The vulnerabilities of face-based biometric systems to presentation attacks have been finally recognized but yet we lack generalized software-based face presentation attack detection (PAD) methods performing robustly in practical mobile authentication scenarios. This is mainly due to the fact that the existing public face PAD datasets are beginning to cover a variety of attack scenarios and acquisition conditions but their standard evaluation protocols do not encourage researchers to assess the generalization capabilities of their methods across these variations. In this present work, we introduce a new public face PAD database, OULU-NPU, aiming at evaluating the generalization of PAD methods in more realistic mobile authentication scenarios across three covariates: unknown environmental conditions (namely illumination and background scene), acquisition devices and presentation attack instruments (PAI). This publicly available database consists of 5940 videos corresponding to 55 subjects recorded in three different environments using high-resolution frontal cameras of six different smartphones. The high-quality print and videoreplay attacks were created using two different printers and two different display devices. Each of the four unambiguously defined evaluation protocols introduces at least one previously unseen condition to the test set, which enables a fair comparison on the generalization capabilities between new and existing approaches. The baseline results using color texture analysis based face PAD method demonstrate the challenging nature of the database.

The increasing popularity of academic social networking sites (ASNSs) requires studies on the usage of ASNSs among scholars and evaluations of the effectiveness of these ASNSs. However, it is unclear whether current ASNSs have fulfilled their design goal, as scholars' actual online interactions on these platforms remain unexplored. To fill the gap, this article presents a study based on data collected from ResearchGate. Adopting a mixed‐method design by conducting qualitative content analysis and statistical analysis on 1,128 posts collected from ResearchGate Q&A, we examine how scholars exchange information and resources, and how their practices vary across three distinct disciplines: library and information services, history of art, and astrophysics. Our results show that the effect of a questioner's intention (i.e., seeking information or discussion) is greater than disciplinary factors in some circumstances. Across the three disciplines, responses to questions provide various resources, including experts' contact details, citations, links to Wikipedia, images, and so on. We further discuss several implications of the understanding of scholarly information exchange and the design of better academic social networking interfaces, which should stimulate scholarly interactions by minimizing confusion, improving the clarity of questions, and promoting scholarly content management.

No abstract

No abstract

The Jinlongshan gold deposit, Shaanxi Province, is a large Carlin‐type deposit in the Western Qinling Gold Province (WQGP) in the Qinling Orogenic Belt, Central China. In this study, we present new EPMA and LA–ICP–MS geochemical data on the Fe–S–As minerals at Jinlongshan, which consist mainly of pyrite, arsenian pyrite and arsenopyrite. Diagenetic pyrite (Py0) is preferentially hosted in the sedimentary rocks as rounded or framboidal aggregates. Three types of hydrothermal pyrite (Py1, Py2 and Py3) and two types of arsenopyrite (Apy1 and Apy2) were newly identified in the ores and wall‐rocks: fine‐grained Py1 and Apy1 were formed at the early mineralization stage; coarse‐grained Py2 and Apy2 were formed at the middle mineralization stage; and Py3 with stibnite were formed at the late mineralization stage. Their trace elements differ in concentrations, associations and rim–core zonations, implying different genesis and crystallization processes. Py0 is As‐poor (Au < 0.105 wt% and As < 0.094 wt%); Py1 and Py2 are Au–As‐rich (Au = 1.9–113 ppm and As up to 7.270 wt%); Py3 is Au–As‐poor (Au = 0.03–11.4 ppm and As < 0.56 wt%); and Apy1 and Apy2 show the highest Au concentration (up to 576 ppm). Co, Ni and As (±Se and Te) occur in pyrite as isomorphism, and Ag, Cu, Pb and Zn are distributed primarily as invisible or visible sulphide inclusions. The dominant state of Au in the hydrothermal Fe–S–As minerals at Jinlongshan is lattice bound Au+1, and possibly invisible Au0 nanoparticles, whereas in the diagenetic Py0, the dominant state is mainly Au0. By integrating the evidence from ore deposit geology, mineral morphology, internal structure and trace element geochemistry of the Au‐bearing Fe–S–As minerals, it is concluded that the formation of the Jinlongshan Au deposit may have been related to the regional metallogenic episode at around 170 Ma in the WQGP, which was coeval with the regional tectonic transition from a collisional compression to a late‐ to post‐collisional extension in the Qinling Orogen. Copyright © 2014 John Wiley & Sons, Ltd.

Smart transportation system is a cross-field research topic that involves both the organizations that manage the large-scaled system and individual end-users who enjoy these services. Recent advancement of machine learning-based algorithms has either enabled or improved a wide range of applications due to its strength in making accurate predictions for complex problems with a minimal amount of domain knowledge and great ability of generalization. These nice properties imply potential to be explored for building smart transportation system. This paper studies how deep reinforcement learning (DRL) can be used to optimize the operating policy in modern bike sharing systems. As a case study, the authors demonstrate the potential power of the modern DRL by showing a policy-gradient-based reinforcement learning approach to the rebalancing problem in a bike sharing system, which can simultaneously improve both the user experience and reduce the operational expense.

No abstract

No abstract