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With the rapid development of machine intelligence, an increasing number of websites and servers have been amicably visited or sometimes attached by intelligent machines intensively. Therefore, how to empower a host machine to intelligently distinguish intelligent machines from humans is a challenging work. In this paper, the Mirror Turing Test (MTT) is conceived and implemented. Unlike the standard Turing Test, the tester in the MTT is replaced by a machine instead of a human. Current advancements on deep learning enable machines to recognize subtle differences between genuine and counterfeit works. Sometimes, the ability of machines is even superior to that of humans. Will machines transcend humans in an irreversible trend? Not completely right. The detection of soul in an artwork remains far beyond the capacity of machines. The two sets of MTT based on poetry generated by a machine and a novel imitated by a human were conducted in this paper and neither of them passed the MTT. Poetry is one of the art forms in which authors reveal their souls. Thus, we chose poetry in the MTT experiments on the basis of our soul computing model, thus clearly discriminating machine from human. [ABSTRACT FROM AUTHOR]

Despite its small land coverage, urban land and its expansion have exhibited profound impacts on global environments. Here, we present the scenario projections of global urban land expansion under the framework of the shared socioeconomic pathways (SSPs). Our projections feature a fine spatial resolution of 1 km to preserve spatial details. The projections reveal that although global urban land continues to expand rapidly before the 2040s, China and many other Asian countries are expected to encounter substantial pressure from urban population decline after the 2050s. Approximately 50–63% of the newly expanded urban land is expected to occur on current croplands. Global crop production will decline by approximately 1–4%, corresponding to the annual food needs for a certain crop of 122–1389 million people. These findings stress the importance of governing urban land development as a key measure to mitigate its negative impacts on food production. Shared socioeconomic pathways (SSPs) is a crucial scenario describing the potential of future socio-economic development. The authors here investigate long-term effects of various government policies suggested by different SSPs on urban land and reveal the impact of future urban expansion on other land and food production. [ABSTRACT FROM AUTHOR]

The description of user behavior in social networks is an important issue for studying social networks. Given that Petri nets can describe the resource flow problem, this study utilizes the features of Petri nets to portray the user behavior states during the message propagation of a micro-blog network and presents an information propagation formalized representation method of a micro-blog network. On this basis, this study analyzed the proposed formalized representation method in detail. We provide examples of applying formalized representation (e.g., micro-blog network addiction of users, user behavior influence, and public opinion analysis). In addition, we introduce the algorithms of formalized representation. We conduct experiments using Sina micro-blog data. Results show that the information propagation formalized representation method of micro-blog network based on Petri nets can depict user behaviors of micro-blog network intuitively and accurately. This study reveals a new perspective for information transmission of a micro-blog network and provides some tools to support public opinion monitoring and micro-blog marketing applications. [ABSTRACT FROM AUTHOR]

Very large-scale social networks are typically sparse and dynamic and often have millions of nodes and billions of links. Link prediction in very large-scale networks is a challenging task for most existing methods. This paper investigates the link prediction problem in very large-scale networks. We propose a model, namely, a very large-scale network co-occurrence embedding algorithm (Hsem), which learns the co-occurrence features of node pairs to embed nodes into a vector with a lower and fixed dimension. A damping-based random walk algorithm is also developed to extract the hierarchical structural information of node pairs. Moreover, we design an incremental learning algorithm based on Hsem to meet the requirements of highly dynamic evolution in very large-scale networks, which significantly improves learning speed while maintaining accuracy. Finally, we present controlled experiments employing the proposed and baseline methods on eight challenging real-world large-scale datasets with a fixed dropout percentage and calculate the time overhead and receiver operating characteristics. The results show that Hsem performs comparably to current state-of-the-art methods and consistently outperforms the baselines in different experimental settings. [ABSTRACT FROM AUTHOR]

Most of the current studies on social network (SN) mainly focused on a single SN platform. Integration of SNs can provide more sufficient user behaviour data and more complete network structure, and thus is rewarding to an ocean of studies on social computing. Recognizing the identical users across SNs, or user identification, naturally bridges the SNs through users and has attracted extensive attentions. Due to the fragmentation, inconsistency and disruption of the accessible information among SNs, user identification is still an intractable problem. Different from the efforts implemented on user profiles and users' content, many studies have noticed the accessibility and reliability of network structure in most of the SNs for addressing this issue. Although substantial achievements have been made, most of the current network structure-based solutions are supervised or semi-supervised and require some given identified users or seed users. In the scenarios where seed users are hard to obtain, it is laborious to label the seed users manually. In this study, we proposed an unsupervised scheme by employing the reliability and consistence of friend relationships in different SNs, termed Unsupervised Friend Relationship-based User Identification algorithm (UFRUI). The UFRUI first models the network structure and embeds the feature of each user into a vector using network embedding technique, and then converts the user identification problem into a nearest neighbour problem. Finally, the matching user is computed using the scalable nearest neighbour algorithm. Results of experiments demonstrated that UFRUI performs much better than current state-of-art network structure-based algorithm without seed users. [ABSTRACT FROM AUTHOR]

Online social networks have continued to attract increased attention since the introduction of this concept nearly three decades ago. Consequently, a study about the workings of online social networks may help in understanding the structure of human society and the characteristics of generic complex networks. Over the past few years, interest in neighboring nodes, which are the number of nodes between any two nodes, the number of neighbors and how many triangles are present in a social network have produced the concepts of a six-degree separation (Milgram, Psychol Today 2(1):60–67, 1967; Backstrom et al., Proceedings of the 4th annual ACM web science conference, p 33–42, 2012), a heavy tail in the degree distribution (Barabási, Albert, Science 286(5439):509–512, 1999) and a high clustering coefficient (Luce, Perry, Psychometrika 14(2):95–116, 1949; Watts, Strogatz, Nature 393(6684):440–442, 1998; Amaral et al., Proc Natl Acad Sci USA 97(21):11149–11152, 2000). In a similar manner, researchers have also been curious about how many polygons are present in a given online social network. Although much effort has been expended (Dantzig et al., International symposium on theory of graphs, p 77–83, 1967; Kamae, IEEE Trans Circuit Theory 14(2):166–171, 1967; Gotlieb, Corneil, Commun ACM 10(12):780–783, 1967; Welch, J ACM 13(2):205–210, 1966; Tiernan, Commun ACM 13(12):722–726, 1970; Tarjan, SIAM J Comput 2(3):211–216, 1973; Johnson, SIAM J Comput 4:77–84, 1975; Mateti, Deo, SIAM J Comput 5(5):90–99, 1976; Marinari et al., Europhys Lett 73(3):301–307, 2005), studying this subject, the inability to enumerate polygons has stymied an in depth understanding of the properties of polygons in an online social network. In the study described in this paper, the estimated number of polygons in an online social network is revealed. It was found that in the current widely used online social networks, e.g., Facebook, Twitter, the number of polygons increases drastically when the length of a polygon is below a set value and then it decreases rapidly. The average length of the network polygons was calculated and it was found that online social networks contain a relatively large average length of polygons. Based on this perspective, a massive labyrinth of polygons would make the online social networks appear to be very complicated. To further investigate this area, a generalized clustering coefficient was explored. Results showed that the generalized clustering coefficient appeared to descend exponentially with the length of polygon and expeditiously approached zero. This result suggested that the polygons with large lengths should be ignored in many scenarios. Since the polygons with lengths greater than five appeared to have little impact on the network, the online social networks appeared to be less complex than anticipated. The polygon is one of the fundamental problems in graph theory and complex networks, so that the work reported here may be beneficial for many disciplines, including transportation [7], engineering [8], computer science [9–16], physics (Birmelé et al., Proceedings of the 24th annual ACM–SIAM symposium on discrete algorithms, p 1884–1896, 2013), sociology (Motter, Albert, Phys Today 65:43, 2012), epidemiology (Feld, Am J Sociol 96(6):1464–1477, 1991; Cohen et al., Phys Rev Lett 91(24):12343, 2002), psychology (Sun et al., Sci Rep 4(6188):5099–5099, 2014), biology (Feiler, Kleinbaum, Psychol Sci, 2015), medicine (Kincaid, Pilette, Comput Appl Biosci Cabios 8:267–273, 1992), geography (Kim et al., Lancet 386(9989):145–153, 2015), etc. [ABSTRACT FROM AUTHOR]

Ancient Chinese cartography dates back to the Western Jin Dynasty in the third century. Cartography was initiated during this period by Pei Xiu, a minister and cartographic expert, who suggested six principles of cartography. Pei Xiu, who is known as the father of Chinese cartography, oversaw the completion of the 'Yu Gong Regional Maps', along with 18 articles and the 'Terrain Fangzhang Map' (AD 224-271). This led to a number of subsequent cartographic initiatives including the 'Wooden Fangzhang Map' (an administrative map) which was completed by Xie Zhuang, a minister during the Southern Dynasties (AD 502-557), 'Haineihuayi Tu' (Map of China and its neighbouring countries) (AD 730-805), drawn by Jia Dan, a cartographer of the Tang Dynasty, and 'Shouling Tu' (an administrative map, AD 1031-1095), drawn by Shen Kuo, a scientist during the Northern Song Dynasty. Throughout the 16th century, ancient Chinese cartography developed continuously and cumulatively, and formed the specific characteristics of China ancient cartography. Although Western latitude- based and longitude-based mapping techniques introduced to China in AD 1460, the theories and methods of Chinese and Western mapping systems co-existed for over 400 years. The cultural heritage of Chinese cartography can be seen in many famous ancient Chinese maps, including astronomical figures and atlases (world, Chinese, regional, military, water conservancy, historical, and educational maps), charts, and maps of scenic spots. These have hitherto been kept in well-known archives and institutions across the world. They form an important part of the global cultural heritage of ancient maps and cartography. Given their high cultural value, these maps remain an important point of study. This paper provides a preliminary discussion on the rarity, application, and the historical, scientific, and artistic value of ancient Chinese maps. [ABSTRACT FROM AUTHOR]

Survival motor neuron protein (SMN) plays essential roles in cell viability and stress responses in higher eukaryotes. However, little is known about the orthologs in fungi except for the only studied yeast SMN. Here, we identified an S MN-like gene in M agnaporthe o ryzae ( MOS). Bioinformatics and Southern blot confirmed that MOS is a single copy located on M. oryzae chromosome. But it was detected to contain three transcript isoforms according to RT-PCR identification. Expression patterns of these transcripts revealed that MOS is associated with stress response and conidiogenesis process in M. oryzae. Disruption mutant and its complemented strain (Δ MOS, Δ MOS/MOS) were genetically created in this study. Through biological comparison, we found that Δ MOS, with no more phenotypic defects except for reduced pigmentation and conidiation, was sensitive to oxidative, osmotic, and nitrogen or carbon-starved stresses, suggesting MOS is required for environmental adaption and basic metabolism. Infection assay further indicated MOS contributed to the development of rice blast. Therefore, we suggest that MOS as a pathogenic gene may be a potential target for reducing disease and implications in crop protection. [ABSTRACT FROM AUTHOR]

Optimizing the training speed of support vector machines (SVMs) is one of the most important topics in the SVM research. In this paper, we propose an algorithm in which the size of working set is reduced to one in order to obtain a faster training speed. Instead of the complex heuristic criteria, the random order for selecting the elements into the working set is adopted. The proposed algorithm shows a better performance in linear SVM training, especially in the large-scale scenario. [ABSTRACT FROM AUTHOR]

A new method of pruning away hidden neurons in neural networks is presented in this paper. The hidden neuron is removed by analyzing the orthogonal projection correlations among the outputs of other hidden neurons. The method guarantees the least loss of weight information in terms of orthogonal projection. The remaining weights and thresholds are updated based on the weight crosswise propagation. A practical technique for penalizing the superfluous hidden neurons is explored. Retraining is needed after pruning. Extensive experiments are conducted, and the results demonstrate that the method gives better initial points for retraining and retraining costs less epochs. [ABSTRACT FROM AUTHOR]

Opinions shared publicly in online social networks spread broadly and at an extremely high speed. However, modelling information diffusion in online social networks is still a challenge that is intriguing to many researchers. To monitor public opinions online, it is necessary to model the process of information dissemination. In this paper, we first study information diffusion based on the network structure and time occupation. By taking into consideration the availability of a user, e.g., his online or offline state, we present the discrete-time bi-probability independent cascade model. We next analyse the information diffusion from a macro perspective. A diffusion model is established by merging the interferences from other events and the cumulative effect that occurs over time. Finally, we observe the factors in online social networks that impact a message’s diffusion from a micro perspective and discuss more complex user behaviour and various types of interferences with their effects from a macro perspective. Experiments are conducted with real world data, and the experimental results justify our models. [ABSTRACT FROM AUTHOR]

Paths and cycles are the two pivotal elements in a network. Here, we demonstrate that paths, particularly the shortest ones, are incomplete in information network. However, based on such paths, many network centrality measures are designed. While extensive explorations on paths have been made, modest studies focus on the cycles on measuring network centrality. We study the relationship between the shortest cycle and the shortest path from extensive real-world networks. The results illustrate the incompleteness of the shortest paths on measuring network centrality. Noticing that the shortest cycle is much more robust than the shortest path, we propose two novel cycle-based network centrality measures to address the incompleteness of paths: the shortest cycle closeness centrality (SCC) and the all cycle betweenness centrality (ACC). Notwithstanding we focus on the network centrality problem, our findings on cycles can be applied to explain the incompleteness of paths in applications and could improve the applicability into more scenarios where the paths are employed in network science. [ABSTRACT FROM AUTHOR]