Scientific consensus,the law,and same sex parenting outcomes

While the US Supreme Court was considering two related cases involving the constitutionality of same-sex marriage, one major question informing that decision was whether scientific research had achieved consensus regarding how children of same-sex couples fare. Determining the extent of consensus has become a key aspect of how social science evidence and testimony is accepted by the courts. Here, we show how a method of analyzing temporal patterns in citation networks can be used to assess the state of social scientific literature as a means to inform just such a question. Patterns of clustering within these citation networks reveal whether and when consensus arises within a scientific field. We find that the literature on outcomes for children of same-sex parents is marked by scientific consensus that they experience “no differences” compared to children from other parental configurations.     

How do countries choose their monetary policy frameworks?

This paper investigates the determinants of countries’ choices of monetary policy framework. A brief narrative focused on groupings of countries motivates an econometric analysis which draws on previous work on the determinants of exchange rate regimes, bringing in standard factors as well as the trade networks of potential anchor currency blocs and the financial market depth that are emphasised in the narrative. The model turns out to be able to predict three quarters of countries’ choices, and there is no obvious systematic pattern in the errors. The results have important implications for how countries should choose their monetary policy frameworks.     

Developing ethical approaches to data and civil society: from availability to accessibility

This research note reflects on the gaps and limitations confronting the development of ethical principles regarding the accessibility of large-scale data for civil society organizations (CSOs). Drawing upon a systematic scoping study on the use of data in the United Kingdom (UK) civil society, it finds that there are twin needs to conceptualize accessibility as more than mere availability of data, as well as examine the use of data among CSOs more generally. In order to deal with the apparent “digital divide” in UK civil society – where, despite extensive government rhetoric about data openness, organizations face not only the barriers of limited time, funds, and expertise to harness data but also the lack of representation within existing data – we present a working model in which ethical concerns accompanying data utilization by civil society may be better accounted. This suggests there is a need for further research into the nexus of civil society and data upon which interdisciplinary discussion about the ethical dimensions of engagement with data, particularly informed by insight from the social sciences, can be predicated.     

大数据背景下基于社交网络的聚类随机游走抽样算法研究

数字经济时代,社交网络作为数字化平台经济的重要载体,受到了国内外学者的广泛关注。大数据背景下,社交网络的商业应用价值巨大,但由于其网络规模空前庞大,传统的网络分析方法 因计算成本过高而不再适用。而通过网络抽样算法获取样本网络,再推断整体网络,可节约计算资源, 因此抽样算法的好坏将直接影响社交网络分析结论的准确性。现有社交网络抽样算法存在忽略网络内部拓扑结构、容易陷入局部网络、抽样效率过低等缺陷。为了弥补现有社交网络抽样算法的缺陷,本文结合大数据社交网络的社区特征,提出了一种聚类随机游走抽样算法。该方法首先使用社区聚类算法将原始网络节点进行社区划分,得到多个社区网络,然后分别对每个社区进行随机游走抽样获取样本网 络。数值模拟和案例应用的结果均表明,聚类随机游走抽样算法克服了传统网络抽样算法的缺点,能够在降低网络规模的同时较好地保留原始网络的结构特征。此外,该抽样算法还可以并行运算,有效提升抽样效率,对于大数据背景下大规模社交网络的抽样实践具有重大现实意义。     

城域网MAC层协议的优先级结构及性能

主要研究城域网MAC层标准“IEEE 802.6 DQDB”协议的优先故排队性能;提出了一种DQDB协议的优先级实现方案—β分级结构。通过本文给出的文件传输性能分析以及计算机模拟证明,这种新的优先级结构可取得比DQDB现有结构更好的性能,而且易于实现。     

罗敷夸夫中的误注

半个世纪以来,各种选本及大学、中学教材,对罗敷夸夫的几处注释,不够严肃,忽略、歪曲了罗敷的原意,贬损了罗敷夸夫的美学内涵.从罗敷的感情流动和具体的语言环境着眼,本文对罗敷夸夫中的几处误注,作了辨析.对几部大型工具书的注释,本文也提出了不同的见解.     

随机型细胞神经网络的稳定性

借助于李雅谱洛夫理论、矩阵分析方法和It?公式,结合不等式分析技巧,研究了随机细胞神经网络系统的均方指数稳定性,给出了系统的解的二阶矩Liapunov指数估计式和均方指数稳定的充分条件。     

基于定位辅助按需拓扑维护的超宽带自组网路由算法

提出了一种基于定位辅助按需拓扑维护的超宽带自组网路由算法,该算法利用超宽带技术精确定位信息所获得的网络拓扑信息和路由信息进行分组转发,路由维护阶段在定位信息辅助下采用按需方式进行断链路由的修复和拓扑维护,通过基于位置信息的按需路由发现和限制路由查找范围,以及定位信息和网络拓扑信息的及时更新,在降低协议开销的同时保证了算法的有效性。仿真表明,该算法在分组丢失率、平均端到端时延和路由附加开销等方面具有良好性能,其优良的分布式控制特征能适应超宽带自组网的动态环境。     

基于贝叶斯神经网络的非参数回归

提高神经网络模型推广能力的关键是控制模型的复杂度。该文探索了贝叶斯神经网络的非参数回归的建模方法,通过融入模型参数的先验知识,在给定数据样本及模型假设下进行后验概率的贝叶斯推理,使用马尔可夫链蒙特卡罗算法来优化模型控制参数,实现了对神经网络模型中不同部分复杂度的控制,获得了模型参数的后验分布及预测分布。在5个含噪二维函数回归问题上的应用显示了模型的复杂度能根据数据的复杂度而自适应调整,并给出了较好的预测结果。