Abstract: Devices and techniques are generally described for three dimensional room modeling. In various examples, a three-dimensional (3D) room model comprising at least a first wall and a floor may be received. A first sphere sized and shaped such that the 3D room model fits within the first sphere may be determined and a virtual camera may be positioned on the first sphere at a first position. A first command may be received to move the virtual camera in a first direction. The virtual camera may be translated in the first direction along a surface of the first sphere to a second position. A view of the interior of the 3D room model may be displayed from a second viewpoint of the virtual camera at the second position.

Abstract: Provided herein are acyclic oxazepinyl compounds useful in the treatment on cancers.

Abstract: A method for training a probabilistic encoder-decoder having a latent space, the method including: extracting different types of medical data for a group of individuals; creating a data matrix X including the extracted medical data, wherein each row of the data matrix X includes data for one of the group of individuals; creating condition matrix C including features to define a clinical condition, wherein each row of the condition matrix C includes the condition data for one of the group of individuals; and training the encoder and the decoder to learn the latent space by minimizing the reconstruction loss and using a regularization effect to force clinically similar inputs to be close together in the latent space.

Abstract: A method for training a probabilistic encoder-decoder having a latent space, the method including: extracting different types of medical data for a group of individuals; creating a data matrix X including the extracted medical data, wherein each row of the data matrix X includes data for one of the group of individuals; creating condition matrix C including features to define a clinical condition, wherein each row of the condition matrix C includes the condition data for one of the group of individuals; and training the encoder and the decoder to learn the latent space by minimizing the reconstruction loss and using a regularization effect to force clinically similar inputs to be close together in the latent space.