Invariant analysis and explicit solutions of the time fractional nonlinear perturbed Burgers equation

The Lie group analysis method is performed for the nonlinear perturbed Burgers equation and the time fractional nonlinear perturbed Burgers equation. All of the point symmetries of the equations are constructed. In view of the point symmetries, the vector fields of the equations are constructed. Subsequently, the symmetry reductions are investigated. In particular, some novel exact and explicit solutions are obtained

Selective-Stereo: Adaptive Frequency Information Selection for Stereo Matching

Stereo matching methods based on iterative optimization, like RAFT-Stereo and IGEV-Stereo, have evolved into a cornerstone in the field of stereo matching. However, these methods struggle to simultaneously capture high-frequency information in edges and low-frequency information in smooth regions due to the fixed receptive field. As a result, they tend to lose details, blur edges, and produce false matches in textureless areas. In this paper, we propose Selective Recurrent Unit (SRU), a novel iterative update operator for stereo matching. The SRU module can adaptively fuse hidden disparity information at multiple frequencies for edge and smooth regions. To perform adaptive fusion, we introduce a new Contextual Spatial Attention (CSA) module to generate attention maps as fusion weights. The SRU empowers the network to aggregate hidden disparity information across multiple frequencies, mitigating the risk of vital hidden disparity information loss during iterative processes. To verify SRU's universality, we apply it to representative iterative stereo matching methods, collectively referred to as Selective-Stereo. Our Selective-Stereo ranks $1^{st}$ on KITTI 2012, KITTI 2015, ETH3D, and Middlebury leaderboards among all published methods. Code is available at https://github.com/Windsrain/Selective-Stereo.Comment: Accepted to CVPR 202

HDRFlow: Real-Time HDR Video Reconstruction with Large Motions

Reconstructing High Dynamic Range (HDR) video from image sequences captured with alternating exposures is challenging, especially in the presence of large camera or object motion. Existing methods typically align low dynamic range sequences using optical flow or attention mechanism for deghosting. However, they often struggle to handle large complex motions and are computationally expensive. To address these challenges, we propose a robust and efficient flow estimator tailored for real-time HDR video reconstruction, named HDRFlow. HDRFlow has three novel designs: an HDR-domain alignment loss (HALoss), an efficient flow network with a multi-size large kernel (MLK), and a new HDR flow training scheme. The HALoss supervises our flow network to learn an HDR-oriented flow for accurate alignment in saturated and dark regions. The MLK can effectively model large motions at a negligible cost. In addition, we incorporate synthetic data, Sintel, into our training dataset, utilizing both its provided forward flow and backward flow generated by us to supervise our flow network, enhancing our performance in large motion regions. Extensive experiments demonstrate that our HDRFlow outperforms previous methods on standard benchmarks. To the best of our knowledge, HDRFlow is the first real-time HDR video reconstruction method for video sequences captured with alternating exposures, capable of processing 720p resolution inputs at 25ms.Comment: CVPR 2024; Project website: https://openimaginglab.github.io/HDRFlow

Accurate and Efficient Stereo Matching via Attention Concatenation Volume

Stereo matching is a fundamental building block for many vision and robotics applications. An informative and concise cost volume representation is vital for stereo matching of high accuracy and efficiency. In this paper, we present a novel cost volume construction method, named attention concatenation volume (ACV), which generates attention weights from correlation clues to suppress redundant information and enhance matching-related information in the concatenation volume. The ACV can be seamlessly embedded into most stereo matching networks, the resulting networks can use a more lightweight aggregation network and meanwhile achieve higher accuracy. We further design a fast version of ACV to enable real-time performance, named Fast-ACV, which generates high likelihood disparity hypotheses and the corresponding attention weights from low-resolution correlation clues to significantly reduce computational and memory cost and meanwhile maintain a satisfactory accuracy. The core idea of our Fast-ACV is volume attention propagation (VAP) which can automatically select accurate correlation values from an upsampled correlation volume and propagate these accurate values to the surroundings pixels with ambiguous correlation clues. Furthermore, we design a highly accurate network ACVNet and a real-time network Fast-ACVNet based on our ACV and Fast-ACV respectively, which achieve the state-of-the-art performance on several benchmarks (i.e., our ACVNet ranks the 2nd on KITTI 2015 and Scene Flow, and the 3rd on KITTI 2012 and ETH3D among all the published methods; our Fast-ACVNet outperforms almost all state-of-the-art real-time methods on Scene Flow, KITTI 2012 and 2015 and meanwhile has better generalization ability)Comment: Accepted to TPAMI 2023. arXiv admin note: substantial text overlap with arXiv:2203.0214

Memory-Efficient Optical Flow via Radius-Distribution Orthogonal Cost Volume

The full 4D cost volume in Recurrent All-Pairs Field Transforms (RAFT) or global matching by Transformer achieves impressive performance for optical flow estimation. However, their memory consumption increases quadratically with input resolution, rendering them impractical for high-resolution images. In this paper, we present MeFlow, a novel memory-efficient method for high-resolution optical flow estimation. The key of MeFlow is a recurrent local orthogonal cost volume representation, which decomposes the 2D search space dynamically into two 1D orthogonal spaces, enabling our method to scale effectively to very high-resolution inputs. To preserve essential information in the orthogonal space, we utilize self attention to propagate feature information from the 2D space to the orthogonal space. We further propose a radius-distribution multi-scale lookup strategy to model the correspondences of large displacements at a negligible cost. We verify the efficiency and effectiveness of our method on the challenging Sintel and KITTI benchmarks, and real-world 4K ($2160\!\times\!3840$) images. Our method achieves competitive performance on both Sintel and KITTI benchmarks, while maintaining the highest memory efficiency on high-resolution inputs.Comment: 10 pages, 9 figure

MC-Stereo: Multi-peak Lookup and Cascade Search Range for Stereo Matching

Stereo matching is a fundamental task in scene comprehension. In recent years, the method based on iterative optimization has shown promise in stereo matching. However, the current iteration framework employs a single-peak lookup, which struggles to handle the multi-peak problem effectively. Additionally, the fixed search range used during the iteration process limits the final convergence effects. To address these issues, we present a novel iterative optimization architecture called MC-Stereo. This architecture mitigates the multi-peak distribution problem in matching through the multi-peak lookup strategy, and integrates the coarse-to-fine concept into the iterative framework via the cascade search range. Furthermore, given that feature representation learning is crucial for successful learn-based stereo matching, we introduce a pre-trained network to serve as the feature extractor, enhancing the front end of the stereo matching pipeline. Based on these improvements, MC-Stereo ranks first among all publicly available methods on the KITTI-2012 and KITTI-2015 benchmarks, and also achieves state-of-the-art performance on ETH3D. Code is available at https://github.com/MiaoJieF/MC-Stereo.Comment: Accepted to 3DV 202

Validating automated eye disease screening AI algorithm in community and in-hospital scenarios

Purpose:To assess the accuracy and robustness of the AI algorithm for detecting referable diabetic retinopathy (RDR), referable macular diseases (RMD), and glaucoma suspect (GCS) from fundus images in community and in-hospital screening scenarios.MethodsWe collected two color fundus image datasets, namely, PUMCH (556 images, 166 subjects, and four camera models) and NSDE (534 images, 134 subjects, and two camera models). The AI algorithm generates the screening report after taking fundus images. The images were labeled as RDR, RMD, GCS, or none of the three by 3 licensed ophthalmologists. The resulting labels were treated as “ground truth” and then were used to compare against the AI screening reports to validate the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of the AI algorithm.ResultsOn the PUMCH dataset, regarding the prediction of RDR, the AI algorithm achieved overall results of 0.950 ± 0.058, 0.963 ± 0.024, and 0.954 ± 0.049 on sensitivity, specificity, and AUC, respectively. For RMD, the overall results are 0.919 ± 0.073, 0.929 ± 0.039, and 0.974 ± 0.009. For GCS, the overall results are 0.950 ± 0.059, 0.946 ± 0.016, and 0.976 ± 0.025.ConclusionThe AI algorithm can work robustly with various fundus camera models and achieve high accuracies for detecting RDR, RMD, and GCS

Research Progress on Effects of Gut Microbiome on Efficacy of Immune Checkpoint Inhibitors in Colorectal Cancer

With the rapid development of immunotherapy, an increasing number of immune checkpoint inhibitors have been used in clinical settings. Immunotherapy provides a new treatment option for patients with advanced colorectal cancer metastasis. Studies have confirmed that patients with metastatic colorectal cancer with dMMR/MSI-H status are more sensitive to immunotherapy and have a more objective and sustained clinical response than their counterparts. Gut microbiome has been proved to play a certain regulatory role in tumor immunotherapy response, and some bacteria can affect the efficacy of immune checkpoint inhibitors through the immune system or metabolic function of the body. With the progress of the study, the gut microbiome is expected to become not only the predictive biomarkers of curative effect of colorectal cancer immunotherapy, but it can also be a key regulatory factor influencing the results of colorectal cancer immunotherapy. For future clinical treatment, the use of immune checkpoint inhibitors may benefit patients with advanced colorectal cancer

Mechanism of Secondary Breakage in the Overlying Strata during Repetitious Mining of an Ultrathick Coal Seam in Design Stage

When designing the mining of an ultrathick coal seam, the laws governing movement in the overlying strata during mining are a fundamental issue based on which several problems are addressed, including determining the mining method and the roadway arrangement, controlling the surrounding strata, and selecting the devices. The present paper considers possible problems related to strata overlying a large mining space subjected to repeated disturbances during the mining of an ultrathick coal seam, including repeatedly broken strata and the existence or inexistence of the structure. The BM coal seam in the No. 2 coal mine of the Dajing mining area in the East Junggar coalfield is studied. Physical simulations are performed on the movements of the overlying strata during slicing mining of the ultrathick coal seam, revealing the new feature of “break-joint stability-instability-secondary breakage” in the overlying strata. Mechanical models are constructed of the secondary breakage of the overlying strata blocks under both static and impact loading, and mechanical criteria are proposed for such breakage. Based on the research findings, methods for controlling the surrounding strata during slicing mining of an ultrathick coal seam are proposed, including increasing the mining rate and designing reasonable heights for the slicing mining