New Periodic Solutions for Some Planar N+3N+3-Body Problems with Newtonian Potentials

For some planar Newtonian $N+3$-body problems, we use variational minimization methods to prove the existence of new periodic solutions satisfying that $N$ bodies chase each other on a curve, and the other 3 bodies chase each other on another curve. From the definition of the group action in equations $(3.1)-(3.3)$, we can find that they are new solutions which are also different from all the examples of Ferrario and Terracini (2004)$[22]$

Periodic orbits of the planar anisotropic Manev problem and of the perturbed hydrogen atom problem

In this paper we use the averaging theory for studying the periodic solutions of the planar anisotropic Manev problem and of two perturbations of the hydrogen atom problem. When a convenient parameter is sufficiently small we prove that for every value e∈ (0, 1) a unique elliptic periodic solution with eccentricity e of the Kepler problem can be continued to the mentioned three problems

DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration

We present DeepICP - a novel end-to-end learning-based 3D point cloud registration framework that achieves comparable registration accuracy to prior state-of-the-art geometric methods. Different from other keypoint based methods where a RANSAC procedure is usually needed, we implement the use of various deep neural network structures to establish an end-to-end trainable network. Our keypoint detector is trained through this end-to-end structure and enables the system to avoid the inference of dynamic objects, leverages the help of sufficiently salient features on stationary objects, and as a result, achieves high robustness. Rather than searching the corresponding points among existing points, the key contribution is that we innovatively generate them based on learned matching probabilities among a group of candidates, which can boost the registration accuracy. Our loss function incorporates both the local similarity and the global geometric constraints to ensure all above network designs can converge towards the right direction. We comprehensively validate the effectiveness of our approach using both the KITTI dataset and the Apollo-SouthBay dataset. Results demonstrate that our method achieves comparable or better performance than the state-of-the-art geometry-based methods. Detailed ablation and visualization analysis are included to further illustrate the behavior and insights of our network. The low registration error and high robustness of our method makes it attractive for substantial applications relying on the point cloud registration task.Comment: 10 pages, 6 figures, 3 tables, typos corrected, experimental results updated, accepted by ICCV 201

Understanding Convolution for Semantic Segmentation

Recent advances in deep learning, especially deep convolutional neural networks (CNNs), have led to significant improvement over previous semantic segmentation systems. Here we show how to improve pixel-wise semantic segmentation by manipulating convolution-related operations that are of both theoretical and practical value. First, we design dense upsampling convolution (DUC) to generate pixel-level prediction, which is able to capture and decode more detailed information that is generally missing in bilinear upsampling. Second, we propose a hybrid dilated convolution (HDC) framework in the encoding phase. This framework 1) effectively enlarges the receptive fields (RF) of the network to aggregate global information; 2) alleviates what we call the "gridding issue" caused by the standard dilated convolution operation. We evaluate our approaches thoroughly on the Cityscapes dataset, and achieve a state-of-art result of 80.1% mIOU in the test set at the time of submission. We also have achieved state-of-the-art overall on the KITTI road estimation benchmark and the PASCAL VOC2012 segmentation task. Our source code can be found at https://github.com/TuSimple/TuSimple-DUC .Comment: WACV 2018. Updated acknowledgements. Source code: https://github.com/TuSimple/TuSimple-DU

CD147 promotes melanoma cell growth via SOX4-mediated glycolytic metabolism

Purpose: To determine the functional roles of cluster of differentiation 147 (CD147) in glycolysis in melanoma cells.Methods: CD147 expression in melanoma tissue and adjacent normal tissue was determined using quantitative real time polymrase chain reaction (qRT-PCR) and immunohistochemistry. Cell Counting Kit-8 (CCK-8) and colony formation assays were used to evaluate cell viability and colony formation, respectively. The role of CD147 in glycolysis in melanoma cells was investigated by determining glucose uptake, production of lactate, and cellular level of ATP.Results: CD147 was enhanced more in melanoma tissue than that in the adjacent normal tissue (p < 0.001). CD147 overexpression promoted the viability and colony formation of melanoma cells. On the other hand, CD147 silencing decreased the viability and colony formation of melanoma cells. Glucose uptake, production of lactate, and cellular level of ATP were upregulated in melanoma cells by CD147 overexpression and downregulated by shRNA-mediated depletion of CD147. CD147 increased expression of C-X-C motif chemokine ligand 1 (CXCL1) to activate the sex-determining region Y-related high-mobility group box 4 (SOX4) pathway. Knockdown of CXCL1 attenuated the positive regulatory effect of CD147 on SOX4. Besides, overexpression of SOX4 reversed the suppressive effects of CD147 silencing on melanoma cell viability, colony formation, and glycolysis.Conclusion: CD147 contributes to melanoma cell growth via upregulation of SOX-mediated glycolysis, thus providing a therapeutic avenue for the management of melanoma. Keywords: Cluster of differentiation 147, CD147, Sex-determining region Y-related high-mobility group box 4, Melanoma, Cell growth, Glycolysi