We study large networks of parametric oscillators as heuristic solvers random Ising models. In these networks, known coherent machines, the model to be solved is encoded in coupling between oscillators, and a solution offered by steady state network. This approach relies on assumption that mode competition steers network ground-state model. By considering broad family frustrated models, we show...

This paper discusses parametric, nonparametric, and semiparametric models of urban growth. To illustrate differences across approaches, we test Gibrat's law in the long run, using three methods different datasets: Spanish capital cities regions (1900–2011, annual data) US MSAs (1900–2000, decennial data). Our results reveal that estimation relationship between growth initial size can significan...

Parametric optimization is an important product design technique, especially in the context of modern parametric feature-based CAD paradigm. Realizing its full potential, however, requires a closed loop between and CAE (i.e., seamless CAD/CAE integration) with automatic modifications simulation updates. Conventionally approach model conversion often employed to form loop, but this way working h...

background: precise diagnosis of disease risk factors via efficient statistical models is the primary step for reducing the heavy costs of breast cancer, as one of the most highly prevalent cancer throughout the world. therefore, the aim of this study was to present a recently introduced statistical model in order to assess its proficiency for model fitting. methods: the information of 1465 eli...

We present a distributed optimization algorithm for solving online personalized problems over network of computing and communicating nodes, each which linked to specific user. The local objective functions are assumed have composite structure consist known time-varying (engineering) part an unknown (user-specific) part. Regarding the part, it is parametric (e.g., quadratic) <italic xmlns:mml="h...