Smoothing algorithms for nonsmooth and nonconvex minimization over the stiefel manifold

We consider a class of nonsmooth and nonconvex optimization problems over the Stiefel manifold where the objective function is the summation of a nonconvex smooth function and a nonsmooth Lipschitz continuous convex function composed with an linear mapping. We propose three numerical algorithms for solving this problem, by combining smoothing methods and some existing algorithms for smooth optimization over the Stiefel manifold. In particular, we approximate the aforementioned nonsmooth convex function by its Moreau envelope in our smoothing methods, and prove that the Moreau envelope has many favorable properties. Thanks to this and the scheme for updating the smoothing parameter, we show that any accumulation point of the solution sequence generated by the proposed algorithms is a stationary point of the original optimization problem. Numerical experiments on building graph Fourier basis are conducted to demonstrate the efficiency of the proposed algorithms.Comment: 22 page

The art of defense: letting networks fool the attacker

Some deep neural networks are invariant to some input transformations, such as Pointnet is permutation invariant to the input point cloud. In this paper, we demonstrated this property could be powerful in defense of gradient-based attacks. Specifically, we apply random input transformation which is invariant to the networks we want to defend. Extensive experiments demonstrate that the proposed scheme defeats various gradient-based attackers in the targeted attack setting, and breaking the attack accuracy into nearly zero. Our code is available at: {\footnotesize{\url{https://github.com/cuge1995/IT-Defense}}}

Multi-functional anodes boost the transient power and durability of proton exchange membrane fuel cells.

Proton exchange membrane fuel cells have been regarded as the most promising candidate for fuel cell vehicles and tools. Their broader adaption, however, has been impeded by cost and lifetime. By integrating a thin layer of tungsten oxide within the anode, which serves as a rapid-response hydrogen reservoir, oxygen scavenger, sensor for power demand, and regulator for hydrogen-disassociation reaction, we herein report proton exchange membrane fuel cells with significantly enhanced power performance for transient operation and low humidified conditions, as well as improved durability against adverse operating conditions. Meanwhile, the enhanced power performance minimizes the use of auxiliary energy-storage systems and reduces costs. Scale fabrication of such devices can be readily achieved based on the current fabrication techniques with negligible extra expense. This work provides proton exchange membrane fuel cells with enhanced power performance, improved durability, prolonged lifetime, and reduced cost for automotive and other applications

Angelica Polysaccharide Ameliorates Sepsis-Induced Acute Lung Injury through Inhibiting NLRP3 and NF-κB Signaling Pathways in Mice

Objective. This study aimed to explore the role of angelica polysaccharide (AP) in sepsis-induced acute lung injury (ALI) and its underlying molecular mechanism. Methods. A sepsis model of cecal ligation and puncture (CLP) in male BALB/C mice was used. Then, 24 h after CLP, histopathological changes in lung tissue, lung wet/dry weight ratio, and inflammatory cell infiltration were analyzed. Next, levels of inflammatory cytokines (tumor necrosis factor-α (TNF-α), interleukin (IL)-1β, IL-6, and IL-18), as well as the activity of myeloperoxidase (MPO), malondialdehyde (MDA), superoxide dismutase (SOD), and glutathione (GSH), were measured to assess the role of AP. The protein expression of NF-κB p65, p-NF-κB p65, IκBα, p-IκBα, nucleotide-binding domain- (NOD-) like receptor protein 3 (NLRP3), ASC, and caspase-1 was detected by western blot. In addition, the expression of p-NF-κB p65 and NLRP3 was detected by immunohistochemistry. Results. AP treatment ameliorated CLP-induced lung injury and lung edema, as well as decreased the number of total cells, neutrophils, and macrophages in bronchoalveolar lavage fluid (BALF). AP reduced the levels of TNF-α, IL-1β, IL-6, and IL-18 in BALF, as well as in serum. Moreover, AP decreased MPO activity and MDA content, whereas increased SOD and GSH levels. AP inhibited the expression of p-NF-κB p65, p-IκBα, NLRP3, ASC, and caspase-1, while promoted IκBα expression. Conclusion. This study demonstrated that AP exhibits protective effects against sepsis-induced ALI by inhibiting NLRP3 and NF-κB signaling pathways in mice

<i>HeLoDL</i>: Hedgerow Localization Based on Deep Learning

Accurate localization of hedges in 3D space is a key step in automatic pruning. However, due to the irregularity of the hedge shape, the localization accuracy based on traditional algorithms is poor. In this paper, we propose a deep learning approach based on a bird’s-eye view to overcoming this problem, which we call HeLoDL. Specifically, we first project the hedge point cloud top-down as a single image and, then, augment the image with morphological operations and rotation. Finally, we trained a convolutional neural network, HeLoDL, based on transfer learning, to regress the center axis and radius of the hedge. In addition, we propose an evaluation metric OIoU that can respond to the radius error, as well as the circle center error in an integrated way. In our test set, HeLoDL achieved an accuracy of 90.44% within the error tolerance, which greatly exceeds the 61.74% of the state-of-the-art algorithm. The average OIoU of HeLoDL is 92.65%; however, the average OIoU of the best conventional algorithm is 83.69%. Extensive experiments demonstrated that HeLoDL shows considerable accuracy in the 3D spatial localization of irregular models

HeLoDL: Hedgerow Localization Based on Deep Learning

Accurate localization of hedges in 3D space is a key step in automatic pruning. However, due to the irregularity of the hedge shape, the localization accuracy based on traditional algorithms is poor. In this paper, we propose a deep learning approach based on a bird&rsquo;s-eye view to overcoming this problem, which we call HeLoDL. Specifically, we first project the hedge point cloud top-down as a single image and, then, augment the image with morphological operations and rotation. Finally, we trained a convolutional neural network, HeLoDL, based on transfer learning, to regress the center axis and radius of the hedge. In addition, we propose an evaluation metric OIoU that can respond to the radius error, as well as the circle center error in an integrated way. In our test set, HeLoDL achieved an accuracy of 90.44% within the error tolerance, which greatly exceeds the 61.74% of the state-of-the-art algorithm. The average OIoU of HeLoDL is 92.65%; however, the average OIoU of the best conventional algorithm is 83.69%. Extensive experiments demonstrated that HeLoDL shows considerable accuracy in the 3D spatial localization of irregular models