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.
Benchmarking Nonlinear Readouts in Linear Reservoir Networks
This paper systematically benchmarks a spectrum of nonlinear readouts within linear RC frameworks.
Lagomarsini, G., Ceni, A., Gallicchio, C. (2026). ICANN 2025 International Workshops and Special Sessions. ICANN 2025. Lecture Notes in Computer Science, vol 16072. DOI: 10.1007/978-3-032-04552-2_17
BerryTwist: A Twisting-Tube Soft Robotic Gripper for Blackberry Harvesting
This paper introduces BerryTwist, a prototype robotic gripper specifically designed for blackberry harvesting.
J. F. Elfferich, E. Shahabi, C. D. Santina and D. Dodou, in IEEE Robotics and Automation Letters, vol. 10, no. 1, pp. 429-435, Jan. 2025, doi: 10.1109/LRA.2024.3505813.
Building Trustworthiness by Minimizing the Sim-to-Real Gap in Fault Detection for Robot Swarms
In this work, the authors implement metric extraction in a real-world environment and evaluate whether the extracted metrics can overcome the “sim-to-real gap”
Suet Lee and Sabine Hauert. 2023. In Proceedings of the First International Symposium on Trustworthy Autonomous Systems (TAS '23). Association for Computing Machinery, New York, NY, USA, Article 47, 1–3. doi: 10.1145/3597512.3597527
Calibration of Continual Learning Models
This paper provides the first empirical study of the behavior of calibration approaches in CL, showing that CL strategies do not inherently learn calibrated models.
L. Li, E. Piccoli, A. Cossu, D. Bacciu and V. Lomonaco, 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA, 2024, pp. 4160-4169, doi: 10.1109/CVPRW63382.2024.00419.
Cellular Au-Tonnetz: A Unified Audio-Visual MIDI Generator Using Tonnetz, Cellular Automata, and IoT
This paper presents a detailed description of an innovative tool for music creation that merges sound and light through a unified system.
Didiot-Cook, T. (2025). Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2025. Lecture Notes in Computer Science, vol 15611. DOI: 10.1007/978-3-031-90167-6_4
Co-perceiving: Bringing the social into perception
In this comprehensive review, the authors advocate for a broader and more mechanistic understanding of the phenomenon called co-perception.
Deroy, O., Longin, L., & Bahrami, B. (2024). WIREs Cognitive Science, e1681. doi: 10.1002/wcs.1681
Consensus in the weighted voter model with noise-free and noisy observations
This work presents an exact finite-population analysis of the best-of-two model on complete as well as regular network topologies.
Ganesh, A., Hauert, S. & Valla, E. Swarm Intell 19, 173–214 (2025). DOI: 10.1007/s11721-025-00248-z
Contact-Aware Safety in Soft Robots Using High-Order Control Barrier and Lyapunov Functions
This letter introduces a comprehensive framework that enforces strict contact force limits across the entire soft-robot body during environmental interactions.
K. Wong, M. Stölzle, W. Xiao, C. D. Santina, D. Rus and G. Zardini, in IEEE Robotics and Automation Letters, vol. 10, no. 12, pp. 12485-12492, Dec. 2025, doi: 10.1109/LRA.2025.3621965
Continual pre-training mitigates forgetting in language and vision
This paper investigates the characteristics of the Continual Pre-Training scenario, where a model is continually pre-trained on a stream of incoming data and only later fine-tuned to different downstream tasks.
Andrea Cossu, Antonio Carta, Lucia Passaro, Vincenzo Lomonaco, Tinne Tuytelaars, Davide Bacciu, Neural Networks, 179, 2024, 106492, doi: 10.1016/j.neunet.2024.106492.
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
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.
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
