A common change to object-oriented software is to add a new type of data that is a subtype of some existing type in the program. However, due to message passing unchanged parts of the program may now call operations of the new type. To avoid reverification of unchanged code, such operations should have specifications that are related to the specifications of the appropriate operations in their supertypes. This paper presents a specification technique that uses inheritance of specifications to force the appropriate behavior on the subtype objects. This technique is simple, requires little effort by the specifier, and avoids reverification of unchanged code. We present two notions of such behavioral subtyping, one of which is new. We show how to use these techniques to specify examples in C++. Abstract A common change to object-oriented software is to add a new type of data that is a subtype of some existing type in the program. However, due to message passing unchanged parts of the program may n o w call operations of the new type. To a void reveri cation of unchanged code such operations should have speci cations that are related to the speci cations of the appropriate operations in their supertypes. This paper presents a speci cation technique that uses inheritance of speci cations to force the appropriate behavior on the subtype objects. This technique is simple, requires little e ort by the speci er, and avoids reveri cation of unchanged code. We present t wo notions of such behavioral subtyping, one of which is new. We show how to use these techniques to specify examples in C++.

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Purpose To investigate a method to identification of early progression of keratoconus using deep learning neural networks. Methods Retrospective evaluation of medical records of patients with progressive keratoconus and had more than one followup visits. Images extracted from the single scheimplug analyzer for analysis were captured during the patient visits. The baseline progression of keratoconus is detected by a change in flat or steep K of ≥1.0D which is labeled as keratometric progression (KP) and progression detected by image based deep learning convolutional neural network (CNN) models, is labeled as latent progression (LP). Patient data consisted of model data (385 eyes of 351patients) to train and test the learning models and prediction data (1331 eyes of 828 patients) to determine the LP based on the learning models. Results The LP prediction model was able to identify progression at a mean of 11.1 months earlier than KP (p < 0.001). LP prediction model was able to identify progression earlier than KP irrespective of age category, gender, the severity of keratoconus, presenting visual acuity, astigmatism, and spherical equivalent (P < 0.001). When compared to the first visit the corrected distance visual acuity was more stable in 71% of the eyes at LP prediction visit compared to 50% at KP visit (p < 0.001). Conclusion Through this study, we propose a possible solution to address the shortcomings noted in the current approaches of detecting progression relying only on KP. Avoiding bias towards feature selection from tomography images as done in the current study aids in identifying very subtle changes on the images between visits.

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