In this algorithm, multitasking optimization is mainly based on two strategies: firstly, a spatial feature enhancement (SFE) module is introduced to extract the location and spatial relationships of objects from the coarse prediction, which are then sent to the refined segmentation model for spatial ...
fast-turnover H3K4me3, slow-turnover H3K4me3, and persistent H3K4me3 (persisting in early 1-pachytene but excluding early-forming H3K4me3; Supplementary information, TableS5). A striking feature was that the average width around
Bottom: refined single-cell resolution expression levels of the corresponding marker genes by SpatialScope. e Visualization of some representative molecular interactions detected in the 3D aligned single-cell resolution spatially resolved transcriptomic data produced by SpatialScope. The scatter plot shows ...
An efficient duplicate image detection method based on Affine-SIFT feature In this paper, we present a novel scheme to tackle the task of near-duplicate image detection. Given two input images, the algorithm based on the refined s... Y Cao,H Zhang,Y Gao,... - IEEE International Conference...
Channel-Refined Feature 1x1 SRU CRU Conv ⨁ Next ConvBlock ResBlock + SCConv Figure 1. The architecture of SCConv integrated with Spatial Reconstruction Unit (SRU) and Channel Reconstruction Unit (CRU). This figure shows the exact position of our SCConv modul...
Thespatial localizationof an element in 3D space can be estimated by describing its position with reference to amorphologic feature, such as an enclosing surface. This information can then be organized into groups to determine the distribution of elements by computing the frequency of elements that ...
LGTCN: A Spatial–Temporal Traffic Flow Prediction Model Based on Local–Global Feature Fusion Temporal Convolutional Network High-precision traffic flow prediction facilitates intelligent traffic control and refined management decisions. Previous research has built a variety of e... J Li - 《Applied Sc...
et al. Refined and dynamic susceptibility assessment of landslides using InSAR and machine learning models. Geoscience Frontiers, 2024, 15(6): 101890. DOI:10.1016/j.gsf.2024.101890 7. Teke, A., Kavzoglu, T. Exploring the decision-making process of ensemble learning algorithms in landslide ...
Subsequently, these refined features are mapped to various potential action categories, yielding probability distributions for each category. Furthermore, both the ODE-TCN and GCN-TCN modules conduct feature extraction in parallel across different layers. Following feature fusion, these modules seamlessly ...
CARD can also impute cell-type compositions and gene expression levels at unmeasured tissue locations to enable the construction of a refined spatial tissue map with a resolution arbitrarily higher than that measured in the original study and can perform deconvolution without an scRNA-seq reference. ...