Topic
RTML
About: RTML is a research topic. Over the lifetime, 16 publications have been published within this topic receiving 215 citations.
Papers
TL;DR: A robust transfer metric learning (RTML) framework to effectively assist the unlabeled target learning by transferring the knowledge from the well-labeled source domain by designing an explicit rank constraint regularizer to replace the rank minimization NP-hard problem to guide the low-rank metric learning.
Abstract: Metric learning has attracted increasing attention due to its critical role in image analysis and classification. Conventional metric learning always assumes that the training and test data are sampled from the same or similar distribution. However, to build an effective distance metric, we need abundant supervised knowledge (i.e., side/label information), which is generally inaccessible in practice, because of the expensive labeling cost. In this paper, we develop a robust transfer metric learning (RTML) framework to effectively assist the unlabeled target learning by transferring the knowledge from the well-labeled source domain. Specifically, RTML exploits knowledge transfer to mitigate the domain shift in two directions, i.e., sample space and feature space. In the sample space, domain-wise and class-wise adaption schemes are adopted to bridge the gap of marginal and conditional distribution disparities across two domains. In the feature space, our metric is built in a marginalized denoising fashion and low-rank constraint, which make it more robust to tackle noisy data in reality. Furthermore, we design an explicit rank constraint regularizer to replace the rank minimization NP-hard problem to guide the low-rank metric learning. Experimental results on several standard benchmarks demonstrate the effectiveness of our proposed RTML by comparing it with the state-of-the-art transfer learning and metric learning algorithms.
205 citations
TL;DR: In this article, a generic transformation of XML data into the Resource Description Framework (RDF) and its implementation by XSLT transformations is presented, which was developed by the grid integration project for robotic telescopes of AstroGrid-D.
Abstract: A generic transformation of XML data into the Resource Description Framework (RDF) and its implementation by XSLT transformations is presented. It was developed by the grid integration project for robotic telescopes of AstroGrid-D to provide network communication through the Remote Telescope Markup Language (RTML) to its RDF based information service. The transformation's generality is explained by this example. It automates the transformation of XML data into RDF and thus solves this problem of semantic computing. Its design also permits the inverse transformation but this is not yet implemented (© 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
30 citations
TL;DR: The Remote Telescope Markup Language (RTML) as mentioned in this paper is a non-homogeneous network of imaging telescopes capable of processing requests for the acquisition and retrieval of simple astronomical images, which is designed to be independent of the specific instrumentation and software that control the remote and/or robotic telescopes.
Abstract: The scientific need for a homogenous remote telescope image request system is rapidly escalating as more remote or robotic telescopes are brought to function and scientific programs are created or adapted to use such powerful telescopes. To respond to this need, we have drafted a protocol - "Remote Telescope Markup Language" (Version 2.1) - which has enabled us to implement a non-homogeneous network of imaging telescopes capable of processing requests for the acquisition and retrieval of simple astronomical images. This protocol is designed to be independent of the specific instrumentation and software that control the remote and/or robotic telescopes. It embeds traditional astronomical features such as coordinates and exposure times, and allows for prioritized queue scheduling of telescopes while protecting the telescope operating system. The prioritization supports high-stakes interruption of other observations - "Targets of Opportunity" like optical detection of gamma-ray bursts or other transient events. Some generality in this definition and flexibility is desirable, so that a broad variety of objects and observations can be accommodated within this standard. A number of professional observatories, telescope hardware/software companies, and amateur astronomers are already working with this version of RTML and a large body of additional professional and amateur users willing to share observing time and/or provide observations for scientific or educational use could easily adopt this protocol. The next generation mark-up language (RTML 3) will include elements necessary to schedule more complex observations, enabling its use in practically all ground-based and satellite observatories.
29 citations
TL;DR: Remote Telescope Markup Language is an XML-based document format for the generic description of astronomical observation requests, incorporating a highly restructured syntax and many new features necessary in order to permit the use of RTML to organize and operate heterogeneous networks of telescopes with complex instruments.
Abstract: Remote Telescope Markup Language (RTML) is an XML-based document format for the generic description of astronomical observation requests. The latest versions (3.x) constitute a substantial modification of the previous published standard (RTML 2.1), incorporating a highly restructured syntax and many new features necessary in order to permit the use of RTML to organize and operate heterogeneous networks of telescopes with complex instruments and to assist both the users and the servers in the preparation, modification, and execution of remote observing requests. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
14 citations
TL;DR: The e-STAR (e-Science Telescopes for Astronomical Research) project uses GRID techniques to develop the software infrastructure for a global network of robotic telescopes, with a distributed approach to telescope scheduling.
Abstract: The e-STAR (e-Science Telescopes for Astronomical Research) project uses GRID techniques to develop the software infrastructure for a global network of robotic telescopes. The basic architecture is based around Intelligent Agents which request data from Discovery Nodes that may be telescopes or databases. Communication is based on a development of the XML RTML language secured using the Globus I/O library, with status serving provided via LDAP. We describe the system architecture and protocols devised to give a distributed approach to telescope scheduling, as well as giving details of the implementation of prototype Intelligent Agent and Discovery Node systems.
7 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2017 | 1 |
| 2012 | 2 |
| 2011 | 1 |
| 2010 | 2 |
| 2009 | 1 |
| 2008 | 1 |