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A new version of the US National Science Foundation National Center forAtmospheric Research (NSF NCAR) thermosphere-ionosphere-electrodynamicsgeneral circulation model (TIEGCM) has been developed and released. Thispaper describes the changes and improvements of the new version 3.0since its last major release (2.0) in 2016. These include: 1) increasingthe model resolution in both the horizontal and vertical dimensions, aswell as the ionospheric dynamo solver; 2) upward extension of the modelupper boundary to enable more accurate simulations of the topsideionosphere and neutral density in the lower exosphere; 3) improvedparameterization for thermal electron heating rate; 4) resolvingtransport of minor species N(2D); 5) treating helium as a major species;6) parameterization for additional physical processes, such as SAPS andelectrojet turbulent heating; 7) including parallel ion drag in theneutral momentum equation; 8) nudging of prognostic fields near thelower boundary from external data; 9) modification to the NO reactionrate and auroral heating rate; 10) outputs of diagnostic analysis termsof the equations; 11) new functionalities enabling model simulations ofcertain recurrent phenomena, such as solar flares and eclipse. Wepresent examples of the model validation during a moderate storm andcompare simulation results by turning on/off new functionalities todemonstrate the related new model capabilities. Furthermore, the modelis upgraded to comply with the new computer software environment at NSFNCAR for easy installation and run setup and with new visualizationtools. Finally, the model limitations and future development plans arediscussed. 

Embeddings, low-dimensional vector representation of objects, are fundamental in building modern machine learning systems. In industrial settings, there is usually an embedding team that trains an embedding model to solve intended tasks (e.g., product recommendation). The produced embeddings are then widely consumed by consumer teams to solve their unintended tasks (e.g., fraud detection). However, as the embedding model gets updated and retrained to improve performance on the intended task, the newly-generated embeddings are no longer compatible with the existing consumer models. This means that historical versions of the embeddings can never be retired or all consumer teams have to retrain their models to make them compatible with the latest version of the embeddings, both of which are extremely costly in practice. Here we study the problem of embedding version updates and their backward compatibility. We formalize the problem where the goal is for the embedding team to keep updating the embedding version, while the consumer teams do not have to retrain their models. We develop a solution based on learning backward compatible embeddings, which allows the embedding model version to be updated frequently, while also allowing the latest version of the embedding to be quickly transformed into any backward compatible historical version of it, so that consumer teams do not have to retrain their models. Our key idea is that whenever a new embedding model is trained, we learn it together with a light-weight backward compatibility transformation that aligns the new embedding to the previous version of it. Our learned backward transformations can then be composed to produce any historical version of embedding. Under our framework, we explore six methods and systematically evaluate them on a real-world recommender system application. We show that the best method, which we call BC-Aligner, maintains backward compatibility with existing unintended tasks even after multiple model version updates. Simultaneously, BC-Aligner achieves the intended task performance similar to the embedding model that is solely optimized for the intended task.