17,749 research outputs found
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 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}\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
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