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Yutaro Kabata

Publications and source records attributed to Yutaro Kabata.

At least 19 recordsLinked to original sources

At least seven modes in a heteroscedastic three-component bivariate Gaussian mixture

A Gaussian mixture density can have more modes than components. It has been conjectured that the maximum number of modes of a $d$-variate $k$-component Gaussian mixture density is $\binom{d+k-1}{d}$, which equals six for $(d,k)=(2,3)$. We construct an explicit family of equally weighted heteroscedastic three-component bivariate Gaussian mixture densities with at least seven distinct nondegenerate modes, showing that this conjectured upper bound fails for $(d,k)=(2,3)$. To the best of our knowledge, this provides the first counterexample to the conjecture across all pairs $(d,k)$.

math.ST

A curvature-based criterion for harmonic circadian waveforms

Experimental and theoretical studies of circadian rhythms have focused largely on the period, on mutants that alter it and on phase shifts, and this focus has driven the identification of clock genes and clarified how clocks entrain to light--dark cycles. The waveform of the oscillation itself, by contrast, has attracted little attention as an indicator of the properties of the underlying oscillator. To assess the waveform directly, we focus on whether the trajectory in the plane spanned by a variable and its time derivative possesses an inflection point, and we define an oscillation to be harmonic when no inflection point is present. Bioluminescence recordings from cyanobacteria and from the mammalian SCN were harmonic in this sense, as were most of the core clock components in mathematical models of the circadian clock. Numerical analysis of the Goodwin model, a minimal representation of the core circadian oscillator, yielded harmonic oscillation throughout. We confirmed this numerical trend semi-analytically using a piecewise-linearized Goodwin model. Because it evaluates the properties of a waveform without assuming a model structure, the approach we propose offers a new perspective on the waveform analysis of biological rhythms in general, not only circadian ones.

q-bio.QM

On Mixtures of Three Homoscedastic Gaussian Densities: An Unconditional Sharper Bound on the Number of Modes

It is known that a mixture of three homoscedastic multivariate Gaussian densities whose centers form an equilateral triangle can have four modes, owing to the emergence of a ``ghost'' mode at the center. Nevertheless, obtaining a sharp upper bound on the number of modes remains open even in this seemingly simple setting. The best previously available upper bounds applicable to the homoscedastic three-component setting were 42592 and, more recently, 72. These bounds were derived for substantially more general classes of Gaussian mixtures and are therefore not optimized for the present setting. Moreover, they are conditional on the assumption that the modal set is finite. In this paper, as a sharper bound, we prove that every mixture of three homoscedastic Gaussian densities has at most 8 modes, without imposing any finiteness or non-degeneracy assumption a priori. To the best of our knowledge, this is the first unconditional finiteness result for the modal set that holds uniformly over the full class of multivariate three-component homoscedastic Gaussian mixtures. In particular, it covers genuinely multivariate asymmetric configurations with arbitrary non-collinear centers and arbitrary positive mixture weights.

math.ST

Shapes of light: singularities of slant functions and the Gauss map of a surface

The slant function on a regular surface in three-space, introduced by Koenderink, is the inner product of a fixed light direction with the unit normal; under the standard Lambertian model it gives the brightness of the surface illuminated from that direction. We study the singularities of this function and show that their type is characterised by the differential geometry of the parabolic set and its spherical image under the Gauss map. At every point of the surface, we first describe the light directions along which the slant function has a degenerate singularity; such singularities arise only at the parabolic points. We then determine the precise singularity type at representative parabolic points. At the first-order inflection and first-order vertex of Gauss, and at the cusp of Gauss at which the parabolic set is regular, the finer geometry of the spherical image is reflected in the singularity of the slant function.

math.DG

Algebraic Approach to Ridge-Regularized Mean Squared Error Minimization in Minimal ReLU Neural Network

This paper investigates a perceptron, a simple neural network model, with ReLU activation and a ridge-regularized mean squared error (RR-MSE). Our approach leverages the fact that the RR-MSE for ReLU perceptron is piecewise polynomial, enabling a systematic analysis using tools from computational algebra. In particular, we develop a Divide-Enumerate-Merge strategy that exhaustively enumerates all local minima of the RR-MSE. By virtue of the algebraic formulation, our approach can identify not only the typical zero-dimensional minima (i.e., isolated points) obtained by numerical optimization, but also higher-dimensional minima (i.e., connected sets such as curves, surfaces, or hypersurfaces). Although computational algebraic methods are computationally very intensive for perceptrons of practical size, as a proof of concept, we apply the proposed approach in practice to minimal perceptrons with a few hidden units.

stat.ML

Visualization of curvature on curve and surface by tangential angle parametrization

We propose a unified method to visualize curvature on planar curves and surfaces of revolution using the tangential angle parameter. For plane curves, placing markers at equal increments of the tangential angle reveals local bending features and naturally highlights inflection points and vertices. This approach extends to surfaces of revolution, where curvature lines drawn at equal tangential angle steps reflect principal curvature variations and naturally expose ridge and parabolic curves. Our method provides clear, consistent visualizations without arbitrary parameter tuning, offering geometric insight for both analysis and design applications.

math.DG

Algebraic Approach for Orthomax Rotations

In exploratory factor analysis, rotation techniques are employed to derive interpretable factor loading matrices. Factor rotations deal with equality-constrained optimization problems aimed at determining a loading matrix based on measure of simplicity, such as ``perfect simple structure'' and ``Thurstone simple structure.'' Numerous criteria have been proposed, since the concept of simple structure is fundamentally ambiguous and involves multiple distinct aspects. However, most rotation criteria may fail to consistently yield a simple structure that is optimal for analytical purposes, primarily due to two challenges. First, existing optimization techniques, including the gradient projection descent method, exhibit strong dependence on initial values and frequently become trapped in suboptimal local optima. Second, multifaceted nature of simple structure complicates the ability of any single criterion to ensure interpretability across all aspects. In certain cases, even when a global optimum is achieved, other rotations may exhibit simpler structures in specific aspects. To address these issues, obtaining all equality-constrained stationary points -- including both global and local optima -- is advantageous. Fortunately, many rotation criteria are expressed as algebraic functions, and the constraints in the optimization problems in factor rotations are formulated as algebraic equations. Therefore, we can employ computational algebra techniques that utilize operations within polynomial rings to derive exact all equality-constrained stationary points. Unlike existing optimization methods, the computational algebraic approach can determine global optima and all stationary points, independent of initial values. We conduct Monte Carlo simulations to examine the properties of the orthomax rotation criteria, which generalizes various orthogonal rotation methods.

math.ST

Singularities in bivariate normal mixtures

We investigate mappings $F = (f_1, f_2) \colon \mathbb{R}^2 \to \mathbb{R}^2 $ where $ f_1, f_2 $ are bivariate normal densities from the perspective of singularity theory of mappings, motivated by the need to understand properties of two-component bivariate normal mixtures. We show a classification of mappings $ F = (f_1, f_2) $ via $\mathcal{A}$-equivalence and characterize them using statistical notions. Our analysis reveals three distinct types, each with specific geometric properties. Furthermore, we determine the upper bounds for the number of modes in the mixture for each type.

math.ST

A view-parametric extension of the d'Ocagne formula for a surface in $\mathbb{R}^3$

In this paper, we consider the orthogonal projection of a surface in $\mathbb{R}^3$ for a given view direction. We then introduce and investigate several invariants of the families of the plane curves that locally configure the projection image of the surface. Using the invariants, we also show an extension of the d'Ocagne formula that associates a local behavior of the projection image of a surface with Gaussian curvature of the surface.

math.DG

Algebraic approach to maximum likelihood factor analysis

In exploratory factor analysis, model parameters are usually estimated by maximum likelihood method. The maximum likelihood estimate is obtained by solving a complicated multivariate algebraic equation. Since the solution to the equation is usually intractable, it is typically computed with continuous optimization methods, such as Newton-Raphson methods. With this procedure, however, the solution is inevitably dependent on the estimation algorithm and initial value since the log-likelihood function is highly non-concave. Particularly, the estimates of unique variances can result in zero or negative, referred to as improper solutions; in this case, the maximum likelihood estimate can be severely unstable. To delve into the issue of the instability of the maximum likelihood estimate, we compute exact solutions to the multivariate algebraic equation by using algebraic computations. We provide a computationally efficient algorithm based on the algebraic computations specifically optimized for maximum likelihood factor analysis. To be specific, Gröebner basis and cylindrical decomposition are employed, powerful tools for solving the multivariate algebraic equation. Our proposed procedure produces all exact solutions to the algebraic equation; therefore, these solutions are independent of the initial value and estimation algorithm. We conduct Monte Carlo simulations to investigate the characteristics of the maximum likelihood solutions.

math.ST

A novel symmetry in nanocarbons: pre-constant discrete principal curvature structure

Since the first-principles calculations in quantum chemistry precisely provide possible configurations of carbon atoms in nanocarbons, we have analyzed the geometrical structure of the possible carbon configurations and found that there exists a novel symmetry in the nanocarbons, i.e., the pre-constant discrete principal curvature (pCDPC) structure. In terms of the discrete principal curvature based on the discrete geometry for trivalent oriented graphs developed by Kotani, Naito, and Omori (Comput. Aided Geom. Design, $\bf{58}$, (2017), 24-54), we numerically investigated discrete principal curvature distribution of the nanocarbons, C$_{60}$, carbon nanotubes, C$_{120}$ (C$_{60}$ dimer), and C$_{60}$-polymers (peanut-shaped fullerene polymers). While the C$_{60}$ and nanotubes have the constant discrete principal curvature (CDPC) as we expected, it is interesting to note that the C$_{60}$-polymers and C$_{60}$ dimer also have the almost constant discrete principal curvature, i.e., pCDPC, which is surprising. A nontrivial pCDPC structure with revolutionary symmetry is available due to discreteness, though it has been overlooked in geometry. In discrete geometry, there appears a center axisoid which is the discrete analogue of the center axis in the continuum differential geometry but has three-dimensional structure rather than a one-dimensional curve due to its discrete nature. We demonstrated that such pCDPC structure exists in nature, namely in the C$_{60}$-polymers. Furthermore, since we found that there is a positive correlation between the degree of the CDPC structure and stability of the configurations for certain class of the C$_{60}$-polymers, we also revealed the origin of the pCDPC structure from an aspect of materials science.

cond-mat.mtrl-sci

Contact cylindrical surfaces and a projection of a surface around a parabolic point

We investigate differential geometric properties of a parabolic point of a surface in the Euclidean three space. We introduce the contact cylindrical surface which is a cylindrical surface having a degenerate contact type with the original surface at a parabolic point. Furthermore, we show that such a contact property gives a characterization to the $\mathcal{A}$-singularity of the orthogonal projection of a surface from the asymptotic direction.

math.DG

Capturing information on curves and surfaces from their projected images

Obtaining complete information about the shape of an object by looking at it from a single direction is impossible in general. In this paper, we theoretically study obtaining differential geometric information of an object from orthogonal projections in a number of directions. We discuss relations between (1) a space curve and the projected curves from several distinct directions, and (2) a surface and the apparent contours of projections from several distinct directions, in terms of differential geometry and singularity theory. In particular, formulae for recovering certain information on the original curves or surfaces from their projected images are given.

math.DG

Topology of Pareto sets of strongly convex problems

A multiobjective optimization problem is simplicial if the Pareto set and front are homeomorphic to a simplex and, under the homeomorphisms, each face of the simplex corresponds to the Pareto set and front of a subproblem. In this paper, we show that strongly convex problems are simplicial under a mild assumption on the ranks of the differentials of the objective mappings. We further prove that one can make any strongly convex problem satisfy the assumption by a generic linear perturbation, provided that the dimension of the source is sufficiently larger than that of the target. We demonstrate that the location problems, a biological modeling, and the ridge regression can be reduced to multiobjective strongly convex problems via appropriate transformations preserving the Pareto ordering and the topology.

math.OC

Projection of crosscap

We determine the precise bifurcation diagrams of the apparent contours of generic crosscaps, which contain the information of bifurcations with respect to the images of the singular sets of crosscaps: crosscap points and double point curves. Especially, three different kinds of equivalences play key roles.

math.DG

Binary differential equations at parabolic and umbilical points for $2$-parameter families of surfaces

We determine local topological types of binary differential equations of asymptotic curves at parabolic and flat umbilical points for generic $2$-parameter families of surfaces in $\mathbb P^3$ by comparing our projective classification of Monge forms and classification of general BDE obtained by Tari and Oliver. In particular, generic bifurcations of the parabolic curve are classified. The flecnodal curve is also examined by direct computations, and we present new bifurcation diagrams in typical examples.

math.DG

Projective classification of jets of surfaces in 3-space

We present a local classification of smooth projective surfaces in 3-space via projective transformations in accordance with singularity types of central projections up to codimension 4. We also discuss relations between our classification of Monge forms and bifurcations of parabolic curves and flecnodal curves.

math.DG