摘要：过去的研究中已发现偏滤器上螺旋条带状的热负荷分布，其磁场的结构对如何增大热负荷湿区以降低偏滤器材料的耐热要求至为关键。本文将磁拓扑相关的概念形式化以利用动力系统中的知识。对于一般三维向量场拓扑的理解，环和鞍环上生长出的不变流形至为关键，本文进行有详细分析。Poincaré 映射的 Jacobian 在环上如何演化，文中给出其公式。鞍环的不变流形从 Poincaré 映射的 Jacobian 矩阵的特征向量上长出，它们对混沌场区的确定十分重要，混沌场会在等离子体中造成一定的混合效应。就三维连续时间动力系统，本文推导得到柱坐标中不变流形的生长公式。
摘要：It is with great pride to bring you this new journal of Data Intelligence. This journal has at least two major purposes that we hope embrace. First, it will embrace the traditional role of a journal in helping to facilitate the communication of research and best practices in scientific data sharing, especially across disciplines, an area that is continually growing in importance for the modern practice of science. Second, we will be experimenting with new methods of enhancing the sharing of this communication, and examples of the field, by utilizing the increasing power of intelligent computing systems to further facilitate the growth of the field. The journal’s title, combining “data,” the field we will support, and “intelligence,” a means to that end, is meant to connote this growing interaction.
摘要：AMiner is a novel online academic search and mining system, and it aims to provide a systematic modeling approach to help researchers and scientists gain a deeper understanding of the large and heterogeneous networks formed by authors, papers, conferences, journals and organizations. The system is subsequently able to extract researchers’ profiles automatically from the Web and integrates them with published papers by a way of a process that first performs name disambiguation. Then a generative probabilistic model is devised to simultaneously model the different entities while providing a topic-level expertise search. In addition, AMiner offers a set of researcher-centered functions, including social influence analysis, relationship mining, collaboration recommendation, similarity analysis and community evolution. The system has been in operation since 2006 and has been accessed from more than 8 million independent IP addresses residing in more than 200 countries and regions.
New middle Eocene rodent fossils discovered from the lower part of the Shara Murun Formation of Ula Usu, Erlian Basin, Nei Mongol, China, the classical locality of Sharamurunian mammalian fauna, were identified as 9 separate species (the ctenodactyloids Yuomys cavioides, Gobiomys neimongolensis, G. exiguus, and G. asiaticus, the dipodids Allosminthus uniconjugatus and Primisminthus shanghenus, the cricetid Pappocricetodon rencunensis, the ischyromyid Hulgana cf. H. ertnia, and the cylindrodontid Proardynomys ulausuensis) belonging to 7 genera, 4 families, and 1 superfamily of Rodentia. The Ula Usu rodent assemblage shares a high degree of similarity with that from the “Lower Red” beds of the Erden Obo, and they both represent the typical Sharamurunian rodent assemblages found in northern China. The Sharamurunian rodent fauna in the Erlian Basin is analyzed by the minimum number of individuals based on the rodent materials from the lower part of the Shara Murun Formation in the Ula Usu and the “Lower Red” beds of the Erden Obo. In the Sharamurunian rodent fauna of the Erlian Basin, ctenodactyloids are the most dominant elements, and dipodids and cricetids follow next in prevalence. By analyzing the evolution of the rodent species richness in the Erlian Basin, the rodent faunas show a transformation from a ctenodactyloid dominant assemblage to a cricetid-dipodid dominant one in chronological order. The Sharamurunian rodent fauna from the Erlian Basin differs from that of the Yuanqu Basin and the differences in the rodent assemblages may be a response to the differences between the regional environments.
The pelvic morphology, and whether the pelvic fin is present or absent in the earliest jawed vertebrates are key in interpreting the origin of vertebrate paired fins. Parayunnanolepis xitunensis, an antiarch placoderm from the Early Devonian of Yunnan, South China, was previously described to possess the earliest evidence of both dermal and endoskeletal pelvic girdles, presumably for the attachment of the pelvic fins. Here, we redescribe the pelvic region of the holotype based on high-resolution computed tomographic data. Instead of having two large plates previously designated as dermal pelvic girdles, Parayunnanolepis possesses three pairs of lateral pelvic plates, and one large oval median pelvic plate. The paired pelvic plates are flat ventral plates, and differ from other dermal pelvic girdles in lacking a dorsal extension. There is no definitive evidence for the presence of an endoskeletal pelvic girdle in Parayunnanolepis, although the possibility cannot be ruled out. A comparison of the dermal pelvic plates in various jawed stem-gnathostomes suggests the presence of both paired and median pelvic plates is shared by different lineages and might be plesiomorphic. The jawed stem-gnathostomes may have recruited the ventral dermal skeleton of the post-thoracic body into different functional units.
摘要：As the world population continues to increase, world food production is not keeping up. This means that to continue to feed the world, we will need to optimize the production and utilization of food around the globe. Optimization of a process on a global scale requires massive data. Agriculture is no exception, but also brings its own unique issues, based on how wide spread agricultural data are, and the wide variety of data that is relevant to optimization of food production and supply. This suggests that we need a global data ecosystem for agriculture and nutrition. Such an ecosystem already exists to some extent, made up of data sets, metadata sets and even search engines that help to locate and utilize data sets. A key concept behind this is sustainability—how do we sustain our data sets, so that we can sustain our production and distribution of food? In order to make this vision a reality, we need to navigate the challenges for sustainable data management on a global scale. Starting from the current state of practice, how do we move forward to a practice in which we make use of global data to have an impact on world hunger? In particular, how do we find, collect and manage the data? How can this be effectively deployed to improve practice in the field? And how can we make sure that these practices are leading to the global goals of improving production, distribution and sustainability of the global food supply? These questions cannot be answered yet, but they are the focus of ongoing and future research to be published in this journal and elsewhere.
摘要：Data-intensive science is reality in large scientific organizations such as the Max Planck Society, but due to the inefficiency of our data practices when it comes to integrating data from different sources, many projects cannot be carried out and many researchers are excluded. Since about 80% of the time in data#2;intensive projects is wasted according to surveys we need to conclude that we are not fit for the challenges that will come with the billions of smart devices producing continuous streams of data—our methods do not scale. Therefore experts worldwide are looking for strategies and methods that have a potential for the future. The first steps have been made since there is now a wide agreement from the Research Data Alliance to the FAIR principles that data should be associated with persistent identifiers (PIDs) and metadata (MD). In fact after 20 years of experience we can claim that there are trustworthy PID systems already in broad use. It is argued, however, that assigning PIDs is just the first step. If we agree to assign PIDs and also use the PID to store important relationships such as pointing to locations where the bit sequences or different metadata can be accessed, we are close to defining Digital Objects (DOs) which could indeed indicate a solution to solve some of the basic problems in data management and processing. In addition to standardizing the way we assign PIDs, metadata and other state information we could also define a Digital Object Access Protocol as a universal exchange protocol for DOs stored in repositories using different data models and data organizations. We could also associate a type with each DO and a set of operations allowed working on its content which would facilitate the way to automatic processing which has been identified as the major step for scalability in data science and data industry. A globally connected group of experts is now working on establishing testbeds for a DO-based data infrastructure.
摘要：Knowledge bases (KBs) are often greatly incomplete, necessitating a demand for KB completion. Although XLORE is an English-Chinese bilingual knowledge graph, there are only 423,974 cross-lingual links between English instances and Chinese instances. We present XLORE2, an extension of the XLORE that is built automatically from Wikipedia, Baidu Baike and Hudong Baike. We add more facts by making cross-lingual knowledge linking, cross-lingual property matching and fine-grained type inference. We also design an entity linking system to demonstrate the effectiveness and broad coverage of XLORE2.
摘要：In a world awash with fragmented data and tools, the notion of Open Science has been gaining a lot of momentum, but simultaneously, it caused a great deal of anxiety. Some of the anxiety may be related to crumbling kingdoms, but there are also very legitimate concerns, especially about the relative role of machines and algorithms as compared to humans and the combination of both (i.e., social machines). There are also grave concerns about the connotations of the term “open”, but also regarding the unwanted side effects as well as the scalability of the approaches advocated by early adopters of new methodological developments. Many of these concerns are associated with mind-machine interaction and the critical role that computers are now playing in our day to day scientific practice. Here we address a number of these concerns and provide some possible solutions. FAIR (machine-actionable) data and services are obviously at the core of Open Science (or rather FAIR science). The scalable and transparent routing of data, tools and compute (to run the tools on) is a key central feature of the envisioned Internet of FAIR Data and Services (IFDS). Both the European Commission in its Declaration on the European Open Science Cloud, the G7, and the USA data commons have identified the need to ensure a solid and sustainable infrastructure for Open Science. Here we first define the term FAIR science as opposed to Open Science. In FAIR science, data and the associated tools are all Findable, Accessible under well defined conditions, Interoperable and Reusable, but not necessarily “open”; without restrictions and certainly not always “gratis”. The ambiguous term “open” has already caused considerable confusion and also opt-out reactions from researchers and other data#2;intensive professionals who cannot make their data open for very good reasons, such as patient privacy or national security. Although Open Science is a definition for a way of working rather than explicitly requesting for all data to be available in full Open Access, the connotation of openness of the data involved in Open Science is very strong. In FAIR science, data and the associated services to run all processes in the data stewardship cycle from design of experiment to capture to curation, processing, linking and analytics all have minimally FAIR metadata, which specify the conditions under which the actual underlying research objects are reusable, first for machines and then also for humans. This effectively means that—properly conducted— Open Science is part of FAIR science. However, FAIR science can also be done with partly closed, sensitive and proprietary data. As has been emphasized before, FAIR is not identical to “open”. In FAIR/Open Science, data should be as open as possible and as closed as necessary. Where data are generated using public funding, the default will usually be that for the FAIR data resulting from the study the accessibility will be as high as possible, and that more restrictive access and licensing policies on these data will have to be explicitly justified and described. In all cases, however, even if the reuse is restricted, data and related services should be findable for their major uses, machines, which will make them also much better findable for human users. With a tendency to make good data stewardship the norm, a very significant new market for distributed data analytics and learning is opening and a plethora of tools and reusable data objects are being developed and released. These all need FAIR metadata to be routed to each other and to be effective.