The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study

Due to the exponential growth of scientific publications on the Web, there is a pressing need to tag each paper with fine-grained topics so that researchers can track their interested fields of study rather than drowning in the whole literature. Scientific literature tagging is beyond a pure multi-label text classification task because papers on the Web are prevalently accompanied by metadata information such as venues, authors, and references, which may serve as additional signals to infer relevant tags. Although there have been studies making use of metadata in academic paper classification, their focus is often restricted to one or two scientific fields (e.g., computer science and biomedicine) and to one specific model. In this work, we systematically study the effect of metadata on scientific literature tagging across 19 fields. We select three representative multi-label classifiers (i.e., a bag-of-words model, a sequence-based model, and a pre-trained language model) and explore their performance change in scientific literature tagging when metadata are fed to the classifiers as additional features. We observe some ubiquitous patterns of metadata's effects across all fields (e.g., venues are consistently beneficial to paper tagging in almost all cases), as well as some unique patterns in fields other than computer science and biomedicine, which are not explored in previous studies.Comment: 11 pages; Accepted to WWW 202

Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT

Physical-Layer Authentication (PLA) has been recently believed as an endogenous-secure and energy-efficient technique to recognize IoT terminals. However, the major challenge of applying the state-of-the-art PLA schemes directly to 6G-enabled IoT is the inaccurate channel fingerprint estimation in low Signal-Noise Ratio (SNR) environments, which will greatly influence the reliability and robustness of PLA. To tackle this issue, we propose a configurable-fingerprint-based PLA architecture through Intelligent Reflecting Surface (IRS) that helps create an alternative wireless transmission path to provide more accurate fingerprints. According to Baye's theorem, we propose a Gaussian Process Classification (GPC)-based PLA scheme, which utilizes the Expectation Propagation (EP) method to obtain the identities of unknown fingerprints. Considering that obtaining sufficient labeled fingerprint samples to train the GPC-based authentication model is challenging for future 6G systems, we further extend the GPC-based PLA to the Efficient-GPC (EGPC)-based PLA through active learning, which requires fewer labeled fingerprints and is more feasible. We also propose three fingerprint selecting algorithms to choose fingerprints, whose identities are queried to the upper-layers authentication mechanisms. For this reason, the proposed EGPC-based scheme is also a lightweight cross-layer authentication method to offer a superior security level. The simulations conducted on synthetic datasets demonstrate that the IRS-assisted scheme reduces the authentication error rate by 98.69% compared to the non-IRS-based scheme. Additionally, the proposed fingerprint selection algorithms reduce the authentication error rate by 65.96% to 86.93% and 45.45% to 70.00% under perfect and imperfect channel estimation conditions, respectively, when compared with baseline algorithms.Comment: 12 pages, 9 figure

Effects of photoperiod on body mass, thermogenesis and body composition in Eothenomys miletus during cold exposure

Many small mammals respond to seasonal changes in photoperiod by altering body mass and adiposity. These animals may provide valuable models for understanding the regulation of energy balance. In present study, we examined the effect on body mass, rest metabolic rate, food intake and body composition in cold-acclimated (5 °C) in Eothenomys miletus by transferring them from a short (SD, 8h :16h L: D) to long day photoperiod (LD, 16h: 8h L:D). During the first 4 weeks of exposure to SD, E. miletus decreased body mass. After the next 4 weeks of exposure to LD, which the average difference between body masses of LD and SD voles was 4.76 g. This 14.74% increase in body mass reflected significant increases in absolute amounts of body components, including wet carcass mass, dry carcass mass and body fat mass. After correcting body composition and organ morphology data for the differences in body mass, only livers, kidney, and small intestine were enlarged due to photoperiod treatment during cold exposure. E. miletus increased RMR and energy intake exposure to LD, but maintained a stable level to SD after 28 days. Serum leptin levels were positively correlated with body mass, body fat mass, RMR as well as energy intake. All of the results indicated that E. miletus may provide an attractive novel animal model for investigation of the regulation of body mass and energy balance at organism levels. Leptin is potentially involved in the photoperiod induced body mass regulation and thermogenesis in E. miletus during cold exposure

Orbit- and Atom-Resolved Spin Textures of Intrinsic, Extrinsic and Hybridized Dirac Cone States

Combining first-principles calculations and spin- and angle-resolved photoemission spectroscopy measurements, we identify the helical spin textures for three different Dirac cone states in the interfaced systems of a 2D topological insulator (TI) of Bi(111) bilayer and a 3D TI Bi2Se3 or Bi2Te3. The spin texture is found to be the same for the intrinsic Dirac cone of Bi2Se3 or Bi2Te3 surface state, the extrinsic Dirac cone of Bi bilayer state induced by Rashba effect, and the hybridized Dirac cone between the former two states. Further orbit- and atom-resolved analysis shows that s and pz orbits have a clockwise (counterclockwise) spin rotation tangent to the iso-energy contour of upper (lower) Dirac cone, while px and py orbits have an additional radial spin component. The Dirac cone states may reside on different atomic layers, but have the same spin texture. Our results suggest that the unique spin texture of Dirac cone states is a signature property of spin-orbit coupling, independent of topology

Empirical study on clique-degree distribution of networks

The community structure and motif-modular-network hierarchy are of great importance for understanding the relationship between structures and functions. In this paper, we investigate the distribution of clique-degree, which is an extension of degree and can be used to measure the density of cliques in networks. The empirical studies indicate the extensive existence of power-law clique-degree distributions in various real networks, and the power-law exponent decreases with the increasing of clique size.Comment: 9 figures, 4 page