Isoindigo-Thiophene D-A-D-Type Conjugated Polymers: Electrosynthesis and Electrochromic Performances.

Four novel isoindigo-thiophene D-A-D-type precursors are synthesized by Stille coupling and electrosynthesized to yield corresponding hybrid polymers with favorable electrochemical and electrochromic performances. Intrinsic structure-property relationships of precursors and corresponding polymers, including surface morphology, band gaps, electrochemical properties, and electrochromic behaviors, are systematically investigated. The resultant isoindigo-thiophene D-A-D-type polymer combines the merits of isoindigo and polythiophene, including the excellent stability of isoindigo-based polymers and the extraordinary electrochromic stability of polythiophene. The low onset oxidation potential of precursors ranges from 1.10 to 1.15 V vs. Ag/AgCl, contributing to the electrodeposition of high-quality polymer films. Further kinetic studies illustrate that isoindigo-thiophene D-A-D-type polymers possess favorable electrochromic performances, including high optical contrast (53%, 1000 nm), fast switching time (0.8 s), and high coloration efficiency (124 cm2 C-1). These features of isoindigo-thiophene D-A-D-type conjugated polymers could provide a possibility for rational design and application as electrochromic materials.

Federated Learning Via Inexact ADMM.

One of the crucial issues in federated learning is how to develop efficient optimization algorithms. Most of the current ones require full device participation and/or impose strong assumptions for convergence. Different from the widely-used gradient descent-based algorithms, in this article, we develop an inexact alternating direction method of multipliers (ADMM), which is both computation- and communication-efficient, capable of combating the stragglers' effect, and convergent under mild conditions. Furthermore, it has high numerical performance compared with several state-of-the-art algorithms for federated learning.

Self-Distilled Hierarchical Network for Unsupervised Deformable Image Registration.

Unsupervised deformable image registration benefits from progressive network structures such as Pyramid and Cascade. However, existing progressive networks only consider the single-scale deformation field in each level or stage and ignore the long-term connection across non-adjacent levels or stages. In this paper, we present a novel unsupervised learning approach named Self-Distilled Hierarchical Network (SDHNet). By decomposing the registration procedure into several iterations, SDHNet generates hierarchical deformation fields (HDFs) simultaneously in each iteration and connects different iterations utilizing the learned hidden state. Specifically, hierarchical features are extracted to generate HDFs through several parallel gated recurrent units, and HDFs are then fused adaptively conditioned on themselves as well as contextual features from the input image. Furthermore, different from common unsupervised methods that only apply similarity loss and regularization loss, SDHNet introduces a novel self-deformation distillation scheme. This scheme distills the final deformation field as the teacher guidance, which adds constraints for intermediate deformation fields on deformation-value and deformation-gradient spaces respectively. Experiments on five benchmark datasets, including brain MRI and liver CT, demonstrate the superior performance of SDHNet over state-of-the-art methods with a faster inference speed and a smaller GPU memory. Code is available at https://github.com/Blcony/SDHNet.

Sparse SVM for Sufficient Data Reduction.

Kernel-based methods for support vector machines (SVM) have shown highly advantageous performance in various applications. However, they may incur prohibitive computational costs for large-scale sample datasets. Therefore, data reduction (reducing the number of support vectors) appears to be necessary, which gives rise to the topic of the sparse SVM. Motivated by this problem, the sparsity constrained kernel SVM optimization has been considered in this paper in order to control the number of support vectors. Based on the established optimality conditions associated with the stationary equations, a Newton-type method is developed to handle the sparsity constrained optimization. This method is found to enjoy the one-step convergence property if the starting point is chosen to be close to a local region of a stationary point, thereby leading to a super-high computational speed. Numerical comparisons with several powerful solvers demonstrate that the proposed method performs exceptionally well, particularly for large-scale datasets in terms of a much lower number of support vectors and shorter computational time.

Recursive Decomposition Network for Deformable Image Registration.

Deformation decomposition serves as a good solution for deformable image registration when the deformation is large. Current deformation decomposition methods can be categorized into cascade-based methods and pyramid-based methods. However, cascade-based methods suffer from heavy computational burdens and long inference time due to their structures of repeated subnetworks, while the effectiveness of pyramid-based methods is constrained by their limited numbers of resolution levels. In this paper, to address both the insufficient and inefficient decomposition problems in current deformation decomposition methods, we propose a recursive decomposition network (RDN) to offer a novel solution for deformable image registration. Stage-wise recursion can efficiently decompose a large deformation into different pyramid estimation stages without using repeated subnetworks like in cascade-based methods. Level-wise recursion can sufficiently decompose the deformation inside each resolution level instead of only one-time estimation like in pyramid-based methods. Extensive experiments and ablation studies on two representative datasets validate the effectiveness and efficiency of our proposed RDN.

Support Vector Machine Classifier via L0/1 Soft-Margin Loss.

Support vector machines (SVM) have drawn wide attention for the last two decades due to its extensive applications, so a vast body of work has developed optimization algorithms to solve SVM with various soft-margin losses. To distinguish all, in this paper, we aim at solving an ideal soft-margin loss SVM: L0/1 soft-margin loss SVM (dubbed as L0/1-SVM). Many of the existing (non)convex soft-margin losses can be viewed as one of the surrogates of the L0/1 soft-margin loss. Despite its discrete nature, we manage to establish the optimality theory for the L0/1-SVM including the existence of the optimal solutions, the relationship between them and P-stationary points. These not only enable us to deliver a rigorous definition of L0/1 support vectors but also allow us to define a working set. Integrating such a working set, a fast alternating direction method of multipliers is then proposed with its limit point being a locally optimal solution to the L0/1-SVM. Finally, numerical experiments demonstrate that our proposed method outperforms some leading classification solvers from SVM communities, in terms of faster computational speed and a fewer number of support vectors. The bigger the data size is, the more evident its advantage appears.

SPI-2/CrmA inhibits IFN-ß induction by targeting TBK1/IKKε.

Viruses modulate the host immune system to evade host antiviral responses. The poxvirus proteins serine proteinase inhibitor 2 (SPI-2) and cytokine response modifier A (CrmA) are involved in multiple poxvirus evasion strategies. SPI-2 and CrmA target caspase-1 to prevent apoptosis and cytokine activation. Here, we identified SPI-2 and CrmA as negative regulators of virus-triggered induction of IFN-ß. Ectopic expression of SPI-2 or CrmA inhibited virus-triggered induction of IFN-ß and its downstream genes. Consistently, knockdown of SPI-2 by RNAi potentiated VACV-induced transcription of antiviral genes. Further studies revealed that SPI-2 and CrmA associated with TBK1 and IKKε to disrupt the MITA-TBK1/IKKε-IRF3 complex. These findings reveal a novel mechanism of SPI-2/CrmA-mediated poxvirus immune evasion.