TY - JOUR A1 - Axenie, Cristian A1 - Halilov, Ertan A1 - Main, Julian A1 - Weiss, David T1 - Edge neuro-statistical learning for event-based visual motion detection and tracking in roadside safety systems JF - Neuromorphic Computing and Engineering N2 - The Vision Zero Program's purpose is to reduce traffic-related fatalities and serious injuries while promoting equitable, safe, and healthy mobility for all. Ultimately, the challenge is to detect pedestrians during the day and especially at night to implement safety measures. The current study introduces an award-winning low-power solution employing neuromorphic visual sensing and hybrid neuro-statistical processing developed by the Technische Hochschule Nürnberg team for the TinyML Vision Zero San Jose Competition. The solution proposes a novel neuromorphic edge fusion of spiking neural networks and event-based expectation maximization for the detection and tracking of pedestrians and bicyclists. We provide a deployment-ready evaluation of the detection performance along with robustness, energy footprint, and weatherization while emphasizing the advantages of the neuro-statistical edge solution and its city-level scaling capabilities. Y1 - 2025 U6 - https://doi.org/10.1088/2634-4386/adcbcb SN - 2634-4386 PB - IOP Publishing ER - TY - JOUR A1 - Axenie, Cristian T1 - Antifragile control systems in neuronal processing: a sensorimotor perspective JF - Biological Cybernetics N2 - The stability–robustness–resilience–adaptiveness continuum in neuronal processing follows a hierarchical structure that explains interactions and information processing among the different time scales. Interestingly, using “canonical” neuronal computational circuits, such as Homeostatic Activity Regulation, Winner-Take-All, and Hebbian Temporal Correlation Learning, one can extend the behavior spectrum towards antifragility. Cast already in both probability theory and dynamical systems, antifragility can explain and define the interesting interplay among neural circuits, found, for instance, in sensorimotor control in the face of uncertainty and volatility. This perspective proposes a new framework to analyze and describe closed-loop neuronal processing using principles of antifragility, targeting sensorimotor control. Our objective is two-fold. First, we introduce antifragile control as a conceptual framework to quantify closed-loop neuronal network behaviors that gain from uncertainty and volatility. Second, we introduce neuronal network design principles, opening the path to neuromorphic implementations and transfer to technical systems. Y1 - 2025 U6 - https://doi.org/10.1007/s00422-025-01003-7 SN - 1432-0770 VL - 119 IS - 2-3 PB - Springer Nature ER - TY - CHAP A1 - Gheorghe, Lisca A1 - Matthias, Kindler A1 - Tanja, Lerchl A1 - Axenie, Cristian A1 - Thomas, Grauschopf A1 - Veit, Senner T1 - The potential of reinforcement learning for lumbar load prediction in multi body models of the spine N2 - Multi body models (MBS) of the spine are an integral part of clinical and biomechanical research. Their noninvasive and adaptive character makes them a promising tool to address a large variety of questions regarding spinal loading, its causes, and consequences for the healthy and pathological spine. In sports science and athletic training, the predictive simulations of human movement can explain how changes in training, technique, or equipment affect performance and the body's biomechanical responses, helping athletes and coaches to make informed decisions. For solving predictive simulations optimization algorithms like collocation method are the most performant ones. The Reinforcement Learning (RL) algorithms propose a transition from optimization to learning. They formulate the problem of predictive simulation as learning to generate new data that describes human movement. This formulation enables them to leverage the generative power of the Artificial Neural Networks (ANNs), capable of learning models from high-dimensional and nonlinear movement data, and subsequently using these models to generate new ones within new boundary conditions. Most studies including MBS of the spine use a combination of inverse kinematics and optimization for muscle force and lumbar load estimation. However, these approaches either use generic assumptions for loading tasks of low complexity or require kinematic data from experimental studies. In this study, we address the following question: What is the potential of RL algorithms for predicting the spine’s joint torques during an extension? Y1 - 2024 U6 - https://doi.org/10.17028/rd.lboro.27045154.v1 ER - TY - JOUR A1 - Axenie, Cristian A1 - López-Corona, Oliver A1 - Makridis, Michail A. A1 - Akbarzadeh, Meisam A1 - Saveriano, Matteo A1 - Stancu, Alexandru A1 - West, Jeffrey T1 - Antifragility in complex dynamical systems JF - npj Complexity N2 - Antifragility characterizes the benefit of a dynamical system derived from the variability in environmental perturbations. Antifragility carries a precise definition that quantifies a system’s output response to input variability. Systems may respond poorly to perturbations (fragile) or benefit from perturbations (antifragile). In this manuscript, we review a range of applications of antifragility theory in technical systems (e.g., traffic control, robotics) and natural systems (e.g., cancer therapy, antibiotics). While there is a broad overlap in methods used to quantify and apply antifragility across disciplines, there is a need for precisely defining the scales at which antifragility operates. Thus, we provide a brief general introduction to the properties of antifragility in applied systems and review relevant literature for both natural and technical systems’ antifragility. We frame this review within three scales common to technical systems: intrinsic (input–output nonlinearity), inherited (extrinsic environmental signals), and induced (feedback control), with associated counterparts in biological systems: ecological (homogeneous systems), evolutionary (heterogeneous systems), and interventional (control). We use the common noun in designing systems that exhibit antifragile behavior across scales and guide the reader along the spectrum of fragility–adaptiveness–resilience–robustness–antifragility, the principles behind it, and its practical implications. Y1 - 2024 U6 - https://doi.org/10.1038/s44260-024-00014-y SN - 2731-8753 VL - 1 IS - 1 PB - Springer Science and Business Media LLC ER - TY - JOUR A1 - Axenie, Cristian A1 - Saveriano, Matteo T1 - Antifragile Control Systems: The Case of Mobile Robot Trajectory Tracking Under Uncertainty and Volatility JF - IEEE Access N2 - Mobile robots are ubiquitous. Such vehicles benefit from well-designed and calibrated control algorithms ensuring their task execution under precise uncertainty bounds. Yet, in tasks involving humans in the loop, such as elderly or mobility impaired, the problem takes a new dimension. In such cases, the system needs not only to compensate for uncertainty and volatility in its operation but at the same time to anticipate and offer responses that go beyond robust. Such robots operate in cluttered, complex environments, akin to human residences, and need to face during their operation sensor and, even, actuator faults, and still operate. This is where our thesis comes into the foreground. We propose a new control design framework based on the principles of antifragility. Such a design is meant to offer a high uncertainty anticipation given previous exposure to failures and faults, and exploit this anticipation capacity to provide performance beyond robust. In the current instantiation of antifragile control applied to mobile robot trajectory tracking, we provide controller design steps, the analysis of performance under parametrizable uncertainty and faults, as well as an extended comparative evaluation against state-of-the-art controllers. We believe in the potential antifragile control has in achieving closed-loop performance in the face of uncertainty and volatility by using its exposures to uncertainty to increase its capacity to anticipate and compensate for such events. KW - Antifragile Control; Mobile Robotics; Trajectory Tracking; Uncertainty Y1 - 2023 U6 - https://doi.org/10.1109/ACCESS.2023.3339988 SN - 2169-3536 VL - 11 SP - 138188 EP - 138200 PB - Institute of Electrical and Electronics Engineers (IEEE) ER - TY - JOUR A1 - Cogno, Nicolò A1 - Axenie, Cristian A1 - Bauer, Roman A1 - Vavourakis, Vasileios T1 - Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation JF - Cancer Biology & Therapy N2 - Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial and temporal scales, but also because they can integrate information from well-established in vitro and in vivo models and test new hypotheses in cancer biomedicine. Agent-based models and simulations are especially interesting candidates among computational modeling procedures in cancer research due to the capability to, for instance, recapitulate the dynamics of neoplasia and tumor – host interactions. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature that explores strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on verification approached as simulation calibration. KW - Agent-based modeling; multi-scale; multi-level; calibration; validation; optimization; biomechanics; biophysics; cancer simulation; precision oncolog Y1 - 2024 U6 - https://doi.org/10.1080/15384047.2024.2344600 SN - 1538-4047 VL - 25 IS - 1 PB - Informa UK Limited ER - TY - BOOK A1 - Axenie, Cristian A1 - Bauer, Roman A1 - López Corona, Oliver A1 - West, Jeffrey T1 - Applied Antifragility in Natural Systems BT - From Principles to Applications N2 - As coined in the book of Nassim Taleb, antifragility is a property of a system to gain from uncertainty, randomness, and volatility, opposite to what fragility would incur. An antifragile system’s response to external perturbations is beyond robust, such that small stressors can strengthen the future response of the system by adding a strong anticipation component. Such principles are already well suited for describing behaviors in natural systems but also in approaching therapy designs and eco-system modelling and eco-system analysis. The purpose of this book is to build a foundational knowledge base by applying antifragile system design, analysis, and development in natural systems, including biomedicine, neuroscience, and ecology as main fields. We are interested in formalizing principles and an apparatus that turns the basic concept of antifragility into a tool for designing and building closed-loop systems that behave beyond robust in the face of uncertainty when characterizing and intervening in biomedical and ecological (eco)systems. The book introduces the framework of applied antifragility and possible paths to build systems that gain from uncertainty. We draw from the body of literature on natural systems (e.g. cancer therapy, antibiotics, neuroscience, and agricultural pest management) in an attempt to unify the scales of antifragility in one framework. The work of the Applied Antifragility Group in oncology, neuroscience, and ecology led by the authors provides a good overview on the current research status. T2 - with a foreword from Nassim Taleb Y1 - 2025 SN - 9783031903908 U6 - https://doi.org/10.1007/978-3-031-90391-5 SN - 2191-5768 PB - Springer Nature Switzerland CY - Cham ER - TY - BOOK A1 - Axenie, Cristian A1 - Akbarzadeh, Meisam A1 - Makridis, Michail A. A1 - Saveriano, Matteo A1 - Stancu, Alexandru T1 - Applied Antifragility in Technical Systems BT - From Principles to Applications N2 - The book purpose is to build a foundational knowledge base by applying antifragile system design, analysis, and development in technical systems, with a focus on traffic engineering, robotics, and control engineering. The authors are interested in formalizing principles and an apparatus that turns the basic concept of antifragility into a tool for designing and building closed-loop technical systems that behave beyond robust in the face of uncertainty. As coined in the book of Nassim Taleb, antifragility is a property of a system to gain from uncertainty, randomness, and volatility, opposite to what fragility would incur. An antifragile system’s response to external perturbations is beyond robust, such that small stressors can strengthen the future response of the system by adding a strong anticipation component. The work of the Applied Antifragility Group in traffic control and robotics, led by the authors, provides a good overview on the current research status. T2 - with a foreword from Nassim Taleb Y1 - 2025 SN - 9783031904240 U6 - https://doi.org/10.1007/978-3-031-90425-7 SN - 2191-5768 PB - Springer Nature Switzerland CY - Cham ER - TY - CHAP A1 - Sun, Linghang A1 - Zhang, Yifan A1 - Axenie, Cristian A1 - Grossi, Margherita A1 - Kouvelas, Anastasios A1 - Makridis, Michail T1 - The fragile nature of road transportation systems : conference paper N2 - Major cities worldwide experience problems with the performance of their road transportation systems, and the continuous increase in traffic demand presents a substantial challenge to the optimal operation of urban road networks and the efficiency of traffic control strategies. Although robust and resilient transportation systems have been extensively researched over the past decades, their performance under an ever-growing traffic demand can still be questionable. The operation of transportation systems is widely believed to display fragile property, i.e., the loss in performance increases exponentially with the linearly increasing magnitude of disruptions, which undermines their continuous operation. Nowadays, the risk engineering community is embracing the novel concept of antifragility, which enables systems to learn from historical disruptions and exhibit improved performance as disruption levels reach unprecedented magnitudes. In this study, we demonstrate the fragile nature of road transportation systems when faced with demand or supply disruptions. First, we conducted a rigorous mathematical analysis to establish the fragile nature of the systems theoretically. Subsequently, by taking into account real-world stochasticity, we implemented a numerical simulation with realistic network data to bridge the gap between the theoretical proof and the real-world operations, to reflect the potential impact of uncertainty on the fragile property of the systems. This work aims to demonstrate the fragility of road transportation systems and help researchers better comprehend the necessity to explicitly consider antifragile design for future traffic control strategies, coping with the constantly growing traffic demand and subsequent traffic accidents. KW - (anti-)fragility, road transportation systems, macroscopic fundamental diagram, model stochasticity Y1 - 2024 U6 - https://doi.org/10.3929/ethz-b-000681460 ER - TY - CHAP A1 - Sun, Linghang A1 - Makridis, Michail A1 - Genser, Alexander A1 - Axenie, Cristian A1 - Grossi, Margherita A1 - Kouvelas, Anastasios T1 - Antifragile perimeter control BT - Anticipating and gaining from disruptions with reinforcement learning N2 - The optimal operation of transportation networks is often susceptible to unexpected disruptions, such as traffic incidents and social events. Many established control strategies rely on mathematical models that often struggle to cope with real-world uncertainties, leading to a significant decline in their effectiveness when faced with substantial disruptions. While previous research works have dedicated efforts to enhancing the robustness or resilience of transportation systems against disruptions, in this paper, we use the concept of antifragility to better design a traffic control strategy for urban road networks. Antifragility represents a system's ability to not only withstand stressors, shocks, and volatility but also thrive and enhance performance in the presence of such disruptions. Hence, modern transport systems call for solutions that are antifragile. In this work, we propose a model-free deep Reinforcement Learning (RL) algorithm to regulate perimeter control in a two-region urban traffic network to exploit and strengthen the learning capability of RL under disruptions and achieve antifragility. By incorporating antifragility terms based on the change rate and curvature of the traffic state into the RL framework, the proposed algorithm further gains knowledge of the traffic state, which helps in anticipating imminent disruptions. An additional term is also integrated into the RL algorithm as redundancy to enhance the performance under disruption scenarios. When compared to a state-of-the-art model predictive control approach and a state-of-the-art RL algorithm, our proposed method demonstrates two antifragility-related properties: (a) gradual performance improvement under disruptions of similar magnitude; and (b) increasingly superior performance under growing disruptions. Y1 - 2024 U6 - https://doi.org/10.48550/arXiv.2402.12665 ER -