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| 1 | 苏门答腊地震的速度和大小 现在我们对去年巨大地震的特征有了更清晰的认识显示文摘我们的地震学分析结果表明,2004年12月26日的苏门答腊一安达曼毁灭性地震比最初报告结果大2.5倍,其震级仅次于1960年智利地震。该地震沿1200km长的断层滑动缓慢释放其能量,产生的长破裂引发了随后的海啸。既然整个破裂区已经滑动,由印度板块向缅甸小板块下俯冲所积累的应变也被释放,因此在该部分板块边界上暂时没有产生类似海啸的危险,虽然南段大地震的威胁依然存在。 | S.Stein E.A.Okal 万永革 左玉玲 | 2005 | 世界地震译丛2005，，2： | 19 |
| 2 | Analyzing machine learning models to accelerate generation of fundamental materials insights显示文摘Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding.A primary role of scientists is extraction of fundamental knowledge from data,and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction tool.Convolutional neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces,such as that mapped by a combinatorial materials science experiment.Measuring a performance metric in a given materials space provides direct information about(locally)optimal materials but not the underlying materials science that gives rise to the variation in performance.By building a model that predicts performance(in this case photoelectrochemical power generation of a solar fuels photoanode)from materials parameters(in this case composition and Raman signal),subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical analyses.Human interpretation of these key relationships produces the desired fundamental understanding,demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human scientist.We also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space,such as the addition of specific alloying elements,that may increase performance by moving beyond the confines of existing data. | Mitsutaro Umehara Helge S.Stein Dan Guevarra Paul F.Newhouse David A.Boyd John M.Gregoire | 2019 | npj Computational Materials2019，，1： | 7 |
| 3 | 华北地区两千年以来的地震迁移:大陆内部与板块边缘的地震有怎样的不同显示文摘板块构造理论只解释了板块边缘的地震,但没有解释那些发生在大陆内部的地震。而大陆内部的地震多发生在没有预料到的地区。我们以华北地区两千年以来的记录为例揭示板块边界和板内地震的不同。结果表明,大陆内部的大地震在断层系统之间的迁移可延伸至一个很大的区域,甚至没有一个大地震可以在同一断裂带上重复发生两次。然而,这些地震的空间迁移并不是完全没有规律,因为断层系统间地震能量的释放是互补的,表明这些系统之间在力学上是耦合的。我们同时针对大陆内部的地震提出了一个简单的概念模型。在此模型中,大陆内部缓慢的构造负荷被一个复杂交错的断层系统全部吸收,而每条断层在长期休眠后都出现一个短暂的活跃期。由此产生的大地震呈现间歇性与空间迁移性,这与板块边缘的地震所具有的更加规则的时空模型形成鲜明对比。 | M.Liu S.Stein H.Wang 翟阳琳(译) 余中元(校) | 2020 | 世界地震译丛2020，51，2： | 1 |
| 4 | Tracking materials science data lineage to manage millions of materials experiments and analyses显示文摘In an era of rapid advancement of algorithms that extract knowledge from data,data and metadata management are increasingly critical to research success.In materials science,there are few examples of experimental databases that contain many different types of information,and compared with other disciplines,the database sizes are relatively small.Underlying these issues are the challenges in managing and linking data across disparate synthesis and characterization experiments,which we address with the development of a lightweight data management framework that is generally applicable for experimental science and beyond.Five years of managing experiments with this system has yielded the Materials Experiment and Analysis Database(MEAD)that contains raw data and metadata from millions of materials synthesis and characterization experiments,as well as the analysis and distillation of that data into property and performance metrics via software in an accompanying open source repository.The unprecedented quantity and diversity of experimental data are searchable by experiment and analysis attributes generated by both researchers and data processing software.The search web interface allows users to visualize their search results and download zipped packages of data with full annotations of their lineage.The enormity of the data provides substantial challenges and opportunities for incorporating data science in the physical sciences,and MEAD’s data and algorithm management framework will foster increased incorporation of automation and autonomous discovery in materials and chemistry research. | Edwin Soedarmadji Helge S.Stein Santosh K.Suram Dan Guevarra John M.Gregoire | 2019 | npj Computational Materials2019，，1： | 0 |
| 5 | Multi-component background learning automates signal detection for spectroscopic data显示文摘Automated experimentation has yielded data acquisition rates that supersede human processing capabilities.Artificial Intelligence offers new possibilities for automating data interpretation to generate large,high-quality datasets.Background subtraction is a long-standing challenge,particularly in settings where multiple sources of the background signal coexist,and automatic extraction of signals of interest from measured signals accelerates data interpretation.Herein,we present an unsupervised probabilistic learning approach that analyzes large data collections to identify multiple background sources and establish the probability that any given data point contains a signal of interest.The approach is demonstrated on X-ray diffraction and Raman spectroscopy data and is suitable to any type of data where the signal of interest is a positive addition to the background signals.While the model can incorporate prior knowledge,it does not require knowledge of the signals since the shapes of the background signals,the noise levels,and the signal of interest are simultaneously learned via a probabilistic matrix factorization framework.Automated identification of interpretable signals by unsupervised probabilistic learning avoids the injection of human bias and expedites signal extraction in large datasets,a transformative capability with many applications in the physical sciences and beyond. | Sebastian E.Ament Helge S.Stein Dan Guevarra Lan Zhou Joel A.Haber David A.Boyd Mitsutaro Umehara John M.Gregoire Carla P.Gomes | 2019 | npj Computational Materials2019，，1： | 0 |
| 6 | 答复:新成果证明不同模型的公开讨论是正确的显示文摘1000年前,犹太哲人写道:“学者间的争论可增进智慧”。与之相比,Schweig等人要求对选择与他们在新马德里地震带(NMSZ)高危险性模型不同的模型要十分谨慎。我们发现这有点不可思议。 | A.Newman S.Stein J.Weber J.Engeln Ailin Mao T.Dixon 何玉林 | 1999 | 世界地震译丛1999，，5： | 0 |
| 7 | 2012年4月11日M8.6印度洋地震的深远影响:短期全球触发后为更长期全球阴影显示文摘2012年4月11日发生的M8.6印度洋地震是一次不寻常的洋内走滑大地震。在几天中,距离震中很远(数千千米)处的全球M≥4.5和M≥6.5地震活动发生率都有提高。走滑型主震通过其勒夫波触发了全球性的走滑余震,并持续了数天。但在随后的95天中,M≥6.5地震的活动率下降到零,而在此期间M≤6.0的全球地震发生率则与背景接近。在过去的一个世纪中,震后95天的时间内很少不发生M≥6.5的地震,大型主震之后更是没有过。同时在过去的一个世纪中,历次大主震(M≥8)后的静止期或者是非常短,或者是在给定主震后经过很长时间才开始,对于这一点我们无法给出相应的物理解释。2012年主震的独特之处体现在两个方面:短暂的全球地震活动增多和随后很长的静止期。我们认为,这两者之间是有联系的,且可以将这种模式解释为全球断层系动态应力作用的结果。瞬时动态应力可以促进短期的触发,但自相矛盾的是,它也可以暂时抑制破裂,直到背景构造负荷将断层系恢复到主震前的应力水平。 | Fred F.Pollitz Roland Bürgmann Ross S.Stein Volkan Sevilgen 孙素梅 | 2015 | 世界地震译丛2015，46，2： | 0 |
| 8 | 2000年伊豆群岛震群应力加载率控制地震活动的证据显示文摘岩浆侵入和喷发通常会使远离岩浆通道处地震活动发生突然改变(Weaver et dl,1981;Mori et al,1996;Bjornsson et al,1977;Dvorak et al,1986),而这些岩浆通道与孔隙流体或热量的传播扩散并没有联系(Delaney,1982)。这种群发地震活动也随时间发生迁移,经常呈现出“狗骨”状分布(Bjornsson et al,1977;Dvorak et al,1986;Hill,1977;Klein et al,1977;Ukawa and Tsukahara,1996;Aoki et al,1999)。震群中最大地震所产生的余震服从大森类型(指数型)时间衰减规律(Watanabe,1989;Kisslinger and Jones,1991;Gross and Kisslinger,1994),但余震序列的持续时间相对于正常的地震活动急剧减少(Klein et al,1977;Walter and Weaver,1980)。在这里我们使用记录到的一个最活跃的震群来研究这些性质对岩浆侵入所传递应力的依赖性(Ukawa and Tsukahara,1996;Kisslingerand Jones,1991;Chouet,1996;Dieterich et al,2000)。在为期2个月的岩脉侵入期间,地震活动发生率增加了1000倍,而余震的持续时间缩减到1‰。我们发现,地震活动发生率与计算出的应力加载率成比例,并且余震序列的持续时间与应力加载率成反比。这种特点同根据实验室所得的速率/状态本构律(Dieterich,1994)相一致,这为震群的发生提供了一种解释。应力加载率的任何持续增加——无论是由熔岩侵入、喷出产生的还是蠕变事件,都应产生这样的地震行为。 | S.Toda S.Stein T.Sagiya 张晁军 | 2004 | 世界地震译丛2004，，1： | 0 |
| 9 | 人类干细胞技术与生物学研究指南和实验室手册显示文摘如何建立自己的干细胞实验室?如何开展前沿性的干细胞研究?干细胞未来10年的方向在哪里?答案都可以在这部论文集里找到。 | Gary S.Stein(等) 魏玉保 | 2011 | 国外科技新书评介2011，，10： | 0 |
| 10 | 重新评估新马德里地震带显示文摘以新马德里地震带(以下简称NMSZ,图1)而知名的美国大陆中部地区,尽管远离板块边界,但却频繁发生特大地震,其发震机制一直是个未解之谜。NMSZ地区并没有大多数活动形变地区构造运动特征的显著地形起伏,但当面对该整个地区地震危险性在某些概率水平上可以与旧金山湾地区相比的证据时,我们多数人感到需要“拧自己一下以看看我们是否在做梦”。尽管在很多方面,评估NMSZ地区的危险性比评价美国西部地区更具挑战性,不确定性更大,但经过细致地科学研究,已经在地震危险性评估的最关键问题上达成了共识。 [这里给出的一致性看法反映了美国地质调查局和美国中部地震中心(一个由国家科学基金会资助的大学协会组织)2000年1月主办的一次讨论会上与会的二十几位科学家在会上及会后在国际互联网和专业会议上公开发表的一些看法。] | G.Atkinson B.Bakun P.Bodin D.Boore C.Cramer A.Frankel P.Gasperini J.Gomberg T.Hanks B.Heermann S.Hough A.Johnston S.Kenner C.Langston M.Linker P.Mayne M.Petersen C.Powell W.Prescott E.Schweig P.Segall S.Stein B.Stuart M.Tuttle R.VanArsdale 米宏亮 王立新 李娟 吴建平 | 2002 | 世界地震译丛2002，33，3： | 0 |
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