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

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

Moisture availability mediates the relationship between terrestrial gross primary production and solar‐induced chlorophyll fluorescence: Insights from global‐scale variations

Effective use of solar‐induced chlorophyll fluorescence (SIF) to estimate and monitor gross primary production (GPP) in terrestrial ecosystems requires a comprehensive understanding and quantification of the relationship between SIF and GPP. To date, this understanding is incomplete and somewhat controversial in the literature. Here we derived the GPP/SIF ratio from multiple data sources as a diagnostic metric to explore its global‐scale patterns of spatial variation and potential climatic dependence. We found that the growing season GPP/SIF ratio varied substantially across global land surfaces, with the highest ratios consistently found in boreal regions. Spatial variation in GPP/SIF was strongly modulated by climate variables. The most striking pattern was a consistent decrease in GPP/SIF from cold‐and‐wet climates to hot‐and‐dry climates. We propose that the reduction in GPP/SIF with decreasing moisture availability may be related to stomatal responses to aridity. Furthermore, we show that GPP/SIF can be empirically modeled from climate variables using a machine learning (random forest) framework, which can improve the modeling of ecosystem production and quantify its uncertainty in global terrestrial biosphere models. Our results point to the need for targeted field and experimental studies to better understand the patterns observed and to improve the modeling of the relationship between SIF and GPP over broad scales

Overexpression of Phosphate Transporter Gene CmPht1;2 Facilitated Pi Uptake and Alternated the Metabolic Profiles of Chrysanthemum Under Phosphate Deficiency

Low availability of phosphorus (P) in the soil is the principal limiting factor for the growth of cut chrysanthemum. Plant phosphate transporters (PTs) facilitate acquisition of inorganic phosphate (Pi) and its homeostasis within the plant. In the present study, CmPht1;2 of the Pht1 family was cloned from chrysanthemum. CmPht1;2 is composed of 12 transmembrane domains and localized to the plasma membrane. Expression of CmPht1;2 in roots was induced by Pi starvation. Chrysanthemum plants with overexpression of CmPht1;2 (Oe) showed higher Pi uptake, as compared to the wild type (WT), both under Pi-starvation and Pi-sufficient conditions, and also showed a higher root biomass compared to WT in the Pi-starvation conditions. Seven days after the P-deficiency treatment, 85 distinct analytes were identified in the roots and 27 in the shoots between the Oe1 plant and WT, in which sophorose, sorbitol (sugars), hydroxybutyric acid (organic acids), and ornithine (amino acid) of CmPht1;2 overexpressing chrysanthemum are specific responses to P-starvation

The Abundance and Diversity of Soil Fungi in Continuously Monocropped Chrysanthemum

Chrysanthemum is an important ornamental plant which is increasingly being monocropped. Monocropping is known to affect both fungal abundance and species diversity. Here, quantitative PCR allied with DGGE analysis was used to show that fungi were more abundant in the rhizosphere than in the bulk soil and that the fungal populations changed during the growth cycle of the chrysanthemum. The majority of amplified fragments appeared to derive from Fusarium species, and F. oxysporum and F. solani proved to be the major pathogenic species which are built up by monocropping

EphB2 represents an independent prognostic marker in patients with gastric cancer and promotes tumour cell aggressiveness

Dysregulated expression of ephrin type-B receptor 2 (EphB2) has been linked with development and progression of solid tumours. In the current study we attempted to investigate the clinical relevance in GC and the effect of EphB2 expression on gastric cancer (GC) cells. EphB2 protein levels in GC and benign gastric tissues were determined using immunohistochemistry. EphB2 transcript expression in a GC cohort with GC tissue samples (n=171) and paired adjacent normal gastric tissues (n=97) was determined using qPCR. The EphB2 expression was over-activated using a CRISPR activator for the investigation of its cellular function. The expression levels of the EphB2 protein in the tumour tissues of tissue arrays were higher than the benign non-cancerous gastric tissues (P<0.05). EphB2 mRNA expression in GC tissues was also significantly elevated when compared with adjacent non-cancerous tissues (P<0.01). EphB2 activation promoted the migration and invasion abilities of the GC cell lines (P<0.01, respectively). In contrast, EphB2 activation significantly decreased the adhesion in GC cells (P<0.0001, respectively). The enrichment analysis of the correlated genes in a GC cohort indicates that EphB2 may function through mediating the cytokine-cytokine interaction, JAK-STAT and TP53 signaling pathways. In conclusion, EphB2 represents as a novel independent prognostic marker in GC. And activation of the EphB2 gene expression elevated the levels of migration and invasion, but suppressed adhesion of GC cells, indicating that EphB2 may act as a tumour promotor in GC. Our findings thus provide fundamental evidence for the consideration of the therapeutic potential of targeting EphB2 in GC

Direct field-to-pattern monolithic design of holographic metasurface via residual encoder-decoder convolutional neural network

Complex-amplitude holographic metasurfaces (CAHMs) with the flexibility in modulating phase and amplitude profiles have been used to manipulate the propagation of wavefront with an unprecedented level, leading to higher image-reconstruction quality compared with their natural counterparts. However, prevailing design methods of CAHMs are based on Huygens-Fresnel theory, meta-atom optimization, numerical simulation and experimental verification, which results in a consumption of computing resources. Here, we applied residual encoder-decoder convolutional neural network to directly map the electric field distributions and input images for monolithic metasurface design. A pretrained network is firstly trained by the electric field distributions calculated by diffraction theory, which is subsequently migrated as transfer learning framework to map the simulated electric field distributions and input images. The training results show that the normalized mean pixel error is about 3% on dataset. As verification, the metasurface prototypes are fabricated, simulated and measured. The reconstructed electric field of reverse-engineered metasurface exhibits high similarity to the target electric field, which demonstrates the effectiveness of our design. Encouragingly, this work provides a monolithic field-to-pattern design method for CAHMs, which paves a new route for the direct reconstruction of metasurfaces