We study the front dynamics of the bistable reaction-diffusion equations with periodic diffusion and/or convection coefficients in several space dimensions. When traveling wave solutions exist, the solutions of the initial value problem behave as wave fronts propagating with the effective speeds of traveling waves under various initial conditions. Yet due to the bistable nature of the nonlinearity, traveling waves may not always exist when the medium variations from the mean states are large enough. Their existence is closely related to the detailed forms of diffusion and convection coefficients, more so in multidimension than in one. We present a simple sufficient condition for the nonexistence of traveling waves (quenching) using perturbation method. Our two dimensional finite difference numerical computations show a variety of front behaviors, such as: the propagation, quenching and retreat of fronts. We found

We construct a smooth Artin stack parameterizing the stable weighted curves of genus one with twisted fields and prove that it is isomorphic to the blowup stack of the moduli of genus one weighted curves studied by Hu and Li. This leads to a blowup-free construction of Vakil-Zinger's desingularization of the moduli of genus one stable maps to projective spaces. This construction provides the cornerstone of the theory of stacks with twisted fields, which is thoroughly studied in arXiv:2005.03384 and leads to a blowup-free resolution of the stable map moduli of genus two.

In a Data-Generating Experiment (DGE), the data, X, is often obtained either from a Black-Box with inputs θ and Y, or from a Quantile function or a learning machine, f(Y, θ); θ is unknown, element of metric space (Θ, ρ), Y is random. If X has intractable or unknown c.d.f., Fθ, non-identifiability of θ cannot be confirmed and when present, among others, limits the predictive accuracy of the learned model, f(Y, \hat{θ}); \hat{θ} estimate of θ. In Machine Learning, non-identifiability of θ is ubiquitous and its extent is a criterion for selecting a learning machine. Empirical indices, EDI and PPVI, are introduced using P-values of Kolmogorov-Smirnov tests: i) to confirm almost surely, using generated data, the discrimination of θ from θ^∗, namely that the Kolmogorov distance, dK(Fθ, Fθ^∗), is positive, ii) to confirm identifiability of θ(∈ Θ) by repeating i) for θ^∗ in a sieve of Θ, since neighboring parameter values are in practice indistinguishable, and iii) most important, to compare EDI-graphs of DGEs, preferring more discrimination and less non-identifiability among parameters, and select one DGE to use. In applications, EDI-graphs confirm nonidentifiability in mixture models and in models parametrised with sums of parameters. EDI and PPVI explain why Tukey’s g-and-h model (DGE1) has better g-discrimination than the g-and-k model (DGE2), unless the sample size is extremely large; h_0 = k_0. EDIgraphs indicate that Normal learning machines have better parameter discrimination thanSigmoid learning machines and their parameters are non-identifiable.

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