MATLABER: Material-Aware Text-to-3D via LAtent BRDF auto-EncodeR

Based on powerful text-to-image diffusion models, text-to-3D generation has made significant progress in generating compelling geometry and appearance. However, existing methods still struggle to recover high-fidelity object materials, either only considering Lambertian reflectance, or failing to disentangle BRDF materials from the environment lights. In this work, we propose Material-Aware Text-to-3D via LAtent BRDF auto-EncodeR (\textbf{MATLABER}) that leverages a novel latent BRDF auto-encoder for material generation. We train this auto-encoder with large-scale real-world BRDF collections and ensure the smoothness of its latent space, which implicitly acts as a natural distribution of materials. During appearance modeling in text-to-3D generation, the latent BRDF embeddings, rather than BRDF parameters, are predicted via a material network. Through exhaustive experiments, our approach demonstrates the superiority over existing ones in generating realistic and coherent object materials. Moreover, high-quality materials naturally enable multiple downstream tasks such as relighting and material editing. Code and model will be publicly available at \url{https://sheldontsui.github.io/projects/Matlaber}

Comparing Infrared Dirac-Born-Infeld Brane Inflation to Observations

We compare the Infrared Dirac-Born-Infeld (IR DBI) brane inflation model to observations using a Bayesian analysis. The current data cannot distinguish it from the \LambdaCDM model, but is able to give interesting constraints on various microscopic parameters including the mass of the brane moduli potential, the fundamental string scale, the charge or warp factor of throats, and the number of the mobile branes. We quantify some distinctive testable predictions with stringy signatures, such as the large non-Gaussianity, and the large, but regional, running of the spectral index. These results illustrate how we may be able to probe aspects of string theory using cosmological observations.Comment: 54 pages, 13 figures. v2: non-Gaussianity constraint has been applied to the model; parameter constraints have tightened significantly, conclusions unchanged. References added; v3, minor revision, PRD versio

Swainsonine Activates Mitochondria-mediated Apoptotic Pathway in Human Lung Cancer A549 Cells and Retards the Growth of Lung Cancer Xenografts

Swainsonine (1, 2, 8-trihyroxyindolizidine, SW), a natural alkaloid, has been reported to exhibit anti-cancer activity on several mouse models of human cancer and human cancers in vivo. However, the mechanisms of SW-mediated tumor regression are not clear. In this study, we investigated the effects of SW on several human lung cancer cell lines in vitro. The results showed that SW significantly inhibited these cells growth through induction of apoptosis in different extent in vitro. Further studies showed that SW treatment up-regulated Bax, down-regulated Bcl-2 expression, promoted Bax translocation to mitochondria, activated mitochondria-mediated apoptotic pathway, which in turn caused the release of cytochrome c, the activation of caspase-9 and caspase-3, and the cleavage of poly (ADP-ribose) polymerase (PARP), resulting in A549 cell apoptosis. However, the expression of Fas, Fas ligand (FasL) or caspase-8 activity did not appear significant changes in the process of SW-induced apoptosis. Moreover, SW treatment inhibited Bcl-2 expression, promoted Bax translocation, cytochrome c release and caspase-3 activity in xenograft tumor cells, resulting in a significant decrease of tumor volume and tumor weight in the SW-treated xenograft mice groups in comparison to the control group. Taken together, this study demonstrated for the first time that SW inhibited A549 cancer cells growth through a mitochondria-mediated, caspase-dependent apoptotic pathway in vitro and in vivo

Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstruction

Neural surface reconstruction aims to reconstruct accurate 3D surfaces based on multi-view images. Previous methods based on neural volume rendering mostly train a fully implicit model with MLPs, which typically require hours of training for a single scene. Recent efforts explore the explicit volumetric representation to accelerate the optimization via memorizing significant information with learnable voxel grids. However, existing voxel-based methods often struggle in reconstructing fine-grained geometry, even when combined with an SDF-based volume rendering scheme. We reveal that this is because 1) the voxel grids tend to break the color-geometry dependency that facilitates fine-geometry learning, and 2) the under-constrained voxel grids lack spatial coherence and are vulnerable to local minima. In this work, we present Voxurf, a voxel-based surface reconstruction approach that is both efficient and accurate. Voxurf addresses the aforementioned issues via several key designs, including 1) a two-stage training procedure that attains a coherent coarse shape and recovers fine details successively, 2) a dual color network that maintains color-geometry dependency, and 3) a hierarchical geometry feature to encourage information propagation across voxels. Extensive experiments show that Voxurf achieves high efficiency and high quality at the same time. On the DTU benchmark, Voxurf achieves higher reconstruction quality with a 20x training speedup compared to previous fully implicit methods

Assessing rice chlorophyll content with vegetation indices from hyperspectral data

Abstract. Leaf chlorophyll content is not only an important biochemical parameter for determinating the capacity of rice photosynthesis, but also a good indicator of crop stress, nutritional state. Due to the reliable, operational and non-destructive advantages, hyperspectral remote sensing plays a significant role for assessing and monitoring chlorophyll content. In the study, a few of typical vegetation indices (VI) with the combination of 670nm and 800nm band reflectance, Normalized Difference Vegetation Index (NDVI), Modified Simple Ratio index (MSR), Modified Chlorophyll Absorption Ratio Index (MCARI), Transformed Chlorophyll Absorption Ratio Index (TCARI), and Optimized Soil-Adjusted Vegetation Index (OSAVI) are modified by using 705nm and 750nm band reflectance so as to reduce the effect of spectral saturation in 660-680nm absorptive band region, and then used to assess the rice chlorophyll content. The result shows that the five mentioned VIs have better correlation with rice chlorophyll content while using 705nm and 750nm. In addition, in the study the Weight optimization combination (WOC) principle is utilized to further assess the capacity of the five modified VIs for estimating rice chlorophyll content, it is proved that OSAVI and MSR display the better performance