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arXiv 2025年

作者： Wolter, Moritz Veeramacheneni, Lokesh High-Performance Computing and Analytics Lab Bonn University Bonn Germany

Experimental verification and falsification of scholarly work are part of the scientific method’s core. To improve the Machine Learning (ML)communities’ ability to verify results from prior work, we argue for more robust software engineering. We estimate the adoption of common engineering best practices by examining repository links from all recently accepted International Conference on Machine Learning (ICML), International Conference on Learning Representations (ICLR) and Conference on Neural Information Processing Systems (NeurIPS) papers as well as International Conference on Machine Learning (ICML) papers over time. Based on the results, we recommend how we, as a community, can improve reproducibility in Machine Learning (ML)research. © 2025, CC BY.

关键词： Adversarial machine learning

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Quark and Gluon Momentum Fractions in the Pion and in the Kaon

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Physical Review Letters 2025年第13期134卷 131902-131902页

作者： Constantia Alexandrou Simone Bacchio Martha Constantinou Joseph Delmar Jacob Finkenrath Bartosz Kostrzewa Marcus Petschlies Luis Alberto Rodriguez Chacon Gregoris Spanoudes Department of Physics University of Cyprus Nicosia Cyprus Computation-based Science and Technology Research Center The Cyprus Institute Nicosia Cyprus Department of Physics Temple University Philadelphia Pennsylvania 19122–1801 USA Department of Physics Bergische Universität Wuppertal Gaußstraße 20 Wuppertal 42119 Germany High Performance Computing and Analytics Lab Rheinische Friedrich-Wilhelms-Universität Bonn Germany Helmholtz-Institut für Strahlen- und Kernphysik University of Bonn Germany Bethe Center for Theoretical Physics University of Bonn Germany Department of Physics and Earth Science University of Ferrara & INFN Via Saragat 1 44122 Ferrara Italy

We present the full decomposition of the momentum fraction carried by quarks and gluons in the pion and the kaon. We employ three gauge ensembles generated with Nf=2+1+1 Wilson twisted-mass clover-improved fermions at the physical quark masses. For both mesons we perform a continuum extrapolation directly at the physical pion mass, which allows us to determine for the first time the momentum decomposition at the physical point. We find that the total momentum fraction carried by quarks is 0.575(79) and 0.683(50) and by gluons 0.402(53) and 0.422(67) in the pion and in the kaon, respectively, in the MS¯ scheme and at the renormalization scale of 2 GeV. Having computed both the quark and gluon contributions in the continuum limit, we find that the momentum sum is 0.984(89) for the pion and 1.13(11) for the kaon, verifying the momentum sum rule.

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Correction to: Inductive linearization for binary quadratic programs with linear constraints

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4OR 2021年第4期19卷 638-639页

作者： Mallach, Sven High Performance Computing and Analytics Lab University of Bonn Bonn Germany

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Towards generalizing deep-audio fake detection networks

arXiv

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arXiv 2023年

作者： Gasenzer, Konstantin Wolter, Moritz High Performance Computing and Analytics Lab University of Bonn Germany

Today's generative neural networks allow the creation of high-quality synthetic speech at scale. While we welcome the creative use of this new technology, we must also recognize the risks. As synthetic speech is abused for monetary and identity theft, we require a broad set of deepfake identification tools. Furthermore, previous work reported a limited ability of deep classifiers to generalize to unseen audio generators. We study the frequency domain fingerprints of current audio generators. Building on top of the discovered frequency footprints, we train excellent lightweight detectors that generalize. We report improved results on the WaveFake dataset and an extended version. To account for the rapid progress in the field, we extend the WaveFake dataset by additionally considering samples drawn from the novel Avocodo and BigVGAN networks. For illustration purposes, the supplementary material contains audio samples of generator artifacts. © 2023, CC BY-NC-SA.

关键词： Frequency domain analysis

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Characterizing the performance-energy tradeoff of small ARM Cores in HPC computation 1

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20th International Conference on Parallel Processing, Euro-Par 2014

作者： Laurenzano, Michael A. Tiwari, Ananta Jundt, Adam Peraza, Joshua Ward Jr., William A. Campbell, Roy Carrington, Laura EP Analytics United States Dept. of Computer Science and Engineering University of Michigan United States Performance Modeling and Characterization Lab San Diego Supercomputer Center United States High Performance Computing Modernization Program U.S. Dept. of Defense United States

ISBN: (数字)9783319098739

ISBN: (纸本)9783319098722

Deploying large numbers of small, low-power cores has been gaining traction recently as a system design strategy in high performance computing (HPC). The ARM platform that dominates the embedded and mobile computing segments is now being considered as an alternative to high-end x86 processors that largely dominate HPC because peak performance per watt may be substantially improved using off-the-shelf commodity processors. In this work we methodically characterize the performance and energy of HPC computations drawn from a number of problem domains on current ARM and x86 processors. Unsurprisingly, we find that the performance, energy and energy-delay product of applications running on these platforms varies significantly across problem types and inputs. Using static program analysis we further show that this variation can be explained largely in terms of the capabilities of two processor subsystems: single instruction multiple data (SIMD)/floating point and the cache/memory hierarchy;and that static analysis of this kind is sufficient to predict which platform is best for a particular application/input pair. In the context of these findings, we evaluate how some of the key architectural changes being made for upcoming 64-bit ARM platforms may impact HPC application performance. © 2014 Springer International Publishing Switzerland.

关键词： Static analysis

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ptwt - the PyTorch wavelet toolbox

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 3892-3898页

作者： Moritz Wolter Felix Blanke Jochen Garcke Charles Tapley Hoyt High-Performance Computing and Analytics Lab University of Bonn Germany Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin Germany Institute for Numerical Simulation University of Bonn and Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin Germany Northeastern University Boston

The fast wavelet transform is an important workhorse in signal processing. Wavelets are local in the spatial- or temporal- and the frequency-domain. This property enables frequency domain analysis while preserving some spatiotemporal information. Until recently, wavelets rarely appeared in the machine learning literature. We provide the PyTorch Wavelet Toolbox to make wavelet methods more accessible to the deep learning community. Our PyTorch Wavelet Toolbox is well documented. A pip package is installable with pip install ptwt.

关键词： PyTorch wavelet wavelet-packets wavelet-analysis wavelet-transform

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Twisted mass ensemble generation on GPU machines 39

Twisted mass ensemble generation on GPU machines

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39th International Symposium on Lattice Field Theory, LATTICE 2022

作者： Kostrzewa, Bartosz Bacchio, Simone Finkenrath, Jacob Garofalo, Marco Pittler, Ferenc Romiti, Simone Urbach, Carsten High-Performance Computing and Analytics Lab Rheinische Friedrich-Wilhelms-Universität Bonn Friedrich-Hirzebruch-Allee 8 Bonn53115 Germany Bethe Center for Theoretical Physics Rheinische Friedrich-Wilhelms-Universität Bonn Nussallee 14-16 Bonn53115 Germany The Cyprus Institute CASTORC 20 Konstantinou Kavafi Street Nicosia2121 Cyprus

We present how we ported the Hybrid Monte Carlo implementation in the tmLQCD software suite to GPUs through offloading its most expensive parts to the QUDA library. We discuss our motivations and some of the technical challenges that we encountered as we added the required functionality to both tmLQCD and QUDA. We further present some performance details, focussing in particular on the usage of QUDA's multigrid solver for poorly conditioned light quark monomials as well as the multi-shift solver for the non-degenerate strange and charm sector in Nf = 2 + 1 + 1 simulations using twisted mass clover fermions, comparing the efficiency of state-of-the-art simulations on CPU and GPU machines. We also take a look at the performance-portability question through preliminary tests of our HMC on a machine based on AMD's MI250 GPU, finding good performance after a very minor additional porting effort. Finally, we conclude that we should be able to achieve GPU utilisation factors acceptable for the current generation of (pre-)exascale supercomputers with subtantial efficiency improvements and real time speedups compared to just running on CPUs. At the same time, we find that future challenges will require different approaches and, most importantly, a very significant investment of personnel for software development. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

关键词： Graphics processing unit

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Fréchet Wavelet Distance: A Domain-Agnostic Metric for Image Generation

arXiv

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arXiv 2023年

作者： Veeramacheneni, Lokesh Wolter, Moritz Kuehne, Hildegard Gall, Juergen High-Performance Computing and Analytics Lab University of Bonn Bonn53115 Germany Multimodal Learning Group University of Bonn Bonn53115 Germany Computer Vision Group University of Bonn Bonn53115 Germany

Modern metrics for generative learning like Fréchet Inception Distance (FID) demonstrate impressive performance. However, they suffer from various shortcomings, like a bias towards specific generators and datasets. To address this problem, we propose the Fréchet Wavelet Distance (FWD) as a domain-agnostic metric based on Wavelet Packet Transform (Wp). FWD provides a sight across a broad spectrum of frequencies in images with a high resolution, along with preserving both spatial and textural aspects. Specifically, we use Wp to project generated and dataset images to packet coefficient space. Further, we compute Fréchet distance with the resultant coefficients to evaluate the quality of a generator. This metric is general-purpose and dataset-domain agnostic, as it does not rely on any pre-trained network while being more interpretable because of frequency band transparency. We conclude with an extensive evaluation of a wide variety of generators across various datasets that the proposed FWD is able to generalize and improve robustness to domain shift and various corruptions compared to other metrics. © 2023, CC BY.

关键词： Quality control

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Inclusive Hadronic Decay Rate of the τ Lepton from Lattice QCD: The u¯s Flavor Channel and the Cabibbo Angle

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Physical Review Letters 2024年第26期132卷 261901-261901页

作者： Constantia Alexandrou Simone Bacchio Alessandro De Santis Antonio Evangelista Jacob Finkenrath Roberto Frezzotti Giuseppe Gagliardi Marco Garofalo Bartosz Kostrzewa Vittorio Lubicz Simone Romiti Francesco Sanfilippo Silvano Simula Nazario Tantalo Carsten Urbach Urs Wenger Department of Physics Computation-based Science and Technology Research Center Dipartimento di Fisica and Gaustrae 20 42119 Wuppertal Germany Istituto Nazionale di Fisica Nucleare HISKP (Theory) High Performance Computing and Analytics Lab Dipartimento di Matematica e Fisica Institute for Theoretical Physics Albert Einstein Center for Fundamental Physics

We present a lattice determination of the inclusive decay rate of the process τ↦Xusντ in which the τ lepton decays into a generic hadronic state Xus with u¯s flavor quantum numbers. Our results have been obtained in nf=2+1+1 isosymmetric QCD with full nonperturbative accuracy, without any operator product expansion approximation and, except for the presently missing long-distance isospin-breaking corrections, include a solid estimate of all sources of theoretical uncertainties. This has been possible by using the Hansen-Lupo-Tantalo method [M. Hansen et al., Phys. Rev. D 99, 094508 (2019)] that we have already successfully applied [A. Evangelista et al., Phys. Rev. D 108, 074513 (2023)] to compute the inclusive decay rate of the process τ↦Xudντ in the u¯d flavor channel. By combining our first-principles theoretical results with the presently available experimental data, we extract the Cabibbo-Kobayashi-Maskawa matrix element |Vus|, the Cabibbo angle, with a 0.9% accuracy, dominated by the experimental error.

关键词： Cabibbo–Kobayashi–Maskawa matrix Hadronic decays Lattice field theory Lattice gauge theory Lepton-hadron interactions Non-Abelian gauge theories Nonperturbative effects in field theory Phenomenology Quantum chromodynamics Strong interaction Total cross sections

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Wavelet-Packets for Deepfake Image Analysis and Detection

arXiv

引用

arXiv 2021年

作者： Wolter, Moritz Blanke, Felix Heese, Raoul Garcke, Jochen High Performance Computing & Analytics Lab Universität Bonn Germany Fraunhofer SCAI University of Bonn Germany Fraunhofer Center for Machine Learning Fraunhofer ITWM Germany Institute for Numerical Simulation University of Bonn Germany Fraunhofer Center for Machine Learning Fraunhofer SCAI Germany

As neural networks become able to generate realistic artificial images, they have the potential to improve movies, music, video games and make the internet an even more creative and inspiring place. Yet, the latest technology potentially enables new digital ways to lie. In response, the need for a diverse and reliable method toolbox arises to identify artificial images and other content. Previous work primarily relies on pixel-space convolutional neural networks or the Fourier transform. Synthesized fake image analysis and detection methods based on a multi-scale wavelet representation, localized in both space and frequency, have been absent thus far. The wavelet transform conserves spatial information to a degree, allowing us to present a new analysis. Comparing the wavelet coefficients of real and fake images significant differences are identified, where this representation also allows further interpretations. Additionally, this paper proposes to learn a model for the detection of synthetic images based on the wavelet-packet representation of natural and generative adversarial neural network (GAN)-generated images. Our forensic classifiers exhibit competitive or improved performance at small network sizes, as we demonstrate on the Flickr Faces high Quality (FFHQ), Large-scale Celeb Faces Attributes (CelebA) and Large-scale Scene UNderstanding (LSUN) source identification problems. Furthermore, we study the binary Face Forensics++ (ff++) fake-detection problem. © 2021, CC BY-NC-SA.

关键词： Fake detection

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