Robust and Reversible Audio Watermarking by Modifying Statistical Features in Time Domain by Shijun Xiang, Le Yang, Yi Wang

“…This paper presents a novel robust reversible audio watermarking method by modifying the statistic features in time domain in the way that the histogram of these statistical values is shifted for data hiding. …”
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CoreTIA: a modular, statistically robust transduction inhibition assay for AAV neutralization by Beatrix Kovács, Beatrix Kovács, Beatrix Kovács, Fanni Somogyi, Fanni Somogyi, Viktória Szabó, Zoltán Zsolt Nagy, István Hernádi, Ferenc Mátyás, Wim Vanduffel, Zsuzsanna Szemlaky, Balázs Rózsa, Balázs Rózsa, István Ulbert, István Ulbert, István Ulbert, István Ulbert, Dániel Hillier, Dániel Hillier, Dániel Hillier

“…We present coreTIA (core Transduction Inhibition Assay), a comprehensive framework providing a modular experimental protocol and a statistically robust analysis pipeline. This integrated method delivers precise, reproducible NAb titers with quantified uncertainty for every result. coreTIA’s statistical framework enables robust estimation of neutralization even when dilution series are incomplete, helping to reduce repeat testing and minimizing sample volume requirements. …”
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Enabling Robust Exoplanet Atmospheric Retrievals with Gaussian Processes by Yoav Rotman, Luis Welbanks, Michael R. Line, Peter McGill, Michael Radica, Matthew C. Nixon

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FORECASTING MONTHLY RAINFALL IN PANGKEP REGENCY USING STATISTICAL DOWNSCALING MODEL WITH ROBUST PRINCIPAL COMPONENT REGRESSION TECHNIQUE by Sitti Sahriman, Anisa Anisa

“…However, the spatial resolution of GCMs is typically on a global scale, which is inadequate for predicting local climate. Statistical downscaling (SD) is used to transform climate information from a global scale to a smaller scale for local-scale climate predictions. …”
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REMLA: An R package for robust expectation-maximization estimation for latent variable models by Kenneth J. Nieser, Bryan Saúl Ortiz-Torres, Gabriel Zayas-Cabán, Amy Cochran

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Noise-robust analysis of X-Ray absorption near-edge structure based on poisson distribution by Shuhei Kashiwamura, Shun Katakami, Taiga Yamasaki, Kazunori Iwamitsu, Hiroyuki Kumazoe, Kenji Nagata, Toshihiro Okajima, Ichiro Akai, Masato Okada

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Bayesian spatiotemporal nonstationary model quantifies robust increases in daily extreme rainfall across the Western Gulf Coast by Yuchen Lu, Benjamin Seiyon Lee, James Doss-Gollin

“…This model draws from extreme value theory, spatial statistics, and Bayesian statistics, and is validated through cross-validation and multiple performance metrics. …”
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Alpha helices are more evolutionarily robust to environmental perturbations than beta sheets: Bayesian learning and statistical mechanics for protein evolution by Tomoei Takahashi, George Chikenji, Kei Tokita, Yoshiyuki Kabashima

“…In this study, through Bayesian learning and statistical mechanics we formalize the stability of the free energy in the space of amino acid sequences that can design particular protein structure against perturbations of the chemical potential of water surrounding a protein as such robustness. …”
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Enhancing AI-driven forecasting of diabetes burden: a comparative analysis of deep learning and statistical models by Rasool Esmaeilyfard, Mohsen Bayati

“…This study evaluates the trade-offs between predictive accuracy, robustness, and computational efficiency in diabetes forecasting. …”
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Robust Data-Driven State of Health Estimation of Lithium-Ion Batteries Based on Reconstructed Signals by Byron Alejandro Acuña Acurio, Diana Estefanía Chérrez Barragán, Juan Carlos Rodríguez, Felipe Grijalva, Luiz Carlos Pereira da Silva

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Robust consensus nuclear and cell segmentation by Melis O. Irfan, Melis O. Irfan, Eduardo A. González-Solares, Tristan Whitmarsh, Alireza Molaeinezhad, Mohammad Al Sa’d, Claire M. Mulvey, Marta Páez Ribes, Atefeh Fatemi, Dario Bressan, Nicholas A. Walton

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Note on Qualitative Robustness of Multivariate Sample Mean and Median by Evgueni Gordienko, Andrey Novikov, J. Ruiz de Chávez

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Robust and Scalable Quantum Repeaters Using Machine Learning by Diego Fuentealba, Jackson Dahn, James Steck, Elizabeth Behrman

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Robust speech parametrization based on pitch synchronized cepstral solutions by Stanisław Gmyrek, Robert Hossa

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Robust response of the Amundsen Sea Low to stratospheric ozone depletion by Mark R. England, Lorenzo M. Polvani, Karen L. Smith, Laura Landrum, Marika M. Holland

“…Using state‐of‐the‐art climate models, we here show that stratospheric ozone depletion can cause a statistically significant deepening of the ASL in summer with an amplitude of approximately 1 hPa per decade. …”
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Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness by Junhui Song, Zhangqi Zheng, Afei Li, Zhixin Xia, Yongshan Liu

“…To mitigate this issue, a heterogeneity-aware and robust FL framework is proposed to enhance model generalization and stability under non-IID conditions. …”
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Robust handling of extremes in quantile mapping – “Murder your darlings” by P. Berg, T. Bosshard, D. Bozhinova, L. Bärring, J. Löw, C. Nilsson, G. Strandberg, J. Södling, J. Thuresson, R. Wilcke, W. Yang

“…The main moments of the distributions are typically well transformed by quantile mapping, but statistical uncertainty increases towards the extreme tails, making robust transformations challenging. …”
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Robustness Measurement of Comprehensive Evaluation Model Based on the Intraclass Correlation Coefficient by Shilai Xian, Li Zhang

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Conformal Segmentation in Industrial Surface Defect Detection with Statistical Guarantees by Cheng Shen, Yuewei Liu

“…Furthermore, our model shows robust and efficient control over the expected test set error rate when calibration-to-test partitioning ratios vary.…”
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Statistical signature of subtle behavioral changes in large-scale assays. by Alexandre Blanc, François Laurent, Alex Barbier-Chebbah, Hugues Van Assel, Benjamin T Cocanougher, Benjamin M W Jones, Peter Hague, Marta Zlatic, Rayan Chikhi, Christian L Vestergaard, Tihana Jovanic, Jean-Baptiste Masson, Chloé Barré

“…We here introduce several statistically robust methods for analyzing behavioral data in response to these challenges: 1) A generative physical model that regularizes the inference of larval shapes across the entire dataset. 2) An unsupervised kernel-based method for statistical testing in learned behavioral spaces aimed at detecting subtle deviations in behavior. 3) A generative model for larval behavioral sequences, providing a benchmark for identifying higher-order behavioral changes. 4) A comprehensive analysis technique using suffix trees to categorize genetic lines into clusters based on common action sequences. …”
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