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The importance of data assimilation components for initial conditions and subsequent error growth

Science China Earth Sciences 2024年第1期67卷 105-116页

作者： zhongrui wang Haohao SUN Lili LEI Zhe-Min TAN Yi ZHANG Key Laboratory of Mesoscale Severe Weather Ministry of Educationand School of Atmospheric SciencesNanjing UniversityNanjing 210023China Frontiers Science Center for Critical Earth Material Cycling Nanjing UniversityNanjing 210023China

Despite a specific data assimilation method,data assimilation(DA)in general can be decomposed into components of the prior information,observation forward operator that is given by the observation type,observation error covariances,and background error *** a classic Lorenz model,the influences of the DA components on the initial conditions(ICs)and subsequent forecasts are systematically investigated,which could provide a theoretical basis for the design of DA for different scales of *** forecast errors undergo three typical stages:a slow growth stage from 0 h to 5 d,a fast growth stage from 5 d to around 15 d with significantly different error growth rates for ensemble and deterministic forecasts,and a saturation stage after 15 *** strategies that provide more accurate ICs can improve the *** assimilation is superior to offline assimilation,and a flow-dependent background error covariance matrix(Pf)provides better analyses than a static background error covariance matrix(B)for instantaneous observations and frequent time-averaged observations;but the opposite is true for infrequent time-averaged observations,since cycling simulation cannot construct informative priors when the model lacks predictive skills and the flow-dependent Pf cannot effectively extract information from low-informative observations as the static *** observations contain more information than time-averaged observations,thus the former is preferred,especially for infrequent observing ***,ensemble forecasts have advantages over deterministic forecasts,and the advantages are enlarged with less informative observations and lower predictive-skill model priors.

关键词： Data assimilation Atmospheric predictability Background error covariances Ensemble forecasts

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CMN: a co-designed neural architecture search for efficient computing-in-memory-based mixture-of-experts

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Science China(Information Sciences) 2024年第10期67卷 100-114页

作者： Shihao HAN Sishuo LIU Shucheng DU Mingzi LI Zijian YE Xiaoxin XU Yi LI zhongrui wang Dashan SHANG Department of Electrical and Electronic Engineering The University of Hong Kong ACCESS-AI Chip Center for Emerging Smart Systems InnoHK Centers Key Lab of Fabrication Technologies for Integrated Circuits Chinese Academy of Sciences Laboratory of Microelectronics Devices and Integrated Technology Institute of MicroelectronicsChinese Academy of Sciences University of Chinese Academy of Sciences School of Microelectronics Southern University of Science and Technology

Artificial intelligence(AI) has experienced substantial advancements recently, notably with the advent of large-scale language models(LLMs) employing mixture-of-experts(MoE) techniques, exhibiting human-like cognitive skills. As a promising hardware solution for edge MoE implementations, the computing-in-memory(CIM) architecture collocates memory and computing within a single device, significantly reducing the data movement and the associated energy consumption. However, due to diverse edge application scenarios and constraints, determining the optimal network structures for MoE, such as the expert's location,quantity, and dimension on CIM systems remains elusive. To this end, we introduce a software-hardware co-designed neural architecture search(NAS) framework, CIM-based MoE NAS(CMN), focusing on identifying a high-performing MoE structure under specific hardware constraints. The results of the NYUD-v2 dataset segmentation on the RRAM(SRAM) CIM system reveal that CMN can discover optimized MoE configurations under energy, latency, and performance constraints, achieving 29.67×(43.10×) energy savings,175.44×(109.89×) speedup, and 12.24× smaller model size compared to the baseline MoE-enabled Visual Transformer, respectively. This co-design opens up an avenue toward high-performance MoE deployments in edge CIM systems.

关键词： mixture-of-experts computing-in-memory neural architecture search resistive random-access memory static random-access memory

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业态融合视域下鞍山铁西民族文化商业街设计研究

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丝网印刷 2024年第10期 66-68页

作者：王忠睿李延鹏辽宁科技大学

分析工业城市发展以及业态融合与民族文化的现状,提出结合传统民俗元素与现代商业活动街区的设计理论。通过鞍山铁西民族文化街实践案例,验证业态融合对提升商业街区吸引力和竞争力的设想。

关键词：业态融合民族文化街区改造

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中老年2型糖尿病合并稳定型心绞痛患者中西医预后模型探讨

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中国实验方剂学杂志 2024年第14期30卷 138-144页

作者：王中瑞朱蓉甄倩赵瑞霞燕树勋邵明义余海滨符宇河南中医药大学郑州450000 河南中医药大学第一附属医院郑州450000

目的:基于中医真实世界临床数据探索中老年2型糖尿病合并稳定型心绞痛(T2DM-SAP)患者发生主要心脑血管不良事件(MACCE)的危险因素,建立COX比例风险预测模型并使用列线图将预测结果可视化。方法:基于临床科研信息共享系统回顾性收集2012... 详细信息

目的:基于中医真实世界临床数据探索中老年2型糖尿病合并稳定型心绞痛(T2DM-SAP)患者发生主要心脑血管不良事件(MACCE)的危险因素,建立COX比例风险预测模型并使用列线图将预测结果可视化。方法:基于临床科研信息共享系统回顾性收集2012年1月至2019年12月中老年(45~94岁)T2DM-SAP患者病历资料586例,包括年龄、性别、病程、主要病史、实验室检查、舌象、脉象、中医证候、主要治疗药物等资料,通过电话、再次住院记录获得随访患者MACCE结局指标。按7∶3将数据分为训练集与验证集。在训练集中,采用COX单因素分析确定T2DM-SAP患者发生MACCE的危险因素,再通过向前向后逐步回归方法筛选变量,建立MACCE风险预测模型,并构建列线图。通过C指数(C-index)、受试者工作特征曲线、校准图、临床决策曲线反映模型的预测效能。结果:脑血管病史[风险比(HR)=1.983,95%置信区间(CI,1.314~2.993)]、低密度脂蛋白胆固醇(LDL-C/mmol·L^(-1))≥4.1[HR=2.683,95%CI(1.461~4.925)]、暗红舌[HR=1.955,95%C(I 1.273~3.002)]、暗紫舌[HR=4.214,95%CI(2.017~8.803)]、白厚苔[HR=3.030,95%CI(1.634~9.293)]、细弱脉[HR=2.233,95%CI(1.283~3.888)]、风痰阻络证[HR=2.007,95%CI(1.179~3.418)]是中老年T2DM-SAP患者危险因素;胰岛素[HR=0.604,95%CI(0.399~0.914)]、糖苷酶抑制剂[HR=0.627,95%CI(0.409~0.962)]、服用中药[HR=0.328,95%CI(0.214~0.503)]是中老年T2DM-SAP患者保护因素;基于筛选出以上危险因素构建预测模型,该模型C-index在训练集中为0.818[95%CI(0.777~0.859)],在验证集中为0.814[95%CI(0.773~0.855)],并绘制C-index随时间的变化图;在训练集中5年、10年、15年受试者工作特征曲线下面积(AUC)分别为0.71、0.67、0.61;在验证集中5年、10年、15年AUC分别为0.60、0.68、0.63;在训练集与验证集分别绘制5年、10年、15年的校准图与临床决策曲线,该模型校准度良好并且存在临床有效性。结论:脑血管病史、低密度脂蛋白、暗红舌、暗紫舌、白厚苔、细弱脉、风痰阻络证是中老年T2DM-SAP患者出现MACCE的危险因素,胰岛素、糖苷酶抑制剂、中药是中老年T2DM-SAP患者出现MACCE的保护因素,并以此建立临床预测模型。该模型具有良好的区分度、校准度及临床有效性,为防治中老年T2DM-SAP患者出现MACCE提供科学依据。

关键词：中老年糖尿病合并稳定型心绞痛中医预测模型心脑血管不良事件列线图

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抑郁障碍动物模型在抑郁障碍机制和抗抑郁治疗研究中的进展

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中国药理学与毒理学杂志 2024年第9期38卷 681-693页

作者：贾靖仪张志博文睿婷曾令高王钟锐梁建辉北京大学药学院分子与细胞药理学系北京100191 北京大学附属人民医院药学部北京100044 重庆市食品药品检验检测研究院国家药品监督管理局麻醉精神药品质量监测重点实验室重庆401121

动物模型是研究抑郁障碍机制、筛选抗抑郁活性药物的有力工具,但目前尚无理想的模拟临床患者状态的模型。本文首先简述经典抑郁障碍动物模型的研究进展,而后综述与应激相关的动物模型,尤其是身体应激源和社交心理应激源诱导的模型,并介... 详细信息

动物模型是研究抑郁障碍机制、筛选抗抑郁活性药物的有力工具,但目前尚无理想的模拟临床患者状态的模型。本文首先简述经典抑郁障碍动物模型的研究进展,而后综述与应激相关的动物模型,尤其是身体应激源和社交心理应激源诱导的模型,并介绍评估动物抑郁样行为的测试,讨论各模型优缺点和应用中的注意事项,最后针对抑郁障碍的高度异质性,阐述难治性抑郁障碍、躁郁症、围产期抑郁和经前心境恶劣障碍4种抑郁障碍亚型的动物模型研究进展。

关键词：动物模型抑郁障碍应激

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基于数据驱动中医证治规律研究的核心问题及解决策略

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中国全科医学 2024年第32期27卷 4029-4032,4039页

作者：甄倩朱蓉王中瑞崔伟锋燕树勋邵明义余海滨符宇河南中医药大学河南省郑州市450046 河南省中西医结合医院河南省郑州市450003 河南中医药大学第一附属医院河南省郑州市450000

辨证论治是中医核心诊疗思维,是决定临床疗效的关键。如今,基于临床数据研究是探索中医证治规律的主要方法,但未真正而全面地剖析出“病-证-方-药-效”关键因素的内在关系,导致研究结果的临床价值较低。因此,笔者系统梳理了电子病历与... 详细信息

辨证论治是中医核心诊疗思维,是决定临床疗效的关键。如今,基于临床数据研究是探索中医证治规律的主要方法,但未真正而全面地剖析出“病-证-方-药-效”关键因素的内在关系,导致研究结果的临床价值较低。因此,笔者系统梳理了电子病历与临床研究匹配性差、数据治理影响数据准确性、数据分析方法难以发掘中医证治规律等核心问题,并在数据驱动的背景下,建立中医临床科研大数据平台、开发以人工智能为核心的数据治理与分析技术,从而实现临床科研一体化,为中医证治规律研究提供新思路与方法,推动中医药的发展。

关键词：中医药疗法证治规律数据驱动数据挖掘电子病历核心问题解决策略

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In-materio reservoir computing based on nanowire networks:fundamental,progress,and perspective

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Materials Futures 2023年第2期2卷 79-93页

作者： Renrui Fang Woyu Zhang Kuan Ren Peiwen Zhang Xiaoxin Xu zhongrui wang Dashan Shang State Key Laboratory of Fabrication Technologies for Integrated Circuits Institute of Microelectronics of Chinese Academy of SciencesBeijing 100029People’s Republic of China Department of Electrical and Electronic Engineering The University of Hong KongHong Kong 999077People’s Republic of China University of Chinese Academy of Sciences Beijing 100049People’s Republic of China

The reservoir computing(RC)system,known for its ability to seamlessly integrate memory and computing functions,is considered as a promising solution to meet the high demands for time and energy-efficient computing in the current big data landscape,compared with traditional silicon-based computing systems that have a noticeable disadvantage of separate storage and *** review focuses on in-materio RC based on nanowire networks(NWs)from the perspective of materials,extending to reservoir devices and *** common methods used in preparing nanowires-based reservoirs,including the synthesis of nanowires and the construction of networks,are firstly systematically *** physical principles of memristive and memcapacitive junctions are then ***,the dynamic characteristics of nanowires-based reservoirs and their computing capability,as well as the neuromorphic applications of NWs-based RC systems in recognition,classification,and forecasting tasks,are explicated in ***,the current challenges and future opportunities facing NWs-based RC are highlighted,aiming to provide guidance for further research.

关键词： reservoir computing nanowire networks short-term memory dynamic characteristics neuromorphic computing

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基于“内热-气血津液不归正化-脉积”探讨糖尿病大血管并发症病因病机

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辽宁中医杂志 2024年第2期51卷 69-71页

作者：王中瑞燕树勋邵明义韩景辉潘研朱蓉甄倩李真符宇河南中医药大学河南郑州450000 河南中医药大学第一附属医院河南郑州450000

大血管病变是糖尿病患者致残、致死的主要原因之一。中医药治疗效果确切,但理论发展相对滞后。传统的消渴病理论未能与大血管并发症联系起来以指导现代中医临床诊疗,缺少原发病与并发症之间的“桥梁性”理论。因此,基于临床实践、文献... 详细信息

大血管病变是糖尿病患者致残、致死的主要原因之一。中医药治疗效果确切,但理论发展相对滞后。传统的消渴病理论未能与大血管并发症联系起来以指导现代中医临床诊疗,缺少原发病与并发症之间的“桥梁性”理论。因此,基于临床实践、文献梳理和科学研究,文章提出了“内热-气血津液不归正化-脉积”的观点,认为糖尿病大血管病变早期以壮火为枢,煎灼水谷为陈气,中期以阴火为枢,引动陈气为痰瘀,晚期以虚火为枢,固化痰瘀为癥积。在治疗上,以除热消积为治疗原则,依据内热的偏重以及“陈气-痰瘀-癥积”的动态演变灵活用药。早期清透壮火,除陈气,中期调理气火,除痰瘀,晚期补虚除热,消癥积,冀为临床防治提供新的思路与方法。

关键词：糖尿病大血管并发症内热陈气痰瘀癥积

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Audio2AB:Audio-driven collaborative generation of virtual character animation

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虚拟现实与智能硬件（中英文） 2024年第1期6卷 56-70页

作者： Lichao NIU Wenjun XIE Dong wang zhongrui CAO Xiaoping LIU School of Computer Science and Information Engineering Hefei University of TechnologyHeifei 230009China School of Software Hefei University of TechnologyHeifei 230009China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of TechnologyHefei 230601China

Background Considerable research has been conducted in the areas of audio-driven virtual character gestures and facial animation with some degree of ***,few methods exist for generating full-body animations,and the portability of virtual character gestures and facial animations has not received sufficient *** Therefore,we propose a deep-learning-based audio-to-animation-and-blendshape(Audio2AB)network that generates gesture animations and ARK it's 52 facial expression parameter blendshape weights based on audio,audio-corresponding text,emotion labels,and semantic relevance labels to generate parametric data for full-body *** parameterization method can be used to drive full-body animations of virtual characters and improve their *** the experiment,we first downsampled the gesture and facial data to achieve the same temporal resolution for the input,output,and facial *** Audio2AB network then encoded the audio,audio-corresponding text,emotion labels,and semantic relevance labels,and then fused the text,emotion labels,and semantic relevance labels into the audio to obtain better audio ***,we established links between the body,gestures,and facial decoders and generated the corresponding animation sequences through our proposed GAN-GF loss *** By using audio,audio-corresponding text,and emotional and semantic relevance labels as input,the trained Audio2AB network could generate gesture animation data containing blendshape ***,different 3D virtual character animations could be created through *** The experimental results showed that the proposed method could generate significant gestures and facial animations.

关键词： Audio-driven Virtual character Full-body animation Audio2AB Blendshape GAN-GF

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Structural plasticity-based hydrogel optical Willshaw model for one-shot on-the-fly edge learning

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InfoMat 2023年第4期5卷 48-59页

作者： Dingchen wang Dingyao Liu Yinan Lin Anran Yuan Woyu Zhang Yaping Zhao Shaocong wang Xi Chen Hegan Chen Yi Zhang Yang Jiang Shuhui Shi Kam Chi Loong Jia Chen Songrui Wei Qing wang Hongyu Yu Renjing Xu Dashan Shang Han Zhang Shiming Zhang zhongrui wang Department of Electrical and Electronic Engineering The University of Hong KongHong Kongthe People's Republic of China ACCESS-AI Chip Center for Emerging Smart Systems InnoHK CentersHong Kong Science ParkHong Kongthe People's Republic of China School of Computer Science and Engineering Faculty of Innovation EngineeringMacao University of Science and TechnologyMacaothe People's Republic of China Key Laboratory of Microelectronics Devices and Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijingthe People's Republic of China Collaborative Innovation Center for Optoelectronic Science Technology International Collaborative Laboratory of 2D Materials for OptoelectronicsScience and Technology of Ministry of EducationInstitute of Microscale OptoelectronicsShenzhen UniversityShenzhenthe People's Republic of China School of Microelectronics Southern University of Science and TechnologyShenzhenthe People's Republic of China Microelectronics Thrust Function Hub of the Hong Kong University of Science and Technology(Guangzhou)Guagndongthe People's Republic of China

Autonomous one-shot on-the-fly learning copes with the high privacy,small dataset,and in-stream data at the *** such learning on digital hardware suffers from the well-known von-Neumann and scaling *** optical neural networks featuring large parallelism,low latency,and high efficiency offer a promising ***,ex-situ training of conventional optical networks,where optical path configuration and deep learning model optimization are separated,incurs hardware,energy and time overheads,and defeats the advantages in edge ***,we introduced a bio-inspired material-algorithm co-design to construct a hydrogel-based optical Willshaw model(HOWM),manifesting Hebbian-rule-based structural plasticity for simultaneous optical path configuration and deep learning model optimization thanks to the underlying opto-chemical *** first employed the HOWM as an all optical in-sensor AI processor for one-shot pattern classification,association and *** then leveraged HOWM to function as a ternary content addressable memory(TCAM)of an optical memory augmented neural network(MANN)for one-shot learning the Omniglot *** HOWM empowered one-shot on-the-fly edge learning leads to 1000boost of energy efficiency and 10boost of speed,which paves the way for the next-generation autonomous,efficient,and affordable smart edge systems.

关键词： associative memory hydrogel one-shot learning optical neural network structural plasticity Willshaw model

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