An Iterative Co-Saliency Framework for RGBD Images

As a newly emerging and significant topic in computer vision community, co-saliency detection aims at discovering the common salient objects in multiple related images. The existing methods often generate the co-saliency map through a direct forward pipeline which is based on the designed cues or initialization, but lack the refinement-cycle scheme. Moreover, they mainly focus on RGB image and ignore the depth information for RGBD images. In this paper, we propose an iterative RGBD co-saliency framework, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD cosaliency map by using a refinement-cycle model. Three schemes are employed in the proposed RGBD co-saliency framework, which include the addition scheme, deletion scheme, and iteration scheme. The addition scheme is used to highlight the salient regions based on intra-image depth propagation and saliency propagation, while the deletion scheme filters the saliency regions and removes the non-common salient regions based on interimage constraint. The iteration scheme is proposed to obtain more homogeneous and consistent co-saliency map. Furthermore, a novel descriptor, named depth shape prior, is proposed in the addition scheme to introduce the depth information to enhance identification of co-salient objects. The proposed method can effectively exploit any existing 2D saliency model to work well in RGBD co-saliency scenarios. The experiments on two RGBD cosaliency datasets demonstrate the effectiveness of our proposed framework.Comment: 13 pages, 13 figures, Accepted by IEEE Transactions on Cybernetics 2017. Project URL: https://rmcong.github.io/proj_RGBD_cosal_tcyb.htm

Protein-Protein and Protein-DNA Interactions at The Bacteriophage T4 DNA Replication Fork. Characterization of a Fluorescently Labeled DNA Polymerase Sliding Clamp

The T4 DNA polymerase holoenzyme is composed of the polymerase enzyme complexed to the sliding clamp (the 45 protein), which is loaded onto DNA by an ATP-dependent clamp loader (the 44/62 complex). This paper describes a new method to directly investigate the mechanism of holoenzyme assembly using a fluorescently labeled cysteine mutant of the 45 protein. This protein possessed unaltered function yet produced substantial changes in probe fluorescence intensity upon interacting with other components of the holoenzyme. These fluorescence changes provide insight into the role of ATP hydrolysis in holoenzyme assembly. Using either ATP or the non-hydrolyzable ATP analog, adenosine 5′-O-(3-thiophosphate), events in holoenzyme assembly were assigned as either dependent or independent of ATP hydrolysis. A holoenzyme assembly mechanism is proposed in which the 44/62 complex mediates the association of the 45 protein with DNA in an ATP-dependent manner not requiring ATP hydrolysis. Upon ATP hydrolysis, the 44/62 complex triggers a conformational change in the 45 protein that may be attributed to the clamp loading onto DNA

Practice Teaching System Establishment in the Major of Tourism Management

Based on the review of related national and international literature, it illustrated the current condition of practice teaching in the major of tourism management in China, specifically analyzed the existing problems, and then established the practice teaching system on the basis of competency based education (CBE), which include system establishing principles, system contents and measures to carry out the system.

From Large Language Models to Databases and Back: A discussion on research and education

This discussion was conducted at a recent panel at the 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023), held April 17-20, 2023 in Tianjin, China. The title of the panel was "What does LLM (ChatGPT) Bring to Data Science Research and Education? Pros and Cons". It was moderated by Lei Chen and Xiaochun Yang. The discussion raised several questions on how large language models (LLMs) and database research and education can help each other and the potential risks of LLMs.Comment: 7 pages, 2 figures, the Panel at the 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023

KinD-LCE Curve Estimation And Retinex Fusion On Low-Light Image

Low-light images often suffer from noise and color distortion. Object detection, semantic segmentation, instance segmentation, and other tasks are challenging when working with low-light images because of image noise and chromatic aberration. We also found that the conventional Retinex theory loses information in adjusting the image for low-light tasks. In response to the aforementioned problem, this paper proposes an algorithm for low illumination enhancement. The proposed method, KinD-LCE, uses a light curve estimation module to enhance the illumination map in the Retinex decomposed image, improving the overall image brightness. An illumination map and reflection map fusion module were also proposed to restore the image details and reduce detail loss. Additionally, a TV(total variation) loss function was applied to eliminate noise. Our method was trained on the GladNet dataset, known for its diverse collection of low-light images, tested against the Low-Light dataset, and evaluated using the ExDark dataset for downstream tasks, demonstrating competitive performance with a PSNR of 19.7216 and SSIM of 0.8213.Comment: Accepted by Signal, Image and Video Processin