人工智能可以帮我们找到下一个优良的新材料吗？| 复杂文摘翻译
目录
12. 人工智能可以帮我们找到下一个优良的新材料吗？
(Can artificial intelligence create the next wonder material?)
13. 从成功度中理清技能表现的影响
(Untangling performance from success)
14. 网络中社区数目的估算
(Estimating the number of communities in a network)
15. 合作还是不合作：为什么行为机制很重要
(To Cooperate or Not to Cooperate: Why Behavioural Mechanisms Matter)
16. 开源数据揭示网络和街头抗议活动之间的关联
(Open source data reveals connection between online and on-street protest activity)
（更多文摘请点击阅读原文。）
12. 人工智能可以帮我们找到下一个优良的新材料吗？
(Can artificial intelligence create the next wonder material?)
May 7, 4:14 PM From www.nature.com
by Nicola Nosengo
(Translated by - 彭程，edited by 傅渥成)
Instead of continuing to develop new materials the old-fashioned way — stumbling across them by luck, then painstakingly measuring their properties in the laboratory — Marzari and like-minded researchers are using computer modelling and machine-learning techniques to generate libraries of candidate materials by the tens of thousands. Even data from failed experiments can provide useful input1. Many of these candidates are completely hypothetical, but engineers are already beginning to shortlist those that are worth synthesizing and testing for specific applications by searching through their predicted properties — for example, how well they will work as a conductor or an insulator, whether they will act as a magnet, and how much heat and pressure they can withstand.
译文：
与传统的开发新材料方法不同，Marzari 和志同道合的研究者们不再凭借偶然发现新材料的运气在实验室精心测试材料性能，而是利用计算机建模和机器学习技术通过数以万计次的训练生成候选材料库 。即使来自失败实验的数据也能够提供有效的输入。虽然很多候选材料都是完全假设的，但是工程师们已经开始通过搜索那些预测出来的性能（例如材料作为导体或者绝缘体时的工作效果、能否作为磁体、以及耐高温或高压的性质等），将那些值得合成和测试的具有特定功能的材料列入候选名单。
原文链接：
http://www.nature.com/news/can-artificial-intelligence-create-the-next-wonder-material-1.19850?WT.ec_id=NATURE-20160505&spMailingID=51301208&spUserID=MjA1NzczNDc3MgS2&spJobID=920498312&spReportId=OTIwNDk4MzEyS0
13. 从成功度中理清技能表现的影响
(Untangling performance from success)
May 8, 9:10 AM From epjdatascience.springeropen.com
by Burcu Yucesoy, Albert-László Barabási
(Translated by - jeffersonchou)
Fame, popularity and celebrity status, frequently used tokens of success, are often loosely related to, or even divorced from professional performance. This dichotomy is partly rooted in the difficulty to distinguish performance, an individual measure that captures the actions of a performer, from success, a collective measure that captures a community’s reactions to these actions. Yet, finding the relationship between the two measures is essential for all areas that aim to objectively reward excellence, from science to business. Here we quantify the relationship between performance and success by focusing on tennis, an individual sport where the two quantities can be independently measured. We show that a predictive model, relying only on a tennis player’s performance in tournaments, can accurately predict an athlete’s popularity, both during a player’s active years and after retirement. Hence the model establishes a direct link between performance and momentary popularity. The agreement between the performance-driven and observed popularity suggests that in most areas of human achievement exceptional visibility may be rooted in detectable performance measures.
译文：
常被用作成功度标签的名声、人气和（明星）地位等因素，往往与个人的专业技能表现仅有松散的联系，甚至相互背离。这种背离部分源于难以从成功中区分技能表现的影响，技能表现是对选手职业活动的独立衡量，而成功则是社会群体对上述职业活动所产生反应的综合标识。然而，确定这两个评价体系之间的关系，对于客观地激励从学界到商界等所有领域的杰出人士非常必要。在本文中，我们通过专注于网球——这种可以对这两个评价体系进行独立测量的运动，来量化技能表现和成功度之间的关系。我们建立了一个根据网球选手巡回赛表现，精确推断其现役及退役后受欢迎程度的预测模型。这个模型在选手的技能表现与即时欢迎度之间建立了直接联系。技能表现与观察到的受欢迎程度变化趋势一致，这表明在人类成就的大多数领域，非比寻常的知名度很可能根源于可察觉的技能表现评价。
原文链接：
http://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-016-0079-z
14. 网络中社区数目的估算
(Estimating the number of communities in a network)
May 11, 6:18 PM From arxiv.org
by M.E.J.Newman, Gesine Reinert
(Translated by - 张皓，edited by 傅渥成)
Community detection, the division of a network into dense subnetworks with only sparse connections between them, has been a topic of vigorous study in recent years. However, while there exist a range of powerful and flexible methods for dividing a network into a specified number of communities, it is an open question how to determine exactly how many communities one should use. Here we describe a mathematically principled approach for finding the number of communities in a network using a maximum-likelihood method. We demonstrate this approach on a range of real-world examples with known community structure, finding that it is able to determine the number of communities correctly in every case.
译文：
社区识别，将网络划分为彼此具有稀疏联系的密集子网络，在近年一直是研究者们非常关心的课题。尽管已有大量强大且富有弹性的方法将一个网络划分为特定数目的社区，然而在应用这些方法时，如何准确确定社区的数目仍然是一个开放的问题。这里我们描述了一个基于数学原则的方法，利用最大似然法来确定一个网络中社区的数目。通过现实世界中已知社区结构的一些案例，我们证明了这个方法的有效性，发现这个方法能正确地为每个案例确定其社区的数目。
原文链接：
http://arxiv.org/abs/1605.02753
15. 合作还是不合作：为什么行为机制很重要
(To Cooperate or Not to Cooperate: Why Behavioural Mechanisms Matter)
May 12, 3:15 PM From journals.plos.org
by Arthur Bernard, Jean-Baptiste André, Nicolas Bredeche
(Translated by - boboyang，edited by 傅渥成)
Mutualistic behaviours wherein several individuals act together for a common benefit, such as a collective hunt, are often deemed of minor interest by theoreticians in evolutionary biology. These behaviours benefit all the individuals involved, and therefore, so the argument goes, their evolution is straightforward. However, mutualistic behaviours do pose a specific kind of evolutionary problem: they require the coordination of several partners. Indeed, a single individual expressing a preference for cooperation cannot benefit if others wish to remain solitary. Here we use simulations in evolutionary robotics to study the consequences of this problem. We show that it constitutes a far more serious obstacle for the evolution of cooperation than was previously thought on the basis of game theoretical analyses. We find that the transition from solitary to cooperative strategies is very unlikely, and we also observe that successful cooperation requires the evolution of a specific and rather complex behaviour, necessary for individuals to coordinate with each other. This reveals the critical role of the practical mechanics of behaviour in evolution.
译文：
互惠行为（若干人为了共同利益一起行动，如集体狩猎）往往不能引起进化生物学理论学家的兴趣。这些行为有利于所有参与的个人，因此（这样的争论还在继续）其进化是很显然的。然而，互惠行为引发了一种特定的进化问题：他们需要几个合作伙伴的协调。事实上，如果某人表达了合作的偏好但其他人想保持不合作，那么这人将不能获益。这篇文章里我们使用进化机器人模拟来研究这个问题的后果。我们发现，它对合作进化构成了（比以前在博弈论分析基础上所认为的）更为严重的障碍。我们发现，从非合作到合作策略的转变是极为不可能的，我们也观察到，成功的合作需要一个特定的、相当复杂的行为演化，有必要对个体进行相互协调。这揭示了在进化中的实际行为机制的关键作用。
原文链接：
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004886
16. 开源数据揭示网络和街头抗议活动之间的关联
(Open source data reveals connection between online and on-street protest activity)
May 12, 6:56 PM From epjdatascience.springeropen.com
by Hong Qi, Pedro Manrique, Daniela Johnson, Elvira Restrepo and Neil F Johnson
(Translated by - F7，edited by 唐璐)
There is enormous interest in inferring features of human behavior in the real world from potential digital footprints created online - particularly at the collective level, where the sheer volume of online activity may indicate some changing mood within the population regarding a particular topic. Civil unrest is a prime example, involving the spontaneous appearance of large crowds of otherwise unrelated people on the street on a certain day. While indicators of brewing protests might be gleaned from individual online communications or account content (e.g. Twitter, Facebook) societal concerns regarding privacy can make such probing a politically delicate issue. Here we show that instead, a simple low-level indicator of civil unrest can be obtained from online data at the aggregate level through Google Trends or similar tools. Our study covers countries across Latin America during 2011-2014 in which diverse civil unrest events took place. In each case, we find that the combination of the volume and momentum of searches from Google Trends surrounding pairs of simple keywords, tailored for the specific cultural setting, provide good indicators of periods of civil unrest. This proof-of-concept study motivates the search for more geographically specific indicators based on geo-located searches at the urban level.
译文：
根据上网记录推断人类的行为特征——尤其是在群体层面上——引起了很多兴趣，在线活动的突然转向的规模可能表明群体对特定话题的情绪发生了变化。民众动乱就是一个典型的例子，大批互不相识的人同时聚集到街上。而抗议活动正在酝酿的苗头可以从个人在线聊天的内容或者账户内容（比如Twitter, Facebook）获取，不过对个人隐私的社会关切会让这种做法充满争议。在此，我们提出了一个替代办法，利用Google Trend或类似工具的总体在线数据就可以获得一个简单的民众骚乱的低层次预测指标。我们研究了拉美地区2011-2014年间发生了各种民众骚乱的地区。在每一个案例里，我们发现Google Trend上围绕几个简单关键词的搜索数量和趋势的组合，在排除特定的文化设定后，为民众骚乱的时期提供了很好的指标。这一实证结果启发我们基于搜索的城市级别地理位置寻找更具地理针对性的指标。
原文链接：
http://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-016-0081-5
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