Particle Swarm Optimization (PSO) is one of the most effective optimization methods for the black-box optimization problem. PSO involves a large number of particles sharing information with each other to search for the optimal solution. The method in which a large number of search individuals cooperate to search for the optimal solution is called swarm intelligence optimization. In the group intelligence optimization, the balance between exploration and exploitation is important. However, in PSO, it is unclear to what extent each parameter affects exploration and exploitation. Therefore, we proposed a deterministic PSO without probabilistic elements and analyzed the dynamics of PSO using dynamical systems theory. Each particle in deterministic PSO has its motion determined by its eigenvalue. In order to make this motion clearer, a canonical deterministic PSO on a regularized phase space was proposed. The results of these analyses clarified what is attributed to the parameters for exploration and exploitation, i.e., global and local search capabilities. Based on this fact, we proposed a nonlinear map optimization (NMO) with improved local search capability. In this paper, we present the background of our proposal and consider the solution-search capability of nonlinear map optimization.

Physics-informed machine learning has recently been shown to efficiently learn complex trajectories of nonlinear dynamical systems, even when order and chaos coexist. However, care must be taken when one or more variables are unbounded, such as in rotations. Here we use the framework of Hamiltonian Neural Networks (HNN) to learn the complex dynamics of nonlinear single and double pendulums, which can both librate and rotate, by mapping the unbounded phase space onto a compact cylinder. We clearly demonstrate that our approach can successfully forecast the motion of these challenging systems, capable of both bounded and unbounded motion. It is also evident that HNN can yield an energy surface that closely matches the surface generated by the true Hamiltonian function. Further we observe that the relative energy error for HNN decreases as a power law with number of training pairs, with HNN clearly outperforming conventional neural networks quantitatively.

We propose a hierarchical network model based on predictive coding and reservoir computing as a model of multi-modal sensory integration in the brain. The network is composed of visual, auditory, and integration areas. In each area, the dynamical reservoir acts as a generative model that reproduces the time-varying sensory signal. The states of the visual and auditory reservoir are spatially compressed and are sent to the integration area. We evaluate the model with a dataset of time courses, including a pair of visual (hand-written characters) and auditory (read utterances) signal. We show that the model learns the association of multiple modalities of the sensory signals and that the model reconstructs the visual signal from a given corresponding auditory signal. Our approach presents a novel dynamical mechanism of the multi-modal information processing in the brain and the fundamental technology for a brain like an artificial intelligence system.

Flaming phenomena represent the divergence in the strength of user dynamics as created by user interactions in online social networks (OSNs). Although it has been known that flaming phenomena occur when the Laplacian matrix of the OSN has non-real eigenvalues, it was recently shown that flaming phenomena may occur even if all the eigenvalues are real numbers. This effect appears only in the situation that some eigenvalues are degenerate, and a special unitary transformation is applied to the equations representing user dynamics; whether actual OSNs satisfy this condition has not been fully discussed. In this paper, we clarify that the user dynamics caused by the degeneration of eigenvalue 0 is one specific example of the above condition. We also investigate the mechanism and characteristics of flaming phenomena generated by degenerated eigenvalues. Furthermore, we demonstrate through numerical simulations that the degeneration of eigenvalues can cause divergence.

Reservoir computing derived from recurrent neural networks is more applicable to real world systems than deep learning because of its low computational cost and potential for physical implementation. Specifically, physical reservoir computing, which replaces the dynamics of reservoir units with physical phenomena, has recently received considerable attention. In this study, we propose a method of exploiting the dynamics of road traffic as a reservoir, and numerically confirm its feasibility by applying several prediction tasks based on a simple mathematical model of the traffic flow.

Power packets are proposed as a transmission unit that can deliver power and information simultaneously. They are transferred using the store-and-forward method of power routers. A system that achieves power supply/demand in this manner is called a power packet network (PPN). A PPN is expected to enhance structural robustness and operational reliability in an energy storage system (ESS) with recent diverse distributed sources. However, this technology is still in its early stage and faces numerous challenges, such as high cost of implementation and complicated energy management. In this paper, we propose a novel power control based on decentralized algorithms for a PPN. Specifically, the power supply is triggered and managed by communications between power routers. We also discuss the mechanism of the decentralized algorithm for the operation of power packets and reveal the feasibility of the given control method and application by forming biased power flows on the consensus-based distribution.

Localized modes in resonant circuit array consisting of circular coils and capacitors are analytically investigated. First, an appropriate approximation of magnetic couplings between the coils is introduced. Then, analytical solutions are successfully derived by solving simultaneous polynomials for two different positions of the external coil. These solutions are evaluated by comparing with numerically exact solutions in the original problem. It is confirmed that the obtained solutions fit well the exact solutions when the overlapping and the diameter of the external coil are not too large.

A seagull (Larus crassirostris) has a high ability to realize its safe, accurate and smooth landing. We examined how a seagull changes its position when landing on a specific target. First, we recorded the landing behavior of an actual seagull by two video cameras and quantified the flight trajectory and the wing angle as time series data. Second, we introduced a simple mathematical model that describes how a seagull changes its position depending on its wing angle. By performing the numerical simulation combining the mathematical model and empirical data, we examined behavioral mechanism reproducing the flight trajectory of an actual seagull during landing behavior.

The growing demand of high-bandwidth and low-latency information transfer in information and communication technologies such as data centers and in-vehicle networks has increased the importance of optical communication networks in recent years. However, complicated arbitration schemes can impose significant overheads in data transfer, which may inhibit the full exploitation of the potential of optical interconnects. Herein, we propose an arbitration protocol based on precision time synchronization via wireless two-way interferometry (Wi-Wi), and numerically validate its efficiency including the ability to impose a strict upper bound on the latency of data transfer. We introduce the notion of arbitration point (AP) for a designated time duration, which is shared by all nodes thanks to the time synchronization by Wi-Wi, to determine data transmission while ensuring avoiding collision. Compared with the conventional carrier sense multiple access/collision detection (CSMA/CD)-based approach, a significant improvement in the data transfer was observed especially in the cases with high traffic flow rate. Furthermore, we conducted a proof-of-principle experiment for Wi-Wi-based data transfer between two electrically connected nodes and confirmed that the skew was less than 300 ns and remained stable over time. Conversely, non-Wi-Wi-based data transfer exhibited huge and unstable skew. These results indicate that precision time synchronization is a promising resource to reduce the communication overheads and ensure low latency for future networks and real-time applications.