Learning-based methods, particularly Reinforcement Learning (RL), hold great promise for streamlining deployment, enhancing performance, and achieving generalization in the control of autonomous multirotor aerial vehicles. Deep RL has been able to control complex systems with impressive fidelity...
01a Articolo in rivista
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We present a Robust Intrinsically Stable Model Predictive Control (RIS-MPC) framework for humanoid gait generation, which realizes as closely as possible a predefined sequence of footsteps in the presence of both persistent and impulsive perturbations. The MPC-based controller has two modes of...
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Background/Objectives: Non-small cell lung cancer (NSCLC) patients without gene driver mutations receive anti-PD1 treatments either as monotherapy or in combination with chemotherapy based on PD-L1 expression in tumor tissue. Anti-PD1 antibodies target various immune system components, perturbing...
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A current open challenge in precision medicine is sex-specific medicine: the study of how sex-based biological differences influence people's health. With recent advancements in high-throughput technologies, large-scale molecular data are being generated for individual cancer patients, however,...
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The article presents a case exercise that teaches students how to apply mathematical programming to a real-life context. The case deals with the management of the food donation supply chain. The case, using a project-based approach, proposes a realistic scenario that simulates the consulting...
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The case addresses a real-world challenge encountered by the nonprofit organization Logica&Co. It revolves around optimizing the logistics involved in collecting food donations from local businesses and delivering them to soup kitchens, utilizing a fleet of bike riders. The focus is identifying...
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Managing all the mobility and transportation services with autonomous vehicles for users of a smart city requires determining the assignment of the vehicles to the users and their routing in conjunction with their speed. Such decisions must ensure low emission, efficiency, and high service quality...
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In this work, we consider minimizing the average of a very large number of smooth and possibly non-convex functions, and we focus on two widely used minibatch frameworks to tackle this optimization problem: Incremental Gradient (IG) and Random Reshuffling (RR). We define ease-controlled...
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Service compositions à la Roman model consist of realizing a virtual service by orchestrating suitably, a set of already available services, where all services are described procedurally as (possibly nondeterministic) transition systems. In this paper, we study a goal-oriented variant of the...
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With the arrival of 5G technology, networks face critical challenges in detecting anomalies that can significantly impact performance and reliability. This paper introduces QAED (Quantized Auto Encoder Detector), a novel deep learning approach for anomaly detection in 5G networks with three key...