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

Attack Deterministic Conditional Image Generative Models for Diverse and Controllable Generation

Existing generative adversarial network (GAN) based conditional image generative models typically produce fixed output for the same conditional input, which is unreasonable for highly subjective tasks, such as large-mask image inpainting or style transfer. On the other hand, GAN-based diverse image generative methods require retraining/fine-tuning the network or designing complex noise injection functions, which is computationally expensive, task-specific, or struggle to generate high-quality results. Given that many deterministic conditional image generative models have been able to produce high-quality yet fixed results, we raise an intriguing question: is it possible for pre-trained deterministic conditional image generative models to generate diverse results without changing network structures or parameters? To answer this question, we re-examine the conditional image generation tasks from the perspective of adversarial attack and propose a simple and efficient plug-in projected gradient descent (PGD) like method for diverse and controllable image generation. The key idea is attacking the pre-trained deterministic generative models by adding a micro perturbation to the input condition. In this way, diverse results can be generated without any adjustment of network structures or fine-tuning of the pre-trained models. In addition, we can also control the diverse results to be generated by specifying the attack direction according to a reference text or image. Our work opens the door to applying adversarial attack to low-level vision tasks, and experiments on various conditional image generation tasks demonstrate the effectiveness and superiority of the proposed method.Comment: 9 pages, 7 figures, accepted by AAAI2

PNeSM: Arbitrary 3D Scene Stylization via Prompt-Based Neural Style Mapping

3D scene stylization refers to transform the appearance of a 3D scene to match a given style image, ensuring that images rendered from different viewpoints exhibit the same style as the given style image, while maintaining the 3D consistency of the stylized scene. Several existing methods have obtained impressive results in stylizing 3D scenes. However, the models proposed by these methods need to be re-trained when applied to a new scene. In other words, their models are coupled with a specific scene and cannot adapt to arbitrary other scenes. To address this issue, we propose a novel 3D scene stylization framework to transfer an arbitrary style to an arbitrary scene, without any style-related or scene-related re-training. Concretely, we first map the appearance of the 3D scene into a 2D style pattern space, which realizes complete disentanglement of the geometry and appearance of the 3D scene and makes our model be generalized to arbitrary 3D scenes. Then we stylize the appearance of the 3D scene in the 2D style pattern space via a prompt-based 2D stylization algorithm. Experimental results demonstrate that our proposed framework is superior to SOTA methods in both visual quality and generalization.Comment: Accepted to AAAI 202

Psoriasin overexpression confers drug resistance to cisplatin by activating ERK in gastric cancer

Psoriasin, a member of the S100 multigenic family, which is aberrantly expressed in a variety of human tumors, is considered as an attractive molecular target for cancer treatment. The present study aimed to characterize the role of psoriasin in gastric cancer (GC), the associated pathways through which it contributes to cancer development and progression, and the effect of psoriasin on cellular response to pre-operative chemotherapy in patients with GC. Expression of psoriasin mRNA and protein were analyzed using quantitative polymerase chain reaction and immunohistochemistry of gastric cancer cohorts, respectively. Gastric cancer cell models with differential expression of psoriasin were generated using stable cell lines that overexpressed psoriasin. The in vitro biological functions of the cells in response to psoriasin overexpression and to chemotherapeutic agents were assessed using various cell-based assays. Psoriasin was overexpressed in patients with advanced GC, and high psoriasin levels led to poor clinical outcomes. Increasing psoriasin expression in GC cell lines promoted cell proliferation, migration and invasion in vitro. Furthermore, psoriasin overexpression caused alterations in the levels of epithelial-mesenchymal transition-associated proteins, and activated the extracellular signal-regulated kinase signaling pathway. Additionally, higher levels of psoriasin expression were significantly associated a lack of response to neoadjuvant chemotherapy in patients with GC. Psoriasin overexpression tended to decrease the sensitivity of GC cells to cisplatin, potentially by inhibiting apoptosis or increasing the S-phase population. Taken together, these results indicate that psoriasin may be a promising therapeutic target for GC treatment, and a potential molecular marker to predict patient response to pre-operative chemotherapy

Coexistence of Microbial Species in Structured Communities by Forming a Hawk-Dove Game Like Interactive Relationship

Microorganisms evolve kinds of elaborate interaction models that can form relatively stable communities in a wide range of ecosystems. It is recognized that the spatial genetic structure of microbes in surface-attached environments lays a good foundation for the persistence of polymicrobial communities in adverse conditions. However, the interacting dynamics of microbes in facilitating the formation and stabilization of community structure still remains elusive. In this study, we identify a hawk-dove game like interspecific relationship between the two Gram-negative opportunistic pathogens Pseudomonas aeruginosa and Klebsiella pneumoniae, which naturally coexist in insect gut and can cocolonize human tissues. Specifically, although P. aeruginosa had significant competitive advantage over cocultured K. pneumoniae on solid medium with rich nutrient factors, K. pneumoniae could resist the suppression of P. aeruginosa by enhancing the expression of membrane transporters induced by the extracellular metabolites of P. aeruginosa. By contrast, under the condition that K. pneumoniae had a growth advantage but P. aeruginosa met a metabolic burden in producing quorum-sensing-controlled extracellular products, the frequency of K. pneumoniae would be slightly higher than P. aeruginosa during the coexistence because K. pneumoniae was also capable of exploiting the extracellular metabolite from P. aeruginosa. In addition, P. aeruginosa quorum-sensing variant could reap benefits from K. pneumoniae in turn and reach a relatively stable two species equilibrium. These findings provide an explanation for the formation and maintenance of polymicrobial communities in different spatially structured environments, and thus may contribute to understanding the complex interspecific interactions of microbes in local communities and shed new light on the development of social microbiology

Thermal Impact on Spiking Properties in Hodgkin-Huxley Neuron with Synaptic Stimulus

The effect of environmental temperature on neuronal spiking behaviors is investigated by numerically simulating the temperature dependence of spiking threshold of the Hodgkin-Huxley neuron subject to synaptic stimulus. We find that the spiking threshold exhibits a global minimum in a "comfortable temperature" range where spike initiation needs weakest synaptic strength, indicating the occurrence of optimal use of synaptic transmission in neural system. We further explore the biophysical origin of this phenomenon in ion channel gating kinetics and also discuss its possible biological relevance in information processing in neural systems.Comment: 10 pages, 4 figure

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

Pseudomonas aeruginosa Quorum-Sensing and Type VI Secretion System Can Direct Interspecific Coexistence During Evolution

It is reported that a wide range of bacterial infections are polymicrobial, and the members in a local microcommunity can influence the growth of neighbors through physical and chemical interactions. Pseudomonas aeruginosa is an important opportunistic pathogen that normally causes a variety of acute and chronic infections, and clinical evidences suggest that P. aeruginosa can be frequently coisolated with other pathogens from the patients with chronic infections. However, the interspecific interaction and the coexisting mechanism of P. aeruginosa with coinfecting bacterial species during evolution still remain largely unclear. In this study, the relationships of P. aeruginosa with other Gram-positive (Staphylococcus aureus) and Gram-negative (Klebsiella pneumoniae) are investigated by using a series of on-plate proximity assay, in vitro coevolution assay, and RNA-sequencing. We find that although the development of a quorum-sensing system contributes P. aeruginosa a significant growth advantage to compete with S. aureus and K. pneumoniae, the quorum-sensing regulation of P. aeruginosa will be decreased during evolution and thus provides a basis for the formation of interspecific coexistence. The results of comparative transcriptomic analyses suggest that the persistent survival of S. aureus in the microcommunity has no significant effect on the intracellular transcriptional pattern of P. aeruginosa, while a more detailed competition happens between P. aeruginosa and K. pneumoniae. Specifically, the population of P. aeruginosa with decreased quorum-sensing regulation can still restrict the proportion increase of K. pneumoniae by enhancing the type VI secretion system-elicited cell aggressivity during further coevolution. These findings provide a general explanation for the formation of a dynamic stable microcommunity consisting of more than two bacterial species, and may contribute to the development of population biology and clinical therapy