In each sample, the gene expression vector of each archetype is determined by the sample-level variables (plus an additional variance that is not explained by sample characteristics), resulting in a ground-truth DE vector for each archetype and, by extension, any given cell state. Finally, ...
The PLS model tuning and ensuing classification performance depend on selecting an appropriate number of latent components, as supported by our data at hand. As each subsequently extracted latent component captures a complementary pattern in the transcriptome profiles, not already explained by a previous...
The initial StyleGAN model introduced the idea of an intermediate latent spaceWWwhich is created by passing the latent vectors in the original latent spaceZZthrough a learned mapping network. The reason for transforming the original vectorsz∈Zz∈Zinto the intermediate representationsw∈Ww∈Wis to ac...
the same time equipping diffusion models with the low-dimensional VAE inferred latent code which can be used for downstream tasks like controllable synthesis and image attribute manipulation. In short, DiffuseVAE presents a generative model which combines the benefits of both VAEs and Diffusion models...
Unless otherwise explained, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It is further to be understood that all base sizes or amino acid sizes, and all molecular weight or mole...
Accordingly, an experimental analysis was performed that explained the dependence of diffusion length on the photoreceptor formulation. The experimental result showed the validity of the principle in the proposed model for all OPC formulations tested, and the existence of a significant dependence of ...
First, the data collection and pre-processing are explained in Subsection 2.2. In Section 2.3, we elaborate on the use of NLP and text mining to uncover the latent characteristics underlying the reviews. Finally, a statistical analysis is presented in Section 2.4. One of our goals in ...
This section is structured as follows: the DMD technique is first explained followed by the resulting state space formulation of the load system. Important practical details are then discussed including: i) the choice of the observables (i.e., state variables) of the wave load system, ii) th...
Altogether, these data demonstrate that HeLa cells latently infected with HIV-1 display rare bursts of viral transcription, which are best explained by stochastic pausing. Fig. 8: Bursting of the HIV-1 promoter in latently infected HeLa cells. A Schematic of the HIV-1 reporter construct used ...
After prompt building, Stable Diffusion (c), an open-source latent diffusion model (LDM), is trained using either of the prompt-building approaches from (b). Stable Diffusion is based on a variational autoencoder (VAE) and a UNet. The VAE uses its encoder (E) to reduce the dimensionality...