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Award ID contains: 1934884

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  1. ; ; ; ; ( , ESEC/FSE'2023: The 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering)
    Free, publicly-accessible full text available December 3, 2024
  2. ; ; ( , ESEC/FSE'2023: The 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering)
    Free, publicly-accessible full text available December 3, 2024
  3. ; ; ; ( , 8th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023))
    Free, publicly-accessible full text available September 11, 2024
  4. ; ; ( , ICSE'23: The 45th International Conference on Software Engineering)
    Free, publicly-accessible full text available May 1, 2024
  5. ; ; ; ; ; ( , ICSE'23: The 45th International Conference on Software Engineering)
    Free, publicly-accessible full text available May 1, 2024
  6. ; ( , ICSE'23: The 45th International Conference on Software Engineering)
    Free, publicly-accessible full text available May 1, 2024
  7. ; ; ; ; ( , Empirical software engineering)
    Free, publicly-accessible full text available March 2, 2024
  8. ; ( , Journal of Social Computing)
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  9. ; ( , New Directions in Function Theory: From Complex to Hypercomplex to Non-Commutative)
    null ; null ; Shoikhet, D. ; Vajiac, E. (Ed.)
    We consider the existence and structure properties of Parseval frames of kernel functions in vector valued de Branges spaces. We develop some sufficient conditions for Parseval sequences by identifying the main construction with Naimark dilation of frames. The dilation occurs by embedding the de Branges space of vector valued functions into a dilated de Branges space of vector valued functions. The embedding also maps the kernel functions associated with a frame sequence of the original space into a Riesz basis for the embedding space. We also develop some sufficient conditions for a dilated de Branges space to have the Kramer sampling property. 
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  10. ; ( , Axioms)
    The Kaczmarz algorithm is an iterative method for solving systems of linear equations. We introduce a randomized Kaczmarz algorithm for solving systems of linear equations in a distributed environment, i.e., the equations within the system are distributed over multiple nodes within a network. The modification we introduce is designed for a network with a tree structure that allows for passage of solution estimates between the nodes in the network. We demonstrate that the algorithm converges to the solution, or the solution of minimal norm, when the system is consistent. We also prove convergence rates of the randomized algorithm that depend on the spectral data of the coefficient matrix and the random control probability distribution. In addition, we demonstrate that the randomized algorithm can be used to identify anomalies in the system of equations when the measurements are perturbed by large, sparse noise. 
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