An Adaptive Mechanism for Accurate Query Answering under Differential Privacy

We propose a novel mechanism for answering sets of count- ing queries under differential privacy. Given a workload of counting queries, the mechanism automatically selects a different set of "strategy" queries to answer privately, using those answers to derive answers to the workload. The main algorithm proposed in this paper approximates the optimal strategy for any workload of linear counting queries. With no cost to the privacy guarantee, the mechanism improves significantly on prior approaches and achieves near-optimal error for many workloads, when applied under (\epsilon, \delta)-differential privacy. The result is an adaptive mechanism which can help users achieve good utility without requiring that they reason carefully about the best formulation of their task.Comment: VLDB2012. arXiv admin note: substantial text overlap with arXiv:1103.136

An Alternating Direction Algorithm for Matrix Completion with Nonnegative Factors

This paper introduces an algorithm for the nonnegative matrix factorization-and-completion problem, which aims to find nonnegative low-rank matrices X and Y so that the product XY approximates a nonnegative data matrix M whose elements are partially known (to a certain accuracy). This problem aggregates two existing problems: (i) nonnegative matrix factorization where all entries of M are given, and (ii) low-rank matrix completion where nonnegativity is not required. By taking the advantages of both nonnegativity and low-rankness, one can generally obtain superior results than those of just using one of the two properties. We propose to solve the non-convex constrained least-squares problem using an algorithm based on the classic alternating direction augmented Lagrangian method. Preliminary convergence properties of the algorithm and numerical simulation results are presented. Compared to a recent algorithm for nonnegative matrix factorization, the proposed algorithm produces factorizations of similar quality using only about half of the matrix entries. On tasks of recovering incomplete grayscale and hyperspectral images, the proposed algorithm yields overall better qualities than those produced by two recent matrix-completion algorithms that do not exploit nonnegativity

Exoplanet Transmission Spectroscopy using KMOS

KMOS (K-Band Multi Object Spectrograph) is a novel integral field spectrograph installed in the VLT's ANTU unit. The instrument offers an ability to observe 24 2.8"$\times$2.8" sub-fields positionable within a 7.2' patrol field, each sub-field producing a spectrum with a 14$\times$14-pixel spatial resolution. The main science drivers for KMOS are the study of galaxies, star formation, and molecular clouds, but its ability to simultaneously measure spectra of multiple stars makes KMOS an interesting instrument for exoplanet atmosphere characterization via transmission spectroscopy. We set to test whether transmission spectroscopy is practical with KMOS, and what are the conditions required to achieve the photometric precision needed, based on observations of a partial transit of WASP-19b, and full transits of GJ 1214b and HD 209458b. Our analysis uses the simultaneously observed comparison stars to reduce the effects from instrumental and atmospheric sources, and Gaussian processes to model the residual systematics. We show that KMOS can, in theory, deliver the photometric precision required for transmission spectroscopy. However, this is shown to require a) pre-imaging to ensure accurate centering and b) a very stable night with optimal observing conditions (seeing $\sim$0.8"). Combining these two factors with the need to observe several transits, each with a sufficient out-of-transit baseline (and with the fact that similar or better precision can be reached with telescopes and instruments with smaller pressure,) we conclude that transmission spectroscopy is not the optimal science case to take advantage of the abilities offered by KMOS and VLT.Comment: 11 pages, accepted to MNRA

LSDCat: Detection and cataloguing of emission-line sources in integral-field spectroscopy datacubes

We present a robust, efficient, and user-friendly algorithm for detecting faint emission-line sources in large integral-field spectroscopic datacubes together with the public release of the software package LSDCat (Line Source Detection and Cataloguing). LSDCat uses a 3-dimensional matched filter approach, combined with thresholding in signal-to-noise, to build a catalogue of individual line detections. In a second pass, the detected lines are grouped into distinct objects, and positions, spatial extents, and fluxes of the detected lines are determined. LSDCat requires only a small number of input parameters, and we provide guidelines for choosing appropriate values. The software is coded in Python and capable to process very large datacubes in a short time. We verify the implementation with a source insertion and recovery experiment utilising a real datacube taken with the MUSE instrument at the ESO Very Large Telescope.Comment: 14 pages. Accepted for publication in Astronomy & Astrophysics. The LSDCat software is available at https://bitbucket.org/Knusper2000/lsdcat, v2 corrected typos and language editin

3D Spectrophotometry of Planetary Nebulae in the Bulge of M31

We introduce crowded field integral field (3D) spectrophotometry as a useful technique for the study of resolved stellar populations in nearby galaxies. As a methodological test, we present a pilot study with selected extragalactic planetary nebulae (XPN) in the bulge of M31, demonstrating how 3D spectroscopy is able to improve the limited accuracy of background subtraction which one would normally obtain with classical slit spectroscopy. It is shown that due to the absence of slit effects, 3D is a most suitable technique for spectrophometry. We present spectra and line intensities for 5 XPN in M31, obtained with the MPFS instrument at the Russian 6m BTA, INTEGRAL at the WHT, and with PMAS at the Calar Alto 3.5m Telescope. Using 3D spectra of bright standard stars, we demonstrate that the PSF is sampled with high accuracy, providing a centroiding precision at the milli-arcsec level. Crowded field 3D spectrophotometry and the use of PSF fitting techniques is suggested as the method of choice for a number of similar observational problems, including luminous stars in nearby galaxies, supernovae, QSO host galaxies, gravitationally lensed QSOs, and others.Comment: (1) Astrophysikalisches Institut Potsdam, (2) University of Durham. 18 pages, 11 figures, accepted for publication in Ap

The MUSE-Wide Survey: A first catalogue of 831 emission line galaxies

We present a first instalment of the MUSE-Wide survey, covering an area of 22.2 arcmin$^2$ (corresponding to $\sim$20% of the final survey) in the CANDELS/Deep area of the Chandra Deep Field South. We use the MUSE integral field spectrograph at the ESO VLT to conduct a full-area spectroscopic mapping at a depth of 1h exposure time per 1 arcmin$^2$ pointing. We searched for compact emission line objects using our newly developed LSDCat software based on a 3-D matched filtering approach, followed by interactive classification and redshift measurement of the sources. Our catalogue contains 831 distinct emission line galaxies with redshifts ranging from 0.04 to 6. Roughly one third (237) of the emission line sources are Lyman $\alpha$ emitting galaxies with $3 < z < 6$, only four of which had previously measured spectroscopic redshifts. At lower redshifts 351 galaxies are detected primarily by their [OII] emission line ($0.3 \lesssim z \lesssim 1.5$), 189 by their [OIII] line ($0.21 \lesssim z \lesssim 0.85$), and 46 by their H$\alpha$ line ($0.04 \lesssim z \lesssim 0.42$). Comparing our spectroscopic redshifts to photometric redshift estimates from the literature, we find excellent agreement for $z<1.5$ with a median $\Delta z$ of only $\sim 4 \times 10^{-4}$ and an outlier rate of 6%, however a significant systematic offset of $\Delta z = 0.26$ and an outlier rate of 23% for Ly$\alpha$ emitters at $z>3$. Together with the catalogue we also release 1D PSF-weighted extracted spectra and small 3D datacubes centred on each of the 831 sources.Comment: 24 pages, 14 figures, accepted for publication in A&A, data products are available for download from http://muse-vlt.eu/science/muse-wide-survey/ and later via the CD

Imaging White Blood Cells using a Snapshot Hyper-Spectral Imaging System

Automated white blood cell (WBC) counting systems process an extracted whole blood sample and provide a cell count. A step that would not be ideal for onsite screening of individuals in triage or at a security gate. Snapshot Hyper-Spectral imaging systems are capable of capturing several spectral bands simultaneously, offering co-registered images of a target. With appropriate optics, these systems are potentially able to image blood cells in vivo as they flow through a vessel, eliminating the need for a blood draw and sample staining. Our group has evaluated the capability of a commercial Snapshot Hyper-Spectral imaging system, specifically the Arrow system from Rebellion Photonics, in differentiating between white and red blood cells on unstained and sealed blood smear slides. We evaluated the imaging capabilities of this hyperspectral camera as a platform to build an automated blood cell counting system. Hyperspectral data consisting of 25, 443x313 hyperspectral bands with ~3nm spacing were captured over the range of 419 to 494nm. Open-source hyperspectral datacube analysis tools, used primarily in Geographic Information Systems (GIS) applications, indicate that white blood cells\u27 features are most prominent in the 428-442nm band for blood samples viewed under 20x and 50x magnification over a varying range of illumination intensities. The system has shown to successfully segment blood cells based on their spectral-spatial information. These images could potentially be used in subsequent automated white blood cell segmentation and counting algorithms for performing in vivo white blood cell counting

Food Tray Sealing Fault Detection in Multi-Spectral Images Using Data Fusion and Deep Learning Techniques

A correct food tray sealing is required to preserve food properties and safety for consumers. Traditional food packaging inspections are made by human operators to detect seal defects. Recent advances in the field of food inspection have been related to the use of hyperspectral imaging technology and automated vision-based inspection systems. A deep learning-based approach for food tray sealing fault detection using hyperspectral images is described. Several pixel-based image fusion methods are proposed to obtain 2D images from the 3D hyperspectral image datacube, which feeds the deep learning (DL) algorithms. Instead of considering all spectral bands in region of interest around a contaminated or faulty seal area, only relevant bands are selected using data fusion. These techniques greatly improve the computation time while maintaining a high classification ratio, showing that the fused image contains enough information for checking a food tray sealing state (faulty or normal), avoiding feeding a large image datacube to the DL algorithms. Additionally, the proposed DL algorithms do not require any prior handcraft approach, i.e., no manual tuning of the parameters in the algorithms are required since the training process adjusts the algorithm. The experimental results, validated using an industrial dataset for food trays, along with different deep learning methods, demonstrate the effectiveness of the proposed approach. In the studied dataset, an accuracy of 88.7%, 88.3%, 89.3%, and 90.1% was achieved for Deep Belief Network (DBN), Extreme Learning Machine (ELM), Stacked Auto Encoder (SAE), and Convolutional Neural Network (CNN), respectively