We designed and constructed an A-sized base autonomous underwater vehicle (AUV), augmented with a stack of modular and extendable hardware and software, including autonomy, navigation, control and high fidelity simulation capabilities (A-size stands for the standard sonobuoy form factor, with a maximum diameter of 124 mm). Subsequently, we extended this base vehicle with a novel tuna-inspired morphing fin payload module (referred to as the Morpheus AUV), to achieve good directional stability and exceptional maneuverability; properties that are highly desirable for rigid hull AUVs, but are presently difficult to achieve because they impose contradictory requirements. The morphing fin payload allows the base AUV to dynamically change its stability-maneuverability qualities by using morphing fins, which can be deployed, deflected and retracted, as needed. The base vehicle and Morpheus AUV were both extensively field tested in-water in the Charles river, Massachusetts, USA; by conducting hundreds of hours of operations over a period of two years. The maneuvering capability of the Morpheus AUV was evaluated with and without the use of morphing fins to quantify the performance improvement. The Morpheus AUV was able to showcase an exceptional turning rate of around 25-35 deg/s. A maximum turn rate improvement of around 35% - 50% was gained through the use of morphing fins.

Estimating the probability of failure for complex real-world systems using high-fidelity computational models is often prohibitively expensive, especially when the probability is small. Exploiting low-fidelity models can make this process more feasible, but merging information from multiple low-fidelity and high-fidelity models poses several challenges. This paper presents a robust multi-fidelity surrogate modeling strategy in which the multi-fidelity surrogate is assembled using an active learning strategy using an on-the-fly model adequacy assessment set within a subset simulation framework for efficient reliability analysis. The multi-fidelity surrogate is assembled by first applying a Gaussian process correction to each low-fidelity model and assigning a model probability based on the model's local predictive accuracy and cost. Three strategies are proposed to fuse these individual surrogates into an overall surrogate model based on model averaging and deterministic/stochastic model selection. The strategies also dictate which model evaluations are necessary. No assumptions are made about the relationships between low-fidelity models, while the high-fidelity model is assumed to be the most accurate and most computationally expensive model. Through two analytical and two numerical case studies, including a case study evaluating the failure probability of Tristructural isotropic-coated (TRISO) nuclear fuels, the algorithm is shown to be highly accurate while drastically reducing the number of high-fidelity model calls (and hence computational cost).

We introduce a novel gated recurrent unit (GRU) with a weighted time-delay feedback mechanism in order to improve the modeling of long-term dependencies in sequential data. This model is a discretized version of a continuous-time formulation of a recurrent unit, where the dynamics are governed by delay differential equations (DDEs). By considering a suitable time-discretization scheme, we propose $\tau$-GRU, a discrete-time gated recurrent unit with delay. We prove the existence and uniqueness of solutions for the continuous-time model, and we demonstrate that the proposed feedback mechanism can help improve the modeling of long-term dependencies. Our empirical results show that $\tau$-GRU can converge faster and generalize better than state-of-the-art recurrent units and gated recurrent architectures on a range of tasks, including time-series classification, human activity recognition, and speech recognition.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

The research area of algorithms with predictions has seen recent success showing how to incorporate machine learning into algorithm design to improve performance when the predictions are correct, while retaining worst-case guarantees when they are not. Most previous work has assumed that the algorithm has access to a single predictor. However, in practice, there are many machine learning methods available, often with incomparable generalization guarantees, making it hard to pick a best method a priori. In this work we consider scenarios where multiple predictors are available to the algorithm and the question is how to best utilize them. Ideally, we would like the algorithm's performance to depend on the quality of the best predictor. However, utilizing more predictions comes with a cost, since we now have to identify which prediction is the best. We study the use of multiple predictors for a number of fundamental problems, including matching, load balancing, and non-clairvoyant scheduling, which have been well-studied in the single predictor setting. For each of these problems we introduce new algorithms that take advantage of multiple predictors, and prove bounds on the resulting performance.

新的AUV技术的发展增加了AUV可以应对的任务范围及其运营的长度。结果，AUV能够处理高度复杂的操作。但是，这些任务并不容易适合将任务定义为一系列预先计划的航路点的传统方法，因为不可能事先知道，在任务过程中可能发生的一切。这会导致操作员的期望和实际操作绩效之间存在差距。因此，这可能会在操作员和AUV之间产生降低的信任程度，从而导致不必要的任务中断。为了弥合机器人行为和运营商的期望之间的这一差距，这项工作旨在提供一个框架，以易于理解的方式解释任务期间自动驾驶汽车采取的决策和行动。此外，目的是拥有一个自治性系统，可以在任何自治体系结构之上添加为附加层。为了使该方法适用于配备不同自主权的不同自主系统，这项工作将自主权的内部运作与决策点以及应用知识蒸馏的由此产生的执行动作。最后，为了以更自然的方式向操作员介绍解释，蒸馏决策树的输出与自然语言解释相结合，并将其报告给操作员作为句子。因此，在解释管道的末尾添加了一个称为Concept2Text生成的附加步骤。

本文报告了对使用一辆或多种无人地面车辆（USV）快速识别通道的快速识别通道问题的研究。一种称为基于建议的自适应通道搜索（PBAC）的新算法作为一种潜在的解决方案，可改善当前方法。将PBAC的经验性能与割草机测量和马尔可夫决策过程（MDP）计划进行了比较，该计划具有两个最先进的奖励功能：上限置信度（UCB）和最大价值信息（MVI）。通过比较使用一个，两个，三个或四个USV识别连续通道的时间来评估每种方法的性能。在十个模拟的测深场景和一个野外区域中比较每种方法的性能，每种方法都有不同的频道布局。模拟和现场试验的结果表明，平均多车辆PBAC优于基于割草机，UCB和基于MVI的方法，尤其是在使用至少三辆车辆时。

深度学习技术的最新进展引发了地面车辆的自主权的根本性进步。定期用于监视，监视和其他常规任务的海洋沿海自动级别的表面车辆（ASV）可以从这种自治中受益。长期的深海运输活动是额外的机会。这两个用例的地形非常不同 - 第一个是沿海水域 - 具有许多障碍，结构和人类的存在，而后者大多没有这样的障碍。环境条件的变化都是两种地形的共同点。绘制此类地形的强大标记数据集对于提高可以推动自主权的情境意识至关重要。但是，只有此类海事数据集有限，这些数据集主要由光学图像组成。虽然，长浪红外（LWIR）是对极端光条件下有助于的光谱的强烈补充，但目前尚不存在带有LWIR图像的标记的公共数据集。在本文中，我们通过在不同条件下呈现在沿海海上环境中捕获的2,900多个LWIR分段图像的标签数据集来填补这一空白。这些图像使用实例分割标记，并分为七个类别 - 天空，水，障碍物，生活障碍，桥梁，自我和背景。我们还评估了三个深度学习体系结构（UNET，PSPNET，DEEPLABV3）的数据集，并对其功效提供了详细的分析。尽管数据集专注于沿海地形，但可以同样有助于深海用例。这种地形的流量将较小，在混乱环境中训练的分类器将能够有效地处理稀疏场景。我们与研究界分享此数据集，希望它刺激新的场景理解海上环境中的能力。

半监督学习（SSL）有望通过对许多未标记图像进行培训，与小标签数据集中的培训分类器相比，准确性的提高。在诸如医学成像之类的现实应用中，将收集未标记的集合，以提高权宜之计，因此未贴上：可能与代表类或类频率中的标记集合不同。不幸的是，现代的深SSL通常会使未经保证的未标记的集合变得更糟。最近的补救措施表明，过滤方法可以检测出分布未标记的示例，然后将其丢弃或减轻重量。相反，我们认为所有未标记的示例可能会有所帮助。我们介绍了一个称为Fix-A-Step的程序，该程序尽管缺乏策划，但仍可以提高常见的深SSL方法的持有准确性。关键的创新是受所有未标记数据启发的标签集的增强，并修改了梯度下降更新，以防止遵循多任务SSL损失损害标签集的精度。尽管我们的方法比替代方案更简单，但我们在所有测试的人工污染水平上显示了无标记集的所有测试水平的CIFAR-10和CIFAR-100基准的准确性提高。我们进一步建议SSL的真实医疗基准：识别心脏超声图像的视图类型。我们的方法可以从353,500个真正未经贴标记的图像中学习，以提供跨医院的概括的收益。