Extending and Exploring the 2 cm Survey Sample

We present new results from the VLBA 2 cm Survey, an imaging survey of active galactic nuclei (AGN) at sub-milliarcsecond resolution. We extend the structural variability monitoring program of more than 130 extragalactic parsec-scale radio jets from over 170 AGN to a total of eight years. The sample is explored further in time for all sources, and intensively for individual objects. We report new detailed results on the compact sources III Zw 2, AO 0235+16, and NRAO 512.Comment: Proceedings of the 7th European VLBI Network Symposium (October 12-15 2004, Toledo, Spain), eds. Bachiller, R., Colomer, F., Desmurs, J. F., & de Vicente, P., 2 tables, 3 figures, needs evn2004.cl

Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization

The separability assumption (Donoho & Stodden, 2003; Arora et al., 2012) turns non-negative matrix factorization (NMF) into a tractable problem. Recently, a new class of provably-correct NMF algorithms have emerged under this assumption. In this paper, we reformulate the separable NMF problem as that of finding the extreme rays of the conical hull of a finite set of vectors. From this geometric perspective, we derive new separable NMF algorithms that are highly scalable and empirically noise robust, and have several other favorable properties in relation to existing methods. A parallel implementation of our algorithm demonstrates high scalability on shared- and distributed-memory machines.Comment: 15 pages, 6 figure

On the nature of the near-UV extended light in Seyfert galaxies

We study the nature of the extended near-UV emission in the inner kiloparsec of a sample of 15 Seyfert galaxies which have both near-UV (F330W) and narrow band [OIII] high resolution Hubble images. For the majority of the objects we find a very similar morphology in both bands. From the [OIII] images we construct synthetic images of the nebular continuum plus the emission line contribution expected through the F330W filter, which can be subtracted from the F330W images. We find that the emission of the ionised gas dominates the near-UV extended emission in half of the objects. A further broad band photometric study, in the bands F330W (U), F547M (V) and F160W (H), shows that the remaining emission is dominated by the underlying galactic bulge contribution. We also find a blue component whose nature is not clear in 4 out of 15 objects. This component may be attributed to scattered light from the AGN, to a young stellar population in unresolved star clusters, or to early-disrupted clusters. Star forming regions and/or bright off-nuclear star clusters are observed in 4/15 galaxies of the sample.Comment: 23 pages, 6 figures, 3 tables; accepted for publication in MNRA

GALFIT-CORSAIR: implementing the core-Sersic model into GALFIT

We introduce GALFIT-CORSAIR: a publicly available, fully retro-compatible modification of the 2D fitting software GALFIT (v.3) which adds an implementation of the core-Sersic model. We demonstrate the software by fitting the images of NGC 5557 and NGC 5813, which have been previously identified as core-Sersic galaxies by their 1D radial light profiles. These two examples are representative of different dust obscuration conditions, and of bulge/disk decomposition. To perform the analysis, we obtained deep Hubble Legacy Archive (HLA) mosaics in the F555W filter (~V-band). We successfully reproduce the results of the previous 1D analysis, modulo the intrinsic differences between the 1D and the 2D fitting procedures. The code and the analysis procedure described here have been developed for the first coherent 2D analysis of a sample of core-Sersic galaxies, which will be presented in a forth-coming paper. As the 2D analysis provides better constraining on multi-component fitting, and is fully seeing-corrected, it will yield complementary constraints on the missing mass in depleted galaxy cores.Comment: Accepted for publication in PASP; A binary version of GALFIT-CORSAIR is publicly available at http://astronomy.swin.edu.au/~pbonfini/galfit-corsair

Does the extension of primary care practice opening hours reduce the use of emergency services?

Over-crowding in Emergency Departments (EDs) generates potential inefficiencies. Using regional administrative data, we investigate the impact of an increase in the accessibility of primary care on ED visits in Italy. We test whether extending practice opening hours up to 12 hours/day reduces inappropriate ED visits. We estimate count data models, considering different measures for ED visits recorded at the list level. Since the extension programme is voluntary, we also account for the potential endogeneity of participation, using a two-stage residual inclusion and a GMM approach. Our results show that improving primary care accessibility favours a more appropriate use of EDs

ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks

Hash codes are efficient data representations for coping with the ever growing amounts of data. In this paper, we introduce a random forest semantic hashing scheme that embeds tiny convolutional neural networks (CNN) into shallow random forests, with near-optimal information-theoretic code aggregation among trees. We start with a simple hashing scheme, where random trees in a forest act as hashing functions by setting `1' for the visited tree leaf, and `0' for the rest. We show that traditional random forests fail to generate hashes that preserve the underlying similarity between the trees, rendering the random forests approach to hashing challenging. To address this, we propose to first randomly group arriving classes at each tree split node into two groups, obtaining a significantly simplified two-class classification problem, which can be handled using a light-weight CNN weak learner. Such random class grouping scheme enables code uniqueness by enforcing each class to share its code with different classes in different trees. A non-conventional low-rank loss is further adopted for the CNN weak learners to encourage code consistency by minimizing intra-class variations and maximizing inter-class distance for the two random class groups. Finally, we introduce an information-theoretic approach for aggregating codes of individual trees into a single hash code, producing a near-optimal unique hash for each class. The proposed approach significantly outperforms state-of-the-art hashing methods for image retrieval tasks on large-scale public datasets, while performing at the level of other state-of-the-art image classification techniques while utilizing a more compact and efficient scalable representation. This work proposes a principled and robust procedure to train and deploy in parallel an ensemble of light-weight CNNs, instead of simply going deeper.Comment: Accepted to ECCV 201