检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Weng, Kangyu Cheng, Aohua Zhang, Ziyang Sun, Pei Tian, Yang Tsien Excellence in Engineering Program Xingjian College Tsinghua University Beijing100084 China Laboratory of Advanced Computing and Storage Central Research Institute 2012 Laboratories Huawei Technologies Co. Ltd. Beijing100084 China Department of Psychology & Tsinghua Brain and Intelligence Lab Tsinghua University Beijing100084 China

In deep learning, neural networks serve as noisy channels between input data and its representation. This perspective naturally relates deep learning with the pursuit of constructing channels with optimal performance in information transmission and representation. While considerable efforts are concentrated on realizing optimal channel properties during network optimization, we study a frequently overlooked possibility that neural networks can be initialized toward optimal channels. Our theory, consistent with experimental validation, identifies primary mechanics underlying this unknown possibility and suggests intrinsic connections between statistical physics and deep learning. Unlike the conventional theories that characterize neural networks applying the classic mean-filed approximation, we offer analytic proof that this extensively applied simplification scheme is not valid in studying neural networks as information channels. To fill this gap, we develop a corrected mean-field framework applicable for characterizing the limiting behaviors of information propagation in neural networks without strong assumptions on inputs. Based on it, we propose an analytic theory to prove that mutual information maximization is realized between inputs and propagated signals when neural networks are initialized at dynamic isometry, a case where information transmits via norm-preserving mappings. These theoretical predictions are validated by experiments on real neural networks, suggesting the robustness of our theory against finite-size effects. Finally, we analyze our findings with information bottleneck theory to confirm the precise relations among dynamic isometry, mutual information maximization, and optimal channel properties in deep learning. Our work may lay a cornerstone for promoting deep learning in terms of network initialization and suggest general statistical physics mechanisms underlying diverse deep learning *** Codes 82C44, 82C05 © 2022, CC BY.

关键词： Statistical Physics

来源：评论

学校读者我要写书评

暂无评论

A unified theory of information transfer and causal relation

arXiv

引用

arXiv 2022年

作者： Tian, Yang Hou, Hedong Wang, Yaoyuan Zhang, Ziyang Sun, Pei Department of Psychology Tsinghua Laboratory of Brain and Intelligence Tsinghua University Beijing100084 China UFR de Mathematiques Universite de Paris Paris75013 France Department of Psychology Tsinghua Brain and Intelligence Lab Tsinghua University Beijing100084 China Laboratory of Advanced Computing and Storage Central Research Institute 2012 Laboratories Huawei Technologies Co. Ltd. Beijing100084 China

Information transfer between coupled stochastic dynamics, measured by transfer entropy and information flow, is suggested as a physical process underlying the causal relation of systems. While information transfer analysis has booming applications in both science and engineering fields, critical mysteries about its foundations remain unsolved. Fundamental yet difficult questions concern how information transfer and causal relation originate, what they depend on, how they differ from each other, and if they are created by a unified and general quantity. These questions essentially determine the validity of causal relation measurement via information transfer. Here we pursue to lay a complete theoretical basis of information transfer and causal relation. Beyond the well-known relations between these concepts that conditionally hold, we demonstrate that information transfer and causal relation universally originate from specific information synergy and redundancy phenomena characterized by high-order mutual information. More importantly, our theory analytically explains the mechanisms for information transfer and causal relation to originate, vanish, and differ from each other. Moreover, our theory naturally defines the effect sizes of information transfer and causal relation based on high-dimensional coupling events. These results may provide a unified view of information, synergy, and causal relation to bridge Pearl's causal inference theory in computer science and information transfer analysis in physics. © 2022, CC BY.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Token-Mol 1.0：tokenized drug design with large language model

arXiv

引用

arXiv 2024年

作者： Wang, Jike Qin, Rui Wang, Mingyang Fang, Meijing Zhang, Yangyang Zhu, Yuchen Su, Qun Wan, Xiaozhe Gou, Qiaolin Shen, Chao Zhang, Odin Wu, Zhenxing Jiang, Dejun Zhang, Xujun Zhao, Huifeng Ge, Jingxuan Wu, Zhourui Liu, Liwei Kang, Yu Hsieh, Chang-Yu Hou, Tingjun College of Pharmaceutical Sciences Zhejiang University Zhejiang Hangzhou310058 China Advanced Computing and Storage Laboratory Central Research Institute 2012 Laboratories Huawei Technologies Co. Ltd. Jiangsu Nanjing210000 China Paul Allen School University of Washington Seattle United States Key Laboratory of Spine and Spinal cord Injury Repair and Regeneration Ministry of Education Tongji University Shanghai China

Significant interests have recently risen in leveraging sequence-based large language models (LLMs) for drug design. However, most current applications of LLMs in drug discovery lack the ability to comprehend 3D structures, thereby limiting their effectiveness in tasks that specifically involve molecular conformations. In this study, we introduce Token-Mol, a token-only 3D drug design model. This model encodes all pertinent molecular information, including both 2D and 3D structures, as well as molecular property data, into tokens. It transforms classification and regression tasks in drug discovery into probabilistic prediction problems, ultimately enabling a comprehensive and cohesive learning approach through a unified paradigm. Token-Mol is constructed on the foundation of the transformer decoder architecture, and trained using random causal masking techniques. Moreover, we propose the Gaussian cross-entropy loss function to overcome the challenges encountered in regression tasks, which significantly enhances the capacity of LLMs to learn continuous numerical values. Experimental validations demonstrate that Token-Mol surpasses state-of-the-art methods in the molecular conformation generation task, achieving improvements across various metrics of over 10% and 20% on two datasets, respectively. In a series of molecular property prediction tasks, Token-Mol exhibits an average improvement of 30% in regression tasks compared to existing token-only models. In the pocket-based molecular generation task, Token-Mol can generate molecules with a notable improvement in the drug-likeness (QED) and synthetic accessibility by ~11% and 14%, respectively, meanwhile maintaining a comparable level of affinity, as estimated by the Vina score, compared to existing methods. When compared to expert models based on diffusion architecture that exhibit similar performance, Token-Mol demonstrates an impressive generation speed that is 35 times faster. In real-world drug design scenarios,

关键词： Structural dynamics

来源：评论

学校读者我要写书评

暂无评论

Network comparison via encoding, decoding, and causality

arXiv

引用

arXiv 2022年

作者： Tian, Yang Hou, Hedong Xu, Guangzheng Zhang, Ziyang Sun, Pei Department of Psychology Tsinghua Laboratory of Brain and Intelligence Tsinghua University Beijing100084 China Laboratory of Advanced Computing and Storage Central Research Institute 2012 Laboratories Huawei Technologies Co. Ltd. Beijing100084 China Institut de Mathématiques d’Orsay 14 Rue Joliot-Curie Gif-sur-Yvette91190 France Department of Computer Science University College London LondonWC1E 6AE United Kingdom

Quantifying the relations (e.g., similarity) between complex networks paves the way for studying the latent information shared across networks. However, fundamental relation metrics are not well-defined between networks. As a compromise, prevalent techniques measure network relations in data-driven manners, which are inapplicable to analytic derivations in physics. To resolve this issue, we present a theory for obtaining an optimal characterization of network topological properties. We show that a network can be fully represented by a Gaussian variable defined by a function of the Laplacian, which simultaneously satisfies network-topology-dependent smoothness and maximum entropy properties. Based on it, we can analytically measure diverse relations between complex networks. As illustrations, we define encoding (e.g., information divergence and mutual information), decoding (e.g., Fisher information), and causality (e.g., Granger causality and conditional mutual information) between networks. We validate our framework on representative networks (e.g., random networks, protein structures, and chemical compounds) to demonstrate that a series of science and engineering challenges (e.g., network evolution, embedding, and query) can be tackled from a new perspective. An implementation of our theory is released as a multi-platform *** Codes 05C62, 05C80, 05C90, 68P01 © 2022, CC BY.

关键词： Complex networks

来源：评论

学校读者我要写书评

暂无评论

High-Ni Low-Co High-Capacity Li-Rich Cathode Materials Via Adjusting Li2mno3 Content and Li/Ni Mixing Structures

SSRN

引用

SSRN 2023年

作者： Wu, Zhen Zou, Kunyang Dai, Xin Zhang, Yu-Han Zhang, Xudong Wang, Hao Hou, Zhufeng Ma, Lijing Liu, Yan Chen, Yuanzhen Guo, Shengwu Liu, Yongning State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong University Xi’an710049 China Qingdao Industrial Energy Storage Research Institute Qingdao Institute of Bioenergy and Bioprocess Technology Chinese Academy of Sciences Qingdao266101 China Center for High Performance Computing Network Information Center Xi’an Jiaotong University Xi’an710049 China Hefei Advanced Computing Center Operation Management Corp Ltd Hefei230088 China State Key Laboratory of Structural Chemistry Fujian Institute of Research the Structure of Matter Chinese Academy of Sciences Fuzhou350002 China International Research Center for Renewable Energy State Key Laboratory of Multiphase Flow in Power Engineering Xi’an Jiaotong University Xi’an710049 China

Li-rich layered oxides (LLOs) are considered as the promising cathode materials for next-generation high energy density lithium-ion batteries (LIBs). However, severe voltage fade and capacity decay hinder their commercial applications. Increasing the Ni content in LLOs effectively improves the stability and discharge voltage, but shows reduced capacities, typically © 2023, The Authors. All rights reserved.

关键词： Lithium compounds

来源：评论

学校读者我要写书评

暂无评论

Theoretical foundations of studying criticality in the brain

arXiv

引用

arXiv 2023年

作者： Tian, Yang Tan, Zeren Hou, Hedong Li, Guoqi Cheng, Aohua Qiu, Yike Weng, Kangyu Chen, Chun Sun, Pei Department of Psychology Tsinghua Laboratory of Brain and Intelligence Tsinghua University Beijing100084 China Laboratory of Advanced Computing and Storage Central Research Institute 2012 Laboratories Huawei Technologies Co. Ltd. Beijing100084 China Institute for Interdisciplinary Information Science Tsinghua University Beijing100084 China UFR de Mathématiques Université de Paris Paris75013 France Institute of Automation Chinese Academy of Science Beijing100190 China University of Chinese Academy of Science Beijing100049 China Tsien Excellence in Engineering Program School of Aerospace Engineering Tsinghua University Beijing100084 China

Criticality is hypothesized as a physical mechanism underlying efficient transitions between cortical states and remarkable information processing capacities in the brain. While considerable evidence generally supports this hypothesis, non-negligible controversies persist regarding the ubiquity of criticality in neural dynamics and its role in information processing. Validity issues frequently arise during identifying potential brain criticality from empirical data. Moreover, the functional benefits implied by brain criticality are frequently misconceived or unduly generalized. These problems stem from the non-triviality and immaturity of the physical theories that analytically derive brain criticality and the statistic techniques that estimate brain criticality from empirical data. To help solve these problems, we present a systematic review and reformulate the foundations of studying brain criticality, i.e., ordinary criticality (OC), quasi-criticality (qC), self-organized criticality (SOC), and self-organized quasi-criticality (SOqC), using the terminology of neuroscience. We offer accessible explanations of the physical theories and statistic techniques of brain criticality, providing step-by-step derivations to characterize neural dynamics as a physical system with avalanches. We summarize error-prone details and existing limitations in brain criticality analysis and suggest possible solutions. Moreover, we present a forward-looking perspective on how optimizing the foundations of studying brain criticality can deepen our understanding of various neuroscience questions. © 2023, CC BY.

关键词： Brain

来源：评论

学校读者我要写书评

暂无评论

Highly Stable Zinc Anodes Enabled by a Dual-Function Additive of In-Situ Film Formation and Electrostatic Shielding

SSRN

引用

SSRN 2024年

作者： Ji, Rui-Han Fu, Hao Wen, Qing Li, Pei-Yao Yang, Pei Chen, Yu-Jing Chen, He-Zhang Zhang, Jia-Feng Li, Ling-Jun Wang, Jie-Xi Wu, Qing Zhang, Xia-Hui Zheng, Jun-Chao Washington State University United States School of Metallurgy and Environment Central South University Hunan Changsha410083 China Engineering Research Center of the Ministry of Education for Advanced Battery Materials Central South University Hunan Changsha410083 China National Energy Metal Resources and New Materials Key Laboratory Central South University Hunan Changsha410083 China School of Chemistry and Chemical Engineering Hunan University of Science and Technology Hunan Xiangtan411201 China School of Materials Science and Engineering Changsha University of Science and Technology Changsha410114 China National Engineering Research Centre of Advanced Energy Storage Materials Changsha410205 China School of Information and Network Center Central South University Hunan Changsha410083 China Center for High-Performance Computing United States

The practical application of aqueous Zn-ion batteries (AZIBs) is significantly hindered by the short lifespan and low utilization of zinc metal anodes caused by the notorious hydrogen evolution reaction (HER), corrosion, and dendritic growth. The in-situ formation of a dense Zn-ion conducting solid electrolyte interphase (SEI) is an ideal approach to handle these problems. Herein, we present a twofold-protective strategy involving the dense in-situ SEI layer and the electrostatic shielding layer by incorporating 0.01 M yttrium nitrate (YN) additive into 2 M zinc sulfate electrolyte. The robust oxidizing characteristic of the nitrate induces rapid passivation on the surface of Zn anodes, by which these inevitable passivation byproducts are utilized for in-situ fabricating a dense SEI protection layer with excellent wettability. Besides, the infusion of high-valence rare-earth Y3+ forms a durable, dynamic electrostatic shielding layer around the growth tip of the protuberances. This can regulate Zn2+ deposition and aid in rectifying the irregular and sharp surface on zinc anodes. Benefiting from the synergistic effect of the static film protection and the dynamic electrostatic rectification, zinc anodes exhibit outstanding stability, as evidenced by remarkable cycling lifespan of exceeding 2000 h at 1 mAh cm-2 and 600 h at 10 mAh cm-2 in Zn symmetric cells and the preeminent CE of 99.83% with stably cycling over 2200 times in half-cells at 4 mA cm-2. This study reveals a straightforward yet pragmatic avenue of synergistic dual-protection effect to achieve anti-corrosion and dendrite-free Zn anodes. © 2024, The Authors. All rights reserved.

关键词： Passivation

来源：评论

学校读者我要写书评

暂无评论

KoopmanLab: Machine learning for solving complex physics equations

APL Machine Learning

引用

APL Machine Learning 2023年第3期1卷

作者： Wei Xiong Muyuan Ma Xiaomeng Huang Ziyang Zhang Pei Sun Yang Tian Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing 100084 China Laboratory of Advanced Computing and Storage Central Research Institute 2012 Laboratories Huawei Technologies Co. Ltd. Beijing 100084 China Department of Psychology and Tsinghua Laboratory of Brain and Intelligence Tsinghua University Beijing 100084 China

Numerous physics theories are rooted in partial differential equations (PDEs). However, the increasingly intricate physics equations, especially those that lack analytic solutions or closed forms, have impeded the further development of physics. Computationally solving PDEs by classic numerical approaches suffers from the trade-off between accuracy and efficiency and is not applicable to the empirical data generated by unknown latent PDEs. To overcome this challenge, we present KoopmanLab, an efficient module of the Koopman neural operator (KNO) family, for learning PDEs without analytic solutions or closed forms. Our module consists of multiple variants of the KNO, a kind of mesh-independent neural-network-based PDE solvers developed following the dynamic system theory. The compact variants of KNO can accurately solve PDEs with small model sizes, while the large variants of KNO are more competitive in predicting highly complicated dynamic systems govern by unknown, high-dimensional, and non-linear PDEs. All variants are validated by mesh-independent and long-term prediction experiments implemented on representative PDEs (e.g., the Navier–Stokes equation and the Bateman–Burgers equation in fluid mechanics) and ERA5 (i.e., one of the largest high-resolution global-scale climate datasets in earth physics). These demonstrations suggest the potential of KoopmanLab to be a fundamental tool in diverse physics studies related to equations or dynamic systems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Perspectives for self-driving labs in synthetic biology

arXiv

引用

arXiv 2022年

作者： Martin, Hector Garcia Radivojevic, Tijana Zucker, Jeremy Bouchard, Kristofer Sustarich, Jess Peisert, Sean Arnold, Dan Hillson, Nathan Babnigg, Gyorgy Marti, Jose Manuel Mungall, Christopher J. Beckham, Gregg T. Waldburger, Lucas Carothers, James Sundaram, ShivShankar Agarwal, Deb Simmons, Blake A. Backman, Tyler Banerjee, Deepanwita Tanjore, Deepti Ramakrishnan, Lavanya Singh, Anup Lawrence Berkeley National Laboratory Biological Systems and Engineering Division BerkeleyCA United States Department of Energy Agile BioFoundry EmeryvilleCA United States Joint BioEnergy Institute EmeryvilleCA United States BCAM Basque Center for Applied Mathematics Bilbao Spain Lawrence Berkeley National Laboratory Scientific Data Division BerkeleyCA United States Helen Wills Neuroscience Institute Redwood Center for Theoretical Neuroscience BerkeleyCA United States Lawrence Berkeley National Laboratory Energy Storage and Distributed Resources Division BerkeleyCA United States University of California Davis Department of Computer Science DavisCA United States Global Security Computing Applications Division Lawrence Livermore National Laboratory LivermoreCA United States Engineering Directorate Lawrence Livermore National Laboratory LivermoreCA United States Center for Bioengineering Lawrence Livermore National Laboratory LivermoreCA United States Department of Chemical Engineering Molecular Engineering & Sciences Institute Center for Synthetic Biology University of Washington SeattleWA United States Biosciences Division Argonne National Laboratory ArgonneIL United States Advanced Biofuels and Bioproducts Process Development Unit Lawrence Berkeley National Laboratory BerkeleyCA United States Biomaterials and Biomanufacturing Division Sandia National Laboratories LivermoreCA United States Earth and Biological Sciences Division Pacific Northwest National Laboratories RichlandWA United States Resources and Enabling Sciences Center National Renewable Energy Laboratory GoldenCO80401 United States Department of Bioengineering University of California BerkeleyCA United States

Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed towards solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology. © 2022, CC BY.

关键词： Synthetic biology

来源：评论

学校读者我要写书评

暂无评论

Thermal Conductivity: Thermal Conductivity of Amorphous Materials (Adv. Funct. Mater. 8/2020)

引用

advanced Functional Materials 2020年第8期30卷

作者： Wu‐Xing Zhou Yuan Cheng Ke‐Qiu Chen Guofeng Xie Tian Wang Gang Zhang School of Materials Science and Engineering & Hunan Provincial Key Laboratory of Advanced Materials for New Energy Storage and Conversion Hunan University of Science and Technology Xiangtan 411201 China Institute of High Performance Computing A*STAR Singapore 138632 Singapore Department of Applied Physics School of Physics and Electronics Hunan University Changsha 410082 China School of Automation Science and Electrical Engineering Beihang University Beijing 100083 China

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：