Antifragile Control Systems: The case of an oscillator-based network model of urban road traffic dynamics

Existing traffic control systems only possess a local perspective over the multiple scales of traffic evolution, namely the intersection level, the corridor level, and the region level respectively. But luckily, despite its complex mechanics, traffic is described by various periodic phenomena. Workday flow distributions in the morning and evening commuting times can be exploited to make traffic adaptive and robust to disruptions. Additionally, controlling traffic is also based on a periodic process, choosing the phase of green time to allocate to opposite directions right of the pass and complementary red time phase for adjacent directions. In our work, we consider a novel system for road traffic control based on a network of interacting oscillators. Such a model has the advantage to capture temporal and spatial interactions of traffic light phasing as well as the network-level evolution of the traffic macroscopic features (i.e. flow, density). In this study, we propose a new realization of the antifragile control framework to control a network of interacting oscillator-based traffic light models to achieve region-level flow optimization. We demonstrate that antifragile control can capture the volatility of the urban road environment and the uncertainty about the distribution of the disruptions that can occur. We complement our control-theoretic design and analysis with experiments on a real-world setup comparatively discussing the benefits of an antifragile design for traffic control

Role of Kinematics Assessment and Multimodal Sensorimotor Training for Motion Deficits in Breast Cancer Chemotherapy-Induced Polyneuropathy: A Perspective on Virtual Reality Avatars

Chemotherapy-induced polyneuropathy (CIPN), one of the most severe and incapacitating side effects of chemotherapeutic drugs, is a serious concern in breast cancer therapy leading to dose diminution, delay, or cessation. The reversibility of CIPN is of increasing importance since active chemotherapies prolong survival. Clinical assessment tools show that patients experiencing sensorimotor CIPN symptoms not only do they have to cope with loss in autonomy and life quality, but CIPN has become a key restricting factor in treatment. CIPN incidence poses a clinical challenge and has lacked established and efficient therapeutic options up to now. Complementary, non-opioid therapies are sought for both prevention and management of CIPN. In this perspective, we explore the potential that digital interventions have for sensorimotor CIPN rehabilitation in breast cancer patients. Our primary goal is to emphasize the benefits and impact that Virtual Reality (VR) avatars and Machine Learning have in combination in a digital intervention aiming at (1) assessing the complete kinematics of deficits through learning underlying patient sensorimotor parameters, and (2) parameterize a multimodal VR simulation to drive personalized deficit compensation. We support our perspective by evaluating sensorimotor effects of chemotherapy, the metrics to assess sensorimotor deficits, and relevant clinical studies. We subsequently analyse the neurological substrate of VR sensorimotor rehabilitation, with multisensory integration acting as a key element. Finally, we propose a closed-loop patient-centered design recommendation for CIPN sensorimotor rehabilitation. Our aim is to provoke the scientific community toward the development and use of such digital interventions for more efficient and targeted rehabilitation

Antifragile Control Systems: The case of mobile robot trajectory tracking in the presence of uncertainty

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

Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation

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. We consolidate our review with an outline of modern approaches for agent-based models’ validation and provide an ambitious outlook toward rigorous and reliable calibration

Reinforcement learning estimates muscle activations

A digital twin of the human neuromuscular system can substantially improve the prediction of injury risks and the evaluation of the readiness to return to sport. Reinforcement learning (RL) algorithms already learn physical quantities unmeasurable in biomechanics, and hence can contribute to the development of the digital twin. Our preliminary results confirm the potential of RL algorithms to estimate the muscle activations of an athlete’s moves.Ein digitaler Zwilling des menschlichen neuromuskulären Systems kann die Vorhersage von Verletzungsrisiken und die Bewertung der Bereitschaft zur Rückkehr in den Sport erheblich verbessern. Algorithmen des bestärkenden Lernens (Reinforcement Learning, RL) lernen bereits physikalische Größen, die in der Biomechanik nicht messbar sind, und können daher zur Entwicklung des digitalen Zwillings beitragen. Unsere vorläufigen Ergebnisse bestätigen das Potenzial von RL-Algorithmen zur Schätzung der Muskelaktivierung bei den Bewegungen eines Sportlers

Recipes for calibration and validation of agent-based models in cancer biomedicine

Computational models and simulations are not just appealing because of their intrinsic characteristics across spatiotemporal scales, scalability, and predictive power, but also because the set of problems in cancer biomedicine that can be addressed computationally exceeds the set of those amenable to analytical solutions. Agent-based models and simulations are especially interesting candidates among computational modelling strategies in cancer research due to their capabilities to replicate realistic local and global interaction dynamics at a convenient and relevant scale. 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 to explore strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on validation approached as simulation calibration. We argue that simulation calibration goes beyond parameter optimization by embedding informative priors to generate plausible parameter configurations across multiple dimensions

From Adaptive Reasoning to Cognitive Factory: Bringing Cognitive Intelligence to Manufacturing Technology

There are two important aspects that will play important roles in future manufacturing systems: changeability and human-machine collaboration. The first aspect, changeability, concerns with the ability of production tools to reconfigure themselves to the new manufacturing settings, possibly with unknown prior information, while maintaining their reliability at lowest cost. The second aspect, human-machine collaboration, emphasizes the ability of production tools to put themselves on the position as humans’ co-workers. The interplay between these two aspects will not only determine the economical accomplishment of a manufacturing process, but it will also shape the future of the technology itself. To address this future challenge of manufacturing systems, the concept of Cognitive Factory was proposed. Along this line, machines and processes are equipped with cognitive capabilities in order to allow them to assess and increase their scope of operation autonomously. However, the technical implementation of such a concept is still widely open for research, since there are several stumbling blocks that limit practicality of the proposed methods. In this paper, we introduce our method to achieve the goal of the Cognitive Factory. Our method is inspired by the working mechanisms of a human’s brain; it works by harnessing the reasoning capabilities of cognitive architecture. By utilizing such an adaptive reasoning mechanism, we envision the future manufacturing systems with cognitive intelligence. We provide illustrative examples from our current research work to demonstrate that our proposed method is notable to address the primary issues of the Cognitive Factory: changeability and human-machine collaboration

Antifragile Perimeter Control: Anticipating and Gaining from Disruptions with Reinforcement Learning

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 struggle to cope with real-world uncertainties, leading to a significant decline in effectiveness when faced with substantial disruptions. While previous research works have dedicated efforts to improving the robustness or resilience of transportation systems against disruptions, this paper applies the cutting-edge concept of antifragility to better design a traffic control strategy for urban road networks. Antifragility sets itself apart from robustness and resilience as it represents a system's ability to not only withstand stressors, shocks, and volatility but also thrive and enhance performance in the presence of such adversarial events. Hence, modern transportation systems call for solutions that are antifragile. In this work, we propose a model-free deep Reinforcement Learning (RL) scheme to control a two-region urban traffic perimeter network. The system exploits the learning capability of RL under disruptions to achieve antifragility. By monitoring the change rate and curvature of the traffic state with the RL framework, the proposed algorithm anticipates imminent disruptions. An additional term is also integrated into the RL algorithm as redundancy to improve 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 constant magnitude; and (b) increasingly superior performance under growing disruptions.Comment: 32 pages, 13 figure