Deep learningSuper resolutionDNBSEQ employs a patterned array to facilitate massively parallel sequencing of DNA nanoballs (DNBs), leading to a considerable boost in throughput. By employing the ultra-high-density (UHD) array with an increased density of DNB binding sites, the throughput of DNBSEQ ...
where λ is the wavelength,Δϕis the phase shift of neutron grating interferometer,Δxis the displacement of the sample in the y-direction in Fig.1,tis the sample thickness in the x-direction,Nis the atomic density, andbcis the neutron scattering length. So, theαcaused by one side of...
The DPA package is the scikit-learn compatible implementation of the Density Peaks Advanced clustering algorithm. The algorithm provides robust and visual information about the clusters, their statistical reliability and their hierarchical organization. ...
In the deep trench, a layer of TiN, followed by a layer of high-k dielectric, followed by a second layer of TiN. The resulting capacitor is completely buried b... TW Dyer,EA Cartier,MP Chudzik,... - US 被引量: 21发表: 2009年 Development of fabrication techniques for high-density ...
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Specifically, data from the Multispectral Instrument (MSI, 10–20 m), Operational Land Imager (OLI, 30 m), WorldView-II (WV-2, 2 m), and PlanetScope/Dove (3 m) are used with a deep convolution neural network (DCNN) to extract Sargassum features and quantify Sargassum biomass density ...
traditional CPUs, in applications ranging from energy exploration todeep learning. NVIDIA’s accelerators also deliver the horsepower needed to run bigger simulations faster than ever before. Plus, NVIDIA GPUs deliver the highest performance and user density forvirtual desktops, applications, and work...
The failure probability of structures is defined as [1], [2]: Pf=Pg(X)≤0=∫g(X)≤0f(x)dxwhere g(X) is the structural limit state function, X is the vector of random variables and f(x) is its joint probability density function. The prevalent approach to estimating the structural ...
a CNN tower aimed at learning distributional features and a GNN tower that detects structural regularities. For an ordered pair (Xi, Xj), the CNN tower captures distributional information via a density estimate that traverses the tower to form an embedding. The GNN tower extracts a subgraph ...
Obtaining image representations that are highly correlated with protein localization and invariant to other sources of heterogeneity (that is, cell state, density and shape) is only the first step for biological interpretation. Indeed, while these representations are lower dimensional than the images the...