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Fourier Series for Singular Measures in Higher Dimensions

https://doi.org/10.1007/s00041-024-10133-8

Berner, Chad; Herr, John E; Jorgensen, Palle_E T; Weber, Eric S (February 2025, Journal of Fourier Analysis and Applications)

Free, publicly-accessible full text available February 1, 2026
Fourier Series for Fractals in Two Dimensions

Herr, John E; Jorgensen, Palle E_T; Weber, Eric S (October 2023, Alpine cryosphere and geomorphology studies)
Ruiz, P; Hinz, M; Okoudjou, K; Rogers, L; Teplyaev, A (Ed.)
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On the use of real-world datasets for reaction yield prediction

https://doi.org/10.1039/d2sc06041h

Saebi, Mandana; Nan, Bozhao; Herr, John E.; Wahlers, Jessica; Guo, Zhichun; Zurański, Andrzej M.; Kogej, Thierry; Norrby, Per-Ola; Doyle, Abigail G.; Chawla, Nitesh V.; et al (March 2023, Chemical Science)

The lack of publicly available, large, and unbiased datasets is a key bottleneck for the application of machine learning (ML) methods in synthetic chemistry. Data from electronic laboratory notebooks (ELNs) could provide less biased, large datasets, but no such datasets have been made publicly available. The first real-world dataset from the ELNs of a large pharmaceutical company is disclosed and its relationship to high-throughput experimentation (HTE) datasets is described. For chemical yield predictions, a key task in chemical synthesis, an attributed graph neural network (AGNN) performs as well as or better than the best previous models on two HTE datasets for the Suzuki–Miyaura and Buchwald–Hartwig reactions. However, training the AGNN on an ELN dataset does not lead to a predictive model. The implications of using ELN data for training ML-based models are discussed in the context of yield predictions.
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Few-Shot Graph Learning for Molecular Property Prediction

https://doi.org/10.1145/3442381.3450112

Guo, Zhichun; Zhang, Chuxu; Yu, Wenhao; Herr, John; Wiest, Olaf; Jiang, Meng; Chawla, Nitesh V. (April 2021, Few-Shot Graph Learning for Molecular Property Prediction)

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On the existence of complex Hadamard submatrices of the Fourier matrices

https://doi.org/10.1515/dema-2019-0001

Bond, Bailey Madison; Glickfield, R. Alexander; Herr, John E. (January 2019, Demonstratio Mathematica)

Abstract We use a theorem of Lam and Leung to prove that a submatrix of a Fourier matrix cannot be Hadamard for particular cases when the dimension of the submatrix does not divide the dimension of the Fourier matrix. We also make some observations on the trace-spectrum relationship of dephased Hadamard matrices of low dimension.
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