{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T13:40:11Z","timestamp":1755870011869,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":84,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,6,8]]},"DOI":"10.1145\/3721145.3725763","type":"proceedings-article","created":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T12:57:17Z","timestamp":1755867437000},"page":"26-42","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["NeurLZ: An Online Neural Learning-based Method to Enhance Scientific Lossy Compression"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9473-6703","authenticated-orcid":false,"given":"Wenqi","family":"Jia","sequence":"first","affiliation":[{"name":"University of Texas at Arlington, Arlington, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3516-0431","authenticated-orcid":false,"given":"Zhewen","family":"Hu","sequence":"additional","affiliation":[{"name":"Texas A&M University, College Station, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6353-3659","authenticated-orcid":false,"given":"Youyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Temple University, Philadelphia, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8937-4067","authenticated-orcid":false,"given":"Boyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Indiana University, Bloomington, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6317-2940","authenticated-orcid":false,"given":"Jinzhen","family":"Wang","sequence":"additional","affiliation":[{"name":"UNC Charlotte, Charlotte, NC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0177-502X","authenticated-orcid":false,"given":"Jinyang","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Houston, Houston, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2697-7042","authenticated-orcid":false,"given":"Wei","family":"Niu","sequence":"additional","affiliation":[{"name":"University of Georgia, Athens, GA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7543-2479","authenticated-orcid":false,"given":"Stavros","family":"Kalafatis","sequence":"additional","affiliation":[{"name":"Texas A&M University, College Station, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9548-1227","authenticated-orcid":false,"given":"Junzhou","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Texas at Arlington, Arlington, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9250-0611","authenticated-orcid":false,"given":"Sian","family":"Jin","sequence":"additional","affiliation":[{"name":"Temple University, Philadelphia, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4444-3634","authenticated-orcid":false,"given":"Daoce","family":"Wang","sequence":"additional","affiliation":[{"name":"Indiana University, Bloomington, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1101-9148","authenticated-orcid":false,"given":"Jiannan","family":"Tian","sequence":"additional","affiliation":[{"name":"University of Kentucky, Lexington, KY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5554-5417","authenticated-orcid":false,"given":"Miao","family":"Yin","sequence":"additional","affiliation":[{"name":"University of Texas at Arlington, Arlington, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,22]]},"reference":[{"key":"e_1_3_3_1_2_2","unstructured":"[n. d.]. Community Earth Simulation Model (CESM). https:\/\/www2.cesm.ucar.edu\/."},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"crossref","unstructured":"Ann\u00a0S Almgren John\u00a0B Bell Mike\u00a0J Lijewski Zarija Luki\u0107 and Ethan Van\u00a0Andel. 2013. Nyx: A massively parallel amr code for computational cosmology. The Astrophysical Journal 765 1 (2013) 39.","DOI":"10.1088\/0004-637X\/765\/1\/39"},{"key":"e_1_3_3_1_4_2","unstructured":"Amazon Web Services. n. d.. Amazon S3 Glacier. https:\/\/aws.amazon.com\/glacier\/. Accessed: 2025-01-07."},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"publisher","unstructured":"Pontus Andersson Jim Nilsson Tomas Akenine-M\u00f6ller Magnus Oskarsson Kalle \u00c5str\u00f6m and Mark\u00a0D. Fairchild. 2020. FLIP: A Difference Evaluator for Alternating Images. Proc. ACM Comput. Graph. Interact. Tech. 3 2 Article 15 (Aug. 2020) 23\u00a0pages. 10.1145\/3406183","DOI":"10.1145\/3406183"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Saeed Anwar Salman Khan and Nick Barnes. 2020. A deep journey into super-resolution: A survey. ACM computing surveys (CSUR) 53 3 (2020) 1\u201334.","DOI":"10.1145\/3390462"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Anakha\u00a0V Babu Tao Zhou Saugat Kandel Tekin Bicer Zhengchun Liu William Judge Daniel\u00a0J Ching Yi Jiang Sinisa Veseli Steven Henke et\u00a0al. 2023. Deep learning at the edge enables real-time streaming ptychographic imaging. Nature Communications 14 1 (2023) 7059.","DOI":"10.1038\/s41467-023-41496-z"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/2600212.2600217"},{"key":"e_1_3_3_1_9_2","first-page":"351","volume-title":"11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 14)","author":"Balakrishnan Shobana","year":"2014","unstructured":"Shobana Balakrishnan, Richard Black, Austin Donnelly, Paul England, Adam Glass, Dave Harper, Sergey Legtchenko, Aaron Ogus, Eric Peterson, and Antony Rowstron. 2014. Pelican: A Building Block for Exascale Cold Data Storage. In 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 14). USENIX Association, Broomfield, CO, 351\u2013365. https:\/\/www.usenix.org\/conference\/osdi14\/technical-sessions\/presentation\/balakrishnan"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"crossref","unstructured":"Rafael Ballester-Ripoll Peter Lindstrom and Renato Pajarola. 2019. TTHRESH: Tensor compression for multidimensional visual data. IEEE transactions on visualization and computer graphics 26 9 (2019) 2891\u20132903.","DOI":"10.1109\/TVCG.2019.2904063"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"crossref","unstructured":"Rafael Ballester-Ripoll Susanne\u00a0K Suter and Renato Pajarola. 2015. Analysis of tensor approximation for compression-domain volume visualization. Computers & Graphics 47 (2015) 34\u201347.","DOI":"10.1016\/j.cag.2014.10.002"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Franck Cappello Sheng Di Sihuan Li Xin Liang Ali\u00a0Murat Gok Dingwen Tao Chun\u00a0Hong Yoon Xin-Chuan Wu Yuri Alexeev and Frederic\u00a0T Chong. 2019. Use cases of lossy compression for floating-point data in scientific data sets. The International Journal of High Performance Computing Applications 33 6 (2019) 1201\u20131220.","DOI":"10.1177\/1094342019853336"},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511546921"},{"key":"e_1_3_3_1_14_2","unstructured":"Xiangyu Chen Xintao Wang Jiantao Zhou Yu Qiao and Chao Dong. 2023. Activating More Pixels in Image Super-Resolution Transformer. arxiv:https:\/\/arXiv.org\/abs\/2205.04437\u00a0[eess.IV] https:\/\/arxiv.org\/abs\/2205.04437"},{"key":"e_1_3_3_1_15_2","unstructured":"Yann Collet and EM Kucherawy. 2015. Zstandard-real-time data compression algorithm."},{"key":"e_1_3_3_1_16_2","unstructured":"Andrew Cook and William Cabot. 2024. Miranda Simulation Code. https:\/\/wci.llnl.gov\/simulation\/computer-codes\/miranda. Accessed: [Date you accessed the site]."},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"crossref","unstructured":"P. Deutsch. 1996. RFC1952: GZIP file format specification version 4.3.","DOI":"10.17487\/rfc1952"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2016.11"},{"key":"e_1_3_3_1_19_2","unstructured":"Sheng Di Jinyang Liu Kai Zhao Xin Liang Robert Underwood Zhaorui Zhang Milan Shah Yafan Huang Jiajun Huang Xiaodong Yu Congrong Ren Hanqi Guo Grant Wilkins Dingwen Tao Jiannan Tian Sian Jin Zizhe Jian Daoce Wang MD\u00a0Hasanur Rahman Boyuan Zhang Jon\u00a0C. Calhoun Guanpeng Li Kazutomo Yoshii Khalid\u00a0Ayed Alharthi and Franck Cappello. 2024. A Survey on Error-Bounded Lossy Compression for Scientific Datasets. arxiv:https:\/\/arXiv.org\/abs\/2404.02840\u00a0[cs.DC] https:\/\/arxiv.org\/abs\/2404.02840"},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/iscas48785.2022.9937486"},{"key":"e_1_3_3_1_21_2","unstructured":"Jean-loup Gailly and Mark Adler. 2004. zlib Compression Library. http:\/\/www.dspace.cam.ac.uk\/handle\/1810\/3486 Accessed: 2025-01-08."},{"key":"e_1_3_3_1_22_2","unstructured":"Hanan Gani Muzammal Naseer and Mohammad Yaqub. 2022. How to Train Vision Transformer on Small-scale Datasets? arxiv:https:\/\/arXiv.org\/abs\/2210.07240\u00a0[cs.CV] https:\/\/arxiv.org\/abs\/2210.07240"},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.5555\/2750482.2750484"},{"key":"e_1_3_3_1_24_2","unstructured":"Ali Hassani Steven Walton Nikhil Shah Abulikemu Abuduweili Jiachen Li and Humphrey Shi. 2022. Escaping the Big Data Paradigm with Compact Transformers. arxiv:https:\/\/arXiv.org\/abs\/2104.05704\u00a0[cs.CV] https:\/\/arxiv.org\/abs\/2104.05704"},{"key":"e_1_3_3_1_25_2","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2015. Deep Residual Learning for Image Recognition. arxiv:https:\/\/arXiv.org\/abs\/1512.03385\u00a0[cs.CV] https:\/\/arxiv.org\/abs\/1512.03385"},{"key":"e_1_3_3_1_26_2","doi-asserted-by":"crossref","unstructured":"Steven\u00a0CH Hoi Doyen Sahoo Jing Lu and Peilin Zhao. 2021. Online learning: A comprehensive survey. Neurocomputing 459 (2021) 249\u2013289.","DOI":"10.1016\/j.neucom.2021.04.112"},{"key":"e_1_3_3_1_27_2","unstructured":"Steven\u00a0C.H. Hoi Jialei Wang and Peilin Zhao. 2014. LIBOL: A Library for Online Learning Algorithms. Journal of Machine Learning Research 15 15 (2014) 495\u2013499. http:\/\/jmlr.org\/papers\/v15\/hoi14a.html"},{"key":"e_1_3_3_1_28_2","doi-asserted-by":"crossref","unstructured":"Kurt Hornik Maxwell Stinchcombe and Halbert White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2 5 (1989) 359\u2013366.","DOI":"10.1016\/0893-6080(89)90020-8"},{"key":"e_1_3_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00073"},{"key":"e_1_3_3_1_30_2","unstructured":"Hurricane ISABEL dataset. 2004. http:\/\/sciviscontest-staging.ieeevis.org\/2004\/data.html. Online."},{"key":"e_1_3_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3659995.3660041"},{"key":"e_1_3_3_1_32_2","unstructured":"Narine Kokhlikyan Vivek Miglani Miguel Martin Edward Wang Bilal Alsallakh Jonathan Reynolds Alexander Melnikov Natalia Kliushkina Carlos Araya Siqi Yan and Orion Reblitz-Richardson. 2020. Captum: A unified and generic model interpretability library for PyTorch. arxiv:https:\/\/arXiv.org\/abs\/2009.07896\u00a0[cs.LG]"},{"key":"e_1_3_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_19"},{"key":"e_1_3_3_1_34_2","doi-asserted-by":"crossref","unstructured":"Yu Kong and Yun Fu. 2022. Human action recognition and prediction: A survey. International Journal of Computer Vision 130 5 (2022) 1366\u20131401.","DOI":"10.1007\/s11263-022-01594-9"},{"key":"e_1_3_3_1_35_2","doi-asserted-by":"crossref","unstructured":"Sriram Lakshminarasimhan Neil Shah Stephane Ethier Seung-Hoe Ku Choong-Seock Chang Scott Klasky Rob Latham Rob Ross and Nagiza\u00a0F Samatova. 2013. ISABELA for effective in situ compression of scientific data. Concurrency and Computation: Practice and Experience 25 4 (2013) 524\u2013540.","DOI":"10.1002\/cpe.2887"},{"key":"e_1_3_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS54959.2023.00104"},{"key":"e_1_3_3_1_37_2","unstructured":"Xiao Li Jaemoon Lee Anand Rangarajan and Sanjay Ranka. 2024. Attention Based Machine Learning Methods for Data Reduction with Guaranteed Error Bounds. arxiv:https:\/\/arXiv.org\/abs\/2409.05357\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2409.05357"},{"key":"e_1_3_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3295500.3356193"},{"key":"e_1_3_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622520"},{"key":"e_1_3_3_1_40_2","doi-asserted-by":"crossref","unstructured":"Xin Liang Ben Whitney Jieyang Chen Lipeng Wan Qing Liu Dingwen Tao James Kress David Pugmire Matthew Wolf Norbert Podhorszki et\u00a0al. 2021. Mgard+: Optimizing multilevel methods for error-bounded scientific data reduction. IEEE Trans. Comput. 71 7 (2021) 1522\u20131536.","DOI":"10.1109\/TC.2021.3092201"},{"key":"e_1_3_3_1_41_2","doi-asserted-by":"crossref","unstructured":"Xin Liang Kai Zhao Sheng Di Sihuan Li Robert Underwood Ali\u00a0M Gok Jiannan Tian Junjing Deng Jon\u00a0C Calhoun Dingwen Tao et\u00a0al. 2022. Sz3: A modular framework for composing prediction-based error-bounded lossy compressors. IEEE Transactions on Big Data 9 2 (2022) 485\u2013498.","DOI":"10.1109\/TBDATA.2022.3201176"},{"key":"e_1_3_3_1_42_2","doi-asserted-by":"crossref","unstructured":"Peter Lindstrom. 2014. Fixed-rate compressed floating-point arrays. IEEE transactions on visualization and computer graphics 20 12 (2014) 2674\u20132683.","DOI":"10.1109\/TVCG.2014.2346458"},{"key":"e_1_3_3_1_43_2","doi-asserted-by":"crossref","unstructured":"Peter Lindstrom and Martin Isenburg. 2006. Fast and efficient compression of floating-point data. IEEE transactions on visualization and computer graphics 12 5 (2006) 1245\u20131250.","DOI":"10.1109\/TVCG.2006.143"},{"key":"e_1_3_3_1_44_2","volume-title":"Fpzip","author":"Lindstrom Peter\u00a0G","year":"2017","unstructured":"Peter\u00a0G Lindstrom. 2017. Fpzip. Technical Report. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)."},{"key":"e_1_3_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData59044.2023.10386682"},{"key":"e_1_3_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/Cluster48925.2021.00034"},{"key":"e_1_3_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/SC41404.2022.00067"},{"key":"e_1_3_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/3577193.3593721"},{"key":"e_1_3_3_1_49_2","doi-asserted-by":"publisher","unstructured":"Jinyang Liu Sheng Di Kai Zhao Xin Liang Sian Jin Zizhe Jian Jiajun Huang Shixun Wu Zizhong Chen and Franck Cappello. 2024. High-performance Effective Scientific Error-bounded Lossy Compression with Auto-tuned Multi-component Interpolation. Proc. ACM Manag. Data 2 1 Article 4 (March 2024) 27\u00a0pages. 10.1145\/3639259","DOI":"10.1145\/3639259"},{"key":"e_1_3_3_1_50_2","doi-asserted-by":"crossref","unstructured":"Li Liu Wanli Ouyang Xiaogang Wang Paul Fieguth Jie Chen Xinwang Liu and Matti Pietik\u00e4inen. 2020. Deep learning for generic object detection: A survey. International journal of computer vision 128 (2020) 261\u2013318.","DOI":"10.1007\/s11263-019-01247-4"},{"key":"e_1_3_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/SCW63240.2024.00046"},{"key":"e_1_3_3_1_52_2","unstructured":"Yahui Liu Enver Sangineto Wei Bi Nicu Sebe Bruno Lepri and Marco\u00a0De Nadai. 2021. Efficient Training of Visual Transformers with Small Datasets. arxiv:https:\/\/arXiv.org\/abs\/2106.03746\u00a0[cs.CV] https:\/\/arxiv.org\/abs\/2106.03746"},{"key":"e_1_3_3_1_53_2","doi-asserted-by":"crossref","unstructured":"Zarija Luki\u0107 Casey\u00a0W Stark Peter Nugent Martin White Avery\u00a0A Meiksin and Ann Almgren. 2015. The Lyman \u03b1 forest in optically thin hydrodynamical simulations. Monthly Notices of the Royal Astronomical Society 446 4 (2015) 3697\u20133724.","DOI":"10.1093\/mnras\/stu2377"},{"key":"e_1_3_3_1_54_2","unstructured":"Julia Lust and Alexandru Condurache. 2020. A Survey on Assessing the Generalization Envelope of Deep Neural Networks at Inference Time for Image Classification. ArXiv abs\/2008.09381 (2020). https:\/\/api.semanticscholar.org\/CorpusID:221246352"},{"key":"e_1_3_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3511987"},{"key":"e_1_3_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/3329785.3329921"},{"key":"e_1_3_3_1_57_2","unstructured":"Microsoft Azure. n. d.. Azure Archive Storage. https:\/\/azure.microsoft.com\/en-us\/services\/storage\/archive\/. Accessed: 2025-01-07."},{"key":"e_1_3_3_1_58_2","doi-asserted-by":"crossref","unstructured":"Jose\u00a0G Moreno-Torres Troy Raeder Roc\u00edo Alaiz-Rodr\u00edguez Nitesh\u00a0V Chawla and Francisco Herrera. 2012. A unifying view on dataset shift in classification. Pattern recognition 45 1 (2012) 521\u2013530.","DOI":"10.1016\/j.patcog.2011.06.019"},{"key":"e_1_3_3_1_59_2","unstructured":"NERSC High-Dimension Nyx Dataset. [n. d.]. https:\/\/portal.nersc.gov\/cfs\/nyx\/highz\/. Online."},{"key":"e_1_3_3_1_60_2","doi-asserted-by":"crossref","unstructured":"Jose O\u00f1orbe FB Davies Z Luki\u0107 JF Hennawi and D Sorini. 2019. Inhomogeneous reionization models in cosmological hydrodynamical simulations. Monthly Notices of the Royal Astronomical Society 486 3 (2019) 4075\u20134097.","DOI":"10.1093\/mnras\/stz984"},{"key":"e_1_3_3_1_61_2","doi-asserted-by":"crossref","unstructured":"Cyril Pernet Claus Svarer Ross Blair John\u00a0D Van\u00a0Horn and Russell\u00a0A Poldrack. 2023. On the long-term archiving of research data. Neuroinformatics 21 2 (2023) 243\u2013246.","DOI":"10.1007\/s12021-023-09621-x"},{"key":"e_1_3_3_1_62_2","unstructured":"Linus Pithan Vladimir Starostin David Mare\u010dek Lukas Petersdorf Constantin V\u00f6lter Valentin Munteanu Maciej Jankowski Oleg Konovalov Alexander Gerlach Alexander Hinderhofer Bridget Murphy Stefan Kowarik and Frank Schreiber. 2023. Closing the loop: Autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments. arxiv:https:\/\/arXiv.org\/abs\/2306.11899\u00a0[physics.data-an] https:\/\/arxiv.org\/abs\/2306.11899"},{"key":"e_1_3_3_1_63_2","doi-asserted-by":"crossref","unstructured":"Waseem Rawat and Zenghui Wang. 2017. Deep convolutional neural networks for image classification: A comprehensive review. Neural computation 29 9 (2017) 2352\u20132449.","DOI":"10.1162\/neco_a_00990"},{"key":"e_1_3_3_1_64_2","doi-asserted-by":"publisher","unstructured":"Chris Rohlfs. 2025. Generalization in neural networks: A broad survey. Neurocomputing 611 (2025) 128701. 10.1016\/j.neucom.2024.128701","DOI":"10.1016\/j.neucom.2024.128701"},{"key":"e_1_3_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_3_1_66_2","doi-asserted-by":"crossref","unstructured":"Umme Sara Morium Akter and Mohammad\u00a0Shorif Uddin. 2019. Image Quality Assessment through FSIM SSIM MSE and PSNR\u2014A Comparative Study. Journal of Computer and Communications (2019). https:\/\/api.semanticscholar.org\/CorpusID:104425037","DOI":"10.4236\/jcc.2019.73002"},{"key":"e_1_3_3_1_67_2","doi-asserted-by":"crossref","unstructured":"Shai Shalev-Shwartz et\u00a0al. 2012. Online learning and online convex optimization. Foundations and Trends\u00ae in Machine Learning 4 2 (2012) 107\u2013194.","DOI":"10.1561\/2200000018"},{"key":"e_1_3_3_1_68_2","doi-asserted-by":"publisher","unstructured":"Ran Shao and Xiao-Jun Bi. 2022. Transformers Meet Small Datasets. IEEE Access 10 (2022) 118454\u2013118464. 10.1109\/ACCESS.2022.3221138","DOI":"10.1109\/ACCESS.2022.3221138"},{"key":"e_1_3_3_1_69_2","first-page":"3319","volume-title":"International conference on machine learning","author":"Sundararajan Mukund","year":"2017","unstructured":"Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017. Axiomatic attribution for deep networks. In International conference on machine learning. PMLR, 3319\u20133328."},{"key":"e_1_3_3_1_70_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.486"},{"key":"e_1_3_3_1_71_2","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2017.115"},{"key":"e_1_3_3_1_72_2","doi-asserted-by":"crossref","unstructured":"Dingwen Tao Sheng Di Xin Liang Zizhong Chen and Franck Cappello. 2019. Optimizing lossy compression rate-distortion from automatic online selection between SZ and ZFP. IEEE Transactions on Parallel and Distributed Systems 30 8 (2019) 1857\u20131871.","DOI":"10.1109\/TPDS.2019.2894404"},{"key":"e_1_3_3_1_73_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00077"},{"key":"e_1_3_3_1_74_2","unstructured":"Amirsina Torfi Rouzbeh\u00a0A. Shirvani Yaser Keneshloo Nader Tavaf and Edward\u00a0A. Fox. 2021. Natural Language Processing Advancements By Deep Learning: A Survey. arxiv:https:\/\/arXiv.org\/abs\/2003.01200\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2003.01200"},{"key":"e_1_3_3_1_75_2","doi-asserted-by":"crossref","unstructured":"Zhihao Wang Jian Chen and Steven\u00a0CH Hoi. 2020. Deep learning for image super-resolution: A survey. IEEE transactions on pattern analysis and machine intelligence 43 10 (2020) 3365\u20133387.","DOI":"10.1109\/TPAMI.2020.2982166"},{"key":"e_1_3_3_1_76_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.163"},{"key":"e_1_3_3_1_77_2","doi-asserted-by":"publisher","DOI":"10.1109\/SCW63240.2024.00048"},{"key":"e_1_3_3_1_78_2","doi-asserted-by":"publisher","unstructured":"Taolue Yang Youyuan Liu Chong Li Xinhua\u00a0Mindy Shi and Sian Jin. 2024. SeqBench: A Benchmark Suite for Lossless and Lossy Compression of Sequence Data(BCB \u201924). Association for Computing Machinery New York NY USA Article 58 7\u00a0pages. 10.1145\/3698587.3701386","DOI":"10.1145\/3698587.3701386"},{"key":"e_1_3_3_1_79_2","unstructured":"Taketoshi Yoshida Ken Iizawa and Masahisa Tamura. 2020. Mass Data Archiving Acceleration Technology for Magnetic Tape Storage. Fujitsu Technical Review 5 (2020)."},{"key":"e_1_3_3_1_80_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240625"},{"key":"e_1_3_3_1_81_2","doi-asserted-by":"crossref","unstructured":"Kai Zhang Wangmeng Zuo Yunjin Chen Deyu Meng and Lei Zhang. 2017. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE transactions on image processing 26 7 (2017) 3142\u20133155.","DOI":"10.1109\/TIP.2017.2662206"},{"key":"e_1_3_3_1_82_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00145"},{"key":"e_1_3_3_1_83_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData50022.2020.9378449"},{"key":"e_1_3_3_1_84_2","doi-asserted-by":"publisher","DOI":"10.1145\/3369583.3392688"},{"key":"e_1_3_3_1_85_2","doi-asserted-by":"crossref","unstructured":"Jacob Ziv and Abraham Lempel. 1977. A universal algorithm for sequential data compression. IEEE Transactions on information theory 23 3 (1977) 337\u2013343.","DOI":"10.1109\/TIT.1977.1055714"}],"event":{"name":"ICS '25: 2025 International Conference on Supercomputing","location":"Salt Lake City USA","acronym":"ICS '25","sponsor":["SIGARCH ACM Special Interest Group on Computer Architecture"]},"container-title":["Proceedings of the 39th ACM International Conference on Supercomputing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3721145.3725763","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T13:01:52Z","timestamp":1755867712000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3721145.3725763"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,8]]},"references-count":84,"alternative-id":["10.1145\/3721145.3725763","10.1145\/3721145"],"URL":"https:\/\/doi.org\/10.1145\/3721145.3725763","relation":{},"subject":[],"published":{"date-parts":[[2025,6,8]]},"assertion":[{"value":"2025-08-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}