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The MS¯ -scheme αs5 QCD contributions to the Adler function and Bjorken polarized sum rule in the Crewther-type two-fold {β}-expanded representation

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Journal of High Energy Physics 2022年第5期2022卷 1-34页

作者： Goriachuk, I.O. Kataev, A.L. Molokoedov, V.S. Department of Theoretical Physics Faculty of Physics Moscow State University Moscow 119991 Russian Federation Institute for Nuclear Research of the Russian Academy of Science Moscow 117312 Russian Federation Research Computing Center Moscow State University Moscow 119991 Russian Federation Moscow Center for Fundamental and Applied Mathematics Moscow 119992 Russian Federation Moscow Institute of Physics and Technology Moscow Region Dolgoprudny 141700 Russian Federation

We consider the two-fold expansion in powers of the conformal anomaly and of the strong coupling αs for the non-singlet contributions to Adler D-function and Bjorken polarized sum rule calculated previously in the MS... 详细信息

We consider the two-fold expansion in powers of the conformal anomaly and of the strong coupling α_s for the non-singlet contributions to Adler D-function and Bjorken polarized sum rule calculated previously in the MS¯ -scheme at the four-loop level. This representation provides relations between definite terms of different loop orders appearing within the {β}-expansion of these quantities. Supposing the validity of this two-fold representation at the five-loop order and using these relations, we obtain some O(αs5) corrections to the D-function, to the R-ratio of e⁺e⁻-annihilation into hadrons and to Bjorken polarized sum rule. These corrections are presented both analytically in the case of the generic simple gauge group and numerically for the SU(3) color group. The arguments in the favor of validity of the two-fold representation are given at least at the four-loop level. Within the {β}-expansion procedure the analytical Riemann ζ₄-contributions to the five-loop expressions for the Adler function and Bjorken polarized sum rule are also fixed for the case of the generic simple gauge group. © 2022, The Author(s).

关键词： Conformal and W Symmetry Perturbative QCD Renormalization Group

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Learning not to Regret

arXiv

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arXiv 2023年

作者： Sychrovský, David Šustr, Michal Davoodi, Elnaz Bowling, Michael Lanctot, Marc Schmid, Martin Department of Applied Mathematics Charles University Czech Republic Artificial Intelligence Center Czech Technical University Czech Republic Google DeepMind United Kingdom Department of Computing Science University of Alberta Canada EquiLibre Technologies

The literature on game-theoretic equilibrium finding predominantly focuses on single games or their repeated play. Nevertheless, numerous real-world scenarios feature playing a game sampled from a distribution of similar, but not identical games, such as playing poker with different public cards or trading correlated assets on the stock market. As these similar games feature similar equilibra, we investigate a way to accelerate equilibrium finding on such a distribution. We present a novel "learning not to regret" framework, enabling us to meta-learn a regret minimizer tailored to a specific distribution. Our key contribution, Neural Predictive Regret Matching, is uniquely meta-learned to converge rapidly for the chosen distribution of games, while having regret minimization guarantees on any game. We validated our algorithms’ faster convergence on a distribution of river poker games. Our experiments show that the meta-learned algorithms outpace their non-meta-learned counterparts, achieving more than tenfold improvements. © 2023, CC BY.

关键词： Commerce

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Multi-Granularity Open Intent Classification via Adaptive Granular-Ball Decision Boundary 39

Multi-Granularity Open Intent Classification via Adaptive Gr...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Yanhua Ouyang, Xiaocao Pan, Chaofan Zhang, Jie Zhao, Sen Xia, Shuyin Yang, Xin Wang, Guoyin Li, Tianrui School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu 611130 China Engineering Research Center of Intelligent Finance Ministry of Education Chengdu 611130 China Key Laboratory of Big Data Intelligent Computing Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing 400065 China National Center for Applied Mathematics in Chongqing Chongqing Normal University Chongqing 401331 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 611756 China

ISBN: (纸本)157735897X

Open intent classification is critical for the development of dialogue systems, aiming to accurately classify known intents into their corresponding classes while identifying unknown intents. Prior boundary-based methods assumed known intents fit within compact spherical regions, focusing on coarse-grained representation and precise spherical decision boundaries. However, these assumptions are often violated in practical scenarios, making it difficult to distinguish known intent classes from unknowns using a single spherical boundary. To tackle these issues, we propose a Multi-granularity Open intent classification method via adaptive Granular-Ball decision boundary (MOGB). Our MOGB method consists of two modules: representation learning and decision boundary acquiring. To effectively represent the intent distribution, we design a hierarchical representation learning method. This involves iteratively alternating between adaptive granular-ball clustering and nearest sub-centroid classification to capture fine-grained semantic structures within known intent classes. Furthermore, multi-granularity decision boundaries are constructed for open intent classification by employing granular-balls with varying centroids and radii. Extensive experiments conducted on three public datasets demonstrate the effectiveness of our proposed method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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QCLAB++: Simulating Quantum Circuits on GPUs

arXiv

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arXiv 2023年

作者： Van Beeumen, Roel Camps, Daan Mehta, Neil Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States

We introduce qclab++, a light-weight, fully-templated C++ package for GPU-accelerated quantum circuit simulations. The code offers a high degree of portability as it has no external dependencies and the GPU kernels are generated through OpenMP offloading. qclab++ is designed for performance and numerical stability through highly optimized gate simulation algorithms for 1-qubit, controlled 1-qubit, and 2-qubit gates. Furthermore, we also introduce qclab a quantum circuit toolbox for Matlab with a syntax that mimics qclab++. This provides users the flexibility and ease of use of a scripting language like Matlab for studying their quantum algorithms, while offering high-performance GPU acceleration when required. As such, the qclab++ library offers a unique combination of features. We compare the CPU simulator in qclab++ with the GPU kernels generated by OpenMP and observe a speedup of over 40×. Furthermore, we also compare qclab++ to other circuit simulation packages, such as cirq-qsim and qibo, in a series of benchmarks conducted on NERSC’s Perlmutter system and illustrate its competitiveness. Copyright © 2023, The Authors. All rights reserved.

关键词： Circuit simulation

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Efficient Quantum Counting and Quantum Content-Addressable Memory for DNA similarity

arXiv

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arXiv 2023年

作者： Balewski, Jan Klymko, Katherine Camps, Daan Tritt, Andrew National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States

We present QCAM, a quantum analogue of Content-Addressable Memory (CAM), useful for finding matches in two sequences of bit-strings. Our QCAM implementation takes advantage of Grover’s search algorithm and proposes a highly-optimized quantum circuit implementation of the QCAM oracle. Our circuit construction uses the parallel uniformly controlled rotation gates, which were used in previous work to generate QBArt encodings. These circuits have a high degree of quantum parallelism which reduces their critical depth. The optimal number of repetitions of the Grover iterator used in QCAM depends on the number of true matches and hence is input dependent. We additionally propose a hardware-efficient implementation of the quantum counting algorithm (HEQC) that can infer the optimal number of Grover iterations from the measurement of a single observable. We demonstrate the QCAM application for computing the Jaccard similarity between two sets of k-mers obtained from two DNA sequences. Copyright © 2023, The Authors. All rights reserved.

关键词： DNA

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Developing Efficient Implementation of Label Propagation Algorithm for Modern NVIDIA GPUs 6th

Developing Efficient Implementation of Label Propagation Alg...

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6th Russian Supercomputing Days, RuSCDays 2020

作者： Afanasyev, Ilya V. Lichmanov, Dmitry I. Research Computing Center of Moscow State University Moscow Russia Moscow Center of Fundamental and Applied Mathematics Moscow Russia

ISBN: (纸本)9783030646158

Systems equipped with modern NVIDIA GPUs nowadays are capable of solving many real-world problems, including large-scale graph processing. Efficiently implementing graph algorithms on GPUs is a challenging task, since modern real-world graphs have irregular structure. Current paper describes approaches, which can be used for developing efficient implementation of label propagation algorithm, frequently used to solve graph clustering and community detection problems. Compared to already existing GPU-based implementations, new optimization techniques have been proposed, including graph preprocessing, efficient load-balancing, using unified memory for out-of-core graph processing, and several others. The performance of the developed implementation has been evaluated on synthetic and medium-scaled real-world graphs, resulting in up to 2 times better results compared to existing approaches. © 2020, Springer Nature Switzerland AG.

关键词： Graphics processing unit

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The System Adaptability Evaluation Index System of Military Communication Equipment System 21

The System Adaptability Evaluation Index System of Military ...

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5th International Conference on Computer Science and Application Engineering, CSAE 2021

作者： Hou, Jian Wang, Ruihua Wang, Jiajia Yang, Zhou He, Danyu Beijing Jinghang Research Institute of Computing and Communication Corporation Applied Mathematics Research Center of China Aerospace Science and Industry Innovation Center for Intelligent System on Recognition and Decision Pla Academy of Military Science Beijing Zhenxing Institute of Metrology and Measurement

ISBN: (纸本)9781450389853

As an objective reflection of equipment combat performance and battlefield satisfaction, the equipment combat test evaluation index system is the premise and basis for equipment combat test planning, design and implementation. It is to assess the system integration ability and matching ability of military communication equipment under actual combat conditions and the ability to join the equipment system for operation and training. This paper analyzes the concept connotation of system adaptability, system integration degree and system contribution rate, defines the construction principles and ideas of equipment system adaptability evaluation index system, and establishes an index system reflecting the adaptability of military communication equipment system from two aspects of system integration degree and system contribution rate, so as to provide theoretical reference and technical support for military communication equipment system adaptability evaluation.. © 2021 Association for computing Machinery. All rights reserved.

关键词： Military communications

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Projected gradient descent algorithm for ab initio crystal structure relaxation under a fixed unit cell volume

arXiv

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arXiv 2024年

作者： Hu, Yukuan Yin, Junlei Gao, Xingyu Liu, Xin Song, Haifeng State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Solidification Processing Northwestern Polytechnical University Shaanxi Xi’an710072 China Innovation Center NPU Chongqing Chongqing401135 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing100088 China

This paper is concerned with ab initio crystal structure relaxation under a fixed unit cell volume, which is a step in calculating the static equations of state and forms the basis of thermodynamic property calculations for materials. The task can be formulated as an energy minimization with a determinant constraint. Widely used line minimization-based methods (e.g., conjugate gradient method) lack both efficiency and convergence guarantees due to the nonconvex nature of the feasible region as well as the significant differences in the curvatures of the potential energy surface with respect to atomic and lattice components. To this end, we propose a projected gradient descent algorithm named PANBB. It is equipped with (i) search direction projections onto the tangent spaces of the nonconvex feasible region for lattice vectors, (ii) distinct curvature-aware initial trial step sizes for atomic and lattice updates, and (iii) a nonrestrictive line minimization criterion as the stopping rule for the inner loop. It can be proved that PANBB favors theoretical convergence to equilibrium states. Across a benchmark set containing 223 structures from various categories, PANBB achieves average speedup factors of approximately 1.41 and 1.45 over the conjugate gradient method and direct inversion in the iterative subspace implemented in off-the-shelf simulation software, respectively. Moreover, it normally converges on all the systems, manifesting its unparalleled robustness. As an application, we calculate the static equations of state for the high-entropy alloy AlCoCrFeNi, which remains elusive owing to 160 atoms representing both chemical and magnetic disorder and the strong local lattice distortion. The results are consistent with the previous calculations and are further validated by experimental thermodynamic data. © 2024, CC BY.

关键词： Conjugate gradient method

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Anisotropy of glancing angle deposited films: Results of atomistic simulation 9

Anisotropy of glancing angle deposited films: Results of ato...

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9th International Conference on Mathematical Modeling in Physical Sciences, IC-MSQUARE 2020

作者： Grigoriev, F.V. Sulimov, V.B. Tikhonravov, A.V. Research Computing Center M.V. Lomonosov Moscow State University Leninskye Gory 1 build.4 Moscow119991 Russia Moscow Center of Fundamental and Applied Mathematics 119234 Leninskie Gory 1 Moscow Russia

Anisotropy of SiO2 films fabricated by glancing angle deposition is investigated using the classical atomistic simulation and anisotropic Bruggeman effective medium theory. The voids between the slanted columns, occurring as a result of glancing angle deposition, are considered as ellipsoids. Averaged shape parameters of these ellipsoids are defined using the density gradient tensor. Calculated values of difference of refractive index components of glancing angle deposited SiO2 films are in accordance with experiment. © Published under licence by IOP Publishing Ltd.

关键词： Anisotropy

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Automated Inference of Domain Knowledge in Scientific Machine Learning 19thth

Automated Inference of Domain Knowledge in Scientific Mach...

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19th International Conference on Computer Aided Systems Theory, EUROCAST 2024

作者： Bachinger, Florian Haider, Christian Zenisek, Jan de França, Fabrício Olivetti Affenzeller, Michael Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg Austria Johannes Kepler University Linz Austria Institute for Symbolic Artificial Intelligence Johannes Kepler University Linz Austria Center for Mathematics Computing and Cognition Federal University of ABC Santo André Brazil

ISBN: (纸本)9783031829512

The integration of prior knowledge into the training of machine learning (ML) models can improve their inter- and extrapolation capabilities and increases the trust of domain experts in model predictions. Shape-constrained regression is one category of ML algorithms capable of integrating knowledge about the shape of the model. Such knowledge is represented by boundary information of partial derivatives of different orders. However, the translation or formulation of (intrinsic) domain expert knowledge into such constraints is challenging and requires experience. Sometimes, this knowledge may even be unavailable for certain domains. We propose an approach that can automatically infer such knowledge from observational data. We envision this approach as an additional tool in the data analysis toolbox that provides suggestions, which can be incorporated into the training of prediction models. In this work, we describe our approach for automated knowledge inference from data. Additionally, we show the applicability of our approach by testing it on synthetic data generated from a set of physics equations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Inference engines

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