Improving Retrieval Results with discipline-specific Query Expansion

Choosing the right terms to describe an information need is becoming more difficult as the amount of available information increases. Search-Term-Recommendation (STR) systems can help to overcome these problems. This paper evaluates the benefits that may be gained from the use of STRs in Query Expansion (QE). We create 17 STRs, 16 based on specific disciplines and one giving general recommendations, and compare the retrieval performance of these STRs. The main findings are: (1) QE with specific STRs leads to significantly better results than QE with a general STR, (2) QE with specific STRs selected by a heuristic mechanism of topic classification leads to better results than the general STR, however (3) selecting the best matching specific STR in an automatic way is a major challenge of this process.Comment: 6 pages; to be published in Proceedings of Theory and Practice of Digital Libraries 2012 (TPDL 2012

Computation of biochemical pathway fluctuations beyond the linear noise approximation using iNA

The linear noise approximation is commonly used to obtain intrinsic noise statistics for biochemical networks. These estimates are accurate for networks with large numbers of molecules. However it is well known that many biochemical networks are characterized by at least one species with a small number of molecules. We here describe version 0.3 of the software intrinsic Noise Analyzer (iNA) which allows for accurate computation of noise statistics over wide ranges of molecule numbers. This is achieved by calculating the next order corrections to the linear noise approximation's estimates of variance and covariance of concentration fluctuations. The efficiency of the methods is significantly improved by automated just-in-time compilation using the LLVM framework leading to a fluctuation analysis which typically outperforms that obtained by means of exact stochastic simulations. iNA is hence particularly well suited for the needs of the computational biology community.Comment: 5 pages, 2 figures, conference proceeding IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 201

Progenitors of ultra-stripped supernovae

The explosion of ultra-stripped stars in close binaries may explain new discoveries of weak and fast optical transients. We have demonstrated that helium star companions to neutron stars (NSs) may evolve into naked metal cores as low as ~1.5 Msun, barely above the Chandrasekhar mass limit, by the time they explode. Here we present a new systematic investigation of the progenitor evolution leading to such ultra-stripped supernovae (SNe), in some cases yielding pre-SN envelopes of less than 0.01 Msun. We discuss the nature of these SNe (electron-capture vs iron core-collapse) and their observational light-curve properties. Ultra-stripped SNe are highly relevant for binary pulsars, as well as gravitational wave detection of merging NSs by LIGO/VIRGO, since these events are expected to produce mainly low-kick NSs in the mass range 1.10-1.80 Msun.Comment: 7 pages, 5 figures, NS4 talk presented at the Marcel Grossmann Meeting (MG14), Rome, July 201

U-Net: Convolutional Networks for Biomedical Image Segmentation

There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .Comment: conditionally accepted at MICCAI 201