Mechanical modeling of incompressible particle-reinforced neo-Hookean composites based on numerical homogenization

In this paper, the mechanical response of incompressible particle-reinforced neo-Hookean composites (IPRNC) under general finite deformations is investigated numerically. Threedimensional Representative Volume Element (RVE) models containing 27 non-overlapping identical randomly distributed spheres are created to represent neo-Hookean composites consisting of incompressible neo-Hookean elastomeric spheres embedded within another incompressible neo-Hookean elastomeric matrix. Four types of finite deformation (i.e., uniaxial tension, uniaxial compression, simple shear and general biaxial deformation) are simulated using the finite element method (FEM) and the RVE models with periodic boundary condition (PBC) enforced. The simulation results show that the overall mechanical response of the IPRNC can be well-predicted by another simple incompressible neo-Hookean model up to the deformation the FEM simulation can reach. It is also shown that the effective shear modulus of the IPRNC can be well-predicted as a function of both particle volume fraction and particle/matrix stiffness ratio, using the classical linear elastic estimation within the limit of current FEM software

Accurate Single Stage Detector Using Recurrent Rolling Convolution

Most of the recent successful methods in accurate object detection and localization used some variants of R-CNN style two stage Convolutional Neural Networks (CNN) where plausible regions were proposed in the first stage then followed by a second stage for decision refinement. Despite the simplicity of training and the efficiency in deployment, the single stage detection methods have not been as competitive when evaluated in benchmarks consider mAP for high IoU thresholds. In this paper, we proposed a novel single stage end-to-end trainable object detection network to overcome this limitation. We achieved this by introducing Recurrent Rolling Convolution (RRC) architecture over multi-scale feature maps to construct object classifiers and bounding box regressors which are "deep in context". We evaluated our method in the challenging KITTI dataset which measures methods under IoU threshold of 0.7. We showed that with RRC, a single reduced VGG-16 based model already significantly outperformed all the previously published results. At the time this paper was written our models ranked the first in KITTI car detection (the hard level), the first in cyclist detection and the second in pedestrian detection. These results were not reached by the previous single stage methods. The code is publicly available.Comment: CVPR 201

2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly Detection

This technical report introduces the winning solution of the team Segment Any Anomaly for the CVPR2023 Visual Anomaly and Novelty Detection (VAND) challenge. Going beyond uni-modal prompt, e.g., language prompt, we present a novel framework, i.e., Segment Any Anomaly + (SAA$+$), for zero-shot anomaly segmentation with multi-modal prompts for the regularization of cascaded modern foundation models. Inspired by the great zero-shot generalization ability of foundation models like Segment Anything, we first explore their assembly (SAA) to leverage diverse multi-modal prior knowledge for anomaly localization. Subsequently, we further introduce multimodal prompts (SAA$+$) derived from domain expert knowledge and target image context to enable the non-parameter adaptation of foundation models to anomaly segmentation. The proposed SAA$+$ model achieves state-of-the-art performance on several anomaly segmentation benchmarks, including VisA and MVTec-AD, in the zero-shot setting. We will release the code of our winning solution for the CVPR2023 VAN.Comment: The first two author contribute equally. CVPR workshop challenge report. arXiv admin note: substantial text overlap with arXiv:2305.1072

Numerical Simulation of the Effect of Smooth Muscle Layer Thickening on Stress Distribution in the Airway Wall

Many chronic respiratory diseases are associated with airway remodeling such as hyperplasia and/or hypertrophy of the smooth muscle cells. It is well known that the hyperplasia and hypertrophy of the smooth muscle cells directly affects the mechanical properties of the smooth muscle layer. Consequently, it may cause uneven distribution of stress and thus local stress stimulation of the cells and tissues in the airway wall, possibly leading to pathogenesis of airway dysfunction such as airway hyperresponsiveness. However, it is difficult to experimentally study the effect of smooth muscle layer on stress distribution in the airway wall. Therefore, in the present work, we built a finite element model which simplified the anatomical structure of the airway wall as a three-layer structure that included an inner wall layer, a smooth muscle layer and an adventitia layer. Based on this model, we varied the smooth muscle layer thickness either uniformly or locally and then computed the stress distribution in the modeled airway wall. The results revealed that the minimum stress occurred in the adventitia layer, and the maximum stress occurred in the smooth muscle layer. More importantly, the smooth muscle layer thickening, occurred either uniformly or locally, led to elevated stress level and enhanced stress concentration in the smooth muscle layer. And the enhancement of stress level and concentration was variable depending on the pattern of smooth muscle layer thickening. For a given extent of smooth muscle layer thickening, the stress level and concentration appeared to be determined by the number of locations and the separation distance between the locations at which the smooth muscle layer thickening occurred. In other words, the maximum stress level in the smooth muscle layer increased from 2.712kPa to 2.842KPa depending on whether the local thickening occurred at one location, 3 or 5 equally separated locations, 2 connected and 1 distanced location, or 3 all connected locations. These simulation results provide important insight for better understanding the mechanism through which the airway smooth muscle is involved in the alteration of airway dysfunction in health and disease, which may be helpful in developing novel diagnosis/therapy via targeting smooth muscle hyperplasia and/or hypertrophy for the prevention/treatment of asthma

Msk is required for nuclear import of TGF-{beta}/BMP-activated Smads

Nuclear translocation of Smad proteins is a critical step in signal transduction of transforming growth factor beta (TGF-beta) and bone morphogenetic proteins (BMPs). Using nuclear accumulation of the Drosophila Smad Mothers against Decapentaplegic (Mad) as the readout, we carried out a whole-genome RNAi screening in Drosophila cells. The screen identified moleskin (msk) as important for the nuclear import of phosphorylated Mad. Genetic evidence in the developing eye imaginal discs also demonstrates the critical functions of msk in regulating phospho-Mad. Moreover, knockdown of importin 7 and 8 (Imp7 and 8), the mammalian orthologues of Msk, markedly impaired nuclear accumulation of Smad1 in response to BMP2 and of Smad2/3 in response to TGF-beta. Biochemical studies further suggest that Smads are novel nuclear import substrates of Imp7 and 8. We have thus identified new evolutionarily conserved proteins that are important in the signal transduction of TGF-beta and BMP into the nucleus

PanoVOS: Bridging Non-panoramic and Panoramic Views with Transformer for Video Segmentation

Panoramic videos contain richer spatial information and have attracted tremendous amounts of attention due to their exceptional experience in some fields such as autonomous driving and virtual reality. However, existing datasets for video segmentation only focus on conventional planar images. To address the challenge, in this paper, we present a panoramic video dataset, PanoVOS. The dataset provides 150 videos with high video resolutions and diverse motions. To quantify the domain gap between 2D planar videos and panoramic videos, we evaluate 15 off-the-shelf video object segmentation (VOS) models on PanoVOS. Through error analysis, we found that all of them fail to tackle pixel-level content discontinues of panoramic videos. Thus, we present a Panoramic Space Consistency Transformer (PSCFormer), which can effectively utilize the semantic boundary information of the previous frame for pixel-level matching with the current frame. Extensive experiments demonstrate that compared with the previous SOTA models, our PSCFormer network exhibits a great advantage in terms of segmentation results under the panoramic setting. Our dataset poses new challenges in panoramic VOS and we hope that our PanoVOS can advance the development of panoramic segmentation/tracking

Msk is required for nuclear import of TGF-β/BMP-activated Smads

Nuclear translocation of Smad proteins is a critical step in signal transduction of transforming growth factor β (TGF-β) and bone morphogenetic proteins (BMPs). Using nuclear accumulation of the Drosophila Smad Mothers against Decapentaplegic (Mad) as the readout, we carried out a whole-genome RNAi screening in Drosophila cells. The screen identified moleskin (msk) as important for the nuclear import of phosphorylated Mad. Genetic evidence in the developing eye imaginal discs also demonstrates the critical functions of msk in regulating phospho-Mad. Moreover, knockdown of importin 7 and 8 (Imp7 and 8), the mammalian orthologues of Msk, markedly impaired nuclear accumulation of Smad1 in response to BMP2 and of Smad2/3 in response to TGF-β. Biochemical studies further suggest that Smads are novel nuclear import substrates of Imp7 and 8. We have thus identified new evolutionarily conserved proteins that are important in the signal transduction of TGF-β and BMP into the nucleus