Simultaneous Spectroscopic and Photometric Observations of Binary Asteroids

We present results of visible wavelengths spectroscopic measurements (0.45 to 0.72 microns) of two binary asteroids, obtained with the 1-m telescope at the Wise Observatory on January 2008. The asteroids (90) Antiope and (1509) Esclangona were observed to search for spectroscopic variations correlated with their rotation while presenting different regions of their surface to the viewer. Simultaneous photometric observations were performed with the Wise Observatory's 0.46-m telescope, to investigate the rotational phase behavior and possible eclipse events. (90) Antiope displayed an eclipse event during our observations. We could not measure any slope change of the spectroscopic albedo within the error range of 3%, except for a steady decrease in the total light flux while the eclipse took place. We conclude that the surface compositions of the two components do not differ dramatically, implying a common origin and history. (1509) Esclangona did not show an eclipse, but rather a unique lightcurve with three peaks and a wide and flat minimum, repeating with a period of 3.2524 hours. Careful measurements of the spectral albedo slopes reveal a color variation of 7 to 10 percent on the surface of (1509) Esclangona, which correlates with a specific region in the photometric lightcurve. This result suggests that the different features on the lightcurve are at least partially produced by color variations and could perhaps be explained by the existence of an exposed fresh surface on (1509) Esclangona.Comment: 21 pages, 14 figures, 1 table, accepted for publication in Meteoritics & Planetary Science (MAPS

TAUVEX: status in 2011

We present a short history of the TAUVEX instrument, conceived to provide multi-band wide-field imaging in the ultraviolet, emphasizing the lack of sufficient and aggressive support on the part of the different space agencies that dealt with this basic science mission. First conceived in 1985 and selected by the Israel Space Agency in 1989 as its first priority payload, TAUVEX is fast becoming one of the longest-living space project of space astronomy. After being denied a launch on a national Israeli satellite, and then not flying on the Spectrum X-Gamma (SRG) international observatory, it was manifested since 2003 as part of ISRO's GSAT-4 Indian satellite to be launched in the late 2000s. However, two months before the launch, in February 2010, it was dismounted from its agreed-upon platform. This proved to be beneficial, since GSAT-4 and its launcher were lost on April 15 2010 due to the failure of the carrier rocket's 3rd stage. TAUVEX is now stored in ISRO's clean room in Bangalore with no firm indications when or on what platform it might be launched.Comment: Invited contribution presented at the "UV Universe 2010". Accepted for publication in Astrophysics & Space Scienc

HeMIS: Hetero-Modal Image Segmentation

We introduce a deep learning image segmentation framework that is extremely robust to missing imaging modalities. Instead of attempting to impute or synthesize missing data, the proposed approach learns, for each modality, an embedding of the input image into a single latent vector space for which arithmetic operations (such as taking the mean) are well defined. Points in that space, which are averaged over modalities available at inference time, can then be further processed to yield the desired segmentation. As such, any combinatorial subset of available modalities can be provided as input, without having to learn a combinatorial number of imputation models. Evaluated on two neurological MRI datasets (brain tumors and MS lesions), the approach yields state-of-the-art segmentation results when provided with all modalities; moreover, its performance degrades remarkably gracefully when modalities are removed, significantly more so than alternative mean-filling or other synthesis approaches.Comment: Accepted as an oral presentation at MICCAI 201

Radar and optical leonids

International audienceWe present joint optical-radar observations of meteors collected near the peak of the leonid activity in 2002. We show four examples of joint detections with a large, phased array L-band radar and with intensified video cameras. The general characteristic of the radar-detected optical meteors is that they show the radar detection below the termination of the optical meteor. Therefore, at least some radar events associated with meteor activity are neither head echoes nor trail echoes, but probably indicate the formation of "charged clouds" after the visual meteor is extinguished

Properties of dust in early-type galaxies

We report optical extinction properties of dust for a sample of 26 early-type galaxies based on the analysis of their multicolour CCD observations. The wavelength dependence of dust extinction for these galaxies is determined and the extinction curves are found to run parallel to the Galactic extinction curve, which implies that the properties of dust in the extragalactic environment are quite similar to those of the Milky Way. For the sample galaxies, value of the parameter $R_V$, the ratio of total extinction in $V$ band to selective extinction in $B$ & $V$ bands, lies in the range 2.03 - 3.46 with an average of 3.02, compared to its canonical value of 3.1 for the Milky Way. A dependence of $R_V$ on dust morphology of the host galaxy is also noticed in the sense that galaxies with a well defined dust lane show tendency to have smaller $R_V$ values compared to the galaxies with disturbed dust morphology. The dust content of these galaxies estimated using total optical extinction is found to lie in the range $10^4$ to 10^6 \rm M_{\sun}, an order of magnitude smaller than those derived from IRAS flux densities, indicating that a significant fraction of dust intermixed with stars remains undetected by the optical method. We examine the relationship between dust mass derived from IRAS flux and the X-ray luminosity of the host galaxies.The issue of the origin of dust in early-type galaxies is also discussed.Comment: 12 pages, 6 figures. Accepted for publication in Astronomy & Astrophysic

Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph-based approaches focus on pairwise similarities without modelling the subjects' individual characteristics and features. On the other hand, relying solely on subject-specific imaging feature vectors fails to model the interaction and similarity between subjects, which can reduce performance. In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. This structure was used to train a GCN model on partially labelled graphs, aiming to infer the classes of unlabelled nodes from the node features and pairwise associations between subjects. We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks. This has a clear impact on the quality of the predictions, leading to 69.5% accuracy for ABIDE (outperforming the current state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion, significantly outperforming standard linear classifiers where only individual features are considered.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

Tversky loss function for image segmentation using 3D fully convolutional deep networks

Fully convolutional deep neural networks carry out excellent potential for fast and accurate image segmentation. One of the main challenges in training these networks is data imbalance, which is particularly problematic in medical imaging applications such as lesion segmentation where the number of lesion voxels is often much lower than the number of non-lesion voxels. Training with unbalanced data can lead to predictions that are severely biased towards high precision but low recall (sensitivity), which is undesired especially in medical applications where false negatives are much less tolerable than false positives. Several methods have been proposed to deal with this problem including balanced sampling, two step training, sample re-weighting, and similarity loss functions. In this paper, we propose a generalized loss function based on the Tversky index to address the issue of data imbalance and achieve much better trade-off between precision and recall in training 3D fully convolutional deep neural networks. Experimental results in multiple sclerosis lesion segmentation on magnetic resonance images show improved F2 score, Dice coefficient, and the area under the precision-recall curve in test data. Based on these results we suggest Tversky loss function as a generalized framework to effectively train deep neural networks