The leftmost data point was computed by pooling P values from the three individual effects (Fisher’s combination test98). Data are shown as mean ± s.e.; *P < 0.05; **P < 0.01; ***P < 0.001; one-tailed binomial test. j, Model comparison. Data are shown in the...
Assigning a set of UDIs to each library in the sequencing pool helps ensure that index 1 and index 2 sequences be designated only once during sample pooling prior to loading of the sequencer. Minimizing the amount of free, un...
resulting in the loss of fine details in the image. Since multiple pixels are combined into one, the pooled feature map cannot accurately preserve subtle variations and edge information present in the original image. Moreover, average pooling is a spatially invariant operation, meaning that regardle...
Characteristic of an expression or variable that can only have a value of true or false. boot BOM A set of files that define the system types, such as string or number, for the Business Action Language (BAL). boot image An image containing the kernel, file systems, libraries, and program...
(LGPL-2.1-only) FlexyPool - Brings metrics and failover strategies to the most common connection pooling solutions. Flyway - Simple database migration tool. H2 - Small SQL database notable for its in-memory functionality. HikariCP - High-performance JDBC connection pool. HSQLDB - HyperSQL 100...
A method that allows a client application, upon the loss of communication with a database server and the predefinition of an alternative server, to continue working with the original database server or the alternative server with only minimal interruption of the work. client/server Pertaining to ...
The distribution of nonzeros in the pooling matrix is equivalent to the design of pooling tests. Importantly, meaningful constructions of M enable one to exactly identify up to a certain number of positive samples using only the matrix and the observed values in w. Two properties that ensure ...
Going through the convolution and pooling layers in a CNN is basically an act of reduction, to filter the information contained in the image to its most important and salient constituents, which in turn form the bottleneck features. Training the CNN molds these values in such a way that items...
Within the convolutional layers, multi-dimensional feature arrays are iteratively updated by convolution (i.e., feature mapping) to extract high-level patterns and by pooling for feature subsampling. The 2D array is flattened into a vector, which can be fed into a fully connected network with k...
First, we introduce a deep learning based algorithm for multi-class semantic segmentation addressing fourteen different tissue types in whole-slide images of colorectal cancer, including not only the primary cancer-associated epithelial and stroma classes but also some other more peripheral tissue types....