Posts by Collection

publications

Cyclic Pattern Containment and Avoidance- Rachel Domagalski, Sergi Elizalde, Jinting Liang, Quinn Minnich, Bruce E. Sagan, Jamie Schmidt, Alexander Sietsema

Published in Advances in Applied Mathematics (vol. 135), 2021

We study a variant of pattern avoidance for cyclic permutations. We prove results including a cyclic variant of the Erdos-Szekeres theorem and results about avoidance of multiple patterns of length 4.

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Cyclic Shuffle Compatibility - Rachel Domagalski, Jinting Liang, Quinn Minnich, Bruce E. Sagan, Jamie Schmidt, Alexander Sietsema

Published in S´eminaire Lotharingien de Combinatoire (vol. 85), 2021

We define and study cyclic shuffle compatibility and show methods by which to lift results for linear permutations to cyclic ones.

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Pinnacle Set Properties - Rachel Domagalski, Jinting Liang, Quinn Minnich, Bruce E. Sagan, Jamie Schmidt, Alexander Sietsema

Published in Discrete Mathematics (vol. 345, iss. 7), 2021

We prove new results about pinnacle sets of permutations, including a simpler bijection which counts the number of admissible pinnacle sets.

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Comparing One-step and Two-step Scatter Correction And Density Reconstruction In X-Ray CT - Alexander N. Sietsema, Michael T. McCann, Marc L. Klasky, Saiprasad Ravishankar

Published in conference proceedings of the 7th International on Image Formation in X-Ray Computed Tomography (CT Meeting), 2022

We use synthetic data to compare the performance separate scatter correction and density reconstruction steps with solving a one-step optimization problem.

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Stochastic Iterative Methods for Online Rank Aggregation from Pairwise Comparisons - Benjamin Jarman, Lara Kassab, Deanna Needell, Alexander Sietsema

Published in BIT Numerical Mathematics vol. 64, 2024

We develop a variant of the Kaczmarz method for inequalities designed for pairwise comparison ranking problems.

Stratified Non-Negative Tensor Factorization - Alexander Sietsema, Zerrin Vural, James Chapman, Yotam Yaniv, Deanna Needell

Published in conference proceedings of 58th Asilomar Conference on Signals, Systems, and Computers, 2024

We develop a stratified non-negative tensor factorization method to learn rank-one differences in data sources.