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Self-Stabilizing Automatic Repeat Request Algorithms for (Bounded Capacity, Omitting, Duplicating and non-FIFO) computer Networks

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arXiv 2020年

作者： Dolev, Shlomi Hanemann, Ariel Schiller, Elad Michael Sharma, Shantanu Department of Computer Science Ben-Gurion University of the Negev Beer-Sheva Israel Department of Computer Science and Engineering Chalmers University of Technology Göteborg Sweden

End-to-end communication over the network layer (or data link in overlay networks) is one of the most important communication tasks in every communication network, including legacy communication networks as well as mobile ad hoc networks, peer-to-peer networks and mesh networks. Reliable end-to-end communications are based on Automatic Repeat reQuest (ARQ) algorithms for dealing with packet failures, such as packet drops. We study ARQ algorithms that exchange packets to deliver (high level) messages in first-in-first-out (FIFO) order without omissions or duplications. We present a self-stabilizing ARQ algorithm that can be applied to networks of bounded capacity that are prone to packet loss, duplication, and reordering. Our analysis considers Lamport’s happened-before relation when demonstrating stabilization without assuming the presence of a fair scheduler. It shows that the length of the longest chain of Lamport’s happened-before relation is 8 for any system run. Copyright © 2020, The Authors. All rights reserved.

关键词： Packet loss

Sensitivity Approximation by the Peano-Baker Series

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arXiv 2021年

作者： Eriksson, Olivia Kramer, Andrei Milinanni, Federica Nyquist, Pierre Department of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Science for Life Laboratory Solna Sweden Department of Neuroscience Karolinska Institute Solna Sweden Department of Mathematics KTH Royal Institute of Technology Stockholm Sweden Department of Mathematical Sciences Chalmers University of Technology University of Gothenburg Gothenburg Sweden

In this paper we develop a new method for numerically approximating sensitivities in parameter-dependent ordinary differential equations (ODEs). Our approach, intended for situations where the standard forward and adjoint sensitivity analyses become too computationally costly for practical purposes, is based on the Peano-Baker series from control theory. Using this series, we construct a representation of the sensitivity matrix S and, from this representation, a numerical method for approximating S. We prove that, under standard regularity assumptions, the error of our method scales as O(∆t2max), where ∆tmax is the largest time step used when numerically solving the ODE. We illustrate the performance of the method in several numerical experiments, taken from both the systems biology setting and more classical dynamical systems. The experiments show the sought-after improvement in running time of our method compared to the forward sensitivity approach. In experiments involving a random linear system, the forward approach requires roughly √n longer computational time, where n is the dimension of the parameter space, than our proposed *** Codes 65L05, 65L20 © 2021, CC BY-SA.

关键词： Sensitivity analysis

Regularity and numerical approximation of fractional elliptic differential equations on compact metric graphs

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arXiv 2023年

作者： Bolin, David Kovács, Mihály Kumar, Vivek Simas, Alexandre B. Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Mathematical Sciences Chalmers University of Technology University of Gothenburg GothenburgSE-41296 Sweden Department of Differential Equations Budapest University of Technology and Economics Mauegyetem rkp. 3. BudapestH-1111 Hungary Faculty of Information Technology and Bionics Pázmány Péter Catholic University Práter utca 50/a. BudapestH-1083 Hungary Theoretical Statistics and Mathematics Unit Indian Statistical Institute Bangalore Centre 8th Mile Mysore Road Karnataka Bangalore560059 India

The fractional differential equation Lβu = f posed on a compact metric graph is considered, where β > 0 and L = κ2 − ∇(a∇) is a second-order elliptic operator equipped with certain vertex conditions and sufficiently smooth and positive coefficients κ, a. We demonstrate the existence of a unique solution for a general class of vertex conditions and derive the regularity of the solution in the specific case of Kirchhoff vertex conditions. These results are extended to the stochastic setting when f is replaced by Gaussian white noise. For the deterministic and stochastic settings under generalized Kirchhoff vertex conditions, we propose a numerical solution based on a finite element approximation combined with a rational approximation of the fractional power L−β. For the resulting approximation, the strong error is analyzed in the deterministic case, and the strong mean squared error as well as the L2(Γ×Γ)error of the covariance function of the solution are analyzed in the stochastic setting. Explicit rates of convergences are derived for all cases. Numerical experiments for L = κ2 − ∆, κ > 0 are performed to illustrate the *** Codes 35R02, 35A01, 35A02, 60H15, 60H40 Copyright © 2023, The Authors. All rights reserved.

关键词： Errors

Infrastructure-Based Multi-camera Calibration Using Radial Projections 16th

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Infrastructure-Based Multi-camera Calibration Using Radial P...

16th European Conference on computer Vision, ECCV 2020

作者： Lin, Yukai Larsson, Viktor Geppert, Marcel Kukelova, Zuzana Pollefeys, Marc Sattler, Torsten Department of Computer Science ETH Zürich Zürich Switzerland VRG Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic Microsoft Mixed Reality & AI Zurich Lab Zürich Switzerland Department of Electrical Engineering Chalmers University of Technology Gothenburg Sweden

ISBN: (纸本)9783030585167

Multi-camera systems are an important sensor platform for intelligent systems such as self-driving cars. Pattern-based calibration techniques can be used to calibrate the intrinsics of the cameras individually. However, extrinsic calibration of systems with little to no visual overlap between the cameras is a challenge. Given the camera intrinsics, infrastructure-based calibration techniques are able to estimate the extrinsics using 3D maps pre-built via SLAM or Structure-from-Motion. In this paper, we propose to fully calibrate a multi-camera system from scratch using an infrastructure-based approach. Assuming that the distortion is mainly radial, we introduce a two-stage approach. We first estimate the camera-rig extrinsics upto a single unknown translation component per camera. Next, we solve for both the intrinsic parameters and the missing translation components. Extensive experiments on multiple indoor and outdoor scenes with multiple multi-camera systems show that our calibration method achieves high accuracy and robustness. In particular, our approach is more robust than the naive approach of first estimating intrinsic parameters and pose per camera before refining the extrinsic parameters of the system. The implementation is available at https://***/youkely/InfrasCal. © 2020, Springer Nature Switzerland AG.

关键词： Cameras

Morphological computation and learning to learn in natural intelligent systems and AI

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arXiv 2020年

作者： Dodig-Crnkovic, Gordana Department of Computer Science and Engineering Chalmers University of Technology University of Gothenburg Sweden School of Innovation Design and Engineering Mälardalen University Sweden

At present, artificial intelligence in the form of machine learning is making impressive progress, especially the field of deep learning (DL) [1]. Deep learning algorithms have been inspired from the beginning by nature, specifically by the human brain, in spite of our incomplete knowledge about its brain function. Learning from nature is a two-way process as discussed in [2][3][4], computing is learning from neuroscience, while neuroscience is quickly adopting information processing models. The question is, what can the inspiration from computational nature at this stage of the development contribute to deep learning and how much models and experiments in machine learning can motivate, justify and lead research in neuroscience and cognitive science and to practical applications of artificial intelligence. Copyright © 2020, The Authors. All rights reserved.

关键词： Neurology

The Hydrogen Intensity Real-Time Analysis eXperiment: Overview and Status Update

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The Hydrogen Intensity Real-Time Analysis eXperiment: Overvi...

European Conference on Antennas and Propagation, EuCAP

作者： Anthony Walters Keshav Bechoo Shruti Bhatporia H. Cynthia Chiang Devin Crichton Jacobus Diener Sindhu Gaddam Kit Gerodias Austin Gumba Neeraj Gupta Surajit Kalita Emily Kuhn Martin Kunz Kavilan Moodley Warren Naidoo Viraj Nistane Tasmiya Papiah Aditi Pattabhiraman Aritha Pillay Alice Pisani Isibabale Qhoboshiyane Alexandre Refregier Edwin Retana-Montenegro Benjamin R. B. Saliwanchik Ajith Sampath Tsepo Sekhoasha Mugundhan Vijayaraghavan Amanda Weltman Astrophysics Research Centre University of KwaZulu-Natal Westville Campus Durban South Africa School of Mathematics Statistics & Computer Science University of KwaZulu-Natal Westville Campus Durban South Africa Department of Mathematics and Applied Mathematics High Energy Physics Cosmology & Astrophysics Theory (HEPCAT) Group University of Cape Town Cape Town South Africa Department of Physics McGill University Quebec Canada Institute for Particle Physics and Astrophysics ETH Zürich Zürich Switzerland Department of Physics and Astronomy Botswana International University of Science and Technology Palapye Botswana Inter-University Centre for Astronomy and Astrophysics Pune India NASA Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA Département de Physique Théorique and Centre for Astroparticle Physics Université de Genève Geneva Switzerland Stanford University Stanford CA Center for Computational Astrophysics Flatiron Institute New York NY USA The Cooper Union for the Advancement of Science and Art New York NY USA Department of Electronic and Computer Engineering Durban University of Technology Durban South Africa Department of Astrophysical Sciences Peyton Hall Princeton University Princeton NJ USA Instrumentation Division Brookhaven National Laboratory Upton NY USA Onsala Space Observatory Chalmers University of Technology Gothenburg Sweden

The Hydrogen Intensity Real-time Analysis eXperiment (HIRAX) will be a large interferometric array of drift-scan radio telescopes designed to map the large-scale spatial fluctuations of neutral hydrogen in the Universe, in order to better understand the nature of dark energy. It will operate between 400–800 MHz, and is currently under construction in the Karoo desert of South Africa. It will also be a powerful tool for studying astronomical transients and the evolution of galaxies. Here we present an overview of the project, its anticipated deployment timelines, and highlight some of the characterisation and calibration efforts that are currently underway, or planned for the future.

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FLOWR – FLOW MATCHING FOR STRUCTURE-AWARE De Novo, INTERACTION- AND FRAGMENT-BASED LIGAND GENERATION

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arXiv 2025年

作者： Cremer, Julian Irwin, Ross Tibo, Alessandro Janet, Jon Paul Olsson, Simon Clevert, Djork-Arné Machine Learning & Computational Sciences Pfizer Worldwide R&D Berlin Germany Molecular AI Discovery Sciences R&D AstraZeneca Gothenburg Sweden Department of Computer Science and Engineering Chalmers University of Technology University of Gothenburg Gothenburg Sweden

We introduce FLOWR, a novel structure-based framework for the generation and optimization of three-dimensional ligands. FLOWR integrates continuous and categorical flow matching with equivariant optimal transport, enhanced by an efficient protein pocket conditioning. Alongside FLOWR, we present SPINDR, a thoroughly curated dataset comprising ligand-pocket co-crystal complexes specifically designed to address existing data quality issues. Empirical evaluations demonstrate that FLOWR surpasses current state-of-the-art diffusion- and flow-based methods in terms of PoseBusters-validity, pose accuracy, and interaction recovery, while offering a significant inference speedup, achieving up to 70-fold faster performance. In addition, we introduce ***, a highly accurate multi-purpose model allowing for the targeted sampling of novel ligands that adhere to predefined interaction profiles and chemical substructures for fragment-based design without the need of re-training or any re-sampling strategies. Collectively, our results indicate that FLOWR and *** represent a significant advancement in AI-driven structure-based drug design, substantially enhancing the reliability and applicability of de novo, interaction- and fragment-based ligand generation in real-world drug discovery settings. © 2025, CC BY.

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Molecular outflow in the reionization-epoch quasar J2054-0005 revealed by OH 119 μm observations

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arXiv 2023年

作者： Salak, Dragan Hashimoto, Takuya Inoue, Akio K. Bakx, Tom J.L.C. Donevski, Darko Tamura, Yoichi Sugahara, Yuma Kuno, Nario Miyamoto, Yusuke Fujimoto, Seiji Suphapolthaworn, Suphakorn Institute for the Advancement of Higher Education Hokkaido University Kita 17 Nishi 8 Kita-ku Hokkaido Sapporo060-0817 Japan Department of Cosmosciences Graduate School of Science Hokkaido University Kita 10 Nishi 8 Kita-ku Hokkaido Sapporo060-0810 Japan Faculty of Pure and Applied Science University of Tsukuba Ibaraki305-8571 Japan Department of Physics School of Advanced Science and Engineering Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo169-8555 Japan Waseda Research Institute for Science and Engineering Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo169-8555 Japan Department of Space Earth & Environment Chalmers University of Technology Chalmersplatsen 4 Gothenburg412 96 Sweden Pasteura 7 Warsaw02-093 Poland SISSA ISAS Via Bonomea 265 TriesteI-34136 Italy IFPU Institute for Fundamental Physics of the Universe Via Beirut 2 TriesteI-34014 Italy Division of Particle and Astrophysical Science Graduate School of Science Nagoya University Aichi 464-8602 Japan National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Department of Electrical Electronic and Computer Engineering Fukui University of Technology 3-6-1 Gakuen Fukui Fukui910-8505 Japan Department of Astronomy The University of Texas at Austin AustinTX78712 United States

Molecular outflows are expected to play a key role in galaxy evolution at high redshift. To study the impact of outflows on star formation at the epoch of reionization, we performed sensitive ALMA observations of OH 119 µm toward J2054-0005, a luminous quasar at z = 6.04. The OH line is detected and exhibits a P-Cygni profile that can be fitted with a broad blue-shifted absorption component, providing unambiguous evidence of an outflow, and an emission component at near-systemic velocity. The mean and terminal outflow velocities are estimated to be vout ≈ 670 km s-1 and 1500 km s-1, respectively, making the molecular outflow in this quasar one of the fastest at the epoch of reionization. The OH line is marginally spatially resolved for the first time in a quasar at z > 6, revealing that the outflow extends over the central 2 kpc region. The mass outflow rate is comparable to the star formation rate (M·out/SFR ∼ 2), indicating rapid (∼ 107 yr) quenching of star formation. The mass outflow rate in a sample of star-forming galaxies and quasars at 4 Copyright © 2023, The Authors. All rights reserved.

关键词： Stars

Hydrodynamic simulations of white dwarf-white dwarf mergers and the origin of R Coronae Borealis stars

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arXiv 2024年

作者： Shiber, Sagiv De Marco, Orsola Motl, Patrick M. Munson, Bradley Marcello, Dominic C. Frank, Juhan Diehl, Patrick Clayton, Geoffrey C. Skinner, Bennett N. Kaiser, Hartmut Daiß, Gregor Pflüger, Dirk Staff, Jan E. Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States School of Mathematical and Physical Sciences Macquarie University SydneyNSW2109 Australia Astronomy Astrophysics and Astrophotonics Research Centre Macquarie University SydneyNSW2109 Australia The School of Sciences Indiana University Kokomo KokomoIN46902 United States LSU Center for Computation & Technology Louisiana State University Baton RougeLA70803 United States Los Alamos National Laboratory Los AlamosNM87545 United States NSF-supported REU Student at LSU United States IPVS University of Stuttgart Stuttgart Stuttgart70174 Germany Division of Computer Science and Engineering Louisiana State University Baton RougeLA70803 United States Department of Space Earth and Environment Chalmers University of Technology GothenburgSE-41296 Sweden

We study the properties of double white dwarf (DWD) mergers by performing hydrodynamic simulations using the new and improved adaptive mesh refinement code Octo-Tiger. We follow the orbital evolution of DWD systems of mass ratio q = 0.7 for tens of orbits until and after the merger to investigate them as a possible origin for R Coronae Borealis (RCB) type stars. We reproduce previous results, finding that during the merger, the helium WD donor star is tidally disrupted within 20-80 minutes since the beginning of the simulation onto the accretor carbon-oxygen WD, creating a high temperature shell around the accretor. We investigate the possible helium burning in this shell and the merged object’s general structure. Specifically, we are interested in the amount of oxygen-16 dredged-up from the accretor to the hot shell and the amount of oxygen-18 produced. This is critical as the discovery of very low oxygen-16 to oxygen-18 ratios in RCB stars pointed out the merger scenario as a favorable explanation for their origin. A small amount of hydrogen in the donor may help keep the oxygen-16 to oxygen-18 ratios within observational bounds, even if moderate dredge-up from the accretor occurs. In addition, we perform a resolution study to reconcile the difference found in the amount of oxygen-16 dredge-up between smoothed-particle hydrodynamics and grid-based simulations. © 2024, CC BY.

关键词： White dwarfs