Error analysis of energy-conservative BDF2-FE scheme for the 2D Navier-Stokes equations with variable density

In this paper, we present an error estimate of a second-order linearized finite element (FE) method for the 2D Navier-Stokes equations with variable density. In order to get error estimates, we first introduce an equivalent form of the original system. Later, we propose a general BDF2-FE method for solving this equivalent form, where the Taylor-Hood FE space is used for discretizing the Navier-Stokes equations and conforming FE space is used for discretizing density equation. We show that our scheme ensures discrete energy dissipation. Under the assumption of sufficient smoothness of strong solutions, an error estimate is presented for our numerical scheme for variable density incompressible flow in two dimensions. Finally, some numerical examples are provided to confirm our theoretical results.Comment: 22 pages, 1 figure

SSformer: A Lightweight Transformer for Semantic Segmentation

It is well believed that Transformer performs better in semantic segmentation compared to convolutional neural networks. Nevertheless, the original Vision Transformer may lack of inductive biases of local neighborhoods and possess a high time complexity. Recently, Swin Transformer sets a new record in various vision tasks by using hierarchical architecture and shifted windows while being more efficient. However, as Swin Transformer is specifically designed for image classification, it may achieve suboptimal performance on dense prediction-based segmentation task. Further, simply combing Swin Transformer with existing methods would lead to the boost of model size and parameters for the final segmentation model. In this paper, we rethink the Swin Transformer for semantic segmentation, and design a lightweight yet effective transformer model, called SSformer. In this model, considering the inherent hierarchical design of Swin Transformer, we propose a decoder to aggregate information from different layers, thus obtaining both local and global attentions. Experimental results show the proposed SSformer yields comparable mIoU performance with state-of-the-art models, while maintaining a smaller model size and lower compute

UperFormer: A Multi-scale Transformer-based Decoder for Semantic Segmentation

While a large number of recent works on semantic segmentation focus on designing and incorporating a transformer-based encoder, much less attention and vigor have been devoted to transformer-based decoders. For such a task whose hallmark quest is pixel-accurate prediction, we argue that the decoder stage is just as crucial as that of the encoder in achieving superior segmentation performance, by disentangling and refining the high-level cues and working out object boundaries with pixel-level precision. In this paper, we propose a novel transformer-based decoder called UperFormer, which is plug-and-play for hierarchical encoders and attains high quality segmentation results regardless of encoder architecture. UperFormer is equipped with carefully designed multi-head skip attention units and novel upsampling operations. Multi-head skip attention is able to fuse multi-scale features from backbones with those in decoders. The upsampling operation, which incorporates feature from encoder, can be more friendly for object localization. It brings a 0.4% to 3.2% increase compared with traditional upsampling methods. By combining UperFormer with Swin Transformer (Swin-T), a fully transformer-based symmetric network is formed for semantic segmentation tasks. Extensive experiments show that our proposed approach is highly effective and computationally efficient. On Cityscapes dataset, we achieve state-of-the-art performance. On the more challenging ADE20K dataset, our best model yields a single-scale mIoU of 50.18, and a multi-scale mIoU of 51.8, which is on-par with the current state-of-art model, while we drastically cut the number of FLOPs by 53.5%. Our source code and models are publicly available at: https://github.com/shiwt03/UperForme

Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image Segmentation

Recently, deep neural networks have greatly advanced histopathology image segmentation but usually require abundant annotated data. However, due to the gigapixel scale of whole slide images and pathologists' heavy daily workload, obtaining pixel-level labels for supervised learning in clinical practice is often infeasible. Alternatively, weakly-supervised segmentation methods have been explored with less laborious image-level labels, but their performance is unsatisfactory due to the lack of dense supervision. Inspired by the recent success of self-supervised learning methods, we present a label-efficient tissue prototype dictionary building pipeline and propose to use the obtained prototypes to guide histopathology image segmentation. Particularly, taking advantage of self-supervised contrastive learning, an encoder is trained to project the unlabeled histopathology image patches into a discriminative embedding space where these patches are clustered to identify the tissue prototypes by efficient pathologists' visual examination. Then, the encoder is used to map the images into the embedding space and generate pixel-level pseudo tissue masks by querying the tissue prototype dictionary. Finally, the pseudo masks are used to train a segmentation network with dense supervision for better performance. Experiments on two public datasets demonstrate that our human-machine interactive tissue prototype learning method can achieve comparable segmentation performance as the fully-supervised baselines with less annotation burden and outperform other weakly-supervised methods. Codes will be available upon publication.Comment: IPMI2023 camera read

PHTrans: Parallelly Aggregating Global and Local Representations for Medical Image Segmentation

The success of Transformer in computer vision has attracted increasing attention in the medical imaging community. Especially for medical image segmentation, many excellent hybrid architectures based on convolutional neural networks (CNNs) and Transformer have been presented and achieve impressive performance. However, most of these methods, which embed modular Transformer into CNNs, struggle to reach their full potential. In this paper, we propose a novel hybrid architecture for medical image segmentation called PHTrans, which parallelly hybridizes Transformer and CNN in main building blocks to produce hierarchical representations from global and local features and adaptively aggregate them, aiming to fully exploit their strengths to obtain better segmentation performance. Specifically, PHTrans follows the U-shaped encoder-decoder design and introduces the parallel hybird module in deep stages, where convolution blocks and the modified 3D Swin Transformer learn local features and global dependencies separately, then a sequence-to-volume operation unifies the dimensions of the outputs to achieve feature aggregation. Extensive experimental results on both Multi-Atlas Labeling Beyond the Cranial Vault and Automated Cardiac Diagnosis Challeng datasets corroborate its effectiveness, consistently outperforming state-of-the-art methods. The code is available at: https://github.com/lseventeen/PHTrans.Comment: 10 pages, 3 figure

Earth’s changing global atmospheric energy cycle in response to climate change

The Lorenz energy cycle is widely used to investigate atmospheres and climates on planets. However, the long-term temporal variations of such an energy cycle have not yet been explored. Here we use three independent meteorological data sets from the modern satellite era, to examine the temporal characteristics of the Lorenz energy cycle of Earth’s global atmosphere in response to climate change. The total mechanical energy of the global atmosphere basically remains constant with time, but the global-average eddy energies show significant positive trends. The spatial investigations suggest that these positive trends are concentrated in the Southern Hemisphere. Significant positive trends are also found in the conversion, generation and dissipation rates of energies. The positive trends in the dissipation rates of kinetic energies suggest that the efficiency of the global atmosphere as a heat engine increased during the modern satellite era

ICG fluorescence imaging technology in laparoscopic liver resection for primary liver cancer: A meta-analysis

Objective: To study the value of ICG molecular fluorescence imaging in laparoscopic hepatectomy for PLC. Methods: CNKI, WD, VIP.com, PM, CL and WOS databases were selected to search for literature on precise and traditional hepatectomy for the treatment of PLC. Results: A total of 33 articles were used, including 3987 patients, 2102 in precision and 1885 in traditional. Meta showed that the operation time of precision was longer, while IBV, HS, PLFI, ALT, TBil, ALB, PCR, PROSIM, RMR and 1-year SR had advantages. Conclusion: Hepatectomy with the concept of PS is a safe and effective method of PLC that can reduce the amount of IB, reduce surgery, reduce PC and improve prognosis and quality of life

A systematic review of the effectiveness of online learning in higher education during the COVID-19 pandemic period

BackgroundThe effectiveness of online learning in higher education during the COVID-19 pandemic period is a debated topic but a systematic review on this topic is absent.MethodsThe present study implemented a systematic review of 25 selected articles to comprehensively evaluate online learning effectiveness during the pandemic period and identify factors that influence such effectiveness.ResultsIt was concluded that past studies failed to achieve a consensus over online learning effectiveness and research results are largely by how learning effectiveness was assessed, e.g., self-reported online learning effectiveness, longitudinal comparison, and RCT. Meanwhile, a set of factors that positively or negatively influence the effectiveness of online learning were identified, including infrastructure factors, instructional factors, the lack of social interaction, negative emotions, flexibility, and convenience.DiscussionAlthough it is debated over the effectiveness of online learning during the pandemic period, it is generally believed that the pandemic brings a lot of challenges and difficulties to higher education and these challenges and difficulties are more prominent in developing countries. In addition, this review critically assesses limitations in past research, develops pedagogical implications, and proposes recommendations for future research