Abstract: Systems adapted to denoise HTML components of a website application and execute a task, using a trained generative artificial intelligence (AI) service comprising at least one trained multimodal machine learning model and at least one trained large language model (LLM), include receiving a request associated with executing the task using the website application, wherein executing the task comprises executing a series of steps; wherein executing the series of steps comprises identifying and using one or more target website application elements corresponding with the series of steps; processing, via the model, the website application to identify a plurality of HTML components of the website application; generating, via the model, a website application structure using the identified plurality of HTML components; and executing the task using the generated website application structure to execute the series of steps by identifying and using the one or more target website application elements.

Abstract: The present disclosure relates to methods of slowing cognitive decline in mild-to-moderate and moderate Alzheimer's disease, and other Tau pathologies, using anti-Tau antibodies. The disclosure provides first-in-class immunotherapy for use in reducing clinical decline in mild-to-moderate AD and moderate AD, in particular, significantly reducing rate of decline in cognitive capacity to a clinically meaningful extent, and significantly retaining memory. The disclosure also relates to use of anti-Tau antibodies to intercept cell-to-cell spread of pathological Tau in the brains of patients with AD or related Tauopathies.

Abstract: A computer implemented method of performing single pass optical character recognition (OCR) including at least one fully convolutional neural network (FCN) engine including at least one processor and at least one memory, the at least one memory including instructions that, when executed by the at least processor, cause the FCN engine to perform a plurality of steps. The steps include preprocessing an input image, extracting image features from the input image, determining at least one optical character recognition feature, building word boxes using the at least one optical character recognition feature, determining each character within each word box based on character predictions and transmitting for display each word box including its predicted corresponding characters.

Abstract: A computer implemented method of performing single pass optical character recognition (OCR) including at least one fully convolutional neural network (FCN) engine including at least one processor and at least one memory, the at least one memory including instructions that, when executed by the at least processor, cause the FCN engine to perform a plurality of steps. The steps include preprocessing an input image, extracting image features from the input image, determining at least one optical character recognition feature, building word boxes using the at least one optical character recognition feature, determining each character within each word box based on character predictions and transmitting for display each word box including its predicted corresponding characters.

Abstract: A computer implemented method of performing single pass optical character recognition (OCR) including at least one fully convolutional neural network (FCN) engine including at least one processor and at least one memory, the at least one memory including instructions that, when executed by the at least processor, cause the FCN engine to perform a plurality of steps. The steps include preprocessing an input image, extracting image features from the input image, determining at least one optical character recognition feature, building word boxes using the at least one optical character recognition feature, determining each character within each word box based on character predictions and transmitting for display each word box including its predicted corresponding characters.