Abstract: A spark spectrometry for inclusions content distribution on the surface of large size metallic materials, comprising the following steps: analyzing the surface of large-size metallic materials by spark discharge continuous excitation scanning, obtaining a mixture intensity distribution data of the solid solution and inclusions of an element on the surface of the large-size metallic materials; the relative frequency distribution diagram of spectral intensity is subjected to peak fitting of normal distribution and Gumbel distribution, obtaining an extreme value distribution data of spectral intensity of the inclusions; a size information of the inclusions in a small sample and that of the largest inclusions are correlated with the spectral intensity distribution data of inclusions, obtaining a result of content distribution of the inclusions on the surface of the large size metallic materials.

Abstract: Disclosed herein are systems (e.g., moving bed redox systems) and methods for supplying thermal energy to an endothermic chemical process.

Abstract: A circuit arrangement includes an array of switches that represent a Boolean satisfiability expression that has a plurality of clauses each defined by a combination of Boolean variables Xi or ¬Xi, a first plane, and a constraints network operatively arranged with the first plane. The constraints network enforces each of the clauses such that values of different ones of the variables continue to randomly or pseudo randomly flip until the values of the variables Xi and ¬Xi stop changing or a predetermined condition occurs.

Abstract: A measurement deviation correction method and system for spark discharge analysis of a large-size metal material includes: performing line-by-line scanning measurement on a surface of the material to obtain a line component content, performing in-line correction on the line component content by adopting a line linear fitting model, and performing inter-line correction by taking a total average value obtained by eliminating an extreme value from a line component content distribution to obtain an inter-line corrected component content; performing column-by-column scanning measurement on the surface of the material to obtain a column component content, performing in-column correction on the column component content by adopting a column linear fitting model, and performing inter-column correction by taking a total average value obtained by eliminating an extreme value from the column component content; and coupling the inter-line corrected component content and the inter-column corrected component content to obta

Abstract: In one embodiment, a method includes accessing a machine-learning model configured to generate an encoding for an utterance by using a module to process data associated with each segment of the utterance in a series of iterations, performing operations associated with an i-th segment during an n-th iteration by the module, which include receiving an input comprising input contextual embeddings generated for the i-th segment in a preceding iteration and a memory bank storing memory vectors generated in the preceding iteration for segments preceding the i-th segment, generating attention outputs and a memory vector based on keys, values, and queries generated using the input, and generating output contextual embeddings for the i-th segment based on the attention outputs, providing the memory vector to the module for performing operations associated with the i-th segment in a next iteration, and performing speech recognition by decoding the encoding of the utterance.