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埋藏木材可作为一种持久的碳移除方法 快报文章
资源环境快报,2024年第19期
作者:  董利苹
Microsoft Word(16Kb)  |  收藏  |  浏览/下载:494/0  |  提交时间:2024/10/15
3775-year-old Wood Burial  “Wood Vaulting”  a Durable Carbon Removal Method  
研究改进对北方生态系统碳平衡的估算方法 快报文章
资源环境快报,2023年第3期
作者:  裴惠娟
Microsoft Word(16Kb)  |  收藏  |  浏览/下载:741/0  |  提交时间:2023/02/16
Carbon Balance  Eddy Covariance Gap-filling Method  Systematic Bias  
新方法助力科研人员探测深层海洋 快报文章
资源环境快报,2020年第16期
作者:  吴秀平
Microsoft Word(21Kb)  |  收藏  |  浏览/下载:373/0  |  提交时间:2020/08/29
New method  ocean particles  ocean's depths  
利用投入产出分析法对英国海洋经济贡献度进行了新的评估 快报文章
资源环境快报,2020年第15期
作者:  王金平,薛明媚
Microsoft Word(16Kb)  |  收藏  |  浏览/下载:372/1  |  提交时间:2020/08/16
Input-output method  Marine economy  assessment  
精确测量地下水变化的新方法 快报文章
资源环境快报,2020年第14期
作者:  吴秀平
Microsoft Word(16Kb)  |  收藏  |  浏览/下载:325/0  |  提交时间:2020/07/31
Groundwater  new method  
全球变暖已达到至少6000年未见的水平 快报文章
气候变化快报,2020年第14期
作者:  董利苹
Microsoft Word(13Kb)  |  收藏  |  浏览/下载:386/0  |  提交时间:2020/07/20
Holocene  Global Mean Surface Temperature  Multi-method Reconstruction Approach  
Efficient Method of Moments for Simulating Atmospheric Aerosol Growth: Model Description, Verification, and Application 期刊论文
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2020, 125 (13)
作者:  Shen, J.;  Yu, M.;  Chan, T. L.;  Tu, C.;  Liu, Y.
收藏  |  浏览/下载:29/0  |  提交时间:2020/08/18
atmospheric aerosol dynamics model  method of moments  secondary nanoparticle formation  vehicle exhaust  simulation  
Methodology of the Constraint Condition in Dynamical Downscaling for Regional Climate Evaluation: A Review 期刊论文
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2020, 125 (11)
作者:  Adachi, S. A.;  Tomita, H.
收藏  |  浏览/下载:19/0  |  提交时间:2020/08/18
dynamical downscaling method  regional climate change  review  
Dynamic spatial-temporal precipitation distribution models for short-duration rainstorms in Shenzhen, China based on machine learning 期刊论文
ATMOSPHERIC RESEARCH, 2020, 237
作者:  Liu, Yuan-Yuan;  Li, Lei;  Liu, Ye-Sen;  Chan, Pak Wai;  Zhang, Wen-Hai
收藏  |  浏览/下载:33/0  |  提交时间:2020/07/02
Short-duration rainstorm  Machine learning  Locally linear embedding method  Dynamic spatial-temporal distribution  Shenzhen  
Nearest neighbours reveal fast and slow components of motor learning 期刊论文
NATURE, 2020, 577 (7791) : 526-+
作者:  Kollmorgen, Sepp;  Hahnloser, Richard H. R.;  Mante, Valerio
收藏  |  浏览/下载:19/0  |  提交时间:2020/07/03

A new method for analysing change in high-dimensional data is based on nearest-neighbour statistics and is applied here to song dynamics during vocal learning in zebra finches, but could potentially be applied to other biological and artificial behaviours.


Changes in behaviour resulting from environmental influences, development and learning(1-5) are commonly quantified on the basis of a few hand-picked features(2-4,6,7) (for example, the average pitch of acoustic vocalizations(3)), assuming discrete classes of behaviours (such as distinct vocal syllables)(2,3,8-10). However, such methods generalize poorly across different behaviours and model systems and may miss important components of change. Here we present a more-general account of behavioural change that is based on nearest-neighbour statistics(11-13), and apply it to song development in a songbird, the zebra finch(3). First, we introduce the concept of '  repertoire dating'  , whereby each rendition of a behaviour (for example, each vocalization) is assigned a repertoire time, reflecting when similar renditions were typical in the behavioural repertoire. Repertoire time isolates the components of vocal variability that are congruent with long-term changes due to vocal learning and development, and stratifies the behavioural repertoire into '  regressions'  , '  anticipations'  and '  typical renditions'  . Second, we obtain a holistic, yet low-dimensional, description of vocal change in terms of a stratified '  behavioural trajectory'  , revealing numerous previously unrecognized components of behavioural change on fast and slow timescales, as well as distinct patterns of overnight consolidation(1,2,4,14,15) across the behavioral repertoire. We find that diurnal changes in regressions undergo only weak consolidation, whereas anticipations and typical renditions consolidate fully. Because of its generality, our nonparametric description of how behaviour evolves relative to itself-rather than to a potentially arbitrary, experimenter-defined goal(2,3,14,16)-appears well suited for comparing learning and change across behaviours and species(17,18), as well as biological and artificial systems(5).