Training a deep neural model is an optimization task. By considering a deep learning model as a functionf(X;θ), whereXis the model input, andθis the set of learnable parameters, you can optimizeθso that it minimizes some loss value based on the training data. For example, optimize...
MMEJ-assisted gene knock-in using TALENs and CRISPR–Cas9 with the PITCh systems. Nat. Protoc. 11, 118–133 (2016). Article CAS PubMed Google Scholar Dorfer, V. et al. MS Amanda, a universal identification algorithm optimized for high accuracy tandem mass spectra. J. Proteome Res. 13,...
case of virtual equivalence between the overall perinasal signal E(x,i) and its negative affect portion EN(x,i), we used only E(x,i) in the main distress analysis described in the Results - Main Analysis section of the article; we also prefer to use the term stress instead of ...
In recent years, the advancement of sequencing techniques has enabled researchers to investigate and understand the involvement of microbes and metabolites in the development of colonic tumorigenesis. Previous studies have identified some site-specific bacteria [14,15,16] and distinct host genetic characte...
Branching (i.e.ifstatements for you non-assembly people) can be a real performance killer. Only do branching if you need to for the correctness of an algorithm, or to save time on an expensive computation (e.g. checking a cache). ...
To do so and to help in identifying new situations of importance or refine existing ones, we propose an approach that uses data-driven approaches and post-hoc explainability methods, in particular SHapley Additive exPlanations (SHAP) algorithm. The first results are shown and discussed over two ...
algorithm Kim et al. (2013) http://bowtie-bio.sourceforge.net/index.shtml; RRID: SCR_005476 SAMtools version 0.1.19. Li et al. (2009a) http://samtools.sourceforge.net/; RRID: SCR_002105 R Project for Statistical Computing The R Foundation http://www.r-project.org/; RRID: SCR_...
Explain the autocorrelation function and the partial autocorrelation function for an A.R.M.A process. Correlation: It shows the relationship between two variables based on the occurrence of a pattern of results simultaneously. ...
For most tasks, you can control the training algorithm details using the trainingOptions and trainnet functions. If the trainingOptions function does not provide the options you need for your task (for example, a custom solver), then you can define your own custom training loop. Define ...
In the present study we quantify stress by measuring transient perspiratory responses on the perinasal area through thermal imaging. These responses prove to be sympathetically driven and hence, a likely indicator of stress processes in the brain. Armed with the unobtrusive measurement methodology we...