We examine the effects of various types of orders and order book states on stock price formation in the Japanese stock market. For the purpose, we use the Queue-Reactive Hawkes (QRH) process to model the order book dynamics since the QRH process can reflect the influence of order book states as well as self-excitation and/or mutual excitation of past orders on the arrival intensities of next orders. As a result, we observe whether the mid price moves or not strongly depends on the order book state.

The present paper proposes a stochastic continuous-time extension of a DICE model, a well-known dynamic integrated climate-economy model. The optimal path of the proposed model is described by a coupled forward-backward stochastic differential equation which is solvable in the sense that it is reduced to solving a system of deterministic linear equations.

The corporate credit rating classification problem has attracted lots of research interests in the literature of financial risk management. This article introduces the least-squares probabilistic classifier to the problem in an attempt to provide a model with better explanatory power. Empirical results show that the least-squares probabilistic classifier outperforms the logistic regression model, random forest, and the support vector machine in prediction accuracy ratios and F1 scores, for the samples of bond issuer firms in Japan.

In astrophysics and astronomy, it is necessary to solve numerically and accurately the Einstein equations, which are 2nd-order partial differential equations for a metric. We propose a method of estimating a numerical scheme in terms of constraints, and we also demonstrate that a numerical scheme with parameters makes it possible to perform a numerical calculation with less constraint violation.

Recently, optimization problems on Riemannian manifolds involving geodesic distances have been attracting considerable research interest. To compute geodesic distances and their Riemannian gradients, we can use logarithmic maps. However, the computational cost of logarithmic maps on Riemannian manifolds is generally higher than that on the Euclidean space. To overcome this computational issue, we propose approximated logarithmic maps. We prove that the definition is closely related to the inverse retractions. Numerical experiments for computing the Riemannian center of mass show that the proposed approximation significantly reduces the computational time while maintaining appropriate precision if the data diameter is sufficiently small.

This study investigates the expectations of the $p$-frame potentials of three typical types of determinantal point processes on the $d$-sphere: (i) spherical ensembles on the $2$-sphere; (ii) harmonic ensembles on the $d$-sphere; and (iii) jittered sampling point processes on the $d$-sphere. By the results, it can be expected that such determinantal point processes have asymptotically smaller expectations of $p$-frame potentials than those of Poisson point processes on the sphere with the same number of points.

Corporate performance prediction has attracted considerable research interest in the investment and financial risk management fields. This study comprehensively examines the ability of machine learning algorithms to integrate analysis of sales growth prediction, with specific focus on random forest, weighted random forest, gradient boosting decision tree, and support vector machine, as well as a least-squares probabilistic classifier. We carried out an experimental comparison study over a dataset comprising real corporate data on the effectiveness of these five machine-learning algorithms. The results showed sufficient performance for some machine-learning algorithms.

In protocols of isogeny-based cryptosystems, we send data of elliptic curves. Then it is necessary to identify supersingularity of the elliptic curves to guarantee the correctness of protocol. Among deterministic algorithms for the purpose, Sutherland proposed an efficient algorithm that utilizes sequences of $2$-isogenies computed by using modular polynomials. In this paper, we improve the efficiency of the algorithm by using some properties of $x$-coordinates of $2$-torsion points instead of modular polynomials. Our experimental result shows that our algorithm succeeded in reducing the computation time by 14 to 32 percent compared to Sutherland's algorithm.

We present an accelerated projected gradient method for solving a topology optimization problem. Specifically, we consider a compliance minimization problem of continua. The proposed method has guaranteed convergence to a stationary point and is easy to implement. By numerical experiments, we show that the computational cost of the proposed method is lower than that of the optimality criteria method.

The Sinc approximation is very efficient for bilateral rapidly decreasing functions. Even for non-bilateral rapidly decreasing functions, the Sinc approximation can work accurately if combined with an appropriate conformal map. Appropriate conformal maps for typical cases have been proposed in the literature. In the case of unilateral rapidly decreasing functions, however, it can be improved further. In this paper, we employ an improved conformal map for the case of unilateral rapidly decreasing functions. In addition, we present a computable error bound for the improved approximation formula.

A method of computing combinatorial Morse decomposition for a system of ordinary differential equations is proposed. It uses numerical solutions by Runge-Kutta method, and it is based on an affine approximation and QR decomposition. In contrast to interval arithmetic, it enables us to compute Morse decomposition at lower computational costs sacrificing for mathematical rigor. Numerical examples for time-T map of 3D ODE and a 3D Poincaré map for 4D ODE are presented for comparison between existing and proposed methods.

In this study, we investigate the shape optimization problem for maximizing heat dissipation in a thermal convection field considering a slight compressibility. This study aims to obtain an optimal shape that will maximize the heat dissipation on arbitrary boundaries considering a slight compressibility. The adjoint variable and traction methods are applied to obtain the gradient for the optimal shape. Consequently, it is found that the trend of increase in heat dissipation is different at certain shape update steps in comparison with the results obtained when considering slight compressibility and using the Boussinesq approximation.

In this paper, we deal with image segmentation by a curvature-driven flow of graphs. We focus on images, where the segmented objects can be represented by 1D graphs of functions. Compared to the usual direct approach, such a description benefits from certain advantages. Three methods to image segmentation are discussed and applied in terms of the graph formulation. Then, all methods are compared in the qualitative computational study.

This paper presents a numerical solution to shape design problem in the field of fluid--structure interaction. The square error integrals between the actual displacement distributions and target distributions to control displacement on the structural field considering geometrical nonlinearity is used as the objective functional. The adjoint variable method is used. A shape derivative is derived for the problem. Further, the $H^1$ gradient method is used to perform reshaping. A numerical analysis program is developed for the problem by using FreeFEM, and its validity is confirmed using the numerical results of 2D problems.

In this paper, we propose combinatorial preconditioning to accelerate the convergence of the parallel block Jacobi method for the symmetric eigenvalue problem. The idea is to gather matrix elements of large modulus near the diagonal prior to each annihilation by permutation of rows and columns and annihilate them at once, thereby leading to large reduction of the off-diagonal norm. Numerical experiments show that the resulting method can actually speedup the convergence and reduce the execution time of the parallel block Jacobi method.

This study discusses the analysis method for obtaining solutions that allow de-escalation in international conflicts within the framework of the Graph Model for Conflict Resolution. CD games, a conflict type in which two decision-makers can choose Cooperate (C) or Defect (D) strategies, are analyzed by examining the state transition and stability of decision makers with a focus on reachability. Based on the analysis, a new conceptual set is proposed, called `de-escalation reachability,' which enables focused analysis on the conflict states while avoiding any influence of arbitrators' attributes.

This paper presents an epidemic model with capacities of treatment and vaccination to discuss their effect on the disease spread. It is numerically shown that a backward bifurcation occurs in the basic reproduction number $\mathscr{R}_0$, where a stable endemic equilibrium co-exists with a stable disease-free equilibrium when $\mathscr{R}_0 < 1$, if the capacities are relatively small. This epidemiological implication is that, when there is not enough capacity for treatment or vaccination, the requirement $\mathscr{R}_0 < 1$ is not sufficient for effective disease control and disease outbreak may happen to a high endemic level even though $\mathscr{R}_0 < 1$.

In this study, we apply a weighted sensitivity approach to topology optimization analysis based on the density method in dynamic oscillation problems. In our proposed technique, strain energy is employed as the performance function. The self-adjoint relation is derived from the strain energy. However, when using this technique, the properties of the mesh prevent the acquisition of a non-oscillatory density distribution. Employing our proposed weighted sensitivity, however, reduces the numerical oscillation of the sensitivity in the search direction, resulting in a clear density distribution. We present the results of a comparison of the sensitivity and density distributions.

In the security evaluation of multivariate public key cryptosystems (MPKCs), constructing algorithms to solve the problem of finding solutions of a system of multivariate quadratic polynomial equations (MQ problem) is an important topic of research. Algorithms for computing a Gröbner basis are often used as a method to solve MQ problems. In this article, we focus on solving MQ problems using Buchberger's algorithm which is a basic algorithm for computing a Gröbner basis. We propose a new method for selecting polynomials to efficiently compute a Gröbner basis for a set of polynomials, which is used for MPKCs.

Anisotropic energies serve to model the boundaries of some types of crystals and liquid crystals. We study the stability of capillary hypersurfaces with anisotropic energy in a wedge, which contact with the edge of the wedge. We prove that stable capillary hypersurfaces in a wedge are, up to rescaling, only parts of the Wulff shape under some boundary conditions. Here the Wulff shape is the model of a single crystal introduced by G. Wulff.

We construct the explicit formula for the isoperimetric deformation of elastica described by the modified KdV equation. We also construct the explicit formulas for the continuous and discrete deformations of the discrete analogue of elastica described by the semi-discrete potential modified KdV equation and the discrete potential modified KdV equation, respectively. The formulas are given in terms of the elliptic theta functions.

The ageing of concrete structures in Japan is becoming an increasingly serious issue. Periodic inspection is necessary to prevent accidents caused by ageing. One of the methods used to inspect concrete is the hammering test. In this study, we aim to develop a system using machine learning for identifying the topology of defects in concrete, based on acceleration response data obtained from the hammering test. As part of the machine learning method, we constructed a neural network based on self-attention. In addition, we evaluated the effect of changing the size of the estimation domain using the machine learning model.

After the publication of [Chiba et al., JSIAM Lett. (2021)], the authors have noticed that there are some errors in the paper and in codes for the numerical experiments.