Abstract: Various aspects of methods, systems, and use cases include techniques for training or using a model to control a robot. A method may include identifying a set of action primitives applicable to a set of robots, receiving information corresponding to a task (e.g., a collaborative task), and determining at least one action primitive based on the received information. The method may include training a model to control operations of at least one robot of the set of robots using the received information and the at least one action primitive.

Abstract: Described herein are compounds and methods for the treatment of coronavirus infection. The compounds can function as an inhibitor of the main protease (Mpro) of coronaviruses. The compounds can include diphenylmethyl piperazine derivatives, diphenylmethyl piperidine derivatives, diphenylmethylidene piperidine derivatives, tricyclo[9.4.0.03,8]pentadeca-1(11),3(8),4,6,12,14-hexaen derivatives, tricyclo[9.4.0.03,8]pentadeca-1(11),3(8),4,6,9,12,14-heptaen derivatives, 6,11-dihydrobenzo[c][1]benzoxepin derivatives, 6,11-dihydrobenzo[c][1]benzothiepin derivatives, 5,5-dioxo-6,11-dihydrobenzo[c][1]benzothiepin derivatives, and 6-oxo-5,11-dihydrobenzo[c][1]benzazepin derivatives, as well as pharmaceutically acceptable salts, hydrates, and prodrugs thereof.

Abstract: Described herein are compounds, and methods for the treatment of coronavirus infection.

Abstract: Systems and methods for generating synthetic data using federated, collaborative, privacy preserving learning models are disclosed. In one embodiment, a method for generating synthetic data from real data for use in a federated learning network may include: (1) conducting, by a backend for a first institution of a plurality of institutions in a federated learning network, a transaction comprising transaction data; (2) generating, by the backend for the first institution, local synthetic data for the transaction data using a local synthetic data generating model; (3) sharing, by the backend for the first institution, the local synthetic data with a plurality of backends for other institutions; (4) receiving, by the backend for the first institution, global synthetic data from the plurality of backends for the other institutions; and (5) training, by the backend for the first institution, a local machine learning model with the global synthetic data.