VGOS: Voxel Grid Optimization for View Synthesis from Sparse Inputs

Neural Radiance Fields (NeRF) has shown great success in novel view synthesis due to its state-of-the-art quality and flexibility. However, NeRF requires dense input views (tens to hundreds) and a long training time (hours to days) for a single scene to generate high-fidelity images. Although using the voxel grids to represent the radiance field can significantly accelerate the optimization process, we observe that for sparse inputs, the voxel grids are more prone to overfitting to the training views and will have holes and floaters, which leads to artifacts. In this paper, we propose VGOS, an approach for fast (3-5 minutes) radiance field reconstruction from sparse inputs (3-10 views) to address these issues. To improve the performance of voxel-based radiance field in sparse input scenarios, we propose two methods: (a) We introduce an incremental voxel training strategy, which prevents overfitting by suppressing the optimization of peripheral voxels in the early stage of reconstruction. (b) We use several regularization techniques to smooth the voxels, which avoids degenerate solutions. Experiments demonstrate that VGOS achieves state-of-the-art performance for sparse inputs with super-fast convergence. Code will be available at https://github.com/SJoJoK/VGOS.Comment: IJCAI 2023 Accepted (Main Track

Early Warning System for Load Distribution in Automated Warehouse Based on Seismic Safety

As an integral part of the storage system in automated warehouse, the structural behaviors of steel rack structures have been extensively studied. However, little research has been conducted on the seismic performance evaluation of racks under different load distributions and also a warning system for safety evaluation of the load distribution that might cause unfavorable seismic performance. The objective of this study is to develop a method of evaluating seismic safety and an early warning system of over-limit load distribution for racks with spine bracings. The genetic algorithm was employed to seek and identify the influence of load distribution on the seismic performance of racks with spine bracing. Then, a formula for safety evaluation for racks with spine bracing was derived and its reliability was validated. An early warning system for over-limit load distribution was established and codified in a software package. Different safety statuses using a color system like Green, Red, and Yellow were employed in the software package as an indicator of the load distribution level of racks. Finally, the system developed was applied to the actual racks in an automated warehouse in Nanjing to demonstrate its applicability and reliability.This work was sponsored by the Natural Science Foundation of Jiangsu Province (BK20191268); and the Project of Central Government for Local Science and Technology Development of Tibetan Autonomous Region (XZ202201YD0032C)

Generative Image Inpainting with Segmentation Confusion Adversarial Training and Contrastive Learning

This paper presents a new adversarial training framework for image inpainting with segmentation confusion adversarial training (SCAT) and contrastive learning. SCAT plays an adversarial game between an inpainting generator and a segmentation network, which provides pixel-level local training signals and can adapt to images with free-form holes. By combining SCAT with standard global adversarial training, the new adversarial training framework exhibits the following three advantages simultaneously: (1) the global consistency of the repaired image, (2) the local fine texture details of the repaired image, and (3) the flexibility of handling images with free-form holes. Moreover, we propose the textural and semantic contrastive learning losses to stabilize and improve our inpainting model's training by exploiting the feature representation space of the discriminator, in which the inpainting images are pulled closer to the ground truth images but pushed farther from the corrupted images. The proposed contrastive losses better guide the repaired images to move from the corrupted image data points to the real image data points in the feature representation space, resulting in more realistic completed images. We conduct extensive experiments on two benchmark datasets, demonstrating our model's effectiveness and superiority both qualitatively and quantitatively.Comment: Accepted to AAAI2023, Ora

Comparative study of two rolling bond process for super-thick Q235B

In paper, two rolling bond processes for heavy-gauge steel plate Q235B were studied and the processes were simulated by MARC software. The mechanical properties and microstructure at the interface were comparative analyzed for the two bonded plates using different rolling process. Using MARC software analysis for two rolling process, the ratio of equivalent stress in rolling process /yield stress in current temperature from surface to center portion was relatively uniform for rolling bonded

Isolation and characterization of centromeric repetitive DNA sequences in Saccharum spontaneum

Sugarcane (Saccharum hybrids spp.) is the most important sugar crop that accounts for ~75% of the world’s sugar production. Recently, a whole-genome sequencing project was launched on the wild species S. spontaneum. To obtain information on the DNA composition of the repeat-enriched region of the centromere, we conducted a genome-wide analysis of the DNA sequences associated with CenH3 (a mutant of histone H3 located in eukaryote centromeres) using chromatin immunoprecipitation followed by sequencing (ChIP-seq) method. We demonstrate that the centromeres contain mainly SCEN-like single satellite repeat (Ss1) and several Ty3/gypsy retrotransposon-related repeats (Ss166, Ss51, and Ss68). Ss1 dominates in the centromeric regions and spans up to 500 kb. In contrast, the Ty3/gypsy retrotransposon-related repeats are either clustered spanning over a short range, or dispersed in the centromere regions. Interestingly, Ss1 exhibits a chromosome-specific enrichment in the wild species S. spontaneum and S. robustum, but not in the domesticated species S. officinarum and modern sugarcane cultivars. This finding suggests an autopolyploid genome identity of S. spontaneum with a high level of homology among its eight sub-genomes. We also conducted a genome-wide survey of the repetitive DNAs in S. spontaneum following a similarity-based sequence clustering strategy. These results provide insight into the composition of sugarcane genome as well as the genome assembly of S. spontaneum

Comprehensively Characterizing the Cytological Features of Saccharum spontaneum by the Development of a Complete Set of Chromosome-Specific Oligo Probes

Chromosome-specific identification is a powerful technique in the study of genome structure and evolution. However, there is no reliable cytogenetic marker to unambiguously identify each of the chromosomes in sugarcane (Saccharum spp., Poaceae), which has a complex genome with a high level of ploidy and heterozygosity. In this study, we developed a set of oligonucleotide (oligo)-based probes through bioinformatic design and massive synthetization. These probes produced a clear and bright single signal in each of the chromosomes and their eight homologous chromosomes in the ancient species Saccharum spontaneum (2n = 8x = 64). Thus, they can be used as reliable markers to robustly label each of the chromosomes in S. spontaneum. We then obtained the karyotype data and established a nomenclature based on chromosomal sizes for the eight chromosomes of the octoploid S. spontaneum. In addition, we also found that the 45S and 5S rDNAs demonstrated high copy number variations among different homologous chromosomes, indicating a rapid evolution of the highly repeated sequence after polyploidization. Our fluorescence in situ hybridization (FISH) assay also demonstrated that these probes could be used as cross-species markers between or within the genera of Sorghum and Saccharum. By comparing FISH analyses, we discovered that several chromosome rearrangement events occurred in S. spontaneum, which might have contributed to the basic chromosome number reduction from 10 in sorghum to 8 in sugarcane. Consistent identification of individual chromosomes makes molecular cytogenetic study possible in sugarcane and will facilitate fine chromosomal structure and karyotype evolution of the genus Saccharum

Dependence of Convective Cloud Properties and Their Transport on Cloud Fraction and GCM Resolution Diagnosed from a Cloud-Resolving Model Simulation

The scale-aware convective parameterization for high resolution global climate models must satisfy the requirement that the parameterized subgrid convective transport diminishes as the model resolution increases to convection-resolving resolutions. A major assumption in current scale-aware convection schemes is that the differences between convective cloud properties and their environmental counterparts are independent of cloud fraction. This study examines convective cloud vertical velocity, moist static energy (MSE), moisture, and the vertical eddy transport of MSE and moisture for different averaging subdomain sizes and fractional convective cloudiness using a cloud resolving model simulation of a midlatitude mesoscale convective system. Results show that convective cloud fraction, mass flux, and vertical transport of MSE and moisture increase with decreasing subdomain size. The differences between convective cloud properties in both updrafts and downdrafts and their environment depend on both cloud fraction and the averaging subdomain size. For a given subdomain size, the differences increase with cloud fraction, in contrast to the assumption used in current scale-aware convection parameterization schemes. A consequence of this is that the parameterized convective eddy transport reaches maximum at a higher cloud fraction than believed in previous studies. This has implications on how fast the subgrid convective transport should diminish as GCM resolution increases