Partial Vessels Annotation-based Coronary Artery Segmentation with Self-training and Prototype Learning

Coronary artery segmentation on coronary-computed tomography angiography (CCTA) images is crucial for clinical use. Due to the expertise-required and labor-intensive annotation process, there is a growing demand for the relevant label-efficient learning algorithms. To this end, we propose partial vessels annotation (PVA) based on the challenges of coronary artery segmentation and clinical diagnostic characteristics. Further, we propose a progressive weakly supervised learning framework to achieve accurate segmentation under PVA. First, our proposed framework learns the local features of vessels to propagate the knowledge to unlabeled regions. Subsequently, it learns the global structure by utilizing the propagated knowledge, and corrects the errors introduced in the propagation process. Finally, it leverages the similarity between feature embeddings and the feature prototype to enhance testing outputs. Experiments on clinical data reveals that our proposed framework outperforms the competing methods under PVA (24.29% vessels), and achieves comparable performance in trunk continuity with the baseline model using full annotation (100% vessels).Comment: Accepted at MICCAI 202

Tone Acquisition of Mandarin Chinese by Italian Adult Learners

This thesis inspected the result of perception experiment and acoustic performance made by Italian students when learning Mandarin. Under the detailed analysis, we tend to master the status quo of the tonal pattern of Italian Mandarin learners with different language levels. The thesis could be generally divided into three parts. In first part, general literature reviews on the history and status quo of experimental study of Mandarin Chinese tones, tonal coarticulation, L2 tonal acquisition, and the bias of tonal acquisition have been elaborated. In second part, we concentrated on introducing the main task, design and methodology of both perception and production experiment. While in third part, statistical analysis of two experiments has been carried out. In perception part, for monosyllabic and disyllabic tones, the overall trend is that the higher level the subject belongs to, the higher accuracy will be shown. But for disyllabic tones in context, this trend has been broken, that the advanced level manifested worse than intermediate level. In terms of production experiment, for monosyllabic part based on different-group discussion, we could conclude that subjects belong to higher level manifest better than those to lower level. But for disyllabic tones pronounced independently and in sentential circumstance, the situation becomes extremely complex. A unified conclusion could not have been easily achieved as we do in monosyllabic part

Visible and Near Infrared Image Fusion Based on Texture Information

Multi-sensor fusion is widely used in the environment perception system of the autonomous vehicle. It solves the interference caused by environmental changes and makes the whole driving system safer and more reliable. In this paper, a novel visible and near-infrared fusion method based on texture information is proposed to enhance unstructured environmental images. It aims at the problems of artifact, information loss and noise in traditional visible and near infrared image fusion methods. Firstly, the structure information of the visible image (RGB) and the near infrared image (NIR) after texture removal is obtained by relative total variation (RTV) calculation as the base layer of the fused image; secondly, a Bayesian classification model is established to calculate the noise weight and the noise information and the noise information in the visible image is adaptively filtered by joint bilateral filter; finally, the fused image is acquired by color space conversion. The experimental results demonstrate that the proposed algorithm can preserve the spectral characteristics and the unique information of visible and near-infrared images without artifacts and color distortion, and has good robustness as well as preserving the unique texture.Comment: 10 pages,11 figure

Dynamic Snake Convolution based on Topological Geometric Constraints for Tubular Structure Segmentation

Accurate segmentation of topological tubular structures, such as blood vessels and roads, is crucial in various fields, ensuring accuracy and efficiency in downstream tasks. However, many factors complicate the task, including thin local structures and variable global morphologies. In this work, we note the specificity of tubular structures and use this knowledge to guide our DSCNet to simultaneously enhance perception in three stages: feature extraction, feature fusion, and loss constraint. First, we propose a dynamic snake convolution to accurately capture the features of tubular structures by adaptively focusing on slender and tortuous local structures. Subsequently, we propose a multi-view feature fusion strategy to complement the attention to features from multiple perspectives during feature fusion, ensuring the retention of important information from different global morphologies. Finally, a continuity constraint loss function, based on persistent homology, is proposed to constrain the topological continuity of the segmentation better. Experiments on 2D and 3D datasets show that our DSCNet provides better accuracy and continuity on the tubular structure segmentation task compared with several methods. Our codes will be publicly available.Comment: Accepted by ICCV 202

Wetting layer evolution and its temperature dependence during self assembly of InAs/GaAs quantum dots

For InAs/GaAs(001) quantum dot (QD) system, the wetting layer (WL) evolution and its temperature dependence were studied using reflectance difference spectroscopy (RDS) and analyzed with a rate equation model. The WL thicknesses showed a monotonic increase at relatively low growth temperatures but a first increase and then decrease at higher temperatures, which were unexpected from the thermodynamic understanding. By adopting a rate equation model, the temperature dependence of QD growth was assigned as the origin of different WL evolutions. A brief discussion on the indium desorption was also given. Those results gave hints of the kinetic aspects of QD self-assembly.Comment: 13 pages, 3 figure

NCAGC: A Neighborhood Contrast Framework for Attributed Graph Clustering

Attributed graph clustering is one of the most fundamental tasks among graph learning field, the goal of which is to group nodes with similar representations into the same cluster without human annotations. Recent studies based on graph contrastive learning method have achieved remarkable results when exploit graph-structured data. However, most existing methods 1) do not directly address the clustering task, since the representation learning and clustering process are separated; 2) depend too much on data augmentation, which greatly limits the capability of contrastive learning; 3) ignore the contrastive message for clustering tasks, which adversely degenerate the clustering results. In this paper, we propose a Neighborhood Contrast Framework for Attributed Graph Clustering, namely NCAGC, seeking for conquering the aforementioned limitations. Specifically, by leveraging the Neighborhood Contrast Module, the representation of neighbor nodes will be 'push closer' and become clustering-oriented with the neighborhood contrast loss. Moreover, a Contrastive Self-Expression Module is built by minimizing the node representation before and after the self-expression layer to constraint the learning of self-expression matrix. All the modules of NCAGC are optimized in a unified framework, so the learned node representation contains clustering-oriented messages. Extensive experimental results on four attributed graph datasets demonstrate the promising performance of NCAGC compared with 16 state-of-the-art clustering methods. The code is available at https://github.com/wangtong627/NCAGC

Response of reinforced mortar‑less interlocking brick wall under seismic loading

Mortar-less construction with interlocking bricks has many advantages, such as improved construction efficiency and relatively low requirements on labour skills. Nevertheless, the seismic performance of interlocking brick structures is not well understood yet. In this paper, laboratory tests and numerical modelling are carried out to investigate the seismic behaviour of interlocking brick walls. Laboratory shaking table tests are performed on a scaled reinforced mortar-less interlocking brick wall. The response and damage modes under in-plane seismic loading are investigated. A detailed numerical model is then generated and validated with the laboratory testing data. Unlike the conventional masonry wall that diagonal shear damage governs the failure, the interlocking brick wall exhibits rocking responses, whose damage is mainly at the two bottom corners of the wall. Full-scale interlocking brick walls are then modelled and compared with conventional concrete masonry unit (CMU) walls bonded by mortar. Comparisons are made between the seismic resistances and damage modes of the two walls. The influences of ground motion intensities, vertical components of seismic excitations and different seismic time histories on the seismic behaviour of the interlocking brick wall are examined. It is found that the interlocking brick wall has a higher seismic resistance capacity than the conventional CMU wall. Inter-brick friction is the main energy dissipation mechanism in the interlocking brick wall. Because of the rocking response, vertical component of the ground motion significantly influences the damage of interlocking brick wall. The interlocking brick wall is insensitive to velocity pulses of ground motions due to its relatively high natural frequency