Lucas Lacasa
@wetuad.bsky.social
Complex Systems | CSIC tenured scientist @ifisc.uib-csic.es
https://sites.google.com/view/lucaslacasa/
https://sites.google.com/view/lucaslacasa/
🚨new preprint !!
How to describe, forecast or control the dynamics of temporal networks? A possible approach includes using fluid mechanical data-driven tools such as #POD and #DMD.
Preprint: arxiv.org/abs/2509.03135
How to describe, forecast or control the dynamics of temporal networks? A possible approach includes using fluid mechanical data-driven tools such as #POD and #DMD.
Preprint: arxiv.org/abs/2509.03135
September 4, 2025 at 9:17 AM
🚨new preprint !!
How to describe, forecast or control the dynamics of temporal networks? A possible approach includes using fluid mechanical data-driven tools such as #POD and #DMD.
Preprint: arxiv.org/abs/2509.03135
How to describe, forecast or control the dynamics of temporal networks? A possible approach includes using fluid mechanical data-driven tools such as #POD and #DMD.
Preprint: arxiv.org/abs/2509.03135
Reposted by Lucas Lacasa
After lunch, we have begun with Lucas Lacasa (@wetuad.bsky.social) who is introducing the use of #federated #learning as implementable solutions for cross-institutional problems. Highlighting industry applications of quantifying #emergence.
@ifisc.uib-csic.es
@ifisc.uib-csic.es
June 23, 2025 at 12:47 PM
After lunch, we have begun with Lucas Lacasa (@wetuad.bsky.social) who is introducing the use of #federated #learning as implementable solutions for cross-institutional problems. Highlighting industry applications of quantifying #emergence.
@ifisc.uib-csic.es
@ifisc.uib-csic.es
🚨New preprint !!
Interpreting the training process of a neural network as a temporal network trajectory, we found a regime where such trajectory becomes chaotic. Rather than a nuisance, such chaotic mixing boosts training!
w/ P. Jiménez, @miguelcsoriano.bsky.social
arxiv.org/abs/2506.08523
Interpreting the training process of a neural network as a temporal network trajectory, we found a regime where such trajectory becomes chaotic. Rather than a nuisance, such chaotic mixing boosts training!
w/ P. Jiménez, @miguelcsoriano.bsky.social
arxiv.org/abs/2506.08523
Leveraging chaos in the training of artificial neural networks
Traditional algorithms to optimize artificial neural networks when confronted with a supervised learning task are usually exploitation-type relaxational dynamics such as gradient descent (GD). Here, w...
arxiv.org
June 15, 2025 at 7:16 PM
🚨New preprint !!
Interpreting the training process of a neural network as a temporal network trajectory, we found a regime where such trajectory becomes chaotic. Rather than a nuisance, such chaotic mixing boosts training!
w/ P. Jiménez, @miguelcsoriano.bsky.social
arxiv.org/abs/2506.08523
Interpreting the training process of a neural network as a temporal network trajectory, we found a regime where such trajectory becomes chaotic. Rather than a nuisance, such chaotic mixing boosts training!
w/ P. Jiménez, @miguelcsoriano.bsky.social
arxiv.org/abs/2506.08523
🚨 Just published !!
How to extract a *scalar time series* that accurately captures the dynamics of a whole temporal network ?
#netsci2025 too bad I missed you.
Great Collab w/ Lluís Arola, Naoki Masuda and F. Javier Marín
Open access --> www.sciencedirect.com/science/arti...
How to extract a *scalar time series* that accurately captures the dynamics of a whole temporal network ?
#netsci2025 too bad I missed you.
Great Collab w/ Lluís Arola, Naoki Masuda and F. Javier Marín
Open access --> www.sciencedirect.com/science/arti...
June 5, 2025 at 7:53 AM
🚨 Just published !!
How to extract a *scalar time series* that accurately captures the dynamics of a whole temporal network ?
#netsci2025 too bad I missed you.
Great Collab w/ Lluís Arola, Naoki Masuda and F. Javier Marín
Open access --> www.sciencedirect.com/science/arti...
How to extract a *scalar time series* that accurately captures the dynamics of a whole temporal network ?
#netsci2025 too bad I missed you.
Great Collab w/ Lluís Arola, Naoki Masuda and F. Javier Marín
Open access --> www.sciencedirect.com/science/arti...
Reposted by Lucas Lacasa
New Study Out Now!
We investigated how deep brain stimulation (DBS) of the nucleus accumbens (NAc) affects memory. www.nature.com/npp/
Title: NAc-DBS selectively enhances memory updating without effect on retrieval
👇 THREAD 👇
We investigated how deep brain stimulation (DBS) of the nucleus accumbens (NAc) affects memory. www.nature.com/npp/
Title: NAc-DBS selectively enhances memory updating without effect on retrieval
👇 THREAD 👇
May 27, 2025 at 1:47 PM
New Study Out Now!
We investigated how deep brain stimulation (DBS) of the nucleus accumbens (NAc) affects memory. www.nature.com/npp/
Title: NAc-DBS selectively enhances memory updating without effect on retrieval
👇 THREAD 👇
We investigated how deep brain stimulation (DBS) of the nucleus accumbens (NAc) affects memory. www.nature.com/npp/
Title: NAc-DBS selectively enhances memory updating without effect on retrieval
👇 THREAD 👇
🆕 Preprint on the use of Machine Learning tools (deep reinforcement learning + transfer learning) for aerodynamic optimization !!
w/ David Ramos, Gonzalo Rubio & Eusebio Valero
arxiv.org/pdf/2505.02634
w/ David Ramos, Gonzalo Rubio & Eusebio Valero
arxiv.org/pdf/2505.02634
May 15, 2025 at 9:45 AM
🆕 Preprint on the use of Machine Learning tools (deep reinforcement learning + transfer learning) for aerodynamic optimization !!
w/ David Ramos, Gonzalo Rubio & Eusebio Valero
arxiv.org/pdf/2505.02634
w/ David Ramos, Gonzalo Rubio & Eusebio Valero
arxiv.org/pdf/2505.02634
New paper on Machine Learning in Aerostructures, now published in Expert Systems With Applications:
shorturl.at/Olfig
Great industry-academia collaboration
@aeroespacialupm.bsky.social
@esa.int
@ifisc.uib-csic.es @EsAirbus
shorturl.at/Olfig
Great industry-academia collaboration
@aeroespacialupm.bsky.social
@esa.int
@ifisc.uib-csic.es @EsAirbus
shorturl.at
March 19, 2025 at 11:25 AM
New paper on Machine Learning in Aerostructures, now published in Expert Systems With Applications:
shorturl.at/Olfig
Great industry-academia collaboration
@aeroespacialupm.bsky.social
@esa.int
@ifisc.uib-csic.es @EsAirbus
shorturl.at/Olfig
Great industry-academia collaboration
@aeroespacialupm.bsky.social
@esa.int
@ifisc.uib-csic.es @EsAirbus
We have looked at how 🧠evolves in graph space. We found that their network trajectories characterize well aging and brain pathologies. Details 👇
Predictability of temporal network dynamics in normal ageing and brain pathology
(preprint with Lisa Caligiuri, Massimiliano Zanin, @wetuad.bsky.social, and others)
arxiv.org/abs/2502.16557
(preprint with Lisa Caligiuri, Massimiliano Zanin, @wetuad.bsky.social, and others)
arxiv.org/abs/2502.16557
Predictability of temporal network dynamics in normal ageing and brain pathology
Spontaneous brain activity generically displays transient spatiotemporal coherent structures, which can selectively be affected in various neurological and psychiatric pathologies. Here we model the f...
arxiv.org
February 28, 2025 at 11:47 AM
We have looked at how 🧠evolves in graph space. We found that their network trajectories characterize well aging and brain pathologies. Details 👇
Reposted by Lucas Lacasa
Today, we are publishing the first-ever International AI Safety Report, backed by 30 countries and the OECD, UN, and EU.
It summarises the state of the science on AI capabilities and risks, and how to mitigate those risks. 🧵
Full Report: assets.publishing.service.gov.uk/media/679a0c...
1/21
It summarises the state of the science on AI capabilities and risks, and how to mitigate those risks. 🧵
Full Report: assets.publishing.service.gov.uk/media/679a0c...
1/21
January 29, 2025 at 1:50 PM
Today, we are publishing the first-ever International AI Safety Report, backed by 30 countries and the OECD, UN, and EU.
It summarises the state of the science on AI capabilities and risks, and how to mitigate those risks. 🧵
Full Report: assets.publishing.service.gov.uk/media/679a0c...
1/21
It summarises the state of the science on AI capabilities and risks, and how to mitigate those risks. 🧵
Full Report: assets.publishing.service.gov.uk/media/679a0c...
1/21
How to describe the dynamical properties of networks *with no labels* that change over time ?
🆕 preprint
(with @_CaligiuriLisa_ @tobiasgalla.bsky.social)
arxiv.org/abs/2412.14864
🆕 preprint
(with @_CaligiuriLisa_ @tobiasgalla.bsky.social)
arxiv.org/abs/2412.14864
December 20, 2024 at 7:06 AM
How to describe the dynamical properties of networks *with no labels* that change over time ?
🆕 preprint
(with @_CaligiuriLisa_ @tobiasgalla.bsky.social)
arxiv.org/abs/2412.14864
🆕 preprint
(with @_CaligiuriLisa_ @tobiasgalla.bsky.social)
arxiv.org/abs/2412.14864
So @naturecomms.bsky.social has highlighted our research home @ifisc.uib-csic.es as an institutional model for interdisciplinary science!
🚀 Cross-disciplinary research is shaping the future! Discover how IFISC has become a global model, breaking silos and fostering innovation in complex systems.
🌐 Read more in @naturecomms.bsky.social: www.nature.com/articles/s41...
#ComplexSystems #ScienceInnovation #CrossDisciplinarity
A cross-disciplinary research framework at institution level and beyond - Nature Communications
Rethinking and restructuring cross-disciplinary research requires innovative models and the Institute for Cross-Disciplinary Physics and Complex Systems (IFISC) stands as a powerful example. Since its...
www.nature.com
December 13, 2024 at 5:29 PM
So @naturecomms.bsky.social has highlighted our research home @ifisc.uib-csic.es as an institutional model for interdisciplinary science!
🚨 New preprint out!
We build **scalar** time series embeddings of temporal networks !
The key enabling insight : the relevant feature of each network snapshot... is just its distance to every other snapshot!
Work w/ FJ Marín, N. Masuda, L. Arola-Fernández
arxiv.org/abs/2412.02715
We build **scalar** time series embeddings of temporal networks !
The key enabling insight : the relevant feature of each network snapshot... is just its distance to every other snapshot!
Work w/ FJ Marín, N. Masuda, L. Arola-Fernández
arxiv.org/abs/2412.02715
December 5, 2024 at 8:30 AM
🚨 New preprint out!
We build **scalar** time series embeddings of temporal networks !
The key enabling insight : the relevant feature of each network snapshot... is just its distance to every other snapshot!
Work w/ FJ Marín, N. Masuda, L. Arola-Fernández
arxiv.org/abs/2412.02715
We build **scalar** time series embeddings of temporal networks !
The key enabling insight : the relevant feature of each network snapshot... is just its distance to every other snapshot!
Work w/ FJ Marín, N. Masuda, L. Arola-Fernández
arxiv.org/abs/2412.02715
Hi Bluesky! My first skytweet is to reflect on how more is different when individual AIs come together arxiv.org/abs/2310.12802 #complexsystems #emergence #machinelearning
November 5, 2023 at 2:46 PM
Hi Bluesky! My first skytweet is to reflect on how more is different when individual AIs come together arxiv.org/abs/2310.12802 #complexsystems #emergence #machinelearning