一个生物体能否大如星系？ | 复杂文摘翻译第3期

复杂文摘翻译小组集智俱乐部 2018-12-21

一个生物体能否大如星系？ Can a Living Creature Be as Big as a Galaxy?

From nautil.us April 1, 7:38 PM

By Gregory Laughlin

(Translated by -陈开壮, Edited by Jake)

译文

我们宇宙中事物的大小几乎都在小至10^-19米以夸克相互作用为尺度到大至10^26米宇宙量级之间。在这45个可能的数量级中，生命正如我们了解到的那样被限定在9个数量级这个非常窄的区间上，这基本上是在宇宙（尺度）范围的中间：细菌和病毒的测量值不到1微米即10^-6米，而高度最高的树粗略计算在100米左右。我们知道生活在俄勒冈蓝山脚下的蜜环菌（译者注：一种广泛分布于北美北部和欧洲的菌）的尺度大约可以绵延4公里，而这被认为是一种单一的生命体。而当我们限定在有知觉的生物，则它们的尺度的范围就更小了，大约为3这个数量级。

原文

Why life is constrained to be about the sizes we see on Earth.
The size of things in our universe runs all the way from the tiny 10^-19 meter scale that characterizes quark interactions, to the cosmic horizon 10^26 meters away. In these 45 possible orders of magnitude, life, as far as we know it, is confined to a relatively tiny bracket of just over nine orders of magnitude, roughly in the middle of the universal range: Bacteria and viruses can measure less than a micron, or 10^-6 meters, and the height of the largest trees reaches roughly 100 meters. The honey fungus that lives under the Blue Mountains in Oregon, and is arguably a single living organism, is about 4 kilometers across.When it comes to known sentient life, the range in scale is even smaller, at about three orders of magnitude.

原文链接：

http://nautil.us/issue/34/adaptation/can-a-living-creature-be-as-big-as-a-galaxy

信息驱动协调现象的动力学：基于转移熵的分析 The dynamics of information-driven coordination phenomena: A transfer entropy analysis

From Science Advances 01 Apr 2016:Vol. 2, no. 4, e1501158 April 2, 8:09 PM

By Javier Borge-Holthoefer, Nicola Perra, Bruno Gonçalves, Sandra González-Bailón, Alex Arenas, Yamir Moreno, and Alessandro Vespignani

(Translated by 蔡嘉文，Edited by Jake)

译文

社会媒体的数据为我们提供了前所未有的机会来研究群体社会现象背后的动力学因素。我们提出了一种基于信息论的方法来定义和测量在集体社会事件出现和爆发时的时序和结构特征。我们利用符号转移熵的方法对微博的时间序列进行分析，提取出社会系统中地理子块相互影响的有向网络。这种方法可以成功捕获社会集体行为开始形成时的系统级动态的涌现。我们通过五个细致的实证案例分析来验证我们的这个框架。具体来说，我们定义了一种在信息传递过程的时间尺度上的变化，这种变化标志着信息驱动的集体现象的开始。进一步地，我们的方法识别出了在社会子单元的有向网络中的有序-无序的相变过程。在缺失明确外源性驱动的情况下，社会集体现象可以表示为内生驱动的信息传输网络的结构性相变。这项研究提供的结果，可以帮助研究者定义模型和预测算法来对基于开源数据的社会事件进行分析。

原文

Data from social media provide unprecedented opportunities to investigate the processes that govern the dynamics of collective social phenomena. We consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence. We use the symbolic transfer entropy analysis of microblogging time series to extract directed networks of influence among geolocalized subunits in social systems. This methodology captures the emergence of system-level dynamics close to the onset of socially relevant collective phenomena. The framework is validated against a detailed empirical analysis of five case studies. In particular, we identify a change in the characteristic time scale of the information transfer that flags the onset of information-driven collective phenomena. Furthermore, our approach identifies an order-disorder transition in the directed network of influence between social subunits. In the absence of clear exogenous driving, social collective phenomena can be represented as endogenously driven structural transitions of the information transfer network. This study provides results that can help define models and predictive algorithms for the analysis of societal events based on open source data.

原文链接：http://dx.doi.org/10.1126/sciadv.1501158

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