拓扑方法可以提供一种提出新的指标和审查数据的方法的方法，否则可能会忽略这一点。在这项工作中，将引入一种量化数据形状的方法，通过称为拓扑数据分析的主题。拓扑数据分析（TDA）中的主要工具是持续的同源性。持续的同源性是一种在长度范围内量化数据形状的方法。在这项工作中简要讨论了所需的背景和计算持续同源性的方法。然后，来自拓扑数据分析的思想被用于非线性动力学，以通过计算其嵌入维度，然后评估其一般拓扑来分析一些常见的吸引子。还将提出一种方法，该方法使用拓扑数据分析来确定时间延迟嵌入的最佳延迟。 TDA还将应用于结构健康监测中的Z24桥案例研究，在该Z24桥梁案例研究中，它将用于仔细检查不同的数据分区，并根据收集数据的条件进行分类。来自拓扑数据分析的度量标准用于比较分区之间的数据。提出的结果表明，损害的存在比温度所产生的影响更大。

基于风险的积极学习是开发用于在线决策支持的统计分类器的方法。在这种方法中，根据初始数据点的完美信息的预期值来指导数据标签查询。对于SHM应用程序，根据维护决策过程评估信息的价值，并且数据标签查询对应于检查结构以确定其健康状态的检查。采样偏见是主动学习范式中的一个已知问题；当一个主动学习过程过多或未示例的特定区域时，就会发生这种情况，从而导致训练集不代表基础分布。这种偏见最终降低了决策绩效，因此导致不必要的费用。当前的论文概述了一种基于风险的主动学习方法，该方法利用了半监督的高斯混合模型。半监督的方法通过通过EM算法合并了未标记的数据来抵消采样偏差。该方法在SHM中发现的决策过程的数值示例中得到了证明。

固有频率的降低通常用作结构健康监测（SHM）目的的损坏指标。但是，操作和环境条件的波动，边界条件的变化以及名义相同结构之间的微小差异也会影响刚度，从而产生模仿或掩盖损坏的频率变化。这种可变性限制了SHM技术的实际实施和概括。这项工作的目的是研究正常变异的效果，并确定解释产生不确定性的方法。这项工作考虑了从四个健康的全尺度复合直升机叶片收集的振动数据。叶片名义上是相同的，但叶片是不同的，并且叶片之间的材料特性和几何形状略有差异，导致频率响应函数的显着差异，这是整个输入空间中四个独立的轨迹。在本文中，使用高斯工艺（OMGP）的重叠混合物来生成标签并量化直升机叶片的正常条件频率响应数据的不确定性。使用基于人群的方法，OMGP模型提供了称为形式的通用表示形式，以表征叶片的正常状况。然后将其他模拟数据与该形式进行比较，并使用边缘样式新颖性指数评估损伤。

获得对结构的操作和维护做出明智决定的能力，为实施结构健康监测（SHM）系统提供了动力。但是，与受监测系统的健康状态相对应的测量数据的描述性标签通常不可用。此问题限制了完全监督的机器学习范例的适用性，用于开发用于SHM系统决策支持的统计分类器。解决此问题的一种方法是基于风险的积极学习。在这种方法中，根据初始数据点的完美信息的预期值来指导数据标签查询。对于基于风险的SHM中的主动学习，可以根据维护决策过程评估信息的价值，并且数据标签查询对应于检查结构以确定其健康状态的检查。在SHM的背景下，仅考虑生成分类器的基于风险的主动学习。当前的论文展示了使用替代类型的分类器 - 判别模型的几个优点。在SHM决策支持的背景下，使用Z24桥数据集作为案例研究，歧视性分类器具有好处，包括改善对采样偏见的鲁棒性以及减少结构检查的支出。

在过去的三十年中，结构性健康监测（SHM）一直是一个活跃的研究领域，并且在此期间积累了许多关键进展，如文献所示。但是，由于损害状态数据，操作和环境波动，可重复性问题以及边界条件的变化，SHM仍然面临挑战。这些问题在被捕获的功能中是不一致的，并且可能会对实际实施产生巨大影响，但更重要的是对技术的概括。基于人群的SHM旨在通过使用从相似结构组收集的数据对缺失信息进行建模和传输信息来解决其中的一些问题。在这项工作中，从四个健康的，名义上相同的全尺度复合直升机叶片收集了振动数据。制造差异（例如，几何形状和/或材料属性的略有差异），在其结构动力学上显示为可变性，这对于基于振动数据的机器学习而对SHM来说可能非常有问题。这项工作旨在通过使用高斯过程的混合物来定义叶片的频率响应函数的通用模型来解决此变异性。

分类模型是物理资产管理技术的基本组成部分，如结构健康监测（SHM）系统和数字双胞胎。以前的工作介绍了\ Texit {基于风险的主动学习}，一种在线方法，用于开发考虑它们所应用的决策支持上下文的统计分类器。通过优先查询数据标签来考虑决策，根据\ Textit {完美信息的预期值}（EVPI）。虽然通过采用基于风险的主动学习方法获得了几种好处，但包括改进的决策性能，但算法遭受与引导查询过程的采样偏差有关的问题。这种采样偏差最终表现为在主动学习后的后期阶段的决策表现的下降，这又对应于丢失的资源/实用程序。目前的论文提出了两种新方法来抵消采样偏置的影响：\纺织{半监督学习}，以及\ extentit {鉴别的分类模型}。首先使用合成数据集进行这些方法，然后随后应用于实验案例研究，具体地，Z24桥数据集。半监督学习方法显示有变量性能;具有稳健性，对采样偏置依赖于对每个数据集选择模型所选择的生成分布的适用性。相反，判别分类器被证明对采样偏压的影响具有优异的鲁棒性。此外，发现在监控运动期间进行的检查数，因此可以通过仔细选择决策支持监测系统中使用的统计分类器的仔细选择来减少。

Merging satellite products and ground-based measurements is often required for obtaining precipitation datasets that simultaneously cover large regions with high density and are more accurate than pure satellite precipitation products. Machine and statistical learning regression algorithms are regularly utilized in this endeavour. At the same time, tree-based ensemble algorithms for regression are adopted in various fields for solving algorithmic problems with high accuracy and low computational cost. The latter can constitute a crucial factor for selecting algorithms for satellite precipitation product correction at the daily and finer time scales, where the size of the datasets is particularly large. Still, information on which tree-based ensemble algorithm to select in such a case for the contiguous United States (US) is missing from the literature. In this work, we conduct an extensive comparison between three tree-based ensemble algorithms, specifically random forests, gradient boosting machines (gbm) and extreme gradient boosting (XGBoost), in the context of interest. We use daily data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and the IMERG (Integrated Multi-satellitE Retrievals for GPM) gridded datasets. We also use earth-observed precipitation data from the Global Historical Climatology Network daily (GHCNd) database. The experiments refer to the entire contiguous US and additionally include the application of the linear regression algorithm for benchmarking purposes. The results suggest that XGBoost is the best-performing tree-based ensemble algorithm among those compared. They also suggest that IMERG is more useful than PERSIANN in the context investigated.

Networks have become indispensable and ubiquitous structures in many fields to model the interactions among different entities, such as friendship in social networks or protein interactions in biological graphs. A major challenge is to understand the structure and dynamics of these systems. Although networks evolve through time, most existing graph representation learning methods target only static networks. Whereas approaches have been developed for the modeling of dynamic networks, there is a lack of efficient continuous time dynamic graph representation learning methods that can provide accurate network characterization and visualization in low dimensions while explicitly accounting for prominent network characteristics such as homophily and transitivity. In this paper, we propose the Piecewise-Velocity Model (PiVeM) for the representation of continuous-time dynamic networks. It learns dynamic embeddings in which the temporal evolution of nodes is approximated by piecewise linear interpolations based on a latent distance model with piecewise constant node-specific velocities. The model allows for analytically tractable expressions of the associated Poisson process likelihood with scalable inference invariant to the number of events. We further impose a scalable Kronecker structured Gaussian Process prior to the dynamics accounting for community structure, temporal smoothness, and disentangled (uncorrelated) latent embedding dimensions optimally learned to characterize the network dynamics. We show that PiVeM can successfully represent network structure and dynamics in ultra-low two-dimensional spaces. It outperforms relevant state-of-art methods in downstream tasks such as link prediction. In summary, PiVeM enables easily interpretable dynamic network visualizations and characterizations that can further improve our understanding of the intrinsic dynamics of time-evolving networks.

Functionality and dialogue experience are two important factors of task-oriented dialogue systems. Conventional approaches with closed schema (e.g., conversational semantic parsing) often fail as both the functionality and dialogue experience are strongly constrained by the underlying schema. We introduce a new paradigm for task-oriented dialogue - Dialog2API - to greatly expand the functionality and provide seamless dialogue experience. The conversational model interacts with the environment by generating and executing programs triggering a set of pre-defined APIs. The model also manages the dialogue policy and interact with the user through generating appropriate natural language responses. By allowing generating free-form programs, Dialog2API supports composite goals by combining different APIs, whereas unrestricted program revision provides natural and robust dialogue experience. To facilitate Dialog2API, the core model is provided with API documents, an execution environment and optionally some example dialogues annotated with programs. We propose an approach tailored for the Dialog2API, where the dialogue states are represented by a stack of programs, with most recently mentioned program on the top of the stack. Dialog2API can work with many application scenarios such as software automation and customer service. In this paper, we construct a dataset for AWS S3 APIs and present evaluation results of in-context learning baselines.

Designing powerful adversarial attacks is of paramount importance for the evaluation of $\ell_p$-bounded adversarial defenses. Projected Gradient Descent (PGD) is one of the most effective and conceptually simple algorithms to generate such adversaries. The search space of PGD is dictated by the steepest ascent directions of an objective. Despite the plethora of objective function choices, there is no universally superior option and robustness overestimation may arise from ill-suited objective selection. Driven by this observation, we postulate that the combination of different objectives through a simple loss alternating scheme renders PGD more robust towards design choices. We experimentally verify this assertion on a synthetic-data example and by evaluating our proposed method across 25 different $\ell_{\infty}$-robust models and 3 datasets. The performance improvement is consistent, when compared to the single loss counterparts. In the CIFAR-10 dataset, our strongest adversarial attack outperforms all of the white-box components of AutoAttack (AA) ensemble, as well as the most powerful attacks existing on the literature, achieving state-of-the-art results in the computational budget of our study ($T=100$, no restarts).