Nature computational science最新文献_第10页

Real-time non-line-of-sight computational imaging using spectrum filtering and motion compensation 利用频谱滤波和运动补偿进行实时非视距计算成像。

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Nature computational science Pub Date : 2024-11-06 DOI: 10.1038/s43588-024-00722-4

Jun-Tian Ye, Yi Sun, Wenwen Li, Jian-Wei Zeng, Yu Hong, Zheng-Ping Li, Xin Huang, Xianghui Xue, Xin Yuan, Feihu Xu, Xiankang Dou, Jian-Wei Pan

{"title":"Real-time non-line-of-sight computational imaging using spectrum filtering and motion compensation","authors":"Jun-Tian Ye, Yi Sun, Wenwen Li, Jian-Wei Zeng, Yu Hong, Zheng-Ping Li, Xin Huang, Xianghui Xue, Xin Yuan, Feihu Xu, Xiankang Dou, Jian-Wei Pan","doi":"10.1038/s43588-024-00722-4","DOIUrl":"10.1038/s43588-024-00722-4","url":null,"abstract":"Non-line-of-sight (NLOS) imaging aims at recovering the shape and albedo of hidden objects. Despite recent advances, real-time video of complex and dynamic scenes remains a major challenge owing to the weak signal of multiply scattered light. Here we propose and demonstrate a framework of spectrum filtering and motion compensation to realize high-quality NLOS video for room-sized scenes. Spectrum filtering leverages a wave-based model for denoising and deblurring in the frequency domain, enabling computational image reconstruction with a small number of sampling points. Motion compensation tailored with an interleaved scanning scheme can compute high-resolution live video during the acquisition of low-quality image sequences. Together, we demonstrate live NLOS videos at 4 fps for a variety of dynamic real-life scenes. The results mark a substantial stride toward real-time, large-scale and low-power NLOS imaging and sensing applications. The authors propose a framework incorporating spectrum filtering and motion compensation, which enables non-line-of-sight live videos at 4 fps for a variety of dynamic real-life scenes.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 12","pages":"920-927"},"PeriodicalIF":12.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142591484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep generative design of RNA aptamers using structural predictions 利用结构预测深度生成设计 RNA 配合物。

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Nature computational science Pub Date : 2024-11-06 DOI: 10.1038/s43588-024-00720-6

Felix Wong, Dongchen He, Aarti Krishnan, Liang Hong, Alexander Z. Wang, Jiuming Wang, Zhihang Hu, Satotaka Omori, Alicia Li, Jiahua Rao, Qinze Yu, Wengong Jin, Tianqing Zhang, Katherine Ilia, Jack X. Chen, Shuangjia Zheng, Irwin King, Yu Li, James J. Collins

{"title":"Deep generative design of RNA aptamers using structural predictions","authors":"Felix Wong, Dongchen He, Aarti Krishnan, Liang Hong, Alexander Z. Wang, Jiuming Wang, Zhihang Hu, Satotaka Omori, Alicia Li, Jiahua Rao, Qinze Yu, Wengong Jin, Tianqing Zhang, Katherine Ilia, Jack X. Chen, Shuangjia Zheng, Irwin King, Yu Li, James J. Collins","doi":"10.1038/s43588-024-00720-6","DOIUrl":"10.1038/s43588-024-00720-6","url":null,"abstract":"RNAs represent a class of programmable biomolecules capable of performing diverse biological functions. Recent studies have developed accurate RNA three-dimensional structure prediction methods, which may enable new RNAs to be designed in a structure-guided manner. Here, we develop a structure-to-sequence deep learning platform for the de novo generative design of RNA aptamers. We show that our approach can design RNA aptamers that are predicted to be structurally similar, yet sequence dissimilar, to known light-up aptamers that fluoresce in the presence of small molecules. We experimentally validate several generated RNA aptamers to have fluorescent activity, show that these aptamers can be optimized for activity in silico, and find that they exhibit a mechanism of fluorescence similar to that of known light-up aptamers. Our results demonstrate how structural predictions can guide the targeted and resource-efficient design of new RNA sequences. A deep learning platform for structure-guided, generative design of RNA sequences is developed and used to discover fluorescent RNA aptamers.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 11","pages":"829-839"},"PeriodicalIF":12.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142591559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extracting reliable quantum outputs for noisy devices 为噪声设备提取可靠的量子输出。

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Nature computational science Pub Date : 2024-11-01 DOI: 10.1038/s43588-024-00713-5

Weikang Li, Dong-Ling Deng

引用次数: 0

Provable bounds for noise-free expectation values computed from noisy samples 从噪声样本计算出的无噪声期望值的可证明边界。

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Nature computational science Pub Date : 2024-11-01 DOI: 10.1038/s43588-024-00709-1

Samantha V. Barron, Daniel J. Egger, Elijah Pelofske, Andreas Bärtschi, Stephan Eidenbenz, Matthis Lehmkuehler, Stefan Woerner

{"title":"Provable bounds for noise-free expectation values computed from noisy samples","authors":"Samantha V. Barron, Daniel J. Egger, Elijah Pelofske, Andreas Bärtschi, Stephan Eidenbenz, Matthis Lehmkuehler, Stefan Woerner","doi":"10.1038/s43588-024-00709-1","DOIUrl":"10.1038/s43588-024-00709-1","url":null,"abstract":"Quantum computing has emerged as a powerful computational paradigm capable of solving problems beyond the reach of classical computers. However, today’s quantum computers are noisy, posing challenges to obtaining accurate results. Here, we explore the impact of noise on quantum computing, focusing on the challenges in sampling bit strings from noisy quantum computers and the implications for optimization and machine learning. We formally quantify the sampling overhead to extract good samples from noisy quantum computers and relate it to the layer fidelity, a metric to determine the performance of noisy quantum processors. Further, we show how this allows us to use the conditional value at risk of noisy samples to determine provable bounds on noise-free expectation values. We discuss how to leverage these bounds for different algorithms and demonstrate our findings through experiments on real quantum computers involving up to 127 qubits. The results show strong alignment with theoretical predictions. In this study, the authors investigate the impact of noise on quantum computing with a focus on the challenges in sampling bit strings from noisy quantum computers, which has implications for optimization and machine learning.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 11","pages":"865-875"},"PeriodicalIF":12.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00709-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142564932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

E-waste challenges of generative artificial intelligence 生成式人工智能面临的电子垃圾挑战。

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Nature computational science Pub Date : 2024-10-28 DOI: 10.1038/s43588-024-00712-6

Peng Wang, Ling-Yu Zhang, Asaf Tzachor, Wei-Qiang Chen

{"title":"E-waste challenges of generative artificial intelligence","authors":"Peng Wang, Ling-Yu Zhang, Asaf Tzachor, Wei-Qiang Chen","doi":"10.1038/s43588-024-00712-6","DOIUrl":"10.1038/s43588-024-00712-6","url":null,"abstract":"Generative artificial intelligence (GAI) requires substantial computational resources for model training and inference, but the electronic-waste (e-waste) implications of GAI and its management strategies remain underexplored. Here we introduce a computational power-driven material flow analysis framework to quantify and explore ways of managing the e-waste generated by GAI, with a particular focus on large language models. Our findings indicate that this e-waste stream could increase, potentially reaching a total accumulation of 1.2–5.0 million tons during 2020–2030, under different future GAI development settings. This may be intensified in the context of geopolitical restrictions on semiconductor imports and the rapid server turnover for operational cost savings. Meanwhile, we show that the implementation of circular economy strategies along the GAI value chain could reduce e-waste generation by 16–86%. This underscores the importance of proactive e-waste management in the face of advancing GAI technologies. Generative artificial intelligence (GAI) is driving a surge in e-waste due to intensive computational infrastructure needs. This study emphasizes the necessity for proactive implementation of circular economy practices throughout GAI value chains.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 11","pages":"818-823"},"PeriodicalIF":12.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142523796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Publisher Correction: Reliable deep learning in anomalous diffusion against out-of-distribution dynamics 出版商更正：针对分布外动态异常扩散的可靠深度学习。

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Nature computational science Pub Date : 2024-10-28 DOI: 10.1038/s43588-024-00729-x

Xiaochen Feng, Hao Sha, Yongbing Zhang, Yaoquan Su, Shuai Liu, Yuan Jiang, Shangguo Hou, Sanyang Han, Xiangyang Ji

引用次数: 0

Improving the prediction of protein stability changes upon mutations by geometric learning and a pre-training strategy 通过几何学习和预训练策略改进突变后蛋白质稳定性变化的预测。

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Nature computational science Pub Date : 2024-10-25 DOI: 10.1038/s43588-024-00716-2

Yunxin Xu, Di Liu, Haipeng Gong

{"title":"Improving the prediction of protein stability changes upon mutations by geometric learning and a pre-training strategy","authors":"Yunxin Xu, Di Liu, Haipeng Gong","doi":"10.1038/s43588-024-00716-2","DOIUrl":"10.1038/s43588-024-00716-2","url":null,"abstract":"Accurate prediction of protein mutation effects is of great importance in protein engineering and design. Here we propose GeoStab-suite, a suite of three geometric learning-based models—GeoFitness, GeoDDG and GeoDTm—for the prediction of fitness score, ΔΔG and ΔTm of a protein upon mutations, respectively. GeoFitness engages a specialized loss function to allow supervised training of a unified model using the large amount of multi-labeled fitness data in the deep mutational scanning database. To further improve the downstream tasks of ΔΔG and ΔTm prediction, the encoder of GeoFitness is reutilized as a pre-trained module in GeoDDG and GeoDTm to overcome the challenge of lacking sufficient labeled data. This pre-training strategy, in combination with data expansion, markedly improves model performance and generalizability. In the benchmark test, GeoDDG and GeoDTm outperform the other state-of-the-art methods by at least 30% and 70%, respectively, in terms of the Spearman correlation coefficient. In this study, the authors propose a strategy to train a unified model to learn the general mutational effects based on multi-labeled deep mutational scanning (DMS) data, and then reutilize this pre-trained model to improve the downstream protein stability prediction tasks.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 11","pages":"840-850"},"PeriodicalIF":12.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fostering discussions on topical issues 促进对热点问题的讨论。

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Nature computational science Pub Date : 2024-10-23 DOI: 10.1038/s43588-024-00719-z

引用次数: 0

Author Correction: Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation 作者更正：用于高效 ab initio 电子结构计算的深度学习密度泛函理论哈密顿。

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Nature computational science Pub Date : 2024-10-23 DOI: 10.1038/s43588-024-00723-3

He Li, Zun Wang, Nianlong Zou, Meng Ye, Runzhang Xu, Xiaoxun Gong, Wenhui Duan, Yong Xu

引用次数: 0

Taking a deep dive with active learning for drug discovery 利用主动学习深入研究药物发现。

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Nature computational science Pub Date : 2024-10-23 DOI: 10.1038/s43588-024-00704-6

Zachary Fralish, Daniel Reker

引用次数: 0