Abstract: A novel AI-driven drug-repurposing method, DeepDrug, is used to identify a lead combination of previously FDA-approved drugs to treat AD by targeting the upstream genetic markers along the AD pathology. A three-step methodology is used. First, a heterogeneous biomedical graph is constructed comprising complex and interconnected genes, proteins, and drug information to capture the network characteristics of the AD pathology, considering the expert known associations between different AD pathways and utilizing node weighting and edge weighting and direction. Second, the curated graph is taken as an input to an artificial intelligence (AI)-driven graphical neural network (GNN) framework, with embeddings of drug and gene nodes as the outputs. Third, a drug scoring and selection analysis is conduced to generate the drug-gene scores and identify a lead combination of repurposed AD drug candidates for clinical verification.

Abstract: In order to maintain a small, stable backlog at an internet router buffer, active queue management (AQM) algorithms drop packets probabilistically at the onset of congestion. This causes Transmission Control Protocol (TCP) data flow rates to reduce. However, wireless losses may be misinterpreted as congestive losses and induce spurious reductions in flow rates. A prior art AQM with random early detection (RED), fails to maintain a stable backlog under time-varying wireless losses. However, the present invention can resolve the problem and provide a robust tracking of the backlog to a preset reference level by applying a control-theoretic vehicle, Internal Model principle, to realize such tracking. An integral controller (IC) is used as an embodiment of the principle that is robust against time-varying wireless losses under various network scenarios.

Abstract: In order to maintain a small, stable backlog at an internet router buffer, active queue management (AQM) algorithms drop packets probabilistically at the onset of congestion. This causes Transmission Control Protocol (TCP) data flow rates to reduce. However, wireless losses may be misinterpreted as congestive losses and induce spurious reductions in flow rates. A prior art AQM with random early detection (RED), fails to maintain a stable backlog under time-varying wireless losses. However, the present invention can resolve the problem and provide a robust tracking of the backlog to a preset reference level by applying a control-theoretic vehicle, Internal Model principle, to realize such tracking. An integral controller (IC) is used as an embodiment of the principle that is robust against time-varying wireless losses under various network scenarios.

Abstract: A data distribution system and a protocol for interactive information services over a network and in particular for video-on-demand applications. A shared buffer and a split-and-merge protocol are implemented to allow multiple users to share the same video stream multicasted over a network with true VOD services including the ability to access any available video at any time, and to perform any VCR-like functions and random access. Video requests for a video program are served in batches. A user interaction is first handled by splitting off the interactive user from a shared video stream in a batch, and by subsequently assigning the user a new dedicated video stream. The interactive user is subsequently merged back to one of the batching video streams shared by multiple users upon resuming normal play mode.