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The exciting potential and daunting challenge of using GPS human-mobility data for epidemic modeling 利用 GPS 人员流动数据进行流行病建模的巨大潜力和艰巨挑战。
IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-19 DOI: 10.1038/s43588-024-00637-0
Francisco Barreras, Duncan J. Watts
Large-scale GPS location datasets hold immense potential for measuring human mobility and interpersonal contact, both of which are essential for data-driven epidemiology. However, despite their potential and widespread adoption during the COVID-19 pandemic, there are several challenges with these data that raise concerns regarding the validity and robustness of its applications. Here we outline two types of challenges—some related to accessing and processing these data, and some related to data quality—and propose several research directions to address them moving forward. While large-scale GPS location datasets have been instrumental to applications in epidemiology, there are still several challenges with these data that should be considered and addressed to make data-driven epidemiology more reliable.
大规模 GPS 定位数据集在测量人类流动性和人际接触方面具有巨大的潜力,而这两方面对于数据驱动的流行病学都至关重要。然而,尽管这些数据具有潜力并在 COVID-19 大流行期间被广泛采用,但这些数据仍面临着一些挑战,使人们对其应用的有效性和稳健性产生了担忧。在此,我们概述了两类挑战--一些与访问和处理这些数据有关,一些与数据质量有关--并提出了解决这些挑战的几个研究方向。
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引用次数: 0
The whole picture in digital pathology 数字病理学的全貌
IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-19 DOI: 10.1038/s43588-024-00655-y
Ananya Rastogi
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引用次数: 0
An algorithmic framework for synthetic cost-aware decision making in molecular design 分子设计中合成成本感知决策的算法框架。
IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-17 DOI: 10.1038/s43588-024-00639-y
Jenna C. Fromer, Connor W. Coley
Small molecules exhibiting desirable property profiles are often discovered through an iterative process of designing, synthesizing and testing sets of molecules. The selection of molecules to synthesize from all possible candidates is a complex decision-making process that typically relies on expert chemist intuition. Here we propose a quantitative decision-making framework, SPARROW, that prioritizes molecules for evaluation by balancing expected information gain and synthetic cost. SPARROW integrates molecular design, property prediction and retrosynthetic planning to balance the utility of testing a molecule with the cost of batch synthesis. We demonstrate, through three case studies, that the developed algorithm captures the non-additive costs inherent to batch synthesis, leverages common reaction steps and intermediates, and scales to hundreds of molecules. The downselection of compounds for synthesis is a key challenge in molecular design cycles that typically relies on expert chemist intuition. Fromer and Coley propose a cost-aware method to automatically select compounds and synthetic routes.
表现出理想特性的小分子通常是通过设计、合成和测试成套分子的反复过程发现的。从所有可能的候选分子中选择要合成的分子是一个复杂的决策过程,通常依赖于化学家的直觉。在此,我们提出了一个定量决策框架 SPARROW,该框架通过平衡预期信息增益和合成成本来确定评估分子的优先次序。SPARROW 整合了分子设计、性质预测和逆合成规划,以平衡测试分子的效用和批量合成的成本。我们通过三个案例研究证明,所开发的算法能够捕捉到批量合成固有的非加成成本,利用常见的反应步骤和中间体,并可扩展到数百个分子。
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引用次数: 0
A nonlinear dimension for machine learning in optical disordered media 光学无序介质中机器学习的非线性维度
IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-14 DOI: 10.1038/s43588-024-00648-x
Tianyu Wang
A recent study shows that, by leveraging nonlinear optical processes in disordered media, photonic processors can transform high-dimensional machine-learning data, using nonlinear functions that are otherwise challenging for digital electronic processors to compute.
最近的一项研究表明,通过利用无序介质中的非线性光学过程,光子处理器可以利用非线性函数转换高维机器学习数据,否则数字电子处理器很难计算这些数据。
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引用次数: 0
Large-scale photonic computing with nonlinear disordered media 利用非线性无序介质进行大规模光子计算。
IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-14 DOI: 10.1038/s43588-024-00644-1
Hao Wang, Jianqi Hu, Andrea Morandi, Alfonso Nardi, Fei Xia, Xuanchen Li, Romolo Savo, Qiang Liu, Rachel Grange, Sylvain Gigan
Neural networks find widespread use in scientific and technological applications, yet their implementations in conventional computers have encountered bottlenecks due to ever-expanding computational needs. Photonic computing is a promising neuromorphic platform with potential advantages of massive parallelism, ultralow latency and reduced energy consumption but mostly for computing linear operations. Here we demonstrate a large-scale, high-performance nonlinear photonic neural system based on a disordered polycrystalline slab composed of lithium niobate nanocrystals. Mediated by random quasi-phase-matching and multiple scattering, linear and nonlinear optical speckle features are generated as the interplay between the simultaneous linear random scattering and the second-harmonic generation, defining a complex neural network in which the second-order nonlinearity acts as internal nonlinear activation functions. Benchmarked against linear random projection, such nonlinear mapping embedded with rich physical computational operations shows improved performance across a large collection of machine learning tasks in image classification, regression and graph classification. Demonstrating up to 27,648 input and 3,500 nonlinear output nodes, the combination of optical nonlinearity and random scattering serves as a scalable computing engine for diverse applications. Nonlinear optical computations have been essential yet challenging for developing optical neural networks with appreciable expressivity. In this paper, light scattering is combined with optical nonlinearity to empower a high-performance, large-scale nonlinear photonic neural system.
神经网络广泛应用于科学和技术领域,但由于计算需求不断扩大,在传统计算机中实现神经网络遇到了瓶颈。光子计算是一种前景广阔的神经形态平台,具有大规模并行、超低延迟和降低能耗等潜在优势,但主要用于计算线性运算。在这里,我们展示了一种基于由铌酸锂纳米晶体组成的无序多晶板的大规模、高性能非线性光子神经系统。在随机准相位匹配和多重散射的介导下,线性和非线性光学斑点特征在同时发生的线性随机散射和二次谐波生成的相互作用下产生,定义了一个复杂的神经网络,其中二阶非线性作为内部非线性激活函数。以线性随机投影为基准,这种嵌入了丰富物理计算操作的非线性映射在图像分类、回归和图分类等大量机器学习任务中显示出更高的性能。光学非线性与随机散射的结合可作为可扩展的计算引擎,适用于各种不同的应用,最多可显示 27648 个输入节点和 3500 个非线性输出节点。
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引用次数: 0
Linguistics-based formalization of the antibody language as a basis for antibody language models 基于语言学的抗体语言形式化是抗体语言模型的基础。
IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-14 DOI: 10.1038/s43588-024-00642-3
Mai Ha Vu, Philippe A. Robert, Rahmad Akbar, Bartlomiej Swiatczak, Geir Kjetil Sandve, Dag Trygve Truslew Haug, Victor Greiff
Apparent parallels between natural language and antibody sequences have led to a surge in deep language models applied to antibody sequences for predicting cognate antigen recognition. However, a linguistic formal definition of antibody language does not exist, and insight into how antibody language models capture antibody-specific binding features remains largely uninterpretable. Here we describe how a linguistic formalization of the antibody language, by characterizing its tokens and grammar, could address current challenges in antibody language model rule mining. The parallels between natural language and antibody sequences could serve as a stepping stone to using deep language models for analyzing antibody sequences. This Perspective discusses how issues in antibody language model rule mining could be addressed by linguistically formalizing the antibody language.
自然语言与抗体序列之间的明显相似性导致了将深度语言模型应用于抗体序列以预测同源抗原识别的热潮。然而,抗体语言的语言学形式定义并不存在,而且对抗体语言模型如何捕捉抗体特异性结合特征的深入了解在很大程度上仍无法解读。在此,我们将介绍如何通过表征抗体语言的词块和语法,对抗体语言进行语言形式化,从而解决目前在抗体语言模型规则挖掘方面所面临的挑战。
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引用次数: 0
Data as the next challenge in atomistic machine learning 数据是原子机器学习的下一个挑战。
IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-12 DOI: 10.1038/s43588-024-00636-1
Chiheb Ben Mahmoud, John L. A. Gardner, Volker L. Deringer
As machine learning models are becoming mainstream tools for molecular and materials research, there is an urgent need to improve the nature, quality, and accessibility of atomistic data. In turn, there are opportunities for a new generation of generally applicable datasets and distillable models.
随着机器学习模型逐渐成为分子和材料研究的主流工具,迫切需要改进原子数据的性质、质量和可获取性。反过来,新一代普遍适用的数据集和可提炼模型也有了机会。
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引用次数: 0
Scalable design of orthogonal DNA barcode libraries 可扩展的正交 DNA 条形码库设计。
IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-07 DOI: 10.1038/s43588-024-00646-z
Gokul Gowri, Kuanwei Sheng, Peng Yin
Orthogonal DNA barcode library design is an essential task in bioengineering. Here we present seqwalk, an efficient method for designing barcode libraries that satisfy a sequence symmetry minimization (SSM) heuristic for orthogonality, with theoretical guarantees of maximal or near-maximal library size under certain design constraints. Seqwalk encodes SSM constraints in a de Bruijn graph representation of sequence space, enabling the application of recent advances in discrete mathematics1 to the problem of orthogonal sequence design. We demonstrate the scalability of seqwalk by designing a library of >106 SSM-satisfying barcode sequences in less than 20 s on a standard laptop. Seqwalk is a scalable method for designing orthogonal DNA barcode libraries, producing one million barcodes in 20 s on a standard laptop.
正交 DNA 条形码文库设计是生物工程中的一项重要任务。在此,我们介绍一种高效的条形码文库设计方法 Seqwalk,该方法满足序列对称性最小化(SSM)启发式正交性要求,理论上保证了在特定设计约束条件下最大或接近最大的文库规模。Seqwalk 将 SSM 约束条件编码为序列空间的 de Bruijn 图表示法,从而将离散数学1 的最新进展应用于正交序列设计问题。我们在一台标准笔记本电脑上用不到 20 秒的时间就设计出了大于 106 个满足 SSM 的条形码序列库,证明了 seqwalk 的可扩展性。
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引用次数: 0
Computational morphology and morphogenesis for empowering soft-matter engineering 通过计算形态学和形态发生学实现软物质工程。
IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-07 DOI: 10.1038/s43588-024-00647-y
Yifan Yang, Fan Xu
Morphing soft matter, which is capable of changing its shape and function in response to stimuli, has wide-ranging applications in robotics, medicine and biology. Recently, computational models have accelerated its development. Here, we highlight advances and challenges in developing computational techniques, and explore the potential applications enabled by such models.
变形软物质能够根据刺激改变形状和功能,在机器人、医学和生物学领域有着广泛的应用。最近,计算模型加速了它的发展。在此,我们将重点介绍在开发计算技术方面取得的进展和面临的挑战,并探讨此类模型的潜在应用。
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引用次数: 0
Advancing computational sustainability in higher education 推进高等教育的计算可持续性。
IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-07 DOI: 10.1038/s43588-024-00638-z
Mayank Kejriwal, Victoria Petryshyn
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引用次数: 0
期刊
Nature computational science
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