Imaging of the Space-time Structure of a Vortex Generator in Supersonic Flow

AbstractThe fine space-time structure of a vortex generator (VG) in supersonic flow is studied with the nanoparticle-based planar laser scattering (NPLS) method in a quiet supersonic wind tunnel. The fine coherent structure at the symmetrical plane of the flow field around the VG is imaged with NPLS. The spatial structure and temporal evolution characteristics of the vortical structure are analyzed, which demonstrate periodic evolution and similar geometry, and the characteristics of rapid movement and slow change. Because the NPLS system yields the flow images at high temporal and spatial resolutions, from these images the position of a large scale structure can be extracted precisely. The position and velocity of the large scale structures can be evaluated with edge detection and correlation algorithms. The shocklet structures induced by vortices are imaged, from which the generation and development of shocklets are discussed in this paper

Learning A Coarse-to-Fine Diffusion Transformer for Image Restoration

Recent years have witnessed the remarkable performance of diffusion models in various vision tasks. However, for image restoration that aims to recover clear images with sharper details from given degraded observations, diffusion-based methods may fail to recover promising results due to inaccurate noise estimation. Moreover, simple constraining noises cannot effectively learn complex degradation information, which subsequently hinders the model capacity. To solve the above problems, we propose a coarse-to-fine diffusion Transformer (C2F-DFT) for image restoration. Specifically, our C2F-DFT contains diffusion self-attention (DFSA) and diffusion feed-forward network (DFN) within a new coarse-to-fine training scheme. The DFSA and DFN respectively capture the long-range diffusion dependencies and learn hierarchy diffusion representation to facilitate better restoration. In the coarse training stage, our C2F-DFT estimates noises and then generates the final clean image by a sampling algorithm. To further improve the restoration quality, we propose a simple yet effective fine training scheme. It first exploits the coarse-trained diffusion model with fixed steps to generate restoration results, which then would be constrained with corresponding ground-truth ones to optimize the models to remedy the unsatisfactory results affected by inaccurate noise estimation. Extensive experiments show that C2F-DFT significantly outperforms diffusion-based restoration method IR-SDE and achieves competitive performance compared with Transformer-based state-of-the-art methods on $3$ tasks, including deraining, deblurring, and real denoising.Comment: 9 pages, 8 figure

Evolving to the Future: Unseen Event Adaptive Fake News Detection on Social Media

With the rapid development of social media, the wide dissemination of fake news on social media is increasingly threatening both individuals and society. In the dynamic landscape of social media, fake news detection aims to develop a model trained on news reporting past events. The objective is to predict and identify fake news about future events, which often relate to subjects entirely different from those in the past. However, existing fake detection methods exhibit a lack of robustness and cannot generalize to unseen events. To address this, we introduce Future ADaptive Event-based Fake news Detection (FADE) framework. Specifically, we train a target predictor through an adaptive augmentation strategy and graph contrastive learning to make more robust overall predictions. Simultaneously, we independently train an event-only predictor to obtain biased predictions. Then we further mitigate event bias by obtaining the final prediction by subtracting the output of the event-only predictor from the output of the target predictor. Encouraging results from experiments designed to emulate real-world social media conditions validate the effectiveness of our method in comparison to existing state-of-the-art approaches

SIRT1 - a new mammalian substrate of nuclear autophagy

Macroautophagic/autophagic degradation of nuclear components (or nuclear autophagy) is a poorly understood area in autophagy research. We previously reported the nuclear lamina protein LMNB1 (lamin B1) as a nuclear autophagy substrate in primary human cells, stimulating the investigation of nuclear autophagy in the mammalian system. We recently reported the sirtuin protein SIRT1 as a new selective substrate of nuclear autophagy in senescence and aging. Upon senescence of primary human cells, SIRT1 degradation is mediated by a direct nuclear SIRT1-LC3 interaction, followed by nucleus-to-cytoplasm shuttling of SIRT1 and autophagosome-lysosome degradation. In vivo, SIRT1 is downregulated by lysosomes in hematopoietic and immune organs upon natural aging in mice and in aged human T cells. Our study identified another substrate of nuclear autophagy and suggests a new strategy to promote SIRT1-mediated health benefits by suppressing its autophagic degradation

P2Seg: Pointly-supervised Segmentation via Mutual Distillation

Point-level Supervised Instance Segmentation (PSIS) aims to enhance the applicability and scalability of instance segmentation by utilizing low-cost yet instance-informative annotations. Existing PSIS methods usually rely on positional information to distinguish objects, but predicting precise boundaries remains challenging due to the lack of contour annotations. Nevertheless, weakly supervised semantic segmentation methods are proficient in utilizing intra-class feature consistency to capture the boundary contours of the same semantic regions. In this paper, we design a Mutual Distillation Module (MDM) to leverage the complementary strengths of both instance position and semantic information and achieve accurate instance-level object perception. The MDM consists of Semantic to Instance (S2I) and Instance to Semantic (I2S). S2I is guided by the precise boundaries of semantic regions to learn the association between annotated points and instance contours. I2S leverages discriminative relationships between instances to facilitate the differentiation of various objects within the semantic map. Extensive experiments substantiate the efficacy of MDM in fostering the synergy between instance and semantic information, consequently improving the quality of instance-level object representations. Our method achieves 55.7 mAP$_{50}$ and 17.6 mAP on the PASCAL VOC and MS COCO datasets, significantly outperforming recent PSIS methods and several box-supervised instance segmentation competitors.Comment: 14 pages, 12 figures, published to ICLR202