Collecting spatially dense meteorological observations is essential for accurate climate modeling and prediction. However, such observations are often costly and difficult to obtain. This limitation motivates the prediction of meteorological variables at locations where direct observations are sparse or unavailable, using limited data from distant observation points. In this study, we address a fundamental question: How does the predictability of meteorological variables depend on the spatial distance between the prediction target and the observation point? To cope with limited data, we employ the echo state network, which is a lightweight machine-learning model within the reservoir computing framework. Predictive models are constructed separately for near-surface temperature and atmospheric pressure using time-series data of meteorological variables obtained from a high-quality climate reanalysis dataset. Our analyses not only confirm the qualitative property that prediction accuracy degrades as the distance between the target point and observation point increases but also provide quantitative estimates of the geographical ranges over which predictions can be made within an acceptable margin of error. These results suggest that prior analysis of spatial correlations in limited observational data can be used to estimate meteorological predictability in advance, thereby contributing to the development of climate modeling with improved efficiency in data collection and computation.

In this article, we experimentally and theoretically analyze practical consensus stimulated by a small number of committed players who stubbornly promote an alternative in a binary-choice quiz with graded confidence. Our study bridges the gap between theory and experiment on practical consensus, since prior work on practical consensus is limited to theoretical analyses. In practical consensus, socially chosen opinions converge to a bounded interval, while admitting some deviations from exact consensus. We formulate a novel heterogeneous population dynamics model that represents the diversity of boundedly rational decision-makers to capture practical consensus in an on-site multiround group experiment. From a theoretical analysis, we prove that practical consensus is shaped by the heterogeneity of boundedly rational decision-makers. Our findings contribute to a better understanding of practical consensus and may provide insights into the spread of new alternatives in binary-choice settings with graded confidence.

This study introduces an uncertainty-aware, mesh-free numerical method for solving Kolmogorov PDEs. In the proposed method, we use Gaussian process regression (GPR) to smoothly interpolate pointwise solutions that are obtained by Monte Carlo methods based on the Feynman–Kac formula. The proposed method has two main advantages: 1. uncertainty assessment, which provides numerical information about the validity of the solution, and 2. mesh-free computation, which allows one to choose any low-dimensional bounded domain for reducing the computational cost. The quality of the solution is improved by adjusting the kernel function and incorporating noise information from the Monte Carlo samples into the GPR noise model. The performance of the method is rigorously analyzed based on a theoretical lower bound on the posterior variance, which serves as a measure of the error between the numerical and true solutions. Extensive tests on three representative PDEs demonstrate the high accuracy and robustness of the method compared to existing methods.

Neural networks capable of approximating complex nonlinearities have found extensive application in data-driven control of nonlinear dynamical systems. However, fast online identification and control of unknown dynamics remain central challenges. This paper integrates echo-state networks (ESNs) – reservoir computing models implemented with recurrent neural networks – and model predictive path integral (MPPI) control – sampling-based variants of model predictive control – to meet these challenges. The proposed reservoir predictive path integral (RPPI) enables fast learning of nonlinear dynamics with ESN and exploits the learned nonlinearities directly in parallelized MPPI control computation without linearization approximations. The framework is further extended to uncertainty-aware RPPI (URPPI), which leverages ESN uncertainty to balance exploration and exploitation: exploratory inputs dominate during early learning, while exploitative inputs prevail as model confidence grows. Experiments on controlling the Duffing oscillator and four-tank systems demonstrate that URPPI improves control performance, reducing control costs by up to 60

In this paper, we analyze a novel continuous-time dynamical binary choice model that unifies several logit dynamics in the presence of a committed minority and internal/external conformity biases. Each logit dynamics represents a population of decision makers in a group (i.e., a subset of society), who choose one from two available strategies. The internal conformity bias is regarded as an inertia which causes people in the group to stick with their own majority. The external conformity bias indicates a social coordination which drives every person in society to the social majority. Based on control theoretic insights, we prove that the committed minority eventually supersedes the majority when every person has an appropriately bounded rationality. Numerical experiments demonstrate the present analytical results.

Realizing smooth traffic flow is important for achieving carbon neutrality. Adaptive traffic signal control, which considers traffic conditions, has thus attracted attention. However, it is difficult to ensure optimal vehicle flow throughout a large city using existing control methods because of their heavy computational load. Here, we propose a control method called AMPIC (Adaptive Model Predictive Ising Controller) that guarantees both scalability and optimality. The proposed method employs model predictive control to solve an optimal control problem at each control interval with explicit consideration of a predictive model of vehicle flow. This optimal control problem is transformed into a combinatorial optimization problem with binary variables that is equivalent to the Ising problem. This transformation allows us to use Ising solvers, which have been widely studied and are expected to offer fast and efficient optimization performance. The method works adaptively according to traffic conditions such as the structure of the road network and feedback from observation of the traffic system. We performed numerical experiments using a microscopic traffic simulator for a realistic city road network. Compared to the classical pattern control method, the results show that AMPIC increases the vehicle cruising speed by 13%, reduces the waiting vehicle ratio to 60%, and lowers the CO 2 emissions to only 25% of the original level. The model predictive approach with a long prediction horizon thus effectively improves control performance. Systematic parametric studies on model cities indicate that the proposed method realizes smoother traffic flows for large city road networks. Among Ising solvers, D-Wave’s quantum annealing is shown to find near-optimal solutions at a reasonable computational cost.

Realizing smooth traffic flow is important for achieving carbon neutrality. Adaptive traffic signal control, which considers traffic conditions, has thus attracted attention. However, it is difficult to ensure optimal vehicle flow throughout a large city using existing control methods because of their heavy computational load. Here, we propose a control method called AMPIC (Adaptive Model Predictive Ising Controller) that guarantees both scalability and optimality. The proposed method employs model predictive control to solve an optimal control problem at each control interval with explicit consideration of a predictive model of vehicle flow. This optimal control problem is transformed into a combinatorial optimization problem with binary variables that is equivalent to the so-called Ising problem. This transformation allows us to use an Ising solver, which has been widely studied and is expected to have fast and efficient optimization performance. We performed numerical experiments using a microscopic traffic simulator for a realistic city road network. The results show that AMPIC enables faster vehicle cruising speed with less waiting time than that achieved by classical control methods, resulting in lower CO2 emissions. The model predictive approach with a long prediction horizon thus effectively improves control performance. Systematic parametric studies on model cities indicate that the proposed method realizes smoother traffic flows for large city road networks. Among Ising solvers, D-Wave's quantum annealing is shown to find near-optimal solutions at a reasonable computational cost.

Collecting time series data spatially distributed in many locations is often important for analyzing climate change and its impacts on ecosystems. However, comprehensive spatial data collection is not always feasible, requiring us to predict climate variables at some locations. This study focuses on a prediction of climatic elements, specifically near-surface temperature and pressure, at a target location apart from a data observation point. Our approach uses two prediction methods: reservoir computing (RC), known as a machine learning framework with low computational requirements, and vector autoregression models (VAR), recognized as a statistical method for analyzing time series data. Our results show that the accuracy of the predictions degrades with the distance between the observation and target locations. We quantitatively estimate the distance in which effective predictions are possible. We also find that in the context of climate data, a geographical distance is associated with data correlation, and a strong data correlation significantly improves the prediction accuracy with RC. In particular, RC outperforms VAR in predicting highly correlated data within the predictive range. These findings suggest that machine learning-based methods can be used more effectively to predict climatic elements in remote locations by assessing the distance to them from the data observation point in advance. Our study on low-cost and accurate prediction of climate variables has significant value for climate change strategies.

In this paper, we conduct on-site group experiments of a multi-alternative decision making game. The objective is to capture a change of socially chosen strategies (i.e., social diffusion) by introducing a couple of committed minority bots and designing the balance between internal/external conformity biases, i.e., inertia and social coordination. We demonstrate the impact of the number of committed minority bots through the experiments. Furthermore, we reveal possible bounded rationality of the subjects from a gap between the designed conformity biases and estimated ones in the multi-alternative decision making game. Copyright (c) 2024 The Authors.

An iterative finite difference scheme for mean field games (MFGs) is proposed. The target MFGs are derived from control problems for multidimensional systems with advection terms. For such MFGs, linearization using the Cole-Hopf transformation and iterative computation using fictitious play are introduced. This leads to an implementation-friendly algorithm that iteratively solves explicit schemes. The convergence properties of the proposed scheme are mathematically proved by tracking the error of the variable through iterations. Numerical calculations show that the proposed method works stably for both one- and two-dimensional control problems.

When controlling multi-agent systems, the trade-off between performance and scalability is a major challenge. Here, we address this difficulty by using mean field games (MFGs), which is a framework that deduces the macroscopic dynamics describing the density profile of agents from their microscopic dynamics. To effectively use the MFG, we propose a model predictive MFG (MP-MFG), which estimates the agent population density profile with using kernel density estimation and manages the input generation with model predictive control. The proposed MP-MFG generates control inputs by monitoring the agent population at each time step, and thus achieves higher robustness than the conventional MFG. Numerical results show that the MP-MFG outperforms the MFG when the agent model has modeling errors or the number of agents in the system is small.

Imaging optics cannot focus light beams emitted from different points onto one point with a lens. Therefore, fabricating a 3D image sensor with a focal plane array is challenging. We developed an imaging optics device with an intentionally shifted focal plane switch array comprising pixels with two optical antennas, a switch, and a receiver on a chip. We successfully illuminated a single point, received the reflected light with the same pixel, and scanned an illuminating target. The proposed system can serve as a 3D image sensor, with a detection range of 204 m for Lambertian reflectors with 94% reflectivity, and as a beam combiner for high-power lasers.

The convergence properties of the upwind difference scheme for the Hamilton-Jacobi-Bellman (HJB) equation, which is a fundamental equation for optimal control theory, are investigated. We first perform a convergence analysis for the solution of the scheme, which eliminates ambiguities in the proofs of existing studies. We then prove the convergence of the spatial difference of the solution in the scheme by the correspondence between the HJB equations and the conservation laws. This result leads to a property of the objective function called epi-convergence, by which the convergence property of the input function is shown. The latter two results have not been addressed in existing studies. Numerical calculations support the obtained results.

We consider a Schelling-like segregation model, in which the behavior of individual agents is determined by a mixed individual and global utility. With a high ratio of global utility being incorporated, the agents are cooperative in order to realize a homogenized state, otherwise the agents are less cooperative, leading to an undesired Nash equilibrium with low utility. In the present study, we introduce a dynamically varying cooperation degree parameter to prevent the agents from falling into such a low-utility equilibrium state. More precisely, a large cooperation degree is assigned when the agents are in high-utility regions, whereas agents having low utility behave more individually. Simulation results show that homogenized phases with globally high utility are achieved with the present dynamical control, even for the case of a low mean value of cooperation degree. Since the cooperation degree represents the magnitude with which Pigouvian tax is enforced in the model of residential movement within a city, this result suggests the possibility of tax intervention to circumvent the undesired segregation of residents.

The rectification of electro-osmotic flows is important in micro- and nano-fluidics applications such as micropumps and energy conversion devices. Here, we propose a simple electro-osmotic diode in which colloidal particles are contained between two parallel membranes with different pore densities. While the flow in the forward direction just pushes the colloidal particles toward the high-pore-density membrane, the backward flow is blocked by the particles near the low-pore-density membrane, which clog the pores. Nonequilibrium molecular dynamics simulations show a strong nonlinear dependence on the electric field for both the electric current and electro-osmotic flow, indicating diode characteristics. A mathematical model to reproduce the electro-osmotic diode behavior is constructed, introducing an effective pore diameter as a model for pores clogged by the colloidal particles. Good agreement is obtained between the proposed model with estimated parameter values and the results of direct molecular dynamics simulations. The proposed electro-osmotic diode has potential application in downsized microfluidic pumps, e.g., the pump induced under AC electric fields.

Swarm robot systems, which consist of many cooperating mobile robots, have attracted attention for their environmental adaptability and fault tolerance advantages. One of the most important tasks for such systems is coverage control, in which robots autonomously deploy to approximate a given spatial distribution. In this study, we formulate a coverage control paradigm using the concept of optimal transport and propose a novel control technique, which we have termed the optimal transport-based coverage control (OTCC) method. The proposed OTCC, derived via the gradient flow of the cost function in the Kantorovich dual problem, is shown to covers a widely used existing control method as a special case. We also perform a Lyapunov stability analysis of the controlled system, and provide numerical calculations to show that the OTCC reproduces target distributions with better performance than the existing control method.

Grating couplers (GCs) are devices for transmitting and receiving the light propagated in a waveguide in silicon photonic integrated circuits (PICs).In this study, we propose a polarization splitting GC (PSGC) for the optical antenna of light detection and ranging (LiDAR).The PSGC was designed on the basis of two GCs coupling in a perfectly vertical direction.Our experiment demonstrated that the designed PSGC transmitted a polarization-multiplexed beam.The transmitting and receiving efficiencies of the optical antenna consists of lenses and PSGCs were calculated, and then the optical parameter for maximizing them was clarified.

The spread of intelligent transportation systems in urban cities has caused heavy computational loads, requiring a novel architecture for managing large-scale traffic. In this study, we develop a method for globally controlling traffic signals arranged on a square lattice by means of a quantum annealing machine, namely the D-Wave quantum annealer. We first formulate a signal optimization problem that minimizes the imbalance of traffic flows in two orthogonal directions. Then we reformulate this problem as an Ising Hamiltonian, which is compatible with quantum annealers. The new control method is compared with a conventional local control method for a large 50-by-50 city, and the results exhibit the superiority of our global control method in suppressing traffic imbalance over wide parameter ranges. Furthermore, the solutions to the global control method obtained with the quantum annealing machine are better than those obtained with conventional simulated annealing. In addition, we prove analytically that the local and the global control methods converge at the limit where cars have equal probabilities for turning and going straight. These results are verified with numerical experiments.

We have developed a light detection and ranging (LiDAR) system with a coin-sized sensor head that includes a scanning mechanism. This powerful detection device, which incorporates optical fiber technology, is based on time-of-flight measurements and can detect targets up to 200 m. The distinguishing characteristic of our system is that it uses optical amplifiers for the receiver. This enables the system to exceed the detection limit set by thermal noise and reach the detection limit of shot noise. In experiments conducted, we achieved a 200-m measurement range using a receiving lens of only 3 mm in diameter. This small and highly sensitive measurement technology could potentially be used in automotive LiDAR.