非常浅的一维AutoEncoder可以提取相关功能，以预测与最先进的SBP和DBP在非常大的数据集上的性能。从模拟-II数据集的一部分的独立测试分别为SBP和DBP提供了2.333和0.713的MAE。在40个主题的外部数据集上，模型在MIMIC-II数据集上培训，分别为SBP和DBP提供2.728和1.166的MAE。对于这种情况来说，结果达到了英国高血压协会（BHS）A级并超越了目前文学的研究。

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.

Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.

Pruning refers to the elimination of trivial weights from neural networks. The sub-networks within an overparameterized model produced after pruning are often called Lottery tickets. This research aims to generate winning lottery tickets from a set of lottery tickets that can achieve similar accuracy to the original unpruned network. We introduce a novel winning ticket called Cyclic Overlapping Lottery Ticket (COLT) by data splitting and cyclic retraining of the pruned network from scratch. We apply a cyclic pruning algorithm that keeps only the overlapping weights of different pruned models trained on different data segments. Our results demonstrate that COLT can achieve similar accuracies (obtained by the unpruned model) while maintaining high sparsities. We show that the accuracy of COLT is on par with the winning tickets of Lottery Ticket Hypothesis (LTH) and, at times, is better. Moreover, COLTs can be generated using fewer iterations than tickets generated by the popular Iterative Magnitude Pruning (IMP) method. In addition, we also notice COLTs generated on large datasets can be transferred to small ones without compromising performance, demonstrating its generalizing capability. We conduct all our experiments on Cifar-10, Cifar-100 & TinyImageNet datasets and report superior performance than the state-of-the-art methods.

We describe a Physics-Informed Neural Network (PINN) that simulates the flow induced by the astronomical tide in a synthetic port channel, with dimensions based on the Santos - S\~ao Vicente - Bertioga Estuarine System. PINN models aim to combine the knowledge of physical systems and data-driven machine learning models. This is done by training a neural network to minimize the residuals of the governing equations in sample points. In this work, our flow is governed by the Navier-Stokes equations with some approximations. There are two main novelties in this paper. First, we design our model to assume that the flow is periodic in time, which is not feasible in conventional simulation methods. Second, we evaluate the benefit of resampling the function evaluation points during training, which has a near zero computational cost and has been verified to improve the final model, especially for small batch sizes. Finally, we discuss some limitations of the approximations used in the Navier-Stokes equations regarding the modeling of turbulence and how it interacts with PINNs.

This paper presents a novel federated reinforcement learning (Fed-RL) methodology to enhance the cyber resiliency of networked microgrids. We formulate a resilient reinforcement learning (RL) training setup which (a) generates episodic trajectories injecting adversarial actions at primary control reference signals of the grid forming (GFM) inverters and (b) trains the RL agents (or controllers) to alleviate the impact of the injected adversaries. To circumvent data-sharing issues and concerns for proprietary privacy in multi-party-owned networked grids, we bring in the aspects of federated machine learning and propose a novel Fed-RL algorithm to train the RL agents. To this end, the conventional horizontal Fed-RL approaches using decoupled independent environments fail to capture the coupled dynamics in a networked microgrid, which leads us to propose a multi-agent vertically federated variation of actor-critic algorithms, namely federated soft actor-critic (FedSAC) algorithm. We created a customized simulation setup encapsulating microgrid dynamics in the GridLAB-D/HELICS co-simulation platform compatible with the OpenAI Gym interface for training RL agents. Finally, the proposed methodology is validated with numerical examples of modified IEEE 123-bus benchmark test systems consisting of three coupled microgrids.

Reinforcement learning (RL) has shown great promise with algorithms learning in environments with large state and action spaces purely from scalar reward signals. A crucial challenge for current deep RL algorithms is that they require a tremendous amount of environment interactions for learning. This can be infeasible in situations where such interactions are expensive; such as in robotics. Offline RL algorithms try to address this issue by bootstrapping the learning process from existing logged data without needing to interact with the environment from the very beginning. While online RL algorithms are typically evaluated as a function of the number of environment interactions, there exists no single established protocol for evaluating offline RL methods.In this paper, we propose a sequential approach to evaluate offline RL algorithms as a function of the training set size and thus by their data efficiency. Sequential evaluation provides valuable insights into the data efficiency of the learning process and the robustness of algorithms to distribution changes in the dataset while also harmonizing the visualization of the offline and online learning phases. Our approach is generally applicable and easy to implement. We compare several existing offline RL algorithms using this approach and present insights from a variety of tasks and offline datasets.

In manufacturing, the production is often done on out-of-the-shelf manufacturing lines, whose underlying scheduling heuristics are not known due to the intellectual property. We consider such a setting with a black-box job-shop system and an unknown scheduling heuristic that, for a given permutation of jobs, schedules the jobs for the black-box job-shop with the goal of minimizing the makespan. Here, the jobs need to enter the job-shop in the given order of the permutation, but may take different paths within the job shop, which depends on the black-box heuristic. The performance of the black-box heuristic depends on the order of the jobs, and the natural problem for the manufacturer is to find an optimum ordering of the jobs. Facing a real-world scenario as described above, we engineer the Monte-Carlo tree-search for finding a close-to-optimum ordering of jobs. To cope with a large solutions-space in planning scenarios, a hierarchical Monte-Carlo tree search (H-MCTS) is proposed based on abstraction of jobs. On synthetic and real-life problems, H-MCTS with integrated abstraction significantly outperforms pure heuristic-based techniques as well as other Monte-Carlo search variants. We furthermore show that, by modifying the evaluation metric in H-MCTS, it is possible to achieve other optimization objectives than what the scheduling heuristics are designed for -- e.g., minimizing the total completion time instead of the makespan. Our experimental observations have been also validated in real-life cases, and our H-MCTS approach has been implemented in a production plant's controller.