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We have developed a new method of data processing for radio telescope observation data to measure time-dependent temporal coherence, and we named it cross-correlation spectrometry (XCS). XCS is an autocorrelation procedure that expands time…

Solar and Stellar Astrophysics · Physics 2016-09-28 Kazuhiro Takefuji , Hiroshi Imai , Mamoru Sekido

Locating lesions is important in the computer-aided diagnosis of X-ray images. However, box-level annotation is time-consuming and laborious. How to locate lesions accurately with few, or even without careful annotations is an urgent…

Computer Vision and Pattern Recognition · Computer Science 2021-02-02 Gangming Zhao , Baolian Qi , Jinpeng Li

A fundamental challenge in physics is controlling the propagation of waves in disordered media despite strong scattering from inhomogeneities. Spatial light modulators enable one to synthesize (shape) the incident wavefront, optimizing the…

Optics · Physics 2017-02-15 Chia Wei Hsu , Seng Fatt Liew , Arthur Goetschy , Hui Cao , A. Douglas Stone

The angular correlation function is a powerful tool for deriving the clustering properties of AGN and hence the mass of the corresponding dark matter halos in which they reside. However, studies based on the application of the angular…

Cosmology and Nongalactic Astrophysics · Physics 2016-05-04 L. Koutoulidis , M. Plionis , I. Georgantopoulos , A. Georgakakis , A. Akylas , S. Basilakos , G. Mountrichas

Canonical correlation analysis (CCA) is a technique for finding correlated sets of features between two datasets. In this paper, we propose a novel extension of CCA to the online, streaming data setting: Sliding Window Informative Canonical…

Machine Learning · Statistics 2026-05-12 Arvind Prasadan

We present a theoretical model which aims at predicting the clustering properties of X-ray clusters in flux-limited surveys for different cosmological scenarios. The model uses the theoretical and empirical relations between mass,…

Generalized Canonical Correlation Analysis (GCCA) is an important tool that finds numerous applications in data mining, machine learning, and artificial intelligence. It aims at finding `common' random variables that are strongly correlated…

Machine Learning · Computer Science 2021-05-19 Mikael Sørensen , Charilaos I. Kanatsoulis , Nicholas D. Sidiropoulos

Representational similarity analysis (RSA) is a multivariate technique to investigate cortical representations of objects or constructs. While avoiding ill-posed matrix inversions that plague multivariate approaches in the presence of many…

Methodology · Statistics 2021-12-03 Roberto Viviani

X-ray absorption spectroscopy (XAS) and resonant inelastic X-ray scattering (RIXS) are powerful probes of electronic structure owing to their chemical and orbital selectivity. For powder samples, however, interpreting RIXS spectral…

Materials Science · Physics 2026-05-12 Sihan Zhang , Oana Bunău , Marius Retegan , Pieter Glatzel

The angular cross-correlation between two galaxy samples separated in redshift is shown to be a useful measure of weak lensing by large-scale structure. Angular correlations in faint galaxies arise due to spatial clustering of the galaxies…

Astrophysics · Physics 2020-11-25 R. Moessner , Bhuvnesh Jain

Split-pulse x-ray photon correlation spectroscopy has been proposed as one of the unique capabilities made possible with the x-ray free electron lasers. It enables characterization of atomic scale structural dynamics that dictates the…

Instrumentation and Detectors · Physics 2020-05-06 Yanwen Sun , Mike Dunne , Paul Fuoss , Taito Osaka , Aymeric Robert , Mark Sutton , Makina Yabashi , Diling Zhu

Principal Component Analysis (PCA) is applied to a variety of blazars to examine X-ray spectral variability. Data from nine different objects are analysed in two ways: long-term, which examines variability trends across years or decades,…

High Energy Astrophysical Phenomena · Physics 2018-08-08 Dennis Gallant , Luigi C. Gallo , Michael L. Parker

Canonical correlation analysis (CCA) is a statistical learning method that seeks to build view-independent latent representations from multi-view data. This method has been successfully applied to several pattern analysis tasks such as…

Computer Vision and Pattern Recognition · Computer Science 2018-12-24 Hichem Sahbi

We review recent results on the anomalous transport in one-dimensional and quasi-one-dimensional systems with bulk and surface disorder. Main attention is paid to the role of long-range correlations in random potentials for the bulk…

Disordered Systems and Neural Networks · Physics 2009-11-11 F. M. Izrailev , N. M. Makarov

Understanding associations between paired high-dimensional longitudinal datasets is a fundamental yet challenging problem that arises across scientific domains, including longitudinal multi-omic studies. The difficulty stems from the…

Methodology · Statistics 2026-01-21 Jianbin Tan , Pixu Shi

We show that the combination of X-ray scattering with a nanofocused beam and X-ray cross correlation analysis is an efficient means for the full structural characterization of mesocrystalline nanoparticle assemblies with a single…

Diagnosis-oriented dialogue system queries the patient's health condition and makes predictions about possible diseases through continuous interaction with the patient. A few studies use reinforcement learning (RL) to learn the optimal…

Computation and Language · Computer Science 2022-12-27 Wei Chen , Cheng Zhong , Jiajie Peng , Zhongyu Wei

Data from a new, wide field, coincident optical and X-ray survey, the X-ray Dark Cluster Survey (XDCS) are presented. This survey comprises simultaneous and independent searches for clusters of galaxies in the optical and X-ray passbands.…

Astrophysics · Physics 2009-11-10 David G. Gilbank , Richard G. Bower , F. J. Castander , B. L. Ziegler

We investigate how simultaneously recorded long-range power-law correlated multi-variate signals cross-correlate. To this end we introduce a two-component ARFIMA stochastic process and a two-component FIARCH process to generate coupled…

Statistical Finance · Quantitative Finance 2009-11-13 Boris Podobnik , Davor Horvatic , Alfonso Lam Ng , H. Eugene Stanley , Plamen Ch. Ivanov

Small- and intermediate-scale galaxy clustering can be used to establish the galaxy-halo connection to study galaxy formation and evolution and to tighten constraints on cosmological parameters. With the increasing precision of galaxy…

Cosmology and Nongalactic Astrophysics · Physics 2016-03-23 Zheng Zheng , Hong Guo
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