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Rotational alignment of imu-camera systems with 1-point ransac 2nd

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Rotational alignment of imu-camera systems with 1-point rans...

2nd Chinese Conference on Pattern Recognition and computer Vision, PRCV 2019

作者： Guan, Banglei Su, Ang Li, Zhang Fraundorfer, Friedrich College of Aerospace Science and Engineering National University of Defense Technology Changsha China Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation Changsha China Institute for Computer Graphics and Vision Graz University of Technology Graz Austria

ISBN: (纸本)9783030317256

In this paper we present a minimal solution for the rotational alignment of IMU-camera systems based on a homography formulation. The image correspondences between two views are related by homography when the motion of the camera can be effectively approximated as a pure rotation. By exploiting the rotational angles of the features obtained by e.g. the SIFT detector, we compute the rotational alignment of IMU-camera systems with only 1 feature correspondence. The novel minimal case solution allows us to cope with feature mismatches efficiently and robustly within a random sample consensus RANSAC scheme. Our method is evaluated on both synthetic and real scene data, demonstrating that our method is suited for the rotational alignment of IMU-camera systems. © Springer Nature Switzerland AG 2019.

关键词： Cameras

Results of the Ontology Alignment Evaluation Initiative 2021 16

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Results of the Ontology Alignment Evaluation Initiative 2021

16th International Workshop on Ontology Matching, OM 2021

作者： Pour, Mina Abd Nikooie Algergawy, Alsayed Amardeilh, Florence Amini, Reihaneh Fallatah, Omaima Faria, Daniel Fundulaki, Irini Harrow, Ian Hertling, Sven Hitzler, Pascal Huschka, Martin Ibanescu, Liliana Jiḿenez-Ruiz, Ernesto Karam, Naouel Laadhar, Amir Lambrix, Patrick Li, Huanyu Li, Ying Michel, Franck Nasr, Engy Paulheim, Heiko Pesquita, Catia Portisch, Jan Roussey, Catherine Saveta, Tzanina Shvaiko, Pavel Splendiani, Andrea Trojahn, Ćassia Vatǎšcinova, Jana Yaman, Beyza Zamaza, Onďrej Zhou, Lu Link¨oping University Swedish E-Science Research Center Link¨oping Sweden Friedrich Schiller University Jena Germany Elzeard.co Paris France Laboratory Kansas State University United States Information School University Of Sheffield Sheffield United Kingdom Faculdade De Cîencias Universidade De Lisboa Lasige Portugal Institute Of Computer Science-FORTH Heraklion Greece Pistoia Alliance Inc. United States University Of Mannheim Germany Fraunhofer Institute For High-Speed Dynamics EMI Germany AgroParisTech Umr MIA-Paris/INRAE France University Of London City United Kingdom Department Of Informatics University Of Oslo Norway Fraunhofer Fokus Berlin Germany University Of Leipzig Germany Department Of Computer Science Aalborg University Denmark University Of G¨avle Sweden University Ĉote d'Azur CNRS Inria France University Of Freiburg Freiburg Galaxy Team Germany Inrae Centre Clermont-ARA Laboratoire Tscf France Trentino Digitale SpA Trento Italy Irit and Universit´e Toulouse II Toulouse France Prague University Of Economics And Business Czech Republic Adapt Centre Dublin City University Ireland

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2021 campaign offered 13 tracks and was attended by 21 participants. This paper is an overall presentation of that campaign. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Ontology

Power-efficient silicon nitride thermo-optic phase shifters for visible light

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arXiv 2021年

作者： Yong, Zheng Chen, Hong Luo, Xianshu Govdeli, Alperen Chua, Hongyao Azadeh, Saeed S. Stalmashonak, Andrei Lo, Guo-Qiang Poon, Joyce K.S. Sacher, Wesley D. Max Planck Institute of Microstructure Physics Weinberg 2 Halle06120 Germany Department of Electrical and Computer Engineering University of Toronto 10 King's College Road TorontoONM5S 3G4 Canada Advanced Micro Foundry Pte. Ltd. Singapore Science Park II 11 Science Park Road 117685 Singapore

We demonstrate power-efficient, thermo-optic, silicon nitride waveguide phase shifters for blue, green, and yellow wavelengths. The phase shifters operated with low power consumption due to a suspended structure and multi-pass waveguide design. The devices were fabricated on 200-mm silicon wafers using deep ultraviolet lithography as part of an active visible-light integrated photonics platform. The measured power consumption to achieve a π phase shift (averaged over multiple devices) was 0.78, 0.93, 1.09, and 1.20 mW at wavelengths of 445, 488, 532, and 561 nm, respectively. The phase shifters were integrated into Mach-Zehnder interferometer switches, and 10 − 90% rise(fall) times of about 570(590) µs were measured. © 2021, CC BY-NC-ND.

关键词： Silicon wafers

Revisiting Clustering as Matrix Factorisation on the Stiefel Manifold 6th

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Revisiting Clustering as Matrix Factorisation on the Stiefel...

6th International Conference on Machine Learning, Optimization, and Data science, LOD 2020

作者： Chrétien, Stéphane Guedj, Benjamin Université Lumière-Lyon-II Bron Cedex69676 France The Alan Turing Institute London United Kingdom The National Physical Laboratory TeddingtonTW11 0LW United Kingdom Inria Lille - Nord Europe Research Centre Lille France Department of Computer Science and Centre for Artificial Intelligence University College London London United Kingdom

ISBN: (纸本)9783030645823

This paper studies clustering for possibly high dimensional data (e.g. images, time series, gene expression data, and many other settings), and rephrase it as low rank matrix estimation in the PAC-Bayesian framework. Our approach leverages the well known Burer-Monteiro factorisation strategy from large scale optimisation, in the context of low rank estimation. Moreover, our Burer-Monteiro factors are shown to lie on a Stiefel manifold. We propose a new generalized Bayesian estimator for this problem and prove novel prediction bounds for clustering. We also devise a componentwise Langevin sampler on the Stiefel manifold to compute this estimator. © 2020, Springer Nature Switzerland AG.

关键词： Non-negative matrix factorization

Forensic Analysis of Synthetically Generated Western Blot Images

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arXiv 2021年

作者： Mandelli, Sara Cozzolino, Davide Cannas, Edoardo Daniele Cardenuto, João Phillipe Moreira, Daniel Bestagini, Paolo Scheirer, Walter Rocha, Anderson Verdoliva, Luisa Tubaro, Stefano Delp, Edward J. The Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan20133 Italy The Dipartimento di Ingegneria Elettrica e Tecnologie Dell'Informazione University Federico Ii of Naples Naples80125 Italy The Dipartimento di Ingegneria Industriale University Federico Ii of Naples Naples80125 Italy The Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States The Artificial Intelligence Lab RECOD.ai Institute of Computing University of Campinas Campinas13083-970 Brazil The School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States

The widespread diffusion of synthetically generated content is a serious threat that needs urgent countermeasures. As a matter of fact, the generation of synthetic content is not restricted to multimedia data like videos, photographs or audio sequences, but covers a significantly vast area that can include biological images as well, such as western blot and microscopic images. In this paper, we focus on the detection of synthetically generated western blot images. These images are largely explored in the biomedical literature and it has been already shown they can be easily counterfeited with few hopes to spot manipulations by visual inspection or by using standard forensics detectors. To overcome the absence of publicly available data for this task, we create a new dataset comprising more than 14K original western blot images and 24K synthetic western blot images, generated using four different state-of-the-art generation methods. We investigate different strategies to detect synthetic western blots, exploring binary classification methods as well as one-class detectors. In both scenarios, we never exploit synthetic western blot images at training stage. The achieved results show that synthetically generated western blot images can be spot with good accuracy, even though the exploited detectors are not optimized over synthetic versions of these scientific images. We also test the robustness of the developed detectors against post-processing operations commonly performed on scientific images, showing that we can be robust to JPEG compression and that some generative models are easily recognizable, despite the application of editing might alter the artifacts they leave. Copyright © 2021, The Authors. All rights reserved.

关键词： Image compression

A distributed neural network architecture for robust non-linear spatio-temporal prediction 28

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A distributed neural network architecture for robust non-lin...

28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020

作者： Karlbauer, Matthias Otte, Sebastian Lensch, Hendrik P.A. Scholten, Thomas Wulfmeyer, Volker Butz, Martin V. University of Tübingen Neuro-Cognitive Modeling Group Sand 14 Tübingen72076 Germany University of Tübingen Computer Graphics Maria-von-Linden-Straße 6 Tübingen72076 Germany University of Tübingen - Soil Science and Geomorphology Rümelinstraße 19-23 Tübingen72070 Germany University of Hohenheim Institute for Physics and Meteorology Garbenstraße 30 Stuttgart70599 Germany

ISBN: (纸本)9782875870742

DISTANA - a distributed spatio-temporal artificial neural network architecture - learns to model and predict spatio-temporal time series dynamics. It learns in a parallel, spatially distributed manner while employing a mesh of recurrent, neural prediction kernels (PKs). Individual PKs predict the local data stream and exchange information laterally. DISTANA essentially assumes that generally applicable causes, which may be locally modified, generate the observed data. We show that DISTANA scales and generalizes to large problem spaces, can approximate complex dynamics, and is robust to overfitting, outperforming other competitive ANNs. © ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

关键词： Network architecture

science with the Global Cosmic-ray Observatory (GCOS) 38

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Science with the Global Cosmic-ray Observatory (GCOS)

38th International Cosmic Ray Conference, ICRC 2023

作者： Batista, Rafael Alves Ahlers, M. Alves Batista, R. Assis, P. Battisti, M. Bellido, J.A. Bhatnagar, S. Bismark, K. Bister, T. Bohacova, M. Caccianiga, L. Caruso, R. Carvalho, W. Castellina, A. Colalillo, R. Convenga, F. Dawson, B. De Mitri, I. de Palma, F. Deval, L. di Matteo, A. DuVernois, M. Engel, R. Eser, J. Fitoussi, T. Flaggs, B. Fujii, T. Garzelli, M.V. Gkika, V. Hamal, P. Hariharan, B. Haungs, A. Hnatyk, B. Hörandel, J. Horváth, P. Hrabovsky, M. Huege, T. Ikeda, D. Isar, P.G. James, R. Johnsen, J. Kampert, K.-H. Kleifges, M. Koundal, P. Liu, J. Longo, F. Mandat, D. Mariş, I.C. Matthews, J. Mayotte, E. Medina-Tanco, G. Mulrey, K. Muzio, M.S. Nellen, L. Niechciol, M. Ogio, S. Oikonomou, F. Ostrowski, M. Pont, B. Porcelli, A. Rautenberg, J. Rieger, F. Risse, M. Roth, M. Sako, T. Salamida, F. Santangelo, A. Santos, E. Sarazin, F. Saveliev, A. Schimp, M. Schmidt, D. Schröder, F. Sergijenko, O. Sigl, G. Soldin, D. Spiczak, G. Tameda, Y. Unger, M. van Vliet, A. Vorobiov, S. Wibig, T. Wundheiler, B. Yushkov, A. Zotov, M. Instituto de Física Teórica UAM-CSIC C Nicolás Cabrera 13-15 Madrid28049 Spain Departamento de Física Teórica Universidad Autónoma de Madrid M-15 Madrid28049 Spain Niels Bohr Institute Copenhagen Denmark Instituto de Física Teórica UAM-CSIC Madrid Spain Universidad Autónoma de Madrid Departamento de Física Teórica Madrid Spain Universidade de Lisboa Instituto Superior Técnico Lisboa Portugal Universidade de Lisboa Laboratório de Instrumentação e Física Experimental de Partículas Lisboa Portugal Université de Paris Astroparticule et Cosmologie Paris France The University of Adelaide Physics Department Adelaide Australia Dayalbagh Educational Institute Department of Physics and Computer Science Agra India Institute for Experimental Particle Physics Karlsruhe Germany Radboud University Institute for Mathematics Astrophysics and Particle Physics Nijmegen Netherlands Amsterdam Netherlands Institute of Physics The Czech Academy of Sciences Prague Czech Republic Sezione di Milano Milan Italy Department of Physics and Astronomy "E. Majorana" University of Catania Catania Italy Sezione di Catania Catania Italy Sezione di Torino Turin Italy Turin Italy Università degli Studi di Napoli "Federico II" Naples Italy Sezione di Napoli Naples Italy Università dell’Aquila Dipartimento di Scienze Fisiche e Chimiche L’Aquila Italy Laboratori Nazionali del Gran Sasso L’Aquila Assergi Italy Gran Sasso Science Institute L’Aquila Italy Università del Salento Dipartimento di Matematica e Fisica "E. De Giorgi" Lecce Italy Institute for Astroparticle Physics Karlsruhe Germany University of Wisconsin-Madison Department of Physics MadisonWI United States University of Wisconsin-Madison Wisconsin IceCube Particle Astrophysics Center MadisonWI United States The University of Chicago Department of Astronomy and Astrophysics ChicagoIL United States University of Delaware Bartol Research Institute NewarkDE United States Osaka Metropolitan Univers

The Global Cosmic-ray Observatory (GCOS) is a proposed large-scale observatory for studying ultra-high-energy cosmic particles, including ultra-high-energy cosmic rays (UHECRs), photons, and neutrinos. Its primary goal is to characterise the properties of the highest-energy particles in Nature with unprecedented accuracy, and to identify their elusive sources. With an aperture at least a ten-fold larger than existing observatories, this next-generation facility should start operating after 2030, when present-day detectors will gradually cease their activities. Here we briefly review the scientific case motivating GCOS. We present the status of the project, preliminary ideas for its design, and some estimates of its capabilities. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

Recommendations for repositories and scientific gateways from a neuroscience perspective

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arXiv 2022年

作者： Sandström, Malin Abrams, Mathew Bjaalie, Jan Hicks, Mona Kennedy, David Kumar, Arvind Poline, J.B. Roy, Prasun Tiesinga, Paul Wachtler, Thomas Goscinski, Wojtek INCF Secretariat Karolinska Institutet Stockholm Sweden Institute Of Basic Medical Sciences University Of Oslo Oslo Norway One Mind Department Of Psychiatry University Of Massachusetts Chan Medical School North. Worcester United States Div. Of Computational Science And Technology School Of Electrical Engineering And Computer Science KTH Royal Institute Of Technology Stockholm Sweden Montreal Neurological Institute Faculty Of Medicine And Health Sciences McGill University Montreal Canada Varanasi India Department Of Neuroinformatics Donders Centre For Neuroscience Faculty Of Science Radboud University Nijmegen Netherlands Department Of Biology II Ludwig-Maximilians-Universität München Planegg-Martinsried Germany National Imaging Facility University Of Queensland St Lucia Australia Monash EResearch Centre Monash University Clayton3800 Australia

Digital services such as repositories and science gateways have become key resources for the neuroscience community, but users often have a hard time orienting themselves in the service landscape to find the best fit for their particular needs. INCF (International Neuroinformatics Coordinating Facility) has developed a set of recommendations and associated criteria for choosing or setting up and running a repository or scientific gateway, intended for the neuroscience community, with a FAIR neuroscience perspective. These recommendations have neurosciences as their primary use case but are often general. Considering the perspectives of researchers and providers of repositories as well as scientific gateways, the recommendations harmonize and complement existing work on criteria for repositories and best practices. The recommendations cover a range of important areas including accessibility, licensing, community responsibility and technical and financial sustainability of a service. © 2022, CC BY.

关键词： Neurology

Improved Upper Bounds on the Growth Constants of Polyominoes and Polycubes

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arXiv 2019年

作者： Barequet, Gill Shalah, Mira Center for Graphics Geometric Computing Computer Science Department Technion—Israel Institute of Technology Haifa3200003 Israel Computer Science Department Stanford University CA United States

A d-dimensional polycube is a facet-connected set of cells (cubes) on the d-dimensional cubical lattice Zd. Let Ad(n) denote the number of d-dimensional polycubes (distinct up to translations) with n cubes, and λd denote the limit of the ratio Ad(n+1)/Ad(n) as n → ∞. The exact value of λd is still unknown rigorously for any dimension d ≥ 2;the asymptotics of λd, as d → ∞, also remained elusive as of today. In this paper, we revisit and extend the approach presented by Klarner and Rivest in 1973 to bound A2(n) from above. Our contributions are: • Using available computing power, we prove that λ2 ≤ 4.5252. This is the first improvement of the upper bound on λ2 in almost half a century;• We prove that λd ≤ (2d−2)e+o(1) for any value of d ≥ 2, using a novel construction of a rational generating function which dominates that of the sequence (Ad(n));• For d = 3, this provides a subtantial improvement of the upper bound on λ3 from 12.2071 to 9.8073;• However, we implement an iterative process in three dimensions, which improves further the upper bound on λ3 to 9.3835;Copyright © 2019, The Authors. All rights reserved.

关键词： Rational functions