Multimodal Subspace Support Vector Data Description

In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification. The proposed method iteratively transforms the data from the original feature space of each modality to a new common feature space along with finding a joint compact description of data coming from all the modalities. For data in each modality, we define a separate transformation to map the data from the corresponding feature space to the new optimized subspace by exploiting the available information from the class of interest only. We also propose different regularization strategies for the proposed method and provide both linear and non-linear formulations. The proposed Multimodal Subspace Support Vector Data Description outperforms all the competing methods using data from a single modality or fusing data from all modalities in four out of five datasets.Comment: 26 pages manuscript (6 tables, 2 figures), 24 pages supplementary material (27 tables, 10 figures). The manuscript and supplementary material are combined as a single .pdf (50 pages) fil

Huddl: the Hydrographic Universal Data Description Language

Since many of the attempts to introduce a universal hydrographic data format have failed or have been only partially successful, a different approach is proposed. Our solution is the Hydrographic Universal Data Description Language (HUDDL), a descriptive XML-based language that permits the creation of a standardized description of (past, present, and future) data formats, and allows for applications like HUDDLER, a compiler that automatically creates drivers for data access and manipulation. HUDDL also represents a powerful solution for archiving data along with their structural description, as well as for cataloguing existing format specifications and their version control. HUDDL is intended to be an open, community-led initiative to simplify the issues involved in hydrographic data access

The HST/ACS Coma Cluster Survey. II. Data Description and Source Catalogs

The Coma cluster was the target of a HST-ACS Treasury program designed for deep imaging in the F475W and F814W passbands. Although our survey was interrupted by the ACS instrument failure in 2007, the partially completed survey still covers ~50% of the core high-density region in Coma. Observations were performed for 25 fields that extend over a wide range of cluster-centric radii (~1.75 Mpc) with a total coverage area of 274 arcmin^2. The majority of the fields are located near the core region of Coma (19/25 pointings) with six additional fields in the south-west region of the cluster. In this paper we present reprocessed images and SExtractor source catalogs for our survey fields, including a detailed description of the methodology used for object detection and photometry, the subtraction of bright galaxies to measure faint underlying objects, and the use of simulations to assess the photometric accuracy and completeness of our catalogs. We also use simulations to perform aperture corrections for the SExtractor Kron magnitudes based only on the measured source flux and half-light radius. We have performed photometry for ~73,000 unique objects; one-half of our detections are brighter than the 10-sigma point-source detection limit at F814W=25.8 mag (AB). The slight majority of objects (60%) are unresolved or only marginally resolved by ACS. We estimate that Coma members are 5-10% of all source detections, which consist of a large population of unresolved objects (primarily GCs but also UCDs) and a wide variety of extended galaxies from a cD galaxy to dwarf LSB galaxies. The red sequence of Coma member galaxies has a constant slope and dispersion across 9 magnitudes (-21<M_F814W<-13). The initial data release for the HST-ACS Coma Treasury program was made available to the public in 2008 August. The images and catalogs described in this study relate to our second data release.Comment: Accepted for publication in ApJS. A high-resolution version is available at http://archdev.stsci.edu/pub/hlsp/coma/release2/PaperII.pd

Peak Criterion for Choosing Gaussian Kernel Bandwidth in Support Vector Data Description

Support Vector Data Description (SVDD) is a machine-learning technique used for single class classification and outlier detection. SVDD formulation with kernel function provides a flexible boundary around data. The value of kernel function parameters affects the nature of the data boundary. For example, it is observed that with a Gaussian kernel, as the value of kernel bandwidth is lowered, the data boundary changes from spherical to wiggly. The spherical data boundary leads to underfitting, and an extremely wiggly data boundary leads to overfitting. In this paper, we propose empirical criterion to obtain good values of the Gaussian kernel bandwidth parameter. This criterion provides a smooth boundary that captures the essential geometric features of the data

Data description of ROV BEAST

This document describes the folder structure to be used to store BEAST ROV data in such a way, that it can be directly ingested by the AWI Raw Data Ingestion Framework. All Path examples are given for the Polarstern expedition PS101. Data structure will adapt to changes in the vehicle composition