相干显微镜技术提供了跨科学和技术领域的材料的无与伦比的多尺度视图,从结构材料到量子设备,从综合电路到生物细胞。在构造更明亮的来源和高速探测器的驱动下,连贯的X射线显微镜方法(如Ptychography)有望彻底改变纳米级材料的特征。但是,相关的数据和计算需求显着增加意味着,常规方法不再足以从高速相干成像实验实时恢复样品图像。在这里,我们演示了一个工作流程,该工作流利用边缘的人工智能和高性能计算,以实现直接从检测器直接从检测器流出的X射线ptychography数据实时反演。拟议的AI支持的工作流程消除了传统的Ptychography施加的采样约束,从而使用比传统方法所需的数据较少的数据级允许低剂量成像。
translated by 谷歌翻译
计算机断层扫描(CT)是一种成像技术,其中以不同角度(称为投影或扫描)收集有关对象的信息。然后,通过解决反问题来产生显示切片的内部结构的横截面图像。受辐射剂量,投影角,产生的图像等某些因素的限制可能是嘈杂的或包含伪像的。受到《变形金刚在自然语言处理》中的成功的启发,这项初步研究的核心思想是将层析成像的投影视为单词令牌,而整个横截面(又称Sinogram)的整体扫描是在句子中作为句子。自然语言处理。然后,我们通过训练蒙版辛图模型(MSM)和微调MSM来探索基础模型的想法,以获取各种下游应用程序,包括数据集合限制(例如,光子预算)和数据驱动的解决方案,以近似于数据驱动的解决方案CT重建的逆问题。本研究中使用的模型和数据可在https://github.com/lzhengchun/tomotx上获得。
translated by 谷歌翻译
研究过程自动化 - 对科学仪器,计算机,数据存储和其他资源的可靠,高效和可重复执行的可靠,高效和可重复执行,这是现代科学的基本要素。我们在此处报告Globus研究数据管理平台内的新服务,该服务可以将各种研究过程的规范作为可重复使用的动作集,流量以及在异质研究环境中执行此类流动的集合。为了以广泛的空间范围(例如,从科学仪器到远程数据中心)和时间范围(从几秒钟到几周),这些Globus自动化服务功能:1)云托管以可靠地执行长期持久的流量,尽管零星的失败,但这些Globus自动化服务功能:1) ; 2)声明性符号和可扩展的异步行动提供商API,用于定义和执行涉及任意资源的各种行动和流动规范; 3)授权授权机制,用于安全调用动作。这些服务允许研究人员将广泛的研究任务的管理外包和自动化为可靠,可扩展和安全的云平台。我们向Globus自动化服务提供用例
translated by 谷歌翻译
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。
translated by 谷歌翻译
从Linac Coohent Light Source(LCLS-II)和高级光子源升级(APS-U)等工具产生的数据中迅速提取可行的信息,由于高(最高(最高为TB/S)数据速率)变得越来越具有挑战性。常规的基于物理的信息检索方法很难快速检测有趣的事件,以便及时关注罕见事件或纠正错误。机器学习〜(ML)学习廉价替代分类器的方法是有希望的替代方法,但是当仪器或样品变化导致ML性能降解时可能会灾难性地失败。为了克服此类困难,我们提出了一个新的数据存储和ML模型培训体系结构,旨在组织大量的数据和模型,以便在检测到模型降解时,可以快速查询先验模型和/或数据。针对新条件进行了微调。我们表明,与当前最新的训练速度提高了200倍和92X端到端模型更新时间的速度相比,我们的方法最多可以达到100倍数据标记的速度。
translated by 谷歌翻译
基于机器学习(ML)的转向可以通过在线选择更科学意义的计算来提高基于合奏的模拟的性能。我们提出了DeepDrivemd,这是ML驱动的科学模拟转向的框架,我们用来通过在大型平行计算机上的有效耦合ML和HPC来实现分子动力学(MD)性能的稳定性提高。我们讨论了DeepDrivemd的设计,并描述了其性能。我们证明,与其他方法相对于其他方法,DeepDrivemd可以在100-1000倍加速度之间达到100-1000倍的加速度,这是通过执行的模拟时间量来衡量的,同时覆盖了模拟过程中采样的状态所量化的相同构象景观。实验是在最多1020个节点的领导级平台上进行的。该结果将DeepDrivemd作为ML驱动的HPC模拟方案的高性能框架建立,该场景支持不同的MD仿真和ML后端,并通过改善当前计算能力来改善长度和时间尺度来实现新的科学见解。
translated by 谷歌翻译
Many scientific fields -- including biology, health, education, and the social sciences -- use machine learning (ML) to help them analyze data at an unprecedented scale. However, ML researchers who develop advanced methods rarely provide detailed tutorials showing how to apply these methods. Existing tutorials are often costly to participants, presume extensive programming knowledge, and are not tailored to specific application fields. In an attempt to democratize ML methods, we organized a year-long, free, online tutorial series targeted at teaching advanced natural language processing (NLP) methods to computational social science (CSS) scholars. Two organizers worked with fifteen subject matter experts to develop one-hour presentations with hands-on Python code for a range of ML methods and use cases, from data pre-processing to analyzing temporal variation of language change. Although live participation was more limited than expected, a comparison of pre- and post-tutorial surveys showed an increase in participants' perceived knowledge of almost one point on a 7-point Likert scale. Furthermore, participants asked thoughtful questions during tutorials and engaged readily with tutorial content afterwards, as demonstrated by 10K~total views of posted tutorial recordings. In this report, we summarize our organizational efforts and distill five principles for democratizing ML+X tutorials. We hope future organizers improve upon these principles and continue to lower barriers to developing ML skills for researchers of all fields.
translated by 谷歌翻译
State-of-the-art deep-learning-based approaches to Natural Language Processing (NLP) are credited with various capabilities that involve reasoning with natural language texts. In this paper we carry out a large-scale empirical study investigating the detection of formally valid inferences in controlled fragments of natural language for which the satisfiability problem becomes increasingly complex. We find that, while transformer-based language models perform surprisingly well in these scenarios, a deeper analysis re-veals that they appear to overfit to superficial patterns in the data rather than acquiring the logical principles governing the reasoning in these fragments.
translated by 谷歌翻译
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.
translated by 谷歌翻译
In this paper, we address the problem of safe trajectory planning for autonomous search and exploration in constrained, cluttered environments. Guaranteeing safe navigation is a challenging problem that has garnered significant attention. This work contributes a method that generates guaranteed safety-critical search trajectories in a cluttered environment. Our approach integrates safety-critical constraints using discrete control barrier functions (DCBFs) with ergodic trajectory optimization to enable safe exploration. Ergodic trajectory optimization plans continuous exploratory trajectories that guarantee full coverage of a space. We demonstrate through simulated and experimental results on a drone that our approach is able to generate trajectories that enable safe and effective exploration. Furthermore, we show the efficacy of our approach for safe exploration of real-world single- and multi- drone platforms.
translated by 谷歌翻译