Perception Driven Texture Generation

This paper investigates a novel task of generating texture images from perceptual descriptions. Previous work on texture generation focused on either synthesis from examples or generation from procedural models. Generating textures from perceptual attributes have not been well studied yet. Meanwhile, perceptual attributes, such as directionality, regularity and roughness are important factors for human observers to describe a texture. In this paper, we propose a joint deep network model that combines adversarial training and perceptual feature regression for texture generation, while only random noise and user-defined perceptual attributes are required as input. In this model, a preliminary trained convolutional neural network is essentially integrated with the adversarial framework, which can drive the generated textures to possess given perceptual attributes. An important aspect of the proposed model is that, if we change one of the input perceptual features, the corresponding appearance of the generated textures will also be changed. We design several experiments to validate the effectiveness of the proposed method. The results show that the proposed method can produce high quality texture images with desired perceptual properties.Comment: 7 pages, 4 figures, icme201

Study of the cytological features of bone marrow mesenchymal stem cells from patients with neuromyelitis optica.

Neuromyelitis optica (NMO) is a refractory autoimmune inflammatory disease of the central nervous system without an effective cure. Autologous bone marrow‑derived mesenchymal stem cells (BM‑MSCs) are considered to be promising therapeutic agents for this disease due to their potential regenerative, immune regulatory and neurotrophic effects. However, little is known about the cytological features of BM‑MSCs from patients with NMO, which may influence any therapeutic effects. The present study aimed to compare the proliferation, differentiation and senescence of BM‑MSCs from patients with NMO with that of age‑ and sex‑matched healthy subjects. It was revealed that there were no significant differences in terms of cell morphology or differentiation capacities in the BM‑MSCs from the patients with NMO. However, in comparison with healthy controls, BM‑MSCs derived from the Patients with NMO exhibited a decreased proliferation rate, in addition to a decreased expression of several cell cycle‑promoting and proliferation‑associated genes. Furthermore, the cell death rate increased in BM‑MSCs from patients under normal culture conditions and an assessment of the gene expression profile further confirmed that the BM‑MSCs from patients with NMO were more vulnerable to senescence. Platelet‑derived growth factor (PDGF), as a major mitotic stimulatory factor for MSCs and a potent therapeutic cytokine in demyelinating disease, was able to overcome the decreased proliferation rate and increased senescence defects in BM‑MSCs from the patients with NMO. Taken together, the results from the present study have enabled the proposition of the possibility of combining the application of autologous BM‑MSCs and PDGF for refractory and severe patients with NMO in order to elicit improved therapeutic effects, or, at the least, to include PDGF as a necessary and standard growth factor in the current in vitro formula for the culture of NMO patient‑derived BM‑MSCs

MRVM-NeRF: Mask-Based Pretraining for Neural Radiance Fields

Most Neural Radiance Fields (NeRFs) have poor generalization ability, limiting their application when representing multiple scenes by a single model. To ameliorate this problem, existing methods simply condition NeRF models on image features, lacking the global understanding and modeling of the entire 3D scene. Inspired by the significant success of mask-based modeling in other research fields, we propose a masked ray and view modeling method for generalizable NeRF (MRVM-NeRF), the first attempt to incorporate mask-based pretraining into 3D implicit representations. Specifically, considering that the core of NeRFs lies in modeling 3D representations along the rays and across the views, we randomly mask a proportion of sampled points along the ray at fine stage by discarding partial information obtained from multi-viewpoints, targeting at predicting the corresponding features produced in the coarse branch. In this way, the learned prior knowledge of 3D scenes during pretraining helps the model generalize better to novel scenarios after finetuning. Extensive experiments demonstrate the superiority of our proposed MRVM-NeRF under various synthetic and real-world settings, both qualitatively and quantitatively. Our empirical studies reveal the effectiveness of our proposed innovative MRVM which is specifically designed for NeRF models

Transcriptome, microRNA, and degradome analyses of the gene expression of Paulownia with phytoplamsa

Primers of P. tomentosa miRNAs for qRT-PCR analysis. (DOCX 20.7 kb

SCFM: Social and crowdsourcing factorization machines for recommendation

With the rapid development of social networks, the exponential growth of social information has attracted much attention. Social information has great value in recommender systems to alleviate the sparsity and cold start problem. On the other hand, the crowd computing empowers recommender systems by utilizing human wisdom. Internal user reviews can be exploited as the wisdom of the crowd to contribute information. In this paper, we propose social and crowdsourcing factorization machines, called SCFM. Our approach fuses social and crowd computing into the factorization machine model. For social computing, we calculate the influence value between users by taking users’ social information and user similarity into account. For crowd computing, we apply LDA (Latent Dirichlet Allocation) on people review to obtain sets of underlying topic probabilities. Furthermore, we impose two important constraints called social regularization and domain inner regularization. The experimental results show that our approach outperforms other state-of-the-art methods.This project is supported by the National Natural Science Foundation of China (Nos. 61672340, 61472240, 61572268)