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   Udaya Sathiyamoorthy; Tiago Cogumbreiro (January 2023, International Symposium on Trends in Functional Programming)

   We present a technique to stress-test the correctness of static analysis tools for CUDA programs, involving code generation and fixed point analysis. Our method revolves around a family of behavioral types called memory access protocols (MAPs), an abstraction used by Faial to determine whether CUDA programs are data-race free. In this paper, we introduce a code generation technique to represent MAPs in a form comprehensible to static analyzers (CUDA code). We use fixed point analysis to detect consistency errors in how programs are represented. We perform white-box testing with Faial, a tool we are already familiar with, to simultaneously fix bugs and facilitate further testing. 

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2. An extended Galerkin analysis for elliptic problems

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   Hong, Qingguo; Wu, Shuonan; Xu, Jinchao (January 2021, Science China Mathematics)

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3. Deep Extended Hazard Models for Survival Analysis

   Qixian Zhong; Jonas W. Mueller, Jane-Ling Wang (December 2021, Advances in neural information processing systems)

   Unlike standard prediction tasks, survival analysis requires modeling right censored data, which must be treated with care. While deep neural networks excel in traditional supervised learning, it remains unclear how to best utilize these models in survival analysis. A key question asks which data-generating assumptions of traditional survival models should be retained and which should be made more flexible via the function-approximating capabilities of neural networks. Rather than estimating the survival function targeted by most existing methods, we introduce a Deep Extended Hazard (DeepEH) model to provide a flexible and general framework for deep survival analysis. The extended hazard model includes the conventional Cox proportional hazards and accelerated failure time models as special cases, so DeepEH subsumes the popular Deep Cox proportional hazard (DeepSurv) and Deep Accelerated Failure Time (DeepAFT) models. We additionally provide theoretical support for the proposed DeepEH model by establishing consistency and convergence rate of the survival function estimator, which underscore the attractive feature that deep learning is able to detect low-dimensional structure of data in high-dimensional space. Numerical experiments also provide evidence that the proposed methods outperform existing statistical and deep learning approaches to survival analysis. 

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4. Analysis of a High-Dimensional Extended B92 Protocol

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5. Finite Key Analysis of the Extended B92 Protocol

   https://doi.org/10.1109/ISIT44484.2020.9174018  

   Amer, Omar; Krawec, Walter O. (June 2020, 2020 IEEE International Symposium on Information Theory (ISIT))

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