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Full-Text Articles in Physical Sciences and Mathematics
Unique Scales Preserve Self-Similar Integrate-And-Fire Functionality Of Neuronal Clusters, Anar Amgalan, Patrick Taylor, Lilianne R. Mujica-Parodi, Hava T. Siegelmann
Unique Scales Preserve Self-Similar Integrate-And-Fire Functionality Of Neuronal Clusters, Anar Amgalan, Patrick Taylor, Lilianne R. Mujica-Parodi, Hava T. Siegelmann
Computer Science Department Faculty Publication Series
Brains demonstrate varying spatial scales of nested hierarchical clustering. Identifying the brain's neuronal cluster size to be presented as nodes in a network computation is critical to both neuroscience and artificial intelligence, as these define the cognitive blocks capable of building intelligent computation. Experiments support various forms and sizes of neural clustering, from handfuls of dendrites to thousands of neurons, and hint at their behavior. Here, we use computational simulations with a brain-derived fMRI network to show that not only do brain networks remain structurally self-similar across scales but also neuron-like signal integration functionality (integrate and fire) is preserved at …
Evolutionary Dynamics Of Bertrand Duopoly, Julian Killingback, Timothy Killingback
Evolutionary Dynamics Of Bertrand Duopoly, Julian Killingback, Timothy Killingback
Computer Science Department Faculty Publication Series
Duopolies are one of the simplest economic situations where interactions between firms determine market behavior. The standard model of a price-setting duopoly is the Bertrand model, which has the unique solution that both firms set their prices equal to their costs-a paradoxical result where both firms obtain zero profit, which is generally not observed in real market duopolies. Here we propose a new game theory model for a price-setting duopoly, which we show resolves the paradoxical behavior of the Bertrand model and provides a consistent general model for duopolies.
Scalable And High-Fidelity Quantum Random Access Memory In Spin-Photon Networks, Kevin C. Chen, Wenhan Dai, Carlos Errando-Herranz, Seth Lloyd, Dirk Englund
Scalable And High-Fidelity Quantum Random Access Memory In Spin-Photon Networks, Kevin C. Chen, Wenhan Dai, Carlos Errando-Herranz, Seth Lloyd, Dirk Englund
Computer Science Department Faculty Publication Series
A quantum random access memory (qRAM) is considered an essential computing unit to enable polynomial speedups in quantum information processing. Proposed implementations include the use of neutral atoms and superconducting circuits to construct a binary tree but these systems still require demonstrations of the elementary components. Here, we propose a photonic-integrated-circuit (PIC) architecture integrated with solid-state memories as a viable platform for constructing a qRAM. We also present an alternative scheme based on quantum teleportation and extend it to the context of quantum networks. Both implementations realize the two key qRAM operations, (1) quantum state transfer and (2) quantum routing, …
Quantifying The Impact Of Non-Stationarity In Reinforcement Learning-Based Traffic Signal Control, Lucas N. Alegre, Ana L.C. Bazzan, Bruno C. Da Silva
Quantifying The Impact Of Non-Stationarity In Reinforcement Learning-Based Traffic Signal Control, Lucas N. Alegre, Ana L.C. Bazzan, Bruno C. Da Silva
Computer Science Department Faculty Publication Series
In reinforcement learning (RL), dealing with non-stationarity is a challenging issue. However, some domains such as traffic optimization are inherently non-stationary. Causes for and effects of this are manifold. In particular, when dealing with traffic signal controls, addressing non-stationarity is key since traffic conditions change over time and as a function of traffic control decisions taken in other parts of a network. In this paper we analyze the effects that different sources of non-stationarity have in a network of traffic signals, in which each signal is modeled as a learning agent. More precisely, we study both the effects of changing …