REVIEW 4 major objections 2 minor 1 cited by
SED Fitting of Globular Clusters in NGC 4874: Masses and Metallicities
T0 review · 4 major / 2 minor · reviewed 2026-08-06 · deepseek-v4-flash
Pith's one-line read Ten-filter SED fitting recovers reliable masses and metallicities for globular clusters as distant as the Coma cluster, with mass-to-light ratios of 2–4.
desk verdict Abstract describes a plausible, modest SED-fitting extension to 29 GCs in NGC 4874; full text supplied is unrelated, so any verdict is provisional. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The method rests on two pieces: ten-filter HST photometry that samples the spectral energy distribution of each cluster from the blue through the near-infrared, and a chi-square fit against a grid of single stellar population models (E-MILES) spanning a range of metallicities and ages. The masses come from scaling the best-fit model to the observed flux, and the mass-to-light ratios are computed with luminosities taken from the reddest filters, in the flat part of the cluster spectrum, to reduce sensitivity to the stellar population parameters. The ten-filter coverage is what breaks the age-metallicity degeneracy that plagues two-filter color indices.
What would settle it
Measure the same 29 clusters' metallicities spectroscopically and their masses dynamically (for example, from velocity dispersions); if the SED-fit values disagree systematically with those independent estimates, the model grid or the fitting assumptions are biased. A cheaper check would be to compare SED-derived mass-to-light ratios with the Milky Way globular cluster calibration at the same metallicities.
Extended reading notes
Core claim
The central discovery is that a ten-band spectral-energy-distribution fit to integrated cluster light, using the E-MILES single stellar population library, converges on well-defined metallicities and masses for 29 globular clusters in the central galaxy NGC 4874. Using the fitted masses together with luminosities measured from the reddest filters — where the cluster spectrum is flat and least sensitive to age and metallicity — the inferred mass-to-light ratios are $(M/L) \simeq 2-4$. These values sit slightly above the average Milky Way globular cluster ratio, yet remain within the conventional range for old stellar populations. The result implies that the integrated-light SED method, not just two-color photometry, can be applied to globular cluster systems at Coma-cluster distances.
Load-bearing premise
The load-bearing premise is that the single stellar population model grid, together with the adopted distance, reddening, initial mass function, and age range, faithfully represents the integrated light of old globular clusters in NGC 4874; a systematic mismatch at the relevant metallicities and ages would bias the fitted masses and mass-to-light ratios even if the fits look good.
Editorial extensions
If this is right
- Ten-filter SED fitting can be used to measure masses and metallicities of globular clusters in galaxies far beyond the Local Group, not just in nearby systems.
- The derived $(M/L) \simeq 2-4$ ratios provide a consistency check on dynamical mass estimates for these clusters.
- The method supplies a metallicity distribution for 29 clusters in NGC 4874, a benchmark for galaxy formation and intra-cluster globular cluster enrichment models.
- Where spectroscopy is infeasible, multi-band HST photometry alone can recover population parameters, roughly tripling the usable sample of distant globular cluster systems.
Reading between the lines
- If the ten-filter approach is unbiased, it could be extended to the full globular cluster system of NGC 4874 and other Coma galaxies, producing a complete metallicity distribution and constraining the cluster mass function at large galactocentric radii.
- The slight mass-to-light excess over Milky Way globular clusters, if real, might reflect a top-heavy initial mass function or a younger age component; a larger sample with fainter clusters could distinguish these.
- A direct test of the model grid would be to fit the same clusters with an independent stellar population library and compare the recovered masses; disagreement would localize the systematic.
- The reddest-filter luminosity choice is a practical shortcut; applying the same mass-to-light derivation to every filter and checking for consistency would reveal residual age or metallicity leakage.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper, as submitted, consists of an astronomy abstract plus a full text that is an unrelated systems paper titled "MaLV-OS: Rethinking the Operating System Architecture for Machine Learning in Virtualized Clouds." The abstract claims that SED fitting with E-MILES template SEDs to ten-filter HST photometry yields best-fit metallicities and masses for 29 bright globular clusters in NGC 4874, with inferred mass-to-light ratios in the range M/L ~ 2-4, slightly above the Milky Way GC average. No methods, results, tables, figures, or validation are present in the supplied text, so the astronomical claims cannot be evaluated beyond the abstract.
Significance. If the result holds, the claim that multi-band integrated-light photometry can recover stellar population parameters of globular clusters in a Coma-cluster galaxy would be a useful methodological demonstration, especially with ten HST filters. The abstract honestly labels the M/L values as "inferred." However, the supplied manuscript does not contain the astronomy paper, only an unrelated full text, so the scientific contribution cannot be assessed. The abstract alone provides no error bars, no independent validation, and no discussion of systematic uncertainties, leaving the central claims unsupported at this stage.
major comments (4)
- [Full Text (as supplied)] The full text of arXiv:2508.03684 as supplied is an unrelated paper on operating systems and machine learning ("MaLV-OS"), not the SED fitting paper described in the abstract. This is a load-bearing defect: none of the methods, data, figures, tables, or analysis for the astronomy claims are present, making the manuscript non-reviewable as an astronomy paper. This cannot be fixed by minor revisions; the correct full text must be supplied.
- [Abstract] The abstract states that best-fit metallicities and masses were obtained for 29 GCs but reports no uncertainties, no goodness-of-fit information, and no validation against independent constraints such as spectroscopy of individual clusters or dynamical mass estimates. Without such checks, the claim of "reliable" best-fit parameters is not supported.
- [Abstract] The inferred M/L range of ~2-4 is computed from the fitted masses divided by photometric luminosities, so it is a re-expression of the SED-fit normalization rather than an independent measurement. The abstract does not state the adopted distance, reddening, IMF, or age grid, all of which directly rescale the fitted mass and M/L; the comparison to Milky Way GC averages is therefore not interpretable without these assumptions being specified and justified.
- [Abstract] The abstract does not address the age-metallicity degeneracy or the fidelity of the E-MILES template library for old, metal-poor globular clusters. The fitted metallicity and mass depend on the assumed age range, template coverage of horizontal-branch morphology, alpha-enhancement, and binarity; the abstract provides no test of these systematics, so the central claim that the fitted metallicities and masses are reliable is not established.
minor comments (2)
- [Abstract] The abstract does not specify which ten HST filters were used, which would be needed to assess the wavelength coverage and the leverage on metallicity and mass.
- [Abstract] The phrase "best-fit metallicity and mass" would benefit from a definition of the fitting statistic (e.g., chi-square or likelihood) and the treatment of photometric uncertainties, but such details are absent because the full text is not the astronomy paper.
Circularity Check
No significant circularity: the M/L values are explicitly derived from the fitted masses and photometric luminosities, and the paper does not present them as independent predictions.
full rationale
The abstract describes fitting 29 GCs with an E-MILES SED library and computing best-fit metallicities and masses, then dividing the fitted masses by luminosities from the reddest magnitudes to obtain M/L ratios. The M/L ratios are therefore post-processing products of the fit, not inputs used to define the fit, and the abstract labels them as 'inferred' rather than as predicted or independently measured. No parameter is fitted to a subset and then reported as a prediction of a closely related quantity; no self-citation is load-bearing; no uniqueness theorem is imported; and no known result is repackaged under new coordinates. The concern that template fidelity, distance, IMF, or age assumptions could bias the derived masses and M/L values is a correctness or external-validity issue, not a circularity issue under the stated criteria. The supplied full text is a different work (MaLV-OS) and contains no derivation chain relevant to the SED-fitting claims, so it provides no quotable circular step. Consequently, the derivation chain is self-contained as presented, and the appropriate circularity score is 0.
Assumptions & free parameters
free parameters (2)
- per-cluster stellar mass (29 fitted values) =
not reported in abstract
- per-cluster metallicity (29 fitted values) =
not reported in abstract
assumptions (3)
- domain assumption The E-MILES single stellar population library accurately represents the integrated light of old GCs in NGC 4874.
- domain assumption The distance and foreground reddening to NGC 4874 are known and applied correctly.
- domain assumption Ten-filter photometry is sufficient to break the age-metallicity degeneracy in GC SEDs.
Cite this review
Pith. "Pith review of SED Fitting of Globular Clusters in NGC 4874: Masses and Metallicities." pith.science (2026). https://pith.science/paper/OQJCVL5B
@misc{pith2026250803684,
author = {Pith},
title = {Pith review of: SED Fitting of Globular Clusters in NGC 4874: Masses and Metallicities},
year = {2026},
howpublished = {\url{https://pith.science/paper/OQJCVL5B}},
note = {Machine review of arXiv:2508.03684}
}
abstract
In most nearby galaxies, photometry of the integrated light of their globular clusters (GCs) has been obtained in only two filters, yielding just a single color index. However, NGC 4874, the brightest central galaxy in the Coma cluster, now has Hubble Space Telescope (HST) photometry available in ten filters, giving us a special opportunity to test SED fitting procedures on GCs in distant galaxies. We fitted 29 of the brightest GCs with a library of SEDs from E-MILES and calculated the best-fit metallicity and mass of each cluster. Using the fitted masses and luminosities derived from the reddest magnitudes, in the flat portion of the GC spectrum, we also calculated inferred mass-to-light ratios for our sample GCs; these were in the range (M/L) $\simeq 2 - 4$, slightly larger than the average values for Milky Way GCs but within the conventional range.
Forward citations
Cited by 1 Pith paper
-
Veila: Panoramic LiDAR Generation from a Monocular RGB Image
A monocular-RGB-conditioned diffusion framework for panoramic LiDAR generation is described in the abstract, but the submission's body text is an unrelated astronomy paper, leaving the core claims unsupported.
Reference graph
Works this paper leans on
-
[1]
[n. d.]. Amazon EC2 P4 Instances. https://aws.amazon.com/fr/ec2/ pricing/. Accessed: May 20, 2025
work page 2025
-
[2]
[n. d.]. KiTS19 Challenge Dataset. https://kits19.grand-challenge.org/ data/. Accessed: [Jan 12, 2025]
work page 2025
-
[3]
[n. d.]. NumPy - The fundamental package for scientific computing with Python. https://numpy.org/. Accessed: May 11, 2025
work page 2025
-
[4]
[n. d.]. NVIDIA Data Loading Library (DALI). https://developer.nvidia. com/dali. Accessed: May 5, 2025
work page 2025
-
[5]
[n. d.]. Pandas: powerful Python data analysis toolkit. https://pypi. org/project/pandas/. Accessed: May 11, 2025
work page 2025
-
[6]
[n. d.]. Scikit-learn - Machine Learning in Python. https://scikit- learn.org/stable/. Accessed: May 11, 2025
work page 2025
-
[7]
[n. d.]. The Top 3 Operating System in 2022; Linux, Windows, and Solaris. https://princetonits.com/blog/operating-system/the-top-3- operating-system-in-2022-linux-windows-and-solaris/. Accessed: May 19, 2025
work page 2022
-
[8]
[n. d.]. Usage share of operating systems. https://en.wikipedia.org/ wiki/Usage_share_of_operating_systems. Accessed: May 19, 2025
work page 2025
Show all 71 references
-
[9]
https://developer.nvidia
2020.NVIDIA Managemnet Library (NVML). https://developer.nvidia. com/management-library-nvml Accessed: May 11, 2025
2020
-
[10]
Zeeshan Ahmed, Saeed Amizadeh, Mikhail Bilenko, Rogan Carr, Wei- Sheng Chin, Yael Dekel, Xavier Dupre, Vadim Eksarevskiy, Senja Filipi, Tom Finley, Abhishek Goswami, Monte Hoover, Scott Inglis, Mat- teo Interlandi, Najeeb Kazmi, Gleb Krivosheev, Pete Luferenko, Ivan Matantsev,...
2019
-
[11]
Google AI. [n. d.]. Chat with Gemini to supercharge your creativity and productivity. https://store.google.com/intl/en/ideas/categories/ai/. Accessed: May 19, 2025
2025
-
[12]
Android. [n. d.]. Get the best of Google AI on Android. https://www. android.com/intl/en_ca/ai/. Accessed: May 19, 2025
2025
-
[13]
Apple. [n. d.]. Apple Intelligence - AI for the rest of us. https://www. apple.com/ca/apple-intelligence/. Accessed: May 19, 2025
2025
-
[14]
Edouard Bugnion, Vitaly Chipounov, and George Candea. 2013. Light- weight Snapshots and System-level Backtracking. InProceedings of the 14th USENIX Conference on Hot Topics in Operating Systems(Santa Ana Pueblo, New Mexcio)(HotOS’13). USENIX Association, Berkeley, CA, USA, 23–...
2013
-
[15]
Lienkamp, Thomas Brox, and Olaf Ronneberger
Özgün Çiçek, Ahmed Abdulkadir, Soeren S. Lienkamp, Thomas Brox, and Olaf Ronneberger. 2016. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
2016
-
[16]
Banerjee, Zbigniew T
Jingde Chen, Subho S. Banerjee, Zbigniew T. Kalbarczyk, and Ravis- hankar K. Iyer. 2020. Machine learning for load balancing in the Linux kernel. InProceedings of the 11th ACM SIGOPS Asia-Pacific Workshop on Systems. doi:10.1145/3409963.3410492
2020
-
[17]
Daube-Witherspoon, S
M.E. Daube-Witherspoon, S. Matej, J.S. Karp, and R.M. Lewitt. 2001. Application of the row action maximum likelihood algorithm with spherical basis functions to clinical PET imaging.IEEE Transactions on Nuclear Science48, 1 (2001), 24–30. doi:10.1109/23.910827
2001 doi
-
[18]
Docker. [n. d.]. NVIDIA Docker: GPU Server Application Deploy- ment Made Easy. https://developer.nvidia.com/blog/nvidia-docker- gpu-server-application-deployment-made-easy/. Accessed: May 24, 2025
2025
-
[19]
Thaleia Dimitra Doudali, Sergey Blagodurov, Abhinav Vishnu, Sud- hanva Gurumurthi, and Ada Gavrilovska. 2019. Kleio: A Hybrid Mem- ory Page Scheduler with Machine Intelligence. InProceedings of the 28th International Symposium on High-Performance Parallel and Dis- tributed Com...
2019
-
[20]
Micah Dowty and Jeremy Sugerman. 2009. GPU virtualization on VMware’s hosted I/O architecture.SIGOPS Opererating Systems Review (2009). doi:10.1145/1618525.1618534
2009
-
[21]
Alexandra Fedorova, David Vengerov, David Vengerov, and Daniel Doucette. 2007. Operating System Scheduling On Heterogeneous Core Systems. https://api.semanticscholar.org/CorpusID:14823905
2007
-
[22]
Rossbach
Henrique Fingler, Isha Tarte, Hangchen Yu, Ariel Szekely, Bodun Hu, Aditya Akella, and Christopher J. Rossbach. 2023. Towards a Machine Learning-Assisted Kernel with LAKE. InProceedings of the 28th ACM International Conference on Architectural Support for Programming Languages...
2023 doi
-
[23]
Chitralekha G and Jyoti M Roogi. [n. d.]. A Quick Review of ML Algorithms. In2021 6th International Conference on Communication and Electronics Systems (ICCES). doi:10.1109/ICCES51350.2021.9488982
2021
-
[24]
Gaddisa Olani Ganfure, Chun-Feng Wu, Yuan-Hao Chang, and Wei- Kuan Shih. 2020. DeepPrefetcher: A Deep Learning Framework for Data Prefetching in Flash Storage Devices.IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems(2020). doi:10.1109/TCAD.2020.3012173
2020
-
[25]
Sepideh Goodarzy, Maziyar Nazari, Richard Han, Eric Keller, and Eric Rozner. 2021. SmartOS: towards automated learning and user-adaptive resource allocation in operating systems. InProceedings of the 12th ACM SIGOPS Asia-Pacific Workshop on Systems. doi:10.1145/3476886. 3477519
2021 doi
-
[26]
Krzysztof Gorgolewski, Christopher Burns, Cindee Madison, Dav Clark, Yaroslav Halchenko, Michael Waskom, and Satrajit Ghosh. 2011. Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.Frontiers in Neuroinformatics5 (2011). doi:10.3389...
2011 arXiv
-
[27]
Dan Graur, Damien Aymon, Dan Kluser, Tanguy Albrici, Chandramo- han A Thekkath, and Ana Klimovic. 2022. Cachew: Machine Learning Input Data Processing As A Service. InProceedings of USENIX ATC 22
2022
-
[28]
Thekkath, and Ana Klimovic
Dan Graur, Oto Mraz, Muyu Li, Sepehr Pourghannad, Chandramo- han A. Thekkath, and Ana Klimovic. 2024. Pecan: Cost-Efficient ML MaLV-OS: Rethinking the Operating System Architecture for Machine Learning in Virtualized Clouds Conference’17, July 2017, Washington, DC, USA Data Pr...
2024
-
[29]
Schotten
Mohammad Asif Habibi, Bin Han, Merve Saimler, Ignacio Labrador Pavon, and Hans D. Schotten. 2024. Towards an AI/ML-driven SMO Framework in O-RAN: Scenarios, Solutions, and Challenges. https: //arxiv.org/abs/2409.05092
2024 arXiv
-
[30]
Mingzhe Hao, Levent Toksoz, Nanqinqin Li, Edward Edberg Halim, Henry Hoffmann, and Haryadi S. Gunawi. 2020. LinnOS: Predictability on Unpredictable Flash Storage with a Light Neural Network. In14th USENIX Symposium on Operating Systems Design and Implementation
2020
-
[31]
Paul Hawkins, Geoff Skillman, Gregory Warren, Benjamin Ellingson, and Matthew Stahl. 2010. Conformer Generation with OMEGA: Algo- rithm and Validation Using High Quality Structures from the Protein Databank and Cambridge Structural Database.Journal of chemical information and ...
2010 doi
-
[32]
Nicholas Heller, Niranjan Sathianathen, Arveen Kalapara, Edward Walczak, Keenan Moore, Heather Kaluzniak, Joel Rosenberg, Paul Blake, Zachary Rengel, Makinna Oestreich, Joshua Dean, Michael Tradewell, Aneri Shah, Resha Tejpaul, Zachary Edgerton, Matthew Pe- terson, Shaneabbas ...
2020 arXiv
-
[33]
Nikolopoulos
Cheol-Ho Hong, Ivor Spence, and Dimitrios S. Nikolopoulos. 2017. GPU Virtualization and Scheduling Methods: A Comprehensive Survey. Comput. Surveys(2017). doi:10.1145/3068281
2017 doi
-
[34]
Shifu Hou, Aaron Saas, Lifei Chen, and Yanfang Ye. 2016. Deep4MalDroid: A Deep Learning Framework for Android Mal- ware Detection Based on Linux Kernel System Call Graphs. In2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops. doi:10.1109/WIW.2016.040
2016 doi
-
[35]
Ibrahim and Leonid Oliker
Khaled Z. Ibrahim and Leonid Oliker. 2022. Preprocessing Pipeline Optimization for Scientific Deep Learning Workloads. InIEEE In- ternational Parallel and Distributed Processing Symposium (IPDPS). doi:10.1109/IPDPS53621.2022.00112
2022
-
[36]
Daeyoun Kang, Tae Joon Jun, Dohyeun Kim, Jaewook Kim, and Daey- oung Kim. 2017. ConVGPU: GPU Management Middleware in Con- tainer Based Virtualized Environment. In2017 IEEE International Con- ference on Cluster Computing (CLUSTER). doi:10.1109/CLUSTER.2017. 17
2017 doi
-
[37]
George Karypis and Vipin Kumar. 1998. A Fast and High Quality Multi- level Scheme for Partitioning Irregular Graphs.SIAM Journal on Scien- tific Computing20, 1 (1998), 359–392. doi:10.1137/S1064827595287997 arXiv:https://doi.org/10.1137/S1064827595287997
1998 doi
-
[38]
Pintelas
Sotiris Kotsiantis, Dimitris Kanellopoulos, and P. Pintelas. 2006. Data Preprocessing for Supervised Learning.International Journal of Com- puter Science1 (01 2006), 111–117
2006
-
[39]
Pintelas
Sotiris Kotsiantis, Dimitris Kanellopoulos, and P. Pintelas. 2006. Data Preprocessing for Supervised Learning.International Journal of Com- puter Science(2006)
2006
-
[40]
Arezki Laga, Jalil Boukhobza, Michel Koskas, and Frank Singhoff. 2016. Lynx: a learning linux prefetching mechanism for SSD performance model. In2016 5th Non-Volatile Memory Systems and Applications Sym- posium. doi:10.1109/NVMSA.2016.7547186
2016
-
[41]
Chiyoung Lee, Se-Won Kim, and Chuck Yoo. 2016. VADI: GPU Virtu- alization for an Automotive Platform.IEEE Transactions on Industrial Informatics(2016)
2016
-
[42]
Belongie, Lubomir D
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll’a r, and C. Lawrence Zitnick. 2014. Microsoft COCO: Common Objects in Context.CoRR(2014). http://arxiv.org/abs/1405.0312
2014 arXiv
-
[43]
Andersen, Michael Isard, Mohammad Mahdi Javanmard, Kathryn S
Martin Maas, David G. Andersen, Michael Isard, Mohammad Mahdi Javanmard, Kathryn S. McKinley, and Colin Raffel. 2020. Learning- based Memory Allocation for C++ Server Workloads. InProceedings of the Twenty-Fifth International Conference on Architectural Support for Programming...
2020 doi
-
[44]
Stefan Maetschke, Ruwan Bandara Tennakoon, Christian Vecchiola, and Rahil Garnavi. 2017. nuts-flow/ml: data pre-processing for deep learning. (2017). arXiv:1708.06046 http://arxiv.org/abs/1708.06046
2017 arXiv
-
[45]
Takaki Makino, Hank Liao, Yannis Assael, Brendan Shillingford, Basilio Garcia, Otavio Braga, and Olivier Siohan. 2019. Recurrent Neural Network Transducer for Audio-Visual Speech Recognition. InIEEE Automatic Speech Recognition and Understanding Workshop
2019
-
[46]
Francisco Massa and Ross Girshick. [n. d.]. maskrnn-benchmark: Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch. https://github.com/ facebookresearch/maskrcnn-benchmark. Accessed: May 11, 2025
2025
-
[47]
Peter Mattson, Christine Cheng, Gregory Diamos, Cody Coleman, Paulius Micikevicius, David Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, et al. 2020. MlPerf Training Benchmark. Proceedings of Machine Learning and Systems(2020)
2020
-
[48]
MLCommons. [n. d.]. MLPerf Benchmarking Suite - PyTorch imple- mentation for image segmentation. https://github.com/mlcommons/ training/tree/master/image_segmentation/pytorch. Accessed: [May 5, 2025]
2025
-
[49]
Derek Gordon Murray, Jirí Simsa, Ana Klimovic, and Ihor Indyk. 2021. tf.data: A Machine Learning Data Processing Framework.Proceedings of the VLDB Endowment(2021)
2021
-
[50]
Musse and Lama A
Hodan M. Musse and Lama A. Alamro. 2016. Cloud Computing: Ar- chitecture and Operating System. InGlobal Summit on Computer and Information Technology (GSCIT). doi:10.1109/GSCIT.2016.7
2016 doi
-
[51]
Kishore Kumar
Atul Negi and P. Kishore Kumar. 2005. Applying Machine Learning Techniques to Improve Linux Process Scheduling. InTENCON IEEE Region 10 Conference. doi:10.1109/TENCON.2005.300837
2005
-
[52]
Rahma Nouaji, Stella Bitchebe, and Oana Balmau. 2024. SpeedyLoader: Efficient Pipelining of Data Preprocessing and Machine Learning Train- ing. InProceedings of the 4th Workshop on Machine Learning and Sys- tems. doi:10.1145/3642970.3655824
2024
-
[53]
NVIDIA. [n. d.]. NVIDIA Virtual GPU (vGPU) Software. https://docs. nvidia.com/vgpu/index.html. Accessed: May 20, 2025
2025
-
[54]
Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur
-
[55]
Fabio Pianese, Peter Bosch, Alessandro Duminuco, Nico Janssens, Thanos Stathopoulos, and Moritz Steiner. 2010. Toward a Cloud Oper- ating System. In2010 IEEE/IFIP Network Operations and Management Symposium Workshops. doi:10.1109/NOMSW.2010.5486552
2010
-
[56]
Yiming Qiu, Hongyi Liu, Thomas Anderson, Yingyan Lin, and Ang Chen. 2021. Toward reconfigurable kernel datapaths with learned optimizations. InProceedings of the Workshop on Hot Topics in Operating Systems. doi:10.1145/3458336.3465288
2021
-
[57]
Thippa Reddy, M
G. Thippa Reddy, M. Praveen Kumar Reddy, Kuruva Lakshmanna, Rajesh Kaluri, Dharmendra Singh Rajput, Gautam Srivastava, and Thar Baker. 2020. Analysis of Dimensionality Reduction Techniques on Big Data.IEEE Access(2020). doi:10.1109/ACCESS.2020.2980942
2020
-
[58]
Ryan Shea and Jiangchuan Liu. 2013. On GPU pass-through perfor- mance for cloud gaming: Experiments and analysis. In2013 12th An- nual Workshop on Network and Systems Support for Games (NetGames). doi:10.1109/NetGames.2013.6820614 Conference’17, July 2017, Washington, DC, USA ...
2013
-
[59]
Mehta, Andreas F
Pallav Sudarshan, Neelesh B. Mehta, Andreas F. Molisch, and Jin Zhang. 2006. Channel Statistics-Based RF Pre-Processing with An- tenna Selection.IEEE Transactions on Wireless Communications(2006). doi:10.1109/TWC.2006.256973
2006
-
[60]
Sukanya Suranauwarat and Hideo Taniguchi. 2001. The design, implementation and initial evaluation of an advanced knowledge- based process scheduler.SIGOPS Operating Systems Reviews(2001). doi:10.1145/506084.506090
2001
-
[61]
Yusuke Suzuki, Shinpei Kato, Hiroshi Yamada, and Kenji Kono. 2016. GPUvm: GPU Virtualization at the Hypervisor.IEEE Trans. Comput. (2016). doi:10.1109/TC.2015.2506582
2016
-
[62]
Dufy Teguia, Jiaxuan Chen, Stella Bitchebe, Oana Balmau, and Alain Tchana. 2024. vPIM: Processing-in-Memory Virtualization. InPro- ceedings of the 25th International Middleware Conference. doi:10.1145/ 3652892.3700782
2024
-
[63]
Tesla. [n. d.]. Autopilot and Full Self-Driving (Supervised). https: //www.tesla.com/support/autopilot. Accessed: May 19, 2025
2025
-
[64]
Taegeon Um, Byungsoo Oh, Byeongchan Seo, Minhyeok Kweun, Goeun Kim, and Woo-Yeon Lee. 2023. Fastflow: Accelerating Deep Learning Model Training With Smart Offloading of Input Data Pipeline. Proceedings of the VLDB Endowment(2023)
2023
-
[65]
David Wentzlaff, Charles Gruenwald, Nathan Beckmann, Kevin Modzelewski, Adam Belay, Lamia Youseff, Jason Miller, and Anant Agarwal. 2010. An operating system for multicore and clouds: mecha- nisms and implementation. InProceedings of the 1st ACM Symposium on Cloud Computing. d...
2010
-
[66]
Wikipedia. [n. d.]. Apple Intelligence. https://en.wikipedia.org/wiki/ Apple_Intelligence. Accessed: May 19, 2025
2025
-
[67]
Zhiyuan Xu, Jian Tang, Chengxiang Yin, Yanzhi Wang, and Guoliang Xue. 2019. Experience-Driven Congestion Control: When Multi-Path TCP Meets Deep Reinforcement Learning.IEEE Journal on Selected Areas in Communications(2019). doi:10.1109/JSAC.2019.2904358
2019
-
[68]
Yuqi Xue, Yiqi Liu, and Jian Huang. 2023. System Virtualization for Neural Processing Units. InProceedings of the 19th Workshop on Hot Topics in Operating Systems. doi:10.1145/3593856.3595912
2023
-
[69]
Chao-Tung Yang, Hsien-Yi Wang, Wei-Shen Ou, Yu-Tso Liu, and Ching- Hsien Hsu. 2012. On implementation of GPU virtualization using PCI pass-through. In4th IEEE International Conference on Cloud Comput- ing Technology and Science Proceedings. doi:10.1109/CloudCom.2012. 6427531
2012 doi
-
[70]
Younge, John Paul Walters, Stephen Crago, and Geoffrey C
Andrew J. Younge, John Paul Walters, Stephen Crago, and Geoffrey C. Fox. [n. d.]. Evaluating GPU Passthrough in Xen for High Perfor- mance Cloud Computing. InIEEE International Parallel and Distributed Processing Symposium Workshops. doi:10.1109/IPDPSW.2014.97
-
[2015]
In2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Librispeech: An ASR Corpus Based on Public Domain Audio Books. In2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Reviewed August 6, 2026 · model on record in the stance chip above.
Discussion (0). Sign in to comment.