IOD-CNN: Integrating Object Detection Networks for Event Recognition

Many previous methods have showed the importance of considering semantically relevant objects for performing event recognition, yet none of the methods have exploited the power of deep convolutional neural networks to directly integrate relevant object information into a unified network. We present a novel unified deep CNN architecture which integrates architecturally different, yet semantically-related object detection networks to enhance the performance of the event recognition task. Our architecture allows the sharing of the convolutional layers and a fully connected layer which effectively integrates event recognition, rigid object detection and non-rigid object detection.Comment: submitted to IEEE International Conference on Image Processing 201

A unified framework for verification techniques for object invariants

Object invariants define the consistency of objects. They have subtle semantics, mainly because of call-backs, multi-object invariants, and subclassing. Several verification techniques for object invariants have been proposed. It is difficult to compare these techniques, and to ascertain their soundness, because of their differences in restrictions on programs and invariants, in the use of advanced type systems (e.g., ownership types), in the meaning of invariants, and in proof obligations. We develop a unified framework for such techniques. We distil seven parameters that characterise a verification technique, and identify sufficient conditions on these parameters which guarantee soundness. We instantiate our framework with three verification techniques from the literature, and use it to assess soundness and compare expressiveness.peer-reviewe

Submission Tool for the DSpace-Based Learning Object Repository

4th International Conference on Open RepositoriesThis presentation was part of the session : Conference PostersThe poster briefly reports our experience with building Learning Object Repository based on DSpace and analyzes some problems we encountered with the submission system. The poster describes custom submission tool that can be used as an alternative to the DSpace submission system and provides useful extensions that allow connecting to the automatic keyword extraction services and generating of IMS and SCORM packages

Detecting and Grouping Identical Objects for Region Proposal and Classification

Often multiple instances of an object occur in the same scene, for example in a warehouse. Unsupervised multi-instance object discovery algorithms are able to detect and identify such objects. We use such an algorithm to provide object proposals to a convolutional neural network (CNN) based classifier. This results in fewer regions to evaluate, compared to traditional region proposal algorithms. Additionally, it enables using the joint probability of multiple instances of an object, resulting in improved classification accuracy. The proposed technique can also split a single class into multiple sub-classes corresponding to the different object types, enabling hierarchical classification.Comment: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Workshop Deep Learning for Robotic Vision, 21 July, 2017, Honolulu, Hawai

Database independent Migration of Objects into an Object-Relational Database

This paper reports on the CERN-based WISDOM project which is studying the serialisation and deserialisation of data to/from an object database (objectivity) and ORACLE 9i.Comment: 26 pages, 18 figures; CMS CERN Conference Report cr02_01

The distribution of mass ratios in compact object binaries

Using the StarTrack population synthesis code we compute the distribution of masses of merging compact object (black hole or neutron star) binaries. The shape of the mass distribution is sensitive to some of the parameters governing the stellar binary evolution. We discuss the possibility of constraining stellar evolution models using mass measurements obtained from the detection of compact object inspiral with the upcoming gravitational-wave observatories.Comment: 10 pages, uses spie.cls, Proc of the SPIE Conference "Astronomical Telescopes and Instrumentation

The Secrets of Salient Object Segmentation

In this paper we provide an extensive evaluation of fixation prediction and salient object segmentation algorithms as well as statistics of major datasets. Our analysis identifies serious design flaws of existing salient object benchmarks, called the dataset design bias, by over emphasizing the stereotypical concepts of saliency. The dataset design bias does not only create the discomforting disconnection between fixations and salient object segmentation, but also misleads the algorithm designing. Based on our analysis, we propose a new high quality dataset that offers both fixation and salient object segmentation ground-truth. With fixations and salient object being presented simultaneously, we are able to bridge the gap between fixations and salient objects, and propose a novel method for salient object segmentation. Finally, we report significant benchmark progress on three existing datasets of segmenting salient objectsComment: 15 pages, 8 figures. Conference version was accepted by CVPR 201

Stellar archaeology with Gaia: the Galactic white dwarf population

Gaia will identify several 1e5 white dwarfs, most of which will be in the solar neighborhood at distances of a few hundred parsecs. Ground-based optical follow-up spectroscopy of this sample of stellar remnants is essential to unlock the enormous scientific potential it holds for our understanding of stellar evolution, and the Galactic formation history of both stars and planets.Comment: Summary of a talk at the 'Multi-Object Spectroscopy in the Next Decade' conference in La Palma, March 2015, to be published in ASP Conference Series (editors Ian Skillen & Scott Trager