EMERGE’s findings will be communicated at scientific conferences and published in open-access journals. Find below the current list of publications.
Access all publication on the project's Zenodo community clicking here.
NiSNN-A: Noniterative Spiking Neural Network With Attention With Application to Motor Imagery EEG Classification
In this work, the authors combine spiking neural networks (SNNs) and the attention mechanisms for electroencephalogram (EEG) classification, aiming to improve precision and reduce energy consumption.
C. Zhang, W. Pan and C. D. Santina, in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2025.3538335.
nOdes: A Networked Constellation of Handheld Orbs for Community Music-Making
This paper presents nOdes, a platform for networked handheld musical objects designed for collaborative music-making.
Thomas Didiot-Cook; Simon Jones; Johanna Blee; Nadine Meertens; Razanne Abu-Aisheh; Ophelia Deroy; Sabine Hauert, in Proc. New Interfaces for Musical Expression 2026.
Non-Dissipative Graph Propagation for Non-Local Community Detection
This work argues that the ability to propagate long-range information during message passing is key to effectively perform community detection in heterophilic graphs.
W. Leeney, A. Gravina and D. Bacciu, 2025 International Joint Conference on Neural Networks (IJCNN), Rome, Italy, 2025, pp. 1-8, doi: 10.1109/IJCNN64981.2025.11228363.
Non-Dissipative Random Oscillators Networks through Negative Feedback Coupling
This paper proposes the Non-Dissipative RON (ND-RON) and benchmark it against popular time series datasets, showcasing its effectiveness against RON and other popular reservoir computing approaches.
Gioele Zerini, Andrea Ceni, Andrea Cossu, Davide Bacciu, and Claudio Gallicchio, in Proc. World Congress on Computational Intelligence, 2026.
Non-dissipative Reservoir Computing Approaches for Time-Series Classification
This paper studies the behavior of a recently introduced class of alternative RC approaches in which the fixed dynamical component implements a stable but non-dissipative system, so that the driving temporal signal can be propagated through multiple time steps effectively.
Gallicchio, C., Ceni, A. (2024). In Artificial Neural Networks and Machine Learning – ICANN 2024. ICANN 2024. Lecture Notes in Computer Science, vol 15025. doi: 10.1007/978-3-031-72359-9_8
Nonlinear Modes as a Tool for Comparing the Mathematical Structure of Dynamic Models of Soft Robots
This paper proposes to use the recent nonlinear extension in modal theory -called eigenmanifolds- as a means to evaluate control-oriented models for soft robots and compare them.
P. Pustina, D. Calzolari, A. Albu-Schäffer, A. D. Luca and C. D. Santina, 2024 IEEE 7th International Conference on Soft Robotics (RoboSoft), 2024, pp. 779-785, doi: 10.1109/RoboSoft60065.2024.10521987.
On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systems
This paper introduces a novel perspective to address oversquashing, leveraging dynamical systems properties of global and local non-dissipativity, that enable the maintenance of a constant information flow rate.
Gravina, A., Eliasof, M., Gallicchio, C., Bacciu, D., & Schönlieb, C.-B. (2025). Proceedings of the AAAI Conference on Artificial Intelligence, 39(16), 16906-16914.
On the ethical governance of swarm robotic systems in the real world
In this paper, the authors address the question: what practices would be required for the responsible design and operation of real-world swarm robotic systems?
Alan F. T. Winfield, Matimba Swana, Jonathan Ives and Sabine Hauert. Phil. Trans. R. Soc. A.38320240142 DOI: 10.1098/rsta.2024.0142
On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning
This paper proposes an interpretation of GNNs as recurrent models and empirically demonstrate that a simple state-space formulation of an GNN effectively alleviates these issues at no extra trainable parameter cost.
Alvaro Arroyo et al. 39th Conference on Neural Information Processing Systems (NeurIPS 2025).
Over-Parameterized Neural Models based on Graph Random Features for fast and accurate graph classification
This paper aims to explore a simple form of a randomized graph neural network.
Nicolò Navarin, Luca Pasa, Claudio Gallicchio, Luca Oneto, and Alessandro Sperduti, in "20th International Workshop on Mining and Learning with Graphs In conjunction with ECMLPKDD 2023"
ParalESN: Enabling parallel information processing in Reservoir Computing
This work revisits Reservoir Computing through the lens of structured operators and state space modeling, introducing Parallel Echo State Network (ParalESN).
Matteo Pinna, Giacomo Lagomarsini, Andrea Ceni, Claudio Gallicchio, in Proc. Forty-third International Conference on Machine Learning, 2026.
Perspectives in Play: A Multi-Perspective Approach for More Inclusive NLP Systems
This work conducts an extensive analysis across diverse subjective text classification tasks including hate speech, irony, abusive language, and stance detection, to highlight the importance of capturing human disagreements, often overlooked by traditional aggregation methods.
Benedetta Muscato, Lucia Passaro, Gizem Gezici, Fosca Giannotti, Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence AI and Social Good. Pages 9827-9835. DOI: 10.24963/ijcai.2025/1092
Physics-Informed Neural Networks to Model and Control Robots: A Theoretical and Experimental Investigation
This work concerns the application of physics-informed neural networks to the modeling and control of complex robotic systems.
Liu, J., Borja, P. and Della Santina, C. (2024), Adv. Intell. Syst., 6: 2300385. doi: 10.1002/aisy.202300385
Piecewise Affine Curvature model: a Reduced-Order Model for Soft Robot-Environment Interaction Beyond PCC
In this work, the authors perform an analysis of the trade-off between computational treatability and modelling accuracy, and propose a new kinematic model, the Piecewise Affine Curvature (PAC).
F. Stella, Q. Guan, C. Della Santina and J. Hughes, in 2023 IEEE International Conference on Soft Robotics (RoboSoft), Singapore, Singapore, 2023, pp. 1-7, doi: 10.1109/RoboSoft55895.2023.10121939.
Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks
This paper introduces port-Hamiltonian Deep Graph Networks, a novel framework that models neural information flow in graphs by building on the laws of conservation of Hamiltonian dynamical systems.
Simon Heilig, Alessio Gravina, Alessandro Trenta, Claudio Gallicchio, Davide Bacciu, The Thirteenth International Conference on Learning Representations, 2025.
Prescribing Cartesian Stiffness of Soft Robots by Co-Optimization of Shape and Segment-Level Stiffness
In this work, the authors propose a strategy to prescribe variations of the physical stiffness and the robot's posture so to implement a desired Cartesian stiffness and location of the contact point.
Francesco Stella, Josie Hughes, Daniela Rus, and Cosimo Della Santina. Soft Robotics. Aug 2023. 701-712. DOI: 10.1089/soro.2022.0025.
Proprioceptive Sensing of Soft Tentacles with Model Based Reconstruction for Controller Optimization
In this work, the authors propose a new sensing approach for soft underwater slender structures based on embedded pressure sensors and use a learning-based pipeline to link the sensor readings to the shape of the soft structure.
A. Vicari et al., 2023 IEEE International Conference on Soft Robotics (RoboSoft), Singapore, Singapore, 2023, pp. 1-6, doi: 10.1109/RoboSoft55895.2023.10121999.
Quasi-Metacognitive Machines: Why We Don’t Need Morally Trustworthy AI and Communicating Reliability is Enough
This paper argues that developing morally trustworthy AI is not only unethical, as it promotes trust in an entity that cannot be trustworthy, but it is also unnecessary for optimal calibration.
Dorsch, J., Deroy, O. Philos. Technol. 37, 62 (2024). doi: 10.1007/s13347-024-00752-w.
