---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '15407'
abstract:
- lang: eng
text: We propose and implement a family of quantum-informed recursive optimization
(QIRO) algorithms for combinatorial optimization problems. Our approach leverages
quantum resources to obtain information that is used in problem-specific classical
reduction steps that recursively simplify the problem. These reduction steps address
the limitations of the quantum component (e.g., locality) and ensure solution
feasibility in constrained optimization problems. Additionally, we use backtracking
techniques to further improve the performance of the algorithm without increasing
the requirements on the quantum hardware. We showcase the capabilities of our
approach by informing QIRO with correlations from classical simulations of shallow
circuits of the quantum approximate optimization algorithm, solving instances
of maximum independent set and maximum satisfiability problems with hundreds of
variables. We also demonstrate how QIRO can be deployed on a neutral atom quantum
processor to find large independent sets of graphs. In summary, our scheme achieves
results comparable to classical heuristics even with relatively weak quantum resources.
Furthermore, enhancing the quality of these quantum resources improves the performance
of the algorithms. Notably, the modular nature of QIRO offers various avenues
for modifications, positioning our work as a template for a broader class of hybrid
quantum-classical algorithms for combinatorial optimization.
acknowledgement: J.R.F. and A.K. thank Libor Caha and Alexander Kliesch for insightful
discussions. The authors thank Lilly Palackal, Maximilian Passek, Carlos Riofrío,
and Gili Rosenberg for thorough reviews of the manuscript, and the Amazon Braket,
BMW, and QuEra teams for their support. C.M. thanks the Munich Quantum Valley initiative,
which is supported by the Bavarian State Government with funds from the Hightech
Agenda Bayern Plus. H.G.K. would like to thank Am Platzl 1A for providing the necessary
environment for creative thinking. An open-source implementation of QIRO is available
online [60].
article_number: '020327'
article_processing_charge: Yes
article_type: original
arxiv: 1
author:
- first_name: Jernej Rudi
full_name: Finžgar, Jernej Rudi
last_name: Finžgar
- first_name: Aron
full_name: Kerschbaumer, Aron
id: ade85a9c-3200-11ee-973b-91c1eb240410
last_name: Kerschbaumer
orcid: 0009-0002-2370-8661
- first_name: Martin J.A.
full_name: Schuetz, Martin J.A.
last_name: Schuetz
- first_name: Christian B.
full_name: Mendl, Christian B.
last_name: Mendl
- first_name: Helmut G.
full_name: Katzgraber, Helmut G.
last_name: Katzgraber
citation:
ama: Finžgar JR, Kerschbaumer A, Schuetz MJA, Mendl CB, Katzgraber HG. Quantum-informed
recursive optimization algorithms. PRX Quantum. 2024;5(2). doi:10.1103/PRXQuantum.5.020327
apa: Finžgar, J. R., Kerschbaumer, A., Schuetz, M. J. A., Mendl, C. B., & Katzgraber,
H. G. (2024). Quantum-informed recursive optimization algorithms. PRX Quantum.
American Physical Society. https://doi.org/10.1103/PRXQuantum.5.020327
chicago: Finžgar, Jernej Rudi, Aron Kerschbaumer, Martin J.A. Schuetz, Christian
B. Mendl, and Helmut G. Katzgraber. “Quantum-Informed Recursive Optimization Algorithms.”
PRX Quantum. American Physical Society, 2024. https://doi.org/10.1103/PRXQuantum.5.020327.
ieee: J. R. Finžgar, A. Kerschbaumer, M. J. A. Schuetz, C. B. Mendl, and H. G. Katzgraber,
“Quantum-informed recursive optimization algorithms,” PRX Quantum, vol.
5, no. 2. American Physical Society, 2024.
ista: Finžgar JR, Kerschbaumer A, Schuetz MJA, Mendl CB, Katzgraber HG. 2024. Quantum-informed
recursive optimization algorithms. PRX Quantum. 5(2), 020327.
mla: Finžgar, Jernej Rudi, et al. “Quantum-Informed Recursive Optimization Algorithms.”
PRX Quantum, vol. 5, no. 2, 020327, American Physical Society, 2024, doi:10.1103/PRXQuantum.5.020327.
short: J.R. Finžgar, A. Kerschbaumer, M.J.A. Schuetz, C.B. Mendl, H.G. Katzgraber,
PRX Quantum 5 (2024).
corr_author: '1'
das_tickbox: '0'
date_created: 2024-05-19T22:01:13Z
date_published: 2024-05-01T00:00:00Z
date_updated: 2026-10-02T11:07:49Z
day: '01'
ddc:
- '530'
department:
- _id: GradSch
doi: 10.1103/PRXQuantum.5.020327
external_id:
arxiv:
- '2308.13607'
file:
- access_level: open_access
checksum: 76bdf0b4dc06d59d073a57bd6957a96c
content_type: application/pdf
creator: dernst
date_created: 2024-05-21T09:35:14Z
date_updated: 2024-05-21T09:35:14Z
file_id: '15409'
file_name: 2024_PRXQuantum_Finzgar.pdf
file_size: 2016085
relation: main_file
success: 1
file_date_updated: 2024-05-21T09:35:14Z
fulldoi: https://doi.org/10.1103/PRXQuantum.5.020327
has_accepted_license: '1'
intvolume: ' 5'
issue: '2'
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
publication: PRX Quantum
publication_identifier:
eissn:
- 2691-3399
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: yes
title: Quantum-informed recursive optimization algorithms
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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 5
year: '2024'
...
---
_id: '15360'
abstract:
- lang: eng
text: We investigate superradiant enhancements in the refrigeration performance
of a set of N three-level systems that are collectively coupled to a hot and a
cold thermal reservoir and are additionally subject to collective periodic (circular)
driving. Assuming the system-reservoir coupling to be weak, we explore the regime
of stronger periodic driving strengths by comparing collective weak driving, Floquet-Lindblad,
and Floquet-Redfield master equations. We identify regimes where the power injected
by the periodic driving is used to pump heat from the cold to the hot reservoir
and derive analytic sufficient conditions for them based on a cycle analysis of
the Floquet-Lindblad master equation. In those regimes, we also argue for which
parameters collective enhancements like a quadratic scaling of the cooling current
with N can be expected and support our arguments by numerical simulations.
acknowledgement: "Financial support by the DFG (project ID 278162697 – SFB 1242) is
gratefully acknowledged.\r\n"
article_number: '044050'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Dmytro
full_name: Kolisnyk, Dmytro
id: 530a7320-5355-11ee-ae5a-82a46997aaa7
last_name: Kolisnyk
orcid: 0000-0002-8612-8202
- first_name: Friedemann
full_name: Queißer, Friedemann
last_name: Queißer
- first_name: Gernot
full_name: Schaller, Gernot
last_name: Schaller
- first_name: Ralf
full_name: Schützhold, Ralf
last_name: Schützhold
citation:
ama: Kolisnyk D, Queißer F, Schaller G, Schützhold R. Floquet analysis of a superradiant
many-qutrit refrigerator. Physical Review Applied. 2024;21(4). doi:10.1103/PhysRevApplied.21.044050
apa: Kolisnyk, D., Queißer, F., Schaller, G., & Schützhold, R. (2024). Floquet
analysis of a superradiant many-qutrit refrigerator. Physical Review Applied.
American Physical Society. https://doi.org/10.1103/PhysRevApplied.21.044050
chicago: Kolisnyk, Dmytro, Friedemann Queißer, Gernot Schaller, and Ralf Schützhold.
“Floquet Analysis of a Superradiant Many-Qutrit Refrigerator.” Physical Review
Applied. American Physical Society, 2024. https://doi.org/10.1103/PhysRevApplied.21.044050.
ieee: D. Kolisnyk, F. Queißer, G. Schaller, and R. Schützhold, “Floquet analysis
of a superradiant many-qutrit refrigerator,” Physical Review Applied, vol.
21, no. 4. American Physical Society, 2024.
ista: Kolisnyk D, Queißer F, Schaller G, Schützhold R. 2024. Floquet analysis of
a superradiant many-qutrit refrigerator. Physical Review Applied. 21(4), 044050.
mla: Kolisnyk, Dmytro, et al. “Floquet Analysis of a Superradiant Many-Qutrit Refrigerator.”
Physical Review Applied, vol. 21, no. 4, 044050, American Physical Society,
2024, doi:10.1103/PhysRevApplied.21.044050.
short: D. Kolisnyk, F. Queißer, G. Schaller, R. Schützhold, Physical Review Applied
21 (2024).
das_tickbox: '0'
date_created: 2024-05-05T22:01:04Z
date_published: 2024-04-26T00:00:00Z
date_updated: 2026-10-02T11:07:26Z
day: '26'
department:
- _id: GradSch
doi: 10.1103/PhysRevApplied.21.044050
external_id:
arxiv:
- '2310.18126'
isi:
- '001226579400001'
fulldoi: https://doi.org/10.1103/PhysRevApplied.21.044050
intvolume: ' 21'
isi: 1
issue: '4'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2310.18126
month: '04'
oa: 1
oa_version: Preprint
publication: Physical Review Applied
publication_identifier:
eissn:
- 2331-7019
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: yes
title: Floquet analysis of a superradiant many-qutrit refrigerator
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 21
year: '2024'
...
---
OA_type: closed access
_id: '18482'
abstract:
- lang: eng
text: "This paper is dedicated to an optimization problem. Let A, B ⊂ Rn be compact
convex sets. Consider the minimal number t0 > 0 such that t0B covers A after a
shift to a vector x0 ∈ \r\nRn. The goal is to find t0 and x0. In the special case
of B being a unit ball centered at zero, x0 and t0 are known as the Chebyshev
center and the Chebyshev radius of A. This paper focuses on the case in which
A and B are defined with their black-box support functions. An algorithm for solving
such problems efficiently is suggested. The algorithm has a superlinear convergence
rate, and it can solve hundred-dimensional test problems in a reasonable time,
but some additional conditions on A and B are required to guarantee the presence
of convergence. Additionally, the behavior of the algorithm for a simple special
case is investigated, which leads to a number of theoretical results. Perturbations
of this special case are also studied."
acknowledgement: The author is grateful to Maxim Balashov for setting the problem,
providing useful literature, important discussions and text review. Also, I thank
Dmitry Tsarev and Kseniia Petukhova for meaningful talks and support.
article_processing_charge: No
article_type: original
author:
- first_name: Pavel
full_name: Arkhipov, Pavel
id: b25f2ab2-1fed-11ee-8599-fe02d211784f
last_name: Arkhipov
citation:
ama: Arkhipov P. An algorithm for finding the generalized Chebyshev center of sets
defined via their support functions. Automation and Remote Control. 2024;85(6):522-532.
doi:10.1134/S0005117924060031
apa: Arkhipov, P. (2024). An algorithm for finding the generalized Chebyshev center
of sets defined via their support functions. Automation and Remote Control.
Springer Nature. https://doi.org/10.1134/S0005117924060031
chicago: Arkhipov, Pavel. “An Algorithm for Finding the Generalized Chebyshev Center
of Sets Defined via Their Support Functions.” Automation and Remote Control.
Springer Nature, 2024. https://doi.org/10.1134/S0005117924060031.
ieee: P. Arkhipov, “An algorithm for finding the generalized Chebyshev center of
sets defined via their support functions,” Automation and Remote Control,
vol. 85, no. 6. Springer Nature, pp. 522–532, 2024.
ista: Arkhipov P. 2024. An algorithm for finding the generalized Chebyshev center
of sets defined via their support functions. Automation and Remote Control. 85(6),
522–532.
mla: Arkhipov, Pavel. “An Algorithm for Finding the Generalized Chebyshev Center
of Sets Defined via Their Support Functions.” Automation and Remote Control,
vol. 85, no. 6, Springer Nature, 2024, pp. 522–32, doi:10.1134/S0005117924060031.
short: P. Arkhipov, Automation and Remote Control 85 (2024) 522–532.
corr_author: '1'
das_tickbox: '0'
date_created: 2024-10-27T23:01:45Z
date_published: 2024-06-01T00:00:00Z
date_updated: 2026-10-02T11:10:45Z
day: '01'
department:
- _id: GradSch
doi: 10.1134/S0005117924060031
external_id:
isi:
- '001338721700007'
fulldoi: https://doi.org/10.1134/S0005117924060031
intvolume: ' 85'
isi: 1
issue: '6'
language:
- iso: eng
month: '06'
oa_version: None
page: 522-532
publication: Automation and Remote Control
publication_identifier:
eissn:
- 1608-3032
issn:
- 0005-1179
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: no
title: An algorithm for finding the generalized Chebyshev center of sets defined via
their support functions
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 85
year: '2024'
...
---
OA_type: closed access
_id: '18450'
abstract:
- lang: eng
text: In this work we present the engineering of a portable platform for sensing
SERS signals in droplets microfluidics. This system comprised not only the read-out
platform but also a design for the microfluidic devices optimized to enhanced
the obtained SERS signals.
acknowledgement: The authors acknowledge the financial support of the project HfPT
– Health from Portugal, with the reference n.° C644937233-00000047, co-funded by
Component C5 – Capitalisation and Business Innovation under the Portuguese Resilience
and Recovery Plan, through the NextGenerationEU Fund.
article_processing_charge: No
author:
- first_name: Sergio
full_name: Quintero, Sergio
last_name: Quintero
- first_name: Maria
full_name: Relvas, Maria
last_name: Relvas
- first_name: Marta
full_name: Aranda, Marta
last_name: Aranda
- first_name: Fernando
full_name: Nodal, Fernando
id: 8ad43d5a-1ffe-11ee-8b67-8176f59de781
last_name: Nodal
orcid: 0009-0006-4119-4376
- first_name: Lorena
full_name: Dieguez, Lorena
last_name: Dieguez
- first_name: Sara
full_name: Abalde-Cela, Sara
last_name: Abalde-Cela
citation:
ama: 'Quintero S, Relvas M, Aranda M, Nodal F, Dieguez L, Abalde-Cela S. Portable
Raman platform for SERS droplets microfludics. In: 2024 International Conference
on Optical MEMS and Nanophotonics. IEEE; 2024. doi:10.1109/OMN61224.2024.10685279'
apa: 'Quintero, S., Relvas, M., Aranda, M., Nodal, F., Dieguez, L., & Abalde-Cela,
S. (2024). Portable Raman platform for SERS droplets microfludics. In 2024
International Conference on Optical MEMS and Nanophotonics. San Sebastian,
Spain: IEEE. https://doi.org/10.1109/OMN61224.2024.10685279'
chicago: Quintero, Sergio, Maria Relvas, Marta Aranda, Fernando Nodal, Lorena Dieguez,
and Sara Abalde-Cela. “Portable Raman Platform for SERS Droplets Microfludics.”
In 2024 International Conference on Optical MEMS and Nanophotonics. IEEE,
2024. https://doi.org/10.1109/OMN61224.2024.10685279.
ieee: S. Quintero, M. Relvas, M. Aranda, F. Nodal, L. Dieguez, and S. Abalde-Cela,
“Portable Raman platform for SERS droplets microfludics,” in 2024 International
Conference on Optical MEMS and Nanophotonics, San Sebastian, Spain, 2024.
ista: 'Quintero S, Relvas M, Aranda M, Nodal F, Dieguez L, Abalde-Cela S. 2024.
Portable Raman platform for SERS droplets microfludics. 2024 International Conference
on Optical MEMS and Nanophotonics. OMN: Conference on Optical MEMS and Nanophotonics.'
mla: Quintero, Sergio, et al. “Portable Raman Platform for SERS Droplets Microfludics.”
2024 International Conference on Optical MEMS and Nanophotonics, IEEE,
2024, doi:10.1109/OMN61224.2024.10685279.
short: S. Quintero, M. Relvas, M. Aranda, F. Nodal, L. Dieguez, S. Abalde-Cela,
in:, 2024 International Conference on Optical MEMS and Nanophotonics, IEEE, 2024.
conference:
end_date: 2024-08-01
location: San Sebastian, Spain
name: 'OMN: Conference on Optical MEMS and Nanophotonics'
start_date: 2024-07-28
das_tickbox: '0'
date_created: 2024-10-20T22:02:07Z
date_published: 2024-09-27T00:00:00Z
date_updated: 2026-10-02T11:10:28Z
day: '27'
department:
- _id: GradSch
doi: 10.1109/OMN61224.2024.10685279
external_id:
isi:
- '001327768000060'
fulldoi: https://doi.org/10.1109/OMN61224.2024.10685279
isi: 1
language:
- iso: eng
month: '09'
oa_version: None
publication: 2024 International Conference on Optical MEMS and Nanophotonics
publication_identifier:
eissn:
- 2160-5041
isbn:
- '9798350384925'
issn:
- 2160-5033
publication_status: published
publisher: IEEE
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: no
title: Portable Raman platform for SERS droplets microfludics
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
OA_place: publisher
_id: '18588'
abstract:
- lang: eng
text: "This thesis is an experimental work about two distinct research projects
that evolved from a single project: non-equilibrium dynamics of an acoustically
vibrated particle and microfabrication of particles with nano-scale 3D printing.
The first project explores non equilibrium dynamics of a particle driven by ultrasonic
vibrations. We design an experimental system consisting of an electromechanical
vibration scheme to drive the particle’s vibrations and an imaging scheme to track
its trajectories. We study the trajectories to determine how the particle’s dynamics
evolve under the driven conditions, considering out of equilibrium systems in
the context of equilibrium statistical mechanics. Using a Langevin framework and
the Boltzmann factor, we characterize the particle’s dynamics as complex; the
particle motion\r\nis not purely diffusive. We extract physical parameters like
spring constant, effective temperature, damping coefficient and resonance frequency.\r\n\r\nIn
the second project, we explore and develop techniques in the design and microfabrication
of particles across scales. Microfabrication involves building structures at the
micron or submicron scale. These designed miniaturized patterns, objects, or devices
are useful in biophysics, pharmacology, medical biology, and nanotechnology. We
specifically apply two-photon polymerization, a form of 3D nano printing. We print
millimetric particles, characterizing different designs to evaluate and showcase
the resolution, aspect ratio integrity and print quality of the printing process.
We also design and fabricate a microsensor to deflect under applicable force of
order 0.1 pN. We present fundamental concepts needed to design the microsensor,
showcasing 3D printing at considerably smaller scales down to the µm or below."
acknowledged_ssus:
- _id: NanoFab
- _id: M-Shop
acknowledgement: "I would like to acknowledge Scott Waitukaitis and Jérémie Palacci,
for their supervision, and their extensive support of my learning. \r\n\r\nFor the
beautiful characterization images used in this work, I would like to thank Dr. Daniel
Grober, Samuel Hajek and Felix Pertl.\r\n\r\nThe Palacci group, particularly Malina
Strugaru and Dan Grober, for their continuous guidance in decoding and following
my streams of thought.\r\n\r\nTo the Waitukaitis group, for helping me find my footing
in science, and making me feel at\r\nhome.\r\n\r\nTo the Nanofabrication Facility
(NFF) at ISTA, for training me in significant aspects of my research. The MIBA Facility,
and particularly Todor Asenov for consistently picking up the phone for my machining
and designing needs.\r\n\r\nTo my friends, Mariana, Lenka, Aaron, Rebecca, Eavan
who provided an ear, wine, and a lot more when I needed to vent, talk through my
crises as well as experiment. For the walks, for the coffees, for reading through
my work and providing edits, for dinners to take me out of blocks and binds and
for cheering me on when it felt insurmountable. \r\n\r\nFinally, I am grateful to
Griff and Fletcher, whose music helped me through several blocks, especially with
my writing.\r\n\r\nMy science would not have been possible without the guidance,
support and contributions of\r\nall these people, and more."
alternative_title:
- ISTA Master's Thesis
article_processing_charge: No
author:
- first_name: Cecelia N
full_name: Mweka, Cecelia N
id: 2a69ab4b-896a-11ed-bdf8-cb8641cf2b21
last_name: Mweka
citation:
ama: Mweka CN. Non equilibrium dynamics of driven individual particles and 3D printing
across scales. 2024. doi:10.15479/at:ista:18588
apa: Mweka, C. N. (2024). Non equilibrium dynamics of driven individual particles
and 3D printing across scales. Institute of Science and Technology Austria.
https://doi.org/10.15479/at:ista:18588
chicago: Mweka, Cecelia N. “Non Equilibrium Dynamics of Driven Individual Particles
and 3D Printing across Scales.” Institute of Science and Technology Austria, 2024.
https://doi.org/10.15479/at:ista:18588.
ieee: C. N. Mweka, “Non equilibrium dynamics of driven individual particles and
3D printing across scales,” Institute of Science and Technology Austria, 2024.
ista: Mweka CN. 2024. Non equilibrium dynamics of driven individual particles and
3D printing across scales. Institute of Science and Technology Austria.
mla: Mweka, Cecelia N. Non Equilibrium Dynamics of Driven Individual Particles
and 3D Printing across Scales. Institute of Science and Technology Austria,
2024, doi:10.15479/at:ista:18588.
short: C.N. Mweka, Non Equilibrium Dynamics of Driven Individual Particles and 3D
Printing across Scales, Institute of Science and Technology Austria, 2024.
corr_author: '1'
das_tickbox: '0'
date_created: 2024-11-27T09:12:02Z
date_published: 2024-11-29T00:00:00Z
date_updated: 2026-10-02T11:11:44Z
day: '29'
ddc:
- '530'
degree_awarded: MS
department:
- _id: GradSch
doi: 10.15479/at:ista:18588
doi_confirm: '1'
file:
- access_level: open_access
checksum: 054ed7a5e5ae6e7220e6bb37ea57a3c3
content_type: application/pdf
creator: cmweka
date_created: 2024-11-28T12:50:32Z
date_updated: 2024-11-28T12:50:32Z
file_id: '18597'
file_name: Cecelia Mweka Master Thesis.pdf
file_size: 3836671
relation: main_file
success: 1
- access_level: closed
checksum: 7d7d9299f090d83e628d65d93116e8c2
content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document
creator: cmweka
date_created: 2024-11-28T12:51:43Z
date_updated: 2024-12-13T11:30:53Z
file_id: '18598'
file_name: Cecelia Mweka Master Thesis.docx
file_size: 7068210
relation: source_file
file_date_updated: 2024-12-13T11:30:53Z
fulldoi: https://doi.org/10.15479/at:ista:18588
has_accepted_license: '1'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
page: '61'
publication_identifier:
issn:
- 2791-4585
publication_status: published
publisher: Institute of Science and Technology Austria
researchdata_availability: no
status: public
supervisor:
- first_name: Scott R
full_name: Waitukaitis, Scott R
id: 3A1FFC16-F248-11E8-B48F-1D18A9856A87
last_name: Waitukaitis
orcid: 0000-0002-2299-3176
- first_name: Jérémie A
full_name: Palacci, Jérémie A
id: 8fb92548-2b22-11eb-b7c1-a3f0d08d7c7d
last_name: Palacci
orcid: 0000-0002-7253-9465
supplementarymaterial: no
title: Non equilibrium dynamics of driven individual particles and 3D printing across
scales
type: dissertation
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
_id: '14705'
abstract:
- lang: eng
text: Since the commercialization of brine shrimp (genus Artemia) in the 1950s,
this lineage, and in particular the model species Artemia franciscana, has been
the subject of extensive research. However, our understanding of the genetic mechanisms
underlying various aspects of their reproductive biology, including sex determination,
are still lacking. This is partly due to the scarcity of genomic resources for
Artemia species and crustaceans in general. Here, we present a chromosome-level
genome assembly of Artemia franciscana (Kellogg 1906), from the Great Salt Lake,
USA. The genome is 1GB, and the majority of the genome (81%) is scaffolded into
21 linkage groups using a previously published high-density linkage map. We performed
coverage and FST analyses using male and female genomic and transcriptomic reads
to quantify the extent of differentiation between the Z and W chromosomes. Additionally,
we quantified the expression levels in male and female heads and gonads and found
further evidence for dosage compensation in this species.
article_processing_charge: No
author:
- first_name: Marwan N
full_name: Elkrewi, Marwan N
id: 0B46FACA-A8E1-11E9-9BD3-79D1E5697425
last_name: Elkrewi
orcid: 0000-0002-5328-7231
citation:
ama: Elkrewi MN. Data from “Chromosome-level assembly of Artemia franciscana sheds
light on sex-chromosome differentiation.” 2024. doi:10.15479/AT:ISTA:14705
apa: Elkrewi, M. N. (2024). Data from “Chromosome-level assembly of Artemia franciscana
sheds light on sex-chromosome differentiation.” Institute of Science and Technology
Austria. https://doi.org/10.15479/AT:ISTA:14705
chicago: Elkrewi, Marwan N. “Data from ‘Chromosome-Level Assembly of Artemia Franciscana
Sheds Light on Sex-Chromosome Differentiation.’” Institute of Science and Technology
Austria, 2024. https://doi.org/10.15479/AT:ISTA:14705.
ieee: M. N. Elkrewi, “Data from ‘Chromosome-level assembly of Artemia franciscana
sheds light on sex-chromosome differentiation.’” Institute of Science and Technology
Austria, 2024.
ista: Elkrewi MN. 2024. Data from ‘Chromosome-level assembly of Artemia franciscana
sheds light on sex-chromosome differentiation’, Institute of Science and Technology
Austria, 10.15479/AT:ISTA:14705.
mla: Elkrewi, Marwan N. Data from “Chromosome-Level Assembly of Artemia Franciscana
Sheds Light on Sex-Chromosome Differentiation.” Institute of Science and Technology
Austria, 2024, doi:10.15479/AT:ISTA:14705.
short: M.N. Elkrewi, (2024).
contributor:
- contributor_type: researcher
first_name: Vincent K
id: 57854184-AAE0-11E9-8D04-98D6E5697425
last_name: Bett
- contributor_type: project_member
first_name: Ariana
id: 2A0848E2-F248-11E8-B48F-1D18A9856A87
last_name: Macon
- contributor_type: supervisor
first_name: Beatriz
id: 49E1C5C6-F248-11E8-B48F-1D18A9856A87
last_name: Vicoso
orcid: 0000-0002-4579-8306
- contributor_type: researcher
first_name: Marwan N
id: 0B46FACA-A8E1-11E9-9BD3-79D1E5697425
last_name: Elkrewi
orcid: 0000-0002-5328-7231
corr_author: '1'
date_created: 2023-12-22T13:40:48Z
date_published: 2024-01-02T00:00:00Z
date_updated: 2026-10-02T11:13:43Z
day: '02'
ddc:
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department:
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- _id: BeVi
doi: 10.15479/AT:ISTA:14705
doi_confirm: '1'
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keyword:
- sex chromosome evolution
- genome assembly
- dosage compensation
month: '01'
oa: 1
oa_version: Published Version
project:
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grant_number: F8810
name: The highjacking of meiosis for asexual reproduction
publisher: Institute of Science and Technology Austria
related_material:
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status: public
title: Data from "Chromosome-level assembly of Artemia franciscana sheds light on
sex-chromosome differentiation"
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
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
_id: '17214'
abstract:
- lang: eng
text: 'Current numerical algorithms for simulating friction fall in one of two camps:
smooth solvers sacrifice the stable treatment of static friction in exchange for
fast convergence, and non-smooth solvers accurately compute friction at convergence
rates that are often prohibitive for large graphics applications. We introduce
a novel bridge between these two ideas that computes static and dynamic friction
stably and efficiently. Our key idea is to convert the highly constrained non-smooth
problem into an unconstrained smooth problem using logarithmic barriers that converges
to the exact solution as accuracy increases. We phrase the problem as an interior
point primal-dual problem that can be solved efficiently with Newton iteration.
We observe quadratic convergence despite the non-smooth nature of the original
problem, and our method is well-suited for large systems of tightly packed objects
with many contact points. We demonstrate the efficacy of our method with stable
piles of grains and stacks of objects, complex granular flows, and robust interlocking
assemblies of rigid bodies.'
acknowledgement: We thank Vincent Acary for his help with Siconos, as well as the
anonymous reviewers and the members of the Visual Computing Group at ISTA for their
helpful comments. This research was funded in part by the European Union (ERC-2021-COG
101045083 CoDiNA).
article_processing_charge: Yes (via OA deal)
author:
- first_name: Yi-Lu
full_name: Chen, Yi-Lu
id: 0b467602-dbcd-11ea-9d1d-ed480aa46b70
last_name: Chen
orcid: 0009-0005-0723-0655
- first_name: Mickaël
full_name: Ly, Mickaël
id: 6340d7f0-b48d-11eb-b10d-b7487e71d9f1
last_name: Ly
- first_name: Christopher J
full_name: Wojtan, Christopher J
id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87
last_name: Wojtan
orcid: 0000-0001-6646-5546
citation:
ama: 'Chen Y-L, Ly M, Wojtan C. Primal-dual non-smooth friction for rigid body animation.
In: Special Interest Group on Computer Graphics and Interactive Techniques
Conference Conference Papers ’24. Association for Computing Machinery; 2024.
doi:10.1145/3641519.3657485'
apa: 'Chen, Y.-L., Ly, M., & Wojtan, C. (2024). Primal-dual non-smooth friction
for rigid body animation. In Special Interest Group on Computer Graphics and
Interactive Techniques Conference Conference Papers ’24. Denver, United States:
Association for Computing Machinery. https://doi.org/10.1145/3641519.3657485'
chicago: Chen, Yi-Lu, Mickaël Ly, and Chris Wojtan. “Primal-Dual Non-Smooth Friction
for Rigid Body Animation.” In Special Interest Group on Computer Graphics and
Interactive Techniques Conference Conference Papers ’24. Association for Computing
Machinery, 2024. https://doi.org/10.1145/3641519.3657485.
ieee: Y.-L. Chen, M. Ly, and C. Wojtan, “Primal-dual non-smooth friction for rigid
body animation,” in Special Interest Group on Computer Graphics and Interactive
Techniques Conference Conference Papers ’24, Denver, United States, 2024.
ista: 'Chen Y-L, Ly M, Wojtan C. 2024. Primal-dual non-smooth friction for rigid
body animation. Special Interest Group on Computer Graphics and Interactive Techniques
Conference Conference Papers ’24. SIGGRAPH: Computer Graphics and Interactive
Techniques Conference.'
mla: Chen, Yi-Lu, et al. “Primal-Dual Non-Smooth Friction for Rigid Body Animation.”
Special Interest Group on Computer Graphics and Interactive Techniques Conference
Conference Papers ’24, Association for Computing Machinery, 2024, doi:10.1145/3641519.3657485.
short: Y.-L. Chen, M. Ly, C. Wojtan, in:, Special Interest Group on Computer Graphics
and Interactive Techniques Conference Conference Papers ’24, Association for Computing
Machinery, 2024.
conference:
end_date: 2024-08-01
location: Denver, United States
name: 'SIGGRAPH: Computer Graphics and Interactive Techniques Conference'
start_date: 2024-07-28
corr_author: '1'
das_tickbox: '0'
date_created: 2024-07-10T11:06:20Z
date_published: 2024-07-01T00:00:00Z
date_updated: 2026-10-02T11:16:26Z
day: '01'
ddc:
- '621'
- '531'
- '006'
department:
- _id: GradSch
- _id: ChWo
doi: 10.1145/3641519.3657485
external_id:
isi:
- '001282218200091'
file:
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checksum: b8b203ed09e3995ba0d7e6a76288663a
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creator: yichen
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date_updated: 2024-07-10T11:03:14Z
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file_size: 47309472
relation: main_file
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creator: yichen
date_created: 2024-07-10T11:03:12Z
date_updated: 2024-07-10T11:03:12Z
file_id: '17216'
file_name: sig24_friction_supplementary.pdf
file_size: 10518286
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content_type: video/mp4
creator: yichen
date_created: 2024-07-10T11:03:51Z
date_updated: 2024-07-10T11:03:51Z
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file_size: 71789192
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creator: yichen
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relation: main_file
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file_date_updated: 2024-07-10T11:03:58Z
fulldoi: https://doi.org/10.1145/3641519.3657485
has_accepted_license: '1'
isi: 1
keyword:
- physical simulation
- frictional contact
- rigid body mechanics
- non-smooth dynamics
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
project:
- _id: 34bc2376-11ca-11ed-8bc3-9a3b3961a088
grant_number: '101045083'
name: Computational Discovery of Numerical Algorithms for Animation and Simulation
of Natural Phenomena
publication: Special Interest Group on Computer Graphics and Interactive Techniques
Conference Conference Papers '24
publication_identifier:
isbn:
- '9798400705250'
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: yes
title: Primal-dual non-smooth friction for rigid body animation
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '17426'
abstract:
- lang: eng
text: "The robustness of neural networks against input perturbations with bounded\r\nmagnitude
represents a serious concern in the deployment of deep learning\r\nmodels in safety-critical
systems. Recently, the scientific community has\r\nfocused on enhancing certifiable
robustness guarantees by crafting 1-Lipschitz\r\nneural networks that leverage
Lipschitz bounded dense and convolutional layers.\r\nAlthough different methods
have been proposed in the literature to achieve this\r\ngoal, understanding the
performance of such methods is not straightforward,\r\nsince different metrics
can be relevant (e.g., training time, memory usage,\r\naccuracy, certifiable robustness)
for different applications. For this reason,\r\nthis work provides a thorough
theoretical and empirical comparison between\r\nmethods by evaluating them in
terms of memory usage, speed, and certifiable\r\nrobust accuracy. The paper also
provides some guidelines and recommendations to\r\nsupport the user in selecting
the methods that work best depending on the\r\navailable resources. We provide
code at\r\nhttps://github.com/berndprach/1LipschitzLayersCompared."
acknowledgement: "This work was partially supported by project SERICS (PE00000014)
under the MUR National Recovery and Resilience Plan funded by the European Union
- NextGenerationEU.\r\n"
article_processing_charge: No
arxiv: 1
author:
- first_name: Bernd
full_name: Prach, Bernd
id: 2D561D42-C427-11E9-89B4-9C1AE6697425
last_name: Prach
- first_name: Fabio
full_name: Brau, Fabio
last_name: Brau
- first_name: Giorgio
full_name: Buttazzo, Giorgio
last_name: Buttazzo
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Prach B, Brau F, Buttazzo G, Lampert C. 1-Lipschitz layers compared: Memory,
speed, and certifiable robustness. In: Proceedings of the IEEE/CVF Conference
on Computer Vision and Pattern Recognition. Computer Vision Foundation; 2024:24574-24583.
doi:10.1109/CVPR52733.2024.02320'
apa: 'Prach, B., Brau, F., Buttazzo, G., & Lampert, C. (2024). 1-Lipschitz layers
compared: Memory, speed, and certifiable robustness. In Proceedings of the
IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 24574–24583).
Seattle, WA, United States: Computer Vision Foundation. https://doi.org/10.1109/CVPR52733.2024.02320'
chicago: 'Prach, Bernd, Fabio Brau, Giorgio Buttazzo, and Christoph Lampert. “1-Lipschitz
Layers Compared: Memory, Speed, and Certifiable Robustness.” In Proceedings
of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 24574–83.
Computer Vision Foundation, 2024. https://doi.org/10.1109/CVPR52733.2024.02320.'
ieee: 'B. Prach, F. Brau, G. Buttazzo, and C. Lampert, “1-Lipschitz layers compared:
Memory, speed, and certifiable robustness,” in Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern Recognition, Seattle, WA, United
States, 2024, pp. 24574–24583.'
ista: 'Prach B, Brau F, Buttazzo G, Lampert C. 2024. 1-Lipschitz layers compared:
Memory, speed, and certifiable robustness. Proceedings of the IEEE/CVF Conference
on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision
and Pattern Recognition, 24574–24583.'
mla: 'Prach, Bernd, et al. “1-Lipschitz Layers Compared: Memory, Speed, and Certifiable
Robustness.” Proceedings of the IEEE/CVF Conference on Computer Vision and
Pattern Recognition, Computer Vision Foundation, 2024, pp. 24574–83, doi:10.1109/CVPR52733.2024.02320.'
short: B. Prach, F. Brau, G. Buttazzo, C. Lampert, in:, Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern Recognition, Computer Vision Foundation,
2024, pp. 24574–24583.
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
corr_author: '1'
das_tickbox: '0'
date_created: 2024-08-14T08:42:32Z
date_published: 2024-06-01T00:00:00Z
date_updated: 2026-10-02T11:16:04Z
day: '01'
department:
- _id: GradSch
- _id: ChLa
doi: 10.1109/CVPR52733.2024.02320
external_id:
arxiv:
- '2311.16833'
isi:
- '001344387500055'
fulldoi: https://doi.org/10.1109/CVPR52733.2024.02320
has_accepted_license: '1'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2311.16833
month: '06'
oa: 1
oa_version: Preprint
page: 24574-24583
publication: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern
Recognition
publication_status: published
publisher: Computer Vision Foundation
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/berndprach/1LipschitzLayersCompared
record:
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relation: dissertation_contains
status: public
researchdata_availability: no
status: public
supplementarymaterial: no
title: '1-Lipschitz layers compared: Memory, speed, and certifiable robustness'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
OA_place: repository
_id: '18874'
abstract:
- lang: eng
text: "Despite extensive research since the community learned about adversarial\r\nexamples
10 years ago, we still do not know how to train high-accuracy\r\nclassifiers that
are guaranteed to be robust to small perturbations of their\r\ninputs. Previous
works often argued that this might be because no classifier\r\nexists that is
robust and accurate at the same time. However, in computer\r\nvision this assumption
does not match reality where humans are usually accurate\r\nand robust on most
tasks of interest. We offer an alternative explanation and\r\nshow that in certain
settings robust generalization is only possible with\r\nunrealistically large
amounts of data. More precisely we find a setting where a\r\nrobust classifier
exists, it is easy to learn an accurate classifier, yet it\r\nrequires an exponential
amount of data to learn a robust classifier. Based on\r\nthis theoretical result,
we explore how well robust classifiers generalize on\r\ndatasets such as CIFAR-10.
We come to the conclusion that on this datasets, the\r\nlimitation of current
robust models also lies in the generalization, and that\r\nthey require a lot
of data to do well on the test set. We also show that the\r\nproblem is not in
the expressiveness or generalization capabilities of current\r\narchitectures,
and that there are low magnitude features in the data which are\r\nuseful for
non-robust generalization but are not available for robust\r\nclassifiers."
article_number: '2412.04245'
article_processing_charge: No
arxiv: 1
author:
- first_name: Bernd
full_name: Prach, Bernd
id: 2D561D42-C427-11E9-89B4-9C1AE6697425
last_name: Prach
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: Prach B, Lampert C. Intriguing properties of robust classification. arXiv.
doi:10.48550/arXiv.2412.04245
apa: Prach, B., & Lampert, C. (n.d.). Intriguing properties of robust classification.
arXiv. https://doi.org/10.48550/arXiv.2412.04245
chicago: Prach, Bernd, and Christoph Lampert. “Intriguing Properties of Robust Classification.”
ArXiv, n.d. https://doi.org/10.48550/arXiv.2412.04245.
ieee: B. Prach and C. Lampert, “Intriguing properties of robust classification,”
arXiv. .
ista: Prach B, Lampert C. Intriguing properties of robust classification. arXiv,
2412.04245.
mla: Prach, Bernd, and Christoph Lampert. “Intriguing Properties of Robust Classification.”
ArXiv, 2412.04245, doi:10.48550/arXiv.2412.04245.
short: B. Prach, C. Lampert, ArXiv (n.d.).
corr_author: '1'
das_tickbox: '0'
date_created: 2025-01-24T16:57:29Z
date_published: 2024-12-05T00:00:00Z
date_updated: 2026-10-02T11:15:43Z
day: '05'
department:
- _id: GradSch
- _id: ChLa
doi: 10.48550/arXiv.2412.04245
external_id:
arxiv:
- '2412.04245'
fulldoi: https://doi.org/10.48550/arXiv.2412.04245
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2412.04245
month: '12'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: draft
related_material:
record:
- id: '20455'
relation: later_version
status: public
- id: '19759'
relation: dissertation_contains
status: public
researchdata_availability: no
status: public
supplementarymaterial: yes
title: Intriguing properties of robust classification
type: preprint
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
OA_place: repository
_id: '17361'
abstract:
- lang: eng
text: We present symplectic structures on the shape space of unparameterized space
curves that generalize the classical Marsden-Weinstein structure. Our method integrates
the Liouville 1-form of the Marsden-Weinstein structure with Riemannian structures
that have been introduced in mathematical shape analysis. We also derive Hamiltonian
vector fields for several classical Hamiltonian functions with respect to these
new symplectic structures.
acknowledgement: The authors are grateful to Boris Khesin for valuable comments on
the MW symplectic structure and S. Ishida thanks Albert Chern for insightful discussions
on space curves and Chris Wojtan for his continuous support. M. Bauer was partially
supported by NSF grant DMS-1953244 and by the Binational Science Foundation (BSF).
S. Ishida was partially supported by ERC Consolidator Grant 101045083 “CoDiNA” funded
by the European Research Council. Some figures were generated by the software Houdini
and its education license was provided by SideFX.
article_processing_charge: No
arxiv: 1
author:
- first_name: Martin
full_name: Bauer, Martin
last_name: Bauer
- first_name: Sadashige
full_name: Ishida, Sadashige
id: 6F7C4B96-A8E9-11E9-A7CA-09ECE5697425
last_name: Ishida
orcid: 0000-0002-3121-3100
- first_name: Peter W.
full_name: Michor, Peter W.
last_name: Michor
citation:
ama: Bauer M, Ishida S, Michor PW. Symplectic structures on the space of space curves.
arXiv. doi:10.48550/arXiv.2407.19908
apa: Bauer, M., Ishida, S., & Michor, P. W. (n.d.). Symplectic structures on
the space of space curves. arXiv. https://doi.org/10.48550/arXiv.2407.19908
chicago: Bauer, Martin, Sadashige Ishida, and Peter W. Michor. “Symplectic Structures
on the Space of Space Curves.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2407.19908.
ieee: M. Bauer, S. Ishida, and P. W. Michor, “Symplectic structures on the space
of space curves,” arXiv. .
ista: Bauer M, Ishida S, Michor PW. Symplectic structures on the space of space
curves. arXiv, 10.48550/arXiv.2407.19908.
mla: Bauer, Martin, et al. “Symplectic Structures on the Space of Space Curves.”
ArXiv, doi:10.48550/arXiv.2407.19908.
short: M. Bauer, S. Ishida, P.W. Michor, ArXiv (n.d.).
das_tickbox: '0'
date_created: 2024-08-01T06:34:08Z
date_published: 2024-07-29T00:00:00Z
date_updated: 2026-10-02T11:16:46Z
day: '29'
department:
- _id: GradSch
- _id: ChWo
doi: 10.48550/arXiv.2407.19908
external_id:
arxiv:
- '2407.19908'
fulldoi: https://doi.org/10.48550/arXiv.2407.19908
keyword:
- space of space curves
- symplectic stuctures
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2407.19908
month: '07'
oa: 1
oa_version: Preprint
project:
- _id: 34bc2376-11ca-11ed-8bc3-9a3b3961a088
grant_number: '101045083'
name: Computational Discovery of Numerical Algorithms for Animation and Simulation
of Natural Phenomena
publication: arXiv
publication_status: draft
related_material:
record:
- id: '20551'
relation: dissertation_contains
status: public
- id: '21743'
relation: later_version
status: public
researchdata_availability: no
status: public
supplementarymaterial: no
title: Symplectic structures on the space of space curves
type: preprint
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
OA_place: publisher
OA_type: hybrid
_id: '17219'
abstract:
- lang: eng
text: 'We introduce a multi-material non-manifold mesh-based surface tracking algorithm
that converts self-intersections into topological changes. Our algorithm generalizes
prior work on manifold surface tracking with topological changes: it preserves
surface features like mesh-based methods, and it robustly handles topological
changes like level set methods. Our method also offers improved efficiency and
robustness over the state of the art. We demonstrate the effectiveness of the
approach on a range of examples, including complex soap film simulations with
thousands of interacting bubbles, and boolean unions of non-manifold meshes consisting
of millions of triangles.'
acknowledgement: Peter Heiss-Synak helped conceive the project, helped formulate the
algorithm structure, contributed ideas and code to Sections 6 & 8, the mesh data
structure, algorithm robustness and benchmarks, helped write the paper, and provided
supervision and conceptual solutions throughout the project. Aleksei Kalinov contributed
ideas and code to Sections 7, 8.5, and 5, the sparse grid data structure, algorithm
robustness and benchmarks, optimized the performance, produced all results, most
figures, and the supplementary video, helped write the text, and provided conceptual
solutions throughout the project. Malina Strugaru helped implement the mesh data
structure and designed re-meshing operations for non-manifold triangle meshes. Arian
Etemadi developed early prototypes for ideas in Sections 8.1 and 8.3 and helped
write the paper. Huidong Yang developed early prototypes for isosurface extraction
and visualization. Chris Wojtan helped conceive the project, helped write the paper,
and provided supervision, prototype grid data structure code, and conceptual solutions
throughout the project. We thank the anonymous reviewers for their helpful comments,
the members of the Visual Computing Group at ISTA for their feedback, Christopher
Batty for discussions about LosTopos, and SideFX for the Houdini Education software
licenses. This research was funded in part by the European Union (ERC-2021-COG
101045083 CoDiNA).
article_number: '54'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Peter
full_name: Synak, Peter
id: 331776E2-F248-11E8-B48F-1D18A9856A87
last_name: Synak
- first_name: Aleksei
full_name: Kalinov, Aleksei
id: 44b7120e-eb97-11eb-a6c2-e1557aa81d02
last_name: Kalinov
orcid: 0000-0003-2189-3904
- first_name: Irina-Malina
full_name: Strugaru, Irina-Malina
id: 2afc607f-f128-11eb-9611-8f2a0dfcf074
last_name: Strugaru
- first_name: Arian
full_name: Etemadihaghighi, Arian
id: 36cea3aa-f38e-11ec-8ae0-c65ae6f6098f
last_name: Etemadihaghighi
- first_name: Huidong
full_name: Yang, Huidong
last_name: Yang
- first_name: Christopher J
full_name: Wojtan, Christopher J
id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87
last_name: Wojtan
orcid: 0000-0001-6646-5546
citation:
ama: Synak P, Kalinov A, Strugaru I-M, Etemadi A, Yang H, Wojtan C. Multi-material
mesh-based surface tracking with implicit topology changes. ACM Transactions
on Graphics. 2024;43(4). doi:10.1145/3658223
apa: Synak, P., Kalinov, A., Strugaru, I.-M., Etemadi, A., Yang, H., & Wojtan,
C. (2024). Multi-material mesh-based surface tracking with implicit topology changes.
ACM Transactions on Graphics. Association for Computing Machinery. https://doi.org/10.1145/3658223
chicago: Synak, Peter, Aleksei Kalinov, Irina-Malina Strugaru, Arian Etemadi, Huidong
Yang, and Chris Wojtan. “Multi-Material Mesh-Based Surface Tracking with Implicit
Topology Changes.” ACM Transactions on Graphics. Association for Computing
Machinery, 2024. https://doi.org/10.1145/3658223.
ieee: P. Synak, A. Kalinov, I.-M. Strugaru, A. Etemadi, H. Yang, and C. Wojtan,
“Multi-material mesh-based surface tracking with implicit topology changes,” ACM
Transactions on Graphics, vol. 43, no. 4. Association for Computing Machinery,
2024.
ista: Synak P, Kalinov A, Strugaru I-M, Etemadi A, Yang H, Wojtan C. 2024. Multi-material
mesh-based surface tracking with implicit topology changes. ACM Transactions on
Graphics. 43(4), 54.
mla: Synak, Peter, et al. “Multi-Material Mesh-Based Surface Tracking with Implicit
Topology Changes.” ACM Transactions on Graphics, vol. 43, no. 4, 54, Association
for Computing Machinery, 2024, doi:10.1145/3658223.
short: P. Synak, A. Kalinov, I.-M. Strugaru, A. Etemadi, H. Yang, C. Wojtan, ACM
Transactions on Graphics 43 (2024).
corr_author: '1'
das_tickbox: '0'
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date_published: 2024-07-01T00:00:00Z
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doi: 10.1145/3658223
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- solid modeling
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grant_number: '101045083'
name: Computational Discovery of Numerical Algorithms for Animation and Simulation
of Natural Phenomena
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publisher: Association for Computing Machinery
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abstract:
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text: Physics simulation in computer graphics can bring triangle meshes into topologically
invalid states. The method in this thesis contributed to Heiss-Synak* and Kalinov*
et al. [2024] who devised a non-manifold hybrid surface tracker—a surface tracker
that repairs explicit non-manifold triangle meshes with the help of the implicit
domain. Specifically, this thesis provides an algorithm for filling the holes
that are left after removing problematic parts of the mesh.
alternative_title:
- ISTA Master's Thesis
article_processing_charge: No
author:
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full_name: Etemadihaghighi, Arian
id: 36cea3aa-f38e-11ec-8ae0-c65ae6f6098f
last_name: Etemadihaghighi
citation:
ama: Etemadi A. Filling the holes of non-manifold self-intersecting meshes for implicit
topology changes in surface tracking. 2024. doi:10.15479/at:ista:18301
apa: Etemadi, A. (2024). Filling the holes of non-manifold self-intersecting
meshes for implicit topology changes in surface tracking. Institute of Science
and Technology Austria. https://doi.org/10.15479/at:ista:18301
chicago: Etemadi, Arian. “Filling the Holes of Non-Manifold Self-Intersecting Meshes
for Implicit Topology Changes in Surface Tracking.” Institute of Science and Technology
Austria, 2024. https://doi.org/10.15479/at:ista:18301.
ieee: A. Etemadi, “Filling the holes of non-manifold self-intersecting meshes for
implicit topology changes in surface tracking,” Institute of Science and Technology
Austria, 2024.
ista: Etemadi A. 2024. Filling the holes of non-manifold self-intersecting meshes
for implicit topology changes in surface tracking. Institute of Science and Technology
Austria.
mla: Etemadi, Arian. Filling the Holes of Non-Manifold Self-Intersecting Meshes
for Implicit Topology Changes in Surface Tracking. Institute of Science and
Technology Austria, 2024, doi:10.15479/at:ista:18301.
short: A. Etemadi, Filling the Holes of Non-Manifold Self-Intersecting Meshes for
Implicit Topology Changes in Surface Tracking, Institute of Science and Technology
Austria, 2024.
corr_author: '1'
das_tickbox: '0'
date_created: 2024-10-11T19:52:20Z
date_published: 2024-10-15T00:00:00Z
date_updated: 2026-10-02T11:19:26Z
day: '15'
ddc:
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degree_awarded: MS
department:
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- _id: ChWo
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relation: source_file
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fulldoi: https://doi.org/10.15479/at:ista:18301
has_accepted_license: '1'
keyword:
- surface tracking
- non-manifold
- hole-filling
- topology change
- multi-material
- solid-modeling
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
page: '39'
publication_identifier:
issn:
- 2791-4585
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
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- id: '17219'
relation: part_of_dissertation
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researchdata_availability: no
status: public
supervisor:
- first_name: Christopher J
full_name: Wojtan, Christopher J
id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87
last_name: Wojtan
orcid: 0000-0001-6646-5546
supplementarymaterial: no
title: Filling the holes of non-manifold self-intersecting meshes for implicit topology
changes in surface tracking
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: dissertation
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '12846'
abstract:
- lang: eng
text: We present a formula for the signed area of a spherical polygon via prequantization.
In contrast to the traditional formula based on the Gauss-Bonnet theorem that
requires measuring angles, the new formula mimics Green's theorem and is applicable
to a wider range of degenerate spherical curves and polygons.
acknowledgement: This work was funded by European Research Council Consolidator grant
101045083 CoDiNA and National Science Foundation CAREER award 2239062. Some figures
in the article were generated by the software Houdini and its education license
was provided by SideFX. The authors acknowledge anonymous referees for their reviews
and insightful suggestions, and Chris Wojtan for his continuous support through
discussions. The second author thanks Anna Sisak for a fruitful discussion on prequantum
bundles.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Albert
full_name: Chern, Albert
last_name: Chern
- first_name: Sadashige
full_name: Ishida, Sadashige
id: 6F7C4B96-A8E9-11E9-A7CA-09ECE5697425
last_name: Ishida
orcid: 0000-0002-3121-3100
citation:
ama: Chern A, Ishida S. Area formula for spherical polygons via prequantization.
SIAM Journal on Applied Algebra and Geometry. 2024;8(3):782-796. doi:10.1137/23M1565255
apa: Chern, A., & Ishida, S. (2024). Area formula for spherical polygons via
prequantization. SIAM Journal on Applied Algebra and Geometry. Society
for Industrial and Applied Mathematics. https://doi.org/10.1137/23M1565255
chicago: Chern, Albert, and Sadashige Ishida. “Area Formula for Spherical Polygons
via Prequantization.” SIAM Journal on Applied Algebra and Geometry. Society
for Industrial and Applied Mathematics, 2024. https://doi.org/10.1137/23M1565255.
ieee: A. Chern and S. Ishida, “Area formula for spherical polygons via prequantization,”
SIAM Journal on Applied Algebra and Geometry, vol. 8, no. 3. Society for
Industrial and Applied Mathematics, pp. 782–796, 2024.
ista: Chern A, Ishida S. 2024. Area formula for spherical polygons via prequantization.
SIAM Journal on Applied Algebra and Geometry. 8(3), 782–796.
mla: Chern, Albert, and Sadashige Ishida. “Area Formula for Spherical Polygons via
Prequantization.” SIAM Journal on Applied Algebra and Geometry, vol. 8,
no. 3, Society for Industrial and Applied Mathematics, 2024, pp. 782–96, doi:10.1137/23M1565255.
short: A. Chern, S. Ishida, SIAM Journal on Applied Algebra and Geometry 8 (2024)
782–796.
corr_author: '1'
das_tickbox: '0'
date_created: 2023-04-18T19:16:06Z
date_published: 2024-09-23T00:00:00Z
date_updated: 2026-10-02T11:17:04Z
day: '23'
ddc:
- '516'
department:
- _id: GradSch
- _id: ChWo
doi: 10.1137/23M1565255
external_id:
arxiv:
- '2303.14555'
isi:
- '001342265800009'
fulldoi: https://doi.org/10.1137/23M1565255
has_accepted_license: '1'
intvolume: ' 8'
isi: 1
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2303.14555
month: '09'
oa: 1
oa_version: Preprint
page: 782-796
project:
- _id: 34bc2376-11ca-11ed-8bc3-9a3b3961a088
grant_number: '101045083'
name: Computational Discovery of Numerical Algorithms for Animation and Simulation
of Natural Phenomena
publication: SIAM Journal on Applied Algebra and Geometry
publication_identifier:
eissn:
- 2470-6566
publication_status: published
publisher: Society for Industrial and Applied Mathematics
quality_controlled: '1'
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scopus_import: '1'
status: public
supplementarymaterial: no
title: Area formula for spherical polygons via prequantization
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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 8
year: '2024'
...
---
OA_place: publisher
_id: '17485'
abstract:
- lang: eng
text: "Large language models (LLMs) have made tremendous progress in the past few
years, from being able to generate coherent text to matching or surpassing humans
in a wide variety of creative, knowledge or reasoning tasks. Much of this can
be attributed to massively increased scale, both in the size of the model as well
as the amount of training data, from 100s of millions to 100s of billions, or
even trillions. This trend is expected to continue, which, although exciting,
also raises major practical concerns. Already today's 100+ billion parameter LLMs
require top-of-the-line hardware just to run. Hence, it is clear that sustaining
these developments will require significant efficiency advances.\r\n\r\nHistorically,
one of the most practical ways of improving model efficiency has been compression,
especially in the form of sparsity or quantization. While this has been studied
extensively in the past, existing accurate methods are all designed for models
around 100 million parameters; scaling them up to ones literally 1000x larger
is highly challenging. In this thesis, we introduce a new unified sparsification
and quantization approach OBC, which through additional algorithmic enhancements
leads to GPTQ and SparseGPT, the first techniques fast and accurate enough to
compress 100+ billion parameter models to 4- or even 3-bit precision and 50% weight-sparsity,
respectively. Additionally, we show how weight-only quantizion does not just bring
space savings but also up to 4.5x faster generation speed, via custom GPU kernels.\r\n\r\nIn
fact, we show for the first time that it is possible to develop an FP16 times
INT4 mixed-precision matrix multiplication kernel, called Marlin, which comes
close to simultaneously maximizing both memory and compute utilization, making
weight-only quantization highly practical even for multi-user serving. Further,
we demonstrate that GPTQ can be scaled to widely overparametrized trillion-parameter
models, where extreme sub-1-bit compression rates can be achieved without any
inference slow-down, by co-designing a bespoke entropy coding scheme together
with an efficient kernel.\r\n\r\nFinally, we also study compression from the perspective
of someone with access to massive amounts of compute resources for training large
models completely from scratch. Here the key questions evolve around the joint
scaling behavior between compression, model size, and amount of training data
used. Based on extensive experimental results for both vision and text models,
we introduce the first scaling law which accurately captures the relationship
between weight-sparsity, number of non-zero weights and data. This further allows
us to characterize the optimal sparsity, which we find to increase the longer
a fixed cost model is being trained.\r\n\r\nOverall, this thesis presents contributions
to three different angles of large model efficiency: affordable but accurate algorithms,
highly efficient systems implementations, and fundamental scaling laws for compressed
training."
acknowledged_ssus:
- _id: ScienComp
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Elias
full_name: Frantar, Elias
id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f
last_name: Frantar
citation:
ama: 'Frantar E. Compressing large neural networks: Algorithms, systems and scaling
laws. 2024. doi:10.15479/at:ista:17485'
apa: 'Frantar, E. (2024). Compressing large neural networks: Algorithms, systems
and scaling laws. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:17485'
chicago: 'Frantar, Elias. “Compressing Large Neural Networks: Algorithms, Systems
and Scaling Laws.” Institute of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:17485.'
ieee: 'E. Frantar, “Compressing large neural networks: Algorithms, systems and scaling
laws,” Institute of Science and Technology Austria, 2024.'
ista: 'Frantar E. 2024. Compressing large neural networks: Algorithms, systems and
scaling laws. Institute of Science and Technology Austria.'
mla: 'Frantar, Elias. Compressing Large Neural Networks: Algorithms, Systems
and Scaling Laws. Institute of Science and Technology Austria, 2024, doi:10.15479/at:ista:17485.'
short: 'E. Frantar, Compressing Large Neural Networks: Algorithms, Systems and Scaling
Laws, Institute of Science and Technology Austria, 2024.'
corr_author: '1'
das_tickbox: '0'
date_created: 2024-09-02T11:01:48Z
date_published: 2024-09-05T00:00:00Z
date_updated: 2026-10-02T11:19:46Z
day: '05'
ddc:
- '000'
degree_awarded: PhD
department:
- _id: GradSch
- _id: DaAl
doi: 10.15479/at:ista:17485
doi_confirm: '1'
ec_funded: 1
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file_id: '17570'
file_name: thesis-final.zip
file_size: 1615167
relation: source_file
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checksum: a9dd1c2d23734986924eb44ebb55fd8f
content_type: application/pdf
creator: efrantar
date_created: 2024-09-06T16:24:59Z
date_updated: 2024-09-06T16:24:59Z
file_id: '17880'
file_name: frantar_thesis_final.pdf
file_size: 2376611
relation: main_file
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has_accepted_license: '1'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: '129'
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '17378'
relation: part_of_dissertation
status: public
- id: '17087'
relation: part_of_dissertation
status: public
- id: '14458'
relation: part_of_dissertation
status: public
- id: '18061'
relation: part_of_dissertation
status: public
- id: '18062'
relation: part_of_dissertation
status: public
researchdata_availability: no
status: public
supervisor:
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
supplementarymaterial: no
title: 'Compressing large neural networks: Algorithms, systems and scaling laws'
type: dissertation
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
_id: '18062'
abstract:
- lang: eng
text: 'We explore the impact of parameter sparsity on the scaling behavior of Transformers
trained on massive datasets (i.e., "foundation models"), in both vision and language
domains. In this setting, we identify the first scaling law describing the relationship
between weight sparsity, number of non-zero parameters, and amount of training
data, which we validate empirically across model and data scales; on ViT/JFT-4B
and T5/C4. These results allow us to characterize the "optimal sparsity", the
sparsity level which yields the best performance for a given effective model size
and training budget. For a fixed number of non-zero parameters, we identify that
the optimal sparsity increases with the amount of data used for training. We also
extend our study to different sparsity structures (such as the hardware-friendly
n:m pattern) and strategies (such as starting from a pretrained dense model).
Our findings shed light on the power and limitations of weight sparsity across
various parameter and computational settings, offering both theoretical understanding
and practical implications for leveraging sparsity towards computational efficiency
improvements. We provide pruning and scaling law fitting code at: github.com/google-research/jaxpruner/tree/main/jaxpruner/projects/bigsparse.'
article_processing_charge: No
arxiv: 1
author:
- first_name: Elias
full_name: Frantar, Elias
id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f
last_name: Frantar
- first_name: Carlos Riquelme
full_name: Ruiz, Carlos Riquelme
last_name: Ruiz
- first_name: Neil
full_name: Houlsby, Neil
last_name: Houlsby
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
- first_name: Utku
full_name: Evci, Utku
last_name: Evci
citation:
ama: 'Frantar E, Ruiz CR, Houlsby N, Alistarh D-A, Evci U. Scaling laws for sparsely-connected
foundation models. In: The Twelfth International Conference on Learning Representations.
; 2024.'
apa: Frantar, E., Ruiz, C. R., Houlsby, N., Alistarh, D.-A., & Evci, U. (2024).
Scaling laws for sparsely-connected foundation models. In The Twelfth International
Conference on Learning Representations. Vienna, Austria.
chicago: Frantar, Elias, Carlos Riquelme Ruiz, Neil Houlsby, Dan-Adrian Alistarh,
and Utku Evci. “Scaling Laws for Sparsely-Connected Foundation Models.” In The
Twelfth International Conference on Learning Representations, 2024.
ieee: E. Frantar, C. R. Ruiz, N. Houlsby, D.-A. Alistarh, and U. Evci, “Scaling
laws for sparsely-connected foundation models,” in The Twelfth International
Conference on Learning Representations, Vienna, Austria, 2024.
ista: 'Frantar E, Ruiz CR, Houlsby N, Alistarh D-A, Evci U. 2024. Scaling laws for
sparsely-connected foundation models. The Twelfth International Conference on
Learning Representations. ICLR: International Conference on Learning Representations.'
mla: Frantar, Elias, et al. “Scaling Laws for Sparsely-Connected Foundation Models.”
The Twelfth International Conference on Learning Representations, 2024.
short: E. Frantar, C.R. Ruiz, N. Houlsby, D.-A. Alistarh, U. Evci, in:, The Twelfth
International Conference on Learning Representations, 2024.
conference:
end_date: 2024-05-07
location: Vienna, Austria
name: 'ICLR: International Conference on Learning Representations'
start_date: 2024-05-07
corr_author: '1'
das_tickbox: '0'
date_created: 2024-09-13T10:31:08Z
date_published: 2024-01-16T00:00:00Z
date_updated: 2026-10-02T11:19:46Z
day: '16'
ddc:
- '000'
department:
- _id: DaAl
external_id:
arxiv:
- '2309.08520'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://openreview.net/forum?id=i9K2ZWkYIP
month: '01'
oa: 1
oa_version: Published Version
publication: The Twelfth International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
related_material:
record:
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researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: no
title: Scaling laws for sparsely-connected foundation models
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
_id: '18061'
abstract:
- lang: eng
text: 'Mixture-of-Experts (MoE) architectures offer a general solution to the high
inference costs of large language models (LLMs) via sparse routing, bringing faster
and more accurate models, at the cost of massive parameter counts. For example,
the SwitchTransformer-c2048 model has 1.6 trillion parameters, requiring 3.2TB
of accelerator memory to run efficiently, which makes practical deployment challenging
and expensive. In this paper, we present a solution to this memory problem, in
form of a new compression and execution framework called QMoE. Specifically, QMoE
consists of a scalable algorithm which accurately compresses trillion-parameter
MoEs to less than 1 bit per parameter, in a custom format co-designed with bespoke
GPU decoding kernels to facilitate efficient end-to-end compressed inference,
with minor runtime overheads relative to uncompressed execution. Concretely, QMoE
can compress the 1.6 trillion parameter SwitchTransformer-c2048 model to less
than 160GB (20x compression, 0.8 bits per parameter) at only minor accuracy loss,
in less than a day on a single GPU. This enables, for the first time, the execution
of a trillion-parameter model on affordable commodity hardware, like a single
server with 4x NVIDIA A6000 or 8x NVIDIA 3090 GPUs, at less than 5% runtime overhead
relative to ideal uncompressed inference. The anonymized code is available at:
github.com/mlsys24-qmoe/qmoe.'
article_processing_charge: No
author:
- 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: 'Frantar E, Alistarh D-A. QMoE: Sub-1-bit compression of trillion parameter
models. In: Proceedings of Machine Learning and Systems. Vol 6. ; 2024.'
apa: 'Frantar, E., & Alistarh, D.-A. (2024). QMoE: Sub-1-bit compression of
trillion parameter models. In Proceedings of Machine Learning and Systems
(Vol. 6). Santa Clara, CA, United States.'
chicago: 'Frantar, Elias, and Dan-Adrian Alistarh. “QMoE: Sub-1-Bit Compression
of Trillion Parameter Models.” In Proceedings of Machine Learning and Systems,
Vol. 6, 2024.'
ieee: 'E. Frantar and D.-A. Alistarh, “QMoE: Sub-1-bit compression of trillion parameter
models,” in Proceedings of Machine Learning and Systems, Santa Clara, CA,
United States, 2024, vol. 6.'
ista: 'Frantar E, Alistarh D-A. 2024. QMoE: Sub-1-bit compression of trillion parameter
models. Proceedings of Machine Learning and Systems. MLSys: Machine Learning and
Systems vol. 6.'
mla: 'Frantar, Elias, and Dan-Adrian Alistarh. “QMoE: Sub-1-Bit Compression of Trillion
Parameter Models.” Proceedings of Machine Learning and Systems, vol. 6,
2024.'
short: E. Frantar, D.-A. Alistarh, in:, Proceedings of Machine Learning and Systems,
2024.
conference:
end_date: 2024-05-16
location: Santa Clara, CA, United States
name: 'MLSys: Machine Learning and Systems'
start_date: 2024-05-13
corr_author: '1'
das_tickbox: '0'
date_created: 2024-09-13T10:01:38Z
date_published: 2024-05-01T00:00:00Z
date_updated: 2026-10-02T11:19:46Z
day: '01'
ddc:
- '000'
department:
- _id: DaAl
intvolume: ' 6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://proceedings.mlsys.org/paper_files/paper/2024/hash/c74b624843218d9b6713fcf299d6d5e4-Abstract-Conference.html
month: '05'
oa: 1
oa_version: Published Version
publication: Proceedings of Machine Learning and Systems
publication_status: published
quality_controlled: '1'
related_material:
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researchdata_availability: yes
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supplementarymaterial: no
title: 'QMoE: Sub-1-bit compression of trillion parameter models'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 6
year: '2024'
...
---
OA_place: publisher
_id: '17490'
abstract:
- lang: eng
text: "Deep learning is essential in numerous applications nowadays, with many recent
advancements made possible by training very large models. Despite their broad
applicability, training neural networks is often time-intensive, and it is usually
impractical to manage large models and datasets on a single machine. To address
these issues, distributed deep learning training has become increasingly important.
However, distributed training requires synchronization among nodes, and the mini-batch
stochastic gradient descent algorithm places a significant load on network connections.
A possible solution to tackle the synchronization bottleneck is to reduce a message
size by lossy compression.\r\n\r\nIn this thesis, we investigate systems and algorithmic
approaches to communication compression during training. From the systems perspective,
we demonstrate that a common approach of expensive hardware overprovisioning can
be replaced through a thorough system design. We introduce a framework that introduces
efficient software support for compressed communication in machine learning applications,
applicable to both multi-GPU single-node training and larger-scale multi-node
training. Our framework integrates with popular ML frameworks, providing up to
3x speedups for multi-GPU nodes based on commodity hardware and order-of-magnitude
improvements in the multi-node setting, with negligible impact on accuracy.\r\n\r\nAlso,
we consider an application of our framework to different communication schemes,
such as Fully Sharded Data Parallel. We provide strong convergence guarantees
for the compression in such a setup. Empirical validation shows that our method
preserves model accuracy for GPT-family models with up to 1.3 billion parameters,
while completely removing the communication bottlenecks of non-compressed alternatives,
providing up to 2.2x speedups end-to-end.\r\n\r\nFrom the algorithmic side, we
propose a general framework that dynamically adjusts the degree of compression
across a model's layers during training. This approach enhances overall compression
and results in significant speedups without compromising accuracy. Our algorithm
utilizes an adaptive algorithm that automatically selects the optimal compression
parameters for model layers, ensuring the best compression ratio while adhering
to an error constraint. Our method is effective across all existing families of
compression methods. It achieves up to 2.5x faster training and up to a 5x improvement
in compression compared to efficient implementations of current approaches. Additionally,
LGreCo can complement existing adaptive algorithms.\r\n"
acknowledged_ssus:
- _id: ScienComp
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Ilia
full_name: Markov, Ilia
id: D0CF4148-C985-11E9-8066-0BDEE5697425
last_name: Markov
citation:
ama: 'Markov I. Communication-efficient distributed training of deep neural networks :
An algorithms and systems perspective. 2024. doi:10.15479/at:ista:17490'
apa: 'Markov, I. (2024). Communication-efficient distributed training of deep
neural networks : An algorithms and systems perspective. Institute of Science
and Technology Austria. https://doi.org/10.15479/at:ista:17490'
chicago: 'Markov, Ilia. “Communication-Efficient Distributed Training of Deep Neural
Networks : An Algorithms and Systems Perspective.” Institute of Science and Technology
Austria, 2024. https://doi.org/10.15479/at:ista:17490.'
ieee: 'I. Markov, “Communication-efficient distributed training of deep neural networks :
An algorithms and systems perspective,” Institute of Science and Technology Austria,
2024.'
ista: 'Markov I. 2024. Communication-efficient distributed training of deep neural
networks : An algorithms and systems perspective. Institute of Science and Technology
Austria.'
mla: 'Markov, Ilia. Communication-Efficient Distributed Training of Deep Neural
Networks : An Algorithms and Systems Perspective. Institute of Science and
Technology Austria, 2024, doi:10.15479/at:ista:17490.'
short: 'I. Markov, Communication-Efficient Distributed Training of Deep Neural Networks :
An Algorithms and Systems Perspective, Institute of Science and Technology Austria,
2024.'
corr_author: '1'
das_tickbox: '0'
date_created: 2024-09-04T08:51:11Z
date_published: 2024-09-04T00:00:00Z
date_updated: 2026-10-02T11:20:06Z
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ddc:
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department:
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- _id: DaAl
doi: 10.15479/at:ista:17490
doi_confirm: '1'
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oa: 1
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page: '102'
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call_identifier: H2020
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name: Elastic Coordination for Scalable Machine Learning
publication_identifier:
issn:
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publication_status: published
publisher: Institute of Science and Technology Austria
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researchdata_availability: no
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supervisor:
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
supplementarymaterial: no
title: 'Communication-efficient distributed training of deep neural networks : An
algorithms and systems perspective'
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legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
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short: CC BY-NC-SA (4.0)
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user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
_id: '17456'
abstract:
- lang: eng
text: "Data-parallel distributed training of deep neural networks (DNN) has gained
very widespread adoption, but can still experience communication bottlenecks.
To address this issue, entire families of compression mechanisms have been developed,
including quantization, sparsification, and low-rank approximation, some of which
are seeing significant practical adoption. Despite this progress, almost all known
compression schemes apply compression uniformly across DNN layers, although layers
are heterogeneous in terms of parameter count and their impact on model accuracy.In
this work, we provide a general framework for adapting the degree of compression
across the model's layers dynamically during training, improving the overall compression,
while leading to substantial speedups, without sacrificing accuracy. Our framework,
called L-GreCo, is based on an adaptive algorithm, which automatically picks the
optimal compression parameters for model layers guaranteeing the best compression
ratio while satisfying an error constraint. Extensive experiments over image classification
and language modeling tasks shows that L-GreCo is effective across all existing
families of compression methods, and achieves up to 2.5\r\n×\r\n training speedup
and up to 5\r\n×\r\n compression improvement over efficient implementations of
existing approaches, while recovering full accuracy. Moreover, L-GreCo is complementary
to existing adaptive algorithms, improving their compression ratio by 50\\% and
practical throughput by 66\\%. An anonymized implementation is available at https://github.com/LGrCo/L-GreCo."
article_processing_charge: No
arxiv: 1
author:
- first_name: Ilia
full_name: Markov, Ilia
id: D0CF4148-C985-11E9-8066-0BDEE5697425
last_name: Markov
- first_name: Kaveh
full_name: Alimohammadi, Kaveh
last_name: Alimohammadi
- 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: 'Markov I, Alimohammadi K, Frantar E, Alistarh D-A. L-GreCo: Layerwise-adaptive
gradient compression for efficient data-parallel deep learning. In: Gibbons P,
Pekhimenko G, De Sa C, eds. Proceedings of Machine Learning and Systems .
Vol 6. Association for Computing Machinery; 2024.'
apa: 'Markov, I., Alimohammadi, K., Frantar, E., & Alistarh, D.-A. (2024). L-GreCo:
Layerwise-adaptive gradient compression for efficient data-parallel deep learning.
In P. Gibbons, G. Pekhimenko, & C. De Sa (Eds.), Proceedings of Machine
Learning and Systems (Vol. 6). Athens, Greece: Association for Computing
Machinery.'
chicago: 'Markov, Ilia, Kaveh Alimohammadi, Elias Frantar, and Dan-Adrian Alistarh.
“L-GreCo: Layerwise-Adaptive Gradient Compression for Efficient Data-Parallel
Deep Learning.” In Proceedings of Machine Learning and Systems , edited
by P. Gibbons, G. Pekhimenko, and C. De Sa, Vol. 6. Association for Computing
Machinery, 2024.'
ieee: 'I. Markov, K. Alimohammadi, E. Frantar, and D.-A. Alistarh, “L-GreCo: Layerwise-adaptive
gradient compression for efficient data-parallel deep learning,” in Proceedings
of Machine Learning and Systems , Athens, Greece, 2024, vol. 6.'
ista: 'Markov I, Alimohammadi K, Frantar E, Alistarh D-A. 2024. L-GreCo: Layerwise-adaptive
gradient compression for efficient data-parallel deep learning. Proceedings of
Machine Learning and Systems . MLSys: Machine Learning and Systems vol. 6.'
mla: 'Markov, Ilia, et al. “L-GreCo: Layerwise-Adaptive Gradient Compression for
Efficient Data-Parallel Deep Learning.” Proceedings of Machine Learning and
Systems , edited by P. Gibbons et al., vol. 6, Association for Computing Machinery,
2024.'
short: I. Markov, K. Alimohammadi, E. Frantar, D.-A. Alistarh, in:, P. Gibbons,
G. Pekhimenko, C. De Sa (Eds.), Proceedings of Machine Learning and Systems ,
Association for Computing Machinery, 2024.
conference:
end_date: 2024-04-22
location: Athens, Greece
name: 'MLSys: Machine Learning and Systems'
start_date: 2024-04-22
corr_author: '1'
das_tickbox: '0'
date_created: 2024-08-22T08:29:25Z
date_published: 2024-04-01T00:00:00Z
date_updated: 2026-10-02T11:20:05Z
day: '01'
ddc:
- '000'
department:
- _id: DaAl
editor:
- first_name: P.
full_name: Gibbons, P.
last_name: Gibbons
- first_name: G.
full_name: Pekhimenko, G.
last_name: Pekhimenko
- first_name: C.
full_name: De Sa, C.
last_name: De Sa
external_id:
arxiv:
- '2210.17357'
intvolume: ' 6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://proceedings.mlsys.org/paper_files/paper/2024/hash/9069a8976ff06f6443e7f4172990a580-Abstract-Conference.html
month: '04'
oa: 1
oa_version: Published Version
publication: 'Proceedings of Machine Learning and Systems '
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
related_material:
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researchdata_availability: no
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supplementarymaterial: no
title: 'L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel
deep learning'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 6
year: '2024'
...
---
OA_place: publisher
_id: '18766'
abstract:
- lang: eng
text: Poxviruses are large pleomorphic double-stranded DNA viruses that include
well known members such as variola virus, the causative agent of smallpox, Mpox
virus, as well as Vaccinia virus (VACV), which serves as a vaccination strain
for formerly mentioned viruses. VACV is a valuable model for studying large pleomorphic
DNA viruses in general and poxviruses specifically, as many features, such as
core morphology and structural proteins, are well conserved within this family.
Despite decades of research, our understanding of the structural components and
proteins that comprise the poxvirus core in mature virions remains limited. Although
major core proteins were identified via indirect experimental evidence, the core's
complexity, with its large size, structure and number of involved proteins, has
hindered efforts to achieve high-resolution insights and to define the roles of
the individual proteins. The specific protein composition of the core's individual
layers, including the palisade layer and the inner core wall, has remained unclear.
In this study, we have merged multiple approaches, including single particle cryo
electron microscopy of purified virus cores, cryo-electron tomography and subtomogram
averaging of mature virions and molecular modeling to elucidate the structural
determinants of the VACV core. Due to the lack of experimentally derived structures,
either in situ or reconstituted in vitro, we used Alphafold to predict models
of the putative major core protein candidates, A10, 23k, A3, A4, and L4. Our results
show that the VACV core is composed of several layers with varying local symmetries,
forming more intricate interactions than observed previously. This allowed us
to identify several molecular building blocks forming the viral core lattice.
In particular, we identified trimers of protein A10 as a major core structure
that forms the palisade layer of the viral core. Additionally, we revealed that
six petals of a flower shaped core pore within the core wall are composed of A10
trimers. Furthermore, we obtained a cryo-EM density for the inner core wall that
could potentially accommodate an A3 dimer. Integrating descriptions of protein
interactions from previous studies enabled us to provide a detailed structural
model of the poxvirus core wall, and our findings indicate that the interactions
within A10 trimers are likely consistent across orthopox- and parapoxviruses.
This combined application of cryo-SPA and cryo-ET can help overcome obstacles
in studying complex virus structures in the future, including their key assembly
proteins, interactions, and the formation into a core lattice. Our work provides
important fundamental new insights into poxvirus core architecture, also considering
the recent re-emergence of poxviruses.
acknowledged_ssus:
- _id: EM-Fac
- _id: LifeSc
- _id: ScienComp
acknowledgement: "This work was funded by the Austrian Science Fund (FWF) grant P31445
and ISTA. I\r\nwould like to express my gratitude to the Scientific Service Units,
particularly the Lab\r\nSupport Facility, the Scientific Computing Facility and
the Electron Microscopy Facility\r\nfor their tremendous support. I want to especially
thank Alois for assisting me with the\r\ninstallation of countless new software
and for troubleshooting cluster issues. A special\r\nthanks goes to Valentin for
his outstanding support in cryo-EM data acquisition and\r\nhis ongoing help in improving
the process to ensure that I obtained the best possible\r\ndata from my sample."
alternative_title:
- ISTA thesis
article_processing_charge: No
author:
- first_name: Julia
full_name: Datler, Julia
id: 3B12E2E6-F248-11E8-B48F-1D18A9856A87
last_name: Datler
orcid: 0000-0002-3616-8580
citation:
ama: Datler J. Elucidating the structural determinants of the poxvirus core using
multi-modal cryo-EM. 2024. doi:10.15479/at:ista:18766
apa: Datler, J. (2024). Elucidating the structural determinants of the poxvirus
core using multi-modal cryo-EM. Institute of Science and Technology Austria.
https://doi.org/10.15479/at:ista:18766
chicago: Datler, Julia. “Elucidating the Structural Determinants of the Poxvirus
Core Using Multi-Modal Cryo-EM.” Institute of Science and Technology Austria,
2024. https://doi.org/10.15479/at:ista:18766.
ieee: J. Datler, “Elucidating the structural determinants of the poxvirus core using
multi-modal cryo-EM,” Institute of Science and Technology Austria, 2024.
ista: Datler J. 2024. Elucidating the structural determinants of the poxvirus core
using multi-modal cryo-EM. Institute of Science and Technology Austria.
mla: Datler, Julia. Elucidating the Structural Determinants of the Poxvirus Core
Using Multi-Modal Cryo-EM. Institute of Science and Technology Austria, 2024,
doi:10.15479/at:ista:18766.
short: J. Datler, Elucidating the Structural Determinants of the Poxvirus Core Using
Multi-Modal Cryo-EM, Institute of Science and Technology Austria, 2024.
corr_author: '1'
das_tickbox: '1'
date_created: 2025-01-07T10:23:12Z
date_published: 2024-12-30T00:00:00Z
date_updated: 2026-10-02T11:21:19Z
day: '30'
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degree_awarded: PhD
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- _id: FlSc
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keyword:
- cryo-EM
- cryo-ET
- cryo-SPA
- Structural Virology
- Poxvirus
- Vaccinia Virus
- Structural Biology
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
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project:
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call_identifier: FWF
grant_number: P31445
name: Structural conservation and diversity in retroviral capsid
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- 978-3-99078-049-7
issn:
- 2663-337X
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researchdata_availability: yes
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supervisor:
- first_name: Florian KM
full_name: Schur, Florian KM
id: 48AD8942-F248-11E8-B48F-1D18A9856A87
last_name: Schur
orcid: 0000-0003-4790-8078
supplementarymaterial: no
title: Elucidating the structural determinants of the poxvirus core using multi-modal
cryo-EM
tmp:
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legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
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short: CC BY-NC-ND (4.0)
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user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
APC_amount: 11700 EUR
OA_place: publisher
OA_type: hybrid
_id: '14979'
abstract:
- lang: eng
text: Poxviruses are among the largest double-stranded DNA viruses, with members
such as variola virus, monkeypox virus and the vaccination strain vaccinia virus
(VACV). Knowledge about the structural proteins that form the viral core has remained
sparse. While major core proteins have been annotated via indirect experimental
evidence, their structures have remained elusive and they could not be assigned
to individual core features. Hence, which proteins constitute which layers of
the core, such as the palisade layer and the inner core wall, has remained enigmatic.
Here we show, using a multi-modal cryo-electron microscopy (cryo-EM) approach
in combination with AlphaFold molecular modeling, that trimers formed by the cleavage
product of VACV protein A10 are the key component of the palisade layer. This
allows us to place previously obtained descriptions of protein interactions within
the core wall into perspective and to provide a detailed model of poxvirus core
architecture. Importantly, we show that interactions within A10 trimers are likely
generalizable over members of orthopox- and parapoxviruses.
acknowledged_ssus:
- _id: ScienComp
- _id: LifeSc
- _id: EM-Fac
acknowledgement: "We thank A. Bergthaler (Research Center for Molecular Medicine of
the Austrian Academy of Sciences) for providing VACV WR. We thank A. Nicholas and
his team at the ISTA proteomics facility, and S. Elefante at the ISTA Scientific
Computing facility for their support. We also thank F. Fäßler, D. Porley, T. Muthspiel
and other members of the Schur group for support and helpful discussions. We also
thank D. Castaño-Díez for support with Dynamo. We thank D. Farrell for his help
optimizing the Rosetta protocol to refine the atomic model into the cryo-EM map
with symmetry.\r\n\r\nF.K.M.S. acknowledges support from ISTA and EMBO. F.K.M.S.
also received support from the Austrian Science Fund (FWF) grant P31445. This publication
has been made possible in part by CZI grant DAF2021-234754 and grant https://doi.org/10.37921/812628ebpcwg
from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community
Foundation (funder https://doi.org/10.13039/100014989) awarded to F.K.M.S.\r\n\r\nThis
research was also supported by the Scientific Service Units (SSUs) of ISTA through
resources provided by Scientific Computing (SciComp), the Life Science Facility
(LSF), and the Electron Microscopy Facility (EMF). We also acknowledge the use of
COSMIC45 and Colabfold46."
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Julia
full_name: Datler, Julia
id: 3B12E2E6-F248-11E8-B48F-1D18A9856A87
last_name: Datler
orcid: 0000-0002-3616-8580
- first_name: Jesse
full_name: Hansen, Jesse
id: 1063c618-6f9b-11ec-9123-f912fccded63
last_name: Hansen
orcid: 0000-0001-7967-2085
- first_name: Andreas
full_name: Thader, Andreas
id: 3A18A7B8-F248-11E8-B48F-1D18A9856A87
last_name: Thader
- first_name: Alois
full_name: Schlögl, Alois
id: 45BF87EE-F248-11E8-B48F-1D18A9856A87
last_name: Schlögl
orcid: 0000-0002-5621-8100
- first_name: Lukas W
full_name: Bauer, Lukas W
id: 0c894dcf-897b-11ed-a09c-8186353224b0
last_name: Bauer
- first_name: Victor-Valentin
full_name: Hodirnau, Victor-Valentin
id: 3661B498-F248-11E8-B48F-1D18A9856A87
last_name: Hodirnau
orcid: 0000-0003-3904-947X
- first_name: Florian KM
full_name: Schur, Florian KM
id: 48AD8942-F248-11E8-B48F-1D18A9856A87
last_name: Schur
orcid: 0000-0003-4790-8078
citation:
ama: Datler J, Hansen J, Thader A, et al. Multi-modal cryo-EM reveals trimers of
protein A10 to form the palisade layer in poxvirus cores. Nature Structural
& Molecular Biology. 2024;31:1114-1123. doi:10.1038/s41594-023-01201-6
apa: Datler, J., Hansen, J., Thader, A., Schlögl, A., Bauer, L. W., Hodirnau, V.-V.,
& Schur, F. K. (2024). Multi-modal cryo-EM reveals trimers of protein A10
to form the palisade layer in poxvirus cores. Nature Structural & Molecular
Biology. Springer Nature. https://doi.org/10.1038/s41594-023-01201-6
chicago: Datler, Julia, Jesse Hansen, Andreas Thader, Alois Schlögl, Lukas W Bauer,
Victor-Valentin Hodirnau, and Florian KM Schur. “Multi-Modal Cryo-EM Reveals Trimers
of Protein A10 to Form the Palisade Layer in Poxvirus Cores.” Nature Structural
& Molecular Biology. Springer Nature, 2024. https://doi.org/10.1038/s41594-023-01201-6.
ieee: J. Datler et al., “Multi-modal cryo-EM reveals trimers of protein A10
to form the palisade layer in poxvirus cores,” Nature Structural & Molecular
Biology, vol. 31. Springer Nature, pp. 1114–1123, 2024.
ista: Datler J, Hansen J, Thader A, Schlögl A, Bauer LW, Hodirnau V-V, Schur FK.
2024. Multi-modal cryo-EM reveals trimers of protein A10 to form the palisade
layer in poxvirus cores. Nature Structural & Molecular Biology. 31, 1114–1123.
mla: Datler, Julia, et al. “Multi-Modal Cryo-EM Reveals Trimers of Protein A10 to
Form the Palisade Layer in Poxvirus Cores.” Nature Structural & Molecular
Biology, vol. 31, Springer Nature, 2024, pp. 1114–23, doi:10.1038/s41594-023-01201-6.
short: J. Datler, J. Hansen, A. Thader, A. Schlögl, L.W. Bauer, V.-V. Hodirnau,
F.K. Schur, Nature Structural & Molecular Biology 31 (2024) 1114–1123.
corr_author: '1'
das_tickbox: '1'
dataavailabilitystatement: "The electron microscopy density maps of the A10 trimer
and the hexameric flower-shaped pore, the subtomogram average of the palisade layer
and representative tomograms for complete viruses as well as isolated cores have
been deposited in the Electron Microscopy Data Bank under accession codes: EMD-17410,
EMD-17411, EMD-17412, EMD-17413, EMD-17414 and EMD-18452.\r\n\r\nThe refined model
of the A10 trimer has been deposited in the Protein Data Bank accession code: PDB
8P4K.\r\n\r\nThe UniProt codes of VACV core proteins used for structure prediction
are: A10 (P16715), A3 (P06440), A4 (P29191) and L4 (P03295).\r\n\r\nGenBank Protein
IDs of variola virus A10 (ABF23487.1), monkeypox virus A10 (YP_010377118.1), rabbitpox
virus A10 (AAS49831.1), cowpox virus A10 (ADZ29251.1), ectromelia virus A10 (NP_671631.1),
orf virus P4a (AY386264.1), Amsacta moorei entomopoxvirus AMV139 (NP_064921.1) and
Melanoplus sanguinipes entomopoxvirus putative core protein P4a homolog (AF063866.1)
were used for protein sequence alignment. Source data for MS experiments are provided
as Supplementary Information accompanying this manuscript."
date_created: 2024-02-12T09:59:45Z
date_published: 2024-07-01T00:00:00Z
date_updated: 2026-10-02T11:21:19Z
day: '01'
ddc:
- '570'
department:
- _id: FlSc
- _id: ScienComp
- _id: EM-Fac
doi: 10.1038/s41594-023-01201-6
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related_material:
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- description: News on ISTA Website
relation: press_release
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supplementarymaterial: yes
title: Multi-modal cryo-EM reveals trimers of protein A10 to form the palisade layer
in poxvirus cores
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
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