Zifan Zheng,Yezhaohui Wang,Yuxin Huang et al.
Zifan Zheng et al.
Large language models (LLMs) have demonstrated performance approaching human levels in tasks such as long-text comprehension and mathematical reasoning, but they remain black-box systems. Understanding the reasoning bottlenecks of LLMs rema...
Model interpretability enhances domain generalization in the case of textual complexity modeling [0.03%]
模型可解释性增强了文本复杂度建模中的领域泛化能力
Frans van der Sluis,Egon L van den Broek
Frans van der Sluis
Balancing prediction accuracy, model interpretability, and domain generalization (also known as [a.k.a.] out-of-distribution testing/evaluation) is a central challenge in machine learning. To assess this challenge, we took 120 interpretable...
Alejandra Alvarado
Alejandra Alvarado
Erratum: Data-driven evaluation of electric vehicle energy consumption for generalizing standard testing to real-world driving [0.03%]
错误！：数据驱动的电动汽车能耗评估，用于将标准测试推广到实际驾驶条件
Xinmei Yuan,Jiangbiao He,Yutong Li et al.
Xinmei Yuan et al.
[This corrects the article DOI: 10.1016/j.patter.2024.100950.]. © 2025 The Author(s).
Published Erratum
Patterns (New York, N.Y.). 2025 Jan 22;6(2):101173. DOI:10.1016/j.patter.2025.101173 2025
Erratum: A latent transfer learning method for estimating hospital-specific post-acute healthcare demands following SARS-CoV-2 infection [0.03%]
erratum:一种潜在的迁移学习方法，用于估计SARS-CoV-2感染后特定医院的后续急性医疗需求量
Qiong Wu,Nathan M Pajor,Yiwen Lu et al.
Qiong Wu et al.
[This corrects the article DOI: 10.1016/j.patter.2024.101079.]. © 2025 The Author(s).
Published Erratum
Patterns (New York, N.Y.). 2025 Jan 23;6(2):101179. DOI:10.1016/j.patter.2025.101179 2025
Unraveling the complexity of rat object vision requires a full convolutional network and beyond [0.03%]
解开大鼠物体视觉的复杂性需要全卷积网络及更多技术
Paolo Muratore,Alireza Alemi,Davide Zoccolan
Paolo Muratore
Despite their prominence as model systems of visual functions, it remains unclear whether rodents are capable of truly advanced processing of visual information. Here, we used a convolutional neural network (CNN) to measure the computationa...
AI-assisted facial analysis in healthcare: From disease detection to comprehensive management [0.03%]
医疗保健中的人工智能面部分析：从疾病检测到综合管理
Chaoyu Lei,Kang Dang,Sifan Song et al.
Chaoyu Lei et al.
Medical conditions and systemic diseases often manifest as distinct facial characteristics, making identification of these unique features crucial for disease screening. However, detecting diseases using facial photography remains challengi...
Erratum: Decorrelative network architecture for robust electrocardiogram classification [0.03%]
Erratum:用于鲁棒心电图分类的解相关网络结构
Christopher Wiedeman,Ge Wang
Christopher Wiedeman
[This corrects the article DOI: 10.1016/j.patter.2024.101116.]. © 2025 The Author(s).
Published Erratum
Patterns (New York, N.Y.). 2025 Jan 22;6(2):101180. DOI:10.1016/j.patter.2025.101180 2025
Why the Environmental Data & Governance Initiative is archiving public environmental data [0.03%]
环境数据与治理计划为何要存档公共环境数据
Eric Nost,Gretchen Gehrke,Lourdes Vera et al.
Eric Nost et al.
Public data help researchers and civic organizations develop solutions and advance accountability around environmental challenges but are vulnerable to political threats. While the Environmental Data & Governance Initiative archives data to...
A deep learning model for characterizing protein-RNA interactions from sequences at single-base resolution [0.03%]
基于序列的单碱基分辨率蛋白质- RNA相互作用特征的深度学习模型
Xilin Shen,Yayan Hou,Xueer Wang et al.
Xilin Shen et al.
Protein-RNA interactions play pivotal roles in regulating transcription, translation, and RNA metabolism. Characterizing these interactions offers key insights into RNA dysregulation mechanisms. Here, we introduce Reformer, a deep learning ...