Global exponential stability for coupled systems of neutral delay differential equations

In this paper, a novel class of neutral delay differential equations (NDDEs) is presented. By using the Razumikhin method and Kirchhoff's matrix tree theorem in graph theory, the global exponential stability for such NDDEs is investigated. By constructing an appropriate Lyapunov function, two different kinds of sufficient criteria which ensure the global exponential stability of NDDEs are derived in the form of Lyapunov functions and coefficients of NDDEs, respectively. A numerical example is provided to demonstrate the effectiveness of the theoretical results

No spin-localization phase transition in the spin-boson model without local field

We explore the spin-boson model in a special case, i.e., with zero local field. In contrast to previous studies, we find no possibility for quantum phase transition (QPT) happening between the localized and delocalized phases, and the behavior of the model can be fully characterized by the even or odd parity as well as the parity breaking, instead of the QPT, owned by the ground state of the system. Our analytical treatment about the eigensolution of the ground state of the model presents for the first time a rigorous proof of no-degeneracy for the ground state of the model, which is independent of the bath type, the degrees of freedom of the bath and the calculation precision. We argue that the QPT mentioned previously appears due to unreasonable treatment of the ground state of the model or of the infrared divergence existing in the spectral functions for Ohmic and sub-Ohmic dissipations.Comment: 5 pages, 1 figure. Comments are welcom

Nest-DGIL: Nesterov-optimized Deep Geometric Incremental Learning for CS Image Reconstruction

Proximal gradient-based optimization is one of the most common strategies for solving image inverse problems as well as easy to implement. However, these techniques often generate heavy artifacts in image reconstruction. One of the most popular refinement methods is to fine-tune the regularization parameter to alleviate such artifacts, but it may not always be sufficient or applicable due to increased computational costs. In this work, we propose a deep geometric incremental learning framework based on second Nesterov proximal gradient optimization. The proposed end-to-end network not only has the powerful learning ability for high/low frequency image features,but also can theoretically guarantee that geometric texture details will be reconstructed from preliminary linear reconstruction.Furthermore, it can avoid the risk of intermediate reconstruction results falling outside the geometric decomposition domains and achieve fast convergence. Our reconstruction framework is decomposed into four modules including general linear reconstruction, cascade geometric incremental restoration, Nesterov acceleration and post-processing. In the image restoration step,a cascade geometric incremental learning module is designed to compensate for the missing texture information from different geometric spectral decomposition domains. Inspired by overlap-tile strategy, we also develop a post-processing module to remove the block-effect in patch-wise-based natural image reconstruction. All parameters in the proposed model are learnable,an adaptive initialization technique of physical-parameters is also employed to make model flexibility and ensure converging smoothly. We compare the reconstruction performance of the proposed method with existing state-of-the-art methods to demonstrate its superiority. Our source codes are available at https://github.com/fanxiaohong/Nest-DGIL.Comment: 15 page