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High-energy lasers can cause severe damage to conventional materials in an extremely short time. Highly reflective coatings are effective against damage, but they cannot withstand high laser power densities. In this study, a composite coating with two characteristics, namely, reflection and energy consumption, was designed and prepared. TiO 2 and BN powders, as light reflective media, were introduced into a boron-modified phenolic resin (BPF) to prepare an anti-laser composite coating. The results show that the initial reflectivity of the composite coating can reach 78.8% at a wavelength of 1064 nm. After damage-free protection for 0.4 s, ablation occurred in the irradiation region. Owing to the formation of residual char, which is the pyrolysis product of BPF, the coating maintained its protective performance. When the composite coating was irradiated at 1000 W/cm2 for 10 s, the back-surface temperature was 186 °C, which indicates decent anti-laser performance. We also studied the role of BN by setting a control experiment. The high conductivity and energy consumption caused by the evaporation of B 2 O 3 , which is the oxidation product of BN, helped in improving the anti-laser performance. The residual char with high porosity and degree of graphitization proved to be effective to isolate laser energy. This study provides a new design for protection of conventional materials against laser damage. [ABSTRACT FROM AUTHOR]

In this paper, we propose an object(s)-of-interest (OOI) segmentation method for images with inhomogeneous intensities. First, we define a discrimination function for each pixel, labelling whether the pixel belongs to OOI based on the characteristics of OOI. This function is then integrated with image gradient to construct a stopping function in an energy functional. Finally, this energy functional is minimized by means of level set evolution, which guides the motion of the zero level set toward object boundaries. The results demonstrate that our model is effective. [ABSTRACT FROM AUTHOR]

Abstract: We propose a novel semi-automatic method for burn scar delineation from Landsat imagery using a modified Chan–Vese model. Burn scars appear reddish-brown in band 742 false-colour composite of Landsat 7 images. This property is used in our algorithm to delineate burn scars. Firstly, we visually choose sample pixels from the burn scar. From these pixels, a discrimination function for burn scars is determined by the principal component analysis and interval estimation. Then we define a modified Chan–Vese functional. The minimizer of the functional corresponds to the boundary of the burn scar. In order to minimize this functional, the corresponding contour evolution equation is given. We use the discrimination function to locate an initial contour that is near the boundary of the burn scar. The evolving curve then efficiently converges to the desired boundary. A Landsat image over Russia is used to examine our algorithm. The result shows that the algorithm is effective. [Copyright &y& Elsevier]

• CAB-UNet: an end-to-end Category Attention Boosting algorithm. • A gradient boosting and deep learning method for 3D medical image segmentation. • Verified on HVSMR 2016 and MM-WHS 2017 Challenge datasets. • Outperform the state-of-the-art algorithms. With the development of machine learning and artificial intelligence, many convolutional neural networks (CNNs) based segmentation methods have been proposed for 3D cardiac segmentation. In this paper, we propose the category attention boosting (CAB) module, which combines the deep network calculation graph with the boosting method. On the one hand, we add the attention mechanism into the gradient boosting process, which enhances the information of coarse segmentation without high computation cost. On the other hand, we introduce the CAB module into the 3D U-Net segmentation network and construct a new multi-scale boosting model CAB U-Net which strengthens the gradient flow in the network and makes full use of the low resolution feature information. Thanks to the advantage that end-to-end networks can adaptively adjust the internal parameters, CAB U-Net can make full use of the complementary effects among different base learners. Extensive experiments on public datasets show that our approach can achieve superior performance over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Generative adversarial imitation learning (GAIL) regards imitation learning (IL) as a distribution matching problem between the state–action distributions of the expert policy and the learned policy. In this paper, we focus on the generalization and computational properties of policy classes. We prove that the generalization can be guaranteed in GAIL when the class of policies is well controlled. With the capability of policy generalization, we introduce distributional reinforcement learning (RL) into GAIL and propose the greedy distributional soft gradient (GDSG) algorithm to solve GAIL. The main advantages of GDSG can be summarized as: (1) Q-value overestimation, a crucial factor leading to the instability of GAIL with off-policy training, can be alleviated by distributional RL. (2) By considering the maximum entropy objective, the policy can be improved in terms of performance and sample efficiency through sufficient exploration. Moreover, GDSG attains a sublinear convergence rate to a stationary solution. Comprehensive experimental verification in MuJoCo environments shows that GDSG can mimic expert demonstrations better than previous GAIL variants. • Prove the generalization property for policy classes of GAIL. • Demonstrate the reasonableness of reproducing kernel functions in computation. • Introduce distributional RL into GAIL and propose GDSG. • Relieve training imbalance between policy and discriminator; enhance GAIL stability. • GDSG converges to a stationary solution with a sublinear rate. [ABSTRACT FROM AUTHOR]

In this paper, numerical methods to compute the Karcher means on the n -order rotation group SO( n ) are considered. First, after recalling Karcher means as solutions of a kind of minimization problems on SO( n ), a super-linearly convergent numerical method, namely conjugate gradient method, has been used to deal with them. By the geometric structure of SO( n ), the proposed algorithm is structure preserving. Then, a variety of numerical experiments are presented to demonstrate the performance and efficiency of the proposed algorithm by comparing with a recent structure preserving method. [ABSTRACT FROM AUTHOR]

Cross-domain recommendation (CDR) leverages knowledge from the source domain to make recommendations for the cold-start users in the target domain. On account of fully utilizing information, various relationships among users and items are taken into account, i.e., the interaction relationship between users and their corresponding items; the relationship among users or items; and the indirect relationship between the user and items related to other users. In order to process these relationships, we propose a novel framework named Memory Pool Variational AutoEncoder (MPVAE). The main advantages of the MPVAE model lie in three aspects: (1) it generates the embedding representations that incorporate more information by a memory pool mechanism in the source and target domains; (2) it involves the relationship among users or items efficiently by the similarity measurement, further, the indirect relationship can be explicitly described, which makes full use of information in the source domain; and (3) it leverages the superiority of the probability model from the perspective of the VAE structure, which ensures generation and robustness. Comprehensive experiments on three real datasets show that the proposed model achieves remarkable superiority over several competitive CDR models. [Display omitted] • Propose MPVAE to generate embeddings that incorporate more information. • MPVAE involves various relationships among data in the source domain. • MPVAE ensures generation and robustness by the VAE structure. • Conduct extensive experiments on three datasets. [ABSTRACT FROM AUTHOR]

• Wide-area power oscillation damper (WPOD) is used to damp interarea oscillation. • Networked predictive control-based WPOD is proposed for DFIG-based wind farm. • The data transmission and packet dropouts of WAMS are modeled in detail. • The proposed WPOD can compensate the time delay and data dropout actively. • Case studies are conducted to verify the effectiveness of the proposed WPOD. Data dropouts and time delays introduced in communication network would degrade the damping performance of the wide-area power oscillation dampers (WPODs) or may even cause instability of the closed-loop system. This paper establishes a data transmission model in the network control system (NCS) and proposes a networked predictive control (NPC) based WPOD of doubly-fed induction generator (DFIG)-based wind farm to damp inter-area oscillations in a large-scale interconnected power system. The major advantage of the proposed WPOD is that it can actively compensate packet dropouts and communication delays involved in wide-area control channels. Additionally, online model identification is employed to handle the parameter uncertainties and operating condition variations of the power system. Case studies are undertaken on the 16-machine 68-bus power system. Simulation results show that the proposed WPOD can provide better damping performances than those of the conventional WPOD over a wide range of operating conditions and different communication delays and packet dropouts. [ABSTRACT FROM AUTHOR]