Blind Super-Resolution Via Meta-Learning and Markov Chain Monte Carlo Simulation.

Learning based approaches have witnessed great successes in blind single image super-resolution (SISR) tasks, however, handcrafted kernel priors and learning based kernel priors are typically required. In this paper, we propose a Meta-learning and Markov Chain Monte Carlo based SISR approach to learn kernel priors from organized randomness. In concrete, a lightweight network is adopted as kernel generator, and is optimized via learning from the MCMC simulation on random Gaussian distributions. This procedure provides an approximation for the rational blur kernel, and introduces a network-level Langevin dynamics into SISR optimization processes, which contributes to preventing bad local optimal solutions for kernel estimation. Meanwhile, a meta-learning based alternating optimization procedure is proposed to optimize the kernel generator and image restorer, respectively. In contrast to the conventional alternating minimization strategy, a meta-learning based framework is applied to learn an adaptive optimization strategy, which is less-greedy and results in better convergence performance. These two procedures are iteratively processed in a plug-and-play fashion, for the first time, realizing a learning-based but plug-and-play blind SISR solution in unsupervised inference. Extensive simulations demonstrate the superior performance and generalization ability of the proposed approach when comparing with state-of-the-arts on synthesis and real-world datasets.

Localized Incomplete Multiple Kernel k-Means With Matrix-Induced Regularization.

Localized incomplete multiple kernel k -means (LI-MKKM) is recently put forward to boost the clustering accuracy via optimally utilizing a quantity of prespecified incomplete base kernel matrices. Despite achieving significant achievement in a variety of applications, we find out that LI-MKKM does not sufficiently consider the diversity and the complementary of the base kernels. This could make the imputation of incomplete kernels less effective, and vice versa degrades on the subsequent clustering. To tackle these problems, an improved LI-MKKM, called LI-MKKM with matrix-induced regularization (LI-MKKM-MR), is proposed by incorporating a matrix-induced regularization term to handle the correlation among base kernels. The incorporated regularization term is beneficial to decrease the probability of simultaneously selecting two similar kernels and increase the probability of selecting two kernels with moderate differences. After that, we establish a three-step iterative algorithm to solve the corresponding optimization objective and analyze its convergence. Moreover, we theoretically show that the local kernel alignment is a special case of its global one with normalizing each base kernel matrices. Based on the above observation, the generalization error bound of the proposed algorithm is derived to theoretically justify its effectiveness. Finally, extensive experiments on several public datasets have been conducted to evaluate the clustering performance of the LI-MKKM-MR. As indicated, the experimental results have demonstrated that our algorithm consistently outperforms the state-of-the-art ones, verifying the superior performance of the proposed algorithm.

Metalearning-Based Alternating Minimization Algorithm for Nonconvex Optimization.

In this article, we propose a novel solution for nonconvex problems of multiple variables, especially for those typically solved by an alternating minimization (AM) strategy that splits the original optimization problem into a set of subproblems corresponding to each variable and then iteratively optimizes each subproblem using a fixed updating rule. However, due to the intrinsic nonconvexity of the original optimization problem, the optimization can be trapped into a spurious local minimum even when each subproblem can be optimally solved at each iteration. Meanwhile, learning-based approaches, such as deep unfolding algorithms, have gained popularity for nonconvex optimization; however, they are highly limited by the availability of labeled data and insufficient explainability. To tackle these issues, we propose a meta-learning based alternating minimization (MLAM) method that aims to minimize a part of the global losses over iterations instead of carrying minimization on each subproblem, and it tends to learn an adaptive strategy to replace the handcrafted counterpart resulting in advance on superior performance. The proposed MLAM maintains the original algorithmic principle, providing certain interpretability. We evaluate the proposed method on two representative problems, namely, bilinear inverse problem: matrix completion and nonlinear problem: Gaussian mixture models. The experimental results validate the proposed approach outperforms AM-based methods.

Efficient and Effective Regularized Incomplete Multi-View Clustering.

Incomplete multi-view clustering (IMVC) optimally combines multiple pre-specified incomplete views to improve clustering performance. Among various excellent solutions, the recently proposed multiple kernel k-means with incomplete kernels (MKKM-IK) forms a benchmark, which redefines IMVC as a joint optimization problem where the clustering and kernel matrix imputation tasks are alternately performed until convergence. Though demonstrating promising performance in various applications, we observe that the manner of kernel matrix imputation in MKKM-IK would incur intensive computational and storage complexities, over-complicated optimization and limitedly improved clustering performance. In this paper, we first propose an Efficient and Effective Incomplete Multi-view Clustering (EE-IMVC) algorithm to address these issues. Instead of completing the incomplete kernel matrices, EE-IMVC proposes to impute each incomplete base matrix generated by incomplete views with a learned consensus clustering matrix. Moreover, we further improve this algorithm by incorporating prior knowledge to regularize the learned consensus clustering matrix. Two three-step iterative algorithms are carefully developed to solve the resultant optimization problems with linear computational complexity, and their convergence is theoretically proven. After that, we theoretically study the generalization bound of the proposed algorithms. Furthermore, we conduct comprehensive experiments to study the proposed algorithms in terms of clustering accuracy, evolution of the learned consensus clustering matrix and the convergence. As indicated, our algorithms deliver their effectiveness by significantly and consistently outperforming some state-of-the-art ones.

Publisher Correction: Science diplomacy for plant health.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Effect of pyramiding Bt and CpTI genes on resistance of cotton to Helicoverpa armigera (Lepidoptera: Noctuidae) under laboratory and field conditions.

Transgenic cotton (Cossypium hirsutum L.) varieties, adapted to China, have been bred that express two genes for resistance to insects, the CrylAc gene from Bacillus thuringiensis (Berliner) (Bt), and a trypsin inhibitor gene from cowpea (CpTI). Effectiveness of the double gene modification in conferring resistance to cotton bollworm, Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae), was studied in laboratory and field experiments. In each experiment, performance of Bt+CpTI cotton was compared with Bt cotton and to a conventional nontransgenic variety. Larval survival was lower on both types of transgenic variety, compared with the conventional cotton. Survival of first-, second-, and third-stage larvae was lower on Bt+CpTI cotton than on Bt cotton. Plant structures differed in level of resistance, and these differences were similar on Bt and Bt + CpTI cotton. Likewise, seasonal trends in level of resistance in different plant structures were similar in Bt and Bt+CpTI cotton. Both types of transgenic cotton interfered with development of sixth-stage larvae to adults, and no offspring was produced by H. armigera that fed on Bt or Bt+CpTI cotton from the sixth stage onward. First-, second-, and third-stage larvae spent significantly less time feeding on transgenic cotton than on conventional cotton, and the reduction in feeding time was significantly greater on Bt+CpTI cotton than on Bt cotton. Food conversion efficiency was lower on transgenic varieties than on conventional cotton, but there was no significant difference between Bt and Bt+CpTI cotton. In 3-yr field experimentation, bollworm densities were greatly suppressed on transgenic as compared with conventional cotton, but no significant differences between Bt and Bt+CpTI cotton were found. Overall, the results from laboratory work indicate that introduction of the CpTI gene in Bt cotton raises some components of resistance in cotton against H. armigera, but enhanced control of H. armigera under field conditions, due to expression of the CpTI gene, was not demonstrated.