Bias-corrected inference for multivariate nonparametric regression: model selection and oracle property

The local polynomial estimator is particularly affected by the curse of di- mensionality. So, the potentialities of such a tool become ineffective for large dimensional applications. Motivated by this, we propose a new estimation procedure based on the local linear estimator and a nonlinearity sparseness condition, which focuses on the number of covariates for which the gradient is not constant. Our procedure, called BID for Bias-Inflation-Deflation, is automatic and easily applicable to models with many covariates without any additive assumption to the model. It simultaneously gives a consistent estimation of a) the optimal bandwidth matrix, b) the multivariate regression function and c) the multivariate, bias-corrected, confidence bands. Moreover, it automatically identify the relevant covariates and it separates the nonlinear from the linear effects. We do not need pilot bandwidths. Some theoretical properties of the method are discussed in the paper. In particular, we show the nonparametric oracle property. For linear models, the BID automatically reaches the optimal rate Op(n−1/2), equivalent to the parametric case. A simulation study shows a good performance of the BID procedure, compared with its direct competitor

A locally adaptive bandwidth selector for kernel based regression

The selection of the smoothing parameter represents a crucial step in the local polynomial regression, because of the implications on the consistency of the nonparametric regression estimator and because of the difficulties in the implementation of the selection procedure. Moreover, to capture the complexity of the unknown regression curve, a local variable bandwidth is needed, which determines an increase in the efficiency and computa- tional costs of such algorithms. This paper focuses on the problem of the automatic selection of a local bandwidth. We propose a slightly different approach with respect to the traditional ones, which does not require ad- ditional computational effort. The empirical performance of the method is shown in the paper through a simulation study

Deterministic Thermal Sculpting of Large-Scale 2D Semiconductor Nanocircuits

Two-dimensional (2D) Transition Metal Dichalcogenide semiconductor (TMDs) nanocircuits are deterministically engineered over large-scale substrates. The original approach combines large-area physical growth of 2D TMDs layer with high resolution thermal - Scanning Probe Lithography (t-SPL), to reshape the ultra-thin semiconducting layers at the nanoscale level. We demonstrate the additive nanofabrication of few-layer MoS2 nanostructures, grown in the 2H-semiconducting TMD phase, as shown by their Raman vibrational fingerprints and by their optoelectronic response. The electronic signatures of the MoS2 nanostructures are locally identified by Kelvin probe force microscopy providing chemical and compositional contrast at the nanometer scale. Finally, the potential role of the 2D TMD nanocircuits as building blocks of deterministic 2D semiconducting interconnections is demonstrated by high-resolution local conductivity maps showing the competitive transport properties of these large-area nanolayers. This work thus provides a powerful approach to scalable nanofabrication of 2D nano-interconnects and van der Waals heterostructures, and to their integration in real-world ultra-compact electronic and photonic nanodevices.Comment: 17 pages, 4 figure

Satellite interferometric data for seismic damage assessment

Radar satellites allow the collection of data on large areas without direct access to structures. Thereby, they appear very attractive for Structural Health Monitoring (SHM) purposes. Data collected by satellites can be processed to obtain temporal histories of displacements through which the health state of a monitored system can be potentially identified. However, anomalies in the time histories of displacements are not necessarily due to damage. Environmental phenomena, such as variations in atmospheric temperature, and rain, can modify the behavior of structures without compromising their safety. The impact of these phenomena on the structural response can hinder the identification of anomalies or lead to false alarms if such alterations are misinterpreted as damage. Furthermore, if the monitored system is a historical structure, uncertainties on the structural behavior are inevitably increased during aging. The purpose of this article is to discuss the possibility of identifying damage due to seismic actions considering the impact of variations of environmental factors on the time histories of the displacements retrieved by satellite data. The structural health condition of a historical structure located in the city of Rome (Italy) hit by the October 2016 Central Italy earthquakes is investigated based on interferometric satellite data. The satellite data are acquired by COSMO-SkyMed (CSM) of the Italian Space Agency between 2010 and 2019 and are processed by CNR IREA

Controlling resonant surface modes by arbitrary light induced optical anisotropies

In this work the sensitivity of Bloch Surface Waves to laser-induced anisotropy of azo-polymeric thin layers is expe rimentally shown . The nanoscale reshaping of the films via thermal-Scanning Probe Lithography allows to couple light to circular photonic nanocavities, tailoring on-demand resonant BSW confined within the nanocavity

Self-Organized Tailoring of Faceted Glass Nanowrinkles for Organic Nanoelectronics

Self-organized wrinkled templates are homogeneously fabricated over a large area (cm2) glass substrates by defocused ion beam irradiation, demonstrating the capability to induce and modify at will the out-of-plane tilt of the nanofacets with selected slope. We identify a region of morphological instability which leads to faceting for incidence angles of the ion beam with respect to the surface, \u3b8, in the range 15\ub0 64 \u3b8 64 45\ub0, while for normal incidence, \u3b8 = 0\ub0, and for grazing incidence at about 55\u201360\ub0 a flat morphology is achieved. The crucial parameter which controls the slope of the sawtooth profile is the local ion beam incidence angle on the facets which corresponds to the maximum erosion velocity. For \u3b8 = 30\ub0, improved lateral order of the templates is found which can be exploited for the anisotropic confinement of functional layers. Here, we highlight the crucial role of the 1D nanopatterned template in driving the anisotropic crystallization of spun-cast conductive polymer thin films in registry with the faceted nanogrooves. In response, anisotropic electrical transport properties of the nanopatterned film are achieved with overall improvement higher than 60% with respect to a flat reference, thus showing the potential of such transparent large-area templates in nanoelectronics, optoelectronics, and biosensing

GRID for model structure discovering in high dimensional regression

Given a nonparametric regression model, we assume that the number of covariates d → ∞ but only some of these covariates are relevant for the model. Our goal is to identify the relevant covariates and to obtain some information about the structure of the model. We propose a new nonparametric procedure, called GRID, having the following features: (a) it automatically identifies the relevant covariates of the regression model, also distinguishing the nonlinear from the linear ones (a covariate is defined linear/nonlinear depending on the marginal relation between the response variable and such a covariate); (b) the interactions between the covariates (mixed effect terms) are automatically identified, without the necessity of considering some kind of stepwise selection method. In particular, our procedure can identify the mixed terms of any order (two way, three way, ...) without increasing the computational complexity of the algorithm; (c) it is completely data-driven, so being easily implementable for the analysis of real datasets. In particular, it does not depend on the selection of crucial regularization parameters, nor it requires the estimation of the nuisance parameter 2 (self scaling). The acronym GRID has a twofold meaning: first, it derives from Gradient Relevant Identification Derivatives, meaning that the procedure is based on testing the significance of a partial derivative estimator; second, it refers to a graphical tool which can help in representing the identified structure of the regression model. The properties of the GRID procedure are investigated theoretically