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39th International Symposium on Lattice Field Theory, LATTICE 2022

作者： Yamamoto, Shuhei Bacchio, Simone Finkenrath, Jacob Computation-based Science and Technology Research Center The Cyprus Institute Cyprus

Lyncs-API is a Python API for Lattice QCD applications. It is designed as a Python toolkit that allows the user to use and run various lattice QCD libraries while programming in Python. The goal is to provide the user an easy programming experience without scarifying performance across multiple platforms, by preparing a common framework for various softwares for lattice QCD calculations. As such, it contains interfaces to, e.g., c-lime, DDalphaAMG, tmLQCD, and QUDA. In this proceeding, we focus on a Lyncs interface to QUDA, named Lyncs-QUDA, and present a small tutorial on how to use this Python interface to perform a HMC simulation using QUDA. © 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： Python

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Deep Learning-based Phylogenetic Analysis of Influenza Protein Sequences: A Siamese Neural Network Approach

Deep Learning-based Phylogenetic Analysis of Influenza Prote...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chrysostomou, Charalambos The Cyprus Institute Computation-Based Science and Technology Research Center Nicosia Cyprus

ISBN: (纸本)9798350337488

Influenza, a highly mutable virus that can infect multiple species, has been a persistent global health challenge. The virus's ability to recombine and evolve necessitates robust and efficient tools for phylogenetic analysis. Traditional methods, however, often rely on sequence alignment, which can be computationally intensive and less effective when dealing with large-scale sequence data. This paper introduces a new approach to phylogenetic analysis based on Siamese Neural Network (SNN). Trained on the influenza virus's Neuraminidase (NA) protein, the proposed SNN model can accurately classify the primary host species - human, avian, and swine - and construct phylogenetic trees without requiring sequence alignment. The empirical results demonstrate the model's robustness, achieving an accuracy of 95.2% in host species classification. © 2023 IEEE.

关键词： Alignment Bioinformatics Classification Deep Learning Influenza Machine Learning Neuraminidase Phylogenetic Analysis Protein Sequences Siamese Neural Network Viruses

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Deep Learning-Assisted Classification of Allergenic Proteins: An Exploration of Amino Acid Indices

Deep Learning-Assisted Classification of Allergenic Proteins...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chrysostomou, Charalambos The Cyprus Institute Computation-Based Science and Technology Research Center Nicosia Cyprus

ISBN: (纸本)9798350337488

The allergenicity prediction is a pivotal yet challenging in bioinformatics, with no universally accepted methodology. Existing strategies primarily hinge on comparing novel protein sequences with known allergenic sequences, using sequence identity thresholds as a benchmark. However, these approaches often overlook other influential factors such as physicochemical properties, protein stability, post-translational modifications, and cleavage sites, all of which may contribute to allergenicity. While bioinformatics methodologies have been devised to analyse and classify allergenic protein sequences, their efficacy in accurately predicting low homology allergenic proteins still needs to be investigated. This study introduces a novel approach to allergenicity prediction, leveraging the capabilities of deep learning techniques, specifically Convolutional Neural Networks (CNNs). We investigate the utility of various amino acid indices for encoding protein sequences into numerical data and evaluate their impact on classification accuracy. Our research is underpinned by a comprehensive dataset, encompassing 3,260 allergenic and 191,001 non-allergenic proteins, collated from diverse online databases. The results of our study underscore the potential of our proposed method in advancing bioinformatics methodologies for allergenicity prediction, thereby contributing to the broader scientific discourse on allergenic proteins. © 2023 IEEE.

关键词： Allergenicity Bioinformatics Convolutional Neural Networks Deep Learning Machine Learning Protein Allergens Protein Sequences Sequence Homology

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Lyncs-API: A Python API for Lattice QCD applications 38

Lyncs-API: A Python API for Lattice QCD applications

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38th International Symposium on Lattice Field Theory, LATTICE 2021

作者： Bacchio, Simone Finkenrath, Jacob Stylianou, Christodoulos Computation-based Science and Technology Research Center The Cyprus Institute Cyprus

We present Lyncs-API, a Python API for Lattice QCD applications currently under development. Lyncs aims to bring several widely used libraries for Lattice QCD under a common framework. Lyncs flexibly links to libraries for CPUs and GPUs in a way that can accommodate additional computing architectures as these arise, achieving performance-portability for the calculations while maintaining the same high-level workflow. Lyncs distributes calculations using Dask and mpi4py, with bindings to the libraries automatically generated by cppyy. While Lyncs is designed to allow linking to multiple libraries, we focus on a set of targeted packages that include DDalphaAMG, tmLQCD, QUDA and c-lime. More libraries will be added in the future. We also develop generic-purpose tools for facilitating the usage of Python in Lattice QCD and HPC in general. The project is open-source, community-oriented and available on Github. © 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： Libraries

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Confining strings, axions and glueballs in the planar limit 20

Confining strings, axions and glueballs in the planar limit

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20th Hellenic School and Workshops on Elementary Particle Physics and Gravity, CORFU 2021

作者： Athenodorou, Andreas Computation-based Science and Technology Research Center The Cyprus Institute Cyprus

We present recent results on the spectrum of a confining flux tube that is closed around a spatial torus as a function of its length as well as the spectrum of glueballs. The extraction of the spectra has been realized by simulating four dimensional SU(N) gauge theories and performing measurements using lattice techniques. Regarding flux-tubes, we have performed calculations for N = 3, 5, 6 and for various values of spin, parity and longitudinal momentum. Long flux-tubes can be thought of as infinitesimally thin strings;hence their spectrum is expected to be described by an effective string theory. Furthermore, the flux-tube's internal structure makes possible the existence of massive states in addition to string modes. Our calculations demonstrate that although most states exhibit a spectrum which can be approximated adequately by Nambu-Goto there is strong evidence for the existence of a massive axion on the world-sheet of the QCD flux-tube as well as a bound state of two such axions. Regarding glueballs, we extracted spectra from N = 2 to N = 12 which enables us to extrapolate to N = ∞. Our main aim was to calculate the lightest glueball masses for all different configurations of the quantum numbers of spin, parity and charge conjugation. This provides a major update on the spectrum of glueballs in the planar limit. © 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： String theory

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Implementation of Simultaneous Inversion of a Multi-shifted Dirac Matrix for Twisted-Mass Fermions within DDαAMG 38

Implementation of Simultaneous Inversion of a Multi-shifted ...

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38th International Symposium on Lattice Field Theory, LATTICE 2021

作者： Yamamoto, Shuhei Bacchio, Simone Finkenrath, Jacob Computation-based Science and Technology Research Center The Cyprus Institute Aglantzia Nicosia Cyprus

At physical light quark masses, efficient linear solvers are crucial for carrying out the millions of inversions of the Dirac matrix required for obtaining high statistics in quark correlation functions. Adaptive algebraic multi-grid methods have proven to be very efficient in such cases, exhibiting mild critical slowing down towards very light quark masses and outperforming traditional solver methods, such as the conjugate gradient method, at the physical point. We will discuss our implementations of simultaneous inversion of a (degenerate) Dirac matrix for twisted-mass fermions for multiple right-hand-sides (rhs) with multi-shifts and block-Krylov solvers. The implementation is carried out within the community library DDαAMG, which implements aggregation-based Domain Decomposition adaptive algebraic multi-grid methods. The block-Krylov solvers are provided via the Fast Accurate Block Linear krylOv Solver (Fabulous) library and can be used at coarser levels. Our code inverts Dirac matrices with different twisted-mass terms and for multiple rhs simultaneously and is thus also suitable for components within a typical lattice QCD simulation workflow, such as the rational approximation. We show preliminary results on scalability and compare the performance of our implementation when using different Block-Krylov solver techniques. © 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： Matrix algebra

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Novel approach for computing gradients of physical observables

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Physical Review D 2023年第9期108卷 L091508-L091508页

作者： Simone Bacchio Computation-based Science and Technology Research Center The Cyprus Institute Nicosia Cyprus

We show that an infinitesimal step of gradient flow can be used for defining a novel approach for computing gradients of physical observables with respect to action parameters. Compared to the commonly used perturbative expansion, this approach does not require calculating any disconnected contribution or vacuum expectation value and can provide results up to 3 orders of magnitudes more precise. On the other hand, it requires a nontrivial condition to be satisfied by the flow action, the calculation of its force and its Laplacian, and the force of the observable, whose gradient needs to be measured. As a proof of concept, we measure gradients in β of Wilson loops in a four-dimensional SU(3) Yang-Mills theory simulated on a 164 lattice using the Wilson action.

关键词： Lattice QCD Lattice field theory Perturbation theory Machine learning

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Symmetry Breaking in Geometric Quantum Machine Learning in the Presence of Noise

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PRX Quantum 2024年第3期5卷 030314-030314页

作者： Cenk Tüysüz Su Yeon Chang Maria Demidik Karl Jansen Sofia Vallecorsa Michele Grossi Zeuthen 15738 Germany Institut für Physik Geneva 1211 Switzerland Institute of Physics Computation-Based Science and Technology Research Center

Geometric quantum machine learning based on equivariant quantum neural networks (EQNNs) recently appeared as a promising direction in quantum machine learning. Despite encouraging progress, studies are still limited to theory, and the role of hardware noise in EQNN training has never been explored. This work studies the behavior of EQNN models in the presence of noise. We show that certain EQNN models can preserve equivariance under Pauli channels, while this is not possible under the amplitude damping channel. We claim that the symmetry breaks linearly in the number of layers and noise strength. We support our claims with numerical data from simulations as well as hardware up to 64 qubits. Furthermore, we provide strategies to enhance the symmetry protection of EQNN models in the presence of noise.

关键词： Artificial neural networks Machine learning Quantum algorithms Quantum algorithms for chemical calculations Quantum circuits Quantum coherence & coherence measures Quantum information theory

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Non-perturbative renormalization of quark and gluon operators using a gauge-invariant scheme 39

Non-perturbative renormalization of quark and gluon operator...

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39th International Symposium on Lattice Field Theory, LATTICE 2022

作者： Spanoudes, G. Alexandrou, C. Finkenrath, J. Hadjiyiannakou, K. Panagopoulos, H. Yamamoto, S. Computation-based Science and Technology Research Center The Cyprus Institute 20 Kavafi Str. Nicosia2121 Cyprus Department of Physics University of Cyprus NicosiaCY-1678 Cyprus

We present preliminary results for the renormalization functions (RFs) of a number of quark and gluon operators studied in lattice QCD using a gauge-invariant renormalization scheme (GIRS). GIRS is a variant of the coordinate-space renormalization prescription, in which Green's functions of gauge-invariant operators are calculated in position space. A novel aspect is that summations over different time slices of the positions of the operators are employed in order to reduce the statistical noise in lattice simulations. We test the reliability of this scheme by calculating RFs for the vector one-derivative quark bilinear operator, which enters the average momentum fraction of the nucleon. We use N f = 4 degenerate twisted mass clover-improved fermion ensembles of different volumes and lattice spacings. We also present first results of applying GIRS when operator mixing occurs: the mixing coefficients of the gluon and quark singlet energy-momentum tensor operators are evaluated by imposing appropriate renormalization conditions on the lattice. © 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： Gages

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Sinogram Denoise based on Fourier Neural Operators

Sinogram Denoise Based on Fourier Neural Operators

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2022 IEEE Nuclear science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Chrysostomou, Charalambos The Cyprus Institute Computation-Based Science and Technology Research Center 20 Konstantinou Kavafi Street Nicosia Aglantzia2121 Cyprus

ISBN: (纸本)9781665488723

A novel method for sinogram denoise based on Fourier Neural Operator (FNO) in SPECT imaging is presented. Projection data from software phantoms were used to train the proposed model. Generated software phantoms with various noise levels added were used to evaluate the method's efficacy in denoising SPECT sinograms. The resulting denoised sinograms are reconstructed using Ordered Subset Expectation Maximization (OSEM) and compared to the reconstructions of the original noised sinograms. The results show that the proposed method can denoise the sinograms and significantly improve current state-of-the-art methodologies, with significantly less training data than other machine learning approaches. Finally, results from real-world DAT-SPECT sinograms demonstrate the proposed method's efficacy and capability. © 2022 IEEE.

关键词： Convolutional Neural Networks (CNN) Ordered Subset Expectation Maximization (OSEM) Single Photon Emission Computerized Tomography (SPECT) SPECT Sinogram Denoise

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