---
OA_place: repository
OA_type: green
_id: '18955'
abstract:
- lang: eng
text: We give a simple proof that assuming the Exponential Time Hypothesis (ETH),
determining the winner of a Rabin game cannot be done in time 2o(k log k) · nO(1),
where k is the number of pairs of vertex subsets involved in the winning condition
and n is the vertex count of the game graph. While this result follows from the
lower bounds provided by Calude et al [SIAM J. Comp. 2022], our reduction is considerably
simpler and arguably provides more insight into the complexity of the problem.
In fact, the analogous lower bounds discussed by Calude et al, for solving Muller
games and multidimensional parity games, follow as simple corollaries of our approach.
Our reduction also highlights the usefulness of a certain pivot problem — Permutation
SAT — which may be of independent interest.
acknowledgement: This work is a part of projects CUTACOMBS (Ma. Pilipczuk), BOBR (Mi.
Pilipczuk), and VAMOS (K. S. Thejaswini) that have received funding from the European
Research Council (ERC) under the European Union's Horizon 2020 research and innovation
programme, grant agreements No 714704, 948057, and 101020093, respectively. Ma.
Pilipczuk is also partially supported by Polish National Science Centre SONATA BIS-12
grant number 2022/46/E/ST6/00143.
article_processing_charge: No
arxiv: 1
author:
- first_name: Antonio
full_name: Casares, Antonio
last_name: Casares
- first_name: Marcin
full_name: Pilipczuk, Marcin
last_name: Pilipczuk
- first_name: Michał
full_name: Pilipczuk, Michał
last_name: Pilipczuk
- first_name: Uéverton S.
full_name: Souza, Uéverton S.
last_name: Souza
- first_name: K. S.
full_name: Thejaswini, K. S.
id: 3807fb92-fdc1-11ee-bb4a-b4d8a431c753
last_name: Thejaswini
citation:
ama: 'Casares A, Pilipczuk M, Pilipczuk M, Souza US, Thejaswini KS. Simple and tight
complexity lower bounds for solving Rabin games. In: 2024 Symposium on Simplicity
in Algorithms. Society for Industrial and Applied Mathematics; 2024:160-167.
doi:10.1137/1.9781611977936.16'
apa: 'Casares, A., Pilipczuk, M., Pilipczuk, M., Souza, U. S., & Thejaswini,
K. S. (2024). Simple and tight complexity lower bounds for solving Rabin games.
In 2024 Symposium on Simplicity in Algorithms (pp. 160–167). Alexandria,
VA, United States: Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611977936.16'
chicago: Casares, Antonio, Marcin Pilipczuk, Michał Pilipczuk, Uéverton S. Souza,
and K. S. Thejaswini. “Simple and Tight Complexity Lower Bounds for Solving Rabin
Games.” In 2024 Symposium on Simplicity in Algorithms, 160–67. Society
for Industrial and Applied Mathematics, 2024. https://doi.org/10.1137/1.9781611977936.16.
ieee: A. Casares, M. Pilipczuk, M. Pilipczuk, U. S. Souza, and K. S. Thejaswini,
“Simple and tight complexity lower bounds for solving Rabin games,” in 2024
Symposium on Simplicity in Algorithms, Alexandria, VA, United States, 2024,
pp. 160–167.
ista: 'Casares A, Pilipczuk M, Pilipczuk M, Souza US, Thejaswini KS. 2024. Simple
and tight complexity lower bounds for solving Rabin games. 2024 Symposium on Simplicity
in Algorithms. SOSA: Symposium on Simplicity in Algorithms, 160–167.'
mla: Casares, Antonio, et al. “Simple and Tight Complexity Lower Bounds for Solving
Rabin Games.” 2024 Symposium on Simplicity in Algorithms, Society for Industrial
and Applied Mathematics, 2024, pp. 160–67, doi:10.1137/1.9781611977936.16.
short: A. Casares, M. Pilipczuk, M. Pilipczuk, U.S. Souza, K.S. Thejaswini, in:,
2024 Symposium on Simplicity in Algorithms, Society for Industrial and Applied
Mathematics, 2024, pp. 160–167.
conference:
end_date: 2024-01-10
location: Alexandria, VA, United States
name: 'SOSA: Symposium on Simplicity in Algorithms'
start_date: 2024-01-08
date_created: 2025-01-29T11:55:50Z
date_published: 2024-01-01T00:00:00Z
date_updated: 2025-04-14T07:55:54Z
day: '01'
department:
- _id: ToHe
doi: 10.1137/1.9781611977936.16
ec_funded: 1
external_id:
arxiv:
- '2310.20433'
fulldoi: https://doi.org/10.1137/1.9781611977936.16
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2310.20433
month: '01'
oa: 1
oa_version: Preprint
page: 160-167
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: 2024 Symposium on Simplicity in Algorithms
publication_identifier:
isbn:
- '9781611977936'
publication_status: published
publisher: Society for Industrial and Applied Mathematics
quality_controlled: '1'
scopus_import: '1'
status: public
title: Simple and tight complexity lower bounds for solving Rabin games
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '18956'
abstract:
- lang: eng
text: 'Group Activity Recognition (GAR) aims to detect the activity performed by
multiple actors in a scene. Prior works model the spatio-temporal features based
on the RGB, optical flow or keypoint data types. On the contrary, our hypothesis
is that by only using the RGB data without temporality, the performance can be
maintained with a negligible loss in accuracy. To that end, we propose a novel
GAR technique for volleyball videos, DECOMPL, which consists of two complementary
branches. In the visual branch, it extracts the features using attention pooling.
In the coordinate branch, it considers the configuration of the players and extracts
the spatial information from the box coordinates. Moreover, we analyzed the Volleyball
dataset that the recent literature is mostly based on, and systematically reannotated
it to emphasize the group concept. Experimental results demonstrated the effectiveness
of the proposed model DECOMPL, which delivered the best/second best GAR performance
with the reannotations/original annotations among the comparable state-of-the-art
methods. Code and new annotations are available at GitHub: https://github.com/berkerdemirel/decompl'
article_processing_charge: No
arxiv: 1
author:
- first_name: Berker
full_name: Demirel, Berker
id: 8b4bc47f-3200-11ee-973b-8f0e7be21a9f
last_name: Demirel
- first_name: Huseyin
full_name: Ozkan, Huseyin
last_name: Ozkan
citation:
ama: 'Demirel B, Ozkan H. Decompl: Decompositional learning with attention pooling
for group activity recognition from a single volleyball image. In: 2024 IEEE
International Conference on Image Processing. IEEE; 2024:977-983. doi:10.1109/icip51287.2024.10647499'
apa: 'Demirel, B., & Ozkan, H. (2024). Decompl: Decompositional learning with
attention pooling for group activity recognition from a single volleyball image.
In 2024 IEEE International Conference on Image Processing (pp. 977–983).
Abu Dhabi, United Arab Emirates: IEEE. https://doi.org/10.1109/icip51287.2024.10647499'
chicago: 'Demirel, Berker, and Huseyin Ozkan. “Decompl: Decompositional Learning
with Attention Pooling for Group Activity Recognition from a Single Volleyball
Image.” In 2024 IEEE International Conference on Image Processing, 977–83.
IEEE, 2024. https://doi.org/10.1109/icip51287.2024.10647499.'
ieee: 'B. Demirel and H. Ozkan, “Decompl: Decompositional learning with attention
pooling for group activity recognition from a single volleyball image,” in 2024
IEEE International Conference on Image Processing, Abu Dhabi, United Arab
Emirates, 2024, pp. 977–983.'
ista: 'Demirel B, Ozkan H. 2024. Decompl: Decompositional learning with attention
pooling for group activity recognition from a single volleyball image. 2024 IEEE
International Conference on Image Processing. ICIP: International Conference on
Image Processing, 977–983.'
mla: 'Demirel, Berker, and Huseyin Ozkan. “Decompl: Decompositional Learning with
Attention Pooling for Group Activity Recognition from a Single Volleyball Image.”
2024 IEEE International Conference on Image Processing, IEEE, 2024, pp.
977–83, doi:10.1109/icip51287.2024.10647499.'
short: B. Demirel, H. Ozkan, in:, 2024 IEEE International Conference on Image Processing,
IEEE, 2024, pp. 977–983.
conference:
end_date: 2024-10-30
location: Abu Dhabi, United Arab Emirates
name: 'ICIP: International Conference on Image Processing'
start_date: 2024-10-27
corr_author: '1'
date_created: 2025-01-29T12:22:24Z
date_published: 2024-11-01T00:00:00Z
date_updated: 2025-09-09T12:13:12Z
day: '01'
department:
- _id: FrLo
doi: 10.1109/icip51287.2024.10647499
external_id:
arxiv:
- '2303.06439'
isi:
- '001442947000143'
fulldoi: https://doi.org/10.1109/icip51287.2024.10647499
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2303.06439
month: '11'
oa: 1
oa_version: Preprint
page: 977-983
publication: 2024 IEEE International Conference on Image Processing
publication_identifier:
eisbn:
- '9798350349399'
eissn:
- 2381-8549
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/berkerdemirel/decompl
status: public
title: 'Decompl: Decompositional learning with attention pooling for group activity
recognition from a single volleyball image'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
OA_place: repository
OA_type: hybrid
_id: '18957'
abstract:
- lang: eng
text: Sui Lutris is the first smart-contract platform to sustainably achieve sub-second
finality. It achieves this significant decrease by employing consensusless agreement
not only for simple payments but for a large variety of transactions. Unlike prior
work, Sui Lutris neither compromises expressiveness nor throughput and can run
perpetually without restarts. Sui Lutris achieves this by safely integrating consensuless
agreement with a high-throughput consensus protocol that is invoked out of the
critical finality path but ensures that when a transaction is at risk of inconsistent
concurrent accesses, its settlement is delayed until the total ordering is resolved.
Building such a hybrid architecture is especially delicate during reconfiguration
events, where the system needs to preserve the safety of the consensusless path
without compromising the long-term liveness of potentially misconfigured clients.
We thus develop a novel reconfiguration protocol, the first to provably show the
safe and efficient reconfiguration of a consensusless blockchain. Sui Lutris is
currently running in production and underpins the Sui smart-contract platform.
Combined with the use of Objects instead of accounts it enables the safe execution
of smart contracts that expose objects as a first-class resource. In our experiments
Sui Lutris achieves latency lower than 0.5 seconds for throughput up to 5,000
certificates per second (150k ops/s with transaction blocks), compared to the
state-of-the-art real-world consensus latencies of 3 seconds. Furthermore, it
gracefully handles validators crash-recovery and does not suffer visible performance
degradation during reconfiguration.
acknowledgement: 'This work is funded by MystenLabs. We thank the Mysten Labs Engineering
teams for valuable feedback broadly, and specifically Dmitry Perelman and Todd Fiala
for managing the implementation effort. A number of folks contributed to specific
aspects of the implementation of Sui Lutris (amongst many other contributions to
the overall blockchain): Francois Garillot, Laura Makdah, Mingwei Tian, Andrew Schran,
Sadhan Sood and William Smith implemented and optimized aspects of both Sui Lutris
and Narwhal / Bullshark consensus; Alonso de Gortari oversaw the cryptoeconomics
of the blockchain, and Emma Zhong, Ade Adepoju, Tim Zakia and Dario Russi designed
and implemented staking and gas mechanisms. Adam Welc designed several Move tools
and provided great feedback on the manuscript. We also extend our thanks to Patrick
Kuo, Ge Gao, Chris Li, and Arun Koshy for their work on the Sui Lutris SDK, clients,
and RPC layer; Kostas Chalkias, Jonas Lindstrøm, and Joy Wang built cryptographic
components.'
article_processing_charge: No
arxiv: 1
author:
- first_name: Sam
full_name: Blackshear, Sam
last_name: Blackshear
- first_name: Andrey
full_name: Chursin, Andrey
last_name: Chursin
- first_name: George
full_name: Danezis, George
last_name: Danezis
- first_name: Anastasios
full_name: Kichidis, Anastasios
last_name: Kichidis
- first_name: Eleftherios
full_name: Kokoris Kogias, Eleftherios
id: f5983044-d7ef-11ea-ac6d-fd1430a26d30
last_name: Kokoris Kogias
- first_name: Xun
full_name: Li, Xun
last_name: Li
- first_name: Mark
full_name: Logan, Mark
last_name: Logan
- first_name: Ashok
full_name: Menon, Ashok
last_name: Menon
- first_name: Todd
full_name: Nowacki, Todd
last_name: Nowacki
- first_name: Alberto
full_name: Sonnino, Alberto
last_name: Sonnino
- first_name: Brandon
full_name: Williams, Brandon
last_name: Williams
- first_name: Lu
full_name: Zhang, Lu
last_name: Zhang
citation:
ama: 'Blackshear S, Chursin A, Danezis G, et al. Sui Lutris: A blockchain combining
broadcast and consensus. In: Proceedings of the 2024 on ACM SIGSAC Conference
on Computer and Communications Security. ACM; 2024:2606-2620. doi:10.1145/3658644.3670286'
apa: 'Blackshear, S., Chursin, A., Danezis, G., Kichidis, A., Kokoris Kogias, E.,
Li, X., … Zhang, L. (2024). Sui Lutris: A blockchain combining broadcast and consensus.
In Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications
Security (pp. 2606–2620). Salt Lake City, UT, United States: ACM. https://doi.org/10.1145/3658644.3670286'
chicago: 'Blackshear, Sam, Andrey Chursin, George Danezis, Anastasios Kichidis,
Eleftherios Kokoris Kogias, Xun Li, Mark Logan, et al. “Sui Lutris: A Blockchain
Combining Broadcast and Consensus.” In Proceedings of the 2024 on ACM SIGSAC
Conference on Computer and Communications Security, 2606–20. ACM, 2024. https://doi.org/10.1145/3658644.3670286.'
ieee: 'S. Blackshear et al., “Sui Lutris: A blockchain combining broadcast
and consensus,” in Proceedings of the 2024 on ACM SIGSAC Conference on Computer
and Communications Security, Salt Lake City, UT, United States, 2024, pp.
2606–2620.'
ista: 'Blackshear S, Chursin A, Danezis G, Kichidis A, Kokoris Kogias E, Li X, Logan
M, Menon A, Nowacki T, Sonnino A, Williams B, Zhang L. 2024. Sui Lutris: A blockchain
combining broadcast and consensus. Proceedings of the 2024 on ACM SIGSAC Conference
on Computer and Communications Security. CCS: Conference on Computer and Communications
Security, 2606–2620.'
mla: 'Blackshear, Sam, et al. “Sui Lutris: A Blockchain Combining Broadcast and
Consensus.” Proceedings of the 2024 on ACM SIGSAC Conference on Computer and
Communications Security, ACM, 2024, pp. 2606–20, doi:10.1145/3658644.3670286.'
short: S. Blackshear, A. Chursin, G. Danezis, A. Kichidis, E. Kokoris Kogias, X.
Li, M. Logan, A. Menon, T. Nowacki, A. Sonnino, B. Williams, L. Zhang, in:, Proceedings
of the 2024 on ACM SIGSAC Conference on Computer and Communications Security,
ACM, 2024, pp. 2606–2620.
conference:
end_date: 2024-10-18
location: Salt Lake City, UT, United States
name: 'CCS: Conference on Computer and Communications Security'
start_date: 2024-10-14
date_created: 2025-01-29T12:42:21Z
date_published: 2024-12-09T00:00:00Z
date_updated: 2025-09-09T12:13:55Z
day: '09'
department:
- _id: ElKo
doi: 10.1145/3658644.3670286
external_id:
arxiv:
- '2310.18042'
isi:
- '001436367300178'
fulldoi: https://doi.org/10.1145/3658644.3670286
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2310.18042
month: '12'
oa: 1
oa_version: Preprint
page: 2606-2620
publication: Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications
Security
publication_identifier:
isbn:
- '9798400706363'
publication_status: published
publisher: ACM
quality_controlled: '1'
status: public
title: 'Sui Lutris: A blockchain combining broadcast and consensus'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
OA_place: publisher
OA_type: gold
_id: '18961'
abstract:
- lang: eng
text: "Automated contact tracing (ACT) emerged as a promising measure to curb the
spread of Covid-19. Users enable ACT on their smartphones to automatically record
contacts with other users. If a user tests positive for the disease, they report
their diagnosis to alert their contacts.\r\nDesigning effective ACT protocols
is challenging since they need to be efficient and secure while also ensuring
users' privacy. As ACT protocols necessarily leak some information by design,
defining privacy is difficult. For example, a user cannot deny having met another
user. Ideally, however, the user can plausibly deny everything else, in particular,
when they met. We call this privacy property contact-time deniability.\r\nWhile
some early works discussed contact-time deniability informally, it has received
little attention since then. We investigate deniability from a rigorous, theoretical
point of view and arrive at the following impossibility result:\r\nA decentralized
protocol with unidirectional communication cannot be contact-time deniable and
replay-secure. This holds even if malicious users treat smartphones as black-boxes.\r\n
Unidirectional protocols are usually very efficient and many proposals are unidirectional,
e.g., the widely-deployed Google-Apple Exposure Notifications. So the impossibility
result considerably constrains the design space of efficient, secure, and private
ACT protocols. However, it can also be used as a guide; we discuss several possibilities
to achieve contact-time deniability in practice."
acknowledgement: "We thank Raluca-Georgia Diugan for her initial contributions and
support afterward.\r\nThis research was funded in whole or in part by the Austrian
Science Fund (FWF) 10.55776/F85."
article_processing_charge: No
article_type: original
author:
- first_name: Christoph Ullrich
full_name: Günther, Christoph Ullrich
id: ec98511c-eb8e-11eb-b029-edd25d7271a1
last_name: Günther
- first_name: Krzysztof Z
full_name: Pietrzak, Krzysztof Z
id: 3E04A7AA-F248-11E8-B48F-1D18A9856A87
last_name: Pietrzak
orcid: 0000-0002-9139-1654
citation:
ama: 'Günther CU, Pietrzak KZ. Deniability in automated contact tracing: Impossibilities
and possibilities. Proceedings on Privacy Enhancing Technologies. 2024;2024(4):636-648.
doi:10.56553/popets-2024-0134'
apa: 'Günther, C. U., & Pietrzak, K. Z. (2024). Deniability in automated contact
tracing: Impossibilities and possibilities. Proceedings on Privacy Enhancing
Technologies. Bristol, UK/Virtual: Privacy Enhancing Technologies Symposium
Advisory Board. https://doi.org/10.56553/popets-2024-0134'
chicago: 'Günther, Christoph Ullrich, and Krzysztof Z Pietrzak. “Deniability in
Automated Contact Tracing: Impossibilities and Possibilities.” Proceedings
on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium
Advisory Board, 2024. https://doi.org/10.56553/popets-2024-0134.'
ieee: 'C. U. Günther and K. Z. Pietrzak, “Deniability in automated contact tracing:
Impossibilities and possibilities,” Proceedings on Privacy Enhancing Technologies,
vol. 2024, no. 4. Privacy Enhancing Technologies Symposium Advisory Board, pp.
636–648, 2024.'
ista: 'Günther CU, Pietrzak KZ. 2024. Deniability in automated contact tracing:
Impossibilities and possibilities. Proceedings on Privacy Enhancing Technologies.
2024(4), 636–648.'
mla: 'Günther, Christoph Ullrich, and Krzysztof Z. Pietrzak. “Deniability in Automated
Contact Tracing: Impossibilities and Possibilities.” Proceedings on Privacy
Enhancing Technologies, vol. 2024, no. 4, Privacy Enhancing Technologies Symposium
Advisory Board, 2024, pp. 636–48, doi:10.56553/popets-2024-0134.'
short: C.U. Günther, K.Z. Pietrzak, Proceedings on Privacy Enhancing Technologies
2024 (2024) 636–648.
conference:
end_date: 2024-07-20
location: Bristol, UK/Virtual
name: 'PETs: Privacy Enhancing Technologies Symposium '
start_date: 2024-07-15
corr_author: '1'
date_created: 2025-01-29T13:39:34Z
date_published: 2024-07-01T00:00:00Z
date_updated: 2025-04-15T08:16:04Z
day: '01'
ddc:
- '000'
department:
- _id: KrPi
- _id: GradSch
doi: 10.56553/popets-2024-0134
file:
- access_level: open_access
checksum: 348ed6adcf6ad2f925227bde1758cae6
content_type: application/pdf
creator: dernst
date_created: 2025-01-29T13:44:47Z
date_updated: 2025-01-29T13:44:47Z
file_id: '18962'
file_name: 2024_ProcPrivacyEnhTech_Guenther.pdf
file_size: 611567
relation: main_file
success: 1
file_date_updated: 2025-01-29T13:44:47Z
fulldoi: https://doi.org/10.56553/popets-2024-0134
has_accepted_license: '1'
intvolume: ' 2024'
issue: '4'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '07'
oa: 1
oa_version: Published Version
page: 636-648
project:
- _id: 34a34d57-11ca-11ed-8bc3-a2688a8724e1
grant_number: F8509
name: Security and Privacy by Design for Complex Systems
publication: Proceedings on Privacy Enhancing Technologies
publication_identifier:
issn:
- 2299-0984
publication_status: published
publisher: Privacy Enhancing Technologies Symposium Advisory Board
quality_controlled: '1'
status: public
title: 'Deniability in automated contact tracing: Impossibilities and possibilities'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2024
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '18964'
abstract:
- lang: eng
text: Object-centric learning (OCL) extracts the representation of objects with
slots, offering an exceptional blend of flexibility and interpretability for abstracting
low-level perceptual features. A widely adopted method within OCL is slot attention,
which utilizes attention mechanisms to iteratively refine slot representations.
However, a major draw-back of most object-centric models, including slot attention,
is their reliance on predefining the number of slots. This not only necessitates
prior knowledge of the dataset but also overlooks the inherent variability in
the number of objects present in each instance. To overcome this fundamental limitation,
we present a novel complexity-aware object auto-encoder framework. Within this
framework, we introduce an adaptive slot attention (AdaSlot) mecha-nism that dynamically
determines the optimal number of slots based on the content of the data. This
is achieved by proposing a discrete slot sampling module that is responsible for
selecting an appropriate number of slots from a candidate list. Furthermore, we
introduce a masked slot decoder that suppresses unselected slots during the decoding
process. Our framework, tested extensively on object discovery tasks with various
datasets, shows performance matching or exceeding top fixed-slot models. Moreover,
our analysis substantiates that our method exhibits the capability to dynamically
adapt the slot number according to each instance's complexity, offering the potential
for further exploration in slot attention research. Project will be available
at https://kfan21.github.io/AdaSlot/
acknowledgement: Yanwei Fu is the corresponding authour. Yanwei Fu is with School
of Data Science, Fudan University, Shanghai Key Lab of Intelligent Information Processing,
Fudan University, and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence,
Zhejiang Normal University, Jinhua, China.
article_processing_charge: No
arxiv: 1
author:
- first_name: Ke
full_name: Fan, Ke
last_name: Fan
- first_name: Zechen
full_name: Bai, Zechen
last_name: Bai
- first_name: Tianjun
full_name: Xiao, Tianjun
last_name: Xiao
- first_name: Tong
full_name: He, Tong
last_name: He
- first_name: Max
full_name: Horn, Max
last_name: Horn
- first_name: Yanwei
full_name: Fu, Yanwei
last_name: Fu
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Zheng
full_name: Zhang, Zheng
last_name: Zhang
citation:
ama: 'Fan K, Bai Z, Xiao T, et al. Adaptive slot attention: Object discovery with
dynamic slot number. In: 2024 IEEE/CVF Conference on Computer Vision and Pattern
Recognition. IEEE; 2024. doi:10.1109/cvpr52733.2024.02176'
apa: 'Fan, K., Bai, Z., Xiao, T., He, T., Horn, M., Fu, Y., … Zhang, Z. (2024).
Adaptive slot attention: Object discovery with dynamic slot number. In 2024
IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA,
United States: IEEE. https://doi.org/10.1109/cvpr52733.2024.02176'
chicago: 'Fan, Ke, Zechen Bai, Tianjun Xiao, Tong He, Max Horn, Yanwei Fu, Francesco
Locatello, and Zheng Zhang. “Adaptive Slot Attention: Object Discovery with Dynamic
Slot Number.” In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
IEEE, 2024. https://doi.org/10.1109/cvpr52733.2024.02176.'
ieee: 'K. Fan et al., “Adaptive slot attention: Object discovery with dynamic
slot number,” in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition,
Seattle, WA, United States, 2024.'
ista: 'Fan K, Bai Z, Xiao T, He T, Horn M, Fu Y, Locatello F, Zhang Z. 2024. Adaptive
slot attention: Object discovery with dynamic slot number. 2024 IEEE/CVF Conference
on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision
and Pattern Recognition.'
mla: 'Fan, Ke, et al. “Adaptive Slot Attention: Object Discovery with Dynamic Slot
Number.” 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition,
IEEE, 2024, doi:10.1109/cvpr52733.2024.02176.'
short: K. Fan, Z. Bai, T. Xiao, T. He, M. Horn, Y. Fu, F. Locatello, Z. Zhang, in:,
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2024.
conference:
end_date: 2024-06-22
location: Seattle, WA, United States
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2024-06-16
date_created: 2025-01-29T14:27:39Z
date_published: 2024-06-15T00:00:00Z
date_updated: 2025-09-09T12:15:17Z
day: '15'
department:
- _id: FrLo
doi: 10.1109/cvpr52733.2024.02176
external_id:
arxiv:
- '2406.09196'
isi:
- '001342515506043'
fulldoi: https://doi.org/10.1109/cvpr52733.2024.02176
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2406.09196
month: '06'
oa: 1
oa_version: Preprint
publication: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition
publication_identifier:
eisbn:
- '9798350353006'
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://kfan21.github.io/AdaSlot/
status: public
title: 'Adaptive slot attention: Object discovery with dynamic slot number'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
OA_place: publisher
OA_type: hybrid
_id: '18967'
abstract:
- lang: eng
text: "Background: We identified small molecule tricyclic pyrone compound CP2 as
a mild mitochondrial complex I (MCI) inhibitor that induces neuroprotection in
multiple mouse models of AD. One of the major concerns while targeting mitochondria
is the production of reactive oxygen species (ROS). CP2 consists of two diastereoisomers,
D1 and D2, with distinct activity and toxicity profiles. This study was designed
to understand how structure of D1 and D2 affects their binding to MCI and the
consequential impact on ROS production.\r\n\r\nMethod: The X-ray crystallography
and cryo-electron microscopy (cryo-EM) at global resolution of 3.25-3.27Å were
employed to identify the molecular structure of D1 and D2 and the D1 binding to
the isolated ovine MCI. The assessment of the MCI inhibition and the extent of
ROS generation were done in isolated MCI and human neuroblastoma MC65 cells using
flow cytometry, a Seahorse extracellular flux analyzer, and the kinetic studies.\r\n\r\nResult:
In the closed conformation of MCI, D1 selectively binds to the deep Quinone-site
(Qd) but not to the shallow Q-site (Qs), sharing the same binding pocket as rotenone.
In the open MCI state, D1 exclusively binds to the Qs in contrast to rotenone,
which binds Qd and Qs in both closed and open states. At the same concentrations,
D1 inhibits respiration to a greater extent compared to D2 (5:1 ratio) and produces
higher level of ROS.\r\n\r\nConclusion:Cryo-EM unambiguously identified binding
of D1 to both the Qd and Qs sites, contingent upon the conformational state of
MCI. In contrast to rotenone, D1 binds Qd only in the closed conformation during
catalytic cycle, leading to mild inhibition. Superimposing X-ray crystallography
data of D1 and D2 onto cryo-EM data suggests that the orientation of the methyl
group in D2 induces a flatter conformation, resulting in lower binding affinity
to MCI, which correlates with lower inhibition and toxicity compared to D1. At
physiologically relevant concentrations, CP2 (D1:D2 = 1:1) demonstrates low MCI
inhibition yielding negligible ROS levels. This observation provides new insight
into the absence of toxicity associated with CP2 treatment in vivo, further highlighting
feasibility for the development of safe and efficacious MCI inhibitors."
article_number: e085971
article_processing_charge: Yes (in subscription journal)
author:
- first_name: Olga
full_name: Petrova, Olga
id: 5D8C9660-5D49-11EA-8188-567B3DDC885E
last_name: Petrova
- first_name: Sergey A
full_name: Trushin, Sergey A
last_name: Trushin
- first_name: Thi Kim Oanh
full_name: Nguyen, Thi Kim Oanh
last_name: Nguyen
- first_name: Mark
full_name: Ostroot, Mark
last_name: Ostroot
- first_name: Matthew
full_name: Schellenberg, Matthew
last_name: Schellenberg
- first_name: Graham
full_name: Johnson, Graham
last_name: Johnson
- first_name: Eugenia
full_name: Trushina, Eugenia
last_name: Trushina
- first_name: Leonid A
full_name: Sazanov, Leonid A
id: 338D39FE-F248-11E8-B48F-1D18A9856A87
last_name: Sazanov
orcid: 0000-0002-0977-7989
citation:
ama: Petrova O, Trushin SA, Nguyen TKO, et al. Structure‐activity Relationship
Study of Neuroprotective Complex I Inhibitor CP2. Vol 20. Wiley; 2024. doi:10.1002/alz.085971
apa: Petrova, O., Trushin, S. A., Nguyen, T. K. O., Ostroot, M., Schellenberg, M.,
Johnson, G., … Sazanov, L. A. (2024). Structure‐activity relationship study
of neuroprotective complex I inhibitor CP2. Alzheimer’s & Dementia
(Vol. 20). Wiley. https://doi.org/10.1002/alz.085971
chicago: Petrova, Olga, Sergey A Trushin, Thi Kim Oanh Nguyen, Mark Ostroot, Matthew
Schellenberg, Graham Johnson, Eugenia Trushina, and Leonid A Sazanov. Structure‐activity
Relationship Study of Neuroprotective Complex I Inhibitor CP2. Alzheimer’s
& Dementia. Vol. 20. Wiley, 2024. https://doi.org/10.1002/alz.085971.
ieee: O. Petrova et al., Structure‐activity relationship study of neuroprotective
complex I inhibitor CP2, vol. 20, no. S6. Wiley, 2024.
ista: Petrova O, Trushin SA, Nguyen TKO, Ostroot M, Schellenberg M, Johnson G, Trushina
E, Sazanov LA. 2024. Structure‐activity relationship study of neuroprotective
complex I inhibitor CP2, Wiley,p.
mla: Petrova, Olga, et al. “Structure‐activity Relationship Study of Neuroprotective
Complex I Inhibitor CP2.” Alzheimer’s & Dementia, vol. 20, no. S6,
e085971, Wiley, 2024, doi:10.1002/alz.085971.
short: O. Petrova, S.A. Trushin, T.K.O. Nguyen, M. Ostroot, M. Schellenberg, G.
Johnson, E. Trushina, L.A. Sazanov, Structure‐activity Relationship Study of Neuroprotective
Complex I Inhibitor CP2, Wiley, 2024.
date_created: 2025-01-29T15:21:40Z
date_published: 2024-12-01T00:00:00Z
date_updated: 2025-01-29T15:29:22Z
day: '01'
ddc:
- '570'
department:
- _id: LeSa
doi: 10.1002/alz.085971
file:
- access_level: open_access
checksum: e914bd5f3a701659ab79d497a122811f
content_type: application/pdf
creator: dernst
date_created: 2025-01-29T15:24:50Z
date_updated: 2025-01-29T15:24:50Z
file_id: '18968'
file_name: 2024_AlzheimerDementia_Petrova.pdf
file_size: 70870
relation: main_file
success: 1
file_date_updated: 2025-01-29T15:24:50Z
fulldoi: https://doi.org/10.1002/alz.085971
has_accepted_license: '1'
intvolume: ' 20'
issue: S6
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
publication: Alzheimer's & Dementia
publication_identifier:
eissn:
- 1552-5279
issn:
- 1552-5260
publication_status: published
publisher: Wiley
quality_controlled: '1'
status: public
title: Structure‐activity relationship study of neuroprotective complex I inhibitor
CP2
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: other_academic_publication
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 20
year: '2024'
...
---
OA_place: publisher
OA_type: hybrid
_id: '18970'
abstract:
- lang: eng
text: Given a smooth projective curve C, nonabelian Hodge theory gives a diffeomorphism
between two different moduli spaces associated to C. The first is the moduli space
of Higgs bundles on C of rank n, which is equipped with the structure of an algebraic
completely integrable Hamiltonian system. The second is the character variety
of representations of the fundamental group of C into GL(n). In 2012, de Cataldo,
Hausel, and Migliorini [1] proposed the P=W conjecture which identifies the perverse
filtration on the cohomology of the Higgs moduli space with the weight filtration
on the cohomology of the character variety. Recently, in 2022, two independent
proofs of the P=W Conjecture appeared, in work of Maulik &Shen [2] and Hausel,
Mellit, Minets &Schiffmann [6]. The aim of the Arbeitsgemeinschaft was to understand
the P=W Conjecture and these two recent proofs.
acknowledgement: "The MFO and the workshop organizers would like to thank the\r\nNational
Science Foundation for supporting the participation of junior researchers\r\nby
the grant DMS-2230648, “US Junior Oberwolfach Fellows”. Moreover, the\r\nMFO and
the workshop organizers would like to thank the Oberwolfach Foundation for supporting
the participation of junior researchers in the Arbeitsgemeinschaft."
article_processing_charge: No
article_type: original
author:
- first_name: Tamás
full_name: Hausel, Tamás
id: 4A0666D8-F248-11E8-B48F-1D18A9856A87
last_name: Hausel
orcid: 0000-0002-9582-2634
- first_name: Davesh
full_name: Maulik, Davesh
last_name: Maulik
- first_name: Anton
full_name: Mellit, Anton
last_name: Mellit
- first_name: Olivier
full_name: Schiffmann, Olivier
last_name: Schiffmann
- first_name: Junliang
full_name: Shen, Junliang
last_name: Shen
citation:
ama: 'Hausel T, Maulik D, Mellit A, Schiffmann O, Shen J. Arbeitsgemeinschaft: Geometry
and representation theory around the P=W conjecture. Oberwolfach Reports.
2024;21(2):949-1004. doi:10.4171/owr/2024/16'
apa: 'Hausel, T., Maulik, D., Mellit, A., Schiffmann, O., & Shen, J. (2024).
Arbeitsgemeinschaft: Geometry and representation theory around the P=W conjecture.
Oberwolfach Reports. EMS Press. https://doi.org/10.4171/owr/2024/16'
chicago: 'Hausel, Tamás, Davesh Maulik, Anton Mellit, Olivier Schiffmann, and Junliang
Shen. “Arbeitsgemeinschaft: Geometry and Representation Theory around the P=W
Conjecture.” Oberwolfach Reports. EMS Press, 2024. https://doi.org/10.4171/owr/2024/16.'
ieee: 'T. Hausel, D. Maulik, A. Mellit, O. Schiffmann, and J. Shen, “Arbeitsgemeinschaft:
Geometry and representation theory around the P=W conjecture,” Oberwolfach
Reports, vol. 21, no. 2. EMS Press, pp. 949–1004, 2024.'
ista: 'Hausel T, Maulik D, Mellit A, Schiffmann O, Shen J. 2024. Arbeitsgemeinschaft:
Geometry and representation theory around the P=W conjecture. Oberwolfach Reports.
21(2), 949–1004.'
mla: 'Hausel, Tamás, et al. “Arbeitsgemeinschaft: Geometry and Representation Theory
around the P=W Conjecture.” Oberwolfach Reports, vol. 21, no. 2, EMS Press,
2024, pp. 949–1004, doi:10.4171/owr/2024/16.'
short: T. Hausel, D. Maulik, A. Mellit, O. Schiffmann, J. Shen, Oberwolfach Reports
21 (2024) 949–1004.
date_created: 2025-01-29T15:34:22Z
date_published: 2024-05-05T00:00:00Z
date_updated: 2025-01-29T15:39:55Z
day: '05'
ddc:
- '500'
department:
- _id: TaHa
doi: 10.4171/owr/2024/16
fulldoi: https://doi.org/10.4171/owr/2024/16
has_accepted_license: '1'
intvolume: ' 21'
issue: '2'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-sa/4.0/
main_file_link:
- open_access: '1'
url: https://doi.org/10.4171/owr/2024/16
month: '05'
oa: 1
oa_version: Published Version
page: 949-1004
publication: Oberwolfach Reports
publication_identifier:
eissn:
- 1660-8941
issn:
- 1660-8933
publication_status: published
publisher: EMS Press
quality_controlled: '1'
status: public
title: 'Arbeitsgemeinschaft: Geometry and representation theory around the P=W conjecture'
tmp:
image: /images/cc_by_sa.png
legal_code_url: https://creativecommons.org/licenses/by-sa/4.0/legalcode
name: Creative Commons Attribution-ShareAlike 4.0 International Public License (CC
BY-SA 4.0)
short: CC BY-SA (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 21
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '18971'
abstract:
- lang: eng
text: 'Models prone to spurious correlations in training data often produce brittle
predictions and introduce unintended biases. Addressing this challenge typically
involves methods relying on prior knowledge and group annotation to remove spurious
correlations, which may not be readily available in many applications. In this
paper, we establish a novel connection between unsupervised object-centric learning
and mitigation of spurious correlations. Instead of directly inferring subgroups
with varying correlations with labels, our approach focuses on discovering concepts:
discrete ideas that are shared across input samples. Leveraging existing object-centric
representation learning, we introduce CoBalT: a concept balancing technique that
effectively mitigates spurious correlations without requiring human labeling of
subgroups. Evaluation across the benchmark datasets for sub-population shifts
demonstrate superior or competitive performance compared state-of-the-art baselines,
without the need for group annotation. Code is available at https://github.com/rarefin/CoBalT'
acknowledgement: "We acknowledge the support of the Canada CIFAR AI Chair Program
and IVADO. We thank Mila and Compute Canada for providing computational resources.\r\n"
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Rifat
full_name: Arefin, Rifat
last_name: Arefin
- first_name: Yan
full_name: Zhang, Yan
last_name: Zhang
- first_name: Aristide
full_name: Baratin, Aristide
last_name: Baratin
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Irina
full_name: Rish, Irina
last_name: Rish
- first_name: Dianbo
full_name: Liu, Dianbo
last_name: Liu
- first_name: Kenji
full_name: Kawaguchi, Kenji
last_name: Kawaguchi
citation:
ama: 'Arefin R, Zhang Y, Baratin A, et al. Unsupervised concept discovery mitigates
spurious correlations. In: Proceedings of the 41st International Conference
on Machine Learning. Vol 235. ML Research Press; 2024:1672-1688.'
apa: 'Arefin, R., Zhang, Y., Baratin, A., Locatello, F., Rish, I., Liu, D., &
Kawaguchi, K. (2024). Unsupervised concept discovery mitigates spurious correlations.
In Proceedings of the 41st International Conference on Machine Learning
(Vol. 235, pp. 1672–1688). Vienna, Austria: ML Research Press.'
chicago: Arefin, Rifat, Yan Zhang, Aristide Baratin, Francesco Locatello, Irina
Rish, Dianbo Liu, and Kenji Kawaguchi. “Unsupervised Concept Discovery Mitigates
Spurious Correlations.” In Proceedings of the 41st International Conference
on Machine Learning, 235:1672–88. ML Research Press, 2024.
ieee: R. Arefin et al., “Unsupervised concept discovery mitigates spurious
correlations,” in Proceedings of the 41st International Conference on Machine
Learning, Vienna, Austria, 2024, vol. 235, pp. 1672–1688.
ista: 'Arefin R, Zhang Y, Baratin A, Locatello F, Rish I, Liu D, Kawaguchi K. 2024.
Unsupervised concept discovery mitigates spurious correlations. Proceedings of
the 41st International Conference on Machine Learning. ICML: International Conference
on Machine Learning, PMLR, vol. 235, 1672–1688.'
mla: Arefin, Rifat, et al. “Unsupervised Concept Discovery Mitigates Spurious Correlations.”
Proceedings of the 41st International Conference on Machine Learning, vol.
235, ML Research Press, 2024, pp. 1672–88.
short: R. Arefin, Y. Zhang, A. Baratin, F. Locatello, I. Rish, D. Liu, K. Kawaguchi,
in:, Proceedings of the 41st International Conference on Machine Learning, ML
Research Press, 2024, pp. 1672–1688.
conference:
end_date: 2024-07-27
location: Vienna, Austria
name: 'ICML: International Conference on Machine Learning'
start_date: 2024-07-21
date_created: 2025-01-30T07:21:57Z
date_published: 2024-07-30T00:00:00Z
date_updated: 2025-01-30T07:23:10Z
day: '30'
department:
- _id: FrLo
external_id:
arxiv:
- '2402.13368'
intvolume: ' 235'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2402.13368
month: '07'
oa: 1
oa_version: Preprint
page: 1672-1688
publication: Proceedings of the 41st International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/rarefin/CoBalT
scopus_import: '1'
status: public
title: Unsupervised concept discovery mitigates spurious correlations
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 235
year: '2024'
...
---
OA_place: publisher
OA_type: green
_id: '18974'
abstract:
- lang: eng
text: Reinforcement Learning (RL) from temporal logical specifications is a fundamental
problem in sequential decision making. One of the basic and core such specification
is the reachability specification that requires a target set to be eventually
visited. Despite strong empirical results for RL from such specifications, the
theoretical guarantees are bleak, including the impossibility of Probably Approximately
Correct (PAC) guarantee for reachability specifications. Given the impossibility
result, in this work we consider the problem of RL from reachability specifications
along with the information of expected conditional distance (ECD). We present
(a) lower bound results which establish the necessity of ECD information for PAC
guarantees and (b) an algorithm that establishes PAC-guarantees given the ECD
information. To the best of our knowledge, this is the first RL from reachability
specifications that does not make any assumptions on the underlying environment
to learn policies.
alternative_title:
- PMLR
article_processing_charge: No
author:
- first_name: Jakub
full_name: Svoboda, Jakub
id: 130759D2-D7DD-11E9-87D2-DE0DE6697425
last_name: Svoboda
orcid: 0000-0002-1419-3267
- first_name: Suguman
full_name: Bansal, Suguman
last_name: Bansal
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
citation:
ama: 'Svoboda J, Bansal S, Chatterjee K. Reinforcement learning from reachability
specifications: PAC guarantees with expected conditional distance. In: 41st
International Conference on Machine Learning. Vol 235. ML Research Press;
2024:47331-47344.'
apa: 'Svoboda, J., Bansal, S., & Chatterjee, K. (2024). Reinforcement learning
from reachability specifications: PAC guarantees with expected conditional distance.
In 41st International Conference on Machine Learning (Vol. 235, pp. 47331–47344).
Vienna, Austria: ML Research Press.'
chicago: 'Svoboda, Jakub, Suguman Bansal, and Krishnendu Chatterjee. “Reinforcement
Learning from Reachability Specifications: PAC Guarantees with Expected Conditional
Distance.” In 41st International Conference on Machine Learning, 235:47331–44.
ML Research Press, 2024.'
ieee: 'J. Svoboda, S. Bansal, and K. Chatterjee, “Reinforcement learning from reachability
specifications: PAC guarantees with expected conditional distance,” in 41st
International Conference on Machine Learning, Vienna, Austria, 2024, vol.
235, pp. 47331–47344.'
ista: 'Svoboda J, Bansal S, Chatterjee K. 2024. Reinforcement learning from reachability
specifications: PAC guarantees with expected conditional distance. 41st International
Conference on Machine Learning. ICML: International Conference on Machine Learning,
PMLR, vol. 235, 47331–47344.'
mla: 'Svoboda, Jakub, et al. “Reinforcement Learning from Reachability Specifications:
PAC Guarantees with Expected Conditional Distance.” 41st International Conference
on Machine Learning, vol. 235, ML Research Press, 2024, pp. 47331–44.'
short: J. Svoboda, S. Bansal, K. Chatterjee, in:, 41st International Conference
on Machine Learning, ML Research Press, 2024, pp. 47331–47344.
conference:
end_date: 2024-07-27
location: Vienna, Austria
name: 'ICML: International Conference on Machine Learning'
start_date: 2024-07-21
corr_author: '1'
date_created: 2025-01-30T07:45:22Z
date_published: 2024-07-29T00:00:00Z
date_updated: 2025-01-30T07:46:16Z
day: '29'
department:
- _id: KrCh
intvolume: ' 235'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://openreview.net/forum?id=mXUDDL4r1Q
month: '07'
oa: 1
oa_version: Preprint
page: 47331-47344
publication: 41st International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Reinforcement learning from reachability specifications: PAC guarantees with
expected conditional distance'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 235
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '18975'
abstract:
- lang: eng
text: Leveraging second-order information about the loss at the scale of deep networks
is one of the main lines of approach for improving the performance of current
optimizers for deep learning. Yet, existing approaches for accurate full-matrix
preconditioning, such as Full-Matrix Adagrad (GGT) or Matrix-Free Approximate
Curvature (M-FAC) suffer from massive storage costs when applied even to small-scale
models, as they must store a sliding window of gradients, whose memory requirements
are multiplicative in the model dimension. In this paper, we address this issue
via a novel and efficient error-feedback technique that can be applied to compress
preconditioners by up to two orders of magnitude in practice, without loss of
convergence. Specifically, our approach compresses the gradient information via
sparsification or low-rank compression before it is fed into the preconditioner,
feeding the compression error back into future iterations. Extensive experiments
on deep neural networks show that this approach can compress full-matrix preconditioners
to up to 99% sparsity without accuracy loss, effectively removing the memory overhead
of fullmatrix preconditioners such as GGT and M-FAC.
acknowledged_ssus:
- _id: CampIT
acknowledgement: The authors thank Adrian Vladu, Razvan Pascanu, Alexandra Peste,
Mher Safaryan for their valuable feedback, the IT department from Institute of Science
and Technology Austria for the hardware support and Weights and Biases for the infrastructure
to track all our experiments.
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Ionut-Vlad
full_name: Modoranu, Ionut-Vlad
id: 449f7a18-f128-11eb-9611-9b430c0c6333
last_name: Modoranu
- first_name: Aleksei
full_name: Kalinov, Aleksei
id: 44b7120e-eb97-11eb-a6c2-e1557aa81d02
last_name: Kalinov
orcid: 0000-0003-2189-3904
- first_name: Eldar
full_name: Kurtic, Eldar
id: 47beb3a5-07b5-11eb-9b87-b108ec578218
last_name: Kurtic
- first_name: Elias
full_name: Frantar, Elias
id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f
last_name: Frantar
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Modoranu I-V, Kalinov A, Kurtic E, Frantar E, Alistarh D-A. Error feedback
can accurately compress preconditioners. In: 41st International Conference
on Machine Learning. Vol 235. ML Research Press; 2024:35910-35933.'
apa: 'Modoranu, I.-V., Kalinov, A., Kurtic, E., Frantar, E., & Alistarh, D.-A.
(2024). Error feedback can accurately compress preconditioners. In 41st International
Conference on Machine Learning (Vol. 235, pp. 35910–35933). Vienna, Austria:
ML Research Press.'
chicago: Modoranu, Ionut-Vlad, Aleksei Kalinov, Eldar Kurtic, Elias Frantar, and
Dan-Adrian Alistarh. “Error Feedback Can Accurately Compress Preconditioners.”
In 41st International Conference on Machine Learning, 235:35910–33. ML
Research Press, 2024.
ieee: I.-V. Modoranu, A. Kalinov, E. Kurtic, E. Frantar, and D.-A. Alistarh, “Error
feedback can accurately compress preconditioners,” in 41st International Conference
on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 35910–35933.
ista: 'Modoranu I-V, Kalinov A, Kurtic E, Frantar E, Alistarh D-A. 2024. Error feedback
can accurately compress preconditioners. 41st International Conference on Machine
Learning. ICML: International Conference on Machine Learning, PMLR, vol. 235,
35910–35933.'
mla: Modoranu, Ionut-Vlad, et al. “Error Feedback Can Accurately Compress Preconditioners.”
41st International Conference on Machine Learning, vol. 235, ML Research
Press, 2024, pp. 35910–33.
short: I.-V. Modoranu, A. Kalinov, E. Kurtic, E. Frantar, D.-A. Alistarh, in:, 41st
International Conference on Machine Learning, ML Research Press, 2024, pp. 35910–35933.
conference:
end_date: 2024-07-27
location: Vienna, Austria
name: 'ICML: International Conference on Machine Learning'
start_date: 2024-07-21
corr_author: '1'
date_created: 2025-01-30T07:53:22Z
date_published: 2024-07-30T00:00:00Z
date_updated: 2025-01-30T07:54:16Z
day: '30'
department:
- _id: DaAl
external_id:
arxiv:
- '2306.06098'
intvolume: ' 235'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2306.06098
month: '07'
oa: 1
oa_version: Preprint
page: 35910-35933
publication: 41st International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Error feedback can accurately compress preconditioners
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 235
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '18976'
abstract:
- lang: eng
text: We analyze asynchronous-type algorithms for distributed SGD in the heterogeneous
setting, where each worker has its own computation and communication speeds, as
well as data distribution. In these algorithms, workers compute possibly stale
and stochastic gradients associated with their local data at some iteration back
in history and then return those gradients to the server without synchronizing
with other workers. We present a unified convergence theory for non-convex smooth
functions in the heterogeneous regime. The proposed analysis provides convergence
for pure asynchronous SGD and its various modifications. Moreover, our theory
explains what affects the convergence rate and what can be done to improve the
performance of asynchronous algorithms. In particular, we introduce a novel asynchronous
method based on worker shuffling. As a by-product of our analysis, we also demonstrate
convergence guarantees for gradient-type algorithms such as SGD with random reshuffling
and shuffle-once mini-batch SGD. The derived rates match the best-known results
for those algorithms, highlighting the tightness of our approach. Finally, our
numerical evaluations support theoretical findings and show the good practical
performance of our method.
acknowledgement: "The authors thank all anonymous reviewers for their valuable comments
and suggestions on how to improve the manuscript. This work was done when Rustem
Islamov was a Master’s student at Institut Polytechnique de Paris (IP Paris) and
an intern at Institute of Science and Technology Austria (ISTA). The research of
Rustem Islamov was supported by ISTA internship\r\nprogram. Mher Safaryan has received
funding from the European Union’s Horizon 2020 research and innovation program under
the Marie Skłodowska-Curie grant agreement No 101034413."
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Rustem
full_name: Islamov, Rustem
last_name: Islamov
- first_name: Mher
full_name: Safaryan, Mher
id: dd546b39-0804-11ed-9c55-ef075c39778d
last_name: Safaryan
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Islamov R, Safaryan M, Alistarh D-A. AsGrad: A sharp unified analysis of asynchronous-SGD
algorithms. In: Proceedings of The 27th International Conference on Artificial
Intelligence and Statistics. Vol 238. ML Research Press; 2024:649-657.'
apa: 'Islamov, R., Safaryan, M., & Alistarh, D.-A. (2024). AsGrad: A sharp unified
analysis of asynchronous-SGD algorithms. In Proceedings of The 27th International
Conference on Artificial Intelligence and Statistics (Vol. 238, pp. 649–657).
Valencia, Spain: ML Research Press.'
chicago: 'Islamov, Rustem, Mher Safaryan, and Dan-Adrian Alistarh. “AsGrad: A Sharp
Unified Analysis of Asynchronous-SGD Algorithms.” In Proceedings of The 27th
International Conference on Artificial Intelligence and Statistics, 238:649–57.
ML Research Press, 2024.'
ieee: 'R. Islamov, M. Safaryan, and D.-A. Alistarh, “AsGrad: A sharp unified analysis
of asynchronous-SGD algorithms,” in Proceedings of The 27th International Conference
on Artificial Intelligence and Statistics, Valencia, Spain, 2024, vol. 238,
pp. 649–657.'
ista: 'Islamov R, Safaryan M, Alistarh D-A. 2024. AsGrad: A sharp unified analysis
of asynchronous-SGD algorithms. Proceedings of The 27th International Conference
on Artificial Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence
and Statistics, PMLR, vol. 238, 649–657.'
mla: 'Islamov, Rustem, et al. “AsGrad: A Sharp Unified Analysis of Asynchronous-SGD
Algorithms.” Proceedings of The 27th International Conference on Artificial
Intelligence and Statistics, vol. 238, ML Research Press, 2024, pp. 649–57.'
short: R. Islamov, M. Safaryan, D.-A. Alistarh, in:, Proceedings of The 27th International
Conference on Artificial Intelligence and Statistics, ML Research Press, 2024,
pp. 649–657.
conference:
end_date: 2024-05-04
location: Valencia, Spain
name: 'AISTATS: Conference on Artificial Intelligence and Statistics'
start_date: 2024-05-02
corr_author: '1'
date_created: 2025-01-30T08:15:49Z
date_published: 2024-05-15T00:00:00Z
date_updated: 2025-04-14T07:54:52Z
day: '15'
department:
- _id: DaAl
ec_funded: 1
external_id:
arxiv:
- '2310.20452'
intvolume: ' 238'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2310.20452
month: '05'
oa: 1
oa_version: Preprint
page: 649-657
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: Proceedings of The 27th International Conference on Artificial Intelligence
and Statistics
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'AsGrad: A sharp unified analysis of asynchronous-SGD algorithms'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 238
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '18977'
abstract:
- lang: eng
text: "Recent advances in large language model (LLM) pretraining have led to high-quality
LLMs with impressive abilities. By compressing such LLMs via quantization to 3-4
bits per parameter, they can fit into memory-limited devices such as laptops and
mobile phones, enabling personalized use. Quantizing models to 3-4 bits per parameter
can lead to moderate to high accuracy losses, especially for smaller models (1-10B
parameters), which are suitable for edge deployment. To address this accuracy
issue, we introduce the Sparse-Quantized Representation (SpQR), a new compressed
format and quantization technique that enables for the first time \\emph{near-lossless}
compression of LLMs across model scales while reaching similar compression levels
to previous methods. SpQR works by identifying and isolating \\emph{outlier weights},
which cause particularly large quantization errors, and storing them in higher
precision while compressing all other weights to 3-4 bits, and achieves relative
accuracy losses of less than \r\n in perplexity for highly-accurate LLaMA and
Falcon LLMs. This makes it possible to run a 33B parameter LLM on a single 24
GB consumer GPU without performance degradation at 15% speedup, thus making powerful
LLMs available to consumers without any downsides. SpQR comes with efficient algorithms
for both encoding weights into its format, as well as decoding them efficiently
at runtime. Specifically, we provide an efficient GPU inference algorithm for
SpQR, which yields faster inference than 16-bit baselines at similar accuracy
while enabling memory compression gains of more than 4x."
acknowledgement: "Denis Kuznedelev acknowledges the support from the Russian Ministry
of Science and Higher\r\nEducation, grant No. 075-10-2021-068. Ruslan Svirschevski
and Vage Egiazarian and Denis\r\nKuznedelev were supported by the grant for research
centers in the field of AI provided by the\r\nAnalytical Center for the Government
of the Russian Federation (ACRF) in accordance with the\r\nagreement on the provision
of subsidies (identifier of the agreement 000000D730321P5Q0002) and the agreement
with HSE University No. 70-2021-00139."
article_processing_charge: No
arxiv: 1
author:
- first_name: Tim
full_name: Dettmers, Tim
last_name: Dettmers
- first_name: Ruslan A.
full_name: Svirschevski, Ruslan A.
last_name: Svirschevski
- first_name: Vage
full_name: Egiazarian, Vage
last_name: Egiazarian
- first_name: Denis
full_name: Kuznedelev, Denis
last_name: Kuznedelev
- first_name: Elias
full_name: Frantar, Elias
id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f
last_name: Frantar
- first_name: Saleh
full_name: Ashkboos, Saleh
last_name: Ashkboos
- first_name: Alexander
full_name: Borzunov, Alexander
last_name: Borzunov
- first_name: Torsten
full_name: Hoefler, Torsten
last_name: Hoefler
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Dettmers T, Svirschevski RA, Egiazarian V, et al. SpQR: A sparse-quantized
representation for near-lossless LLM weight compression. In: 12th International
Conference on Learning Representations. OpenReview; 2024.'
apa: 'Dettmers, T., Svirschevski, R. A., Egiazarian, V., Kuznedelev, D., Frantar,
E., Ashkboos, S., … Alistarh, D.-A. (2024). SpQR: A sparse-quantized representation
for near-lossless LLM weight compression. In 12th International Conference
on Learning Representations. Vienna, Austria: OpenReview.'
chicago: 'Dettmers, Tim, Ruslan A. Svirschevski, Vage Egiazarian, Denis Kuznedelev,
Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, and Dan-Adrian
Alistarh. “SpQR: A Sparse-Quantized Representation for near-Lossless LLM Weight
Compression.” In 12th International Conference on Learning Representations.
OpenReview, 2024.'
ieee: 'T. Dettmers et al., “SpQR: A sparse-quantized representation for near-lossless
LLM weight compression,” in 12th International Conference on Learning Representations,
Vienna, Austria, 2024.'
ista: 'Dettmers T, Svirschevski RA, Egiazarian V, Kuznedelev D, Frantar E, Ashkboos
S, Borzunov A, Hoefler T, Alistarh D-A. 2024. SpQR: A sparse-quantized representation
for near-lossless LLM weight compression. 12th International Conference on Learning
Representations. ICLR: International Conference on Learning Representations.'
mla: 'Dettmers, Tim, et al. “SpQR: A Sparse-Quantized Representation for near-Lossless
LLM Weight Compression.” 12th International Conference on Learning Representations,
OpenReview, 2024.'
short: T. Dettmers, R.A. Svirschevski, V. Egiazarian, D. Kuznedelev, E. Frantar,
S. Ashkboos, A. Borzunov, T. Hoefler, D.-A. Alistarh, in:, 12th International
Conference on Learning Representations, OpenReview, 2024.
conference:
end_date: 2024-05-11
location: Vienna, Austria
name: 'ICLR: International Conference on Learning Representations'
start_date: 2024-05-07
date_created: 2025-01-30T08:26:59Z
date_published: 2024-05-15T00:00:00Z
date_updated: 2025-01-30T08:27:47Z
day: '15'
department:
- _id: DaAl
external_id:
arxiv:
- '2306.03078'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2306.03078
month: '05'
oa: 1
oa_version: Preprint
publication: 12th International Conference on Learning Representations
publication_status: published
publisher: OpenReview
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'SpQR: A sparse-quantized representation for near-lossless LLM weight compression'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...
---
OA_place: repository
_id: '18981'
abstract:
- lang: eng
text: We establish several results combining discrete Morse theory and microlocal
sheaf theory in the setting of finite posets and simplicial complexes. Our primary
tool is a computationally tractable description of the bounded derived category
of sheaves on a poset with the Alexandrov topology. We prove that each bounded
complex of sheaves on a finite poset admits a unique (up to isomorphism of complexes)
minimal injective resolution, and we provide algorithms for computing minimal
injective resolution of an injective complex, as well as several useful functors
between derived categories of sheaves. For the constant sheaf on a simplicial
complex, we give asymptotically tight bounds on the complexity of computing the
minimal injective resolution using those algorithms. Our main result is a novel
definition of the discrete microsupport of a bounded complex of sheaves on a finite
poset. We detail several foundational properties of the discrete microsupport,
as well as a microlocal generalization of the discrete homological Morse theorem
and Morse inequalities.
acknowledgement: "This project has received funding from the European Research Council
(ERC) under the European\r\nUnion’s Horizon 2020 research and innovation programme,
grant no. 788183, from the Wittgenstein Prize,\r\nAustrian Science Fund (FWF), grant
no. Z 342-N31, and from the DFG Collaborative Research Center TRR\r\n109, ‘Discretization
in Geometry and Dynamics’, Austrian Science Fund (FWF), grant no. I 02979-N35."
article_processing_charge: No
arxiv: 1
author:
- first_name: Adam
full_name: Brown, Adam
last_name: Brown
- first_name: Ondrej
full_name: Draganov, Ondrej
id: 2B23F01E-F248-11E8-B48F-1D18A9856A87
last_name: Draganov
orcid: 0000-0003-0464-3823
citation:
ama: Brown A, Draganov O. Discrete microlocal Morse theory. arXiv. doi:10.48550/arXiv.2209.14993
apa: Brown, A., & Draganov, O. (n.d.). Discrete microlocal Morse theory. arXiv.
https://doi.org/10.48550/arXiv.2209.14993
chicago: Brown, Adam, and Ondrej Draganov. “Discrete Microlocal Morse Theory.” ArXiv,
n.d. https://doi.org/10.48550/arXiv.2209.14993.
ieee: A. Brown and O. Draganov, “Discrete microlocal Morse theory,” arXiv.
.
ista: Brown A, Draganov O. Discrete microlocal Morse theory. arXiv, 10.48550/arXiv.2209.14993.
mla: Brown, Adam, and Ondrej Draganov. “Discrete Microlocal Morse Theory.” ArXiv,
doi:10.48550/arXiv.2209.14993.
short: A. Brown, O. Draganov, ArXiv (n.d.).
corr_author: '1'
date_created: 2025-01-31T17:03:04Z
date_published: 2024-06-09T00:00:00Z
date_updated: 2026-04-07T11:47:29Z
day: '09'
department:
- _id: HeEd
doi: 10.48550/arXiv.2209.14993
ec_funded: 1
external_id:
arxiv:
- '2209.14993'
fulldoi: https://doi.org/10.48550/arXiv.2209.14993
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2209.14993
month: '06'
oa: 1
oa_version: Preprint
project:
- _id: 266A2E9E-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '788183'
name: Alpha Shape Theory Extended
- _id: 268116B8-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z00342
name: Mathematics, Computer Science
- _id: 2561EBF4-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: I02979-N35
name: Persistence and stability of geometric complexes
publication: arXiv
publication_status: draft
related_material:
record:
- id: '20323'
relation: later_version
status: public
- id: '18979'
relation: dissertation_contains
status: public
status: public
title: Discrete microlocal Morse theory
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '18996'
abstract:
- lang: eng
text: 'We consider the linear causal representation learning setting where we observe
a linear mixing of d unknown latent factors, which follow a linear structural
causal model. Recent work has shown that it is possible to recover the latent
factors as well as the underlying structural causal model over them, up to permutation
and scaling, provided that we have at least d environments, each of which corresponds
to perfect interventions on a single latent node (factor). After this powerful
result, a key open problem faced by the community has been to relax these conditions:
allow for coarser than perfect single-node interventions, and allow for fewer
than d of them, since the number of latent factors d could be very large. In this
work, we consider precisely such a setting, where we allow a smaller than d number
of environments, and also allow for very coarse interventions that can very coarsely
\textit{change the entire causal graph over the latent factors}. On the flip side,
we relax what we wish to extract to simply the \textit{list of nodes that have
shifted between one or more environments}. We provide a surprising identifiability
result that it is indeed possible, under some very mild standard assumptions,
to identify the set of shifted nodes. Our identifiability proof moreover is a
constructive one: we explicitly provide necessary and sufficient conditions for
a node to be a shifted node, and show that we can check these conditions given
observed data. Our algorithm lends itself very naturally to the sample setting
where instead of just interventional distributions, we are provided datasets of
samples from each of these distributions. We corroborate our results on both synthetic
experiments as well as an interesting psychometric dataset. The code can be found
at https://github.com/TianyuCodings/iLCS.'
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Tianyu
full_name: Chen, Tianyu
last_name: Chen
- first_name: Kevin
full_name: Bello, Kevin
last_name: Bello
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Bryon
full_name: Aragam, Bryon
last_name: Aragam
- first_name: Pradeep Kumar
full_name: Ravikumar, Pradeep Kumar
last_name: Ravikumar
citation:
ama: 'Chen T, Bello K, Locatello F, Aragam B, Ravikumar PK. Identifying general
mechanism shifts in linear causal representations. In: 38th Conference on Neural
Information Processing Systems. Vol 37. Neural Information Processing Systems
Foundation; 2024.'
apa: 'Chen, T., Bello, K., Locatello, F., Aragam, B., & Ravikumar, P. K. (2024).
Identifying general mechanism shifts in linear causal representations. In 38th
Conference on Neural Information Processing Systems (Vol. 37). Vancouver,
Canada: Neural Information Processing Systems Foundation.'
chicago: Chen, Tianyu, Kevin Bello, Francesco Locatello, Bryon Aragam, and Pradeep
Kumar Ravikumar. “Identifying General Mechanism Shifts in Linear Causal Representations.”
In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural
Information Processing Systems Foundation, 2024.
ieee: T. Chen, K. Bello, F. Locatello, B. Aragam, and P. K. Ravikumar, “Identifying
general mechanism shifts in linear causal representations,” in 38th Conference
on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.
ista: 'Chen T, Bello K, Locatello F, Aragam B, Ravikumar PK. 2024. Identifying general
mechanism shifts in linear causal representations. 38th Conference on Neural Information
Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in
Neural Information Processing Systems, vol. 37.'
mla: Chen, Tianyu, et al. “Identifying General Mechanism Shifts in Linear Causal
Representations.” 38th Conference on Neural Information Processing Systems,
vol. 37, Neural Information Processing Systems Foundation, 2024.
short: T. Chen, K. Bello, F. Locatello, B. Aragam, P.K. Ravikumar, in:, 38th Conference
on Neural Information Processing Systems, Neural Information Processing Systems
Foundation, 2024.
conference:
end_date: 2024-12-16
location: Vancouver, Canada
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2024-12-16
date_created: 2025-02-04T13:09:34Z
date_published: 2024-09-25T00:00:00Z
date_updated: 2025-07-07T13:23:49Z
day: '25'
ddc:
- '000'
department:
- _id: FrLo
external_id:
arxiv:
- '2410.24059'
file:
- access_level: open_access
checksum: 75c3091e70bd2916cd94afbf40a0c425
content_type: application/pdf
creator: dernst
date_created: 2025-02-04T13:09:08Z
date_updated: 2025-02-04T13:09:08Z
file_id: '18997'
file_name: 2024_NeurIPS_Chen.pdf
file_size: 5659119
relation: main_file
success: 1
file_date_updated: 2025-02-04T13:09:08Z
has_accepted_license: '1'
intvolume: ' 37'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
publication: 38th Conference on Neural Information Processing Systems
publication_identifier:
eissn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: Identifying general mechanism shifts in linear causal representations
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2024'
...
---
OA_place: publisher
OA_type: gold
_id: '18998'
abstract:
- lang: eng
text: Word embeddings represent language vocabularies as clouds of d-dimensional
points. We investigate how information is conveyed by the general shape of these
clouds, instead of representing the semantic meaning of each token. Specifically,
we use the notion of persistent homology from topological data analysis (TDA)
to measure the distances between language pairs from the shape of their unlabeled
embeddings. These distances quantify the degree of non-isometry of the embeddings.
To distinguish whether these differences are random training errors or capture
real information about the languages, we use the computed distance matrices to
construct language phylogenetic trees over 81 Indo-European languages. Careful
evaluation shows that our reconstructed trees exhibit strong and statistically-significant
similarities to the reference.
article_processing_charge: No
arxiv: 1
author:
- first_name: Ondrej
full_name: Draganov, Ondrej
id: 2B23F01E-F248-11E8-B48F-1D18A9856A87
last_name: Draganov
orcid: 0000-0003-0464-3823
- first_name: Steven
full_name: Skiena, Steven
last_name: Skiena
citation:
ama: 'Draganov O, Skiena S. The shape of word embeddings: Quantifying non-isometry
with topological data analysis. In: Findings of the Association for Computational
Linguistics: EMNLP 2024. Association for Computational Linguistics; 2024:12080-12099.
doi:10.18653/v1/2024.findings-emnlp.705'
apa: 'Draganov, O., & Skiena, S. (2024). The shape of word embeddings: Quantifying
non-isometry with topological data analysis. In Findings of the Association
for Computational Linguistics: EMNLP 2024 (pp. 12080–12099). Miami, FL, United
States: Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-emnlp.705'
chicago: 'Draganov, Ondrej, and Steven Skiena. “The Shape of Word Embeddings: Quantifying
Non-Isometry with Topological Data Analysis.” In Findings of the Association
for Computational Linguistics: EMNLP 2024, 12080–99. Association for Computational
Linguistics, 2024. https://doi.org/10.18653/v1/2024.findings-emnlp.705.'
ieee: 'O. Draganov and S. Skiena, “The shape of word embeddings: Quantifying non-isometry
with topological data analysis,” in Findings of the Association for Computational
Linguistics: EMNLP 2024, Miami, FL, United States, 2024, pp. 12080–12099.'
ista: 'Draganov O, Skiena S. 2024. The shape of word embeddings: Quantifying non-isometry
with topological data analysis. Findings of the Association for Computational
Linguistics: EMNLP 2024. EMNLP: Conference on Empirical Methods in Natural Language
Processing, 12080–12099.'
mla: 'Draganov, Ondrej, and Steven Skiena. “The Shape of Word Embeddings: Quantifying
Non-Isometry with Topological Data Analysis.” Findings of the Association for
Computational Linguistics: EMNLP 2024, Association for Computational Linguistics,
2024, pp. 12080–99, doi:10.18653/v1/2024.findings-emnlp.705.'
short: 'O. Draganov, S. Skiena, in:, Findings of the Association for Computational
Linguistics: EMNLP 2024, Association for Computational Linguistics, 2024, pp.
12080–12099.'
conference:
end_date: 2024-11-16
location: Miami, FL, United States
name: 'EMNLP: Conference on Empirical Methods in Natural Language Processing'
start_date: 2024-11-12
corr_author: '1'
date_created: 2025-02-04T16:19:28Z
date_published: 2024-11-01T00:00:00Z
date_updated: 2025-02-10T08:21:37Z
day: '01'
ddc:
- '500'
department:
- _id: GradSch
- _id: HeEd
doi: 10.18653/v1/2024.findings-emnlp.705
external_id:
arxiv:
- '2404.00500'
file:
- access_level: open_access
checksum: f4416a5962194f0181ab0dc7f9ef93c0
content_type: application/pdf
creator: dernst
date_created: 2025-02-10T08:20:34Z
date_updated: 2025-02-10T08:20:34Z
file_id: '19016'
file_name: 2024_EMNLP_Draganov.pdf
file_size: 1312638
relation: main_file
success: 1
file_date_updated: 2025-02-10T08:20:34Z
fulldoi: https://doi.org/10.18653/v1/2024.findings-emnlp.705
has_accepted_license: '1'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
page: 12080-12099
publication: 'Findings of the Association for Computational Linguistics: EMNLP 2024'
publication_status: published
publisher: Association for Computational Linguistics
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'The shape of word embeddings: Quantifying non-isometry with topological data
analysis'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '18999'
abstract:
- lang: eng
text: Exploring the shape of point configurations has been a key driver in the evolution
of TDA (short for topological data analysis) since its infancy. This survey illustrates
the recent efforts to broaden these ideas to model spatial interactions among
multiple configurations, each distinguished by a color. It describes advances
in this area and prepares the ground for further exploration by mentioning unresolved
questions and promising research avenues while focusing on the overlap with discrete
geometry.
article_number: '2406.04102'
article_processing_charge: No
arxiv: 1
author:
- first_name: Sebastiano
full_name: Cultrera di Montesano, Sebastiano
id: 34D2A09C-F248-11E8-B48F-1D18A9856A87
last_name: Cultrera di Montesano
orcid: 0000-0001-6249-0832
- first_name: Ondrej
full_name: Draganov, Ondrej
id: 2B23F01E-F248-11E8-B48F-1D18A9856A87
last_name: Draganov
orcid: 0000-0003-0464-3823
- first_name: Herbert
full_name: Edelsbrunner, Herbert
id: 3FB178DA-F248-11E8-B48F-1D18A9856A87
last_name: Edelsbrunner
orcid: 0000-0002-9823-6833
- first_name: Morteza
full_name: Saghafian, Morteza
id: f86f7148-b140-11ec-9577-95435b8df824
last_name: Saghafian
citation:
ama: Cultrera di Montesano S, Draganov O, Edelsbrunner H, Saghafian M. Chromatic
topological data analysis. arXiv. doi:10.48550/ARXIV.2406.04102
apa: Cultrera di Montesano, S., Draganov, O., Edelsbrunner, H., & Saghafian,
M. (n.d.). Chromatic topological data analysis. arXiv. https://doi.org/10.48550/ARXIV.2406.04102
chicago: Cultrera di Montesano, Sebastiano, Ondrej Draganov, Herbert Edelsbrunner,
and Morteza Saghafian. “Chromatic Topological Data Analysis.” ArXiv, n.d.
https://doi.org/10.48550/ARXIV.2406.04102.
ieee: S. Cultrera di Montesano, O. Draganov, H. Edelsbrunner, and M. Saghafian,
“Chromatic topological data analysis,” arXiv. .
ista: Cultrera di Montesano S, Draganov O, Edelsbrunner H, Saghafian M. Chromatic
topological data analysis. arXiv, 2406.04102.
mla: Cultrera di Montesano, Sebastiano, et al. “Chromatic Topological Data Analysis.”
ArXiv, 2406.04102, doi:10.48550/ARXIV.2406.04102.
short: S. Cultrera di Montesano, O. Draganov, H. Edelsbrunner, M. Saghafian, ArXiv
(n.d.).
corr_author: '1'
date_created: 2025-02-04T16:21:21Z
date_published: 2024-06-06T00:00:00Z
date_updated: 2025-02-10T08:14:27Z
day: '06'
ddc:
- '510'
department:
- _id: GradSch
- _id: HeEd
doi: 10.48550/ARXIV.2406.04102
external_id:
arxiv:
- '2406.04102'
fulldoi: https://doi.org/10.48550/ARXIV.2406.04102
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2406.04102
month: '06'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Chromatic topological data analysis
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...
---
OA_place: publisher
OA_type: gold
_id: '19005'
abstract:
- lang: eng
text: "Causal representation learning promises to extend causal models to hidden
causal\r\nvariables from raw entangled measurements. However, most progress has
focused\r\non proving identifiability results in different settings, and we are
not aware of any\r\nsuccessful real-world application. At the same time, the field
of dynamical systems\r\nbenefited from deep learning and scaled to countless applications
but does not allow\r\nparameter identification. In this paper, we draw a clear
connection between the two\r\nand their key assumptions, allowing us to apply
identifiable methods developed\r\nin causal representation learning to dynamical
systems. At the same time, we can\r\nleverage scalable differentiable solvers
developed for differential equations to build\r\nmodels that are both identifiable
and practical. Overall, we learn explicitly controllable models that isolate the
trajectory-specific parameters for further downstream\r\ntasks such as out-of-distribution
classification or treatment effect estimation. We\r\nexperiment with a wind simulator
with partially known factors of variation. We\r\nalso apply the resulting model
to real-world climate data and successfully answer\r\ndownstream causal questions
in line with existing literature on climate change.\r\nCode is available at https://github.com/CausalLearningAI/crl-dynamical-systems."
acknowledgement: "We thank Niklas Boers for recommending the SpeedyWeather simulator
and Valentino Maiorca\r\nfor guidance on Fourier transformation for SST data. We
are also grateful to Shimeng Huang and Riccardo Cadei for their feedback on the
treatment effect estimation experiment and to Jiale Chen and Adeel Pervez for their
assistance with the solver implementation. Finally, we appreciate the anonymous
reviewers for their insightful suggestions, which helped improve the manuscript. "
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Dingling
full_name: Yao, Dingling
id: d3e02e50-48a8-11ee-8f62-c108061797fa
last_name: Yao
- first_name: Caroline J
full_name: Muller, Caroline J
id: f978ccb0-3f7f-11eb-b193-b0e2bd13182b
last_name: Muller
orcid: 0000-0001-5836-5350
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Yao D, Muller CJ, Locatello F. Marrying causal representation learning with
dynamical systems for science. In: 38th Conference on Neural Information Processing
Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.'
apa: 'Yao, D., Muller, C. J., & Locatello, F. (2024). Marrying causal representation
learning with dynamical systems for science. In 38th Conference on Neural Information
Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing
Systems Foundation.'
chicago: Yao, Dingling, Caroline J Muller, and Francesco Locatello. “Marrying Causal
Representation Learning with Dynamical Systems for Science.” In 38th Conference
on Neural Information Processing Systems, Vol. 37. Neural Information Processing
Systems Foundation, 2024.
ieee: D. Yao, C. J. Muller, and F. Locatello, “Marrying causal representation learning
with dynamical systems for science,” in 38th Conference on Neural Information
Processing Systems, Vancouver, Canada, 2024, vol. 37.
ista: 'Yao D, Muller CJ, Locatello F. 2024. Marrying causal representation learning
with dynamical systems for science. 38th Conference on Neural Information Processing
Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information
Processing Systems, vol. 37.'
mla: Yao, Dingling, et al. “Marrying Causal Representation Learning with Dynamical
Systems for Science.” 38th Conference on Neural Information Processing Systems,
vol. 37, Neural Information Processing Systems Foundation, 2024.
short: D. Yao, C.J. Muller, F. Locatello, in:, 38th Conference on Neural Information
Processing Systems, Neural Information Processing Systems Foundation, 2024.
conference:
end_date: 2024-12-16
location: Vancouver, Canada
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2024-12-16
corr_author: '1'
date_created: 2025-02-05T07:49:00Z
date_published: 2024-12-01T00:00:00Z
date_updated: 2025-07-10T11:51:32Z
day: '01'
ddc:
- '000'
- '550'
department:
- _id: CaMu
- _id: FrLo
external_id:
arxiv:
- '2405.13888'
file:
- access_level: open_access
checksum: fe8832367e7143876f178244385d859e
content_type: application/pdf
creator: dernst
date_created: 2025-02-05T07:44:58Z
date_updated: 2025-02-05T07:44:58Z
file_id: '19006'
file_name: 2024_NeurIPS_Yao.pdf
file_size: 2595855
relation: main_file
success: 1
file_date_updated: 2025-02-05T07:44:58Z
has_accepted_license: '1'
intvolume: ' 37'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
publication: 38th Conference on Neural Information Processing Systems
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/CausalLearningAI/crl-dynamical-systems
scopus_import: '1'
status: public
title: Marrying causal representation learning with dynamical systems for science
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2024'
...
---
OA_place: publisher
OA_type: hybrid
_id: '19007'
abstract:
- lang: eng
text: "Learning modular object-centric representations is crucial for systematic
generalization. Existing methods show promising object-binding capabilities empirically,\r\nbut
theoretical identifiability guarantees remain relatively underdeveloped. Understanding
when object-centric representations can theoretically be identified is\r\ncrucial
for scaling slot-based methods to high-dimensional images with correctness\r\nguarantees.
To that end, we propose a probabilistic slot-attention algorithm that\r\nimposes
an aggregate mixture prior over object-centric slot representations, thereby\r\nproviding
slot identifiability guarantees without supervision, up to an equivalence\r\nrelation.
We provide empirical verification of our theoretical identifiability result\r\nusing
both simple 2-dimensional data and high-resolution imaging datasets.\r\n"
acknowledgement: A. Kori is supported by UKRI (grant number EP/S023356/1), as part
of the UKRI Centre for Doctoral Training in Safe and Trusted AI. B. Glocker and
F.D.S. Ribeiro acknowledge the support of the UKRI AI programme, and the Engineering
and Physical Sciences Research Council, for CHAI - EPSRC Causality in Healthcare
AI Hub (grant number EP/Y028856/1).
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Avinash
full_name: Kori, Avinash
last_name: Kori
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Ainkaran
full_name: Santhirasekaram, Ainkaran
last_name: Santhirasekaram
- first_name: Francesca
full_name: Toni, Francesca
last_name: Toni
- first_name: Ben
full_name: Glocker, Ben
last_name: Glocker
- first_name: Fabio
full_name: De Sousa Ribeiro, Fabio
last_name: De Sousa Ribeiro
citation:
ama: 'Kori A, Locatello F, Santhirasekaram A, Toni F, Glocker B, De Sousa Ribeiro
F. Identifiable object-centric representation learning via probabilistic slot
attention. In: 38th Conference on Neural Information Processing Systems.
Vol 37. Neural Information Processing Systems Foundation; 2024.'
apa: 'Kori, A., Locatello, F., Santhirasekaram, A., Toni, F., Glocker, B., &
De Sousa Ribeiro, F. (2024). Identifiable object-centric representation learning
via probabilistic slot attention. In 38th Conference on Neural Information
Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing
Systems Foundation.'
chicago: Kori, Avinash, Francesco Locatello, Ainkaran Santhirasekaram, Francesca
Toni, Ben Glocker, and Fabio De Sousa Ribeiro. “Identifiable Object-Centric Representation
Learning via Probabilistic Slot Attention.” In 38th Conference on Neural Information
Processing Systems, Vol. 37. Neural Information Processing Systems Foundation,
2024.
ieee: A. Kori, F. Locatello, A. Santhirasekaram, F. Toni, B. Glocker, and F. De
Sousa Ribeiro, “Identifiable object-centric representation learning via probabilistic
slot attention,” in 38th Conference on Neural Information Processing Systems,
Vancouver, Canada, 2024, vol. 37.
ista: 'Kori A, Locatello F, Santhirasekaram A, Toni F, Glocker B, De Sousa Ribeiro
F. 2024. Identifiable object-centric representation learning via probabilistic
slot attention. 38th Conference on Neural Information Processing Systems. NeurIPS:
Neural Information Processing Systems, Advances in Neural Information Processing
Systems, vol. 37.'
mla: Kori, Avinash, et al. “Identifiable Object-Centric Representation Learning
via Probabilistic Slot Attention.” 38th Conference on Neural Information Processing
Systems, vol. 37, Neural Information Processing Systems Foundation, 2024.
short: A. Kori, F. Locatello, A. Santhirasekaram, F. Toni, B. Glocker, F. De Sousa
Ribeiro, in:, 38th Conference on Neural Information Processing Systems, Neural
Information Processing Systems Foundation, 2024.
conference:
end_date: 2024-12-16
location: Vancouver, Canada
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2024-12-16
date_created: 2025-02-05T08:36:22Z
date_published: 2024-12-01T00:00:00Z
date_updated: 2025-05-14T11:29:10Z
day: '01'
ddc:
- '000'
department:
- _id: FrLo
external_id:
arxiv:
- '2406.07141'
file:
- access_level: open_access
checksum: d27b3c7102adc28e798fe41001f0b919
content_type: application/pdf
creator: dernst
date_created: 2025-02-05T08:34:25Z
date_updated: 2025-02-05T08:34:25Z
file_id: '19008'
file_name: 2024_NeurIPS_Kori.pdf
file_size: 6943800
relation: main_file
success: 1
file_date_updated: 2025-02-05T08:34:25Z
has_accepted_license: '1'
intvolume: ' 37'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
publication: 38th Conference on Neural Information Processing Systems
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: Identifiable object-centric representation learning via probabilistic slot
attention
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2024'
...
---
OA_place: publisher
OA_type: hybrid
_id: '19028'
abstract:
- lang: eng
text: The stochastic nature of modern Monte Carlo (MC) rendering methods inevitably
produces noise in rendered images for a practical number of samples per pixel.
The problem of denoising these images has been widely studied, with most recent
methods relying on data-driven, pretrained neural networks. In contrast, in this
paper we propose a statistical approach to the denoising problem, treating each
pixel as a random variable and reasoning about its distribution. Considering a
pixel of the noisy rendered image, we formulate fast pair-wise statistical tests—based
on online estimators—to decide which of the nearby pixels to exclude from the
denoising filter. We show that for symmetric pixel weights and normally distributed
samples, the classical Welch t-test is optimal in terms of mean squared error.
We then show how to extend this result to handle non-normal distributions, using
more recent confidence-interval formulations in combination with the Box-Cox transformation.
Our results show that our statistical denoising approach matches the performance
of state-of-the-art neural image denoising without having to resort to any computation-intensive
pretraining. Furthermore, our approach easily generalizes to other quantities
besides pixel intensity, which we demonstrate by showing additional applications
to Russian roulette path termination and multiple importance sampling.
acknowledgement: 'We would like to thank Lukas Lipp for fruitful discussions, Károly
Zsolnai-Fehér and Jaroslav Křivánek for valuable contributions to early versions
of this work, and Bernhard Kerbl for help with our CUDA implementation. Moreover,
we would like to thank the creators of the scenes we have used: Wig42 for “Wooden
Staircase” (Fig. 1), “Grey and White Room” (Fig. S6), and “Modern Living Room” (Fig.
S8); nacimus for “Bathroom” (Fig. 3, S5); NovaZeeke for “Japanese Classroom” (Fig.
4, 6); Beeple for “Zero-Day” (Fig. 8); Jay-Artist for “White Room” (Fig. S7); Mareck
for “Contemporary Bathroom” (Fig. 2); Christian Freude for “Glass Caustics” (Fig.
S10); and Benedikt Bitterli for “Veach Ajar” (Fig. 7, S2), “Veach MIS” (Fig. S4),
and “Fur Ball” (Fig. S11). This work has received funding from the Vienna Science
and Technology Fund (WWTF) project ICT22-028 (“Toward Optimal Path Guiding for Photorealistic
Rendering”) and the Austrian Science Fund (FWF) project F 77 (SFB “Advanced Computational
Design”).'
article_number: '68'
article_processing_charge: Yes (in subscription journal)
author:
- first_name: Hiroyuki
full_name: Sakai, Hiroyuki
last_name: Sakai
- first_name: Christian
full_name: Freude, Christian
last_name: Freude
- first_name: Thomas
full_name: Auzinger, Thomas
id: 4718F954-F248-11E8-B48F-1D18A9856A87
last_name: Auzinger
orcid: 0000-0002-1546-3265
- first_name: David
full_name: Hahn, David
id: 357A6A66-F248-11E8-B48F-1D18A9856A87
last_name: Hahn
- first_name: Michael
full_name: Wimmer, Michael
last_name: Wimmer
citation:
ama: 'Sakai H, Freude C, Auzinger T, Hahn D, Wimmer M. A statistical approach to
Monte Carlo denoising. In: Proceedings - SIGGRAPH Asia 2024 Conference Papers.
Association for Computing Machinery; 2024. doi:10.1145/3680528.3687591'
apa: 'Sakai, H., Freude, C., Auzinger, T., Hahn, D., & Wimmer, M. (2024). A
statistical approach to Monte Carlo denoising. In Proceedings - SIGGRAPH Asia
2024 Conference Papers. Tokyo, Japan: Association for Computing Machinery.
https://doi.org/10.1145/3680528.3687591'
chicago: Sakai, Hiroyuki, Christian Freude, Thomas Auzinger, David Hahn, and Michael
Wimmer. “A Statistical Approach to Monte Carlo Denoising.” In Proceedings -
SIGGRAPH Asia 2024 Conference Papers. Association for Computing Machinery,
2024. https://doi.org/10.1145/3680528.3687591.
ieee: H. Sakai, C. Freude, T. Auzinger, D. Hahn, and M. Wimmer, “A statistical approach
to Monte Carlo denoising,” in Proceedings - SIGGRAPH Asia 2024 Conference Papers,
Tokyo, Japan, 2024.
ista: 'Sakai H, Freude C, Auzinger T, Hahn D, Wimmer M. 2024. A statistical approach
to Monte Carlo denoising. Proceedings - SIGGRAPH Asia 2024 Conference Papers.
SA: SIGGRAPH Asia, 68.'
mla: Sakai, Hiroyuki, et al. “A Statistical Approach to Monte Carlo Denoising.”
Proceedings - SIGGRAPH Asia 2024 Conference Papers, 68, Association for
Computing Machinery, 2024, doi:10.1145/3680528.3687591.
short: H. Sakai, C. Freude, T. Auzinger, D. Hahn, M. Wimmer, in:, Proceedings -
SIGGRAPH Asia 2024 Conference Papers, Association for Computing Machinery, 2024.
conference:
end_date: 2024-12-06
location: Tokyo, Japan
name: 'SA: SIGGRAPH Asia'
start_date: 2024-12-03
date_created: 2025-02-16T23:02:34Z
date_published: 2024-12-03T00:00:00Z
date_updated: 2025-12-02T13:58:56Z
day: '03'
ddc:
- '000'
doi: 10.1145/3680528.3687591
external_id:
isi:
- '001441591200068'
file:
- access_level: open_access
checksum: 89f63b9237224362ec33430af9152700
content_type: application/pdf
creator: dernst
date_created: 2025-04-15T12:53:24Z
date_updated: 2025-04-15T12:53:24Z
file_id: '19563'
file_name: 2024_SIGGRAPH_Sakai.pdf
file_size: 14791980
relation: main_file
success: 1
file_date_updated: 2025-04-15T12:53:24Z
fulldoi: https://doi.org/10.1145/3680528.3687591
has_accepted_license: '1'
isi: 1
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
publication: Proceedings - SIGGRAPH Asia 2024 Conference Papers
publication_identifier:
isbn:
- '9798400711312'
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: A statistical approach to Monte Carlo denoising
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '19307'
abstract:
- lang: eng
text: "This repository contains the data, scripts, SAM codes and files required
to reproduce the results of the manuscript \"The Unreasonable Efficiency of Total
Rain Evaporation Removal in Triggering Convective Self-Aggregation\" submitted
to the Geophysical Research Letters (GRL).\r\n\r\nBrief description of project:
This project aims to examine the impact of rain evaporation removal or reduction
in the planetary boundary layer (PBL) on convective self aggregation (CSA). Non-rotating
radiative-convective equilibrium (RCE) simulations were conducted with the System
for Atmospheric Modeling (SAM) cloud resolving model. Rain evaporation in the
lowest 1 km was progressively reduced and the effect on CSA was investigated.
The physical processes underlying this type of aggregation (referred to in the
manuscript as no-evaporation CSA, or NE-CSA) were analyzed and described. \r\nThe
default SAM code base (version 6.10.8) can be downloaded from here: http://rossby.msrc.sunysb.edu/~marat/SAM.html"
article_processing_charge: No
author:
- first_name: Yi-Ling
full_name: Hwong, Yi-Ling
id: 1217aa61-4dd1-11ec-9ac3-f2ba3f17ee22
last_name: Hwong
orcid: 0000-0001-9281-3479
- first_name: Caroline J
full_name: Muller, Caroline J
id: f978ccb0-3f7f-11eb-b193-b0e2bd13182b
last_name: Muller
orcid: 0000-0001-5836-5350
citation:
ama: Hwong Y-L, Muller CJ. Data - The unreasonable efficiency of total rain evaporation
removal in triggering convective self-aggregation. 2024. doi:10.5281/ZENODO.10687169
apa: Hwong, Y.-L., & Muller, C. J. (2024). Data - The unreasonable efficiency
of total rain evaporation removal in triggering convective self-aggregation. Zenodo.
https://doi.org/10.5281/ZENODO.10687169
chicago: Hwong, Yi-Ling, and Caroline J Muller. “Data - The Unreasonable Efficiency
of Total Rain Evaporation Removal in Triggering Convective Self-Aggregation.”
Zenodo, 2024. https://doi.org/10.5281/ZENODO.10687169.
ieee: Y.-L. Hwong and C. J. Muller, “Data - The unreasonable efficiency of total
rain evaporation removal in triggering convective self-aggregation.” Zenodo, 2024.
ista: Hwong Y-L, Muller CJ. 2024. Data - The unreasonable efficiency of total rain
evaporation removal in triggering convective self-aggregation, Zenodo, 10.5281/ZENODO.10687169.
mla: Hwong, Yi-Ling, and Caroline J. Muller. Data - The Unreasonable Efficiency
of Total Rain Evaporation Removal in Triggering Convective Self-Aggregation.
Zenodo, 2024, doi:10.5281/ZENODO.10687169.
short: Y.-L. Hwong, C.J. Muller, (2024).
corr_author: '1'
date_created: 2025-03-07T08:39:40Z
date_published: 2024-02-21T00:00:00Z
date_updated: 2025-09-04T13:16:39Z
day: '21'
ddc:
- '550'
department:
- _id: CaMu
doi: 10.5281/ZENODO.10687169
fulldoi: https://doi.org/10.5281/ZENODO.10687169
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.8369509
month: '02'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
record:
- id: '15186'
relation: used_in_publication
status: public
status: public
title: Data - The unreasonable efficiency of total rain evaporation removal in triggering
convective self-aggregation
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...