The SNAP Strong Lens Survey

Basic considerations of lens detection and identification indicate that a wide field survey of the types planned for weak lensing and Type Ia SNe with SNAP are close to optimal for the optical detection of strong lenses. Such a ``piggy-back'' survey might be expected even pessimistically to provide a catalogue of a few thousand new strong lenses, with the numbers dominated by systems of faint blue galaxies lensed by foreground ellipticals. After sketching out our strategy for detecting and measuring these galaxy lenses using the SNAP images, we discuss some of the scientific applications of such a large sample of gravitational lenses: in particular we comment on the partition of information between lens structure, the source population properties and cosmology. Understanding this partitioning is key to assessing strong lens cosmography's value as a cosmological probe.Comment: 6 pages, 4 figures. To appear in the conference proceedings of "Wide Field Imaging from Space" (published in New Astronomy Reviews), eds. T. McKay, A. Fruchter, and E. Linde

Adaptation of Person Re-identification Models for On-boarding New Camera(s)

Existing approaches for person re-identification have concentrated on either designing the best feature representation or learning optimal matching metrics in a static setting where the number of cameras are fixed in a network. Most approaches have neglected the dynamic and open world nature of the re- identification problem, where one or multiple new cameras may be temporarily on-boarded into an ex- isting system to get additional information or added to expand an existing network. To address such a very practical problem, we propose a novel approach for adapting existing multi-camera re-identification frameworks with limited supervision. First, we formulate a domain perceptive re-identification method based on geodesic flow kernel that can effectively find the best source camera (already installed) to adapt with newly introduced target camera(s), without requiring a very expensive training phase. Second, we introduce a transitive inference algorithm for re-identification that can exploit the information from best source camera to improve the accuracy across other camera pairs in a network of multiple cameras. Third, we develop a target-aware sparse prototype selection strategy for finding an informative subset of source camera data for data-efficient learning in resource constrained environments. Our approach can greatly increase the flexibility and reduce the deployment cost of new cameras in many real-world dy- namic camera networks. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art unsupervised alternatives whilst being extremely efficient to compute

On-line presentation of mineral occurrences in Greenland

The Geological Survey of Denmark and Greenland (GEUS) and the Bureau of Minerals and Petroleum (BMP, under the Government of Greenland) have co-operated on the international promotion of the mineral resources of Greenland for more than ten years. The Government of Greenland follows a strategy aimed at the development of a mining and petroleum sector in Greenland capable of yielding a significant proportion of the national income. To reach this goal it is necessary to attract international investment. In respect of mineral exploration, many parts of Greenland can still be considered virgin territory and it is therefore vital that all data relevant for the identification of possible exploration targets are available to the international mining industry. GEUS has produced many compilations of geoscience data for that purpose in traditional reports, on CD-ROMs and in scientific journals. In 2004, a new source of geoscience information was developed based on an interactive GIS facility on the Internet, and mineral exploration data and information from a region in central West Greenland are now accessible at the Greenland Mineral Occurrence Map (GMOM) website at GEUS (Fig. 1; www.geus.dk/gmom). Technically, this new facility will be maintained and developed in accordance with general principles for Internet services adopted by GEUS (e.g. Tulstrup 2004). New information from other regions of Greenland will gradually be added

Using open source middleware for securing E-Gov applications

Nowadays, a global information infrastructure connects remote parties through the use of large scale networks, and many companies focus on developing e-services based on remote resources and on interaction between remote parties. In such a context, e-Government (e-Gov) systems became of paramount importance for the Public Administration, and many ongoing development projects are targeted on their implementation and release. For open source software to play an important role in this scenario, two main technological requirements must be fulfilled: (i) the identification and optimization of de facto standards for building e-Gov open source software components and (ii) a standard integration strategy of these components into an open source middleware layer, capable of conveying a completely open-source e-Gov solution. In this paper, we argue that e-Gov systems should be constructed on a open source middleware layer, providing full public responsibility in its development

Source Free Domain Adaptation of a DNN for SSVEP-based Brain-Computer Interfaces

This paper presents a source free domain adaptation method for steady-state visually evoked potential (SSVEP) based brain-computer interface (BCI) spellers. SSVEP-based BCI spellers help individuals experiencing speech difficulties, enabling them to communicate at a fast rate. However, achieving a high information transfer rate (ITR) in the current methods requires an extensive calibration period before using the system, leading to discomfort for new users. We address this issue by proposing a method that adapts the deep neural network (DNN) pre-trained on data from source domains (participants of previous experiments conducted for labeled data collection), using only the unlabeled data of the new user (target domain). This adaptation is achieved by minimizing our proposed custom loss function composed of self-adaptation and local-regularity loss terms. The self-adaptation term uses the pseudo-label strategy, while the novel local-regularity term exploits the data structure and forces the DNN to assign the same labels to adjacent instances. Our method achieves striking 201.15 bits/min and 145.02 bits/min ITRs on the benchmark and BETA datasets, respectively, and outperforms the state-of-the-art alternative techniques. Our approach alleviates user discomfort and shows excellent identification performance, so it would potentially contribute to the broader application of SSVEP-based BCI systems in everyday life.Comment: 11 pages (including one page appendix), 5 figure