2) (Fig. 2d). A recent study14 showed that scRNA-seq alone was not capable of separating certain cell types and states in hPBMCs without additional single-cell protein measurements. In a dataset with 200,000 hPBMC transcriptomes generated with 10x Genomics, 228 protein markers (CITE-seq) ...
We use two scanned datasets: 2D-manifold Hand dataset (triangle meshes with 778 vertices and 1538 faces) (Romero et al. 2017) and 3D-manifold Asiadragon dataset (tet meshes with 959 vertices and 10364 faces) (Stanford 2021). For Hand dataset, they are divided into 1554 models for train ...
On the validation dataset of Brats2018, our model achieves dice coefficients of 0.716, 0.761, and 0.874 on the corresponding channels, respectively. Conclusion We propose a new segmentation model that combines the advantages of Vision Transformer and Convolution and achieves a better balance between ...
Johnson and Hebert [17] proposed the SI descriptor, which was one of the earliest 3D local feature descriptors. This descriptor defines the normal of the local surface as a reference axis and spins a 2D plane around the axis, which is then divided into several bins. The number of points f...
(where H = A, C, or T). In order to find out whether different methylated sequential patterns are correlated with each other, we constructed extra CHG and CHH datasets for the three cell lines, respectively. It is worth noting that the number of sequences in CpG dataset is far more ...
Specifically, pre-training a speech or text encoder with a large speech or text corpus, and using the attention mechanism to map the correspondence, then a fine-tuning model with AD or MCI dataset is a general method to build a framework to train the AD classification from scratch. The ...
Assuming 2019 prices, amortisation over 3 years, and full utilisation of the hardware, the capital cost of the GPU-server hardware required to do this represents a few tens of dollars to invert a 3D transcranial dataset on a 500-μm grid. So, although the computational burden of FWI is ...
We choose six previous methods designed for multi-dimensional data integration: concatenation, COCA, iCluster, intNMF, SNF and ANF. The concatenation method is a commonly used method that is simple and has a low computational cost. For each sample, the concatenation method assembles the multiple ...
In the first stage of downsampling, different initial points were selected to ensure sample dataset in the RANSAC, and different point cloud samples were generated through different initial sampling point sequences. Finally, a new enhanced dataset was obtained, including 1260 training sets, 360 test...
Due to the license issue, we are not able to release FFHQ-F dataset that we used to train the model.test_data_imgandtest_data_segare just an example for showing the dataset struture. For the camera pose convention, please refer toEG3D. ...