Smooth quasi-developable surfaces bounded by smooth curves

Computing a quasi-developable strip surface bounded by design curves finds wide industrial applications. Existing methods compute discrete surfaces composed of developable lines connecting sampling points on input curves which are not adequate for generating smooth quasi-developable surfaces. We propose the first method which is capable of exploring the full solution space of continuous input curves to compute a smooth quasi-developable ruled surface with as large developability as possible. The resulting surface is exactly bounded by the input smooth curves and is guaranteed to have no self-intersections. The main contribution is a variational approach to compute a continuous mapping of parameters of input curves by minimizing a function evaluating surface developability. Moreover, we also present an algorithm to represent a resulting surface as a B-spline surface when input curves are B-spline curves.Comment: 18 page

Conditional Lie-Bäcklund Symmetries and Reductions of the Nonlinear Diffusion Equations with Source

Conditional Lie-Bäcklund symmetry approach is used to study the invariant subspace of the nonlinear diffusion equations with source ut=e−qx(epxP(u)uxm)x+Q(x,u), m≠1. We obtain a complete list of canonical forms for such equations admit multidimensional invariant subspaces determined by higher order conditional Lie-Bäcklund symmetries. The resulting equations are either solved exactly or reduced to some finite-dimensional dynamic systems

Fine-grained ship image recognition based on BCNN with inception and AM-Softmax

The fine-grained ship image recognition task aims to identify various classes of ships. However, small inter-class, large intra-class differences between ships, and lacking of training samples are the reasons that make the task difficult. Therefore, to enhance the accuracy of the fine-grained ship image recognition, we design a fine-grained ship image recognition network based on bilinear convolutional neural network (BCNN) with Inception and additive margin Softmax (AM-Softmax). This network improves the BCNN in two aspects. Firstly, by introducing Inception branches to the BCNN network, it is helpful to enhance the ability of extracting comprehensive features from ships. Secondly, by adding margin values to the decision boundary, the AM-Softmax function can better extend the inter-class differences and reduce the intra-class differences. In addition, as there are few publicly available datasets for fine-grained ship image recognition, we construct a Ship-43 dataset containing 47,300 ship images belonging to 43 categories. Experimental results on the constructed Ship-43 dataset demonstrate that our method can effectively improve the accuracy of ship image recognition, which is 4.08% higher than the BCNN model. Moreover, comparison results on the other three public fine-grained datasets (Cub, Cars, and Aircraft) further validate the effectiveness of the proposed method

CaSR Induces Osteoclast Differentiation and Promotes Bone Metastasis in Lung Adenocarcinoma

Objective: Explore the mechanism of CaSR's involvement in bone metastasis in lung adenocarcinoma. Methods: Immunohistochemistry (IHC) was used to detect the expression of calcium-sensing receptor (CaSR) in 120 cases of lung adenocarcinoma with bone metastasis. Stably transfected cell lines with CaSR overexpression and knockdown based on A549 cells were constructed. The expression of CaSR was verified by western blot and qPCR. The proliferation and migration abilities of A549 cells were tested using cholecystokinin-8 (CCK-8) and Transwell assays, respectively. Western blotting was used to detect the expression of matrix metalloproteinases MMP2, MMP9, CaSR, and NF-κB. The supernatant from each cell culture group was collected as a conditional co-culture solution to study the induction of osteoclast precursor cells and osteoblasts. Western blot and qPCR were used to validate the expression of bone matrix degradation-related enzymes cathepsin K and hormone calcitonin receptor (CTR) and osteoblast-induced osteoclast maturation and differentiation enzyme receptor activator of nuclear factor-κB ligand (RANKL), macrophage colony-stimulating factor (M-CSF), osteoprotegerin (OPG), and PTHrP. Immunofluorescent staining was used to detect F-actin ring formation and osteocalcin expression. Western blot results for NF-κB expression identified a regulatory relationship between NF-κB and CaSR. Results: CaSR expression in lung cancer tissues was significantly higher than that in adjacent and normal lung tissues. The expression of CaSR in lung cancer tissues with bone metastasis was higher than that in non-metastatic lung cancer tissues. The proliferation and migration ability of A549 cells increased significantly with overexpressed CaSR. The co-culture solution directly induced osteoclast precursor cells and the expression of bone matrix degradation-related enzymes significantly increased. Osteoblasts were significantly inhibited and osteoblast-induced osteoclast maturation and differentiation enzymes were significantly downregulated. It was found that the expression of NF-κB and PTHrP increased when CaSR was overexpressed. Osteoclast differentiation factor expression was also significantly increased, which directly induces osteoclast differentiation and maturation. These results were reversed when CaSR was knocked down. Conclusions: CaSR can positively regulate NF-κB and PTHrP expression in A549 cells with a high metastatic potential, thereby promoting osteoclast differentiation and maturation, and facilitating the occurrence and development of bone metastasis in lung adenocarcinoma

FoxM1B transcriptionally regulates vascular endothelial growth factor expression and promotes the angiogenesis and growth of glioma cells.

We previously found that FoxM1B is overexpressed in human glioblastomas and that forced FoxM1B expression in anaplastic astrocytoma cells leads to the formation of highly angiogenic glioblastoma in nude mice. However, the molecular mechanisms by which FoxM1B enhances glioma angiogenesis are currently unknown. In this study, we found that vascular endothelial growth factor (VEGF) is a direct transcriptional target of FoxM1B. FoxM1B overexpression increased VEGF expression, whereas blockade of FoxM1 expression suppressed VEGF expression in glioma cells. Transfection of FoxM1 into glioma cells directly activated the VEGF promoter, and inhibition of FoxM1 expression by FoxM1 siRNA suppressed VEGF promoter activation. We identified two FoxM1-binding sites in the VEGF promoter that specifically bound to the FoxM1 protein. Mutation of these FoxM1-binding sites significantly attenuated VEGF promoter activity. Furthermore, FoxM1 overexpression increased and inhibition of FoxM1 expression suppressed the angiogenic ability of glioma cells. Finally, an immunohistochemical analysis of 59 human glioblastoma specimens also showed a significant correlation between FoxM1 overexpression and elevated VEGF expression. Our findings provide both clinical and mechanistic evidence that FoxM1 contributes to glioma progression by enhancing VEGF gene transcription and thus tumor angiogenesis

Pre-trained transformer for adversarial purification

With more and more deep neural networks being deployed as various daily services, their reliability is essential. It's frightening that deep neural networks are vulnerable and sensitive to adversarial attacks, the most common one of which for the services is evasion-based. Recent works usually strengthen the robustness by adversarial training or leveraging the knowledge of an amount of clean data. However, in practical terms, retraining and redeploying the model need a large computational budget, leading to heavy losses to the online service. In addition, when adversarial examples of a certain attack are detected, only limited adversarial examples are available for the service provider, while much clean data may not be accessible. Given the mentioned problems, we propose a new scenario, RaPiD (Rapid Plug-in Defender), which is to rapidly defend against a certain attack for the frozen original service model with limitations of few clean and adversarial examples. Motivated by the generalization and the universal computation ability of pre-trained transformer models, we come up with a new defender method, CeTaD, which stands for Considering Pre-trained Transformers as Defenders. In particular, we evaluate the effectiveness and the transferability of CeTaD in the case of one-shot adversarial examples and explore the impact of different parts of CeTaD as well as training data conditions. CeTaD is flexible, able to be embedded into an arbitrary differentiable model, and suitable for various types of attacks