R-mode instability in compact stars

R-mode oscillations have been identified as viable and promising targets for continuous gravitational wave searches, meanwhile, it would allow us to probe the interior of compact stars directly. As well as emitting gravitational wave, r-modes would strongly affect the thermal and spin evolution of compact stars. In this paper, we reviewed the theory behind the gravitational wave driven r-mode instability in a rapidly rotating compact star. In particular, we will focus on r-mode instability window, r-mode evolution and detectability of r-mode.Comment: contribution to the AIP Proceedings of the Xiamen-CUSTIPEN Workshop on the EOS of Dense Neutron-Rich Matter in the Era of Gravitational Wave Astronomy, Jan. 3-7, 2019, Xiamen, China. arXiv admin note: text overlap with arXiv:0806.1005, arXiv:1510.07051, arXiv:1209.5962 by other author

Zirconium-Catalyzed Atom-Economical Synthesis of 1,1-Diborylalkanes from Terminal and Internal Alkenes

A general and atom-economical synthesis of 1,1-diborylalkanes from alkenes and a borane without the need for an additional H2 acceptor is reported for the first time. The key to our success is the use of an earth-abundant zirconium-based catalyst, which allows a balance of self-contradictory reactivities (dehydrogenative boration and hydroboration) to be achieved. Our method avoids using an excess amount of another alkene as an H2 acceptor, which was required in other reported systems. Furthermore, substrates such as simple long-chain aliphatic alkenes that did not react before also underwent 1,1-diboration in our system. Significantly, the unprecedented 1,1-diboration of internal alkenes enabled the preparation of 1,1-diborylalkanes. © 2020 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA

Effect of Preparation Technologies on Properties of Reactive Powder Concrete with Nano-zirconia

Reactive powder concrete filled with 3% content of nano-zirconia (NZ) are fabricated to investigate the effect of preparation technologies on the mechanical strength. The preparation technologies involve internal (NZ is added in RPC and replaced cement )/external mixing(NZ is added in RPC but not replaced cement), ultrasonic time, high mixing speed, saturated lime water/high temperature curing media(curing in water at 90℃). The influencing mechanisms of processing method are revealed through X-ray powder diffraction (XRD) and thermogravimetry (TG) analysis, scanning electron microscope observation. Experiment results showed that high mixing speed and high temperature curing media can improve the mechanical strength obviously. The compressive strength of NZ filled reactive powder concrete with high mixing speed increase 49.9%. The compressive strength, flexural strength and splitting strength of reactive powder concrete with NZ under high temperature curing media increase 35%, 15% and 17% respectively compared with control concrete

Thermal conductivity of graphene nanoribbons under shear deformation: A molecular dynamics simulation

Tensile strain and compress strain can greatly affect the thermal conductivity of graphene nanoribbons (GNRs). However, the effect of GNRs under shear strain, which is also one of the main strain effect, has not been studied systematically yet. In this work, we employ reverse nonequilibrium molecular dynamics (RNEMD) to the systematical study of the thermal conductivity of GNRs (with model size of 4 nm × 15 nm) under the shear strain. Our studies show that the thermal conductivity of GNRs is not sensitive to the shear strain, and the thermal conductivity decreases only 12–16% before the pristine structure is broken. Furthermore, the phonon frequency and the change of the micro-structure of GNRs, such as band angel and bond length, are analyzed to explore the tendency of thermal conductivity. The results show that the main influence of shear strain is on the in-plane phonon density of states (PDOS), whose G band (higher frequency peaks) moved to the low frequency, thus the thermal conductivity is decreased. The unique thermal properties of GNRs under shear strains suggest their great potentials for graphene nanodevices and great potentials in the thermal managements and thermoelectric applications

Crossover between Weak Antilocalization and Weak Localization of Bulk States in Ultrathin Bi2Se3 Films

We report transport studies on the 5 nm thick Bi2Se3 topological insulator films which are grown via molecular beam epitaxy technique. The angle-resolved photoemission spectroscopy data show that the Fermi level of the system lies in the bulk conduction band above the Dirac point, suggesting important contribution of bulk states to the transport results. In particular, the crossover from weak antilocalization to weak localization in the bulk states is observed in the parallel magnetic field measurements up to 50 Tesla. The measured magneto-resistance exhibits interesting anisotropy with respect to the orientation of B// and I, signifying intrinsic spin-orbit coupling in the Bi2Se3 films. Our work directly shows the crossover of quantum interference effect in the bulk states from weak antilocalization to weak localization. It presents an important step toward a better understanding of the existing three-dimensional topological insulators and the potential applications of nano-scale topological insulator devices

Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation

Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user intent lying in the item transitions plays a pivotal role. Recent graph neural networks (GNNs) based SBR methods regard the item transitions as pairwise relations, which neglect the complex high-order information among items. Hypergraph provides a natural way to capture beyond-pairwise relations, while its potential for SBR has remained unexplored. In this paper, we fill this gap by modeling session-based data as a hypergraph and then propose a hypergraph convolutional network to improve SBR. Moreover, to enhance hypergraph modeling, we devise another graph convolutional network which is based on the line graph of the hypergraph and then integrate self-supervised learning into the training of the networks by maximizing mutual information between the session representations learned via the two networks, serving as an auxiliary task to improve the recommendation task. Since the two types of networks both are based on hypergraph, which can be seen as two channels for hypergraph modeling, we name our model \textbf{DHCN} (Dual Channel Hypergraph Convolutional Networks). Extensive experiments on three benchmark datasets demonstrate the superiority of our model over the SOTA methods, and the results validate the effectiveness of hypergraph modeling and self-supervised task. The implementation of our model is available at https://github.com/xiaxin1998/DHCNComment: 9 pages, 4 figures, accepted by AAAI'2