Abstract: In certain aspects, a pattern generation system includes a pattern generator, a memory, a pin function register, a pin function mapper, and a set of source selectors. The pattern generator generates a plurality of source patterns. The memory stores a lookup table set. The lookup table set describes a mapping relationship between the plurality of source patterns and a set of test channels, and is indexed based on a pin function index. The pin function register stores a value of the pin function index. The pin function mapper executes a pin-mapping operation to generate a set of source selection signals based on the value of the pin function index and the lookup table set. Each source selector selects and outputs a source signal from the plurality of source patterns to a corresponding test channel based on a corresponding source selection signal received from the pin function mapper.

Abstract: The present disclosure discloses an Internet-of-Things-oriented machine learning container image download system and a method. The Internet-of-Things-oriented machine learning container image download system includes a master node and a plurality of computing nodes; the master node is configured to store and convert a machine learning model, and build a machine learning container image from the format-converted machine learning model; and issue an image download instruction to each of the computing nodes after image information of the machine learning container image is completely built; and each of the computing nodes is configured to receive the image download instruction, download the machine learning container image, and start a machine learning container; and receive data collected by Internet-of-Things devices, and return a data processing result to the Internet-of-Things devices.

Abstract: A method for creating a realistic agent-based simulation of an over-the-counter market and using data from the real market to calibrate the simulator is provided. The method includes: assigning, to each of a group of agents, a type value that relates to a state of the respective agent; receiving agent-specific data that relates to market-based observations, market-based actions, and market-based rewards; generating a market simulation based on the assigned type values and the received agent-specific data; and calibrating the market simulation based on actual market data.