Multi-wavelet residual dense convolutional neural network for image denoising

Networks with large receptive field (RF) have shown advanced fitting ability in recent years. In this work, we utilize the short-term residual learning method to improve the performance and robustness of networks for image denoising tasks. Here, we choose a multi-wavelet convolutional neural network (MWCNN), one of the state-of-art networks with large RF, as the backbone, and insert residual dense blocks (RDBs) in its each layer. We call this scheme multi-wavelet residual dense convolutional neural network (MWRDCNN). Compared with other RDB-based networks, it can extract more features of the object from adjacent layers, preserve the large RF, and boost the computing efficiency. Meanwhile, this approach also provides a possibility of absorbing advantages of multiple architectures in a single network without conflicts. The performance of the proposed method has been demonstrated in extensive experiments with a comparison with existing techniques.Comment: 9 pages, 9 figure

A primal-dual interior-point relaxation method with adaptively updating barrier for nonlinear programs

Based on solving an equivalent parametric equality constrained mini-max problem of the classic logarithmic-barrier subproblem, we present a novel primal-dual interior-point relaxation method for nonlinear programs. In the proposed method, the barrier parameter is updated in every step as done in interior-point methods for linear programs, which is prominently different from the existing interior-point methods and the relaxation methods for nonlinear programs. Since our update for the barrier parameter is autonomous and adaptive, the method has potential of avoiding the possible difficulties caused by the unappropriate initial selection of the barrier parameter and speeding up the convergence to the solution. Moreover, it can circumvent the jamming difficulty of global convergence caused by the interior-point restriction for nonlinear programs and improve the ill conditioning of the existing primal-dual interiorpoint methods as the barrier parameter is small. Under suitable assumptions, our method is proved to be globally convergent and locally quadratically convergent. The preliminary numerical results on a well-posed problem for which many line-search interior-point methods fail to find the minimizer and a set of test problems from the CUTE collection show that our method is efficient.Comment: submitted to SIOPT on April 14, 202

Self-Organized Time Crystal in Driven-Dissipative Quantum System

Continuous time crystals (CTCs) are characterized by sustained oscillations that break the time translation symmetry. Since the ruling out of equilibrium CTCs by no-go theorems, the emergence of such dynamical phases has been observed in various driven-dissipative quantum platforms. The current understanding of CTCs is mainly based on mean-field (MF) theories, which fail to address the problem of whether the long-range time crystalline order exists in noisy, spatially extended systems without the protection of all-to-all couplings. Here, we propose a new kind of CTC realized in a quantum contact model through self-organized bistability (SOB). The exotic CTCs stem from the interplay between collective dissipation induced by the first-order absorbing phase transitions (APTs) and slow constant driving provided by an incoherent pump. The stability of such oscillatory phases in finite dimensions under the action of intrinsic quantum fluctuations is scrutinized by the functional renormalization group method and numerical simulations. Occurring at the edge of quantum synchronization, the CTC phase exhibits an inherent period and amplitude with a coherence time diverging with system size, thus also constituting a boundary time crystal (BTC). Our results serve as a solid route towards self-protected CTCs in strongly interacting open systems.Comment: 15 pages, 7 figure

Large-deviation analysis for counting statistics in mesoscopic transports

We present an efficient approach, based on a number-conditioned master equation, for large-deviation analysis in mesoscopic transports. Beyond the conventional full-counting-statistics study, the large-deviation approach encodes complete information of both the typical trajectories and the rare ones, in terms of revealing a continuous change of the dynamical phase in trajectory space. The approach is illustrated with two examples: (i) transport through a single quantum dot, where we reveal the inhomogeneous distribution of trajectories in general case and find a particular scale invariance point in trajectory statistics; and (ii) transport through a double dots, where we find a dynamical phase transition between two distinct phases induced by the Coulomb correlation and quantum interference.Comment: 8 pages, 3 figure