由于极大数量的参数和评估标准和再现性，机器学习长期以来被视为黑盒子，用于预测燃烧化学动力学和缺乏评估标准和再现性。目前的工作旨在了解关于深度神经网络（DNN）方法的两个基本问题：DNN需要的数据以及DNN方法的一般数据。采样和预处理确定DNN训练数据集，进一步影响DNN预测能力。目前的工作建议使用Box-Cox转换（BCT）来预处理燃烧数据。此外，这项工作比较了在没有预处理的情况下进行了不同的采样方法，包括蒙特卡罗方法，歧管采样，生成神经网络方法（Cycle-GaN）和新提出的多尺度采样。我们的研究结果表明，通过歧管数据训练的DNN可以以有限的配置捕获化学动力学，但不能对扰动牢固，这对于与流场联系的DNN是不可避免的。蒙特卡罗和循环甘套采样可以覆盖更宽的相位空间，但不能捕获小规模的中间物种，产生差的预测结果。基于没有特定火焰仿真数据的多尺度方法的三层DNN，允许在各种场景中预测化学动力学并在时间的演变期间保持稳定。该单个DNN易于用几个CFD代码实现并在各种燃烧器中验证，包括（1）。零维自动化，（2）。一维自由传播火焰，（3）。具有三重火焰结构的二维喷射火焰，和（4）。三维湍流升降火焰。结果证明了预先训练的DNN的令人满意的准确性和泛化能力。 DNN和示例代码的FORTRAN和PYTHON版本在补充中附加了再现性。

We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.

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.

Remaining Useful Life (RUL) estimation plays a critical role in Prognostics and Health Management (PHM). Traditional machine health maintenance systems are often costly, requiring sufficient prior expertise, and are difficult to fit into highly complex and changing industrial scenarios. With the widespread deployment of sensors on industrial equipment, building the Industrial Internet of Things (IIoT) to interconnect these devices has become an inexorable trend in the development of the digital factory. Using the device's real-time operational data collected by IIoT to get the estimated RUL through the RUL prediction algorithm, the PHM system can develop proactive maintenance measures for the device, thus, reducing maintenance costs and decreasing failure times during operation. This paper carries out research into the remaining useful life prediction model for multi-sensor devices in the IIoT scenario. We investigated the mainstream RUL prediction models and summarized the basic steps of RUL prediction modeling in this scenario. On this basis, a data-driven approach for RUL estimation is proposed in this paper. It employs a Multi-Head Attention Mechanism to fuse the multi-dimensional time-series data output from multiple sensors, in which the attention on features is used to capture the interactions between features and attention on sequences is used to learn the weights of time steps. Then, the Long Short-Term Memory Network is applied to learn the features of time series. We evaluate the proposed model on two benchmark datasets (C-MAPSS and PHM08), and the results demonstrate that it outperforms the state-of-art models. Moreover, through the interpretability of the multi-head attention mechanism, the proposed model can provide a preliminary explanation of engine degradation. Therefore, this approach is promising for predictive maintenance in IIoT scenarios.

In this paper, we propose a novel variable-length estimation approach for shape sensing of extensible soft robots utilizing fiber Bragg gratings (FBGs). Shape reconstruction from FBG sensors has been increasingly developed for soft robots, while the narrow stretching range of FBG fiber makes it difficult to acquire accurate sensing results for extensible robots. Towards this limitation, we newly introduce an FBG-based length sensor by leveraging a rigid curved channel, through which FBGs are allowed to slide within the robot following its body extension/compression, hence we can search and match the FBGs with specific constant curvature in the fiber to determine the effective length. From the fusion with the above measurements, a model-free filtering technique is accordingly presented for simultaneous calibration of a variable-length model and temporally continuous length estimation of the robot, enabling its accurate shape sensing using solely FBGs. The performances of the proposed method have been experimentally evaluated on an extensible soft robot equipped with an FBG fiber in both free and unstructured environments. The results concerning dynamic accuracy and robustness of length estimation and shape sensing demonstrate the effectiveness of our approach.

Performing 3D dense captioning and visual grounding requires a common and shared understanding of the underlying multimodal relationships. However, despite some previous attempts on connecting these two related tasks with highly task-specific neural modules, it remains understudied how to explicitly depict their shared nature to learn them simultaneously. In this work, we propose UniT3D, a simple yet effective fully unified transformer-based architecture for jointly solving 3D visual grounding and dense captioning. UniT3D enables learning a strong multimodal representation across the two tasks through a supervised joint pre-training scheme with bidirectional and seq-to-seq objectives. With a generic architecture design, UniT3D allows expanding the pre-training scope to more various training sources such as the synthesized data from 2D prior knowledge to benefit 3D vision-language tasks. Extensive experiments and analysis demonstrate that UniT3D obtains significant gains for 3D dense captioning and visual grounding.

Recognizing useful named entities plays a vital role in medical information processing, which helps drive the development of medical area research. Deep learning methods have achieved good results in medical named entity recognition (NER). However, we find that existing methods face great challenges when dealing with the nested named entities. In this work, we propose a novel method, referred to as ASAC, to solve the dilemma caused by the nested phenomenon, in which the core idea is to model the dependency between different categories of entity recognition. The proposed method contains two key modules: the adaptive shared (AS) part and the attentive conditional random field (ACRF) module. The former part automatically assigns adaptive weights across each task to achieve optimal recognition accuracy in the multi-layer network. The latter module employs the attention operation to model the dependency between different entities. In this way, our model could learn better entity representations by capturing the implicit distinctions and relationships between different categories of entities. Extensive experiments on public datasets verify the effectiveness of our method. Besides, we also perform ablation analyses to deeply understand our methods.

We present a retrospective on the state of Embodied AI research. Our analysis focuses on 13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are grouped into three themes: (1) visual navigation, (2) rearrangement, and (3) embodied vision-and-language. We discuss the dominant datasets within each theme, evaluation metrics for the challenges, and the performance of state-of-the-art models. We highlight commonalities between top approaches to the challenges and identify potential future directions for Embodied AI research.

给定数千种同样准确的机器学习（ML）模型，用户如何在其中选择？最近的ML技术使领域专家和数据科学家能够为稀疏决策树生成完整的Rashomon设置，这是一套几乎最理想的可解释的ML模型。为了帮助ML从业者识别具有此Rashomon集合中理想属性的模型，我们开发了Timbertrek，这是第一个交互式可视化系统，该系统总结了数千个稀疏决策树的规模。两种用法方案突出了Timbertrek如何使用户能够轻松探索，比较和策划与域知识和价值观保持一致的模型。我们的开源工具直接在用户的计算笔记本和Web浏览器中运行，从而降低了创建更负责任的ML模型的障碍。Timbertrek可在以下公共演示链接中获得：https：//poloclub.github.io/timbertrek。

在任何给定的机器学习问题中，可能有许多模型可以很好地解释数据。但是，大多数学习算法仅返回这些模型中的一种，使从业者没有实用的方法来探索替代模型，这些模型可能具有超出损失函数中可以表达的内容的理想属性。 Rashomon集是所有这些几乎最佳模型的集合。 Rashomon集可能非常复杂，尤其是对于高度非线性功能类，允许复杂的交互项，例如决策树。我们提供了第一种完全列举稀疏决策树的Rashomon设置的技术；实际上，我们的工作提供了针对高度非线性离散功能类别的非平凡问题的所有Rashomon设置的首次列举。这使用户可以在所有近似同样好的模型中对模型选择的前所未有的控制水平。我们在专门的数据结构中表示Rashomon集，该数据结构支持有效的查询和采样。我们显示了Rashomon集的三个应用：1）它可用于研究一组几乎最佳树的重要性（与一棵树相对），2）Rashomon设置的精确度使Rashomon集可以枚举Rashomon集合。平衡的精度和F1得分，以及3）完整数据集的Rashomon集可以用于生产仅使用数据集的子集构建的Rashomon集。因此，我们能够检查新镜头问题的Rashomon集合，使用户能够选择模型，而不是受到仅产生单个模型的算法的摆布。