3D Neighborhood Convolution: Learning Depth-Aware Features for RGB-D and RGB Semantic Segmentation

A key challenge for RGB-D segmentation is how to effectively incorporate 3D geometric information from the depth channel into 2D appearance features. We propose to model the effective receptive field of 2D convolution based on the scale and locality from the 3D neighborhood. Standard convolutions are local in the image space ($u, v$), often with a fixed receptive field of 3x3 pixels. We propose to define convolutions local with respect to the corresponding point in the 3D real-world space ($x, y, z$), where the depth channel is used to adapt the receptive field of the convolution, which yields the resulting filters invariant to scale and focusing on the certain range of depth. We introduce 3D Neighborhood Convolution (3DN-Conv), a convolutional operator around 3D neighborhoods. Further, we can use estimated depth to use our RGB-D based semantic segmentation model from RGB input. Experimental results validate that our proposed 3DN-Conv operator improves semantic segmentation, using either ground-truth depth (RGB-D) or estimated depth (RGB)

Kernel Learning in Ridge Regression "Automatically" Yields Exact Low Rank Solution

We consider kernels of the form $(x,x') \mapsto \phi(\|x-x'\|^2_\Sigma)$ parametrized by $\Sigma$. For such kernels, we study a variant of the kernel ridge regression problem which simultaneously optimizes the prediction function and the parameter $\Sigma$ of the reproducing kernel Hilbert space. The eigenspace of the $\Sigma$ learned from this kernel ridge regression problem can inform us which directions in covariate space are important for prediction. Assuming that the covariates have nonzero explanatory power for the response only through a low dimensional subspace (central mean subspace), we find that the global minimizer of the finite sample kernel learning objective is also low rank with high probability. More precisely, the rank of the minimizing $\Sigma$ is with high probability bounded by the dimension of the central mean subspace. This phenomenon is interesting because the low rankness property is achieved without using any explicit regularization of $\Sigma$, e.g., nuclear norm penalization. Our theory makes correspondence between the observed phenomenon and the notion of low rank set identifiability from the optimization literature. The low rankness property of the finite sample solutions exists because the population kernel learning objective grows "sharply" when moving away from its minimizers in any direction perpendicular to the central mean subspace.Comment: Add code links and correct a figur

Experimental investigation of dielectric barrier impact on breakdown voltage enhancement of copper wire-plane electrode systems

Non-pressurized air is extensively used as basic insulation media in medium / high voltage equipments. An inherent property of air-insulated designs is that the systems tend to become physically large. Application of Dielectric barrier can increase the breakdown voltage and therefore decrease the size of the equipments. In this paper, the impact of dielectric barrier on breakdown voltage enhancement of a copper wire-plane system is investigated. For this purpose, the copper wire is covered with different dielectric materials. Depending on the air gap and dielectric strength of the barrier the breakdown can be initiated in the solid or gas dielectric. Theoretically, free charges are affected by the electric field between the electrodes and accumulated at the dielectric surface, this leads to the reduction of electric field in air gap and enhancement of the ifield in the dielectric layer. Therefore, with appropriate selection of the barrier thickness and material, it is possible to increase the breakdown voltage of the insulation system. The influence of different parameters like inter-electrode spacing, and dielectric material on the break-down voltage is investigated for applied 50 Hz AC and DC voltages. The results indicate that up to 240% increase of the breakdown voltage can be achieved

Neural Feature Matching in Implicit 3D Representations

Recently, neural implicit functions have achieved impressive results for encoding 3D shapes. Conditioning on low-dimensional latent codes generalises a single implicit function to learn shared representation space for a variety of shapes, with the advantage of smooth interpolation. While the benefits from the global latent space do not correspond to explicit points at local level, we propose to track the continuous point trajectory by matching implicit features with the latent code interpolating between shapes, from which we corroborate the hierarchical functionality of the deep implicit functions, where early layers map the latent code to fitting the coarse shape structure, and deeper layers further refine the shape details. Furthermore, the structured representation space of implicit functions enables to apply feature matching for shape deformation, with the benefits to handle topology and semantics inconsistency, such as from an armchair to a chair with no arms, without explicit flow functions or manual annotations

Dual-agonist occupancy of orexin receptor 1 and cholecystokinin A receptor heterodimers decreases G-protein-dependent signaling and migration in the human colon cancer cell line HT-29

The orexin (OX1R) and cholecystokinin A (CCK1R) receptors play opposing roles in the migration of the human colon cancer cell line HT-29, and may be involved in the pathogenesis and pathophysiology of cancer cell invasion and metastasis. OX1R and CCK1R belong to family A of the G-protein-coupled receptors (GPCRs), but the detailed mechanisms underlying their functions in solid tumor development remain unclear. In this study, we investigated whether these two receptors heterodimerize, and the results revealed novel signal transduction mechanisms. Bioluminescence and Förster resonance energy transfer, as well as proximity ligation assays, demonstrated that OX1R and CCK1R heterodimerize in HEK293 and HT-29 cells, and that peptides corresponding to transmembrane domain 5 of OX1R impaired heterodimer formation. Stimulation of OX1R and CCK1R heterodimers with both orexin-A and CCK decreased the activation of Gαq, Gαi2, Gα12, and Gα13 and the migration of HT-29 cells in comparison with stimulation with orexin-A or CCK alone, but did not alter GPCR interactions with β-arrestins. These results suggest that OX1R and CCK1R heterodimerization plays an anti-migratory role in human colon cancer cells. [Abstract copyright: Copyright © 2017. Published by Elsevier B.V.

MANGO: A Mask Attention Guided One-Stage Scene Text Spotter

Recently end-to-end scene text spotting has become a popular research topic due to its advantages of global optimization and high maintainability in real applications. Most methods attempt to develop various region of interest (RoI) operations to concatenate the detection part and the sequence recognition part into a two-stage text spotting framework. However, in such framework, the recognition part is highly sensitive to the detected results (e.g.), the compactness of text contours). To address this problem, in this paper, we propose a novel Mask AttentioN Guided One-stage text spotting framework named MANGO, in which character sequences can be directly recognized without RoI operation. Concretely, a position-aware mask attention module is developed to generate attention weights on each text instance and its characters. It allows different text instances in an image to be allocated on different feature map channels which are further grouped as a batch of instance features. Finally, a lightweight sequence decoder is applied to generate the character sequences. It is worth noting that MANGO inherently adapts to arbitrary-shaped text spotting and can be trained end-to-end with only coarse position information (e.g.), rectangular bounding box) and text annotations. Experimental results show that the proposed method achieves competitive and even new state-of-the-art performance on both regular and irregular text spotting benchmarks, i.e., ICDAR 2013, ICDAR 2015, Total-Text, and SCUT-CTW1500.Comment: Accepted to AAAI2021. Code is available at https://davar-lab.github.io/publication.html or https://github.com/hikopensource/DAVAR-Lab-OC

Individual phosphorylation sites at the C-terminus of the apelin receptor play different roles in signal transduction

The apelin and Elabela proteins constitute a spatiotemporal double-ligand system that controls apelin receptor (APJ) signal transduction. Phosphorylation of multiple sites within the C-terminus of APJ is essential for the recruitment of β-arrestins. We sought to determine the precise mechanisms by which apelin and Elabela promote APJ phosphorylation, and to elucidate the influence of β-arrestin phosphorylation on G-protein-coupled receptor (GPCR)/β-arrestin-dependent signaling. We used techniques including mass spectrometry (MS), mutation analysis, and bioluminescence resonance energy transfer (BRET) to evaluate the role of phosphorylation sites in APJ-mediated G-protein-dependent and β-dependent signaling. Phosphorylation of APJ occurred at five serine residues in the C-terminal region (Ser335, Ser339, Ser345, Ser348 and Ser369). We also identified two phosphorylation sites in β-arrestin1 and three in β-arrestin2, including three previously identified residues (Ser412, Ser361, and Thr383) and two new sites, Tyr47 in β-arrestin1 and Tyr48 in β-arrestin2. APJ mutations did not affect the phosphorylation of β-arrestins, but it affects the β-arrestin signaling pathway, specifically Ser335 and Ser339. Mutation of Ser335 decreased the ability of the receptor to interact with β-arrestin1/2 and AP2, indicating that APJ affects the β-arrestin signaling pathway by stimulating Elabela. Mutation of Ser339 abolished the capability of the receptor to interact with GRK2 and β-arrestin1/2 upon stimulation with apelin-36, and disrupted receptor internalization and β-arrestin-dependent ERK1/2 activation. Five peptides act on distinct phosphorylation sites at the APJ C-terminus, differentially regulating APJ signal transduction and causing different biological effects. These findings may facilitate screening for drugs to treat cardiovascular and metabolic diseases

PointMixup: Augmentation for Point Clouds

This paper introduces data augmentation for point clouds by interpolation between examples. Data augmentation by interpolation has shown to be a simple and effective approach in the image domain. Such a mixup is however not directly transferable to point clouds, as we do not have a one-to-one correspondence between the points of two different objects. In this paper, we define data augmentation between point clouds as a shortest path linear interpolation. To that end, we introduce PointMixup, an interpolation method that generates new examples through an optimal assignment of the path function between two point clouds. We prove that our PointMixup finds the shortest path between two point clouds and that the interpolation is assignment invariant and linear. With the definition of interpolation, PointMixup allows to introduce strong interpolation-based regularizers such as mixup and manifold mixup to the point cloud domain. Experimentally, we show the potential of PointMixup for point cloud classification, especially when examples are scarce, as well as increased robustness to noise and geometric transformations to points. The code for PointMixup and the experimental details are publicly available.Comment: Accepted as Spotlight presentation at European Conference on Computer Vision (ECCV), 202