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Geotechnical Frontiers 2025: Geotechnics of Natural Hazards

作者： Ji, Xiaohang Xiao, Ming Martin, Eileen Dept. of Civil and Environmental Engineering Pennsylvania State Univ. University ParkPA United States Dept. of Geophysics Dept. of Applied Math and Statistics Hydrologic Science and Engineering Program Colorado School of Mines GoldenCO United States

ISBN: (数字)9780784485996

Increasing air temperatures are driving permafrost warming across the Arctic and sub-Arctic. This in turn degrades the geomechanical properties of soils, disrupts the natural environment and infrastructure systems, and results in long-lasting societal impacts. Understanding the thermal state of degrading permafrost in the Arctic is crucial for maintaining resilient infrastructure in the changing Arctic. This study employs Distributed Temperature Sensing (DTS) to monitor permafrost temperature near Utqiaġvik, Alaska, from 2021 to 2024. Fiber-optic DTS sensors have unique and attractive characteristics that allow their deployment in the Arctic for low-maintenance, cost-effective, and high-resolution data. This paper presents the temperature recordings of a 2-km DTS system on the tundra near Utqiaġvik, Alaska, by the Elson Lagoon. A hysteresis model is developed to link permafrost temperature with air temperature, incorporating meteorological data and ground surface conditions. The study also explores the impact of civil infrastructure on permafrost thermal dynamics. © ASCE.

关键词： Permafrost

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HiPoNet: A Topology-Preserving Multi-View Neural Network For High Dimensional Point Cloud and Single-Cell Data

arXiv

引用

arXiv 2025年

作者： Viswanath, Siddharth Madhu, Hiren Bhaskar, Dhananjay Kovalic, Jake Johnson, Dave Ying, Rex Tape, Christopher Adelstein, Ian Perlmutter, Michael Krishnaswamy, Smita Department of Computer Science Yale University New Haven United States Department of Genetics Yale University New Haven United States Department of Applied Physics Yale University New Haven United States Program in Computing Boise State University Idaho United States Cell Communication Lab Department of Oncology University College London Cancer Institute London United Kingdom Department of Mathematics Yale University New Haven United States Department of Mathematics Boise State University Idaho United States Computational Biology and Bioinformatics Program Yale University New Haven United States Wu-Tsai Institute Yale University New Haven United States Program for Applied Math Yale University New Haven United States

In this paper, we propose HiPoNet, an end-to-end differentiable neural network for regression, classification, and representation learning on high-dimensional point clouds. Single-cell data can have high dimensionality exceeding the capabilities of existing methods for point clouds which are tailored for 3D data. Moreover, modern single-cell and spatial experiments now yield entire cohorts of datasets (i.e. one on every patient), necessitating models that can process large, high-dimensional point clouds at scale. Most current approaches build a single nearest-neighbor graph, discarding important geometric information. In contrast, HiPoNet forms higher-order simplicial complexes through learnable feature reweighting, generating multiple data views that disentangle distinct biological processes. It then employs simplicial wavelet transforms to extract multiscale features—capturing both local and global topology. We empirically show that these components preserve topological information in the learned representations, and that HiPoNet significantly outperforms state-of-the-art point-cloud and graph-based models on single cell. We also show an application of HiPoNet on spatial transcriptomics datasets using spatial co-ordinates as one of the views. Overall, HiPoNet offers a robust and scalable solution for high-dimensional data analysis. © 2025, CC BY.

关键词： Network topology

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Physical basis for the symmetries in the Friedmann-Robert son-Walker metric

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Frontiers of physics 2016年第4期11卷 137-143页

作者： Fulvio Melia Department of Physics The Applied Math Program and Department of Astronomy The University of Arizona Tucson AZ 85721 USA

Modern cosmological theory is based on the Friedmann-Robertson-Walker （FRW） metric. Often written in terms of co-moving coordinates, this well-known solution to Einstein＇s equations owes its elegant and highly practical formulation to the cosmological principle and Weyl＇s postulate, upon which it is founded. However, there is physics behind such symmetries, and not all of it has yet been recognized. In this paper, we derive the FRW metric coefficients from the general form of the spherically symmetric line element and demonstrate that, because the co-moving frame also happens to be in free fall, the symmetries in FRW are valid only for a medium with zero active mass. In other words, the spacetime of a perfect fluid in cosmology may be correctly written as FRW only when its equation of state is p ＋ 3p = 0, in terms of the total pressure p and total energy density p. There is now compelling observational support for this conclusion, including the Alcock-Paczyriski test, which shows that only an FRW cosmology with zero active mass is consistent with the latest model-independent baryon acoustic oscillation data.

关键词： cosmological parameters cosmological observations cosmological theory gravitation

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The zero active mass condition in Friedmann- Robertson-Walker cosmologies

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Frontiers of physics 2017年第1期12卷 141-145页

作者： Fulvio Melia Department of Physics The Applied Math Program and Department of Astronomy The University of Arizona AZ 85721 USA

Many cosmological measurements today suggest that the Universe is expanding at a constant rate. This is inferred from the observed age versus redshift relationship and various distance indicators, all of which point to a cosmic equation of state （EoS） p = -ρ/3, where ρ and p are, respectively, the total energy density and pressure of the cosmic fluid. It has recently been shown that this result is not a coincidence and simply confirms the fact that the symmetries in the Friedmann-Robertson-Walker （FRW） metric appear to be viable only for a medium with zero active mass, i.e., ρ ＋ 3p -= 0. In their latest paper, however, Kim, Lasenby and Hobson （2016） have provided what they believe to be a counter argument to this conclusion. Here, we show that these authors are merely repeating the conventional mistake of incorrectly placing the observer simultaneously in a comoving frame, where the lapse function gtt is coordinate dependent when ρ ＋ 3p ≠ 0, and a supposedly different, free- falling frame, in which gtt = i, implying no time dilation. We demonstrate that the Hubble flow is not inertial when ρ ＋ 3p ≠ 0, so the comoving frame is generally not in free fall, even though in FRW, the comoving and free-falling frames are supposed to be identical at every spacetime point. So this confusion of frames not only constitutes an inconsistency with the fundamental tenets of general relativity but, additionally, there is no possibility of using a gauge transformation to select a set of coordinates for which gtt = 1 when ρ＋ 3p ≠ 0.

关键词： cosmology gravitation spacetime metric

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Principal Curvatures Estimation with Applications to Single Cell Data

Principal Curvatures Estimation with Applications to Single ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yanlei Zhang Lydia Mezrag Xingzhi Sun Charles Xu Kincaid Macdonald Dhananjay Bhaskar Smita Krishnaswamy Guy Wolf Bastian Rieck Dept. of Math. & Stat. Université de Montréal Mila – Quebec AI Institute Montréal CA Dept. of Comp. Sci. Yale University Dept. of Genetics Applied Mathematics Program New Haven CT USA Helmholtz Zentrum München Institute of AI for Health Munich BY DE Department of Informatics Université de Fribourg Fribourg CH

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The rapidly growing field of single-cell transcriptomic sequencing (scRNAseq) presents challenges for data analysis due to its massive datasets. A common method in manifold learning consists in hypothesizing that datasets lie on a lower dimensional manifold. This allows to study the geometry of point clouds by extracting meaningful descriptors like curvature. In this work, we will present Adaptive Local PCA (AdaL-PCA), a data-driven method for accurately estimating various notions of intrinsic curvature on data manifolds, in particular principal curvatures for surfaces. The model relies on local PCA to estimate the tangent spaces. The evaluation of AdaL-PCA on sampled surfaces shows state-of-the-art results. Combined with a PHATE embedding, the model applied to single-cell RNA sequencing data allows us to identify key variations in the cellular differentiation.

关键词： Manifolds Sequential analysis Adaptation models Data analysis RNA Transcriptomics Curve fitting Speech processing Tuning Principal component analysis

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Principal Curvatures Estimation with Applications to Single Cell Data

Principal Curvatures Estimation with Applications to Single ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Yanlei Mezrag, Lydia Sun, Xingzhi Xu, Charles Macdonald, Kincaid Bhaskar, Dhananjay Krishnaswamy, Smita Wolf, Guy Rieck, Bastian Université de Montréal Dept. of Math. & Stat Canada Mila - Quebec AI Institute MontréalQC Canada Yale University Dept. of Comp. Sci. United States Dept. of Genetics United States Applied Mathematics Program New HavenCT United States Université de Fribourg Department of Informatics FR Fribourg Switzerland Helmholtz Zentrum München Institute of AI for Health BY Munich Germany

ISBN: (纸本)9798350368741

关键词： Gaussian curvature Principal curvature principal directions single-cell

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CONDITIONAL AVERAGES IN CONVECTIVE TURBULENCE

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PHYSICA A 1994年第1-4期204卷 341-345页

作者： KADANOFF, LP The Computational and Applied Math Program Ryerson Laboratory The University of Chicago 1100 East 58th Street Chicago IL 60637 USA

Two exact results are presented for the thermal dissipation in a Rayleigh-Benard geometry. The first result expresses the conditional dissipation at each value of theta within the entire volume of the system in terms of the thermal flux and the PDF for theta. The second gives the conditional dissipation in a region of the cell in terms of the conditional flux into the region. These results are used to argue that the observed stretched exponential PDF in the central region must be connected with the behavior near the boundary.

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Principal Curvatures Estimation with Applications to Single Cell Data

arXiv

引用

arXiv 2025年

作者： Zhang, Yanlei Mezrag, Lydia Sun, Xingzhi Xu, Charles Macdonald, Kincaid Bhaskar, Dhananjay Krishnaswamy, Smita Wolf, Guy Rieck, Bastian Université de Montréal Dept. of Math. & Stat. QC Montréal Canada Mila – Quebec AI Institute MontréalQC Canada Yale University Dept. of Comp. Sci. New HavenCT United States Dept. of Genetics New HavenCT United States Applied Mathematics Program New HavenCT United States Université de Fribourg Department of Informatics Fribourg Switzerland Helmholtz Zentrum München Institute of AI for Health Munich Germany

关键词： Spatio-temporal data

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Strategies for polyhedral surface decomposition: An experimental study 95

Strategies for polyhedral surface decomposition: An experime...

引用

Proceedings of the 11th Annual Symposium on Computational Geometry

作者： Chazelle, B. Dobkin, D.P. Shouraboura, N. Tal, A. Department of Computer Science Princeton University Program in Applied and Computer. Math. Princeton University

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Convex surface decomposition 95

Convex surface decomposition

引用

11th Annual Symposium on Computational Geometry, SCG 1995

作者： Chazelle, Bernard Dobkin, David P. Shouraboura, Nadia Tal, Ayellet Department of Computer Science Princeton University United States Program in Applied and Comput. Math. Princeton University United States

No abstract available.

ISBN: (纸本)0897917243

No abstract available.

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