THIS PAPER PROPOSES PARTIAL ANSWERS TO THE FOLLOWING QUESTIONS: in what senses can fitness differences plausibly be considered causes of evolution?What relationships are there between fitness concepts used in empirical research, modeling, and abstract theoretical proposals? How does the relevance of different fitness concepts depend on research questions and methodological constraints? The pape...

We provide a new mathematical technique leading to the construction of the exact parametric or closed form solutions of the classes of Abel’s nonlinear differential equations ODEs of the first kind. These solutions are given implicitly in terms of Bessel functions of the first and the second kind Neumann functions , as well as of the free member of the considered ODE; the parameter ν being intr...

in metropolitan development management, quality of public services is influential in every public sector to satisfaction of citizens on quality of services. nowadays, satisfaction are with such important matters that should be considered in the planning, implementation, management and maintenance of many public services such as subway, transportation, traffics, parks, markets and so on. the pur...

This paper studies stability for parametric mathematical programs with geometric constraints. We show that, under the no nonzero abnormal multiplier constraint qualification and the second-order growth condition or second-order sufficient condition, the locally optimal solution mapping and stationary point mapping are nonempty-valued and continuous with respect to the perturbation parameter and...

This note is concerned with accurate and computationally efficient approximations of moments of Gaussian random variables passed through sigmoid or softmax mappings. These approximations are semi-analytical (i.e. they involve the numerical adjustment of parametric forms) and highly accurate (they yield 5% error at most). We also highlight a few niche applications of these approximations, which ...

Response predictions in structural dynamics are in general very sensitive to random uncertainties associated with the underlying predictive mathematical model. The non-parametric probabilistic model provides the possibility to capture both data and model uncertainties. The uncertainties are introduced at a global level and controlled through one dispersion parameter each, for the mass, damping ...

Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. This book introduces theories, methods, and applications of density ratio estimation, which is a newly emerging paradigm in the machine learning community. Various machine learning problems such as non-stationarity adaptation, outlier det...