This work improves existing noise sampling techniques for training rectified flow models by biasing them towards perceptually relevant scales and presents a novel transformer-based architecture for text-to-image generation that uses separate weights for the two modalities and enables a bidirectional flow of information between image and text tokens.
A framework to be followed during preclinical investigation of nanomedicines to increase their translatability potential is proposed and will help accelerate the clinical translation and maximize the impact of nanomedicines.
Results, where the intrinsic error suppression of the bosonic encodings enables us to use a hardware-efficient outer error-correcting code, indicate that concatenated bosonic codes can be a compelling model for reaching fault-tolerant quantum computation.
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