Abstract: The present disclosure relates to prosthesis systems having trays and liners having an asymmetrical locking mechanism to bias the strength of the liner to resist loading forces and associated methods. The tray has a lateral groove disposed in an inner surface of a lateral circumferential portion of the tray and a medial groove disposed in an inner surface of a medial circumferential portion of the tray. The liner has an upper segment and a lower segment. The liner has a locking portion for lockingly engaging the tray that includes the lower segment. The locking portion has a lateral toe positioned generally diametrically opposite a plurality of resiliently deformable medial fingers defined therein. The liner and the tray are engageable in a lateral-to-medial direction so that the plurality of medial fingers can resiliently deform to engage the medial groove subsequent to engagement of the lateral toe within the lateral groove.

Abstract: A great deal of time and computational resources may be used when developing a machine learning or other data processing workflow. This can be related to the need to re-compute the workflow in response to adjustments to the workflow parameters, in order to assess the benefit of such adjustments so as to develop a workflow that satisfies accuracy or other constraints. Embodiments herein provide time and computational savings by selectively storing and re-loading intermediate results of steps of a data processing workflow. For each step of the workflow, during execution, a decision is made whether to store the intermediate results of the step. Thus, these embodiments can offer storage savings as well as processing speedups when repeatedly re-executing machine learning or other data processing workflows during workflow development.

Abstract: A great deal of time and computational resources may be used when developing a machine learning or other data processing workflow. This can be related to the need to re-compute the workflow in response to adjustments to the workflow parameters, in order to assess the benefit of such adjustments so as to develop a workflow that satisfies accuracy or other constraints. Embodiments herein provide time and computational savings by selectively storing and re-loading intermediate results of steps of a data processing workflow. For each step of the workflow, during execution, a decision is made whether to store the intermediate results of the step. Thus, these embodiments can offer storage savings as well as processing speedups when repeatedly re-executing machine learning or other data processing workflows during workflow development.