Antarctic snowmelt detection for QuikSCAT scatterometer data based on mathematical morphology combined with wavelet transform

225-232Microwave scatterometer is sensitive to the melting snow. When the freeze-thaw phenomenon occurs, the backscatter coefficients will have a sharp rising and falling mutation. Mathematical morphology has the characteristics with edge-preserving filter and wavelet transform has the characteristics with the automatic edge extraction, which does not depend on the priori snowmelt information. A new automatic Antarctic snowmelt detection method was proposed based on mathematical morphology combined with wavelet transform. This method improves the snowmelt detection accuracy, because this method can remove the interference of the edge extraction. Melt onset date, end date and duration can be obtained with high accuracy by identifying and tracking the sharp rising and falling edge. Compare the snowmelt results in this work with the temperature of ten automatic weather stations (AWS), which shows that the snowmelt detection method proposed in the paper improves the detection accuracy from about 50 % to 62.5 % in AWS Cape Denison

Minimax Optimal Transfer Learning for Kernel-based Nonparametric Regression

In recent years, transfer learning has garnered significant attention in the machine learning community. Its ability to leverage knowledge from related studies to improve generalization performance in a target study has made it highly appealing. This paper focuses on investigating the transfer learning problem within the context of nonparametric regression over a reproducing kernel Hilbert space. The aim is to bridge the gap between practical effectiveness and theoretical guarantees. We specifically consider two scenarios: one where the transferable sources are known and another where they are unknown. For the known transferable source case, we propose a two-step kernel-based estimator by solely using kernel ridge regression. For the unknown case, we develop a novel method based on an efficient aggregation algorithm, which can automatically detect and alleviate the effects of negative sources. This paper provides the statistical properties of the desired estimators and establishes the minimax optimal rate. Through extensive numerical experiments on synthetic data and real examples, we validate our theoretical findings and demonstrate the effectiveness of our proposed method

Towards a Unified Analysis of Kernel-based Methods Under Covariate Shift

Covariate shift occurs prevalently in practice, where the input distributions of the source and target data are substantially different. Despite its practical importance in various learning problems, most of the existing methods only focus on some specific learning tasks and are not well validated theoretically and numerically. To tackle this problem, we propose a unified analysis of general nonparametric methods in a reproducing kernel Hilbert space (RKHS) under covariate shift. Our theoretical results are established for a general loss belonging to a rich loss function family, which includes many commonly used methods as special cases, such as mean regression, quantile regression, likelihood-based classification, and margin-based classification. Two types of covariate shift problems are the focus of this paper and the sharp convergence rates are established for a general loss function to provide a unified theoretical analysis, which concurs with the optimal results in literature where the squared loss is used. Extensive numerical studies on synthetic and real examples confirm our theoretical findings and further illustrate the effectiveness of our proposed method.Comment: Poster to appear in Thirty-seventh Conference on Neural Information Processing System

Value Congruence: A Study of Green Transformational Leadership and Employee Green Behavior

This study examined the extent to which the impact of green transformational leadership on employee green behavior through follower perceptions of value congruence. Path analyzing on data from 193 subordinate-leader dyads showed that followers’ value congruence with their leader mediated the effects of green transformational leadership on employee green behavior. Results also supported that green identity moderated the indirect effect of green transformational leadership on employee green behavior through value congruence, such that the indirect effect was more positive when green identity was high than when it was low. These findings provided valuable contribution to green transformational leadership, value congruence, and employee green behavior by exploring the relationship between them. Practical implications and directions for future research are also discussed

Ectopic osteoid and bone formation by three calcium-phosphate ceramics in rats, rabbits and dogs

Calcium phosphate ceramics with specific physicochemical properties have been shown to induce de novo bone formation upon ectopic implantation in a number of animal models. In this study we explored the influence of physicochemical properties as well as the animal species on material-induced ectopic bone formation. Three bioceramics were used for the study: phase-pure hydroxyapatite (HA) sintered at 1200°C and two biphasic calcium phosphate (BCP) ceramics, consisting of 60 wt.% HA and 40 wt.% TCP (β-Tricalcium phosphate), sintered at either 1100°C or 1200°C. 108 samples of each ceramic were intramuscularly implanted in dogs, rabbits, and rats for 6, 12, and 24 weeks respectively. Histological and histomorphometrical analyses illustrated that ectopic bone and/or osteoid tissue formation was most pronounced in BCP sintered at 1100°C and most limited in HA, independent of the animal model. Concerning the effect of animal species, ectopic bone formation reproducibly occurred in dogs, while in rabbits and rats, new tissue formation was mainly limited to osteoid. The results of this study confirmed that the incidence and the extent of material-induced bone formation are related to both the physicochemical properties of calcium phosphate ceramics and the animal model

Synthesis and photocatalytic activity of titania monoliths prepared with controlled macro- and mesopore structure

Herein, we report a one-pot synthesis of crack-free titania monoliths with hierarchical macro-mesoporosity and crystalline anatase walls. Bimodal macroporosity is created through the polymer-induced phase separation of poly(furfuryl alcohol). The cationic polymerization of furfuryl alcohol is performed in situ and subsequently the polymer becomes immiscible with the aqueous phase, which includes titanic acid. Addition of template, Pluronic F127, increases the mesopore volume and diameter of the resulting titania, as the poly(ethylene glycol) block interacts with the titania precursor, leading to assisted assembly of the metal oxide framework. The hydrophobic poly(propylene glycol) micelle core could itself be swollen with monomeric and oligomeric furfuryl alcohol, allowing for mesopores as large as 18 nm. Variations in synthesis parameters affect porosity; for instance furfuryl alcohol content changes the size and texture of the macropores, water content changes the grain size of the titania and Pluronic F127 content changes the size and volume of the mesopore. Morphological manipulation improves the photocatalytic degradation of methylene blue. Light can penetrate several millimeters into the porous monolith, giving these materials possible application in commercial devices.Fil: Drisko, Glenna L.. University of Melbourne; AustraliaFil: Zelcer, Andrés. Comisión Nacional de Energía Atómica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; ArgentinaFil: Wang, Xingdong. Commonwealth Scientific And Industrial Research Organization; AustraliaFil: Caruso, Rachel A.. School Of Chemistry; Australia. Commonwealth Scientific And Industrial Research Organization; AustraliaFil: Soler Illia, Galo Juan de Avila Arturo. Comisión Nacional de Energía Atómica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Bright solitons in a spin-orbit-coupled dipolar Bose-Einstein condensate trapped within a double-lattice

By effectively controlling the dipole-dipole interaction, we investigate the characteristics of the ground state of bright solitons in a spin-orbit coupled dipolar Bose-Einstein condensate. The dipolar atoms are trapped within a double-lattice which consists of a linear and a nonlinear lattice. We derive the motion equations of the different spin components, taking the controlling mechanisms of the diolpe-dipole interaction into account. An analytical expression of dipole-dipole interaction is derived. By adjusting the dipole polarization angle, the dipole interaction can be adjusted from attraction to repulsion. On this basis, we study the generation and manipulation of the bright solitons using both the analytical variational method and numerical imaginary time evolution. The stability of the bright solitons is also analyzed and we map out the stability phase diagram. By adjusting the long-range dipole-dipole interaction, one can achieve manipulation of bright solitons in all aspects, including the existence, width, nodes, and stability. Considering the complexity of our system, our results will have enormous potential applications in quantum simulation of complex systems