Limits from Weak Gravity Conjecture on Dark Energy Models

The weak gravity conjecture has been proposed as a criterion to distinguish the landscape from the swampland in string theory. As an application in cosmology of this conjecture, we use it to impose theoretical constraint on parameters of two types of dark energy models. Our analysis indicates that the Chaplygin-gas-type models realized in quintessence field are in the swampland, whereas the $a$ power-low decay model of the variable cosmological constant can be viable but the parameters are tightly constrained by the conjecture.Comment: Revtex4, 8 pages, 5 figures; References, minor corrections in content, and acknowledgement adde

Ground-State Fidelity and Kosterlitz-Thouless Phase Transition for Spin 1/2 Heisenberg Chain with Next-to-the-Nearest-Neighbor Interaction

The Kosterlitz-Thouless transition for the spin 1/2 Heisenberg chain with the next-to-the-nearest-neighbor interaction is investigated in the context of an infinite matrix product state algorithm, which is a generalization of the infinite time-evolving block decimation algorithm [G. Vidal, Phys. Rev. Lett. \textbf{98}, 070201 (2007)] to accommodate both the next-to-the-nearest-neighbor interaction and spontaneous dimerization. It is found that, in the critical regime, the algorithm automatically leads to infinite degenerate ground-state wave functions, due to the finiteness of the truncation dimension. This results in \textit{pseudo} symmetry spontaneous breakdown, as reflected in a bifurcation in the ground-state fidelity per lattice site. In addition, this allows to introduce a pseudo-order parameter to characterize the Kosterlitz-Thouless transition.Comment: 4 pages, 4 figure

Localization lengths of ultrathin disordered gold and silver nanowires

The localization lengths of ultrathin disordered Au and Ag nanowires are estimated by calculating the wire conductances as functions of wire lengths. We study Ag and Au monoatomic linear chains, and thicker Ag wires with very small cross sections. For the monoatomic chains we consider two types of disorder: bounded random fluctuations of the interatomic distances, and the presence of random substitutional impurities. The effect of impurity atoms on the nanowire conductance is much stronger. Our results show that electrical transport in ultrathin disordered wires may occur in the strong localization regime, and with relatively small amounts of disorder the localization lengths may be rather small. The localization length dependence on wire thickness is investigated for Ag nanowires with different impurity concentrations.Comment: 6 pages, postscript figures included, submitted to PR

Identification of the active compounds and significant pathways of yinchenhao decoction based on network pharmacology

published_or_final_versio

Macroscopic Quantum Phase Interference in Antiferromagnetic Particles

The tunnel splitting in biaxial antiferromagnetic particles is studied with a magnetic field applied along the hard anisotropy axis. We observe the oscillation of tunnel splitting as a function of the magnetic field due to the quantum phase interference of two tunneling paths of opposite windings. The oscillation is similar to the recent experimental result with Fe}$_8$\textrm{\ molecular clusters.}Comment: 8 pages, 2 postscript figures, to appear in J. Phys.: Condes. Matte

Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution

The recent advancement of deep learning techniques has made great progress on hyperspectral image super-resolution (HSI-SR). Yet the development of unsupervised deep networks remains challenging for this task. To this end, we propose a novel coupled unmixing network with a cross-attention mechanism, CUCaNet for short, to enhance the spatial resolution of HSI by means of higher-spatial-resolution multispectral image (MSI). Inspired by coupled spectral unmixing, a two-stream convolutional autoencoder framework is taken as backbone to jointly decompose MS and HS data into a spectrally meaningful basis and corresponding coefficients. CUCaNet is capable of adaptively learning spectral and spatial response functions from HS-MS correspondences by enforcing reasonable consistency assumptions on the networks. Moreover, a cross-attention module is devised to yield more effective spatial-spectral information transfer in networks. Extensive experiments are conducted on three widely-used HS-MS datasets in comparison with state-of-the-art HSI-SR models, demonstrating the superiority of the CUCaNet in the HSI-SR application. Furthermore, the codes and datasets will be available at: https://github.com/danfenghong/ECCV2020_CUCaNet

FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning

Federated learning (FL) enables a decentralized machine learning paradigm for multiple clients to collaboratively train a generalized global model without sharing their private data. Most existing works simply propose typical FL systems for single-modal data, thus limiting its potential on exploiting valuable multimodal data for future personalized applications. Furthermore, the majority of FL approaches still rely on the labeled data at the client side, which is limited in real-world applications due to the inability of self-annotation from users. In light of these limitations, we propose a novel multimodal FL framework that employs a semi-supervised learning approach to leverage the representations from different modalities. Bringing this concept into a system, we develop a distillation-based multimodal embedding knowledge transfer mechanism, namely FedMEKT, which allows the server and clients to exchange the joint knowledge of their learning models extracted from a small multimodal proxy dataset. Our FedMEKT iteratively updates the generalized global encoders with the joint embedding knowledge from the participating clients. Thereby, to address the modality discrepancy and labeled data constraint in existing FL systems, our proposed FedMEKT comprises local multimodal autoencoder learning, generalized multimodal autoencoder construction, and generalized classifier learning. Through extensive experiments on three multimodal human activity recognition datasets, we demonstrate that FedMEKT achieves superior global encoder performance on linear evaluation and guarantees user privacy for personal data and model parameters while demanding less communication cost than other baselines

Working with the homeless: The case of a non-profit organisation in Shanghai

This article addresses a two-pronged objective, namely to bring to the fore a much neglected social issue of homelessness, and to explore the dynamics of state-society relations in contemporary China, through a case study of a non-profit organisation (NPO) working with the homeless in Shanghai. It shows that the largely invisible homelessness in Chinese cities was substantially due to exclusionary institutions, such as the combined household registration and 'detention and deportation' systems. Official policy has become much more supportive since 2003 when the latter was replaced with government-run shelters, but we argue that the NPO case demonstrates the potential for enhanced longer-term support and enabling active citizenship for homeless people. By analysing the ways in which the NPO offers services through collaboration and partnership with the public (and private) actors, we also argue that the transformations in postreform China and the changes within the state and civil society have significantly blurred their boundaries, rendering state-society relations much more complex, dynamic, fluid and mutually embedded

Probing for cosmological parameters with LAMOST measurement

In this paper we study the sensitivity of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) project to the determination of cosmological parameters, employing the Monte Carlo Markov Chains (MCMC) method. For comparison, we first analyze the constraints on cosmological parameters from current observational data, including WMAP, SDSS and SN Ia. We then simulate the 3D matter power spectrum data expected from LAMOST, together with the simulated CMB data for PLANCK and the SN Ia from 5-year Supernovae Legacy Survey (SNLS). With the simulated data, we investigate the future improvement on cosmological parameter constraints, emphasizing the role of LAMOST. Our results show the potential of LAMOST in probing for the cosmological parameters, especially in constraining the equation-of-state (EoS) of the dark energy and the neutrino mass.Comment: 7 pages and 3 figures. Replaced with version accepted for publication in JCA