We introduce an end-to-end computational framework that enables hyperparameter optimization with the DeepHyper library, accelerated training, and interpretable AI inference with a suite of state-of-the-art AI models, including CGCNN, PhysNet, SchNet, MPNN, MPNN-transformer, and TorchMD-Net. We use these AI models and the benchmark QM9, hMOF, and MD17 datasets to showcase the prediction of user-specified materials properties in modern computing environments, and to demonstrate translational applications for the modeling of small molecules, crystals and metal organic frameworks with a unified, stand-alone framework. We deployed and tested this framework in the ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and the Delta supercomputer at the National Center for Supercomputing Applications to provide researchers with modern tools to conduct accelerated AI-driven discovery in leadership class computing environments.

The findable, accessible, interoperable, and reusable (FAIR) data principles have provided a framework for examining, evaluating, and improving how we share data with the aim of facilitating scientific discovery. Efforts have been made to generalize these principles to research software and other digital products. Artificial intelligence (AI) models -- algorithms that have been trained on data rather than explicitly programmed -- are an important target for this because of the ever-increasing pace with which AI is transforming scientific and engineering domains. In this paper, we propose a practical definition of FAIR principles for AI models and create a FAIR AI project template that promotes adherence to these principles. We demonstrate how to implement these principles using a concrete example from experimental high energy physics: a graph neural network for identifying Higgs bosons decaying to bottom quarks. We study the robustness of these FAIR AI models and their portability across hardware architectures and software frameworks, and report new insights on the interpretability of AI predictions by studying the interplay between FAIR datasets and AI models. Enabled by publishing FAIR AI models, these studies pave the way toward reliable and automated AI-driven scientific discovery.

我们介绍了第一个机器学习引力波搜索模拟数据挑战（MLGWSC-1）的结果。在这一挑战中，参与的小组必须从二进制黑洞合并中识别出复杂性和持续时间逐渐嵌入在逐渐更现实的噪声中的引力波信号。 4个提供的数据集中的决赛包含O3A观察的真实噪声，并发出了20秒的持续时间，其中包含进动效应和高阶模式。我们介绍了在提交前从参与者未知的1个月的测试数据中得出的6个输入算法的平均灵敏度距离和运行时。其中4个是机器学习算法。我们发现，最好的基于机器学习的算法能够以每月1个的错误警报率（FAR）的速度（FAR）实现基于匹配过滤的生产分析的敏感距离的95％。相反，对于真实的噪音，领先的机器学习搜索获得了70％。为了更高的范围，敏感距离缩小的差异缩小到某些数据集上选择机器学习提交的范围$ \ geq 200 $以优于传统搜索算法的程度。我们的结果表明，当前的机器学习搜索算法可能已经在有限的参数区域中对某些生产设置有用。为了改善最新的技术，机器学习算法需要降低他们能够检测信号并将其有效性扩展到参数空间区域的虚假警报率，在这些区域中，建模的搜索在计算上很昂贵。根据我们的发现，我们汇编了我们认为，将机器学习搜索提升到重力波信号检测中的宝贵工具，我们认为这是最重要的研究领域。

科学数据的一套简洁且可衡量的公平（可访问，可互操作和可重复使用的）原则正在转变用于数据管理和管理的最新实践，以支持和支持发现和创新。从这项计划中学习，并承认人工智能（AI）在科学和工程实践中的影响，我们为AI模型引入了一套实用，简洁和可衡量的公平原则。我们展示了如何在统一的计算框架内创建和共享公平的数据和AI模型，结合了以下要素：Argonne国家实验室的高级光子源，材料数据设施，科学数据和学习中心，Funcx和Argonne Leadersition的数据和学习中心计算设施（ALCF），尤其是ALCF AI测试台的Thetagpu SuperCuputer和Sambanova Datascale系统。我们描述了如何利用这种域 - 不足的计算框架来实现自主AI驱动的发现。

We present an end-to-end framework to learn partial differential equations that brings together initial data production, selection of boundary conditions, and the use of physics-informed neural operators to solve partial differential equations that are ubiquitous in the study and modeling of physics phenomena. We first demonstrate that our methods reproduce the accuracy and performance of other neural operators published elsewhere in the literature to learn the 1D wave equation and the 1D Burgers equation. Thereafter, we apply our physics-informed neural operators to learn new types of equations, including the 2D Burgers equation in the scalar, inviscid and vector types. Finally, we show that our approach is also applicable to learn the physics of the 2D linear and nonlinear shallow water equations, which involve three coupled partial differential equations. We release our artificial intelligence surrogates and scientific software to produce initial data and boundary conditions to study a broad range of physically motivated scenarios. We provide the source code, an interactive website to visualize the predictions of our physics informed neural operators, and a tutorial for their use at the Data and Learning Hub for Science.

我们使用人工智能（AI）来学习和推断出准圆形，旋转的高阶重力波模式的物理学，旋转，非精确的二进制黑洞合并。我们培训了使用1400万波形的AI模型，并使用代理模型NRHYBSUR3DQ8生产，包括最多$ \ ell \ Leq 4 $和$（5,5）$的模式，除了$（4,0）$和$（4 1）$，描述具有大规模比率$ Q \ LEQ8 $和个人SPINS $ S ^ z _ {\ {1,2 \}} \中的二进制文件。我们使用我们的AI模型来获得质量比，单独的旋转，有效旋转和描述这种信号歧管的数值相对性波形的倾斜角度的确定性和概率估计。我们的研究表明，AI为这些物理参数提供了信息估计。该工作标记第一次AI能够表征该高维信号歧管。我们的AI型号在3.4小时内培训，在峰会超级计算机上的256个节点（1,536个NVIDIA V100 GPU）上的分布式培训培训。

我们介绍了深度学习模型，以估计黑洞兼并的二元组件的群众，$（m_1，m_2）$，以及合并后巧妙剩余滞留的三个天体性质，即最终旋转，$ a_f $，以及ringdown振荡的频率和阻尼时间为基础$ \ ell = m = 2 $酒吧模式，$（\ OMEGA_R，\ OMEGA_I）$。我们的神经网络将修改的$ \ texttt {wavenet} $架构与对比学习和标准化流相结合。我们将这些模型验证在先前分布通过闭合的分析表达描述后的高斯缀合物的先前家庭。确认我们的模型产生统计上一致的结果，我们使用它们来估计五个二进制黑洞的天体物理参数$（m_1，m_2，a_f，\ oomega_r，\ omega_i）：$ \ texttt {gw150914}，\ texttt {gw170104 }，\ texttt {gw170814}，\ texttt {gw190521} $和$ \ texttt {gw190630} $。我们使用$ \ texttt {pycbc推理} $直接比较传统的贝叶斯方法进行参数估计与我们的深度学习的后部分布。我们的研究结果表明，我们的神经网络模型预测编码物理相关性的后分布，以及我们的数据驱动的中值结果和90美元\％$置信区间与引力波贝叶斯分析产生的数据相似。此方法需要单个V100 $ \ TextTT {NVIDIA} $ GPU，以在每次事件中生成2毫秒内的中位值和后部分布。这个神经网络和使用的教程，可在$ \ texttt {scounty} $ \ texttt {scounty hub} $。

Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains.

This paper proposes a novel observer-based controller for Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV) designed to directly receive measurements from a Vision-Aided Inertial Navigation System (VA-INS) and produce the required thrust and rotational torque inputs. The VA-INS is composed of a vision unit (monocular or stereo camera) and a typical low-cost 6-axis Inertial Measurement Unit (IMU) equipped with an accelerometer and a gyroscope. A major benefit of this approach is its applicability for environments where the Global Positioning System (GPS) is inaccessible. The proposed VTOL-UAV observer utilizes IMU and feature measurements to accurately estimate attitude (orientation), gyroscope bias, position, and linear velocity. Ability to use VA-INS measurements directly makes the proposed observer design more computationally efficient as it obviates the need for attitude and position reconstruction. Once the motion components are estimated, the observer-based controller is used to control the VTOL-UAV attitude, angular velocity, position, and linear velocity guiding the vehicle along the desired trajectory in six degrees of freedom (6 DoF). The closed-loop estimation and the control errors of the observer-based controller are proven to be exponentially stable starting from almost any initial condition. To achieve global and unique VTOL-UAV representation in 6 DoF, the proposed approach is posed on the Lie Group and the design in unit-quaternion is presented. Although the proposed approach is described in a continuous form, the discrete version is provided and tested. Keywords: Vision-aided inertial navigation system, unmanned aerial vehicle, vertical take-off and landing, stochastic, noise, Robotics, control systems, air mobility, observer-based controller algorithm, landmark measurement, exponential stability.

Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple degrees of freedom via user-generated signals remains challenging. To address this issue, various machine learning controllers have been developed to better predict movement intent. As these controllers become more intelligent and take on more autonomy in the system, the traditional approach of representing the human-machine interface as a human controlling a tool becomes limiting. One possible approach to improve the understanding of these interfaces is to model them as collaborative, multi-agent systems through the lens of joint action. The field of joint action has been commonly applied to two human partners who are trying to work jointly together to achieve a task, such as singing or moving a table together, by effecting coordinated change in their shared environment. In this work, we compare different prosthesis controllers (proportional electromyography with sequential switching, pattern recognition, and adaptive switching) in terms of how they present the hallmarks of joint action. The results of the comparison lead to a new perspective for understanding how existing myoelectric systems relate to each other, along with recommendations for how to improve these systems by increasing the collaborative communication between each partner.