Using Intelligent Prefetching to Reduce the Energy Consumption of a Large-scale Storage System

Many high performance large-scale storage systems will experience significant workload increases as their user base and content availability grow over time. The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) center hosts one such system that has recently undergone a period of rapid growth as its user population grew nearly 400% in just about three years. When administrators of these massive storage systems face the challenge of meeting the demands of an ever increasing number of requests, the easiest solution is to integrate more advanced hardware to existing systems. However, additional investment in hardware may significantly increase the system cost as well as daily power consumption. In this paper, we present evidence that well-selected software level optimization is capable of achieving comparable levels of performance without the cost and power consumption overhead caused by physically expanding the system. Specifically, we develop intelligent prefetching algorithms that are suitable for the unique workloads and user behaviors of the world\u27s largest satellite images distribution system managed by USGS EROS. Our experimental results, derived from real-world traces with over five million requests sent by users around the globe, show that the EROS hybrid storage system could maintain the same performance with over 30% of energy savings by utilizing our proposed prefetching algorithms, compared to the alternative solution of doubling the size of the current FTP server farm

The stellar halo of isolated central galaxies in the Hyper Suprime-Cam imaging survey

We study the faint stellar halo of isolated central galaxies, by stacking galaxy images in the HSC survey and accounting for the residual sky background sampled with random points. The surface brightness profiles in HSC $r$-band are measured for a wide range of galaxy stellar masses ($9.2<\log_{10}M_\ast/M_\odot<11.4$) and out to 120 kpc. Failing to account for the stellar halo below the noise level of individual images will lead to underestimates of the total luminosity by $\leq 15\%$. Splitting galaxies according to the concentration parameter of their light distributions, we find that the surface brightness profiles of low concentration galaxies drop faster between 20 and 100 kpc than those of high concentration galaxies. Albeit the large galaxy-to-galaxy scatter, we find a strong self-similarity of the stellar halo profiles. They show unified forms once the projected distance is scaled by the halo virial radius. The colour of galaxies is redder in the centre and bluer outside, with high concentration galaxies having redder and more flattened colour profiles. There are indications of a colour minimum, beyond which the colour of the outer stellar halo turns red again. This colour minimum, however, is very sensitive to the completeness in masking satellite galaxies. We also examine the effect of the extended PSF in the measurement of the stellar halo, which is particularly important for low mass or low concentration galaxies. The PSF-corrected surface brightness profile can be measured down to $\sim$31 $\mathrm{mag}/\mathrm{arcsec}^2$ at 3-$\sigma$ significance. PSF also slightly flattens the measured colour profiles.Comment: accepted by MNRAS - Significant changes have been made compared with the first version, including discussions on the extended PSF wings, robustness of our results to source detection and masking thresholds and more detailed investigations on the indications of positive colour gradient

A review of parallel computing for large-scale remote sensing image mosaicking

Interest in image mosaicking has been spurred by a wide variety of research and management needs. However, for large-scale applications, remote sensing image mosaicking usually requires significant computational capabilities. Several studies have attempted to apply parallel computing to improve image mosaicking algorithms and to speed up calculation process. The state of the art of this field has not yet been summarized, which is, however, essential for a better understanding and for further research of image mosaicking parallelism on a large scale. This paper provides a perspective on the current state of image mosaicking parallelization for large scale applications. We firstly introduce the motivation of image mosaicking parallel for large scale application, and analyze the difficulty and problem of parallel image mosaicking at large scale such as scheduling with huge number of dependent tasks, programming with multiple-step procedure, dealing with frequent I/O operation. Then we summarize the existing studies of parallel computing in image mosaicking for large scale applications with respect to problem decomposition and parallel strategy, parallel architecture, task schedule strategy and implementation of image mosaicking parallelization. Finally, the key problems and future potential research directions for image mosaicking are addressed

Studying the photometric and spectroscopic variability of the magnetic hot supergiant ζ\zeta Orionis Aa

Massive stars play a significant role in the chemical and dynamical evolution of galaxies. However, much of their variability, particularly during their evolved supergiant stage, is poorly understood. To understand the variability of evolved massive stars in more detail, we present a study of the O9.2Ib supergiant $\zeta$ Ori Aa, the only currently confirmed supergiant to host a magnetic field. We have obtained two-color space-based BRIght Target Explorer photometry (BRITE) for $\zeta$ Ori Aa during two observing campaigns, as well as simultaneous ground-based, high-resolution optical CHIRON spectroscopy. We perform a detailed frequency analysis to detect and characterize the star's periodic variability. We detect two significant, independent frequencies, their higher harmonics, and combination frequencies: the stellar rotation period $P_{\mathrm{rot}} = 6.82\pm0.18$ d, most likely related to the presence of the stable magnetic poles, and a variation with a period of $10.0\pm0.3$ d attributed to circumstellar environment, also detected in the H$\alpha$ and several He I lines, yet absent in the purely photospheric lines. We confirm the variability with $P_{\mathrm{rot}}$/4, likely caused by surface inhomogeneities, being the possible photospheric drivers of the discrete absorption components. No stellar pulsations were detected in the data. The level of circumstellar activity clearly differs between the two BRITE observing campaigns. We demonstrate that $\zeta$ Ori Aa is a highly variable star with both periodic and non-periodic variations, as well as episodic events. The rotation period we determined agrees well with the spectropolarimetric value from the literature. The changing activity level observed with BRITE could explain why the rotational modulation of the magnetic measurements was not clearly detected at all epochs.Comment: 20 pages, 5 tables, 12 figures, accepted for publication in A&

DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images

The domain adaptation of satellite images has recently gained an increasing attention to overcome the limited generalization abilities of machine learning models when segmenting large-scale satellite images. Most of the existing approaches seek for adapting the model from one domain to another. However, such single-source and single-target setting prevents the methods from being scalable solutions, since nowadays multiple source and target domains having different data distributions are usually available. Besides, the continuous proliferation of satellite images necessitates the classifiers to adapt to continuously increasing data. We propose a novel approach, coined DAugNet, for unsupervised, multi-source, multi-target, and life-long domain adaptation of satellite images. It consists of a classifier and a data augmentor. The data augmentor, which is a shallow network, is able to perform style transfer between multiple satellite images in an unsupervised manner, even when new data are added over the time. In each training iteration, it provides the classifier with diversified data, which makes the classifier robust to large data distribution difference between the domains. Our extensive experiments prove that DAugNet significantly better generalizes to new geographic locations than the existing approaches

High-contrast imaging of Sirius~A with VLT/SPHERE: Looking for giant planets down to one astronomical unit

Sirius has always attracted a lot of scientific interest, especially after the discovery of a companion white dwarf at the end of the 19th century. Very early on, the existence of a potential third body was put forward to explain some of the observed properties of the system. We present new coronagraphic observations obtained with VLT/SPHERE that explore, for the very first time, the innermost regions of the system down to 0.2" (0.5 AU) from Sirius A. Our observations cover the near-infrared from 0.95 to 2.3 $\mu$m and they offer the best on-sky contrast ever reached at these angular separations. After detailing the steps of our SPHERE/IRDIFS data analysis, we present a robust method to derive detection limits for multi-spectral data from high-contrast imagers and spectrographs. In terms of raw performance, we report contrasts of 14.3 mag at 0.2", ~16.3 mag in the 0.4-1.0" range and down to 19 mag at 3.7". In physical units, our observations are sensitive to giant planets down to 11 $M_{Jup}$ at 0.5 AU, 6-7 $M_{Jup}$ in the 1-2 AU range and ~4 $M_{Jup}$ at 10 AU. Despite the exceptional sensitivity of our observations, we do not report the detection of additional companions around Sirius A. Using a Monte Carlo orbital analysis, we show that we can reject, with about 50% probability, the existence of an 8 $M_{Jup}$ planet orbiting at 1 AU. In addition to the results presented in the paper, we provide our SPHERE/IFS data reduction pipeline at http://people.lam.fr/vigan.arthur/ under the MIT license.Comment: 16 pages, 10 figures, accepted for publication in MNRA

Open source tool for DSMs generation from high resolution optical satellite imagery. Development and testing of an OSSIM plug-in

The fully automatic generation of digital surface models (DSMs) is still an open research issue. From recent years, computer vision algorithms have been introduced in photogrammetry in order to exploit their capabilities and efficiency in three-dimensional modelling. In this article, a new tool for fully automatic DSMs generation from high resolution satellite optical imagery is presented. In particular, a new iterative approach in order to obtain the quasi-epipolar images from the original stereopairs has been defined and deployed. This approach is implemented in a new Free and Open Source Software (FOSS) named Digital Automatic Terrain Extractor (DATE) developed at the Geodesy and Geomatics Division, University of Rome ‘La Sapienza’, and conceived as an Open Source Software Image Map (OSSIM) plug-in. DATE key features include: the epipolarity achievement in the object space, thanks to the images ground projection (Ground quasi-Epipolar Imagery (GrEI)) and the coarse-to-fine pyramidal scheme adopted; the use of computer vision algorithms in order to improve the processing efficiency and make the DSMs generation process fully automatic; the free and open source aspect of the developed code. The implemented plug-in was validated through two optical datasets, GeoEye-1 and the newest Pléiades-high resolution (HR) imagery, on Trento (Northern Italy) test site. The DSMs, generated on the basis of the metadata rational polynomial coefficients only, without any ground control point, are compared to a reference lidar in areas with different land use/land cover and morphology. The results obtained thanks to the developed workflow are good in terms of statistical parameters (root mean square error around 5 m for GeoEye-1 and around 4 m for Pléiades-HR imagery) and comparable with the results obtained through different software by other authors on the same test site, whereas in terms of efficiency DATE outperforms most of the available commercial software. These first achievements indicate good potential for the developed plug-in, which in a very near future will be also upgraded for synthetic aperture radar and tri-stereo optical imagery processing