VideoControlNet: A Motion-Guided Video-to-Video Translation Framework by Using Diffusion Model with ControlNet

Recently, diffusion models like StableDiffusion have achieved impressive image generation results. However, the generation process of such diffusion models is uncontrollable, which makes it hard to generate videos with continuous and consistent content. In this work, by using the diffusion model with ControlNet, we proposed a new motion-guided video-to-video translation framework called VideoControlNet to generate various videos based on the given prompts and the condition from the input video. Inspired by the video codecs that use motion information for reducing temporal redundancy, our framework uses motion information to prevent the regeneration of the redundant areas for content consistency. Specifically, we generate the first frame (i.e., the I-frame) by using the diffusion model with ControlNet. Then we generate other key frames (i.e., the P-frame) based on the previous I/P-frame by using our newly proposed motion-guided P-frame generation (MgPG) method, in which the P-frames are generated based on the motion information and the occlusion areas are inpainted by using the diffusion model. Finally, the rest frames (i.e., the B-frame) are generated by using our motion-guided B-frame interpolation (MgBI) module. Our experiments demonstrate that our proposed VideoControlNet inherits the generation capability of the pre-trained large diffusion model and extends the image diffusion model to the video diffusion model by using motion information. More results are provided at our project page

Experimental and Numerical Study of Macro-Cell Corrosion Between Crossed Steel Bars

Reinforcing steel bars embedded in concrete are always intersected with each other to form rebar mesh or three-dimensional steel cage. The present study aims to investigate the phenomenon of severe corrosion observed at stirrups or some intersections of steel rebar mesh, which has not been well studied before. Macro-cell corrosion between crossed steel bars was considered to be the major cause for this phenomenon. In this regard, crossed steel bars were divided into intersected zone (IZ) and non-intersected zone (NIZ). The macro-cell current at the face-to-face IZ was calculated by Ohm’s law. A one-dimensional model based on transmission line method was employed to obtain the distribution of macro-cell current on the NIZ juxtaposed to the IZ. Experiments of steel bars in aqueous solutions and concrete were undertaken to verify the numerical model. The results demonstrated a good match between experiments and numerical model. It was also shown that the distribution of macro-cell current on the non-intersected areas was influenced by the resistivity of electrolyte. Based on the corrosion rate model presented in this study, the severe corrosion observed at stirrups or some intersection zones of rebar mesh can be explained and quantified

Isolation and Identification of Lactic Acid Bacteria Strains and Their Effects on the Fermentation Quality of Elephant Grass (\u3ci\u3eCenchrus purpureus\u3c/i\u3e) Silage

This study aims to isolate and identify lactic acid bacteria and examine their effects on the fermentation quality of elephant grass silage. The isolated strains were identified based on morphological, physiological and biochemical characteristics as well as 16S rRNA analysis. Three strains namely Pediococcus acidilactic (AZZ5), Lactobacillus plantarum subsp. Plantarum (AZZ4), Lactobacillus plantarum subsp. Argentoratensis (AZZ6) were isolated from elephant grass silage. Isolation of the microbes was done by serial dilution method. Three LAB and one commercial bacteria Lactobacillus Plantarum, Ecosyl MTD/1(CB)) were used as additives to fresh material of elephant grass. To follow the fermentation quality during ensiling, samples were taken on days 30, 60 and 90 of ensiling for chemical analysis. The strain AZZ5 was identified as Pediococcus genus while AZZ4 and AZZ6 were Lactobacillus genus. Compared to the control, all the isolates improved the silage quality of elephant grass silage. In conclusion, AZZ4 performed better among all inoculants

Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation

Pulmonary nodules and masses are crucial imaging features in lung cancer screening that require careful management in clinical diagnosis. Despite the success of deep learning-based medical image segmentation, the robust performance on various sizes of lesions of nodule and mass is still challenging. In this paper, we propose a multi-scale neural network with scale-aware test-time adaptation to address this challenge. Specifically, we introduce an adaptive Scale-aware Test-time Click Adaptation method based on effortlessly obtainable lesion clicks as test-time cues to enhance segmentation performance, particularly for large lesions. The proposed method can be seamlessly integrated into existing networks. Extensive experiments on both open-source and in-house datasets consistently demonstrate the effectiveness of the proposed method over some CNN and Transformer-based segmentation methods. Our code is available at https://github.com/SplinterLi/SaTTCAComment: 11 pages, 3 figures, MICCAI 202