Abstract: A display substrate, a manufacturing method therefor, and a display device are provided. The display substrate includes a base substrate, sub-pixels and an isolation portion, and each sub-pixel includes a light-emitting function layer. The isolation portion includes a first sub-isolation portion and a second sub-isolation portion. The first sub-isolation portion is between the second sub-isolation portion and the base substrate. The second sub-isolation portion includes a protrusion protruding relative to an edge of the first sub-isolation portion, the second sub-isolation portion facing one of adjacent sub-pixels is different in shape from the second sub-isolation portion facing the other one; or, a slope angle between a side surface of the first sub-isolation portion or the second sub-isolation portion facing one sub-pixel and a plane parallel to the base substrate is 60° to 120°. At least one film of the light-emitting function layer is disconnected at the isolation portion.

Abstract: Systems and methods for customer journey optimization in email marketing are described. The systems and methods may identify a plurality of messages for a first time period, wherein the plurality of messages are categorized according to a plurality of messages types, identify user information for a customer, wherein the user information includes user interaction data, determine a message type from the plurality of message types for the first time period based on the user information, wherein the message type is determined using a decision making model comprising a deep Q-learning neural network, select a message from the plurality of messages based on the determined message type, and transmit the message to the customer during the first time period based on the selection.

Abstract: Computer vision systems and methods for acceleration of high-resolution mobile deep vision with content-aware parallel offloading are provided. The system allows for acceleration of deep vision applications running on mobile devices via offloading of machine learning computations on edge cloud infrastructure, and speeds up computation via parallel processing on the edge cloud, employing one or more of the following processes: recurrent region proposal prediction, region proposal centric frame partitioning, and resource-aware offloading to multiple processors. Mobile deep vision applications can utilize the systems and methods disclosed herein to accelerate application processing. The system offloads computation to multiple servers to minimize the end-to-end latency, and includes a high-resolution mobile deep vision task acceleration system that offloads computation to multiple (edge located) servers.