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Supervised learning on graphs is a challenging task due to the high dimensionality and inherent structural dependencies in the data, where each edge depends on a pair of vertices. Existing conventional methods are designed for standard…

Methodology · Statistics 2024-06-27 Cencheng Shen , Shangsi Wang , Alexandra Badea , Carey E. Priebe , Joshua T. Vogelstein

Model fitting is frequently used to determine the shape of galaxies and the point spread function, for examples, in weak lensing analyses or morphology studies aiming at probing the evolution of galaxies. However, the number of parameters…

Cosmology and Nongalactic Astrophysics · Physics 2012-10-03 Guoliang Li , Bo Xin , Wei Cui

Uniform sampling from graphical realizations of a given degree sequence is a fundamental component in simulation-based measurements of network observables, with applications ranging from epidemics, through social networks to Internet…

Physics and Society · Physics 2010-04-14 Charo I. Del Genio , Hyunju Kim , Zoltan Toroczkai , Kevin E. Bassler

Automated experiments in scanning transmission electron microscopy (STEM) require rapid image segmentation to optimize data representation for human interpretation, decision-making, site-selective spectroscopies, and atomic manipulation.…

Materials Science · Physics 2024-09-23 Kamyar Barakati , Utkarsh Pratiush , Austin C. Houston , Gerd Duscher , Sergei V. Kalinin

We have developed a data reduction procedure to extract multiple spectra from a single two-dimensional Space Telescope Imaging Spectrograph (STIS) image of a crowded stellar field. This paper provides a description of our new technique,…

Astrophysics · Physics 2009-11-07 Cherie L. Miskey , Fred C. Bruhweiler

Weighted sampling is a fundamental tool in data analysis and machine learning pipelines. Samples are used for efficient estimation of statistics or as sparse representations of the data. When weight distributions are skewed, as is often the…

Machine Learning · Computer Science 2020-08-18 Edith Cohen , Rasmus Pagh , David P. Woodruff

Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar…

We study the photometric properties of stars in the data archive of the Sloan Digital Sky Survey (SDSS), the prime aim being to understand the photometric calibration over the entire data set. It is confirmed that the photometric…

Solar and Stellar Astrophysics · Physics 2015-05-19 Masataka Fukugita , Naoki Yasuda , Mamoru Doi , James E. Gunn , Donald G. York

Astronomical data are typically irregular in time, e.g. the space (HIPPARCOS/TYCHO, KEPLER, GAIA, WISE etc.) and ground-based CCD (NSVS, ASAS, CRTS, SuperWASP etc.) and photographic (Harvard, Sonneberg, Odessa etc.) photometrical surveys.…

Solar and Stellar Astrophysics · Physics 2019-02-05 Ivan L. Andronov

For a given graph $F$, the $F$-saturation number of a graph $G$ is the minimum number of edges in an edge-maximal $F$-free subgraph of $G$. Recently, the $F$-saturation number of the Erd\H{o}s$\text{\bf--}$R\'enyi random graph…

Combinatorics · Mathematics 2017-09-26 A. Mohammadian , B. Tayfeh-Rezaie

We fit model spectral energy distributions to each pixel in 67 nearby (<z>=0.0057) galaxies using broadband photometry from the Sloan Digital Sky Survey and GALEX. For each galaxy, we compare the stellar mass derived by summing the mass of…

Astrophysics of Galaxies · Physics 2015-07-15 Robert Sorba , Marcin Sawicki

Randomly sampling points on surfaces is an essential operation in geometry processing. This sampling is computationally straightforward on explicit meshes, but it is much more difficult on other shape representations, such as widely-used…

Graphics · Computer Science 2025-06-16 Selena Ling , Abhishek Madan , Nicholas Sharp , Alec Jacobson

Slice Sampling has emerged as a powerful Markov Chain Monte Carlo algorithm that adapts to the characteristics of the target distribution with minimal hand-tuning. However, Slice Sampling's performance is highly sensitive to the…

Machine Learning · Statistics 2021-10-05 Minas Karamanis , Florian Beutler

In a number of situations, collecting a function value for every data point may be prohibitively expensive, and random sampling ignores any structure in the underlying data. We introduce a scalable optimization algorithm with no correction…

Machine Learning · Computer Science 2020-06-23 Saeed Vahidian , Baharan Mirzasoleiman , Alexander Cloninger

Using a dataset of stars from a barred galaxy that provides instantaneous positions and velocities in the sky, we propose a method to identify its key structures, such as the central bar and spiral arms, and to determine the bar pattern…

Astrophysics of Galaxies · Physics 2025-06-24 P. Sánchez-Martín , J. Amorós , J. J. Masdemont

This paper introduces the Multi-wavelength Extreme Starburst Sample (MESS), a new catalog of 138 star-forming galaxies (0.1 < z < 0.3) optically selected from the SDSS using emission line strength diagnostics to have high absolute SFR…

Astrophysics of Galaxies · Physics 2015-05-20 Edward Laag , Steve Croft , Gabriela Canalizo , Mark Lacy

Estimating stellar masses for billions of galaxies in upcoming surveys requires methods that are both accurate and computationally efficient. We present a new approach using symbolic regression trained on a simulation to derive simple,…

Series photo selection (SPS) is an important branch of the image aesthetics quality assessment, which focuses on finding the best one from a series of nearly identical photos. While a great progress has been observed, most of the existing…

Computer Vision and Pattern Recognition · Computer Science 2022-03-21 Jin Huang , Lu Zhang , Yongshun Gong , Jian Zhang , Xiushan Nie , Yilong Yin

The distribution of stars in the Hertzsprung-Russell diagram narrates their evolutionary history and directly assesses their properties. Placing stars in this diagram however requires the knowledge of their distances and interstellar…

Solar and Stellar Astrophysics · Physics 2014-10-29 N. Castro , L. Fossati , N. Langer , S. Simón-Díaz , F. R. N. Schneider , R. G. Izzard

We present a method to estimate distances to stars with spectroscopically derived stellar parameters. The technique is a Bayesian approach with likelihood estimated via comparison of measured parameters to a grid of stellar isochrones, and…