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full_name: Chiossi, Heloisa
id: 2BBA502C-F248-11E8-B48F-1D18A9856A87
last_name: Chiossi
citation:
ama: Chiossi HSC. Adaptive hierarchical representations in the hippocampus. 2024.
doi:10.15479/at:ista:14821
apa: Chiossi, H. S. C. (2024). Adaptive hierarchical representations in the hippocampus.
Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:14821
chicago: Chiossi, Heloisa S. C. “Adaptive Hierarchical Representations in the Hippocampus.”
Institute of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:14821.
ieee: H. S. C. Chiossi, “Adaptive hierarchical representations in the hippocampus,”
Institute of Science and Technology Austria, 2024.
ista: Chiossi HSC. 2024. Adaptive hierarchical representations in the hippocampus.
Institute of Science and Technology Austria.
mla: Chiossi, Heloisa S. C. Adaptive Hierarchical Representations in the Hippocampus.
Institute of Science and Technology Austria, 2024, doi:10.15479/at:ista:14821.
short: H.S.C. Chiossi, Adaptive Hierarchical Representations in the Hippocampus,
Institute of Science and Technology Austria, 2024.
date_created: 2024-01-16T14:25:21Z
date_published: 2024-01-19T00:00:00Z
date_updated: 2024-02-01T09:50:29Z
day: '19'
ddc:
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degree_awarded: PhD
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- _id: JoCs
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page: '89'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication_identifier:
issn:
- 2663 - 337X
publication_status: published
publisher: Institute of Science and Technology Austria
status: public
supervisor:
- first_name: Jozsef L
full_name: Csicsvari, Jozsef L
id: 3FA14672-F248-11E8-B48F-1D18A9856A87
last_name: Csicsvari
orcid: 0000-0002-5193-4036
title: Adaptive hierarchical representations in the hippocampus
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2024'
...
---
_id: '15020'
abstract:
- lang: eng
text: "This thesis consists of four distinct pieces of work within theoretical biology,
with two themes in common: the concept of optimization in biological systems,
and the use of information-theoretic tools to quantify biological stochasticity
and statistical uncertainty.\r\nChapter 2 develops a statistical framework for
studying biological systems which we believe to be optimized for a particular
utility function, such as retinal neurons conveying information about visual stimuli.
We formalize such beliefs as maximum-entropy Bayesian priors, constrained by the
expected utility. We explore how such priors aid inference of system parameters
with limited data and enable optimality hypothesis testing: is the utility higher
than by chance?\r\nChapter 3 examines the ultimate biological optimization process:
evolution by natural selection. As some individuals survive and reproduce more
successfully than others, populations evolve towards fitter genotypes and phenotypes.
We formalize this as accumulation of genetic information, and use population genetics
theory to study how much such information can be accumulated per generation and
maintained in the face of random mutation and genetic drift. We identify the population
size and fitness variance as the key quantities that control information accumulation
and maintenance.\r\nChapter 4 reuses the concept of genetic information from Chapter
3, but from a different perspective: we ask how much genetic information organisms
actually need, in particular in the context of gene regulation. For example, how
much information is needed to bind transcription factors at correct locations
within the genome? Population genetics provides us with a refined answer: with
an increasing population size, populations achieve higher fitness by maintaining
more genetic information. Moreover, regulatory parameters experience selection
pressure to optimize the fitness-information trade-off, i.e. minimize the information
needed for a given fitness. This provides an evolutionary derivation of the optimization
priors introduced in Chapter 2.\r\nChapter 5 proves an upper bound on mutual information
between a signal and a communication channel output (such as neural activity).
Mutual information is an important utility measure for biological systems, but
its practical use can be difficult due to the large dimensionality of many biological
channels. Sometimes, a lower bound on mutual information is computed by replacing
the high-dimensional channel outputs with decodes (signal estimates). Our result
provides a corresponding upper bound, provided that the decodes are the maximum
posterior estimates of the signal."
acknowledged_ssus:
- _id: ScienComp
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Michal
full_name: Hledik, Michal
id: 4171253A-F248-11E8-B48F-1D18A9856A87
last_name: Hledik
citation:
ama: Hledik M. Genetic information and biological optimization. 2024. doi:10.15479/at:ista:15020
apa: Hledik, M. (2024). Genetic information and biological optimization.
Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:15020
chicago: Hledik, Michal. “Genetic Information and Biological Optimization.” Institute
of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:15020.
ieee: M. Hledik, “Genetic information and biological optimization,” Institute of
Science and Technology Austria, 2024.
ista: Hledik M. 2024. Genetic information and biological optimization. Institute
of Science and Technology Austria.
mla: Hledik, Michal. Genetic Information and Biological Optimization. Institute
of Science and Technology Austria, 2024, doi:10.15479/at:ista:15020.
short: M. Hledik, Genetic Information and Biological Optimization, Institute of
Science and Technology Austria, 2024.
date_created: 2024-02-23T14:02:04Z
date_published: 2024-02-23T00:00:00Z
date_updated: 2025-06-30T13:21:09Z
day: '23'
ddc:
- '576'
- '519'
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- _id: NiBa
- _id: GaTk
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keyword:
- Theoretical biology
- Optimality
- Evolution
- Information
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oa: 1
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page: '158'
project:
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grant_number: '665385'
name: International IST Doctoral Program
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name: Can evolution minimize spurious signaling crosstalk to reach optimal performance?
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publisher: Institute of Science and Technology Austria
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status: public
supervisor:
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
title: Genetic information and biological optimization
type: dissertation
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...
---
_id: '14711'
abstract:
- lang: eng
text: "In nature, different species find their niche in a range of environments,
each with its unique characteristics. While some thrive in uniform (homogeneous)
landscapes where environmental conditions stay relatively consistent across space,
others traverse the complexities of spatially heterogeneous terrains. Comprehending
how species are distributed and how they interact within these landscapes holds
the key to gaining insights into their evolutionary dynamics while also informing
conservation and management strategies.\r\n\r\nFor species inhabiting heterogeneous
landscapes, when the rate of dispersal is low compared to spatial fluctuations
in selection pressure, localized adaptations may emerge. Such adaptation in response
to varying selection strengths plays an important role in the persistence of populations
in our rapidly changing world. Hence, species in nature are continuously in a
struggle to adapt to local environmental conditions, to ensure their continued
survival. Natural populations can often adapt in time scales short enough for
evolutionary changes to influence ecological dynamics and vice versa, thereby
creating a feedback between evolution and demography. The analysis of this feedback
and the relative contributions of gene flow, demography, drift, and natural selection
to genetic variation and differentiation has remained a recurring theme in evolutionary
biology. Nevertheless, the effective role of these forces in maintaining variation
and shaping patterns of diversity is not fully understood. Even in homogeneous
environments devoid of local adaptations, such understanding remains elusive.
Understanding this feedback is crucial, for example in determining the conditions
under which extinction risk can be mitigated in peripheral populations subject
to deleterious mutation accumulation at the edges of species’ ranges\r\nas well
as in highly fragmented populations.\r\n\r\nIn this thesis we explore both uniform
and spatially heterogeneous metapopulations, investigating and providing theoretical
insights into the dynamics of local adaptation in the latter and examining the
dynamics of load and extinction as well as the impact of joint ecological and
evolutionary (eco-evolutionary) dynamics in the former. The thesis is divided
into 5 chapters.\r\n\r\nChapter 1 provides a general introduction into the subject
matter, clarifying concepts and ideas used throughout the thesis. In chapter 2,
we explore how fast a species distributed across a heterogeneous landscape adapts
to changing conditions marked by alterations in carrying capacity, selection pressure,
and migration rate.\r\n\r\nIn chapter 3, we investigate how migration selection
and drift influences adaptation and the maintenance of variation in a metapopulation
with three habitats, an extension of previous models of adaptation in two habitats.
We further develop analytical approximations for the critical threshold required
for polymorphism to persist.\r\n\r\nThe focus of chapter 4 of the thesis is on
understanding the interplay between ecology and evolution as coupled processes.
We investigate how eco-evolutionary feedback between migration, selection, drift,
and demography influences eco-evolutionary outcomes in marginal populations subject
to deleterious mutation accumulation. Using simulations as well as theoretical
approximations of the coupled dynamics of population size and allele frequency,
we analyze how gene flow from a large mainland source influences genetic load
and population size on an island (i.e., in a marginal population) under genetically
realistic assumptions. Analyses of this sort are important because small isolated
populations, are repeatedly affected by complex interactions between ecological
and evolutionary processes, which can lead to their death. Understanding these
interactions can therefore provide an insight into the conditions under which
extinction risk can be mitigated in peripheral populations thus, contributing
to conservation and restoration efforts.\r\n\r\nChapter 5 extends the analysis
in chapter 4 to consider the dynamics of load (due to deleterious mutation accumulation)
and extinction risk in a metapopulation. We explore the role of gene flow, selection,
and dominance on load and extinction risk and further pinpoint critical thresholds
required for metapopulation persistence.\r\n\r\nOverall this research contributes
to our understanding of ecological and evolutionary mechanisms that shape species’
persistence in fragmented landscapes, a crucial foundation for successful conservation
efforts and biodiversity management."
acknowledged_ssus:
- _id: SSU
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Oluwafunmilola O
full_name: Olusanya, Oluwafunmilola O
id: 41AD96DC-F248-11E8-B48F-1D18A9856A87
last_name: Olusanya
orcid: 0000-0003-1971-8314
date_created: 2023-12-26T22:49:53Z
date_published: 2024-01-19T00:00:00Z
date_updated: 2025-05-26T09:05:10Z
day: '19'
ddc:
- '576'
degree_awarded: MS
department:
- _id: NiBa
- _id: GradSch
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ec_funded: 1
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creator: oolusany
date_created: 2024-01-03T18:30:13Z
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language:
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month: '01'
oa: 1
oa_version: Published Version
page: '183'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: c08d3278-5a5b-11eb-8a69-fdb09b55f4b8
grant_number: P32896
name: Causes and consequences of population fragmentation
- _id: 34c872fe-11ca-11ed-8bc3-8534b82131e6
grant_number: '26380'
name: Polygenic Adaptation in a Metapopulation
publication_identifier:
issn:
- 2663 - 337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
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relation: part_of_dissertation
status: public
- id: '10787'
relation: part_of_dissertation
status: public
- id: '14732'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
- first_name: Jitka
full_name: Polechova, Jitka
last_name: Polechova
- first_name: Himani
full_name: Sachdeva, Himani
last_name: Sachdeva
title: Local adaptation, genetic load and extinction in metapopulations
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...
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_id: '12349'
abstract:
- lang: eng
text: Statistics of natural scenes are not uniform - their structure varies dramatically
from ground to sky. It remains unknown whether these non-uniformities are reflected
in the large-scale organization of the early visual system and what benefits such
adaptations would confer. Here, by relying on the efficient coding hypothesis,
we predict that changes in the structure of receptive fields across visual space
increase the efficiency of sensory coding. We show experimentally that, in agreement
with our predictions, receptive fields of retinal ganglion cells change their
shape along the dorsoventral retinal axis, with a marked surround asymmetry at
the visual horizon. Our work demonstrates that, according to principles of efficient
coding, the panoramic structure of natural scenes is exploited by the retina across
space and cell-types.
acknowledged_ssus:
- _id: ScienComp
- _id: PreCl
- _id: LifeSc
- _id: Bio
acknowledgement: We thank Hiroki Asari for sharing the dataset of naturalistic images,
Anton Sumser for sharing visual stimulus code, Yoav Ben Simon for initial explorative
work with the generation of AAVs, and Tomas Vega-Zuñiga for help with immunostainings.
We also thank Gasper Tkacik and members of the Neuroethology group for their comments
on the manuscript. This research was supported by the Scientific Service Units of
IST Austria through resources provided by Scientific Computing, the Preclinical
Facility, the Lab Support Facility, and the Imaging and Optics Facility. This work
was supported by European Union Horizon 2020 Marie Skłodowska-Curie grant 665385
(DG), Austrian Science Fund (FWF) stand-alone grant P 34015 (WM), Human Frontiers
Science Program LT000256/2018-L (AS), EMBO ALTF 1098-2017 (AS) and the European
Research Council Starting Grant 756502 (MJ).
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Divyansh
full_name: Gupta, Divyansh
id: 2A485EBE-F248-11E8-B48F-1D18A9856A87
last_name: Gupta
orcid: 0000-0001-7400-6665
- first_name: Wiktor F
full_name: Mlynarski, Wiktor F
id: 358A453A-F248-11E8-B48F-1D18A9856A87
last_name: Mlynarski
- first_name: Anton L
full_name: Sumser, Anton L
id: 3320A096-F248-11E8-B48F-1D18A9856A87
last_name: Sumser
orcid: 0000-0002-4792-1881
- first_name: Olga
full_name: Symonova, Olga
id: 3C0C7BC6-F248-11E8-B48F-1D18A9856A87
last_name: Symonova
orcid: 0000-0003-2012-9947
- first_name: Jan
full_name: Svaton, Jan
id: f7f724c3-9d6f-11ed-9f44-e5c5f3a5bee2
last_name: Svaton
orcid: 0000-0002-6198-2939
- first_name: Maximilian A
full_name: Jösch, Maximilian A
id: 2BD278E6-F248-11E8-B48F-1D18A9856A87
last_name: Jösch
orcid: 0000-0002-3937-1330
citation:
ama: Gupta D, Mlynarski WF, Sumser AL, Symonova O, Svaton J, Jösch MA. Panoramic
visual statistics shape retina-wide organization of receptive fields. Nature
Neuroscience. 2023;26:606-614. doi:10.1038/s41593-023-01280-0
apa: Gupta, D., Mlynarski, W. F., Sumser, A. L., Symonova, O., Svaton, J., &
Jösch, M. A. (2023). Panoramic visual statistics shape retina-wide organization
of receptive fields. Nature Neuroscience. Springer Nature. https://doi.org/10.1038/s41593-023-01280-0
chicago: Gupta, Divyansh, Wiktor F Mlynarski, Anton L Sumser, Olga Symonova, Jan
Svaton, and Maximilian A Jösch. “Panoramic Visual Statistics Shape Retina-Wide
Organization of Receptive Fields.” Nature Neuroscience. Springer Nature,
2023. https://doi.org/10.1038/s41593-023-01280-0.
ieee: D. Gupta, W. F. Mlynarski, A. L. Sumser, O. Symonova, J. Svaton, and M. A.
Jösch, “Panoramic visual statistics shape retina-wide organization of receptive
fields,” Nature Neuroscience, vol. 26. Springer Nature, pp. 606–614, 2023.
ista: Gupta D, Mlynarski WF, Sumser AL, Symonova O, Svaton J, Jösch MA. 2023. Panoramic
visual statistics shape retina-wide organization of receptive fields. Nature Neuroscience.
26, 606–614.
mla: Gupta, Divyansh, et al. “Panoramic Visual Statistics Shape Retina-Wide Organization
of Receptive Fields.” Nature Neuroscience, vol. 26, Springer Nature, 2023,
pp. 606–14, doi:10.1038/s41593-023-01280-0.
short: D. Gupta, W.F. Mlynarski, A.L. Sumser, O. Symonova, J. Svaton, M.A. Jösch,
Nature Neuroscience 26 (2023) 606–614.
date_created: 2023-01-23T14:14:19Z
date_published: 2023-04-01T00:00:00Z
date_updated: 2023-10-04T11:41:05Z
day: '01'
ddc:
- '570'
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- _id: GradSch
- _id: MaJö
doi: 10.1038/s41593-023-01280-0
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call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 626c45b5-2b32-11ec-9570-e509828c1ba6
grant_number: P34015
name: Efficient coding with biophysical realism
- _id: 2634E9D2-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '756502'
name: Circuits of Visual Attention
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grant_number: LT000256
name: Neuronal networks of salience and spatial detection in the murine superior
colliculus
- _id: 264FEA02-B435-11E9-9278-68D0E5697425
grant_number: ALTF 1098-2017
name: Connecting sensory with motor processing in the superior colliculus
publication: Nature Neuroscience
publication_identifier:
eissn:
- 1546-1726
issn:
- 1097-6256
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publisher: Springer Nature
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title: Panoramic visual statistics shape retina-wide organization of receptive fields
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user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
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...
---
_id: '12370'
abstract:
- lang: eng
text: 'Statistics of natural scenes are not uniform - their structure varies dramatically
from ground to sky. It remains unknown whether these non-uniformities are reflected
in the large-scale organization of the early visual system and what benefits such
adaptations would confer. Here, by relying on the efficient coding hypothesis,
we predict that changes in the structure of receptive fields across visual space
increase the efficiency of sensory coding. We show experimentally that, in agreement
with our predictions, receptive fields of retinal ganglion cells change their
shape along the dorsoventral retinal axis, with a marked surround asymmetry at
the visual horizon. Our work demonstrates that, according to principles of efficient
coding, the panoramic structure of natural scenes is exploited by the retina across
space and cell-types. '
acknowledged_ssus:
- _id: ScienComp
- _id: M-Shop
- _id: Bio
- _id: PreCl
- _id: LifeSc
article_processing_charge: No
author:
- first_name: Divyansh
full_name: Gupta, Divyansh
id: 2A485EBE-F248-11E8-B48F-1D18A9856A87
last_name: Gupta
orcid: 0000-0001-7400-6665
- first_name: Anton L
full_name: Sumser, Anton L
id: 3320A096-F248-11E8-B48F-1D18A9856A87
last_name: Sumser
orcid: 0000-0002-4792-1881
- first_name: Maximilian A
full_name: Jösch, Maximilian A
id: 2BD278E6-F248-11E8-B48F-1D18A9856A87
last_name: Jösch
orcid: 0000-0002-3937-1330
citation:
ama: 'Gupta D, Sumser AL, Jösch MA. Research Data for: Panoramic visual statistics
shape retina-wide organization of receptive fields. 2023. doi:10.15479/AT:ISTA:12370'
apa: 'Gupta, D., Sumser, A. L., & Jösch, M. A. (2023). Research Data for: Panoramic
visual statistics shape retina-wide organization of receptive fields. Institute
of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:12370'
chicago: 'Gupta, Divyansh, Anton L Sumser, and Maximilian A Jösch. “Research Data
for: Panoramic Visual Statistics Shape Retina-Wide Organization of Receptive Fields.”
Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/AT:ISTA:12370.'
ieee: 'D. Gupta, A. L. Sumser, and M. A. Jösch, “Research Data for: Panoramic visual
statistics shape retina-wide organization of receptive fields.” Institute of Science
and Technology Austria, 2023.'
ista: 'Gupta D, Sumser AL, Jösch MA. 2023. Research Data for: Panoramic visual statistics
shape retina-wide organization of receptive fields, Institute of Science and Technology
Austria, 10.15479/AT:ISTA:12370.'
mla: 'Gupta, Divyansh, et al. Research Data for: Panoramic Visual Statistics
Shape Retina-Wide Organization of Receptive Fields. Institute of Science and
Technology Austria, 2023, doi:10.15479/AT:ISTA:12370.'
short: D. Gupta, A.L. Sumser, M.A. Jösch, (2023).
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date_created: 2023-01-25T12:45:18Z
date_published: 2023-01-26T00:00:00Z
date_updated: 2023-10-04T11:41:04Z
day: '26'
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- _id: MaJö
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call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 626c45b5-2b32-11ec-9570-e509828c1ba6
grant_number: P34015
name: Efficient coding with biophysical realism
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call_identifier: H2020
grant_number: '756502'
name: Circuits of Visual Attention
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grant_number: LT000256
name: Neuronal networks of salience and spatial detection in the murine superior
colliculus
- _id: 264FEA02-B435-11E9-9278-68D0E5697425
grant_number: ALTF 1098-2017
name: Connecting sensory with motor processing in the superior colliculus
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '12349'
relation: used_in_publication
status: public
status: public
title: 'Research Data for: Panoramic visual statistics shape retina-wide organization
of receptive fields'
tmp:
image: /images/cc_by_nc_sa.png
legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC
BY-NC-SA 4.0)
short: CC BY-NC-SA (4.0)
type: research_data
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '12470'
abstract:
- lang: eng
text: "The brain is an exceptionally sophisticated organ consisting of billions
of cells and trillions of \r\nconnections that orchestrate our cognition and behavior.
To decode its complex connectivity, it is \r\npivotal to disentangle its intricate
architecture spanning from cm-sized circuits down to tens of \r\nnm-small synapses.\r\nTo
achieve this goal, I developed CATS – Comprehensive Analysis of nervous Tissue
across \r\nScales, a versatile toolbox for obtaining a holistic view of nervous
tissue context with (super\x02resolution) fluorescence microscopy. CATS combines
comprehensive labeling of the extracellular\r\nspace, that is compatible with
chemical fixation, with information on molecular markers, super\x02resolved data
acquisition and machine-learning based data analysis for segmentation and synapse
\r\nidentification.\r\nI used CATS to analyze key features of nervous tissue connectivity,
ranging from whole tissue \r\narchitecture, neuronal in- and output-fields, down
to synapse morphology.\r\nFocusing on the hippocampal circuitry, I quantified
synaptic transmission properties of mossy \r\nfiber boutons and analyzed the connectivity
pattern of dentate gyrus granule cells with CA3 \r\npyramidal neurons. This shows
that CATS is a viable tool to study hallmarks of neuronal \r\nconnectivity with
light microscopy."
acknowledged_ssus:
- _id: Bio
- _id: LifeSc
- _id: PreCl
- _id: EM-Fac
- _id: M-Shop
- _id: ScienComp
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Julia M
full_name: Michalska, Julia M
id: 443DB6DE-F248-11E8-B48F-1D18A9856A87
last_name: Michalska
orcid: 0000-0003-3862-1235
citation:
ama: Michalska JM. A versatile toolbox for the comprehensive analysis of nervous
tissue organization with light microscopy. 2023. doi:10.15479/at:ista:12470
apa: Michalska, J. M. (2023). A versatile toolbox for the comprehensive analysis
of nervous tissue organization with light microscopy. Institute of Science
and Technology Austria. https://doi.org/10.15479/at:ista:12470
chicago: Michalska, Julia M. “A Versatile Toolbox for the Comprehensive Analysis
of Nervous Tissue Organization with Light Microscopy.” Institute of Science and
Technology Austria, 2023. https://doi.org/10.15479/at:ista:12470.
ieee: J. M. Michalska, “A versatile toolbox for the comprehensive analysis of nervous
tissue organization with light microscopy,” Institute of Science and Technology
Austria, 2023.
ista: Michalska JM. 2023. A versatile toolbox for the comprehensive analysis of
nervous tissue organization with light microscopy. Institute of Science and Technology
Austria.
mla: Michalska, Julia M. A Versatile Toolbox for the Comprehensive Analysis of
Nervous Tissue Organization with Light Microscopy. Institute of Science and
Technology Austria, 2023, doi:10.15479/at:ista:12470.
short: J.M. Michalska, A Versatile Toolbox for the Comprehensive Analysis of Nervous
Tissue Organization with Light Microscopy, Institute of Science and Technology
Austria, 2023.
date_created: 2023-01-31T15:10:53Z
date_published: 2023-01-09T00:00:00Z
date_updated: 2023-08-31T12:26:58Z
day: '09'
ddc:
- '610'
degree_awarded: PhD
department:
- _id: GradSch
- _id: JoDa
doi: 10.15479/at:ista:12470
ec_funded: 1
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creator: cchlebak
date_created: 2023-01-31T15:11:42Z
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has_accepted_license: '1'
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
page: '201'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 26AA4EF2-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: W1232-B24
name: Molecular Drug Targets
publication_identifier:
isbn:
- ' 978-3-99078-026-8'
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '11943'
relation: part_of_dissertation
status: public
- id: '11950'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Johann G
full_name: Danzl, Johann G
id: 42EFD3B6-F248-11E8-B48F-1D18A9856A87
last_name: Danzl
orcid: 0000-0001-8559-3973
title: A versatile toolbox for the comprehensive analysis of nervous tissue organization
with light microscopy
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: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2023'
...
---
_id: '12826'
abstract:
- lang: eng
text: "During navigation, animals can infer the structure of the environment by
computing the optic flow cues elicited by their own movements, and subsequently
use this information to instruct proper locomotor actions. These computations
require a panoramic assessment of the visual environment in order to disambiguate
similar sensory experiences that may require distinct behavioral responses. The
estimation of the global motion patterns is therefore essential for successful
navigation. Yet, our understanding of the algorithms and implementations that
enable coherent panoramic visual perception remains scarce. Here I pursue this
problem by dissecting the functional aspects of interneuronal communication in
the lobula plate tangential cell network in Drosophila melanogaster. The results
presented in the thesis demonstrate that the basis for effective interpretation
of the optic flow in this circuit are stereotyped synaptic connections that mediate
the formation of distinct subnetworks, each extracting a particular pattern of
global motion. \r\nFirstly, I show that gap junctions are essential for a correct
interpretation of binocular motion cues by horizontal motion-sensitive cells.
HS cells form electrical synapses with contralateral H2 neurons that are involved
in detecting yaw rotation and translation. I developed an FlpStop-mediated mutant
of a gap junction protein ShakB that disrupts these electrical synapses. While
the loss of electrical synapses does not affect the tuning of the direction selectivity
in HS neurons, it severely alters their sensitivity to horizontal motion in the
contralateral side. These physiological changes result in an inappropriate integration
of binocular motion cues in walking animals. While wild-type flies form a binocular
perception of visual motion by non-linear integration of monocular optic flow
cues, the mutant flies sum the monocular inputs linearly. These results indicate
that rather than averaging signals in neighboring neurons, gap-junctions operate
in conjunction with chemical synapses to mediate complex non-linear optic flow
computations.\r\nSecondly, I show that stochastic manipulation of neuronal activity
in the lobula plate tangential cell network is a powerful approach to study the
neuronal implementation of optic flow-based navigation in flies. Tangential neurons
form multiple subnetworks, each mediating course-stabilizing response to a particular
global pattern of visual motion. Application of genetic mosaic techniques can
provide sparse optogenetic activation of HS cells in numerous combinations. These
distinct combinations of activated neurons drive an array of distinct behavioral
responses, providing important insights into how visuomotor transformation is
performed in the lobula plate tangential cell network. This approach can be complemented
by stochastic silencing of tangential neurons, enabling direct assessment of the
functional role of individual tangential neurons in the processing of specific
visual motion patterns.\r\n\tTaken together, the findings presented in this thesis
suggest that establishing specific activity patterns of tangential cells via stereotyped
synaptic connectivity is a key to efficient optic flow-based navigation in Drosophila
melanogaster."
acknowledged_ssus:
- _id: Bio
- _id: LifeSc
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Victoria
full_name: Pokusaeva, Victoria
id: 3184041C-F248-11E8-B48F-1D18A9856A87
last_name: Pokusaeva
orcid: 0000-0001-7660-444X
citation:
ama: Pokusaeva V. Neural control of optic flow-based navigation in Drosophila melanogaster.
2023. doi:10.15479/at:ista:12826
apa: Pokusaeva, V. (2023). Neural control of optic flow-based navigation in Drosophila
melanogaster. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:12826
chicago: Pokusaeva, Victoria. “Neural Control of Optic Flow-Based Navigation in
Drosophila Melanogaster.” Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/at:ista:12826.
ieee: V. Pokusaeva, “Neural control of optic flow-based navigation in Drosophila
melanogaster,” Institute of Science and Technology Austria, 2023.
ista: Pokusaeva V. 2023. Neural control of optic flow-based navigation in Drosophila
melanogaster. Institute of Science and Technology Austria.
mla: Pokusaeva, Victoria. Neural Control of Optic Flow-Based Navigation in Drosophila
Melanogaster. Institute of Science and Technology Austria, 2023, doi:10.15479/at:ista:12826.
short: V. Pokusaeva, Neural Control of Optic Flow-Based Navigation in Drosophila
Melanogaster, Institute of Science and Technology Austria, 2023.
date_created: 2023-04-14T14:56:04Z
date_published: 2023-04-18T00:00:00Z
date_updated: 2023-06-23T09:47:36Z
day: '18'
ddc:
- '570'
- '571'
degree_awarded: PhD
department:
- _id: MaJö
- _id: GradSch
doi: 10.15479/at:ista:12826
ec_funded: 1
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file_size: 14507243
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date_updated: 2023-04-20T09:14:44Z
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file_size: 10090711
relation: main_file
success: 1
file_date_updated: 2023-04-20T09:26:51Z
has_accepted_license: '1'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
page: '106'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication_identifier:
issn:
- 2663 - 337X
publication_status: published
publisher: Institute of Science and Technology Austria
status: public
supervisor:
- first_name: Maximilian A
full_name: Jösch, Maximilian A
id: 2BD278E6-F248-11E8-B48F-1D18A9856A87
last_name: Jösch
orcid: 0000-0002-3937-1330
title: Neural control of optic flow-based navigation in Drosophila melanogaster
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: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2023'
...
---
_id: '12885'
abstract:
- lang: eng
text: 'High-performance semiconductors rely upon precise control of heat and charge
transport. This can be achieved by precisely engineering defects in polycrystalline
solids. There are multiple approaches to preparing such polycrystalline semiconductors,
and the transformation of solution-processed colloidal nanoparticles is appealing
because colloidal nanoparticles combine low cost with structural and compositional
tunability along with rich surface chemistry. However, the multiple processes
from nanoparticle synthesis to the final bulk nanocomposites are very complex.
They involve nanoparticle purification, post-synthetic modifications, and finally
consolidation (thermal treatments and densification). All these properties dictate
the final material’s composition and microstructure, ultimately affecting its
functional properties. This thesis explores the synthesis, surface chemistry and
consolidation of colloidal semiconductor nanoparticles into dense solids. In particular,
the transformations that take place during these processes, and their effect on
the material’s transport properties are evaluated. '
acknowledged_ssus:
- _id: EM-Fac
- _id: NanoFab
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Mariano
full_name: Calcabrini, Mariano
id: 45D7531A-F248-11E8-B48F-1D18A9856A87
last_name: Calcabrini
orcid: 0000-0003-4566-5877
citation:
ama: 'Calcabrini M. Nanoparticle-based semiconductor solids: From synthesis to consolidation.
2023. doi:10.15479/at:ista:12885'
apa: 'Calcabrini, M. (2023). Nanoparticle-based semiconductor solids: From synthesis
to consolidation. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:12885'
chicago: 'Calcabrini, Mariano. “Nanoparticle-Based Semiconductor Solids: From Synthesis
to Consolidation.” Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/at:ista:12885.'
ieee: 'M. Calcabrini, “Nanoparticle-based semiconductor solids: From synthesis to
consolidation,” Institute of Science and Technology Austria, 2023.'
ista: 'Calcabrini M. 2023. Nanoparticle-based semiconductor solids: From synthesis
to consolidation. Institute of Science and Technology Austria.'
mla: 'Calcabrini, Mariano. Nanoparticle-Based Semiconductor Solids: From Synthesis
to Consolidation. Institute of Science and Technology Austria, 2023, doi:10.15479/at:ista:12885.'
short: 'M. Calcabrini, Nanoparticle-Based Semiconductor Solids: From Synthesis to
Consolidation, Institute of Science and Technology Austria, 2023.'
date_created: 2023-05-02T07:58:57Z
date_published: 2023-04-28T00:00:00Z
date_updated: 2023-08-14T07:25:26Z
day: '28'
ddc:
- '546'
- '541'
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month: '04'
oa: 1
oa_version: Published Version
page: '82'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication_identifier:
isbn:
- 978-3-99078-028-2
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '10806'
relation: part_of_dissertation
status: public
- id: '10042'
relation: part_of_dissertation
status: public
- id: '12237'
relation: part_of_dissertation
status: public
- id: '9118'
relation: part_of_dissertation
status: public
- id: '10123'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Maria
full_name: Ibáñez, Maria
id: 43C61214-F248-11E8-B48F-1D18A9856A87
last_name: Ibáñez
orcid: 0000-0001-5013-2843
title: 'Nanoparticle-based semiconductor solids: From synthesis to consolidation'
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2023'
...
---
_id: '12964'
abstract:
- lang: eng
text: "Pattern formation is of great importance for its contribution across different
biological behaviours. During developmental processes for example, patterns of
chemical gradients are\r\nestablished to determine cell fate and complex tissue
patterns emerge to define structures such\r\nas limbs and vascular networks. Patterns
are also seen in collectively migrating groups, for\r\ninstance traveling waves
of density emerging in moving animal flocks as well as collectively migrating
cells and tissues. To what extent these biological patterns arise spontaneously
through\r\nthe local interaction of individual constituents or are dictated by
higher level instructions is\r\nstill an open question however there is evidence
for the involvement of both types of process.\r\nWhere patterns arise spontaneously
there is a long standing interest in how far the interplay\r\nof mechanics, e.g.
force generation and deformation, and chemistry, e.g. gene regulation\r\nand signaling,
contributes to the behaviour. This is because many systems are able to both\r\nchemically
regulate mechanical force production and chemically sense mechanical deformation,\r\nforming
mechano-chemical feedback loops which can potentially become unstable towards\r\nspatio
and/or temporal patterning.\r\nWe work with experimental collaborators to investigate
the possibility that this type of\r\ninteraction drives pattern formation in biological
systems at different scales. We focus first on\r\ntissue-level ERK-density waves
observed during the wound healing response across different\r\nsystems where many
previous studies have proposed that patterns depend on polarized cell\r\nmigration
and arise from a mechanical flocking-like mechanism. By combining theory with\r\nmechanical
and optogenetic perturbation experiments on in vitro monolayers we instead find\r\nevidence
for mechanochemical pattern formation involving only scalar bilateral feedbacks\r\nbetween
ERK signaling and cell contraction. We perform further modeling and experiment\r\nto
study how this instability couples with polar cell migration in order to produce
a robust\r\nand efficient wound healing response. In a following chapter we implement
ERK-density\r\ncoupling and cell migration in a 2D active vertex model to investigate
the interaction of\r\nERK-density patterning with different tissue rheologies
and find that the spatio-temporal\r\ndynamics are able to both locally and globally
fluidize a tissue across the solid-fluid glass\r\ntransition. In a last chapter
we move towards lower spatial scales in the context of subcellular\r\npatterning
of the cell cytoskeleton where we investigate the transition between phases of\r\nspatially
homogeneous temporal oscillations and chaotic spatio-temporal patterning in the\r\ndynamics
of myosin and ROCK activities (a motor component of the actomyosin cytoskeleton\r\nand
its activator). Experimental evidence supports an intrinsic chemical oscillator
which we\r\nencode in a reaction model and couple to a contractile active gel
description of the cell cortex.\r\nThe model exhibits phases of chemical oscillations
and contractile spatial patterning which\r\nreproduce many features of the dynamics
seen in Drosophila oocyte epithelia in vivo. However,\r\nadditional pharmacological
perturbations to inhibit myosin contractility leaves the role of\r\ncontractile
instability unclear. We discuss alternative hypotheses and investigate the possibility\r\nof
reaction-diffusion instability."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Daniel R
full_name: Boocock, Daniel R
id: 453AF628-F248-11E8-B48F-1D18A9856A87
last_name: Boocock
orcid: 0000-0002-1585-2631
citation:
ama: Boocock DR. Mechanochemical pattern formation across biological scales. 2023.
doi:10.15479/at:ista:12964
apa: Boocock, D. R. (2023). Mechanochemical pattern formation across biological
scales. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:12964
chicago: Boocock, Daniel R. “Mechanochemical Pattern Formation across Biological
Scales.” Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/at:ista:12964.
ieee: D. R. Boocock, “Mechanochemical pattern formation across biological scales,”
Institute of Science and Technology Austria, 2023.
ista: Boocock DR. 2023. Mechanochemical pattern formation across biological scales.
Institute of Science and Technology Austria.
mla: Boocock, Daniel R. Mechanochemical Pattern Formation across Biological Scales.
Institute of Science and Technology Austria, 2023, doi:10.15479/at:ista:12964.
short: D.R. Boocock, Mechanochemical Pattern Formation across Biological Scales,
Institute of Science and Technology Austria, 2023.
date_created: 2023-05-15T14:52:36Z
date_published: 2023-05-17T00:00:00Z
date_updated: 2023-08-04T11:02:40Z
day: '17'
ddc:
- '530'
degree_awarded: PhD
department:
- _id: GradSch
- _id: EdHa
doi: 10.15479/at:ista:12964
ec_funded: 1
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date_updated: 2023-05-19T07:04:25Z
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date_created: 2023-05-17T13:39:53Z
date_updated: 2023-05-17T14:35:13Z
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language:
- iso: eng
month: '05'
oa_version: Published Version
page: '146'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication_identifier:
isbn:
- 978-3-99078-032-9
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '8602'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Edouard B
full_name: Hannezo, Edouard B
id: 3A9DB764-F248-11E8-B48F-1D18A9856A87
last_name: Hannezo
orcid: 0000-0001-6005-1561
title: Mechanochemical pattern formation across biological scales
tmp:
image: /images/cc_by_nc_sa.png
legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC
BY-NC-SA 4.0)
short: CC BY-NC-SA (4.0)
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2023'
...
---
_id: '13074'
abstract:
- lang: eng
text: "Deep learning has become an integral part of a large number of important
applications, and many of the recent breakthroughs have been enabled by the ability
to train very large models, capable to capture complex patterns and relationships
from the data. At the same time, the massive sizes of modern deep learning models
have made their deployment to smaller devices more challenging; this is particularly
important, as in many applications the users rely on accurate deep learning predictions,
but they only have access to devices with limited memory and compute power. One
solution to this problem is to prune neural networks, by setting as many of their
parameters as possible to zero, to obtain accurate sparse models with lower memory
footprint. Despite the great research progress in obtaining sparse models that
preserve accuracy, while satisfying memory and computational constraints, there
are still many challenges associated with efficiently training sparse models,
as well as understanding their generalization properties.\r\n\r\nThe focus of
this thesis is to investigate how the training process of sparse models can be
made more efficient, and to understand the differences between sparse and dense
models in terms of how well they can generalize to changes in the data distribution.
We first study a method for co-training sparse and dense models, at a lower cost
compared to regular training. With our method we can obtain very accurate sparse
networks, and dense models that can recover the baseline accuracy. Furthermore,
we are able to more easily analyze the differences, at prediction level, between
the sparse-dense model pairs. Next, we investigate the generalization properties
of sparse neural networks in more detail, by studying how well different sparse
models trained on a larger task can adapt to smaller, more specialized tasks,
in a transfer learning scenario. Our analysis across multiple pruning methods
and sparsity levels reveals that sparse models provide features that can transfer
similarly to or better than the dense baseline. However, the choice of the pruning
method plays an important role, and can influence the results when the features
are fixed (linear finetuning), or when they are allowed to adapt to the new task
(full finetuning). Using sparse models with fixed masks for finetuning on new
tasks has an important practical advantage, as it enables training neural networks
on smaller devices. However, one drawback of current pruning methods is that the
entire training cycle has to be repeated to obtain the initial sparse model, for
every sparsity target; in consequence, the entire training process is costly and
also multiple models need to be stored. In the last part of the thesis we propose
a method that can train accurate dense models that are compressible in a single
step, to multiple sparsity levels, without additional finetuning. Our method results
in sparse models that can be competitive with existing pruning methods, and which
can also successfully generalize to new tasks."
acknowledged_ssus:
- _id: ScienComp
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Elena-Alexandra
full_name: Peste, Elena-Alexandra
id: 32D78294-F248-11E8-B48F-1D18A9856A87
last_name: Peste
citation:
ama: Peste E-A. Efficiency and generalization of sparse neural networks. 2023. doi:10.15479/at:ista:13074
apa: Peste, E.-A. (2023). Efficiency and generalization of sparse neural networks.
Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:13074
chicago: Peste, Elena-Alexandra. “Efficiency and Generalization of Sparse Neural
Networks.” Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/at:ista:13074.
ieee: E.-A. Peste, “Efficiency and generalization of sparse neural networks,” Institute
of Science and Technology Austria, 2023.
ista: Peste E-A. 2023. Efficiency and generalization of sparse neural networks.
Institute of Science and Technology Austria.
mla: Peste, Elena-Alexandra. Efficiency and Generalization of Sparse Neural Networks.
Institute of Science and Technology Austria, 2023, doi:10.15479/at:ista:13074.
short: E.-A. Peste, Efficiency and Generalization of Sparse Neural Networks, Institute
of Science and Technology Austria, 2023.
date_created: 2023-05-23T17:07:53Z
date_published: 2023-05-23T00:00:00Z
date_updated: 2023-08-04T10:33:27Z
day: '23'
ddc:
- '000'
degree_awarded: PhD
department:
- _id: GradSch
- _id: DaAl
- _id: ChLa
doi: 10.15479/at:ista:13074
ec_funded: 1
file:
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date_updated: 2023-05-24T16:12:59Z
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file_name: PhD_Thesis_APeste.zip
file_size: 1658293
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has_accepted_license: '1'
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: '147'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _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: '11458'
relation: part_of_dissertation
status: public
- id: '13053'
relation: part_of_dissertation
status: public
- id: '12299'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
title: Efficiency and generalization of sparse neural networks
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2023'
...
---
_id: '13126'
abstract:
- lang: eng
text: Mapping the complex and dense arrangement of cells and their connectivity
in brain tissue demands nanoscale spatial resolution imaging. Super-resolution
optical microscopy excels at visualizing specific molecules and individual cells
but fails to provide tissue context. Here, we developed Comprehensive Analysis
of Tissues across Scales (CATS), a technology to densely map brain tissue architecture
from millimeter regional to nanometer synaptic scales in diverse chemically fixed
brain preparations, including rodent and human. CATS uses fixation-compatible
extracellular labeling and optical imaging, including stimulated emission depletion
or expansion microscopy, to comprehensively delineate cellular structures. It
enables three-dimensional reconstruction of single synapses and mapping of synaptic
connectivity by identification and analysis of putative synaptic cleft regions.
Applying CATS to the mouse hippocampal mossy fiber circuitry, we reconstructed
and quantified the synaptic input and output structure of identified neurons.
We furthermore demonstrate applicability to clinically derived human tissue samples,
including formalin-fixed paraffin-embedded routine diagnostic specimens, for visualizing
the cellular architecture of brain tissue in health and disease.
acknowledged_ssus:
- _id: ScienComp
- _id: Bio
- _id: PreCl
- _id: LifeSc
- _id: M-Shop
- _id: E-Lib
acknowledgement: "We thank Jakob Vorlaufer, Nathalie Agudelo-Dueñas, Wiebke Jahr,
Andreas Wartak for microscope maintenance and troubleshooting, Caroline Kreuzinger,
Anna Freeman, and Irene Erber for technical assistance and Matthias Tomschik for
support with obtaining human samples. We gratefully acknowledge Eder Miguel for
setting up webKnossos and Marek Šuplata for computational support and hardware control.
We are grateful to Ryuichi Shigemoto and Bernd Bickel for generous support, and
Michael Sixt and Scott Boyd (Stanford University) for discussions and critical reading
of the manuscript. PSD95-HaloTag mice were kindly provided by Seth Grant (University
of Edinburgh). We acknowledge expert support by IST Austria’s scientific computing,
imaging and optics, preclinical, and lab support facilities, and by the Library
and Miba machine shop.\r\nWe gratefully acknowledge funding by the following sources:
\r\nAustrian Science Fund (FWF) grant I3600-B27 (JGD)\r\nAustrian Science Fund (FWF)
grant DK W1232 (JGD, JMM)\r\nAustrian Science Fund (FWF) grant Z 312-B27, Wittgenstein
award (PJ)\r\nAustrian Science Funds (FWF) projects I4685-B, I6565-B (SYNABS) and
DOC 33-B27 (RH)\r\nGesellschaft für Forschungsförderung NÖ (NFB) grant LSC18-022
(JGD)\r\nEuropean Union’s Horizon 2020 research and innovation programme, European
Research Council (ERC) grant 715508 – REVERSEAUTISM (GN)\r\nEuropean Union’s Horizon
2020 research and innovation programme, European Research Council (ERC) grant 692692
– GIANTSYN (PJ)\r\nMarie Skłodowska-Curie Actions Fellowship GA no. 665385 under
the EU Horizon 2020 program (JMM, JL)\r\nMarie Skłodowska-Curie Actions Individual
Fellowship 101026635 under the EU Horizon 2020 program (JFW)"
article_processing_charge: No
author:
- first_name: Johann G
full_name: Danzl, Johann G
id: 42EFD3B6-F248-11E8-B48F-1D18A9856A87
last_name: Danzl
orcid: 0000-0001-8559-3973
citation:
ama: Danzl JG. Research data for the publication “Imaging brain tissue architecture
across millimeter to nanometer scales.” 2023. doi:10.15479/AT:ISTA:13126
apa: Danzl, J. G. (2023). Research data for the publication “Imaging brain tissue
architecture across millimeter to nanometer scales.” Institute of Science and
Technology Austria. https://doi.org/10.15479/AT:ISTA:13126
chicago: Danzl, Johann G. “Research Data for the Publication ‘Imaging Brain Tissue
Architecture across Millimeter to Nanometer Scales.’” Institute of Science and
Technology Austria, 2023. https://doi.org/10.15479/AT:ISTA:13126.
ieee: J. G. Danzl, “Research data for the publication ‘Imaging brain tissue architecture
across millimeter to nanometer scales.’” Institute of Science and Technology Austria,
2023.
ista: Danzl JG. 2023. Research data for the publication ‘Imaging brain tissue architecture
across millimeter to nanometer scales’, Institute of Science and Technology Austria,
10.15479/AT:ISTA:13126.
mla: Danzl, Johann G. Research Data for the Publication “Imaging Brain Tissue
Architecture across Millimeter to Nanometer Scales.” Institute of Science
and Technology Austria, 2023, doi:10.15479/AT:ISTA:13126.
short: J.G. Danzl, (2023).
contributor:
- first_name: Julia M
id: 443DB6DE-F248-11E8-B48F-1D18A9856A87
last_name: Michalska
- first_name: Julia
id: 46E28B80-F248-11E8-B48F-1D18A9856A87
last_name: Lyudchik
- first_name: Philipp
id: 39BDC62C-F248-11E8-B48F-1D18A9856A87
last_name: Velicky
orcid: 0000-0002-2340-7431
- first_name: Hana
id: ee3cb6ca-ec98-11ea-ae11-ff703e2254ed
last_name: Stefanickova
- first_name: Jake
id: 63836096-4690-11EA-BD4E-32803DDC885E
last_name: Watson
orcid: 0000-0002-8698-3823
- first_name: Alban
id: 9ac8f577-2357-11eb-997a-e566c5550886
last_name: Cenameri
- first_name: Christoph M
id: 4DF26D8C-F248-11E8-B48F-1D18A9856A87
last_name: Sommer
orcid: 0000-0003-1216-9105
- first_name: Nicole
id: 4CD6AAC6-F248-11E8-B48F-1D18A9856A87
last_name: Amberg
orcid: 0000-0002-3183-8207
- first_name: Alessandro
id: 41CB84B2-F248-11E8-B48F-1D18A9856A87
last_name: Venturino
orcid: 0000-0003-2356-9403
- first_name: Karl
last_name: Roessler
- first_name: Thomas
last_name: Czech
- first_name: Romana
last_name: Höftberger
- first_name: Sandra
id: 36ACD32E-F248-11E8-B48F-1D18A9856A87
last_name: Siegert
orcid: 0000-0001-8635-0877
- first_name: Gaia
id: 3E57A680-F248-11E8-B48F-1D18A9856A87
last_name: Novarino
orcid: 0000-0002-7673-7178
- first_name: Peter M
id: 353C1B58-F248-11E8-B48F-1D18A9856A87
last_name: Jonas
orcid: 0000-0001-5001-4804
date_created: 2023-06-07T07:15:12Z
date_published: 2023-08-04T00:00:00Z
date_updated: 2024-02-21T12:18:19Z
day: '04'
ddc:
- '610'
department:
- _id: JoDa
- _id: SaSi
- _id: GaNo
- _id: PeJo
- _id: Bio
- _id: RySh
doi: 10.15479/AT:ISTA:13126
ec_funded: 1
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- description: 'Original data for Fig. 5d, Fig. 5d (N2V) and Fig. 5f-i, provided
via an external link due to the large size (>10GB) of the datasets. '
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title: Research data for the publication "Imaging brain tissue architecture across
millimeter to nanometer scales"
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image: /images/cc_by_nc_sa.png
legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC
BY-NC-SA 4.0)
short: CC BY-NC-SA (4.0)
type: research_data
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
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...
---
_id: '13142'
abstract:
- lang: eng
text: Reinforcement learning has received much attention for learning controllers
of deterministic systems. We consider a learner-verifier framework for stochastic
control systems and survey recent methods that formally guarantee a conjunction
of reachability and safety properties. Given a property and a lower bound on the
probability of the property being satisfied, our framework jointly learns a control
policy and a formal certificate to ensure the satisfaction of the property with
a desired probability threshold. Both the control policy and the formal certificate
are continuous functions from states to reals, which are learned as parameterized
neural networks. While in the deterministic case, the certificates are invariant
and barrier functions for safety, or Lyapunov and ranking functions for liveness,
in the stochastic case the certificates are supermartingales. For certificate
verification, we use interval arithmetic abstract interpretation to bound the
expected values of neural network functions.
acknowledgement: This work was supported in part by the ERC-2020-AdG 101020093, ERC
CoG 863818 (FoRM-SMArt) and the European Union’s Horizon 2020 research and innovation
programme under the Marie Skłodowska-Curie Grant Agreement No. 665385.
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
citation:
ama: 'Chatterjee K, Henzinger TA, Lechner M, Zikelic D. A learner-verifier framework
for neural network controllers and certificates of stochastic systems. In: Tools
and Algorithms for the Construction and Analysis of Systems . Vol 13993. Springer
Nature; 2023:3-25. doi:10.1007/978-3-031-30823-9_1'
apa: 'Chatterjee, K., Henzinger, T. A., Lechner, M., & Zikelic, D. (2023). A
learner-verifier framework for neural network controllers and certificates of
stochastic systems. In Tools and Algorithms for the Construction and Analysis
of Systems (Vol. 13993, pp. 3–25). Paris, France: Springer Nature. https://doi.org/10.1007/978-3-031-30823-9_1'
chicago: Chatterjee, Krishnendu, Thomas A Henzinger, Mathias Lechner, and Dorde
Zikelic. “A Learner-Verifier Framework for Neural Network Controllers and Certificates
of Stochastic Systems.” In Tools and Algorithms for the Construction and Analysis
of Systems , 13993:3–25. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-30823-9_1.
ieee: K. Chatterjee, T. A. Henzinger, M. Lechner, and D. Zikelic, “A learner-verifier
framework for neural network controllers and certificates of stochastic systems,”
in Tools and Algorithms for the Construction and Analysis of Systems ,
Paris, France, 2023, vol. 13993, pp. 3–25.
ista: 'Chatterjee K, Henzinger TA, Lechner M, Zikelic D. 2023. A learner-verifier
framework for neural network controllers and certificates of stochastic systems.
Tools and Algorithms for the Construction and Analysis of Systems . TACAS: Tools
and Algorithms for the Construction and Analysis of Systems, LNCS, vol. 13993,
3–25.'
mla: Chatterjee, Krishnendu, et al. “A Learner-Verifier Framework for Neural Network
Controllers and Certificates of Stochastic Systems.” Tools and Algorithms for
the Construction and Analysis of Systems , vol. 13993, Springer Nature, 2023,
pp. 3–25, doi:10.1007/978-3-031-30823-9_1.
short: K. Chatterjee, T.A. Henzinger, M. Lechner, D. Zikelic, in:, Tools and Algorithms
for the Construction and Analysis of Systems , Springer Nature, 2023, pp. 3–25.
conference:
end_date: 2023-04-27
location: Paris, France
name: 'TACAS: Tools and Algorithms for the Construction and Analysis of Systems'
start_date: 2023-04-22
date_created: 2023-06-18T22:00:47Z
date_published: 2023-04-22T00:00:00Z
date_updated: 2025-07-14T09:09:52Z
day: '22'
ddc:
- '000'
department:
- _id: KrCh
- _id: ToHe
doi: 10.1007/978-3-031-30823-9_1
ec_funded: 1
file:
- access_level: open_access
checksum: 3d8a8bb24d211bc83360dfc2fd744307
content_type: application/pdf
creator: dernst
date_created: 2023-06-19T08:29:30Z
date_updated: 2023-06-19T08:29:30Z
file_id: '13150'
file_name: 2023_LNCS_Chatterjee.pdf
file_size: 528455
relation: main_file
success: 1
file_date_updated: 2023-06-19T08:29:30Z
has_accepted_license: '1'
intvolume: ' 13993'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
page: 3-25
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: 'Tools and Algorithms for the Construction and Analysis of Systems '
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783031308222'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: A learner-verifier framework for neural network controllers and certificates
of stochastic systems
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13993
year: '2023'
...
---
_id: '13267'
abstract:
- lang: eng
text: Three-dimensional (3D) reconstruction of living brain tissue down to an individual
synapse level would create opportunities for decoding the dynamics and structure–function
relationships of the brain’s complex and dense information processing network;
however, this has been hindered by insufficient 3D resolution, inadequate signal-to-noise
ratio and prohibitive light burden in optical imaging, whereas electron microscopy
is inherently static. Here we solved these challenges by developing an integrated
optical/machine-learning technology, LIONESS (live information-optimized nanoscopy
enabling saturated segmentation). This leverages optical modifications to stimulated
emission depletion microscopy in comprehensively, extracellularly labeled tissue
and previous information on sample structure via machine learning to simultaneously
achieve isotropic super-resolution, high signal-to-noise ratio and compatibility
with living tissue. This allows dense deep-learning-based instance segmentation
and 3D reconstruction at a synapse level, incorporating molecular, activity and
morphodynamic information. LIONESS opens up avenues for studying the dynamic functional
(nano-)architecture of living brain tissue.
acknowledged_ssus:
- _id: ScienComp
- _id: Bio
- _id: PreCl
- _id: E-Lib
- _id: LifeSc
- _id: M-Shop
acknowledgement: "We thank J. Vorlaufer, N. Agudelo and A. Wartak for microscope maintenance
and troubleshooting, C. Kreuzinger and A. Freeman for technical assistance, M. Šuplata
for hardware control support and M. Cunha dos Santos for initial exploration of
software. We\r\nthank P. Henderson for advice on deep-learning training and M. Sixt,
S. Boyd and T. Weiss for discussions and critical reading of the manuscript. L.
Lavis (Janelia Research Campus) generously provided the JF585-HaloTag ligand. We
acknowledge expert support by IST\r\nAustria’s scientific computing, imaging and
optics, preclinical, library and laboratory support facilities and by the Miba machine
shop. We gratefully acknowledge funding by the following sources: Austrian Science
Fund (F.W.F.) grant no. I3600-B27 (J.G.D.), grant no. DK W1232\r\n(J.G.D. and J.M.M.)
and grant no. Z 312-B27, Wittgenstein award (P.J.); the Gesellschaft für Forschungsförderung
NÖ grant no. LSC18-022 (J.G.D.); an ISTA Interdisciplinary project grant (J.G.D.
and B.B.); the European Union’s Horizon 2020 research and innovation programme,\r\nMarie-Skłodowska
Curie grant 665385 (J.M.M. and J.L.); the European Union’s Horizon 2020 research
and innovation programme, European Research Council grant no. 715767, MATERIALIZABLE
(B.B.); grant no. 715508, REVERSEAUTISM (G.N.); grant no. 695568, SYNNOVATE (S.G.N.G.);
and grant no. 692692, GIANTSYN (P.J.); the Simons\r\nFoundation Autism Research
Initiative grant no. 529085 (S.G.N.G.); the Wellcome Trust Technology Development
grant no. 202932 (S.G.N.G.); the Marie Skłodowska-Curie Actions Individual Fellowship
no. 101026635 under the EU Horizon 2020 program (J.F.W.);\r\nthe Human Frontier
Science Program postdoctoral fellowship LT000557/2018 (W.J.); and the National Science
Foundation grant no. IIS-1835231 (H.P.) and NCS-FO-2124179 (H.P.)."
article_processing_charge: Yes
article_type: original
author:
- first_name: Philipp
full_name: Velicky, Philipp
id: 39BDC62C-F248-11E8-B48F-1D18A9856A87
last_name: Velicky
orcid: 0000-0002-2340-7431
- first_name: Eder
full_name: Miguel Villalba, Eder
id: 3FB91342-F248-11E8-B48F-1D18A9856A87
last_name: Miguel Villalba
orcid: 0000-0001-5665-0430
- first_name: Julia M
full_name: Michalska, Julia M
id: 443DB6DE-F248-11E8-B48F-1D18A9856A87
last_name: Michalska
orcid: 0000-0003-3862-1235
- first_name: Julia
full_name: Lyudchik, Julia
id: 46E28B80-F248-11E8-B48F-1D18A9856A87
last_name: Lyudchik
- first_name: Donglai
full_name: Wei, Donglai
last_name: Wei
- first_name: Zudi
full_name: Lin, Zudi
last_name: Lin
- first_name: Jake
full_name: Watson, Jake
id: 63836096-4690-11EA-BD4E-32803DDC885E
last_name: Watson
orcid: 0000-0002-8698-3823
- first_name: Jakob
full_name: Troidl, Jakob
last_name: Troidl
- first_name: Johanna
full_name: Beyer, Johanna
last_name: Beyer
- first_name: Yoav
full_name: Ben Simon, Yoav
id: 43DF3136-F248-11E8-B48F-1D18A9856A87
last_name: Ben Simon
- first_name: Christoph M
full_name: Sommer, Christoph M
id: 4DF26D8C-F248-11E8-B48F-1D18A9856A87
last_name: Sommer
orcid: 0000-0003-1216-9105
- first_name: Wiebke
full_name: Jahr, Wiebke
id: 425C1CE8-F248-11E8-B48F-1D18A9856A87
last_name: Jahr
- first_name: Alban
full_name: Cenameri, Alban
id: 9ac8f577-2357-11eb-997a-e566c5550886
last_name: Cenameri
- first_name: Johannes
full_name: Broichhagen, Johannes
last_name: Broichhagen
- first_name: Seth G.N.
full_name: Grant, Seth G.N.
last_name: Grant
- first_name: Peter M
full_name: Jonas, Peter M
id: 353C1B58-F248-11E8-B48F-1D18A9856A87
last_name: Jonas
orcid: 0000-0001-5001-4804
- first_name: Gaia
full_name: Novarino, Gaia
id: 3E57A680-F248-11E8-B48F-1D18A9856A87
last_name: Novarino
orcid: 0000-0002-7673-7178
- first_name: Hanspeter
full_name: Pfister, Hanspeter
last_name: Pfister
- first_name: Bernd
full_name: Bickel, Bernd
id: 49876194-F248-11E8-B48F-1D18A9856A87
last_name: Bickel
orcid: 0000-0001-6511-9385
- first_name: Johann G
full_name: Danzl, Johann G
id: 42EFD3B6-F248-11E8-B48F-1D18A9856A87
last_name: Danzl
orcid: 0000-0001-8559-3973
citation:
ama: Velicky P, Miguel Villalba E, Michalska JM, et al. Dense 4D nanoscale reconstruction
of living brain tissue. Nature Methods. 2023;20:1256-1265. doi:10.1038/s41592-023-01936-6
apa: Velicky, P., Miguel Villalba, E., Michalska, J. M., Lyudchik, J., Wei, D.,
Lin, Z., … Danzl, J. G. (2023). Dense 4D nanoscale reconstruction of living brain
tissue. Nature Methods. Springer Nature. https://doi.org/10.1038/s41592-023-01936-6
chicago: Velicky, Philipp, Eder Miguel Villalba, Julia M Michalska, Julia Lyudchik,
Donglai Wei, Zudi Lin, Jake Watson, et al. “Dense 4D Nanoscale Reconstruction
of Living Brain Tissue.” Nature Methods. Springer Nature, 2023. https://doi.org/10.1038/s41592-023-01936-6.
ieee: P. Velicky et al., “Dense 4D nanoscale reconstruction of living brain
tissue,” Nature Methods, vol. 20. Springer Nature, pp. 1256–1265, 2023.
ista: Velicky P, Miguel Villalba E, Michalska JM, Lyudchik J, Wei D, Lin Z, Watson
J, Troidl J, Beyer J, Ben Simon Y, Sommer CM, Jahr W, Cenameri A, Broichhagen
J, Grant SGN, Jonas PM, Novarino G, Pfister H, Bickel B, Danzl JG. 2023. Dense
4D nanoscale reconstruction of living brain tissue. Nature Methods. 20, 1256–1265.
mla: Velicky, Philipp, et al. “Dense 4D Nanoscale Reconstruction of Living Brain
Tissue.” Nature Methods, vol. 20, Springer Nature, 2023, pp. 1256–65, doi:10.1038/s41592-023-01936-6.
short: P. Velicky, E. Miguel Villalba, J.M. Michalska, J. Lyudchik, D. Wei, Z. Lin,
J. Watson, J. Troidl, J. Beyer, Y. Ben Simon, C.M. Sommer, W. Jahr, A. Cenameri,
J. Broichhagen, S.G.N. Grant, P.M. Jonas, G. Novarino, H. Pfister, B. Bickel,
J.G. Danzl, Nature Methods 20 (2023) 1256–1265.
date_created: 2023-07-23T22:01:13Z
date_published: 2023-08-01T00:00:00Z
date_updated: 2024-01-10T08:37:48Z
day: '01'
department:
- _id: PeJo
- _id: GaNo
- _id: BeBi
- _id: JoDa
- _id: Bio
doi: 10.1038/s41592-023-01936-6
ec_funded: 1
external_id:
isi:
- '001025621500001'
pmid:
- '37429995'
intvolume: ' 20'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1038/s41592-023-01936-6
month: '08'
oa: 1
oa_version: Published Version
page: 1256-1265
pmid: 1
project:
- _id: 265CB4D0-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: I03600
name: Optical control of synaptic function via adhesion molecules
- _id: 2548AE96-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: W1232-B24
name: Molecular Drug Targets
- _id: 25C5A090-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z00312
name: The Wittgenstein Prize
- _id: 23889792-32DE-11EA-91FC-C7463DDC885E
name: High content imaging to decode human immune cell interactions in health and
allergic disease
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 24F9549A-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '715767'
name: 'MATERIALIZABLE: Intelligent fabrication-oriented Computational Design and
Modeling'
- _id: 25444568-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '715508'
name: Probing the Reversibility of Autism Spectrum Disorders by Employing in vivo
and in vitro Models
- _id: 25B7EB9E-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '692692'
name: Biophysics and circuit function of a giant cortical glumatergic synapse
- _id: fc2be41b-9c52-11eb-aca3-faa90aa144e9
call_identifier: H2020
grant_number: '101026635'
name: Synaptic computations of the hippocampal CA3 circuitry
- _id: 2668BFA0-B435-11E9-9278-68D0E5697425
grant_number: LT00057
name: High-speed 3D-nanoscopy to study the role of adhesion during 3D cell migration
publication: Nature Methods
publication_identifier:
eissn:
- 1548-7105
issn:
- 1548-7091
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/danzllab/LIONESS
record:
- id: '12817'
relation: research_data
status: public
- id: '14770'
relation: shorter_version
status: public
scopus_import: '1'
status: public
title: Dense 4D nanoscale reconstruction of living brain tissue
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 20
year: '2023'
...
---
_id: '14830'
abstract:
- lang: eng
text: We study the problem of learning controllers for discrete-time non-linear
stochastic dynamical systems with formal reach-avoid guarantees. This work presents
the first method for providing formal reach-avoid guarantees, which combine and
generalize stability and safety guarantees, with a tolerable probability threshold
p in [0,1] over the infinite time horizon. Our method leverages advances in machine
learning literature and it represents formal certificates as neural networks.
In particular, we learn a certificate in the form of a reach-avoid supermartingale
(RASM), a novel notion that we introduce in this work. Our RASMs provide reachability
and avoidance guarantees by imposing constraints on what can be viewed as a stochastic
extension of level sets of Lyapunov functions for deterministic systems. Our approach
solves several important problems -- it can be used to learn a control policy
from scratch, to verify a reach-avoid specification for a fixed control policy,
or to fine-tune a pre-trained policy if it does not satisfy the reach-avoid specification.
We validate our approach on 3 stochastic non-linear reinforcement learning tasks.
acknowledgement: This work was supported in part by the ERC-2020-AdG 101020093, ERC
CoG 863818 (FoRM-SMArt) and the European Union’s Horizon 2020 research and innovation
programme under the Marie Skłodowska-Curie Grant Agreement No. 665385.
article_processing_charge: No
arxiv: 1
author:
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
citation:
ama: 'Zikelic D, Lechner M, Henzinger TA, Chatterjee K. Learning control policies
for stochastic systems with reach-avoid guarantees. In: Proceedings of the
37th AAAI Conference on Artificial Intelligence. Vol 37. Association for the
Advancement of Artificial Intelligence; 2023:11926-11935. doi:10.1609/aaai.v37i10.26407'
apa: 'Zikelic, D., Lechner, M., Henzinger, T. A., & Chatterjee, K. (2023). Learning
control policies for stochastic systems with reach-avoid guarantees. In Proceedings
of the 37th AAAI Conference on Artificial Intelligence (Vol. 37, pp. 11926–11935).
Washington, DC, United States: Association for the Advancement of Artificial Intelligence.
https://doi.org/10.1609/aaai.v37i10.26407'
chicago: Zikelic, Dorde, Mathias Lechner, Thomas A Henzinger, and Krishnendu Chatterjee.
“Learning Control Policies for Stochastic Systems with Reach-Avoid Guarantees.”
In Proceedings of the 37th AAAI Conference on Artificial Intelligence,
37:11926–35. Association for the Advancement of Artificial Intelligence, 2023.
https://doi.org/10.1609/aaai.v37i10.26407.
ieee: D. Zikelic, M. Lechner, T. A. Henzinger, and K. Chatterjee, “Learning control
policies for stochastic systems with reach-avoid guarantees,” in Proceedings
of the 37th AAAI Conference on Artificial Intelligence, Washington, DC, United
States, 2023, vol. 37, no. 10, pp. 11926–11935.
ista: 'Zikelic D, Lechner M, Henzinger TA, Chatterjee K. 2023. Learning control
policies for stochastic systems with reach-avoid guarantees. Proceedings of the
37th AAAI Conference on Artificial Intelligence. AAAI: Conference on Artificial
Intelligence vol. 37, 11926–11935.'
mla: Zikelic, Dorde, et al. “Learning Control Policies for Stochastic Systems with
Reach-Avoid Guarantees.” Proceedings of the 37th AAAI Conference on Artificial
Intelligence, vol. 37, no. 10, Association for the Advancement of Artificial
Intelligence, 2023, pp. 11926–35, doi:10.1609/aaai.v37i10.26407.
short: D. Zikelic, M. Lechner, T.A. Henzinger, K. Chatterjee, in:, Proceedings of
the 37th AAAI Conference on Artificial Intelligence, Association for the Advancement
of Artificial Intelligence, 2023, pp. 11926–11935.
conference:
end_date: 2023-02-14
location: Washington, DC, United States
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2023-02-07
date_created: 2024-01-18T07:44:31Z
date_published: 2023-06-26T00:00:00Z
date_updated: 2025-07-14T09:10:02Z
day: '26'
department:
- _id: ToHe
- _id: KrCh
doi: 10.1609/aaai.v37i10.26407
ec_funded: 1
external_id:
arxiv:
- '2210.05308'
intvolume: ' 37'
issue: '10'
keyword:
- General Medicine
language:
- iso: eng
month: '06'
oa_version: Preprint
page: 11926-11935
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: Proceedings of the 37th AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
issn:
- 2159-5399
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
related_material:
record:
- id: '14600'
relation: earlier_version
status: public
status: public
title: Learning control policies for stochastic systems with reach-avoid guarantees
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2023'
...
---
_id: '14058'
abstract:
- lang: eng
text: "Females and males across species are subject to divergent selective pressures
arising\r\nfrom di↵erent reproductive interests and ecological niches. This often
translates into a\r\nintricate array of sex-specific natural and sexual selection
on traits that have a shared\r\ngenetic basis between both sexes, causing a genetic
sexual conflict. The resolution of\r\nthis conflict mostly relies on the evolution
of sex-specific expression of the shared genes,\r\nleading to phenotypic sexual
dimorphism. Such sex-specific gene expression is thought\r\nto evolve via modifications
of the genetic networks ultimately linked to sex-determining\r\ntranscription
factors. Although much empirical and theoretical evidence supports this\r\nstandard
picture of the molecular basis of sexual conflict resolution, there still are
a\r\nfew open questions regarding the complex array of selective forces driving
phenotypic\r\ndi↵erentiation between the sexes, as well as the molecular mechanisms
underlying sexspecific adaptation. I address some of these open questions in my
PhD thesis.\r\nFirst, how do patterns of phenotypic sexual dimorphism vary within
populations,\r\nas a response to the temporal and spatial changes in sex-specific
selective forces? To\r\ntackle this question, I analyze the patterns of sex-specific
phenotypic variation along\r\nthree life stages and across populations spanning
the whole geographical range of Rumex\r\nhastatulus, a wind-pollinated angiosperm,
in the first Chapter of the thesis.\r\nSecond, how do gene expression patterns
lead to phenotypic dimorphism, and what\r\nare the molecular mechanisms underlying
the observed transcriptomic variation? I\r\naddress this question by examining
the sex- and tissue-specific expression variation in\r\nnewly-generated datasets
of sex-specific expression in heads and gonads of Drosophila\r\nmelanogaster.
I additionally used two complementary approaches for the study of the\r\ngenetic
basis of sex di↵erences in gene expression in the second and third Chapters of\r\nthe
thesis.\r\nThird, how does intersex correlation, thought to be one of the main
aspects constraining the ability for the two sexes to decouple, interact with
the evolution of sexual\r\ndimorphism? I develop models of sex-specific stabilizing
selection, mutation and drift\r\nto formalize common intuition regarding the patterns
of covariation between intersex\r\ncorrelation and sexual dimorphism in the fourth
Chapter of the thesis.\r\nAlltogether, the work described in this PhD thesis provides
useful insights into the\r\nlinks between genetic, transcriptomic and phenotypic
layers of sex-specific variation,\r\nand contributes to our general understanding
of the dynamics of sexual dimorphism\r\nevolution."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Gemma
full_name: Puixeu Sala, Gemma
id: 33AB266C-F248-11E8-B48F-1D18A9856A87
last_name: Puixeu Sala
orcid: 0000-0001-8330-1754
citation:
ama: 'Puixeu Sala G. The molecular basis of sexual dimorphism: Experimental and
theoretical characterization of phenotypic, transcriptomic and genetic patterns
of sex-specific adaptation. 2023. doi:10.15479/at:ista:14058'
apa: 'Puixeu Sala, G. (2023). The molecular basis of sexual dimorphism: Experimental
and theoretical characterization of phenotypic, transcriptomic and genetic patterns
of sex-specific adaptation. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:14058'
chicago: 'Puixeu Sala, Gemma. “The Molecular Basis of Sexual Dimorphism: Experimental
and Theoretical Characterization of Phenotypic, Transcriptomic and Genetic Patterns
of Sex-Specific Adaptation.” Institute of Science and Technology Austria, 2023.
https://doi.org/10.15479/at:ista:14058.'
ieee: 'G. Puixeu Sala, “The molecular basis of sexual dimorphism: Experimental and
theoretical characterization of phenotypic, transcriptomic and genetic patterns
of sex-specific adaptation,” Institute of Science and Technology Austria, 2023.'
ista: 'Puixeu Sala G. 2023. The molecular basis of sexual dimorphism: Experimental
and theoretical characterization of phenotypic, transcriptomic and genetic patterns
of sex-specific adaptation. Institute of Science and Technology Austria.'
mla: 'Puixeu Sala, Gemma. The Molecular Basis of Sexual Dimorphism: Experimental
and Theoretical Characterization of Phenotypic, Transcriptomic and Genetic Patterns
of Sex-Specific Adaptation. Institute of Science and Technology Austria, 2023,
doi:10.15479/at:ista:14058.'
short: 'G. Puixeu Sala, The Molecular Basis of Sexual Dimorphism: Experimental and
Theoretical Characterization of Phenotypic, Transcriptomic and Genetic Patterns
of Sex-Specific Adaptation, Institute of Science and Technology Austria, 2023.'
date_created: 2023-08-15T10:20:40Z
date_published: 2023-08-15T00:00:00Z
date_updated: 2023-12-13T12:15:36Z
day: '15'
ddc:
- '576'
degree_awarded: PhD
department:
- _id: GradSch
- _id: NiBa
- _id: BeVi
doi: 10.15479/at:ista:14058
ec_funded: 1
file:
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checksum: 4e44e169f2724ee8c9324cd60bcc2b71
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creator: gpuixeus
date_created: 2023-08-16T18:15:17Z
date_updated: 2023-08-17T06:55:24Z
file_id: '14075'
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file_size: 10891454
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checksum: e10b04cd8f3fecc0d9ef6e6868b6e1e8
content_type: application/pdf
creator: gpuixeus
date_created: 2023-08-18T10:47:55Z
date_updated: 2023-08-18T10:47:55Z
file_id: '14079'
file_name: PhDThesis_PuixeuG.pdf
file_size: 19856686
relation: main_file
success: 1
file_date_updated: 2023-08-18T10:47:55Z
has_accepted_license: '1'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
page: '230'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 9B9DFC9E-BA93-11EA-9121-9846C619BF3A
grant_number: '25817'
name: 'Sexual conflict: resolution, constraints and biomedical implications'
publication_identifier:
isbn:
- 978-3-99078-035-0
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '9803'
relation: research_data
status: public
- id: '12933'
relation: research_data
status: public
- id: '6831'
relation: part_of_dissertation
status: public
- id: '14077'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Beatriz
full_name: Vicoso, Beatriz
id: 49E1C5C6-F248-11E8-B48F-1D18A9856A87
last_name: Vicoso
orcid: 0000-0002-4579-8306
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
title: 'The molecular basis of sexual dimorphism: Experimental and theoretical characterization
of phenotypic, transcriptomic and genetic patterns of sex-specific adaptation'
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: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2023'
...
---
_id: '14077'
abstract:
- lang: eng
text: "The regulatory architecture of gene expression is known to differ substantially
between sexes in Drosophila, but most studies performed\r\nso far used whole-body
data and only single crosses, which may have limited their scope to detect patterns
that are robust across tissues\r\nand biological replicates. Here, we use allele-specific
gene expression of parental and reciprocal hybrid crosses between 6 Drosophila\r\nmelanogaster
inbred lines to quantify cis- and trans-regulatory variation in heads and gonads
of both sexes separately across 3 replicate\r\ncrosses. Our results suggest that
female and male heads, as well as ovaries, have a similar regulatory architecture.
On the other hand,\r\ntestes display more and substantially different cis-regulatory
effects, suggesting that sex differences in the regulatory architecture that\r\nhave
been previously observed may largely derive from testis-specific effects. We also
examine the difference in cis-regulatory variation\r\nof genes across different
levels of sex bias in gonads and heads. Consistent with the idea that intersex
correlations constrain expression\r\nand can lead to sexual antagonism, we find
more cis variation in unbiased and moderately biased genes in heads. In ovaries,
reduced cis\r\nvariation is observed for male-biased genes, suggesting that cis
variants acting on these genes in males do not lead to changes in ovary\r\nexpression.
Finally, we examine the dominance patterns of gene expression and find that sex-
and tissue-specific patterns of inheritance\r\nas well as trans-regulatory variation
are highly variable across biological crosses, although these were performed in
highly controlled\r\nexperimental conditions. This highlights the importance of
using various genetic backgrounds to infer generalizable patterns."
acknowledged_ssus:
- _id: ScienComp
acknowledgement: We thank members of the Vicoso Group for comments on the manuscript,
the Scientific Computing Unit at ISTA for technical support, and 2 anonymous reviewers
for useful feedback. GP is the recipient of a DOC Fellowship of the Austrian Academy
of Sciences at the Institute of Science and Technology Austria (DOC 25817) and received
funding from the European Union’s Horizon 2020 research and innovation program under
the Marie Skłodowska-Curie Grant (agreement no. 665385).
article_processing_charge: Yes
article_type: original
author:
- first_name: Gemma
full_name: Puixeu Sala, Gemma
id: 33AB266C-F248-11E8-B48F-1D18A9856A87
last_name: Puixeu Sala
orcid: 0000-0001-8330-1754
- first_name: Ariana
full_name: Macon, Ariana
id: 2A0848E2-F248-11E8-B48F-1D18A9856A87
last_name: Macon
- first_name: Beatriz
full_name: Vicoso, Beatriz
id: 49E1C5C6-F248-11E8-B48F-1D18A9856A87
last_name: Vicoso
orcid: 0000-0002-4579-8306
citation:
ama: 'Puixeu Sala G, Macon A, Vicoso B. Sex-specific estimation of cis and trans
regulation of gene expression in heads and gonads of Drosophila melanogaster.
G3: Genes, Genomes, Genetics. 2023;13(8). doi:10.1093/g3journal/jkad121'
apa: 'Puixeu Sala, G., Macon, A., & Vicoso, B. (2023). Sex-specific estimation
of cis and trans regulation of gene expression in heads and gonads of Drosophila
melanogaster. G3: Genes, Genomes, Genetics. Oxford University Press. https://doi.org/10.1093/g3journal/jkad121'
chicago: 'Puixeu Sala, Gemma, Ariana Macon, and Beatriz Vicoso. “Sex-Specific Estimation
of Cis and Trans Regulation of Gene Expression in Heads and Gonads of Drosophila
Melanogaster.” G3: Genes, Genomes, Genetics. Oxford University Press, 2023.
https://doi.org/10.1093/g3journal/jkad121.'
ieee: 'G. Puixeu Sala, A. Macon, and B. Vicoso, “Sex-specific estimation of cis
and trans regulation of gene expression in heads and gonads of Drosophila melanogaster,”
G3: Genes, Genomes, Genetics, vol. 13, no. 8. Oxford University Press,
2023.'
ista: 'Puixeu Sala G, Macon A, Vicoso B. 2023. Sex-specific estimation of cis and
trans regulation of gene expression in heads and gonads of Drosophila melanogaster.
G3: Genes, Genomes, Genetics. 13(8).'
mla: 'Puixeu Sala, Gemma, et al. “Sex-Specific Estimation of Cis and Trans Regulation
of Gene Expression in Heads and Gonads of Drosophila Melanogaster.” G3: Genes,
Genomes, Genetics, vol. 13, no. 8, Oxford University Press, 2023, doi:10.1093/g3journal/jkad121.'
short: 'G. Puixeu Sala, A. Macon, B. Vicoso, G3: Genes, Genomes, Genetics 13 (2023).'
date_created: 2023-08-18T06:52:14Z
date_published: 2023-08-01T00:00:00Z
date_updated: 2023-12-13T12:15:37Z
day: '01'
ddc:
- '570'
department:
- _id: BeVi
- _id: NiBa
- _id: GradSch
doi: 10.1093/g3journal/jkad121
ec_funded: 1
external_id:
isi:
- '001002997200001'
file:
- access_level: open_access
checksum: c62e29fc7c5efbf8356f4c60cab4a2d1
content_type: application/pdf
creator: dernst
date_created: 2023-11-07T09:00:19Z
date_updated: 2023-11-07T09:00:19Z
file_id: '14498'
file_name: 2023_G3_Puixeu.pdf
file_size: 845642
relation: main_file
success: 1
file_date_updated: 2023-11-07T09:00:19Z
has_accepted_license: '1'
intvolume: ' 13'
isi: 1
issue: '8'
keyword:
- Genetics (clinical)
- Genetics
- Molecular Biology
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 9B9DFC9E-BA93-11EA-9121-9846C619BF3A
grant_number: '25817'
name: 'Sexual conflict: resolution, constraints and biomedical implications'
publication: 'G3: Genes, Genomes, Genetics'
publication_identifier:
issn:
- 2160-1836
publication_status: published
publisher: Oxford University Press
quality_controlled: '1'
related_material:
record:
- id: '12933'
relation: research_data
status: public
- id: '14058'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Sex-specific estimation of cis and trans regulation of gene expression in heads
and gonads of Drosophila melanogaster
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2023'
...
---
_id: '14242'
abstract:
- lang: eng
text: We study the problem of training and certifying adversarially robust quantized
neural networks (QNNs). Quantization is a technique for making neural networks
more efficient by running them using low-bit integer arithmetic and is therefore
commonly adopted in industry. Recent work has shown that floating-point neural
networks that have been verified to be robust can become vulnerable to adversarial
attacks after quantization, and certification of the quantized representation
is necessary to guarantee robustness. In this work, we present quantization-aware
interval bound propagation (QA-IBP), a novel method for training robust QNNs.
Inspired by advances in robust learning of non-quantized networks, our training
algorithm computes the gradient of an abstract representation of the actual network.
Unlike existing approaches, our method can handle the discrete semantics of QNNs.
Based on QA-IBP, we also develop a complete verification procedure for verifying
the adversarial robustness of QNNs, which is guaranteed to terminate and produce
a correct answer. Compared to existing approaches, the key advantage of our verification
procedure is that it runs entirely on GPU or other accelerator devices. We demonstrate
experimentally that our approach significantly outperforms existing methods and
establish the new state-of-the-art for training and certifying the robustness
of QNNs.
acknowledgement: "This work was supported in part by the ERC-2020-AdG 101020093, ERC
CoG 863818 (FoRM-SMArt) and the European Union’s Horizon 2020 research and innovation
programme under the Marie Skłodowska-Curie Grant Agreement No. 665385. Research
was sponsored by the United\r\nStates Air Force Research Laboratory and the United
States Air Force Artificial Intelligence Accelerator and was accomplished under
Cooperative Agreement Number FA8750-19-2-\r\n1000. The views and conclusions contained
in this document are those of the authors and should not be interpreted as representing
the official policies, either expressed or implied,\r\nof the United States Air
Force or the U.S. Government. The U.S. Government is authorized to reproduce and
distribute reprints for Government purposes notwithstanding any copyright\r\nnotation
herein. The research was also funded in part by the AI2050 program at Schmidt Futures
(Grant G-22-63172) and Capgemini SE."
article_processing_charge: No
arxiv: 1
author:
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
- first_name: Daniela
full_name: Rus, Daniela
last_name: Rus
citation:
ama: 'Lechner M, Zikelic D, Chatterjee K, Henzinger TA, Rus D. Quantization-aware
interval bound propagation for training certifiably robust quantized neural networks.
In: Proceedings of the 37th AAAI Conference on Artificial Intelligence.
Vol 37. Association for the Advancement of Artificial Intelligence; 2023:14964-14973.
doi:10.1609/aaai.v37i12.26747'
apa: 'Lechner, M., Zikelic, D., Chatterjee, K., Henzinger, T. A., & Rus, D.
(2023). Quantization-aware interval bound propagation for training certifiably
robust quantized neural networks. In Proceedings of the 37th AAAI Conference
on Artificial Intelligence (Vol. 37, pp. 14964–14973). Washington, DC, United
States: Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v37i12.26747'
chicago: Lechner, Mathias, Dorde Zikelic, Krishnendu Chatterjee, Thomas A Henzinger,
and Daniela Rus. “Quantization-Aware Interval Bound Propagation for Training Certifiably
Robust Quantized Neural Networks.” In Proceedings of the 37th AAAI Conference
on Artificial Intelligence, 37:14964–73. Association for the Advancement of
Artificial Intelligence, 2023. https://doi.org/10.1609/aaai.v37i12.26747.
ieee: M. Lechner, D. Zikelic, K. Chatterjee, T. A. Henzinger, and D. Rus, “Quantization-aware
interval bound propagation for training certifiably robust quantized neural networks,”
in Proceedings of the 37th AAAI Conference on Artificial Intelligence,
Washington, DC, United States, 2023, vol. 37, no. 12, pp. 14964–14973.
ista: 'Lechner M, Zikelic D, Chatterjee K, Henzinger TA, Rus D. 2023. Quantization-aware
interval bound propagation for training certifiably robust quantized neural networks.
Proceedings of the 37th AAAI Conference on Artificial Intelligence. AAAI: Conference
on Artificial Intelligence vol. 37, 14964–14973.'
mla: Lechner, Mathias, et al. “Quantization-Aware Interval Bound Propagation for
Training Certifiably Robust Quantized Neural Networks.” Proceedings of the
37th AAAI Conference on Artificial Intelligence, vol. 37, no. 12, Association
for the Advancement of Artificial Intelligence, 2023, pp. 14964–73, doi:10.1609/aaai.v37i12.26747.
short: M. Lechner, D. Zikelic, K. Chatterjee, T.A. Henzinger, D. Rus, in:, Proceedings
of the 37th AAAI Conference on Artificial Intelligence, Association for the Advancement
of Artificial Intelligence, 2023, pp. 14964–14973.
conference:
end_date: 2023-02-14
location: Washington, DC, United States
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2023-02-07
date_created: 2023-08-27T22:01:17Z
date_published: 2023-06-26T00:00:00Z
date_updated: 2025-07-14T09:09:56Z
day: '26'
department:
- _id: ToHe
- _id: KrCh
doi: 10.1609/aaai.v37i12.26747
ec_funded: 1
external_id:
arxiv:
- '2211.16187'
intvolume: ' 37'
issue: '12'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2211.16187
month: '06'
oa: 1
oa_version: Preprint
page: 14964-14973
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: Proceedings of the 37th AAAI Conference on Artificial Intelligence
publication_identifier:
isbn:
- '9781577358800'
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
scopus_import: '1'
status: public
title: Quantization-aware interval bound propagation for training certifiably robust
quantized neural networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2023'
...
---
_id: '14243'
abstract:
- lang: eng
text: 'Two-player zero-sum "graph games" are central in logic, verification, and
multi-agent systems. The game proceeds by placing a token on a vertex of a graph,
and allowing the players to move it to produce an infinite path, which determines
the winner or payoff of the game. Traditionally, the players alternate turns in
moving the token. In "bidding games", however, the players have budgets and in
each turn, an auction (bidding) determines which player moves the token. So far,
bidding games have only been studied as full-information games. In this work we
initiate the study of partial-information bidding games: we study bidding games
in which a player''s initial budget is drawn from a known probability distribution.
We show that while for some bidding mechanisms and objectives, it is straightforward
to adapt the results from the full-information setting to the partial-information
setting, for others, the analysis is significantly more challenging, requires
new techniques, and gives rise to interesting results. Specifically, we study
games with "mean-payoff" objectives in combination with "poorman" bidding. We
construct optimal strategies for a partially-informed player who plays against
a fully-informed adversary. We show that, somewhat surprisingly, the "value" under
pure strategies does not necessarily exist in such games.'
acknowledgement: This research was supported in part by ISF grant no.1679/21, by the
ERC CoG 863818 (ForM-SMArt), and the European Union’s Horizon 2020 research and
innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 665385.
article_processing_charge: No
arxiv: 1
author:
- first_name: Guy
full_name: Avni, Guy
id: 463C8BC2-F248-11E8-B48F-1D18A9856A87
last_name: Avni
orcid: 0000-0001-5588-8287
- first_name: Ismael R
full_name: Jecker, Ismael R
id: 85D7C63E-7D5D-11E9-9C0F-98C4E5697425
last_name: Jecker
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
citation:
ama: 'Avni G, Jecker IR, Zikelic D. Bidding graph games with partially-observable
budgets. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence.
Vol 37. ; 2023:5464-5471. doi:10.1609/aaai.v37i5.25679'
apa: Avni, G., Jecker, I. R., & Zikelic, D. (2023). Bidding graph games with
partially-observable budgets. In Proceedings of the 37th AAAI Conference on
Artificial Intelligence (Vol. 37, pp. 5464–5471). Washington, DC, United States.
https://doi.org/10.1609/aaai.v37i5.25679
chicago: Avni, Guy, Ismael R Jecker, and Dorde Zikelic. “Bidding Graph Games with
Partially-Observable Budgets.” In Proceedings of the 37th AAAI Conference on
Artificial Intelligence, 37:5464–71, 2023. https://doi.org/10.1609/aaai.v37i5.25679.
ieee: G. Avni, I. R. Jecker, and D. Zikelic, “Bidding graph games with partially-observable
budgets,” in Proceedings of the 37th AAAI Conference on Artificial Intelligence,
Washington, DC, United States, 2023, vol. 37, no. 5, pp. 5464–5471.
ista: 'Avni G, Jecker IR, Zikelic D. 2023. Bidding graph games with partially-observable
budgets. Proceedings of the 37th AAAI Conference on Artificial Intelligence. AAAI:
Conference on Artificial Intelligence vol. 37, 5464–5471.'
mla: Avni, Guy, et al. “Bidding Graph Games with Partially-Observable Budgets.”
Proceedings of the 37th AAAI Conference on Artificial Intelligence, vol.
37, no. 5, 2023, pp. 5464–71, doi:10.1609/aaai.v37i5.25679.
short: G. Avni, I.R. Jecker, D. Zikelic, in:, Proceedings of the 37th AAAI Conference
on Artificial Intelligence, 2023, pp. 5464–5471.
conference:
end_date: 2023-02-14
location: Washington, DC, United States
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2023-02-07
date_created: 2023-08-27T22:01:18Z
date_published: 2023-06-27T00:00:00Z
date_updated: 2025-07-14T09:09:56Z
day: '27'
department:
- _id: ToHe
- _id: KrCh
doi: 10.1609/aaai.v37i5.25679
ec_funded: 1
external_id:
arxiv:
- '2211.13626'
intvolume: ' 37'
issue: '5'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1609/aaai.v37i5.25679
month: '06'
oa: 1
oa_version: Published Version
page: 5464-5471
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: Proceedings of the 37th AAAI Conference on Artificial Intelligence
publication_identifier:
isbn:
- '9781577358800'
publication_status: published
quality_controlled: '1'
scopus_import: '1'
status: public
title: Bidding graph games with partially-observable budgets
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2023'
...
---
_id: '14257'
abstract:
- lang: eng
text: Mapping the complex and dense arrangement of cells and their connectivity
in brain tissue demands nanoscale spatial resolution imaging. Super-resolution
optical microscopy excels at visualizing specific molecules and individual cells
but fails to provide tissue context. Here we developed Comprehensive Analysis
of Tissues across Scales (CATS), a technology to densely map brain tissue architecture
from millimeter regional to nanometer synaptic scales in diverse chemically fixed
brain preparations, including rodent and human. CATS uses fixation-compatible
extracellular labeling and optical imaging, including stimulated emission depletion
or expansion microscopy, to comprehensively delineate cellular structures. It
enables three-dimensional reconstruction of single synapses and mapping of synaptic
connectivity by identification and analysis of putative synaptic cleft regions.
Applying CATS to the mouse hippocampal mossy fiber circuitry, we reconstructed
and quantified the synaptic input and output structure of identified neurons.
We furthermore demonstrate applicability to clinically derived human tissue samples,
including formalin-fixed paraffin-embedded routine diagnostic specimens, for visualizing
the cellular architecture of brain tissue in health and disease.
acknowledged_ssus:
- _id: ScienComp
- _id: Bio
- _id: PreCl
- _id: LifeSc
- _id: M-Shop
- _id: E-Lib
acknowledgement: 'We thank J. Vorlaufer, N. Agudelo-Dueñas, W. Jahr and A. Wartak
for microscope maintenance and troubleshooting; C. Kreuzinger, A. Freeman and I.
Erber for technical assistance; and M. Tomschik for support with obtaining human
samples. We gratefully acknowledge E. Miguel for setting up webKnossos and M. Šuplata
for computational support and hardware control. We are grateful to R. Shigemoto
and B. Bickel for generous support and M. Sixt and S. Boyd (Stanford University)
for discussions and critical reading of the paper. PSD95-HaloTag mice were kindly
provided by S. Grant (University of Edinburgh). We acknowledge expert support by
Institute of Science and Technology Austria’s scientific computing, imaging and
optics, preclinical and lab support facilities and by the Miba machine shop and
library. We gratefully acknowledge funding by the following sources: Austrian Science
Fund (FWF) grant I3600-B27 (J.G.D.); Austrian Science Fund (FWF) grant DK W1232
(J.G.D. and J.M.M.); Austrian Science Fund (FWF) grant Z 312-B27, Wittgenstein award
(P.J.); Austrian Science Fund (FWF) projects I4685-B, I6565-B (SYNABS) and DOC 33-B27
(R.H.); Gesellschaft für Forschungsförderung NÖ (NFB) grant LSC18-022 (J.G.D.);
European Union’s Horizon 2020 research and innovation programme, European Research
Council (ERC) grant 715508 – REVERSEAUTISM (G.N.); European Union’s Horizon 2020
research and innovation programme, European Research Council (ERC) grant 692692
– GIANTSYN (P.J.); Marie Skłodowska-Curie Actions Fellowship GA no. 665385 under
the EU Horizon 2020 program (J.M.M. and J.L.); and Marie Skłodowska-Curie Actions
Individual Fellowship no. 101026635 under the EU Horizon 2020 program (J.F.W.).'
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Julia M
full_name: Michalska, Julia M
id: 443DB6DE-F248-11E8-B48F-1D18A9856A87
last_name: Michalska
orcid: 0000-0003-3862-1235
- first_name: Julia
full_name: Lyudchik, Julia
id: 46E28B80-F248-11E8-B48F-1D18A9856A87
last_name: Lyudchik
- first_name: Philipp
full_name: Velicky, Philipp
id: 39BDC62C-F248-11E8-B48F-1D18A9856A87
last_name: Velicky
orcid: 0000-0002-2340-7431
- first_name: Hana
full_name: Korinkova, Hana
id: ee3cb6ca-ec98-11ea-ae11-ff703e2254ed
last_name: Korinkova
- first_name: Jake
full_name: Watson, Jake
id: 63836096-4690-11EA-BD4E-32803DDC885E
last_name: Watson
orcid: 0000-0002-8698-3823
- first_name: Alban
full_name: Cenameri, Alban
id: 9ac8f577-2357-11eb-997a-e566c5550886
last_name: Cenameri
- first_name: Christoph M
full_name: Sommer, Christoph M
id: 4DF26D8C-F248-11E8-B48F-1D18A9856A87
last_name: Sommer
orcid: 0000-0003-1216-9105
- first_name: Nicole
full_name: Amberg, Nicole
id: 4CD6AAC6-F248-11E8-B48F-1D18A9856A87
last_name: Amberg
orcid: 0000-0002-3183-8207
- first_name: Alessandro
full_name: Venturino, Alessandro
id: 41CB84B2-F248-11E8-B48F-1D18A9856A87
last_name: Venturino
orcid: 0000-0003-2356-9403
- first_name: Karl
full_name: Roessler, Karl
last_name: Roessler
- first_name: Thomas
full_name: Czech, Thomas
last_name: Czech
- first_name: Romana
full_name: Höftberger, Romana
last_name: Höftberger
- first_name: Sandra
full_name: Siegert, Sandra
id: 36ACD32E-F248-11E8-B48F-1D18A9856A87
last_name: Siegert
orcid: 0000-0001-8635-0877
- first_name: Gaia
full_name: Novarino, Gaia
id: 3E57A680-F248-11E8-B48F-1D18A9856A87
last_name: Novarino
orcid: 0000-0002-7673-7178
- first_name: Peter M
full_name: Jonas, Peter M
id: 353C1B58-F248-11E8-B48F-1D18A9856A87
last_name: Jonas
orcid: 0000-0001-5001-4804
- first_name: Johann G
full_name: Danzl, Johann G
id: 42EFD3B6-F248-11E8-B48F-1D18A9856A87
last_name: Danzl
orcid: 0000-0001-8559-3973
citation:
ama: Michalska JM, Lyudchik J, Velicky P, et al. Imaging brain tissue architecture
across millimeter to nanometer scales. Nature Biotechnology. 2023. doi:10.1038/s41587-023-01911-8
apa: Michalska, J. M., Lyudchik, J., Velicky, P., Korinkova, H., Watson, J., Cenameri,
A., … Danzl, J. G. (2023). Imaging brain tissue architecture across millimeter
to nanometer scales. Nature Biotechnology. Springer Nature. https://doi.org/10.1038/s41587-023-01911-8
chicago: Michalska, Julia M, Julia Lyudchik, Philipp Velicky, Hana Korinkova, Jake
Watson, Alban Cenameri, Christoph M Sommer, et al. “Imaging Brain Tissue Architecture
across Millimeter to Nanometer Scales.” Nature Biotechnology. Springer
Nature, 2023. https://doi.org/10.1038/s41587-023-01911-8.
ieee: J. M. Michalska et al., “Imaging brain tissue architecture across millimeter
to nanometer scales,” Nature Biotechnology. Springer Nature, 2023.
ista: Michalska JM, Lyudchik J, Velicky P, Korinkova H, Watson J, Cenameri A, Sommer
CM, Amberg N, Venturino A, Roessler K, Czech T, Höftberger R, Siegert S, Novarino
G, Jonas PM, Danzl JG. 2023. Imaging brain tissue architecture across millimeter
to nanometer scales. Nature Biotechnology.
mla: Michalska, Julia M., et al. “Imaging Brain Tissue Architecture across Millimeter
to Nanometer Scales.” Nature Biotechnology, Springer Nature, 2023, doi:10.1038/s41587-023-01911-8.
short: J.M. Michalska, J. Lyudchik, P. Velicky, H. Korinkova, J. Watson, A. Cenameri,
C.M. Sommer, N. Amberg, A. Venturino, K. Roessler, T. Czech, R. Höftberger, S.
Siegert, G. Novarino, P.M. Jonas, J.G. Danzl, Nature Biotechnology (2023).
date_created: 2023-09-03T22:01:15Z
date_published: 2023-08-31T00:00:00Z
date_updated: 2024-02-21T12:18:18Z
day: '31'
department:
- _id: SaSi
- _id: GaNo
- _id: PeJo
- _id: JoDa
- _id: Bio
- _id: RySh
doi: 10.1038/s41587-023-01911-8
ec_funded: 1
external_id:
isi:
- '001065254200001'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1038/s41587-023-01911-8
month: '08'
oa: 1
oa_version: Published Version
project:
- _id: 265CB4D0-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: I03600
name: Optical control of synaptic function via adhesion molecules
- _id: 2548AE96-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: W1232-B24
name: Molecular Drug Targets
- _id: 25C5A090-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z00312
name: The Wittgenstein Prize
- _id: 23889792-32DE-11EA-91FC-C7463DDC885E
name: High content imaging to decode human immune cell interactions in health and
allergic disease
- _id: 25444568-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '715508'
name: Probing the Reversibility of Autism Spectrum Disorders by Employing in vivo
and in vitro Models
- _id: 25B7EB9E-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '692692'
name: Biophysics and circuit function of a giant cortical glumatergic synapse
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: fc2be41b-9c52-11eb-aca3-faa90aa144e9
call_identifier: H2020
grant_number: '101026635'
name: Synaptic computations of the hippocampal CA3 circuitry
publication: Nature Biotechnology
publication_identifier:
eissn:
- 1546-1696
issn:
- 1087-0156
publication_status: epub_ahead
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/danzllab/CATS
record:
- id: '13126'
relation: research_data
status: public
scopus_import: '1'
status: public
title: Imaging brain tissue architecture across millimeter to nanometer scales
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14317'
abstract:
- lang: eng
text: "Markov decision processes can be viewed as transformers of probability distributions.
While this view is useful from a practical standpoint to reason about trajectories
of distributions, basic reachability and safety problems are known to be computationally
intractable (i.e., Skolem-hard) to solve in such models. Further, we show that
even for simple examples of MDPs, strategies for safety objectives over distributions
can require infinite memory and randomization.\r\nIn light of this, we present
a novel overapproximation approach to synthesize strategies in an MDP, such that
a safety objective over the distributions is met. More precisely, we develop a
new framework for template-based synthesis of certificates as affine distributional
and inductive invariants for safety objectives in MDPs. We provide two algorithms
within this framework. One can only synthesize memoryless strategies, but has
relative completeness guarantees, while the other can synthesize general strategies.
The runtime complexity of both algorithms is in PSPACE. We implement these algorithms
and show that they can solve several non-trivial examples."
acknowledgement: This work was supported in part by the ERC CoG 863818 (FoRM-SMArt)
and the European Union’s Horizon 2020 research and innovation programme under the
Marie Skłodowska-Curie Grant Agreement No. 665385 as well as DST/CEFIPRA/INRIA project
EQuaVE and SERB Matrices grant MTR/2018/00074.
alternative_title:
- LNCS
article_processing_charge: Yes (in subscription journal)
author:
- first_name: S.
full_name: Akshay, S.
last_name: Akshay
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Tobias
full_name: Meggendorfer, Tobias
id: b21b0c15-30a2-11eb-80dc-f13ca25802e1
last_name: Meggendorfer
orcid: 0000-0002-1712-2165
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
citation:
ama: 'Akshay S, Chatterjee K, Meggendorfer T, Zikelic D. MDPs as distribution transformers:
Affine invariant synthesis for safety objectives. In: International Conference
on Computer Aided Verification. Vol 13966. Springer Nature; 2023:86-112. doi:10.1007/978-3-031-37709-9_5'
apa: 'Akshay, S., Chatterjee, K., Meggendorfer, T., & Zikelic, D. (2023). MDPs
as distribution transformers: Affine invariant synthesis for safety objectives.
In International Conference on Computer Aided Verification (Vol. 13966,
pp. 86–112). Paris, France: Springer Nature. https://doi.org/10.1007/978-3-031-37709-9_5'
chicago: 'Akshay, S., Krishnendu Chatterjee, Tobias Meggendorfer, and Dorde Zikelic.
“MDPs as Distribution Transformers: Affine Invariant Synthesis for Safety Objectives.”
In International Conference on Computer Aided Verification, 13966:86–112.
Springer Nature, 2023. https://doi.org/10.1007/978-3-031-37709-9_5.'
ieee: 'S. Akshay, K. Chatterjee, T. Meggendorfer, and D. Zikelic, “MDPs as distribution
transformers: Affine invariant synthesis for safety objectives,” in International
Conference on Computer Aided Verification, Paris, France, 2023, vol. 13966,
pp. 86–112.'
ista: 'Akshay S, Chatterjee K, Meggendorfer T, Zikelic D. 2023. MDPs as distribution
transformers: Affine invariant synthesis for safety objectives. International
Conference on Computer Aided Verification. CAV: Computer Aided Verification, LNCS,
vol. 13966, 86–112.'
mla: 'Akshay, S., et al. “MDPs as Distribution Transformers: Affine Invariant Synthesis
for Safety Objectives.” International Conference on Computer Aided Verification,
vol. 13966, Springer Nature, 2023, pp. 86–112, doi:10.1007/978-3-031-37709-9_5.'
short: S. Akshay, K. Chatterjee, T. Meggendorfer, D. Zikelic, in:, International
Conference on Computer Aided Verification, Springer Nature, 2023, pp. 86–112.
conference:
end_date: 2023-07-22
location: Paris, France
name: 'CAV: Computer Aided Verification'
start_date: 2023-07-17
date_created: 2023-09-10T22:01:12Z
date_published: 2023-07-17T00:00:00Z
date_updated: 2025-07-14T09:09:56Z
day: '17'
ddc:
- '000'
department:
- _id: KrCh
doi: 10.1007/978-3-031-37709-9_5
ec_funded: 1
file:
- access_level: open_access
checksum: f143c8eedf609f20f2aad2eeb496d53f
content_type: application/pdf
creator: dernst
date_created: 2023-09-20T08:46:43Z
date_updated: 2023-09-20T08:46:43Z
file_id: '14349'
file_name: 2023_LNCS_Akshay.pdf
file_size: 531745
relation: main_file
success: 1
file_date_updated: 2023-09-20T08:46:43Z
has_accepted_license: '1'
intvolume: ' 13966'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 86-112
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
publication: International Conference on Computer Aided Verification
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783031377082'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'MDPs as distribution transformers: Affine invariant synthesis for safety objectives'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13966
year: '2023'
...