arXiv subjects

John F. Hardy II

Publications and source records attributed to John F. Hardy II.

5 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

Using Preformed Resistive Random Access Memory to Create a Strong Physically Unclonable Function

Physically Unclonable Functions (PUFs) are a promising solution for identity verification and asymmetric encryption. In this paper, a new Resistive Random Access Memory (ReRAM) PUF-based protocol is presented to create a physical ReRAM PUF with a large challenge space. This protocol uses differential reads from unformed ReRAM as the method for response generation. Lastly, this paper also provides an experimental hardware demonstration of this protocol on a Physical ReRAM device, along with providing notable results as a PUF, with excellent performance characteristics.

cs.CR↗

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↗

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

Optimizing the Optical Properties of Tin Oxide Aerogels through Defect Passivation

Tin oxide aerogels were synthesized using an epoxide-assisted technique and characterized with Fourier transform infrared, X-ray diffraction, and UV-Vis to study the effects of post-synthesis annealing and peroxide treatment. While bulk tin oxide exhibits an optical bandgap of $3.6$ eV, its aerogel form often displays a larger apparent bandgap around $4.6$ eV due to defects. Our study reveals that annealing induces a partial phase change from SnO$_2$ to SnO, but is ineffective in removing defects. Conversely, peroxide passivation effectively lowers the bandgap and disorder levels, suggesting that dangling bonds are the primary cause of the increased bandgap in tin oxide aerogels. These findings offer insights for optimizing the optical properties of tin oxide aerogels for applications like solar cells.

cond-mat.mtrl-sci↗