Scalable Semidefinite Relaxation for Maximum A Posterior Estimation

Maximum a posteriori (MAP) inference over discrete Markov random fields is a fundamental task spanning a wide spectrum of real-world applications, which is known to be NP-hard for general graphs. In this paper, we propose a novel semidefinite relaxation formulation (referred to as SDR) to estimate the MAP assignment. Algorithmically, we develop an accelerated variant of the alternating direction method of multipliers (referred to as SDPAD-LR) that can effectively exploit the special structure of the new relaxation. Encouragingly, the proposed procedure allows solving SDR for large-scale problems, e.g., problems on a grid graph comprising hundreds of thousands of variables with multiple states per node. Compared with prior SDP solvers, SDPAD-LR is capable of attaining comparable accuracy while exhibiting remarkably improved scalability, in contrast to the commonly held belief that semidefinite relaxation can only been applied on small-scale MRF problems. We have evaluated the performance of SDR on various benchmark datasets including OPENGM2 and PIC in terms of both the quality of the solutions and computation time. Experimental results demonstrate that for a broad class of problems, SDPAD-LR outperforms state-of-the-art algorithms in producing better MAP assignment in an efficient manner.Comment: accepted to International Conference on Machine Learning (ICML 2014

Multi-View Representation is What You Need for Point-Cloud Pre-Training

A promising direction for pre-training 3D point clouds is to leverage the massive amount of data in 2D, whereas the domain gap between 2D and 3D creates a fundamental challenge. This paper proposes a novel approach to point-cloud pre-training that learns 3D representations by leveraging pre-trained 2D networks. Different from the popular practice of predicting 2D features first and then obtaining 3D features through dimensionality lifting, our approach directly uses a 3D network for feature extraction. We train the 3D feature extraction network with the help of the novel 2D knowledge transfer loss, which enforces the 2D projections of the 3D feature to be consistent with the output of pre-trained 2D networks. To prevent the feature from discarding 3D signals, we introduce the multi-view consistency loss that additionally encourages the projected 2D feature representations to capture pixel-wise correspondences across different views. Such correspondences induce 3D geometry and effectively retain 3D features in the projected 2D features. Experimental results demonstrate that our pre-trained model can be successfully transferred to various downstream tasks, including 3D shape classification, part segmentation, 3D object detection, and semantic segmentation, achieving state-of-the-art performance.Comment: 14 pages, 6 figure

An Aquaporin 3-Notch1 Axis in Keratinocyte Differentiation and Inflammation

Aquaporin 3 (AQP3) is an aquaglyceroporin which transports water, glycerol and small solutes across the plasma membrane. Its functions are not limited to fluid transport but also involve the regulation of cell proliferation, migration, skin hydration, wound healing and tumorigenesis. While AQP3 has been reported to play an important role in keratinocyte proliferation, its role in differentiation remains controversial. Our study demonstrated that the expression of AQP3 was regulated during differentiation and that it participated in keratinocyte differentiation control. We further revealed that AQP3 was a transcriptional target of Notch signaling, a critical pathway regulating keratinocyte differentiation and tumor suppression, and it regulated differentiation through a reciprocal negative feedback loop with Notch1. When the expression level of AQP3 was elevated, impaired barrier integrity and increased pro-inflammatory cytokine production ensued, mimicking the pathological conditions in Notch deficient mice and in atopic dermatitis. Dysregulation of AQP3 and Notch receptors has been reported in several skin diseases, including skin cancer. Our discovery of the novel AQP3-Notch1 axis may provide insight into epidermal homeostasis control and possible translational applications, including its potential use as a biomarker for molecular diagnosis in environmental studies

Forest biomass resources and utilization in China

Under the context of climate change, persistent high oil prices and rapidly growing dependence on imported oil prompt China to pay much more attention to biofuels that provide environmental benefits besides fuel. China has rich biodiversity with 30 thousand high plant species and 154 kinds of energy trees could produce seeds containing more than 40% of oil, with total production of the seeds totaling 5 million t, and 200 x109 t of biomass production per year, which is equal to 2 x 109 t of petroleum. There are over 2000 types of wild and cultivated firewood plants in the country. So far there is 4 million ha raising oil-bearing trees planted on some land in different regions. Another 57 million ha of waste land are available and suitable for planting trees for the production of forest bioenergy. On part of these lands, the central government plans to cultivate a total of 13 million ha of high-grade bioenergy forests by 2020. This will yield 6 million tons of diesel that would be enough to fuel power plants with a combined capacity of 11 GW each year. Moreover, forest biomass plantations potentially offer many direct and indirect environmental benefits. In view of climate change their globally significant environmental benefits may result from using forest biomass for energy rather than fossil fuels.Key words: Biomass energy, China, forest biomass resources

Neural Volumetric Mesh Generator

Deep generative models have shown success in generating 3D shapes with different representations. In this work, we propose Neural Volumetric Mesh Generator(NVMG) which can generate novel and high-quality volumetric meshes. Unlike the previous 3D generative model for point cloud, voxel, and implicit surface, the volumetric mesh representation is a ready-to-use representation in industry with details on both the surface and interior. Generating this such highly-structured data thus brings a significant challenge. We first propose a diffusion-based generative model to tackle this problem by generating voxelized shapes with close-to-reality outlines and structures. We can simply obtain a tetrahedral mesh as a template with the voxelized shape. Further, we use a voxel-conditional neural network to predict the smooth implicit surface conditioned on the voxels, and progressively project the tetrahedral mesh to the predicted surface under regularizations. The regularization terms are carefully designed so that they can (1) get rid of the defects like flipping and high distortion; (2) force the regularity of the interior and surface structure during the deformation procedure for a high-quality final mesh. As shown in the experiments, our pipeline can generate high-quality artifact-free volumetric and surface meshes from random noise or a reference image without any post-processing. Compared with the state-of-the-art voxel-to-mesh deformation method, we show more robustness and better performance when taking generated voxels as input

Increased gene copy number of DEFA1/DEFA3 worsens sepsis by inducing endothelial pyroptosis.

Sepsis claims an estimated 30 million episodes and 6 million deaths per year, and treatment options are rather limited. Human neutrophil peptides 1-3 (HNP1-3) are the most abundant neutrophil granule proteins but their neutrophil content varies because of unusually extensive gene copy number polymorphism. A genetic association study found that increased copy number of the HNP-encoding gene DEFA1/DEFA3 is a risk factor for organ dysfunction during sepsis development. However, direct experimental evidence demonstrating that these risk alleles are pathogenic for sepsis is lacking because the genes are present only in some primates and humans. Here, we generate DEFA1/DEFA3 transgenic mice with neutrophil-specific expression of the peptides. We show that mice with high copy number of DEFA1/DEFA3 genes have more severe sepsis-related vital organ damage and mortality than mice with low copy number of DEFA1/DEFA3 or wild-type mice, resulting from more severe endothelial barrier dysfunction and endothelial cell pyroptosis after sepsis challenge. Mechanistically, HNP-1 induces endothelial cell pyroptosis via P2X7 receptor-mediating canonical caspase-1 activation in a NLRP3 inflammasome-dependent manner. Based on these findings, we engineered a monoclonal antibody against HNP-1 to block the interaction with P2X7 and found that the blocking antibody protected mice carrying high copy number of DEFA1/DEFA3 from lethal sepsis. We thus demonstrate that DEFA1/DEFA3 copy number variation strongly modulates sepsis development in vivo and explore a paradigm for the precision treatment of sepsis tailored by individual genetic information

Point Defects and Localized Excitons in 2D WSe2

Identifying the point defects in 2D materials is important for many applications. Recent studies have proposed that W vacancies are the predominant point defect in 2D WSe2, in contrast to theoretical studies, which predict that chalcogen vacancies are the most likely intrinsic point defects in transition metal dichalcogenide semiconductors. We show using first principles calculations, scanning tunneling microscopy (STM) and scanning transmission electron microscopy experiments, that W vacancies are not present in our CVD-grown 2D WSe2. We predict that O-passivated Se vacancies (O_Se) and O interstitials (Oins) are present in 2D WSe2, because of facile O2 dissociation at Se vacancies, or due to the presence of WO3 precursors in CVD growth. These defects give STM images in good agreement with experiment. The optical properties of point defects in 2D WSe2 are important because single photon emission (SPE) from 2D WSe2 has been observed experimentally. While strain gradients funnel the exciton in real space, point defects are necessary for the localization of the exciton at length scales that enable photons to be emitted one at a time. Using state-of-the-art GW-Bethe-Salpeter-equation calculations, we predict that only Oins defects give localized excitons within the energy range of SPE in previous experiments, making them a likely source of previously observed SPE. No other point defects (O_Se, Se vacancies, W vacancies and Se_W antisites) give localized excitons in the same energy range. Our predictions suggest ways to realize SPE in related 2D materials and point experimentalists toward other energy ranges for SPE in 2D WSe2

Cloth2Tex: A Customized Cloth Texture Generation Pipeline for 3D Virtual Try-On

Fabricating and designing 3D garments has become extremely demanding with the increasing need for synthesizing realistic dressed persons for a variety of applications, e.g. 3D virtual try-on, digitalization of 2D clothes into 3D apparel, and cloth animation. It thus necessitates a simple and straightforward pipeline to obtain high-quality texture from simple input, such as 2D reference images. Since traditional warping-based texture generation methods require a significant number of control points to be manually selected for each type of garment, which can be a time-consuming and tedious process. We propose a novel method, called Cloth2Tex, which eliminates the human burden in this process. Cloth2Tex is a self-supervised method that generates texture maps with reasonable layout and structural consistency. Another key feature of Cloth2Tex is that it can be used to support high-fidelity texture inpainting. This is done by combining Cloth2Tex with a prevailing latent diffusion model. We evaluate our approach both qualitatively and quantitatively and demonstrate that Cloth2Tex can generate high-quality texture maps and achieve the best visual effects in comparison to other methods. Project page: tomguluson92.github.io/projects/cloth2tex/Comment: 15 pages, 15 figure