Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization

As a powerful statistical image modeling technique, sparse representation has been successfully used in various image restoration applications. The success of sparse representation owes to the development of l1-norm optimization techniques, and the fact that natural images are intrinsically sparse in some domain. The image restoration quality largely depends on whether the employed sparse domain can represent well the underlying image. Considering that the contents can vary significantly across different images or different patches in a single image, we propose to learn various sets of bases from a pre-collected dataset of example image patches, and then for a given patch to be processed, one set of bases are adaptively selected to characterize the local sparse domain. We further introduce two adaptive regularization terms into the sparse representation framework. First, a set of autoregressive (AR) models are learned from the dataset of example image patches. The best fitted AR models to a given patch are adaptively selected to regularize the image local structures. Second, the image non-local self-similarity is introduced as another regularization term. In addition, the sparsity regularization parameter is adaptively estimated for better image restoration performance. Extensive experiments on image deblurring and super-resolution validate that by using adaptive sparse domain selection and adaptive regularization, the proposed method achieves much better results than many state-of-the-art algorithms in terms of both PSNR and visual perception.Comment: 35 pages. This paper is under review in IEEE TI

Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser

Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose high-level representation guided denoiser (HGD) as a defense for image classification. Standard denoiser suffers from the error amplification effect, in which small residual adversarial noise is progressively amplified and leads to wrong classifications. HGD overcomes this problem by using a loss function defined as the difference between the target model's outputs activated by the clean image and denoised image. Compared with ensemble adversarial training which is the state-of-the-art defending method on large images, HGD has three advantages. First, with HGD as a defense, the target model is more robust to either white-box or black-box adversarial attacks. Second, HGD can be trained on a small subset of the images and generalizes well to other images and unseen classes. Third, HGD can be transferred to defend models other than the one guiding it. In NIPS competition on defense against adversarial attacks, our HGD solution won the first place and outperformed other models by a large margin

Finite element model updating using base excitation response function

Finite element model updating is an effective way to build accurate analytical models for structures. Most of the available updating methods employ information from modal testing. However, in astronautics engineering, information provided by vibration table testing is more valuable than those from modal testing. Therefore, it is necessary to study updating methods which can adopt information from vibration table testing. This paper presents the study on such issue. The base excitation response function is analyzed with the assumption that the vibration table gives the structure a single direction motion excitation. Model updating method which adopts the response function is then proposed. In the numerical simulation, several case studies are constructed for a truss structure with small or significant modeling errors respectively. Data selection, which has great influence on the success of updating, is carefully studied. A novel adaptive data selection approach is suggested. Simulation results show that model updating converge with good accuracy when the adaptive data selection approach is used

Criteria of evaluating initial model for effective dynamic model updating

Finite element model updating is an important research field in structural dynamics. Though a variety of updating methods have been proposed in the past decades, all the methods could be effective only on the assumption that the initial finite element model is updatable. The assumption has led to the fact that many researchers study on how to update the model while little attention is paid to studies on whether the model is updatable. This has become inevitable obstacle between research and engineering applications because the assumption is not a tenable hypothesis in practice. To circumvent this problem, the evaluation of model updatability is studied in this paper. Firstly, two conditional statements about mapping are proved as a theoretical basis. Then, two criteria for evaluation of initial models are deduced. A beam is employed in the numerical simulations. Two different initial models for the beam are constructed with different boundary conditions. The models are evaluated using the proposed criteria. The results indicate that the criteria are able to distinguish the model updatability

Photoinduced High-Chern-Number Quantum Anomalous Hall Effect from Higher-Order Topological Insulators

Quantum anomalous Hall (QAH) insulators with high Chern number host multiple dissipationless chiral edge channels, which are of fundamental interest and promising for applications in spintronics and quantum computing. However, only a limited number of high-Chern-number QAH insulators have been reported to date. Here, we propose a dynamic approach for achieving high-Chern-number QAH phases in periodically driven two-dimensional higher-order topological insulators (HOTIs).In particular, we consider two representative kinds of HOTIs which are characterized by a quantized quadruple moment and the second Stiefel-Whitney number, respectively. Using the Floquet formalism for periodically driven systems, we demonstrate that QAH insulators with tunable Chern number up to four can be achieved. Moreover, we show by first-principles calculations that the monolayer graphdiyne, a realistic HOTI, is an ideal material candidate. Our work not only establishes a strategy for designing high-Chern-number QAH insulators in periodically driven HOTIs, but also provides a powerful approach to investigate exotic topological states in nonequilibrium cases.Comment: 6 pages, 3 figure

Azorhizobium caulinodans c-di-GMP phosphodiesterase Chp1 involved in motility, EPS production, and nodulation of the host plant

Establishment of the rhizobia-legume symbiosis is usually accompanied by hydrogen peroxide (H2O2) production by the legume host at the site of infection, a process detrimental to rhizobia. In Azorhizobium caulinodans ORS571, deletion of chp1, a gene encoding c-di-GMP phosphodiesterase, led to increased resistance against H2O2 and to elevated nodulation efficiency on its legume host Sesbania rostrata. Three domains were identified in the Chp1: a PAS domain, a degenerate GGDEF domain, and an EAL domain. An in vitro enzymatic activity assay showed that the degenerate GGDEF domain of Chp1 did not have diguanylate cyclase activity. The phosphodiesterase activity of Chp1 was attributed to its EAL domain which could hydrolyse c-di-GMP into pGpG. The PAS domain functioned as a regulatory domain by sensing oxygen. Deletion of Chp1 resulted in increased intracellular c-di-GMP level, decreased motility, increased aggregation, and increased EPS (extracellular polysaccharide) production. H2O2-sensitivity assay showed that increased EPS production could provide ORS571 with resistance against H2O2. Thus, the elevated nodulation efficiency of the increment chp1 mutant could be correlated with a protective role of EPS in the nodulation process. These data suggest that c-di-GMP may modulate the A. caulinodans-S. rostrata nodulation process by regulating the production of EPS which could protect rhizobia against H2O2

A Novel Differential Log-Companding Amplifier for Biosignal Sensing

We proposed a new method for designing the CMOS differential log-companding amplifier which achieves significant improvements in linearity, common-mode rejection ratio (CMRR), and output range. With the new nonlinear function used in the log-companding technology, this proposed amplifier has a very small total harmonic distortion (THD) and simultaneously a wide output current range. Furthermore, a differential structure with conventionally symmetrical configuration has been adopted in this novel method in order to obtain a high CMRR. Because all transistors in this amplifier operate in the weak inversion, the supply voltage and the total power consumption are significantly reduced. The novel log-companding amplifier was designed using a 0.18 μm CMOS technology. Improvements in THD, output current range, noise, and CMRR are verified using simulation data. The proposed amplifier operates from a 0.8 V supply voltage, shows a 6.3 μA maximum output current range, and has a 6 μW power consumption. The THD is less than 0.03%, the CMRR of this circuit is 74 dB, and the input referred current noise density is 166.1 fA/Hz. This new method is suitable for biomedical applications such as electrocardiogram (ECG) signal acquisition

Fast generation of mock galaxy catalogues with COLA

We investigate the feasibility of using COmoving Lagrangian Acceleration (COLA) technique to efficiently generate galaxy mock catalogues that can accurately reproduce the statistical properties of observed galaxies. Our proposed scheme combines the subhalo abundance matching (SHAM) procedure with COLA simulations, utilizing only three free parameters: the scatter magnitude ($\sigma_{\rm scat}$) in SHAM, the initial redshift ($z_{\rm init}$) of the COLA simulation, and the time stride ($da$) used by COLA. In this proof-of-concept study, we focus on a subset of BOSS CMASS NGC galaxies within the redshift range $z\in [0.45, 0.55]$. We perform $\mathtt{GADGET}$ simulation and low-resolution COLA simulations with various combinations of $(z_{\rm init}, da)$, each using $1024^{3}$ particles in an $800~h^{-1}{\rm Mpc}$ box. By minimizing the difference between COLA mock and CMASS NGC galaxies for the monopole of the two-point correlation function (2PCF), we obtain the optimal $\sigma_{\rm scat}$. We have found that by setting $z_{\rm init}=29$ and $da=1/30$, we achieve a good agreement between COLA mock and CMASS NGC galaxies within the range of 4 to $20~h^{-1}{\rm Mpc}$, with a computational cost two orders of magnitude lower than that of the N-body code. Moreover, a detailed verification is performed by comparing various statistical properties, such as anisotropic 2PCF, three-point clustering, and power spectrum multipoles, which shows similar performance between GADGET mock and COLA mock catalogues with the CMASS NGC galaxies. Furthermore, we assess the robustness of the COLA mock catalogues across different cosmological models, demonstrating consistent results in the resulting 2PCFs. Our findings suggest that COLA simulations are a promising tool for efficiently generating mock catalogues for emulators and machine learning analyses in exploring the large-scale structure of the Universe.Comment: 24 pages, 14 figures, 4 table