674 research outputs found
Improving controllability of complex networks by rewiring links regularly
Network science have constantly been in the focus of research for the last
decade, with considerable advances in the controllability of their structural.
However, much less effort has been devoted to study that how to improve the
controllability of complex networks. In this paper, a new algorithm is proposed
to improve the controllability of complex networks by rewiring links regularly
which transforms the network structure. Then it is demonstrated that our
algorithm is very effective after numerical simulation experiment on typical
network models (Erd\"os-R\'enyi and scale-free network). We find that our
algorithm is mainly determined by the average degree and positive correlation
of in-degree and out-degree of network and it has nothing to do with the
network size. Furthermore, we analyze and discuss the correlation between
controllability of complex networks and degree distribution index: power-law
exponent and heterogeneit
Complexation of Z-ligustilide with hydroxypropyl-β-cyclodextrin to improve stability and oral bioavailability
To improve the stability and oral bioavailability of Z-ligustilide (LIG), the inclusion complex of LIG with hydroxypropyl-β-cyclodextrin (HP-β-CD) was prepared by the kneading method and characterized by UV-Vis spectroscopy, differential thermal analysis (DTA) and Fourier transform infrared (FTIR) spectroscopy. LIG is capable of forming an inclusion complex with HP-β-CD and the stoichiometry of the complex was 1:1. Stability of the inclusion complex against temperature and light was greatly enhanced compared to that of free LIG. Further, oral bioavailability of LIG and the inclusion complex in rats were studied and the plasma drug concentration-time curves fitted well with the non-compartment model to estimate the absolute bioavailability, which was 7.5 and 35.9 %, respectively. In conclusion, these results show that LIG/HP-β-CD complexation can be of great use for increasing the stability and biological efficacy of LIG
Constrained Multiview Representation for Self-supervised Contrastive Learning
Representation learning constitutes a pivotal cornerstone in contemporary deep learning paradigms, offering a conduit to elucidate distinctive features within the latent space and interpret the deep models. Nevertheless, the inherent complexity of anatomical patterns and the random nature of lesion distribution in medical image segmentation pose significant challenges to the disentanglement of representations and the understanding of salient features. Methods guided by the maximization of mutual information, particularly within the framework of contrastive learning, have demonstrated remarkable success and superiority in decoupling densely intertwined representations. However, the effectiveness of contrastive learning highly depends on the quality of the positive and negative sample pairs, i.e. the unselected average mutual information among multi-views would obstruct the learning strategy so the selection of the views is vital. In this work, we introduce a novel approach predicated on representation distance-based mutual information (MI) maximization for measuring the significance of different views, aiming at conducting more efficient contrastive learning and representation disentanglement. Additionally, we introduce an MI re-ranking strategy for representation selection, benefiting both the continuous MI estimating and representation significance distance measuring. Specifically, we harness multi-view representations extracted from the frequency domain, re-evaluating their significance based on mutual information across varying frequencies, thereby facilitating a multifaceted contrastive learning approach to bolster semantic comprehension. The statistical results under the five metrics demonstrate that our proposed framework proficiently constrains the MI maximization-driven representation selection and steers the multi-view contrastive learning process
Analysis and design of transition radiation in layered uniaxial crystals using Tandem neural networks
With the flourishing development of nanophotonics, Cherenkov radiation
pattern can be designed to achieve superior performance in particle detection
by fine-tuning the properties of metamaterials such as photonic crystals (PCs)
surrounding the swift particle. However, the radiation pattern can be sensitive
to the geometry and material properties of PCs, such as periodicity, unit
thickness, and dielectric fraction, making direct analysis and inverse design
difficult. In this article, we propose a systematic method to analyze and
design PC-based transition radiation, which is assisted by deep learning neural
networks. By matching boundary conditions at the interfaces, Cherenkov-like
radiation of multilayered structures can be resolved analytically using the
cascading scattering matrix method, despite the optical axes not being aligned
with the swift electron trajectory. Once well trained, forward deep learning
neural networks can be utilized to predict the radiation pattern without
further direct electromagnetic simulations; moreover, Tandem neural networks
have been proposed to inversely design the geometry and/or material properties
for desired Cherenkov radiation pattern. Our proposal demonstrates a promising
strategy for dealing with layered-medium-based Cherenkov radiation detectors,
and it can be extended for other emerging metamaterials, such as photonic time
crystals
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