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Journal articles on the topic 'Butterfly factorization'

Author: Grafiati

Published: 31 August 2024

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1

Li, Yingzhou, Haizhao Yang, Eileen R. Martin, Kenneth L. Ho, and Lexing Ying. "Butterfly Factorization." Multiscale Modeling & Simulation 13, no. 2 (January 2015): 714–32. http://dx.doi.org/10.1137/15m1007173.

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2

Li, Yingzhou, and Haizhao Yang. "Interpolative Butterfly Factorization." SIAM Journal on Scientific Computing 39, no. 2 (January 2017): A503—A531. http://dx.doi.org/10.1137/16m1074941.

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3

Li, Yingzhou, Haizhao Yang, and Lexing Ying. "Multidimensional butterfly factorization." Applied and Computational Harmonic Analysis 44, no. 3 (May 2018): 737–58. http://dx.doi.org/10.1016/j.acha.2017.04.002.

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4

Pang, Qiyuan, Kenneth L. Ho, and Haizhao Yang. "Interpolative Decomposition Butterfly Factorization." SIAM Journal on Scientific Computing 42, no. 2 (January 2020): A1097—A1115. http://dx.doi.org/10.1137/19m1294873.

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5

Liu, Yang, Xin Xing, Han Guo, Eric Michielssen, Pieter Ghysels, and Xiaoye Sherry Li. "Butterfly Factorization Via Randomized Matrix-Vector Multiplications." SIAM Journal on Scientific Computing 43, no. 2 (January 2021): A883—A907. http://dx.doi.org/10.1137/20m1315853.

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6

Chen, Ze, Juan Zhang, Kenneth L. Ho, and Haizhao Yang. "Multidimensional phase recovery and interpolative decomposition butterfly factorization." Journal of Computational Physics 412 (July 2020): 109427. http://dx.doi.org/10.1016/j.jcp.2020.109427.

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7

Jaber, Marwan A., and Daniel Massicotte. "Radix-2α/4β Building Blocks for Efficient VLSI’s Higher Radices Butterflies Implementation." VLSI Design 2014 (May 13, 2014): 1–13. http://dx.doi.org/10.1155/2014/690594.

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This paper describes an embedded FFT processor where the higher radices butterflies maintain one complex multiplier in its critical path. Based on the concept of a radix-r fast Fourier factorization and based on the FFT parallel processing, we introduce a new concept of a radix-r Fast Fourier Transform in which the concept of the radix-r butterfly computation has been formulated as the combination of radix-2α/4β butterflies implemented in parallel. By doing so, the VLSI butterfly implementation for higher radices would be feasible since it maintains approximately the same complexity of the radix-2/4 butterfly which is obtained by block building of the radix-2/4 modules. The block building process is achieved by duplicating the block circuit diagram of the radix-2/4 module that is materialized by means of a feed-back network which will reuse the block circuit diagram of the radix-2/4 module.

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8

Bremer, James, Ze Chen, and Haizhao Yang. "Rapid Application of the Spherical Harmonic Transform via Interpolative Decomposition Butterfly Factorization." SIAM Journal on Scientific Computing 43, no. 6 (January 2021): A3789—A3808. http://dx.doi.org/10.1137/20m1333845.

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9

Yang, Haizhao. "A unified framework for oscillatory integral transforms: When to use NUFFT or butterfly factorization?" Journal of Computational Physics 388 (July 2019): 103–22. http://dx.doi.org/10.1016/j.jcp.2019.02.044.

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10

Mardan, Suha Suliman, and Mounir Taha Hamood. "New fast Walsh–Hadamard–Hartley transform algorithm." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (April 1, 2023): 1533. http://dx.doi.org/10.11591/ijece.v13i2.pp1533-1540.

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<span lang="EN-US">This paper presents an efficient fast Walsh–Hadamard–Hartley transform (FWHT) algorithm that incorporates the computation of the Walsh-Hadamard transform (WHT) with the discrete Hartley transform (DHT) into an orthogonal, unitary single fast transform possesses the block diagonal structure. The proposed algorithm is implemented in an integrated butterfly structure utilizing the sparse matrices factorization approach and the Kronecker (tensor) product technique, which proved a valuable and fast tool for developing and analyzing the proposed algorithm. The proposed approach was distinguished by ease of implementation and reduced computational complexity compared to previous algorithms, which were based on the concatenation of WHT and FHT by saving up to 3N-4 of real multiplication and 7.5N-10 of real addition.</span>

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11

Guo, Han, Yang Liu, Jun Hu, and Eric Michielssen. "A butterfly-based direct solver using hierarchical LU factorization for Poggio-Miller-Chang-Harrington-Wu-Tsai equations." Microwave and Optical Technology Letters 60, no. 6 (April 24, 2018): 1381–87. http://dx.doi.org/10.1002/mop.31166.

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12

Guo, Han, Yang Liu, Jun Hu, and Eric Michielssen. "A Butterfly-Based Direct Integral-Equation Solver Using Hierarchical LU Factorization for Analyzing Scattering From Electrically Large Conducting Objects." IEEE Transactions on Antennas and Propagation 65, no. 9 (September 2017): 4742–50. http://dx.doi.org/10.1109/tap.2017.2727511.

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13

Zheng, Léon, Elisa Riccietti, and Rémi Gribonval. "Efficient Identification of Butterfly Sparse Matrix Factorizations." SIAM Journal on Mathematics of Data Science 5, no. 1 (January 28, 2023): 22–49. http://dx.doi.org/10.1137/22m1488727.

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14

Capitain, Charlotte C., Fatemeh Nejati, Martin Zischka, Markus Berzak, Stefan Junne, Peter Neubauer, and Philipp Weller. "Volatilomics-Based Microbiome Evaluation of Fermented Dairy by Prototypic Headspace-Gas Chromatography–High-Temperature Ion Mobility Spectrometry (HS-GC-HTIMS) and Non-Negative Matrix Factorization (NNMF)." Metabolites 12, no. 4 (March 28, 2022): 299. http://dx.doi.org/10.3390/metabo12040299.

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Fermented foods, such as yogurt and kefir, contain a versatile spectrum of volatile organic compounds (VOCs), including ethanol, acetic acid, ethyl acetate, and diacetyl. To overcome the challenge of overlapping peaks regarding these key compounds, the drift tube temperature was raised in a prototypic high-temperature ion mobility spectrometer (HTIMS). This HS-GC-HTIMS was used for the volatilomic profiling of 33 traditional kefir, 13 commercial kefir, and 15 commercial yogurt samples. Pattern recognition techniques, including principal component analysis (PCA) and NNMF, in combination with non-targeted screening, revealed distinct differences between traditional and commercial kefir while showing strong similarities between commercial kefir and yogurt. Classification of fermented dairy samples into commercial yogurt, commercial kefir, traditional mild kefir, and traditional tangy kefir was also possible for both PCA- and NNMF-based models, obtaining cross-validation (CV) error rates of 0% for PCA-LDA, PCA-kNN (k = 5), and NNMF-kNN (k = 5) and 3.3% for PCA-SVM and NNMF-LDA. Through back projection of NNMF loadings, characteristic substances were identified, indicating a mild flavor composition of commercial samples, with high concentrations of buttery-flavored diacetyl. In contrast, traditional kefir showed a diverse VOC profile with high amounts of flavorful alcohols (including ethanol and methyl-1-butanol), esters (including ethyl acetate and 3-methylbutyl acetate), and aldehydes. For validation of the results and deeper understanding, qPCR sequencing was used to evaluate the microbial consortia, confirming the microbial associations between commercial kefir and commercial yogurt and reinforcing the differences between traditional and commercial kefir. The diverse flavor profile of traditional kefir primarily results from the yeast consortium, while commercial kefir and yogurt is primarily, but not exclusively, produced through bacterial fermentation. The flavor profile of fermented dairy products may be used to directly evaluate the microbial consortium using HS-GC-HTIMS analysis.

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15

Claus, Lisa, Pieter Ghysels, Yang Liu, Thái Anh Nhan, Ramakrishnan Thirumalaisamy, Amneet Pal Singh Bhalla, and Sherry Li. "Sparse approximate multifrontal factorization with composite compression methods." ACM Transactions on Mathematical Software, August 2023. http://dx.doi.org/10.1145/3611662.

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This paper presents a fast and approximate multifrontal solver for large sparse linear systems. In a recent paper by Liu et al. we showed the efficiency of a multifrontal solver leveraging the butterfly algorithm and its hierarchical matrix extension, Hierarchically Off-Diagonal Butterfly compression (HODBF), to compress large frontal matrices. The resulting multifrontal solver can attain quasi-linear computation and memory complexity when applied to sparse linear systems arising from spatial discretization of high-frequency wave equations. To further reduce the overall number of operations and especially the factorization memory usage in order to scale to larger problem sizes, in this paper, we develop a composite multifrontal solver that employs the HODBF format for large sized fronts, a reduced-memory version of the non-hierarchical Block Low-Rank format for medium sized fronts and a lossy compression format for small sized fronts. This allows us to solve sparse linear systems of dimension up to 2.7 × larger than before and leads to a memory consumption that is reduced by 70 percent while ensuring the same execution time. The code is made publicly available in github.

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16

Liu, Yang, Pieter Ghysels, Lisa Claus, and Xiaoye Sherry Li. "Sparse Approximate Multifrontal Factorization with Butterfly Compression for High-Frequency Wave Equations." SIAM Journal on Scientific Computing, June 22, 2021, S367—S391. http://dx.doi.org/10.1137/20m1349667.

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17

Yamakawa, Keisuke K., Rena Nishiwaki, and Yasuo Sengoku. "Muscle Coordination During Maximal Butterfly Stroke Swimming: Comparison Between Competitive and Recreational Swimmers." Journal of Applied Biomechanics, 2024, 1–10. http://dx.doi.org/10.1123/jab.2023-0186.

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This study aimed to clarify the differences in muscular coordination during butterfly swimming between high- and low-performance swimmers using muscle synergy analysis. Eight female competitive swimmers and 8 female recreational swimmers participated in this study. The participants swam a 25-m butterfly stroke with maximum effort. Surface electromyography was measured from 12 muscles and muscle synergy analysis was performed from the data using nonnegative matrix factorization algorithms. From the results of the muscle synergy analysis, 4 synergies were extracted from both groups. Synergies 1 and 2 were characterized by coactivation of the upper and lower limb muscles in the recreational swimmers, whereas only synergy 1 was characterized by this in the competitive swimmers. Synergy 3 was involved in arm recovery in both groups. Synergy 4 was only involved in the downward kick in the competitive swimmers. From these results, it can be concluded that muscle synergies with combined coordination of upper and lower limb muscles were extracted more in the recreational swimmers and that the competitive swimmers controlled the downward kick with an independent synergy and that the adjustment of the timing of the downward kick may be an important factor for the efficient performance of butterfly swimming.

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18

Matsuura, Yuiko, Naoto Matsunaga, Hiroshi Akuzawa, Tsuyoshi Kojima, Tomoki Oshikawa, Satoshi Iizuka, Keisuke Okuno, and Koji Kaneoka. "Difference in muscle synergies of the butterfly technique with and without swimmer’s shoulder." Scientific Reports 12, no. 1 (September 6, 2022). http://dx.doi.org/10.1038/s41598-022-18624-8.

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AbstractThis study aimed to investigate whether muscle synergy differs between swimmers with and without swimmer's shoulder in the butterfly technique. Muscle synergies, which can assess muscle coordination, were analyzed using surface electromyography. Twenty elite swimmers were included in this study (swimmer's shoulder: n = 8; control: n = 12). The motions involved in executing the butterfly technique were classified into the early pull-through, late pull-through, and recovery phases. Muscle synergy data analyzed using the nonnegative matrix factorization method were compared between the two groups.The swimming velocities were 1.66 ± 0.09 m・s −1 and 1.69 ± 0.06 m・s −1 for the control and swimmer's shoulder groups, respectively. Four muscle synergies in both groups were identified: synergy #1, which was involved in the early pull; synergy #2, involved in the late pull; synergy #3, involved in the early recovery; and synergy #4, involved in pre- and posthand entry. Compared to the control group, the swimmer's shoulder group had a small contribution from the pectoralis major (p = 0.032) and a high contribution from the rectus femoris during the early pull phase (p = 0.036). In the late pull phase, the contribution of the lower trapezius muscle in the swimmer's shoulder group was low (p = 0.033), while the contribution of the upper trapezius muscle in the pre- and postentry phases was high (p = 0.032). In the rehabilitation of athletes with swimmer's shoulder, it is therefore important to introduce targeted muscle rehabilitation in each phase.

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19

Haehl, Felix M., Alexandre Streicher, and Ying Zhao. "Six-point functions and collisions in the black hole interior." Journal of High Energy Physics 2021, no. 8 (August 2021). http://dx.doi.org/10.1007/jhep08(2021)134.

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Abstract In the eternal AdS black hole geometry, we consider two signals sent from the boundaries into the black hole interior shared between the two asymptotic regions. We compute three different out-of-time-order six-point functions to quantify various properties of the collision of these signals behind the horizons: (i) We diagnose the strength of the collision by probing the two-signal state on a late time slice with boundary operators. (ii) We quantify two-sided operator growth, which provides a dual description of the signals meeting in the black hole interior, in terms of the quantum butterfly effect and quantum circuits. (iii) We consider an explicit coupling between the left and right CFTs to make the wormhole traversable and extract information about the collision product from behind the horizon. At a technical level, our results rely on the method of eikonal resummation to obtain the relevant gravitational contributions to Lorentzian six-point functions at all orders in the GN-expansion. We observe that such correlation functions display an intriguing factorization property. We corroborate these results with geodesic computations of six-point functions in two- and three-dimensional gravity.

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