Evolving team compositions by agent swapping

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Neural methods have become the de-facto choice for the vast majority of data analysis tasks, and entity alignment (EA) is no exception. Not surprisingly, more than 50 different neural EA methods have been published since 2017. However, surprisingly, an ana ...

Vision-based drone swarms have recently emerged as a promising alternative to address the fault-tolerance and flexibility limitations of centralized and communication-based aerial collective systems. Although most vision-based control algorithms rely on th ...

Although often regarded a childhood toy, the design of paper airplanes is subtly complex. The design space and mapping from geometry to distance flown is highly nonlinear and probabilistic where a single airplane design exhibits a multitude of trajectory f ...

An optimal planning algorithm enables swarms of flying robots to explore unknown environments autonomously and safely. ...

Moving walkways are pedestrian dedicated hardware which generally decrease pedestrian travel time. We propose the utilization of these devices to dynamically control pedestrian flows in order to improve pedestrian dynamics. Three variations of a control st ...

In this thesis, we study systems of active particles interacting via generic torques of different nature. We analyze the phase behavior of these systems, which results from the interplay between self-propulsion, excluded-volume and torques. We tackle the p ...

Evolutionary diversity in species can arise in many ways, including local adaptation. Despite the global importance of tropical forest ecosystems, few studies have explored patterns of local adaptation in tropical tree species. We investigated population g ...

Designing optimal soft modular robots is difficult, due to non-trivial interactions between morphology and controller. Evolutionary algorithms (EAs), combined with physical simulators, represent a valid tool to overcome this issue. In this work, we investi ...

A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learni ...

In crowding, perception of a target deteriorates in the presence of nearby flankers. Traditionally, it is thought that visual crowding obeys Bouma's law, i.e., all elements within a certain distance interfere with the target, and that adding more elements ...

In this paper, we propose a simple global optimisation algorithm inspired by Pareto’s principle. This algorithm samples most of its solutions within prominent search domains and is equipped with a self-adaptive mechanism to control the dynamic tightening o ...

Evolving team compositions by agent swapping

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A Critical Re-evaluation of Neural Methods for Entity Alignment

On the Scalability of Vision-based Drone Swarms in the Presence of Occlusions

Robotic automation and unsupervised cluster assisted modeling for solving the forward and reverse design problem of paper airplanes

Swarms of flying robots in unknown environments

Controlling pedestrian flows with moving walkways

Collective behavior and emerging patterns in dense dry aligning active matter

Population structure and signals of local adaptation in Eugenia uniflora (Myrtaceae), a widely distributed species in the Atlantic Forest

Seeking Quality Diversity in Evolutionary Co-design of Morphology and Control of Soft Tensegrity Modular Robots

Population pharmacokinetic model selection assisted by machine learning

Shrinking Bouma's window: How to model crowding in dense displays

Pareto-like sequential sampling heuristic for global optimisation