Nature Machine Intelligence最新文献_第7页

Evolutionary optimization of model merging recipes 模型合并配方的进化优化

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-01-27 DOI: 10.1038/s42256-024-00975-8

Takuya Akiba, Makoto Shing, Yujin Tang, Qi Sun, David Ha

{"title":"Evolutionary optimization of model merging recipes","authors":"Takuya Akiba, Makoto Shing, Yujin Tang, Qi Sun, David Ha","doi":"10.1038/s42256-024-00975-8","DOIUrl":"10.1038/s42256-024-00975-8","url":null,"abstract":"Large language models (LLMs) have become increasingly capable, but their development often requires substantial computational resources. Although model merging has emerged as a cost-effective promising approach for creating new models by combining existing ones, it currently relies on human intuition and domain knowledge, limiting its potential. Here we propose an evolutionary approach that overcomes this limitation by automatically discovering effective combinations of diverse open-source models, harnessing their collective intelligence without requiring extensive additional training data or compute. Our approach operates in both parameter space and data flow space, allowing optimization beyond just the weights of the individual models. This approach even facilitates cross-domain merging, generating models such as a Japanese LLM with math reasoning capabilities. Surprisingly, our Japanese math LLM achieved state-of-the-art performance on a variety of established Japanese LLM benchmarks, even surpassing models with substantially more parameters, despite not being explicitly trained for such tasks. Furthermore, a culturally aware Japanese vision–language model generated through our approach demonstrates its effectiveness in describing Japanese culture-specific content, outperforming previous Japanese vision–language models. This work not only contributes new state-of-the-art models back to the open-source community but also introduces a new paradigm for automated model composition, paving the way for exploring alternative, efficient approaches to foundation model development. Akiba et al. developed an evolutionary approach to automatically merge artificial intelligence models, creating powerful hybrid models without extensive training. The method produces models with enhanced mathematical and visual capabilities that outperform larger models.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 2","pages":"195-204"},"PeriodicalIF":18.8,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42256-024-00975-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143044077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Moving towards genome-wide data integration for patient stratification with Integrate Any Omics 通过整合任意组学，向患者分层的全基因组数据整合迈进

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-01-23 DOI: 10.1038/s42256-024-00942-3

Shihao Ma, Andy G. X. Zeng, Benjamin Haibe-Kains, Anna Goldenberg, John E. Dick, Bo Wang

{"title":"Moving towards genome-wide data integration for patient stratification with Integrate Any Omics","authors":"Shihao Ma, Andy G. X. Zeng, Benjamin Haibe-Kains, Anna Goldenberg, John E. Dick, Bo Wang","doi":"10.1038/s42256-024-00942-3","DOIUrl":"10.1038/s42256-024-00942-3","url":null,"abstract":"High-throughput omics profiling advancements have greatly enhanced cancer patient stratification. However, incomplete data in multi-omics integration present a substantial challenge, as traditional methods like sample exclusion or imputation often compromise biological diversity and dependencies. Furthermore, the critical task of accurately classifying new patients with partial omics data into existing subtypes is commonly overlooked. To address these issues, we introduce Integrate Any Omics (IntegrAO), an unsupervised framework for integrating incomplete multi-omics data and classifying new samples. IntegrAO first combines partially overlapping patient graphs from diverse omics sources and utilizes graph neural networks to produce unified patient embeddings. Our systematic evaluation across five cancer cohorts involving six omics modalities demonstrates IntegrAO’s robustness to missing data and its accuracy in classifying new samples with partial profiles. An acute myeloid leukaemia case study further validates its capability to uncover biological and clinical heterogeneities in incomplete datasets. IntegrAO’s ability to handle heterogeneous and incomplete data makes it an essential tool for precision oncology, offering a holistic approach to patient characterization. Integrating incomplete multi-omics data remains a key challenge in precision oncology. IntegrAO, an unsupervised framework that integrates diverse omics, enables accurate patient classification even with incomplete datasets.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 1","pages":"29-42"},"PeriodicalIF":18.8,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143020700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Author Correction: Kernel approximation using analogue in-memory computing 作者更正：核近似使用模拟内存计算

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-01-21 DOI: 10.1038/s42256-025-00996-x

Julian Büchel, Giacomo Camposampiero, Athanasios Vasilopoulos, Corey Lammie, Manuel Le Gallo, Abbas Rahimi, Abu Sebastian

引用次数: 0

What large language models know and what people think they know 大型语言模型知道什么，以及人们认为他们知道什么

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-01-21 DOI: 10.1038/s42256-024-00976-7

Mark Steyvers, Heliodoro Tejeda, Aakriti Kumar, Catarina Belem, Sheer Karny, Xinyue Hu, Lukas W. Mayer, Padhraic Smyth

{"title":"What large language models know and what people think they know","authors":"Mark Steyvers, Heliodoro Tejeda, Aakriti Kumar, Catarina Belem, Sheer Karny, Xinyue Hu, Lukas W. Mayer, Padhraic Smyth","doi":"10.1038/s42256-024-00976-7","DOIUrl":"10.1038/s42256-024-00976-7","url":null,"abstract":"As artificial intelligence systems, particularly large language models (LLMs), become increasingly integrated into decision-making processes, the ability to trust their outputs is crucial. To earn human trust, LLMs must be well calibrated such that they can accurately assess and communicate the likelihood of their predictions being correct. Whereas recent work has focused on LLMs’ internal confidence, less is understood about how effectively they convey uncertainty to users. Here we explore the calibration gap, which refers to the difference between human confidence in LLM-generated answers and the models’ actual confidence, and the discrimination gap, which reflects how well humans and models can distinguish between correct and incorrect answers. Our experiments with multiple-choice and short-answer questions reveal that users tend to overestimate the accuracy of LLM responses when provided with default explanations. Moreover, longer explanations increased user confidence, even when the extra length did not improve answer accuracy. By adjusting LLM explanations to better reflect the models’ internal confidence, both the calibration gap and the discrimination gap narrowed, significantly improving user perception of LLM accuracy. These findings underscore the importance of accurate uncertainty communication and highlight the effect of explanation length in influencing user trust in artificial-intelligence-assisted decision-making environments. Understanding how people perceive and interpret uncertainty from large language models (LLMs) is crucial, as users often overestimate LLM accuracy, especially with default explanations. Steyvers et al. show that aligning LLM explanations with their internal confidence improves user perception.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 2","pages":"221-231"},"PeriodicalIF":18.8,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42256-024-00976-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142991513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A unified evolution-driven deep learning framework for virus variation driver prediction 用于病毒变异驱动因素预测的统一进化驱动深度学习框架

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-01-17 DOI: 10.1038/s42256-024-00966-9

Zhiwei Nie, Xudong Liu, Jie Chen, Zhennan Wang, Yutian Liu, Haorui Si, Tianyi Dong, Fan Xu, Guoli Song, Yu Wang, Peng Zhou, Wen Gao, Yonghong Tian

{"title":"A unified evolution-driven deep learning framework for virus variation driver prediction","authors":"Zhiwei Nie, Xudong Liu, Jie Chen, Zhennan Wang, Yutian Liu, Haorui Si, Tianyi Dong, Fan Xu, Guoli Song, Yu Wang, Peng Zhou, Wen Gao, Yonghong Tian","doi":"10.1038/s42256-024-00966-9","DOIUrl":"10.1038/s42256-024-00966-9","url":null,"abstract":"The increasing frequency of emerging viral infections necessitates a rapid human response, highlighting the cost-effectiveness of computational methods. However, existing computational approaches are limited by their input forms or incomplete functionalities, preventing a unified prediction of diverse virus variation drivers and hindering in-depth applications. To address this issue, we propose a unified evolution-driven framework for predicting virus variation drivers, named Evolution-driven Virus Variation Driver prediction (E2VD), which is guided by virus evolutionary traits. With evolution-inspired design, E2VD comprehensively and significantly outperforms state-of-the-art methods across various virus mutational driver prediction tasks. Moreover, E2VD effectively captures the fundamental patterns of virus evolution. It not only distinguishes different types of mutations but also accurately identifies rare beneficial mutations that are critical for viruses to survive, while maintaining generalization capabilities across different lineages of SARS-CoV-2 and different types of viruses. Importantly, with predicted biological drivers, E2VD perceives virus evolutionary trends in which potential high-risk mutation sites are accurately recommended. Overall, E2VD represents a unified, structure-free and interpretable approach for analysing and predicting viral evolutionary fitness, providing an ideal alternative to costly wet-lab measurements to accelerate responses to emerging viral infections. A unified evolution-driven deep learning framework is presented, which outperforms state-of-the-art methods across various virus mutational driver predictions, and which captures fundamental patterns of virus evolution.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 1","pages":"131-144"},"PeriodicalIF":18.8,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142987611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A quantitative analysis of knowledge-learning preferences in large language models in molecular science 分子科学中大语言模型中知识学习偏好的定量分析

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-01-17 DOI: 10.1038/s42256-024-00977-6

Pengfei Liu, Jun Tao, Zhixiang Ren

{"title":"A quantitative analysis of knowledge-learning preferences in large language models in molecular science","authors":"Pengfei Liu, Jun Tao, Zhixiang Ren","doi":"10.1038/s42256-024-00977-6","DOIUrl":"10.1038/s42256-024-00977-6","url":null,"abstract":"Deep learning has significantly advanced molecular modelling and design, enabling an efficient understanding and discovery of novel molecules. In particular, large language models introduce a fresh research paradigm to tackle scientific problems from a natural language processing perspective. Large language models significantly enhance our understanding and generation of molecules, often surpassing existing methods with their capabilities to decode and synthesize complex molecular patterns. However, two key issues remain: how to quantify the match between model and data modalities and how to identify the knowledge-learning preferences of models. To address these challenges, we propose a multimodal benchmark, named ChEBI-20-MM, and perform 1,263 experiments to assess the model’s compatibility with data modalities and knowledge acquisition. Through the modal transition probability matrix, we provide insights into the most suitable modalities for tasks. Furthermore, we introduce a statistically interpretable approach to discover context-specific knowledge mapping by localized feature filtering. Our analysis offers an exploration of the learning mechanism and paves the way for advancing large language models in molecular science. Large language models promise substantial advances in molecular modelling and design. A multimodal benchmark is proposed to analyse performance, and 1,263 experiments are conducted to examine the compatibility of a large language model with data modalities and knowledge acquisition.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 2","pages":"315-327"},"PeriodicalIF":18.8,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142987610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning from models beyond fine-tuning 从模型中学习超越微调

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-01-16 DOI: 10.1038/s42256-024-00961-0

Hongling Zheng, Li Shen, Anke Tang, Yong Luo, Han Hu, Bo Du, Yonggang Wen, Dacheng Tao

{"title":"Learning from models beyond fine-tuning","authors":"Hongling Zheng, Li Shen, Anke Tang, Yong Luo, Han Hu, Bo Du, Yonggang Wen, Dacheng Tao","doi":"10.1038/s42256-024-00961-0","DOIUrl":"10.1038/s42256-024-00961-0","url":null,"abstract":"Foundation models have demonstrated remarkable performance across various tasks, primarily due to their abilities to comprehend instructions and access extensive, high-quality data. These capabilities showcase the effectiveness of current foundation models and suggest a promising trajectory. Owing to multiple constraints, such as the extreme scarcity or inaccessibility of raw data used to train foundation models and the high cost of training large-scale foundation models from scratch, the use of pre-existing foundation models or application programming interfaces for downstream tasks has become a new research trend, which we call Learn from Model (LFM). LFM involves extracting and leveraging prior knowledge from foundation models through fine-tuning, editing and fusion methods and applying it to downstream tasks. We emphasize that maximizing the use of parametric knowledge in data-scarce scenarios is critical to LFM. Analysing the LFM paradigm can guide the selection of the most appropriate technology in a given scenario to minimize parameter storage and computational costs while improving the performance of foundation models on new tasks. This Review provides a comprehensive overview of current methods based on foundation models from the perspective of LFM. Large general-purpose models are becoming more prevalent and useful, but also harder to train and find suitable training data for. Zheng et al. discuss how models can be used to train other models.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 1","pages":"6-17"},"PeriodicalIF":18.8,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142986399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A machine learning approach to leveraging electronic health records for enhanced omics analysis 利用电子健康记录增强组学分析的机器学习方法

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-01-16 DOI: 10.1038/s42256-024-00974-9

Samson J. Mataraso, Camilo A. Espinosa, David Seong, S. Momsen Reincke, Eloise Berson, Jonathan D. Reiss, Yeasul Kim, Marc Ghanem, Chi-Hung Shu, Tomin James, Yuqi Tan, Sayane Shome, Ina A. Stelzer, Dorien Feyaerts, Ronald J. Wong, Gary M. Shaw, Martin S. Angst, Brice Gaudilliere, David K. Stevenson, Nima Aghaeepour

{"title":"A machine learning approach to leveraging electronic health records for enhanced omics analysis","authors":"Samson J. Mataraso, Camilo A. Espinosa, David Seong, S. Momsen Reincke, Eloise Berson, Jonathan D. Reiss, Yeasul Kim, Marc Ghanem, Chi-Hung Shu, Tomin James, Yuqi Tan, Sayane Shome, Ina A. Stelzer, Dorien Feyaerts, Ronald J. Wong, Gary M. Shaw, Martin S. Angst, Brice Gaudilliere, David K. Stevenson, Nima Aghaeepour","doi":"10.1038/s42256-024-00974-9","DOIUrl":"10.1038/s42256-024-00974-9","url":null,"abstract":"Omics studies produce a large number of measurements, enabling the development, validation and interpretation of systems-level biological models. Large cohorts are required to power these complex models; yet, the cohort size remains limited due to clinical and budgetary constraints. We introduce clinical and omics multimodal analysis enhanced with transfer learning (COMET), a machine learning framework that incorporates large, observational electronic health record databases and transfer learning to improve the analysis of small datasets from omics studies. By pretraining on electronic health record data and adaptively blending both early and late fusion strategies, COMET overcomes the limitations of existing multimodal machine learning methods. Using two independent datasets, we showed that COMET improved the predictive modelling performance and biological discovery compared with the analysis of omics data with traditional methods. By incorporating electronic health record data into omics analyses, COMET enables more precise patient classifications, beyond the simplistic binary reduction to cases and controls. This framework can be broadly applied to the analysis of multimodal omics studies and reveals more powerful biological insights from limited cohort sizes. COMET, an artificial intelligence method that improves the analysis of small medical studies using large clinical databases, has been created. COMET can help develop better artificial intelligence tools and identify key biomarkers across many diseases, potentially changing medical research.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 2","pages":"293-306"},"PeriodicalIF":18.8,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42256-024-00974-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142986398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning 基于细胞间深度学习的不同老化条件下电池寿命预测

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-01-15 DOI: 10.1038/s42256-024-00972-x

Han Zhang, Yuqi Li, Shun Zheng, Ziheng Lu, Xiaofan Gui, Wei Xu, Jiang Bian

{"title":"Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning","authors":"Han Zhang, Yuqi Li, Shun Zheng, Ziheng Lu, Xiaofan Gui, Wei Xu, Jiang Bian","doi":"10.1038/s42256-024-00972-x","DOIUrl":"10.1038/s42256-024-00972-x","url":null,"abstract":"Accurately predicting battery lifetime in early cycles holds tremendous value in real-world applications. However, this task poses significant challenges due to diverse factors influencing complex battery capacity degradation, such as cycling protocols, ambient temperatures and electrode materials. Moreover, cycling under specific conditions is both resource-intensive and time-consuming. Existing predictive models, primarily developed and validated within a restricted set of ageing conditions, thus raise doubts regarding their extensive applicability. Here we introduce BatLiNet, a deep learning framework tailored to predict battery lifetime reliably across a variety of ageing conditions. The distinctive design is integrating an inter-cell learning mechanism to predict the lifetime differences between two battery cells. This mechanism, when combined with conventional single-cell learning, enhances the stability of lifetime predictions for a target cell under varied ageing conditions. Our experimental results, derived from a broad spectrum of ageing conditions, demonstrate BatLiNet’s superior accuracy and robustness compared to existing models. BatLiNet also exhibits transferring capabilities across different battery chemistries, benefitting scenarios with limited resources. We expect this study could promote exploration of cross-cell insights and facilitate battery research across comprehensive ageing factors. Zhang and colleagues introduce an inter-cell learning mechanism to predict battery lifetime in the presence of diverse ageing conditions.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 2","pages":"270-277"},"PeriodicalIF":18.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42256-024-00972-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142981810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Visual cognition in multimodal large language models 多模态大语言模型中的视觉认知

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-01-15 DOI: 10.1038/s42256-024-00963-y

Luca M. Schulze Buschoff, Elif Akata, Matthias Bethge, Eric Schulz

{"title":"Visual cognition in multimodal large language models","authors":"Luca M. Schulze Buschoff, Elif Akata, Matthias Bethge, Eric Schulz","doi":"10.1038/s42256-024-00963-y","DOIUrl":"10.1038/s42256-024-00963-y","url":null,"abstract":"A chief goal of artificial intelligence is to build machines that think like people. Yet it has been argued that deep neural network architectures fail to accomplish this. Researchers have asserted these models’ limitations in the domains of causal reasoning, intuitive physics and intuitive psychology. Yet recent advancements, namely the rise of large language models, particularly those designed for visual processing, have rekindled interest in the potential to emulate human-like cognitive abilities. This paper evaluates the current state of vision-based large language models in the domains of intuitive physics, causal reasoning and intuitive psychology. Through a series of controlled experiments, we investigate the extent to which these modern models grasp complex physical interactions, causal relationships and intuitive understanding of others’ preferences. Our findings reveal that, while some of these models demonstrate a notable proficiency in processing and interpreting visual data, they still fall short of human capabilities in these areas. Our results emphasize the need for integrating more robust mechanisms for understanding causality, physical dynamics and social cognition into modern-day, vision-based language models, and point out the importance of cognitively inspired benchmarks. Modern vision-based language models face challenges with complex physical interactions, causal reasoning and intuitive psychology. Schulze Buschoff and colleagues demonstrate that while some models exhibit proficient visual data processing capabilities, they fall short of human performance in these cognitive domains.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 1","pages":"96-106"},"PeriodicalIF":18.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42256-024-00963-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142981812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0