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Robust Optimization is becoming increasingly important in machine learning applications. This paper studies the problem of robust submodular minimization subject to combinatorial constraints. Constrained Submodular Minimization arises in…

Machine Learning · Computer Science 2020-01-28 Rishabh Iyer

The absolute visual magnitude as function of the observed colour (B-V), also named Hertzsprung-Russell diagram can be described through five equations; that in presence of calibrated stars means eight constants. The developed framework…

Astrophysics · Physics 2009-11-13 Lorenzo Zaninetti

Obtaining microscopic structure-property relationships for grain boundaries are challenging because of the complex atomic structures that underlie their behavior. This has led to recent efforts to obtain these relationships with machine…

Kaiser & Squires have proposed a technique for mapping the dark matter in galaxy clusters using the coherent weak distortion of background galaxy images caused by gravitational lensing. We investigate the effectiveness of this technique…

Astrophysics · Physics 2015-06-24 Gillian Wilson , Shaun Cole , Carlos S. Frenk

We present an improved calibration of photometric metallicity indicators, derived from the new metallicity scale for Globular Clusters presented by Carretta & Gratton (1997) and based on direct high resolution spectroscopy of 160 stars in…

Astrophysics · Physics 2007-05-23 Eugenio Carretta , Angela Bragaglia

Imperfect photometric calibration of galaxy surveys due to either astrophysical or instrumental effects leads to biases in measuring galaxy clustering and in the resulting cosmological parameter measurements. More interestingly (and…

Cosmology and Nongalactic Astrophysics · Physics 2015-06-12 Dragan Huterer , Carlos E. Cunha , Wenjuan Fang

The learning of domain-invariant representations in the context of domain adaptation with neural networks is considered. We propose a new regularization method that minimizes the discrepancy between domain-specific latent feature…

Nowadays, Machine Learning techniques offer fast and efficient solutions for classification problems that would require intensive computational resources via traditional methods. We examine the use of a supervised Random Forest to classify…

Astrophysics of Galaxies · Physics 2022-06-22 I. Marini , S. Borgani , A. Saro , G. Murante , G. L. Granato , C. Ragone-Figueroa , G. Taffoni

We report weak-lensing masses for 51 of the most X-ray luminous galaxy clusters known. This cluster sample, introduced earlier in this series of papers, spans redshifts 0.15 < z_cl < 0.7, and is well suited to calibrate mass proxies for…

We report new HST/WFPC2 photometry for 10 globular clusters (GC) in M31 observed in F5555W(V) and F814W(I). Additionally we have reanalyzed HST archival data of comparable quality for 2 more GCs. Extraordinary care is taken to account for…

Astrophysics · Physics 2009-11-10 R. M. Rich , C. E. Corsi , C. Cacciari , L. Federici , F. Fusi Pecci , S. G. Djorgovski

Connected clustering denotes a family of constrained clustering problems in which we are given a distance metric and an undirected connectivity graph $G$ that can be completely unrelated to the metric. The aim is to partition the $n$…

Data Structures and Algorithms · Computer Science 2025-11-25 Jan Eube , Heiko Röglin

This paper proposes a novel point-cloud-based place recognition system that adopts a deep learning approach for feature extraction. By using a convolutional neural network pre-trained on color images to extract features from a range image…

Computer Vision and Pattern Recognition · Computer Science 2018-10-24 Ting Sun , Ming Liu , Haoyang Ye , Dit-Yan Yeung

We report on the absolute calibration of a CCD camera by exploiting quantum correlation. This novel method exploits a certain number of spatial pairwise quantum correlated modes produced by spontaneous parametric-down-conversion. We develop…

Quantum Physics · Physics 2014-10-14 I. Ruo-Berchera , A. Meda , I. P. Degiovanni , G. Brida , M. L. Rastello , M. Genovese

We examine how the statistics of the quadrupoles of (projected) cluster masses can discriminate between flat cold dark matter (CDM) universes with or without a cosmological constant term. Even in the era of high precision cosmology that…

Astrophysics · Physics 2009-11-06 A. Maccio` , A. Gardini , S. Ghigna , S. A. Bonometto

The morphology of a galaxy has been shown to encode the evolutionary history and correlates strongly with physical properties such as stellar mass, star formation rates and past merger events. While the majority of galaxies in the local…

Astrophysics of Galaxies · Physics 2023-02-23 Clár-Bríd Tohill , Steven Bamford , Christopher Conselice

The Vera C. Rubin Observatory is slated to observe nearly 20 billion galaxies during its decade-long Legacy Survey of Space and Time. The rich imaging data it collects will be an invaluable resource for probing galaxy evolution across…

Instrumentation and Methods for Astrophysics · Physics 2025-12-05 Alexander T. Gagliano , Yunyi Shen , V. A. Villar

There exist a variety of star-galaxy classification techniques, each with their own strengths and weaknesses. In this paper, we present a novel meta-classification framework that combines and fully exploits different techniques to produce a…

Instrumentation and Methods for Astrophysics · Physics 2015-08-20 Edward J. Kim , Robert J. Brunner , Matias Carrasco Kind

We analyse the clustering of matter on large scales in an extension of the concordance model that allows for spatial curvature. We develop a consistent approach to curvature and wide-angle effects on the galaxy 2-point correlation function…

Cosmology and Nongalactic Astrophysics · Physics 2022-10-04 Julien Bel , Julien Larena , Roy Maartens , Christian Marinoni , Louis Perenon

It is widely recognized that cold dark matter models predict abundant dark matter substructure in halos of all sizes. Galaxy-galaxy lensing provides a unique opportunity to directly measure the presence and the mass of such substructures in…

Astrophysics · Physics 2009-11-10 Priyamvada Natarajan , Volker Springel

Matrices are two-dimensional data structures allowing one to conceptually organize information. For example, adjacency matrices are useful to store the links of a network; correlation matrices are simple ways to arrange gene co-expression…

Disordered Systems and Neural Networks · Physics 2022-09-29 Flaviano Morone