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arXiv 2025年

作者： Xu, Haotian Padilla, Carlos Misael Madrid Padilla, Oscar Hernan Madrid Wang, Daren Department of Statistics University of Warwick United Kingdom Department of Statistics and Data Science Washington University in St. Louis United States Department of Statistics and Data Science University of California Los Angeles United States Department of Applied and Computational Mathematics and Statistics University of Notre Dame United States

In this work, we introduce new matrix- and tensor-based methodologies for estimating multivariate intensity functions of spatial point processes. By modeling intensity functions as infinite-rank tensors within function spaces, we develop new algorithms to reveal optimal bias-variance trade-off for infinite-rank tensor estimation. Our methods dramatically enhance estimation accuracy while simultaneously reducing computational complexity. To our knowledge, this work marks the first application of matrix and tensor techinques to spatial point processes. Extensive numerical experiments further demonstrate that our techniques consistently outperform current state-of-the-art methods. © 2025, CC BY.

关键词： Numerical methods

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Bayesian phylodynamic inference of population dynamics with dormancy

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Proceedings of the National Academy of sciences of the United States of America 2025年第18期122卷 e2501394122页

作者： Cappello, Lorenzo Lo, Wai Tung Zhang, Joy Z. Xu, Peiyu Barrow, Daniel Chopra, Ishani Clark, Andrew G. Wells, Martin T. Kim, Jaehee Universitat Pompeu Fabra Departments of Economics and Business Barcelona 08005 Spain Data Science Center Barcelona School of Economics Barcelona 08005 Spain Department of Computational Biology Cornell University Ithaca NY 14850 Center for Applied Mathematics Cornell University Ithaca NY 14850 Department of Molecular Biology and Genetics Cornell University Ithaca NY 14850 Department of Statistics and Data Science Cornell University Ithaca NY 14850

Many organisms employ reversible dormancy, or seedbank, in response to environmental fluctuations. This life-history strategy alters fundamental ecoevolutionary forces, leading to distinct patterns of genetic diversity. Two models of dormancy have been proposed based on the average duration of dormancy relative to coalescent timescales: weak seedbank, induced by scheduled seasonality (e.g., plants, invertebrates), and strong seedbank, where individuals stochastically switch between active and dormant states (e.g., bacteria, fungi). The weak seedbank coalescent is statistically equivalent to the Kingman coalescent with a scaled mutation rate, allowing the use of existing inference methods. In contrast, the strong seedbank coalescent differs fundamentally, as only active lineages can coalesce, while dormant lineages cannot. Additionally, dormant individuals typically mutate at a slower rate than active ones. Consequently, despite the significant role of dormancy in the ecoevolutionary dynamics of many organisms, no methods currently exist for inferring population dynamics involving dormancy and associated parameters. We present a Bayesian framework for jointly inferring a latent genealogy, seedbank parameters, and evolutionary parameters from molecular sequence data under the strong seedbank coalescent. We derive the exact probability density of genealogies sampled under the strong seedbank coalescent, characterize the corresponding likelihood function, and present efficient computational algorithms for its evaluation based on our theoretical framework. We develop a tailored Markov chain Monte Carlo sampler and implement our inference framework as a package SeedbankTree within BEAST2. Our work provides both a theoretical foundation and practical inference framework for studying the population genetic and genealogical impacts of dormancy. Copyright © 2025 the Author(s).

关键词： Bayesian inference coalescent dormancy phylodynamics population dynamics

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Empirical Power Analysis of a Statistical Test to Quantify Gerrymandering

arXiv

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arXiv 2025年

作者： Clark, Ranthony A. Glenn, Susan Lee, Harlin Villar, Soledad Department of Mathematics Duke University DurhamNC United States Los Alamos National Laboratory Los AlamosNM United States School of Data Science and Society University of North Carolina at Chapel Hill Chapel HillNC United States Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD United States

Gerrymandering is a pervasive problem within the US political system. In the past decade, methods based on Markov Chain Monte Carlo (MCMC) sampling and statistical outlier tests have been proposed to quantify gerrymandering and were used as evidence in several high-profile legal cases. We perform an empirical power analysis of one such hypothesis test from Chikina et al (2020). We generate a family of biased North Carolina congressional district maps using the 2012 and 2016 presidential elections and assess under which conditions the outlier test fails to flag them at the specified Type I error level. The power of the outlier test is found to be relatively stable across political parties, election years, lengths of the MCMC chain and effect sizes. The main effect on the power of the test is shown to be the choice of the bias metric. This is the first work that computationally verifies the power of statistical tests used in gerrymandering cases. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov chains

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Brain MRIs classification based on 2D SWD-MF-DFA

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Journal of Neuroscience Methods 2025年 421卷 110468页

作者： Wang, Jing Wu, Xinpei Wang, Haozhe Wang, Jian School of Mathematics and Statistics Nanjing University of Information Science and Technology Nanjing 210044 China Reading Academy Nanjing University of Information Science and Technology Nanjing 210044 China Center for Applied Mathematics of Jiangsu Province Nanjing University of Information Science and Technology Nanjing 210044 China Jiangsu International Joint Laboratory on System Modeling and Data Analysis Nanjing University of Information Science and Technology Nanjing 210044 China

Background: To improve imaging classification accuracy, we modify the traditional 2D multifractal trend fluctuation analysis (MF-DFA) method to better preserve local feature values. Inspired by MF-DFA, we develop a novel method for extracting eigenvalues, enhancing the precision of imaging analysis. New method: In this paper, we propose an enhanced algorithm building upon the traditional 2D MF-DFA. Our approach introduces a 2D sliding window (SWD) technique for feature value extraction. Initially, the local generalized Hurst index of the imaging is derived using the SWD algorithm, based on MF-DFA principles. Subsequently, the generalized Hurst index is recalculated for the digital matrix formed by these local Hurst indexes. These vectors are then input into a support vector machine (SVM) for classification. This methodology seeks to refine the traditional 2D MF-DFA by more effectively preserving local feature values in imaging. Results: The classification accuracy of the SWD eigenvalue extraction method based on 2D MF-DFA reaches 91.54%. Comparison with existing methods: We employ brain magnetic resonance imaging (MRI) data sets to evaluate the efficacy of both the conventional 2D MF-DFA method and our proposed feature value extraction technique. Both methods are applied alongside a SVM for classification. The findings reveal that the conventional 2D MF-DFA method yields a classification accuracy of 59.40%, while our SWD feature value extraction method attains a classification accuracy of 91.54%. Conclusion: This substantial performance enhancement underscores the superiority of the SWD approach over the conventional method. © 2025 Elsevier B.V.

关键词： 2D MF-DFA 2D SWD MRI SVM

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A New Class of Strong Orthogonal Arrays of Strength Three

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Journal of Systems science & Complexity 2024年第3期37卷 1233-1250页

作者： WANG Chunyan LIU Min-Qian YANG Jinyu Center for Applied Statistics and School of Statistics Renmin University of ChinaBeijing 100872China School of Statistics and Data Science LPMC&KLMDASRNankai UniversityTianjin 300071China

Strong orthogonal arrays(SOAs) were recently introduced and studied as a class of spacefilling designs for computer experiments. To surely realize better space-filling properties, SOAs of strength three or higher are desirable. In addition, orthogonality is also an important property for designs of computer experiments, because it guarantees that the estimates of the main effects are uncorrelated. This paper first provides a systematic study on the construction of(nearly) orthogonal strength-three SOAs with better space-filling properties. The newly proposed strength-three SOAs enjoy almost the same space-filling properties of strength-four SOAs, and can accommodate much more columns than the latter. Moreover, they are(nearly) orthogonal and flexible in run sizes. The construction methods are straightforward to implement, and their theoretical supports are well established. In addition to the theoretical results, many designs are tabulated for practical needs.

关键词： Computer experiment orthogonality space-filling property strong orthogonal array

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The Silent Majority: Demystifying Memorization Effect in the Presence of Spurious Correlations

arXiv

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arXiv 2025年

作者： You, Chenyu Dai, Haocheng Min, Yifei Sekhon, Jasjeet S. Joshi, Sarang Duncan, James S. Department of Electrical & Computer Engineering Yale University New HavenCT United States Scientific Computing and Imaging Institute University of Utah Salt Lake CityUT United States Department of Statistics and Data Science Yale University New HavenCT United States Department of Biomedical Engineering Yale University New HavenCT United States Department of Applied Mathematics & Statistics Stony Brook University Stony BrookNY United States Department of Computer Science Stony Brook University Stony BrookNY United States

Machine learning models often rely on simple spurious features - patterns in training data that correlate with targets but are not causally related to them, like image backgrounds in foreground classification. This reliance typically leads to imbalanced test performance across minority and majority groups. In this work, we take a closer look at the fundamental cause of such imbalanced performance through the lens of memorization, which refers to the ability to predict accurately on atypical examples (minority groups) in the training set but failing in achieving the same accuracy in the testing set. This paper systematically shows the ubiquitous existence of spurious features in a small set of neurons within the network, providing the first-ever evidence that memorization may contribute to imbalanced group performance. Through three experimental sources of converging empirical evidence, we find the property of a small subset of neurons or channels in memorizing minority group information. Inspired by these findings, we articulate the hypothesis: the imbalanced group performance is a byproduct of "noisy" spurious memorization confined to a small set of neurons. To further substantiate this hypothesis, we show that eliminating these unnecessary spurious memorization patterns via a novel framework during training can significantly affect the model performance on minority groups. Our experimental results across various architectures and benchmarks offer new insights on how neural networks encode core and spurious knowledge, laying the groundwork for future research in demystifying robustness to spurious correlation. © 2025, CC BY.

关键词： Neurons

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Visualization of Trochlear Dysplasia Using 3-Dimensional Curvature Analysis in Patients With Patellar Instability Facilitates Understanding and Improves the Reliability of the Entry Point to Trochlea Groove Angle

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Arthroscopy, Sports Medicine, and Rehabilitation 2025年第1期7卷 101010-101010页

作者： Sieberer, Johannes M. Park, Nancy Manafzadeh, Armita R. Desroches, Shelby T. Brennan, Kelsey McDonald, Curtis Tommasini, Steven M. Wiznia, Daniel H. Fulkerson, John P. Department of Orthopaedics and Rehabilitation Yale School of Medicine New Haven CT United States Yale Institute for Biospheric Studies Yale University New Haven CT United States Department of Statistics and Data Science Yale University New Haven CT United States Department of Mechanical Engineering and Material Science Yale School of Engineering and Applied Science New Haven CT United States

Purpose: To examine a method to visualize a 3-dimensional (3D) rendered distal femur using 3D curvature analysis and to compare models of patellofemoral instability (PFI) with controls to study the reliability of the entry point to trochlear groove angle (EPTG) metric. Methods: The 3D models of patients with recurrent patellar instability, defined by at least 2 reported patellar dislocation events, and age- and sex-matched controls were created from computed tomography scans. Curvature was calculated to highlight the proximal trochlear ridges and the trochlear groove by overlaying them on the 3D models. Anteroposterior views with and without curvature visualization were created and used for qualitative comparison and to measure the EPTG. The EPTG was measured by 2 raters with and without the aid of the curvature maps. Significant differences between patients with PFI and controls were compared with a Mann-Whitney U test. Inter-rater reliability was calculated using interclass correlation coefficients, classified according to literature and compared using a permutation test. Significance was assumed at .05. Results: Qualitive analysis between 30 PFI patient knees (age: 23.9 ± 8.4 years, female/male: 24/6) and 30 control knees (age: 21.8 ± 5.6 years, female/male: 22/8) showed that in general, patients with PFI have a lateralized medial ridge and trochlear groove, with the trochlear groove being shorter and shallower. Qualitatively, differences between patients with PFI and controls were significant for measurements both with and without the aid of the curvature maps. Inter-rater reliability was significantly (P = .0349) better when using the curvature visualization. Conclusions: Curvature-based visualization aids overlain on a 3D model have the power to increase the information gained from 3D imaging and corresponding 3D models, amplifying their potential value in clinical decision-making. Such visualizations facilitate both the identification of qualitative differenc

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Testing Serial Correlation in Partially Linear Additive Models

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Acta Mathematicae Applicatae Sinica 2019年第2期35卷 401-411页

作者： Jin YANG Chuan-hua WEI School of Statistics and Data Science Nankai University Department of Applied Mathematics The Hong Kong Polytechnic University Department of Statistics School of ScienceMinzu University of China

As an extension of partially linear models and additive models, partially linear additive model is useful in statistical modelling. This paper proposes an empirical likelihood based approach for testing serial correlation in this semiparametric model. The proposed test method can test not only zero first-order serial correlation, but also higher-order serial correlation. Under the null hypothesis of no serial correlation, the test statistic is shown to follow asymptotically a chi-square distribution. Furthermore, a simulation study is conducted to illustrate the performance of the proposed method.

关键词： Partially linear additive model Backfitting Profile least-squares approach Empirical likelihood Serial correlation

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Structure in conversation: Evidence for the vocabulary, semantics, and syntax of prosody

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Proceedings of the National Academy of sciences of the United States of America 2025年第17期122卷 e2403262122页

作者： Matalon, Nadav Weinreb, Eyal Freche, Dominik Volk, Erez Biron, Tirza Moses, Elisha Biron, David Department of Physics of Complex Systems Weizmann Institute of Science Rehovot 7610001 Israel Department of Linguistics Hebrew University of Jerusalem Jerusalem 9190501 Israel NeuraLight Inc. Tel Aviv 6713818 Israel Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot 7610001 Israel The Data Science Institute The University of Chicago Chicago 60637 IL United States The Department of Statistics The University of Chicago Chicago 60637 IL United States

Prosody, the musical facet of speech, is pivotal in human communication, and its structure and meaning remain subjects of ongoing research. In this study, we introduce a data-driven model for English prosody, based on large-scale analysis of spontaneous conversations. As a first step, we identify approximately 200 discernible prosodic patterns—which we view as building blocks of the prosodic vocabulary—and outline their properties and range of meanings. Next, we reveal a Markovian logic, akin to a syntax, for concatenating these elementary building blocks into coherent utterances. We identify distinct compound functions associated with pairs of consecutive patterns and show that the Markovian syntax is more prevalent in spontaneous prosody, as compared to scripted speech. These findings offer invaluable insights into the underlying mechanisms of conversational prosody: They empirically inform and refine existing theoretical concepts. The methodology we present, combining unsupervised analysis of large datasets of spontaneous speech with manual sampling of the results, could guide future research aimed at refining our model and expanding it to other languages. Copyright © 2025 the Author(s).

关键词： language prosody speech unsupervised analysis

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Less is More? Revisiting the Importance of Frame Rate in Real-Time Zero-Shot Surgical Video Segmentation

arXiv

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arXiv 2025年

作者： Ozbulak, Utku Mousavi, Seyed Amir Tozzi, Francesca Rashidian, Nikdokht Willaert, Wouter De Neve, Wesley Vankerschaver, Joris Center for Biosystems and Biotech Data Science Ghent University Global Campus Incheon Korea Republic of IDLab ELIS Ghent University Ghent Belgium Department of GI Surgery Ghent University Hospital Ghent Belgium Department of Human Structure and Repair Ghent University Ghent Belgium Department of HPB Surgery & Liver Transplantation Ghent University Hospital Ghent Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium

Real-time video segmentation is a promising feature for AI-assisted surgery, providing intraoperative guidance by identifying surgical tools and anatomical structures. However, deploying state-of-the-art segmentation models, such as SAM2, in real-time settings is computationally demanding, which makes it essential to balance frame rate and segmentation performance. In this study, we investigate the impact of frame rate on zero-shot surgical video segmentation, evaluating SAM2’s effectiveness across multiple frame sampling rates for cholecystectomy procedures. Surprisingly, our findings indicate that in conventional evaluation settings, frame rates as low as a single frame per second can outperform 25 FPS, as fewer frames smooth out segmentation inconsistencies. However, when assessed in a real-time streaming scenario, higher frame rates yield superior temporal coherence and stability, particularly for dynamic objects such as surgical graspers. Finally, we investigate human perception of real-time surgical video segmentation among professionals who work closely with such data and find that respondents consistently prefer high FPS segmentation mask overlays, reinforcing the importance of real-time evaluation in AI-assisted surgery. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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