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2020


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Community detection with node attributes in multilayer networks

Martina Contisciani, E. A. P., Bacco, C. D.

Nature Scientific Reports, 10, pages: 15736, September 2020 (article)

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[BibTex]

2020


[BibTex]


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Analytical classical density functionals from an equation learning network

Lin, S., Martius, G., Oettel, M.

The Journal of Chemical Physics, 152(2):021102, 2020, arXiv preprint \url{https://arxiv.org/abs/1910.12752} (article)

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Preprint_PDF DOI [BibTex]

Preprint_PDF DOI [BibTex]


Wearable and Stretchable Strain Sensors: Materials, Sensing Mechanisms, and Applications
Wearable and Stretchable Strain Sensors: Materials, Sensing Mechanisms, and Applications

Souri, H., Banerjee, H., Jusufi, A., Radacsi, N., Stokes, A. A., Park, I., Sitti, M., Amjadi, M.

Advanced Intelligent Systems, 2020 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Fish-like aquatic propulsion studied using a pneumatically-actuated soft-robotic model

Wolf, Z., Jusufi, A., Vogt, D. M., Lauder, G. V.

Bioinspiration & Biomimetics, 15(4):046008, Inst. of Physics, London, 2020 (article)

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DOI [BibTex]

DOI [BibTex]

2016


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Stochastic search with Poisson and deterministic resetting

Bhat, U., De Bacco, C., Redner, S.

Journal of Statistical Mechanics: Theory and Experiment, 2016(8):083401, IOP Publishing, 2016 (article)

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Preprint link (url) [BibTex]

2016


Preprint link (url) [BibTex]


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Dynamics of beneficial epidemics

Berdahl, A., Brelsford, C., De Bacco, C., Dumas, M., Ferdinand, V., Grochow, J. A., Hébert-Dufresne, L., Kallus, Y., Kempes, C. P., Kolchinsky, A., others,

arXiv preprint arXiv:1604.02096, 2016 (article)

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Preprint [BibTex]

Preprint [BibTex]


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Rare events statistics of random walks on networks: localisation and other dynamical phase transitions

De Bacco, C., Guggiola, A., Kühn, R., Paga, P.

Journal of Physics A: Mathematical and Theoretical, 49(18):184003, IOP Publishing, 2016 (article)

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Preprint link (url) [BibTex]

Preprint link (url) [BibTex]


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Extrapolation and learning equations

Martius, G., Lampert, C. H.

2016, arXiv preprint \url{https://arxiv.org/abs/1610.02995} (misc)

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Project Page [BibTex]

Project Page [BibTex]

2015


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The average number of distinct sites visited by a random walker on random graphs

De Bacco, C., Majumdar, S. N., Sollich, P.

Journal of Physics A: Mathematical and Theoretical, 48(20):205004, IOP Publishing, 2015 (article)

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Preprint link (url) [BibTex]

2015


Preprint link (url) [BibTex]


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The edge-disjoint path problem on random graphs by message-passing

Altarelli, F., Braunstein, A., Dall’Asta, L., De Bacco, C., Franz, S.

PloS one, 10(12):e0145222, Public Library of Science, 2015 (article)

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Code Preprint link (url) Project Page [BibTex]

Code Preprint link (url) Project Page [BibTex]


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Non-equilibrium statistical mechanics of the heat bath for two Brownian particles : Internal degrees of freedom found where there shouldn’t be (Special Issue on New Challenges in Complex Systems Science)

De Bacco, C., Baldovin, F., Orlandini, E.

理工研報告特集号 : ASTE : advances in science, technology and environmentology : special issue, 11, pages: 111-113, 早稲田大学理工学術院総合研究所 (理工学研究所), March 2015 (article)

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link (url) [BibTex]

link (url) [BibTex]


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Novel plasticity rule can explain the development of sensorimotor intelligence

Der, R., Martius, G.

Proceedings of the National Academy of Sciences, 112(45):E6224-E6232, 2015 (article)

Abstract
Grounding autonomous behavior in the nervous system is a fundamental challenge for neuroscience. In particular, self-organized behavioral development provides more questions than answers. Are there special functional units for curiosity, motivation, and creativity? This paper argues that these features can be grounded in synaptic plasticity itself, without requiring any higher-level constructs. We propose differential extrinsic plasticity (DEP) as a new synaptic rule for self-learning systems and apply it to a number of complex robotic systems as a test case. Without specifying any purpose or goal, seemingly purposeful and adaptive rhythmic behavior is developed, displaying a certain level of sensorimotor intelligence. These surprising results require no system-specific modifications of the DEP rule. They rather arise from the underlying mechanism of spontaneous symmetry breaking, which is due to the tight brain body environment coupling. The new synaptic rule is biologically plausible and would be an interesting target for neurobiological investigation. We also argue that this neuronal mechanism may have been a catalyst in natural evolution.

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link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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Quantifying Emergent Behavior of Autonomous Robots

Martius, G., Olbrich, E.

Entropy, 17(10):7266, 2015 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]

2012


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Tail-assisted pitch control in lizards, robots and dinosaurs

Libby, T., Moore, T., Chang, E., Li, D., Cohen, D., Jusufi, A., Full, R.

Nature, 2012 (article)

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link (url) [BibTex]

2012


link (url) [BibTex]


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Rapid Inversion: Running Animals and Robots Swing like a Pendulum under Ledges

Mongeau, J., McRae, B., Jusufi, A., Birkmeyer, P., Hoover, A., Fearing, R.

PLoS One, 2012 (article)

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link (url) [BibTex]

link (url) [BibTex]


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Variants of guided self-organization for robot control

Martius, G., Herrmann, J.

Theory in Biosci., 131(3):129-137, Springer Berlin / Heidelberg, 2012 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers

Rolinek, M., Swoboda, P., Zietlow, D., Paulus, A., Musil, V., Martius, G.

Arxiv (article)

Abstract
Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contains unmodified combinatorial solvers. Using the presence of heavily optimized combinatorial solvers together with some improvements in architecture design, we advance state-of-the-art on deep graph matching benchmarks for keypoint correspondence. In addition, we highlight the conceptual advantages of incorporating solvers into deep learning architectures, such as the possibility of post-processing with a strong multi-graph matching solver or the indifference to changes in the training setting. Finally, we propose two new challenging experimental setups

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Arxiv [BibTex]