arXiv subjects

Guilherme S. Y. Giardini

Publications and source records attributed to Guilherme S. Y. Giardini.

7 recordsLinked to original sources

arXiv2026

Evolved Collectives Combine Complex Internal Representations with Simple Outputs

Collective intelligence emerges from local interactions among agents with limited information, yet how internal controller organization relates to emergent collective order remains unclear. Here, we study evolved swarms with shallow neural controllers under explicit sensory and actuation constraints and compare collective order with hidden-layer complexity and output nonlinearity across 3024 conditions. Under these constraints, the most ordered regimes exhibit two simultaneous and seemingly contrasting effects: hidden-layer complexity increases, while the effective output mapping becomes more linear. The diversity of recurrent collective behaviors varies nonmonotonically across the control parameters, with pattern richness shaped by parameter-specific tradeoffs rather than a single generic constraint optimum. Unevolved controls show that output linearization persists without adaptation, whereas the hidden-complexity relation depends on optimization. These two effects are respectively consistent with the law of requisite complexity and ecological rationality, suggesting that adaptive collective intelligence can arise through a partitioned controller organization in which representational complexity and action-level linearization coexist within the same system.

physics.soc-ph↗

arXiv2025

Impact of Switching Layer Architecture on Power Consumption in RRAM

This work demonstrates that porous helical WOx architectures enable a distinct low-power regime for planar ITO/WOx/ITO resistive random-access devices. While thin film and helical devices behave similarly at a 5 mA compliance, only helical devices sustain reproducible operation at 500 uA, where RESET voltages reduce by ~60%, switching currents decrease by 68-75%, and SET/RESET power drops by ~89% and ~83%. With helical devices operating at 500 uA, the memory window expands 400-600% due to selective suppression of high-resistive-state leakage, yielding both lower-power and improved read margin in a regime inaccessible to thin film devices. These results highlight geometry-driven field enhancement and confinement as practical design principles for low-power, high-margin resistive memories and point toward opportunities in transparent, flexible, and high-surface-area material systems.

physics.app-ph↗

arXiv2025

Thermodynamics of Innovation: A Statistical Mechanics Framework of Social Adoption

We develop a thermodynamic framework for modeling innovation adoption and abandonment dynamics using statistical mechanics. Starting from a mathematical model for an adoption distribution that fits empirically obtained date, we construct a canonical ensemble whose equilibrium distribution yields Gompertz-like and Maxwell-Boltzmann-like shapes. By reverse engineering the associated energy landscape, we define an effective potential and derive a dynamical Lagrangian formulation. The resulting field theory captures key features of emergent behaviors in socio-technical systems, from early suppression to peak dynamics and late decline. We interpret effective temperature, entropy, and equilibrium points, and show how these systems exhibit hybrid thermodynamic-statistical signatures.

physics.soc-ph↗

arXiv2024

Evolving Neural Networks Reveal Emergent Collective Behavior from Minimal Agent Interactions

Understanding the mechanisms behind emergent behaviors in multi-agent systems is critical for advancing fields such as swarm robotics and artificial intelligence. In this study, we investigate how neural networks evolve to control agents' behavior in a dynamic environment, focusing on the relationship between the network's complexity and collective behavior patterns. By performing quantitative and qualitative analyses, we demonstrate that the degree of network non-linearity correlates with the complexity of emergent behaviors. Simpler behaviors, such as lane formation and laminar flow, are characterized by more linear network operations, while complex behaviors like swarming and flocking show highly non-linear neural processing. Moreover, specific environmental parameters, such as moderate noise, broader field of view, and lower agent density, promote the evolution of non-linear networks that drive richer, more intricate collective behaviors. These results highlight the importance of tuning evolutionary conditions to induce desired behaviors in multi-agent systems, offering new pathways for optimizing coordination in autonomous swarms. Our findings contribute to a deeper understanding of how neural mechanisms influence collective dynamics, with implications for the design of intelligent, self-organizing systems.

nlin.AO↗

arXiv2024

Chain Reaction of Ideas: Can Radioactive Decay Predict Technological Innovation?

This work demonstrates the application of a birth-death Markov process, inspired by radioactive decay, to capture the dynamics of innovation processes. Leveraging the Bass diffusion model, we derive a Gompertz-like function explaining the long-term innovation trends. The validity of our model is confirmed using citation data, Google trends, and a recurrent neural network, which also reveals short-term fluctuations. Further analysis through an automaton model suggests these fluctuations can arise from the inherent stochastic nature of the underlying physics.

cond-mat.stat-mech↗

arXiv2023

Velocities of Mesenchymal Cells May be Ill-Defined

The dynamics of single cell migration on flat surfaces is usually modeled by a Langevin-like problem consisting of ballistic motion for short periods and random walk. for long periods. Conversely, recent studies have revealed a previously neglected random motion at very short intervals, what would rule out the possibility of defining the cell instantaneous velocity and a robust measurement procedure. A previous attempt to address this issue considered an anisotropic migration model, which takes into account a polarization orientation along which the velocity is well-defined, and a direction orthogonal to the polarization vector that describes the random walk. Although the numerically and analytically calculated mean square displacement and auto-correlation agree with experimental data for that model, the velocity distribution peaks at zero, which contradicts experimental observations of a constant drift in the polarization direction. Moreover, Potts model simulations indicate that instantaneous velocity cannot be measured for any direction. Here, we consider dynamical equations for cell polarization, which is measurable and introduce a polarization-dependent displacement, circumventing the problem of ill defined instantaneous velocity. Polarization is a well-defined quantity, preserves memory for short intervals, and provides a robust measurement procedure for characterizing cell migration. We consider cell polarization dynamics to follow a modified Langevin equation that yields cell displacement distribution that peaks at positive values, in agreement with experiments and Potts model simulations. Furthermore, displacement autocorrelation functions present two different time scales, improving the agreement between theoretical fits and experiments or simulations.

physics.bio-ph↗

arXiv2021

Exact solution for the Anisotropic Ornstein-Uhlenbeck Process

Active Matter models commonly consider particles with overdamped dynamics subject to a force (speed) with constant modulus and random direction. Some models include also random noise in particle displacement (Wiener process) resulting in a diffusive motion at short time scales. On the other hand, Ornstein-Uhlenbeck processes consider Langevin dynamics for the particle velocity and predict a motion that is not diffusive at short time scales. However, experiments show that migrating cells may present a varying speed as well as a short-time diffusive behavior. While Ornstein-Uhlenbeck processes can describe the varying speed, Active Mater models can explain the short-time diffusive behavior. Isotropic models cannot explain both: short-time diffusion renders instantaneous velocity ill-defined, hence impeding dynamical equations that consider velocity time-derivatives. On the other hand, both models apply for migrating biological cells and must, in some limit, yield the same observable predictions. Here we propose and analytically solve an Anisotropic Ornstein-Uhlenbeck process that considers polarized particles, with a Langevin dynamics for the particle movement in the polarization direction while following a Wiener process for displacement in the orthogonal direction. Our characterization provides a theoretically robust way to compare movement in dimensionless simulations to movement in dimensionful experiments, besides proposing a procedure to deal with inevitable finite precision effects in experiments or simulations.

physics.bio-ph↗