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  • Publication . Preprint . Article . Conference object . 2018
    Open Access English
    Authors: 
    Moser, Daniel; Abele, Hartmut; Bosina, Joachim; Fillunger, Harald; Soldner, Torsten; Wang, Xiangzun; Zmeskal, Johann; Konrad, Gertrud;
    Publisher: HAL CCSD
    Country: France
    Project: FWF | Particles and Interaction... (W 1252)

    The beta decay of the free neutron provides several probes to test the Standard Model of particle physics as well as to search for extensions thereof. Hence, multiple experiments investigating the decay have already been performed, are under way or are being prepared. These measure the mean lifetime, angular correlation coefficients or various spectra of the charged decay products (proton and electron). NoMoS, the Neutron decay prOducts MOmentum Spectrometer, presents a novel method of momentum spectroscopy: it utilizes the $R \times B$ drift effect to disperse charged particles dependent on their momentum in an uniformly curved magnetic field. This spectrometer is designed to precisely measure momentum spectra and angular correlation coefficients in free neutron beta decay to test the Standard Model and to search for new physics beyond. With NoMoS, we aim to measure inter alia the electron-antineutrino correlation coefficient $a$ and the Fierz interference term $b$ with an ultimate precision of $\Delta a/a < 0.3\%$ and $\Delta b < 10^{-3}$ respectively. In this paper, we present the measurement principles, discuss measurement uncertainties and systematics, and give a status update. Comment: 7 pages, 4 figures, accepted to the Proceedings of the International Workshop on Particle Physics at Neutron Sources PPNS 2018, Grenoble, France, May 24-26, 2018

  • Publication . Conference object . Part of book or chapter of book . 2018
    Open Access English
    Authors: 
    Florian Kragulj; Alexander Kaiser; Thomas Grisold;
    Publisher: IEEE Computer Society Press
    Country: Austria

    Despite ongoing interest in organizational visions, both in research and practice, there is little understanding of what a vision should entail. What makes a good vision? We approach this question from a knowledge perspective and explore what organizations need to know in order to effectively plan and perform organizational activities. We will review relevant literature and conduct a content analysis of visions of global profit-oriented organizations. By providing a synthesis of theory and practice, we suggest that organizational visions should include three knowledge enablers, which guide the creation as well as the management of (1) knowledge about organizational identity, (2) knowledge about mutual embeddedness, and (3) knowledge about emerging opportunities. Our findings can contribute to research on vision development and vision content. Furthermore, they can inform a recent discussion in the KM community to guide KM activities in organizations.

  • Publication . Conference object . Other literature type . Part of book or chapter of book . 2016
    Open Access English
    Authors: 
    Etienne Parizet; Ryan Robart; Perceval Pondrom; Jean-Christophe Chamard; Guillaume Baudet; David Quinn; Karl Janssens; Manfred Haider;
    Publisher: HAL CCSD
    Country: France
    Project: EC | EVADER (285095)

    International audience; Electric or hybrid vehicles are very silent, which represents a major advantage for the reduction of noise annoyance in urban areas. But this makes them dangerous for pedestrians, especially vulnerable ones as visually-impaired people. Current solutions consist in using warning sound so that the exterior noise of an electric vehicle is as high as for a conventional one. This may cancel the benefit of electric vehicles for the reduction of sound annoyance. The eVADER project (funded by the European Commission) aims at proposing a prototype car which combines a high safety and a low noise level. A part of the the work program consisted in perceptual studies. The goal of these studies was to evaluate the influence of various timbre parameters on the detectability and the unpleasantness of a warning sound. Results show that it is possible to make an electric vehicle easily detected while keeping its sound level much lower than the one of a conventional car.

  • English
    Authors: 
    Maros Blaha; Christoph Vogel; Audrey Richard; Jan Dirk Wegner; Thomas Pock; Konrad Schindler;
    Publisher: IEEE
    Project: EC | HOMOVIS (640156)

    We propose an adaptive multi-resolution formulation of semantic 3D reconstruction. Given a set of images of a scene, semantic 3D reconstruction aims to densely reconstruct both the 3D shape of the scene and a segmentation into semantic object classes. Jointly reasoning about shape and class allows one to take into account class-specific shape priors (e.g., building walls should be smooth and vertical, and vice versa smooth, vertical surfaces are likely to be building walls), leading to improved reconstruction results. So far, semantic 3D reconstruction methods have been limited to small scenes and low resolution, because of their large memory footprint and computational cost. To scale them up to large scenes, we propose a hierarchical scheme which refines the reconstruction only in regions that are likely to contain a surface, exploiting the fact that both high spatial resolution and high numerical precision are only required in those regions. Our scheme amounts to solving a sequence of convex optimizations while progressively removing constraints, in such a way that the energy, in each iteration, is the tightest possible approximation of the underlying energy at full resolution. In our experiments the method saves up to 98% memory and 95% computation time, without any loss of accuracy.

  • Publication . Article . Preprint . Conference object . 2021 . Embargo End Date: 28 Feb 2022
    Open Access English
    Authors: 
    Korhonen, Janne H.; Nikabadi, Amir;
    Publisher: Schloss Dagstuhl - Leibniz-Zentrum f��r Informatik
    Project: ANR | MILYON (ANR-10-LABX-0070), ANR | Avenir L.S.E. (ANR-11-IDEX-0007), EC | ScaleML (805223)

    Subgraph detection has recently been one of the most studied problems in the CONGEST model of distributed computing. In this work, we study the distributed complexity of problems closely related to subgraph detection, mainly focusing on induced subgraph detection. The main line of this work presents lower bounds and parameterized algorithms w.r.t structural parameters of the input graph: - On general graphs, we give unconditional lower bounds for induced detection of cycles and patterns of treewidth 2 in CONGEST. Moreover, by adapting reductions from centralized parameterized complexity, we prove lower bounds in CONGEST for detecting patterns with a 4-clique, and for induced path detection conditional on the hardness of triangle detection in the congested clique. - On graphs of bounded degeneracy, we show that induced paths can be detected fast in CONGEST using techniques from parameterized algorithms, while detecting cycles and patterns of treewidth 2 is hard. - On graphs of bounded vertex cover number, we show that induced subgraph detection is easy in CONGEST for any pattern graph. More specifically, we adapt a centralized parameterized algorithm for a more general maximum common induced subgraph detection problem to the distributed setting. In addition to these induced subgraph detection results, we study various related problems in the CONGEST and congested clique models, including for multicolored versions of subgraph-detection-like problems. LIPIcs, Vol. 217, 25th International Conference on Principles of Distributed Systems (OPODIS 2021), pages 15:1-15:18

  • Publication . Preprint . Conference object . Article . 2020
    Open Access English
    Authors: 
    Susanna F. de Rezende; Or Meir; Jakob Nordström; Toniann Pitassi; Robert Robere; Marc Vinyals;
    Project: EC | UTHOTP (279611), NSERC

    We significantly strengthen and generalize the theorem lifting Nullstellensatz degree to monotone span program size by Pitassi and Robere (2018) so that it works for any gadget with high enough rank, in particular, for useful gadgets such as equality and greater-than. We apply our generalized theorem to solve three open problems: •We present the first result that demonstrates a separation in proof power for cutting planes with unbounded versus polynomially bounded coefficients. Specifically, we exhibit CNF formulas that can be refuted in quadratic length and constant line space in cutting planes with unbounded coefficients, but for which there are no refutations in subexponential length and subpolynomial line space if coefficients are restricted to be of polynomial magnitude. •We give the first explicit separation between monotone Boolean formulas and monotone real formulas. Specifically, we give an explicit family of functions that can be computed with monotone real formulas of nearly linear size but require monotone Boolean formulas of exponential size. Previously only a non-explicit separation was known. •We give the strongest separation to-date between monotone Boolean formulas and monotone Boolean circuits. Namely, we show that the classical GEN problem, which has polynomial-size monotone Boolean circuits, requires monotone Boolean formulas of size $2^{\Omega(n/\text{polylog}(n))}$ . An important technical ingredient, which may be of independent interest, is that we show that the Nullstellensatz degree of refuting the pebbling formula over a DAG $G$ over any field coincides exactly with the reversible pebbling price of $G$ . In particular, this implies that the standard decision tree complexity and the parity decision tree complexity of the corresponding falsified clause search problem are equal. This is an extended abstract. The full version of the paper is available at https://arxiv.org/abs/2001.02144.

  • Closed Access English
    Authors: 
    Gita Babazadeh Eslamlou; Alexander Jung; Norbert Goertz; Mehdi Fereydooni;

    We consider the problem of recovering a graph signal from noisy and incomplete information. In particular, we propose an approximate message passing based iterative method for graph signal recovery. The recovery of the graph signal is based on noisy signal values at a small number of randomly selected nodes. Our approach exploits the smoothness of typical graph signals occurring in many applications, such as wireless sensor networks or social network analysis. The graph signals are smooth in the sense that neighboring nodes have similar signal values. Methodologically, our algorithm is a new instance of the denoising based approximate message passing framework introduced recently by Metzler et. al. We validate the performance of the proposed recovery method via numerical experiments. In certain scenarios our algorithm outperforms existing methods.

  • Publication . Preprint . Article . Conference object . 2012
    Open Access English
    Authors: 
    Roehlly, Y.; Burgarella, D.; Buat, V.; Giovannoli, É.; Noll, S.; Serra, P.;
    Publisher: HAL CCSD
    Country: France

    We present CIGALE (Burgarella et al. 2005; Noll et al. 2009), a software developed at the Laboratoire d'Astrophysique de Marseille to fit galaxy spectral energy distributions from the rest-frame far-UV to far-IR wavelength range, and to derive some of their physical parameters. We also give some examples of scientific results obtained with CIGALE. 4 pages, 2 figures, proceeding of a poster to be presented at ADASS XXI in Paris

  • Publication . Conference object . 2013
    Open Access English
    Authors: 
    Toma Kazmar; Evgeny Z. Kvon; Alexander Stark; Christoph H. Lampert;
    Project: EC | L3VISU (308036)

    In this work we propose a system for automatic classification of Drosophila embryos into developmental stages. While the system is designed to solve an actual problem in biological research, we believe that the principle underlying it is interesting not only for biologists, but also for researchers in computer vision. The main idea is to combine two orthogonal sources of information: one is a classifier trained on strongly invariant features, which makes it applicable to images of very different conditions, but also leads to rather noisy predictions. The other is a label propagation step based on a more powerful similarity measure that however is only consistent within specific subsets of the data at a time. In our biological setup, the information sources are the shape and the staining patterns of embryo images. We show experimentally that while neither of the methods can be used by itself to achieve satisfactory results, their combination achieves prediction quality comparable to human performance.

  • Publication . Article . Conference object . Preprint . 2017
    Open Access English
    Authors: 
    Naman Agarwal; Zeyuan Allen-Zhu; Brian Bullins; Elad Hazan; Tengyu Ma;

    We design a non-convex second-order optimization algorithm that is guaranteed to return an approximate local minimum in time which scales linearly in the underlying dimension and the number of training examples. The time complexity of our algorithm to find an approximate local minimum is even faster than that of gradient descent to find a critical point. Our algorithm applies to a general class of optimization problems including training a neural network and other non-convex objectives arising in machine learning.

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