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.
Continually learn to map visual concepts to language models in resource-constrained environments
This paper proposes a novel learning strategy, Continual Visual Mapping (CVM), which continuously maps visual representations into a fixed knowledge space derived from a language model.
Clea Rebillard, Julio Hurtado, Andrii Krutsylo, Lucia Passaro, Vincenzo Lomonaco, Neurocomputing, Volume 652, 2025, 131013, DOI: 10.1016/j.neucom.2025.131013.
Continuously Deep Recurrent Neural Networks
This paper introduces a new class of recurrent neural models based on a fundamentally different type of topological organization than the conventionally used deep recurrent networks, and directly inspired by the way cortical networks in the brain process information at multiple temporal scales.
Ceni, A., Dominey, P.F., Gallicchio, C., Micheli, A., Pedrelli, L., Tortorella, D. (2024). In Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2024. Lecture Notes in Computer Science(), vol 14947. doi: 10.1007/978-3-031-70368-3_4
Covert neural and autonomic signatures of shared perception Open Access
This work tested whether awareness that a visual stimulus is simultaneously available to another person, without interaction, modulates behavioral performance and neurophysiological signatures of perceptual decision-making.
Mustafa Yavuz, Jamal Esmaily, Bahador Bahrami, Ophelia Deroy, Social Cognitive and Affective Neuroscience, Volume 21, Issue 1, 2026, nsag009, DOI: 10.1093/scan/nsag009
Decentralized Incremental Federated Learning with Echo State Networks
This work broadens the applicability of Federated Echo State Networks to a decentralized setting, where we have a set of peers connected through a logical communication topology and cannot rely on a centralized aggregation entity.
G. Pompei, P. Dazzi, V. De Caro and C. Gallicchio, 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan, 2024, pp. 1-8, doi: 10.1109/IJCNN60899.2024.10650756.
Deep Echo State Networks for Modelling of Industrial Systems
This paper works with an industrial plant with four water tanks, focusing on estimating the levels of two sequentially connected tanks using Deep Echo State Networks (Deep ESNs).
Rodríguez-Ossorio, J.R., Gallicchio, C., Morán, A., Díaz, I., Fuertes, J.J., Domínguez, M. (2024). In Engineering Applications of Neural Networks. EANN 2024. Communications in Computer and Information Science, vol 2141. doi: 10.1007/978-3-031-62495-7_9
Deep Learning for Dynamic Graphs: Models and Benchmarks
With the aim of fostering research in the domain of dynamic graphs, this work surveys recent advantages in learning both temporal and spatial information, providing a comprehensive overview of the current state-of-the-art in the domain of representation learning for dynamic graphs.
A. Gravina and D. Bacciu, in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2024.3379735.
Design Pattern-Based Code Refactoring with LLMs
This paper investigates the potential of Large Language Models (LLMs) to automate design pattern-based code refactoring.
B. Zisa, L. Passaro and J. Soldani, 2026 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), Limassol, Cyprus, 2026, pp. 222-227, doi: 10.1109/SANER67736.2026.00033.
Designing for Children’s Digital Well-being: An Agenda for Research, Policy and Practice
This work aims to co-create an agenda for future actions by mapping the current state-of-the-art research about children’s digital well-being.
Vicky Charisi, Nikoleta Yiannoutsou, Shuli and Gilutz, Matthew and Dennis and Shyamli Suneesh, in Proceedings of the 23rd Annual ACM Interaction Design and Children Conference, 1026–1028, 2024, doi:10.1145/3628516.3661154.
Designing robot swarms: a puzzle, a problem, and a mess
This work characterize the issue of designing collective behaviors for robot swarms and discuss how various research goals have shaped the current state of the field.
D. Garzon Ramos and S. Hauert (2024). 40th Anniversary of the IEEE Conference on Robotics and Automation (ICRA@40), pp. 1600-1602. arXiv:2410.22478
Direct Communication or Stigmergy? Selecting Communication Mechanisms for Robot Swarms via Automatic Modular Design
In this paper, we show that automatic modular design (AutoMoDe) can also select between direct communication and stigmergy within a single design process.
David Garzón Ramos, Juan B. Medina, Sabine Hauert, Mauro Birattari (2025). Proceedings of the ALIFE 2025: Ciphers of Life: Proceedings of the Artificial Life Conference 2025. Kyoto, Japan. (pp. 87). DOI: 10.1162/ISAL.a.915
Direct Feedback Alignment for Recurrent Neural Networks
This work adapts Direct Feedback Alignment (DFA) for both “vanilla” and gated recurrent networks.
Folchini, S., Cossu, A., Ceni, A., Lomonaco, V., Bacciu, D., Gallicchio, C. (2026). In High Performance Computing. ISC High Performance 2025. Lecture Notes in Computer Science, vol 16091. DOI: 10.1007/978-3-032-07612-0_9
Disentangling Dynamics: A Compositional Framework for Temporal Graph Foundation Models
This paper proposes TIDES, a compositional framework that systematically decouples time from space during learning, serving as a stepping stone toward foundation models for temporal graphs.
Fabrizio De Castelli, Alessio Gravina, Davide Bacciu, Moshe Eliasof, in Proc. Forty-third International Conference on Machine Learning, 2026.
Distributed Spatial Awareness for Robot Swarms
This paper uses local observations by robots of each other and Gaussian Belief Propagation message passing combined with continuous swarm movement to build a global and distributed swarm-centric frame of reference.
Jones, S., Hauert, S. (2026). In Distributed Autonomous Robotic Systems. DARS 2024. Springer Proceedings in Advanced Robotics, vol 34. DOI: 10.1007/978-3-032-04584-3_36
Diversifying Non-dissipative Reservoir Computing Dynamics
In this paper, the authors propose alternative formulations of the reservoirs for EuSNs, aiming at improving the diversity of the resulting dynamics.
Gallicchio, C. (2023). In Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14261. Springer, Cham. doi: 10.1007/978-3-031-44198-1_15
DRESS: Distributed Robotic Enhanced Soft System for Non-Uniform Object Transportation
This paper presents the Distributed Robotic Enhanced Soft System (DRESS), a proof-of-concept platform that integrates soft inflatable actuation with a mobile groundbased swarm to explore cooperative transport of varied object types.
S. Terrile et al., 2026 IEEE 9th International Conference on Soft Robotics (RoboSoft), Kanazawa, Japan, 2026, pp. 944-949, doi: 10.1109/RoboSoft67810.2026.11522867.
Drifting explanations in continual learning
This paper studies the behavior of different explanation methods in CL and propose CLEX (ContinuaL EXplanations), an evaluation protocol to robustly assess the change of explanations in Class-Incremental scenarios, where forgetting is pronounced.
Andrea Cossu, Francesco Spinnato, Riccardo Guidotti, Davide Bacciu, Neurocomputing, 597, 2024, 127960, doi: 10.1016/j.neucom.2024.127960.
Edge of Stability Echo State Network
This paper introduces a new ESN architecture called the Edge of Stability (ESN). The introduced model is based on defining the reservoir layer as a convex combination of a nonlinear reservoir (as in the standard ESN), and a linear reservoir that implements an orthogonal transformation.
A. Ceni and C. Gallicchio, in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2024.3400045.
Embedded deep reservoir computing for modelling complex industrial systems
This work proposes the use of Deep Echo State Networks to model an industrial system and evaluate its performance in real-time industrial applications when running on embedded devices.
Ramón Rodríguez-Ossorio J, Gallicchio C, Morán A, Díaz I, Fuertes JJ, Domínguez M. Integrated Computer-Aided Engineering. 2025;0(0). doi:10.1177/10692509251376129
