Insight into cloud processes from unsupervisedclassification with a rotation-invariant autoencoder
Takuya Kurihana∗Dept. of Computer Science
University of Chicago
tkurihana@uchicago.edu

James Franke
Dept. of the Geophysical Sciences
University of Chicago
jfranke@uchicago.edu

Ian T. Foster
University of Chicago &Argonne National Laboratory
foster@uchicago.edu

Ziwei Wang
Dept. of the Geophysical Sciences
University of Chicago
zwwang@uchicago.edu

Elisabeth J. Moyer
Dept. of the Geophysical Sciences
University of Chicago
moyer@uchicago.edu

Abstract
Clouds play a critical role in the Earth’s energy budget and their potential changes
are one of the largest uncertainties in future climate projections. However, the useof satellite observations to understand cloud feedbacks in a warming climate has
been hampered by the simplicity of existing cloud classification schemes, which
are based on single-pixel cloud properties rather than utilizing spatial structures
and textures. Recent advances in computer vision enable the grouping of different
patterns of images without using human-predefined labels, providing a novel
means of automated cloud classification. This unsupervised learning approachallows discovery of unknown climate-relevant cloud patterns, and the automatedprocessing of large datasets. We describe here the use of such methods to generatea new AI-driven Cloud Classification Atlas (AICCA), which leverages 22 years
and 800 terabytes of MODIS satellite observations over the global ocean. We use arotation-invariant cloud clustering (RICC) method to classify those observations
into 42 AI-generated cloud class labels at ∼100 km spatial resolution. As a casestudy, we use AICCA to examine a recent finding of decreasing cloudiness in a
critical part of the subtropical stratocumulus deck, and show that the change isaccompanied by strong trends in cloud classes.

1

Motivation

Stratocumulus—decks of low clouds—are critical for the climate and represent the single biggest
source of uncertainty in climate response to anthropogenic forcing [Zelinka et al., 2020]. The marine
stratocumulus decks off the west coasts of North and South America and Africa in the sub-tropics
cover only ∼6% of the Earth’s surface but reflect a disproportionate amount of solar radiation, keeping
the planet cool. Recent studies based on relatively small-scale numerical simulations suggest thatthese decks are vulnerable to increases in CO2 and may break up after some threshold is reached,∗

Globus Labs: https://labs.globus.org/

Machine Learning and the Physical Sciences workshop, NeurIPS 2022.

causing an additional 8K of global warming [Schneider et al., 2019, 2020]. However, the global
models used in most climate studies are too coarse in resolution to capture the behavior of subtropicalstratocumulus, or indeed marine low clouds in general. Current computing power does not allow
simulating global clouds at the 10m resolution needed to realistically capture their behavior [Morrison
et al., 2020], and will not be able to do so until at least 2060, even if computer power continues to growat historical rates [Schneider et al., 2017]. Understanding the climate risk posed by stratocumulustransitions requires making use of satellite observations instead.
By now, satellite instruments have provided several decades of high-resolution multispectral imagesof the Earth. These datasets, now totaling many petabytes (PBs), should provide unprecedented
opportunity to understand cloud processes. However, satellite imagery is underemployed in cloudstudies because climate scientists have had no practical means of analyzing their spatial-temporal
patterns. Traditional cloud classifications [Rossow and Schiffer, 1999, World Meteorological Organization] have therefore been based only on mean physical properties over some area, ignoring cloudmorphologies and textures.
The difficulty of extracting insight from complex patterns in PB-scale datasets motivates the application of artificial intelligence (AI) algorithms to define and label cloud types that are relevant toclimate research. However, supervised learning approaches [e.g., Lee et al., 1990, Rasp et al., 2019,
Zantedeschi et al., 2019, Zhang et al., 2018] which rely on human-labeled training datasets, are notuseful for research where the relevant classes themselves are not known. Unsupervised learning
methods, on the other hand, can discover unknown cloud types relevant to climate change research,with definitions based on spatial distributions as well as means [Kurihana et al., 2019, 2021]. In thiswork, we leverage the unsupervised deep learning-based, rotation-invariant cloud clustering (RICC)
approach of our previous studies [Kurihana et al., 2019, 2021] to create an atlas of cloud classes, andshow that these classes can help in understanding observed trends in marine clouds.

2

Unsupervised classification of marine clouds in MODIS images

We use an unsupervised cloud clustering approach to reduce the dimensionality of more than two
decades of multispectral satellite imagery from the Moderate Resolution Imaging Spectroradiometer(MODIS) instruments.
2.1

MODIS dataset

The MODIS instruments on the Terra and Aqua satellites have been capturing images of the earth
since 2000 and 2002 in 36 “moderate” resolution spectral bands, from visible to thermal range. Each
instrument captures a ∼2330 km × 2030 km swath every five minutes, covering much of the globe
at a daily cadence. The total dataset of raw spectral imagery is now nearly 1 PB. We use selected
fields from the MODIS Level 1B calibrated radiance product (MOD02), Level 1 geolocation fields(MOD03), and, for validation, Level 2 derived cloud properties (MOD06), all at 1 km resolution.
We then extract ∼331M non-overlapping ocean-cloud patches as the smaller geographical image
unit, each of 128 pixels × 128 pixels (∼100 km × 100km). We use only 6 of the 36 bands, selecting
those most useful for diagnosing cloud optical properties, cloud phase, and cloud height. To avoid
the complications of land reflectance, we further restrict sampling to ocean-only by eliminating any
patches that include non-ocean pixels, using MOD06 land/water indicator. We also eliminate thosewith less than 30% cloud pixels, as indicated by the MOD06 cloud mask, yielding a set, OC-Patches,of 146M ocean-cloud patches.
2.2

Rotation-invariant cloud clustering: RICC

The goal of the RICC method that we employ in this work [Kurihana et al., 2021] is to generate latent
representations from an autoencoder that can assign cloud types to images regardless of orientation
when applying hierarchical agglomerative clustering (HAC). Rotation invariance is needed because
any particular physically driven cloud pattern can occur in different orientations. A common l2 loss
function is insufficient to achieve this goal because it can generate different representations for animage x and a rotated version of the same image R(x) (where R is a set of scalar rotation operators),
which may then be placed in different clusters. The RI autoencoder is inspired by a shifted transforminvariant autoencoder [Matsuo et al., 2017]. It optimizes LRI (see Equation 1), which combines both
2

Ci

Cs

310

Cloud top pressure [hPa]

Dc
1

440

8

Ac

6

2

3

5

4
5

6

As

560

7 11

4

Ns

RFO [%]

180

3

17

9
13
12 10161520
14
19
26 21 3124
27 2925 23
3432 28
3318
22 35
37
41
38
4030 42
39
36

680
800

Cu
10000.5

Sc

1.3

3.6

9.4

23

Cloud optical thickness(b) Example patches from #3.

(c) Example patches from #35.

Cluster 3
RFO 2.2 %COT 6.1 CTP 324.6 hPa

Cluster 35
RFO 1.2 %COT 13.2 CTP 832.3 hPa

2
1
0

St
60
1

(a) Distributions of cluster properties.

5

10

15

20

25

Relative Frequency of Occurrence (%)30(d) Spatial RFO of #3.

1

5

10

15

20

25

Relative Frequency of Occurrence (%)30(e) Spatial RFO of #35.

Figure 1: Demonstration of physical reasonableness of of AICCA42 classes. (a) Distributions of
AICCA42 classes in cloud optical thickness (COT) – cloud top pressure (CTP) space. Dots show
mean values for each cluster and error bars show the standard deviation of properties. For comparison,dashed lines divide the nine regions corresponding to the International Satellite Cloud Climatology
Project (ISCCP) cloud classes [Rossow and Schiffer, 1999, World Meteorological Organization].
Note that AICCA42 allows far richer differentiation of cloud types, especially stratocumulus (“Sc”).
Panels (b–e) show the texture and distribution of example classes #3 (a type of cirrostratus) and #35(the dominant class in the stratocumulus decks off the N. and S. American W. coasts). (b–c) examplesof MODIS true color images [Gumley et al., 2003] for representative patches from each example
class. (d–e) all-season geographic distribution of each example class.
a rotation-invariant loss Linv (which learns the rotation-invariance, to map different orientations ofidentical input images into an identical representation) and a restoration loss Lres (θ) (which learns
the important spatial structures) to restore spatial patterns in inputs via RI autoencoder. The RI loss isformulated as:
X
1 XX
LRI (θ) =||Dθ (Eθ (x)) − Dθ (Eθ (R(x)))||22 +min ||R(x) − Dθ (Eθ (x))||22 , (1)R∈R
Nx∈S R∈R
x∈S
|
{z
} |
{z
}
Linv (θ)

Lres (θ)

where E and D represent the encoder and decoder, respectively; S are training images; and thescalars λinv and λres are the weights of the respective loss terms. (We use (λinv , λres ) = (32, 80).)
Optimizing the values of θ produces a latent representation that preserves the spatial structure in x
and transforms x and a set of R(x) to a canonical orientation. Kurihana et al. train the autoencoder atlearning rate 0.01 for 100 epochs with 32 NVIDIA V100 GPUs. Our code is available in Github:https://github.com/RDCEP/clouds.
2.3

AICCA: An AI-driven Cloud Classification Atlas

We construct AICCA by assigning a cluster label to each of the 146M patches in OC-Patches. For
each OC-Patch xi , we calculate the cluster label assignment ck,i by identifying the cluster centroidµk with the smallest Euclidean distance to its latent representation, z(xi ):ck,i = arg min ||z(xi ) − µk ||2 ,(2)

k={1,··· ,k∗ }where µk is a set of k ∗ cluster centroids by applying a trained RI autoencoder with Equation 1 on 1M
patches selected at random from OC-Patches and applied HAC to the 74 911 ocean-cloud patchesfrom the year 2003. We select k ∗ = 42 by following a five-criteria evaluation protocol based on
quantitative and qualitative tests so that the dataset is formally “AICCA42 ”, but for convenience we
use here simply “AICCA”; see [Kurihana et al., 2021, 2022] for further details on the protocol. The
3

evaluation protocol addresses the difficulty in unsupervised learning that there is no perfect groundtruth against which output can be compared.
The resulting AICCA dataset displays strong explanatory power (Figure 1). It distributes classes in
COT–CTP space in a manner of consistent with occurrence frequencies (panel a). The 42 AICCA
classes allow far richer cloud information about stratocumulus than does the conventional 9-classscheme: 30 of these classes cover the space of the conventional “Sc” cloud type. The AICCA classes
show consistent textures and physically reasonable properties (panels b–c) and physically reasonablegeographic distributions (panels d–e). Examples shown here are tropical high-altitude clouds thatwould be broadly termed “cirrostratus” (class #3) and low-altitude stratocumulus decks that form
along the West coast of N. and S. America (class #35).

3

Trends in subtropical stratocumulus

For our case study, we consider whether the AICCA classes can provide insight into a recently
discovered cloud trend: low cloud cover has been decreasing in the Pacific Ocean off the coast ofSouthern California and the Baja peninsula Andersen et al. [2022]. This finding was based on mean
MODIS cloud properties alone, with no consideration of cloud patterns. Trends in AICCA classes
suggest that the reduction is driven by losses in stratocumulus decks, but that other classes of sparse,
optically thinner clouds actually increase in frequency.

Figure 2: Trends in occurrence of selected AICCA cloud classes over the subtropical N. Pacific Ocean(100–160W and 5–40N). Colors show fitted linear trends over the 18 years 2003–2021, expressed inunits of % of the mean value over this time. Dot size represents mean relative frequency of occurrence
within a class. Dashed curve highlights the approximate edge of the deck.
Figure 2 shows the trends in 18 years (2003–2021) of MODIS observations off the S. California
coast for eight cloud classes. We exclude 2000–2002 because the Aqua and Terra instruments were
not operating simultaneously over an entire year during that period. Closed-cell (or transitional)
stratocumulus are the most dominant cloud types in this part of the Pacific, making up a quarter of all
cloud occurrences (top row, #26, 30, 33, 35, which are four of the five most predominant classes).
All these classes decrease strongly, especially along the southern edge of the deck—by as muchas 2/3 in some locations. However, several classes of optically thinner clouds increase, especiallyjust south of the deck region (bottom row, #8, 17, 20, and 38). These classes represent very sparse
cumulus or alto-cumulus, similar in visual texture but slightly higher in altitude. The combined
effect is that sparse classes infiltrate the stratocumulus deck along its unstable edges. (The same
analysis applied to ISCCP cloud classifications also shows a decrease in stratocumulus and increasein cumulus clouds, but AICCA cloud classes provide finer details on these transitions.) This trend
may be temperature-driven: stratocumulus decks form only when atmospheric conditions are highly
stable, and warmer sea surface temperatures tend to reduce stability. Sea-surface temperatures in theregion from the ERA5 reanalysis [Hersbach et al., 2020] do increase by nearly 1K over this time
period.
The AICCA dataset also lets us examine the temporal evolution of cloud patterns over shorter
timescales. For now we consider evolution at fixed (Eulerian) locations, ignoring advection by winds.
(Stratocumulus textures are driven by a mix of local environmental control and horizontal advection,4

but the Eulerian perspective can provide insight at sub-daily to daily timescales.) Figure 3 shows, asa Sankey diagram, transitions from one time period to the next (∼ daily) for the selected classes andregion shown in Figure 2. We see significant evolution within closed-cell stratocumulus. The mostfrequently occurring class, #35, is unsurprisingly also the most stable, and evolves primarily into
other closed-cell forms (#30 and #26), which in turn transition into a wide variety of classes. Less
than 10% of transitions represent direct evolution from selected closed-cell to selected sparse cloudclasses (though still more than the reverse direction). That is, transitions from closed cell classes
to sparse cloud classes are largely modulated through a complicated network of mixed type classes.
This result suggests that the replacement of closedcell by sparse cloud types in Figure 2 does not
simply result from a reduction in stratocumulus
lifetime.

4

Summary

In summary, the unsupervised cloud clustering approach employed in this work enables reducing thedimensionality of multidecadal MODIS satellite
imagery into a tractable set of physically meaningful cloud clusters. It permits the discovery of
new classes based on both cloud morphology andphysical properties that are unbiased by artificial
assumptions and that capture the diversity of global
cloud types. We have shown an illustrative exampleof how the AICCA dataset produced via this approach can be used to track both long-term trends
and short-term transitions in cloud morphology, important for understanding cloud feedbacks. More
broadly, this work shows the promise of unsupervised learning for unlocking new applications oflarge satellite datasets in Earth science.

5

Figure 3: Sankey diagram of sub-daily and daily
cloud class transitions within gridcells for the
2003–2022 period for the eight classes, and region, in Figure 2. Box widths represent occurrence frequency of each class, and colors areordered by trend. Ribbon width represents therate of transitions from one class type to another.
Loops represent persistence of a class from one
time period to the next.

Broader Impact
The AICCA dataset that we present here, generated via the unsupervised cloud clustering approach of
Kurihana et al., has both immediate uses for cloud research and broader application in Earth sciencegenerally.
The AICCA dataset can be applied immediately to some of the most urgent questions in climate
science: how clouds alter under conditions of higher CO2 and warmer temperatures. The dataset
is designed in particular to address questions about marine stratocumulus clouds, whose potential
breakup is the largest uncertainty in climate projections. While observations will provide the mostdirect insight, the same approach can also be applied to analyzing the fidelity of cloud representations
in Earth system models, which are producing increasingly realistic cloud distributions. Other
promising lines for future analysis include the impacts aerosols or of large scale circulation pattern
impacts such as ENSO.
More broadly, as the volume of data in Earth sciences explodes, unsupervised deep learning-basedclassification methods can find uses in many areas. Advances in measurement techniques havebrought a deluge of new data, from PBs of satellite images or model output to location data fromanimal tracking to 3D scanning of fossil collections. Some means is needed to make these datasets
tractable. Unsupervised deep learning methods can deliver these complex datasets in compact forms
that allow interpretation. Finally, by facilitating access to core data, unsupervised methods can also
help democratize climate research, reducing the barriers to entry in the field.
Unsupervised approaches, like any black-box interpretive tool, do offer some dangers. The proliferation of automated classifications not directly based on physical principles could negatively impact
scientific fields by pulling researchers further from physical insight. As unsupervised classification
grows in popularity, it will be important to produce community standards on how to validate themeaningfulness of the categories produced. Overall, however, the large potential benefits make its
use worthwhile.

Acknowledgments and Disclosure of Funding
Computing resources were provided by the University of Chicago Research Computing Centerand the Argonne Leadership Computing Facility (ALCF). This work was supported in part by the
U. Chicago Center for Robust Decision-making on Climate and Energy Policy (RDCEP), fundedby NSF grant SES-1463644 through the Decision Making Under Uncertainty program, and the
U.S. Department of Energy under Contract DE-AC02-06CH11357. T.K, J.A.F., and Z.W. were
supported by the NSF NRT program (grant DGE-1735359). J.A.F. was supported by the NSF
Graduate Research Fellowship Program (grant DGE-1746045). Codebases are available in Github:https://github.com/RDCEP/clouds.

References
Mark D. Zelinka, Timothy A. Myers, Daniel T. McCoy, Stephen Po-Chedley, Peter M. Caldwell,
Paulo Ceppi, Stephen A. Klein, and Karl E. Taylor. Causes of higher climate sensitivity in
CMIP6 models. Geophysical Research Letters, 47(1), January 2020. ISSN 0094-8276, 19448007. doi: 10.1029/2019GL085782. URL https://onlinelibrary.wiley.com/doi/abs/
10.1029/2019GL085782.
Tapio Schneider, Colleen M. Kaul, and Kyle G. Pressel. Possible climate transitions from breakup of
stratocumulus decks under greenhouse warming. Nature Geoscience, 12(3):163–167, March 2019.
ISSN 1752-0894, 1752-0908. doi: 10.1038/s41561-019-0310-1. URL http://www.nature.
com/articles/s41561-019-0310-1.
Tapio Schneider, Colleen M. Kaul, and Kyle G. Pressel. Solar geoengineering may not preventstrong warming from direct effects of CO2 on stratocumulus cloud cover. Proceedings of the
National Academy of Sciences, page 202003730, November 2020. ISSN 0027-8424, 1091-6490.
doi: 10.1073/pnas.2003730117. URL http://www.pnas.org/lookup/doi/10.1073/pnas.
2003730117.
6

Hugh Morrison, Marcus van Lier-Walqui, Ann M. Fridlind, Wojciech W. Grabowski, Jerry Y.
Harrington, Corinna Hoose, Alexei Korolev, Matthew R. Kumjian, Jason A. Milbrandt, Hanna
Pawlowska, Derek J. Posselt, Olivier P. Prat, Karly J. Reimel, Shin-Ichiro Shima, Bastiaan vanDiedenhoven, and Lulin Xue. Confronting the challenge of modeling cloud and precipitation
microphysics. Journal of Advances in Modeling Earth Systems, 12(8), August 2020. ISSN 19422466, 1942-2466. doi: 10.1029/2019MS001689. URL https://onlinelibrary.wiley.com/
doi/10.1029/2019MS001689.
Tapio Schneider, João Teixeira, Christopher S. Bretherton, Florent Brient, Kyle G. Pressel, Christoph
Schär, and A. Pier Siebesma. Climate goals and computing the future of clouds. Nature Climate
Change, 7(1):3–5, January 2017. ISSN 1758-678X, 1758-6798. doi: 10.1038/nclimate3190. URL
http://www.nature.com/articles/nclimate3190.
William B Rossow and Robert A Schiffer. Advances in understanding clouds from ISCCP. Bulletinof the American Meteorological Society, 80(11):2261–2288, 1999. URL https://doi.org/10.
1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2.
World Meteorological Organization. International Cloud Atlas. https://cloudatlas.wmo.int/.
Jonathan Lee, Ronald C Weger, Sailes K Sengupta, and Ronald M Welch. A neural network approach
to cloud classification. IEEE Transactions on Geoscience and Remote Sensing, 28(5):846–855,
1990. URL https://doi.org/10.1109/36.58972.
S. Rasp, H. Schulz, S. Bony, and B. Stevens. Combining crowd-sourcing and deep learning tounderstand meso-scale organization of shallow convection, 2019. URL https://doi.org/10.
48550/arXiv.1906.01906.
Valentina Zantedeschi, Fabrizio Falasca, Alyson Douglas, Richard Strange, Matt Kusner, and Duncan
Watson-Parris. Cumulo: A dataset for learning cloud classes. In NeurIPS Workshop on Tackling Climate Change with Machine Learning, 2019. URL https://www.climatechange.ai/
papers/neurips2019/11.
J. Zhang, Pu Liu, Feng Zhang, and Qianqian Song. CloudNet: Ground-based cloud classification
with deep convolutional neural network. Geophysical Research Letters, 45:8665–8672, 2018. URL
https://doi.org/10.1029/2018GL077787.
Takuya Kurihana, Ian Foster, Rebecca Willett, Sydney Jenkins, Kathryn Kurihana, Ruby Werman, Ricardo Barros Lourenco, Casper Neo, and Elisabeth Moyer. Cloud classification with unsupervised
deep learning. In 9th International Workshop on Climate Informatics, 2019.
Takuya Kurihana, Elisabeth Moyer, Rebecca Willett, Davis Gilton, and Ian Foster. Data-driven cloudclustering via a rotationally invariant autoencoder. IEEE Transactions on Geoscience and Remote
Sensing, 60:1–25, 2021. URL https://doi.org/10.1109/TGRS.2021.3098008.
Liam Gumley, Jacques Descloitres, and Jeffrey Schmaltz. Creating reprojected true color MODIS
images: A tutorial. University of Wisconsin–Madison, 19, 2003.
Tadashi Matsuo, Hiroya Fukuhara, and Nobutaka Shimada. Transform invariant auto-encoder. In
IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 2359–2364, 2017.
URL https://doi.org/10.1109/IROS.2017.8206047.
Takuya Kurihana, Elisabeth J. Moyer, and Ian T. Foster. AICCA: AI-driven cloud classification
atlas. Remote Sensing, 14(22), 2022. ISSN 2072-4292. doi: 10.3390/rs14225690. URL https://www.mdpi.com/2072-4292/14/22/5690.
Hendrik Andersen, Jan Cermak, Lukas Zipfel, and Timothy A. Myers. Attribution of observed recent
decrease in low clouds over the northeastern Pacific to cloud-controlling factors. Geophysical Research Letters, 49(3), February 2022. ISSN 0094-8276, 1944-8007. doi: 10.1029/2021GL096498.
URL https://onlinelibrary.wiley.com/doi/10.1029/2021GL096498.
Hans Hersbach, Bill Bell, Paul Berrisford, Shoji Hirahara, András Horányi, Joaquín Muñoz-Sabater,
Julien Nicolas, Carole Peubey, Raluca Radu, Dinand Schepers, Adrian Simmons, Cornel Soci,
Saleh Abdalla, Xavier Abellan, Gianpaolo Balsamo, Peter Bechtold, Gionata Biavati, Jean Bidlot,
7

Massimo Bonavita, Giovanna Chiara, Per Dahlgren, Dick Dee, Michail Diamantakis, Rossana
Dragani, Johannes Flemming, Richard Forbes, Manuel Fuentes, Alan Geer, Leo Haimberger, Sean
Healy, Robin J. Hogan, Elías Hólm, Marta Janisková, Sarah Keeley, Patrick Laloyaux, Philippe
Lopez, Cristina Lupu, Gabor Radnoti, Patricia Rosnay, Iryna Rozum, Freja Vamborg, Sebastien
Villaume, and Jean-Noël Thépaut. The ERA5 global reanalysis. Quarterly Journal of the Royal
Meteorological Society, 146(730):1999–2049, July 2020. ISSN 0035-9009, 1477-870X. doi:
10.1002/qj.3803. URL https://onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803.

8

Checklist
If you are using existing assets (e.g., code, data, models) or curating/releasing new assets...
1. For all authors...
(a) Do the main claims made in the abstract and introduction accurately reflect the paper’s
contributions and scope? [Yes](b) Did you describe the limitations of your work? [Yes](c) Did you discuss any potential negative societal impacts of your work? [N/A](d) Have you read the ethics review guidelines and ensured that your paper conforms tothem? [Yes]2. If you are including theoretical results...
(a) Did you state the full set of assumptions of all theoretical results? [N/A](b) Did you include complete proofs of all theoretical results? [N/A]
3. If you ran experiments...
(a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes](b) Did you specify all the training details (e.g., data splits, hyperparameters, how they
were chosen)? [Yes](c) Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)? [No] because we did not need regression tasks.
(d) Did you include the total amount of compute and the type of resources used (e.g., typeof GPUs, internal cluster, or cloud provider)? [Yes]4. If you are using existing assets (e.g., code, data, models) or curating/releasing new assets...
(a) If your work uses existing assets, did you cite the creators? [Yes](b) Did you mention the license of the assets? [N/A](c) Did you include any new assets either in the supplemental material or as a URL? [N/A](d) Did you discuss whether and how consent was obtained from people whose data you’reusing/curating? [N/A](e) Did you discuss whether the data you are using/curating contains personally identifiable
information or offensive content? [N/A]
5. If you used crowdsourcing or conducted research with human subjects...
(a) Did you include the full text of instructions given to participants and screenshots, if
applicable? [N/A](b) Did you describe any potential participant risks, with links to Institutional Review
Board (IRB) approvals, if applicable? [N/A](c) Did you include the estimated hourly wage paid to participants and the total amountspent on participant compensation? [N/A]9