在我们不断变化的气候中，使用模型来评估天气和气候对社会和企业的后续后果的风险及其后续后果至关重要。这种模型的操作在历史上是定制的，并限制在特定的计算基础架构，驱动数据集和预定义的配置上。这些约束通过缩放模型运行并将模型掌握在感兴趣的用户手中。在这里，我们提出了一个基于云的模块化框架，用于部署和操作地理空间模型，最初应用于气候影响。气候冲击建模框架（CIMF）可以以动态和灵活的方式部署模块化工作流程。用户可以以简化的方式指定工作流程组件，然后可以轻松地将这些组件组织成不同的配置，以以不同的方式和不同的尺度评估风险。这还可以使不同的模型（物理模拟或机器学习模型）和工作流程连接以产生合并的风险评估。洪水建模被用作端到端的示例，以证明CIMF的操作。

This paper utilizes an anomaly detection algorithm to check if underwater gliders are operating normally in the unknown ocean environment. Glider pilots can be warned of the detected glider anomaly in real time, thus taking over the glider appropriately and avoiding further damage to the glider. The adopted algorithm is validated by two valuable sets of data in real glider deployments, the University of South Florida (USF) glider Stella and the Skidaway Institute of Oceanography (SkIO) glider Angus.

Despite a sea of interpretability methods that can produce plausible explanations, the field has also empirically seen many failure cases of such methods. In light of these results, it remains unclear for practitioners how to use these methods and choose between them in a principled way. In this paper, we show that for even moderately rich model classes (easily satisfied by neural networks), any feature attribution method that is complete and linear--for example, Integrated Gradients and SHAP--can provably fail to improve on random guessing for inferring model behaviour. Our results apply to common end-tasks such as identifying local model behaviour, spurious feature identification, and algorithmic recourse. One takeaway from our work is the importance of concretely defining end-tasks. In particular, we show that once such an end-task is defined, a simple and direct approach of repeated model evaluations can outperform many other complex feature attribution methods.

We seek methods to model, control, and analyze robot teams performing environmental monitoring tasks. During environmental monitoring, the goal is to have teams of robots collect various data throughout a fixed region for extended periods of time. Standard bottom-up task assignment methods do not scale as the number of robots and task locations increases and require computationally expensive replanning. Alternatively, top-down methods have been used to combat computational complexity, but most have been limited to the analysis of methods which focus on transition times between tasks. In this work, we study a class of nonlinear macroscopic models which we use to control a time-varying distribution of robots performing different tasks throughout an environment. Our proposed ensemble model and control maintains desired time-varying populations of robots by leveraging naturally occurring interactions between robots performing tasks. We validate our approach at multiple fidelity levels including experimental results, suggesting the effectiveness of our approach to perform environmental monitoring.

Any strategy used to distribute a robot ensemble over a set of sequential tasks is subject to inaccuracy due to robot-level uncertainties and environmental influences on the robots' behavior. We approach the problem of inaccuracy during task allocation by modeling and controlling the overall ensemble behavior. Our model represents the allocation problem as a stochastic jump process and we regulate the mean and variance of such a process. The main contributions of this paper are: Establishing a structure for the transition rates of the equivalent stochastic jump process and formally showing that this approach leads to decoupled parameters that allow us to adjust the first- and second-order moments of the ensemble distribution over tasks, which gives the flexibility to decrease the variance in the desired final distribution. This allows us to directly shape the impact of uncertainties on the group allocation over tasks. We introduce a detailed procedure to design the gains to achieve the desired mean and show how the additional parameters impact the covariance matrix, which is directly associated with the degree of task allocation precision. Our simulation and experimental results illustrate the successful control of several robot ensembles during task allocation.

平行操纵器的配置歧管比串行操纵器表现出更多的非线性。从定性上讲，它们可以看到额外的褶皱。通过将这种歧管投射到工程相关性的空间上，例如输出工作区或输入执行器空间，这些折叠式的边缘呈现出表现非滑动行为的边缘。例如，在五杆链接的全局工作空间边界内显示了几个局部工作空间边界，这些边界仅限于该机制的某些输出模式。当专门研究这些投影而不是配置歧管本身时，这种边界的存在在输入和输出投影中都表现出来。特别是，非对称平行操纵器的设计已被其输入和输出空间中的外来投影所困扰。在本文中，我们用半径图表示配置空间，然后通过使用同型延续来量化传输质量来解决每个边缘。然后，我们采用图路径计划器来近似于避免传输质量区域的配置点之间的大地测量。我们的方法会自动生成能够在非邻居输出模式之间过渡的路径，该运动涉及示波多个工作空间边界（局部，全局或两者）。我们将技术应用于两个非对称五杆示例，这些示例表明如何通过切换输出模式来选择工作空间的传输属性和其他特征。

本文研究了“探索性”机器学习分类问题的置信后的事后校准。这些问题的困难源于持续的愿望，即在策划数据集时具有足够的例子来推广哪些类别的界限以及对这些类别的有效性的混乱。我们认为，对于此类问题，必须使用“单一的所有”方法（顶级标签校准），而不是文献中其他地方提倡的“校准 - 满足 - 响应 - 摩托克质”方法。我们介绍并测试了四种旨在处理特定置信度估计的特质的新算法。这些方法中的主要主要是将内核密度比用于置信度校准，包括用于选择带宽的新颖的防弹算法。我们测试了我们的主张，并探讨了生物信息学应用程序（Phanns）1以及经典的MNIST基准2。最后，我们的分析认为，事后校准应始终执行，应仅基于测试数据集，并且应在视觉上进行理智检查。

语音助手等对话用户界面非常受欢迎。然而，它们被设计为默认情况下是单语的，缺乏对双语对话体验的支持或敏感性。在此挑衅论文中，我们强调了双语用户VA互动中面临的语言生产挑战。我们认为，通过促进双语互动中看到的现象，例如代码转换，我们可以为双语用户提供更具包容性和改进的用户体验。我们还通过支持多种语言识别，并对语音输出中代码转换的偏好敏感，探索可以实现这一目标的方法。

我们提出了一种从人类设计的家具布局数据中生成室内家具的布置的方法。我们的方法创建了针对指定多样性的安排，例如房间中所有家具的总价格以及放置的碎片数量。为了产生逼真的家具布置，我们在人类设计的布局上训练生成的对抗网络（GAN）。为了针对安排中的特定多样性，我们通过质量多样性算法优化GAN的潜在空间，以生成多样化的安排集合。实验表明，我们的方法发现了一系列与人类设计的布局相似的布置，但价格和家具的数量也有所不同。

已知新生儿皮质表面受早产的影响，随后对皮质组织的变化与较差的神经发育结局有关。深层生成模型有可能导致临床上可解释的疾病模型，但是在皮质表面开发这些模型是具有挑战性的，因为已建立的学习卷积过滤器的技术不适当地不适合非弹性拓扑。为了缩小这一差距，我们使用混合模型CNN（MONET）实现了基于表面的自行车，以在皮质成熟度不同阶段之间翻译球形新生儿皮质表面特征（曲率和T1W/T2W皮质髓磷脂）。结果表明，我们的方法能够可靠地预测妊娠后期皮质组织单个模式的变化，并通过与纵向数据进行比较验证。并通过与训练有素的术语/早产子进行比较来验证早产和妊娠期（> 37周妊娠）之间的外观。皮质成熟的模拟差异与文献中的观察结果一致。