In this paper, we propose the test statistic to check whether the nonparametric function in partially linear models is linear or not. We estimate the nonparametric function in alternative by using the local linear met...In this paper, we propose the test statistic to check whether the nonparametric function in partially linear models is linear or not. We estimate the nonparametric function in alternative by using the local linear method, and then estimate the parameters by the two stage method. The test statistic under the null hypothesis is calculated, and it is shown to be asymptotically normal.展开更多
We propose the test statistic to check whether the nonpararnetric functions in two partially linear models are equality or not in this paper. We estimate the nonparametric function both in null hypothesis and the alte...We propose the test statistic to check whether the nonpararnetric functions in two partially linear models are equality or not in this paper. We estimate the nonparametric function both in null hypothesis and the alternative by the local linear method, where we ignore the parametric components, and then estimate the parameters by the two stage method. The test statistic is derived, and it is shown to be asymptotically normal under the null hypothesis.展开更多
Mainly due to its implementation simplicity, the non-coherent Ultra-Wide Band (UWB) receiver is attractive for lower data rate applications, which gains much attention again in recent years. In this paper, a General L...Mainly due to its implementation simplicity, the non-coherent Ultra-Wide Band (UWB) receiver is attractive for lower data rate applications, which gains much attention again in recent years. In this paper, a General Likelihood Ratio Test (GLRT) based non-coherent receiver on UWB Pulse-Position-Modulation (PPM) signal in multipath channels is derived, and a novel structure is proposed as well. Subsequently, the closed-form expressions of asymptotic error-rate performance related to the non-coherent receiver are also derived and verified.展开更多
In pulsar timing, timing residuals are the differences between the observed times of arrival and predictions from the timing model. A comprehensive timing model will produce featureless resid- uals, which are presumab...In pulsar timing, timing residuals are the differences between the observed times of arrival and predictions from the timing model. A comprehensive timing model will produce featureless resid- uals, which are presumably composed of dominating noise and weak physical effects excluded from the timing model （e.g. gravitational waves）. In order to apply optimal statistical methods for detecting weak gravitational wave signals, we need to know the statistical properties of noise components in the residuals. In this paper we utilize a variety of non-parametric statistical tests to analyze the whiteness and Gaussianity of the North American Nanohertz Observatory for Gravitational Waves （NANOGrav） 5- year timing data, which are obtained from Arecibo Observatory and Green Bank Telescope from 2005 to 2010. We find that most of the data are consistent with white noise; many data deviate from Gaussianity at different levels, nevertheless, removing outliers in some pulsars will mitigate the deviations.展开更多
A dataset of 35,608 materials with their topological properties is constructed by combining the density functional theory(DFT)results of Materiae and the Topological Materials Database.Thanks to this,machine-learning ...A dataset of 35,608 materials with their topological properties is constructed by combining the density functional theory(DFT)results of Materiae and the Topological Materials Database.Thanks to this,machine-learning approaches are developed to categorize materials into five distinct topological types,with the XGBoost model achieving an impressive 85.2%classification accuracy.By conducting generalization tests on different sub-datasets,differences are identified between the original datasets in terms of topological types,chemical elements,unknown magnetic compounds,and feature space coverage.Their impact on model performance is analyzed.Turning to the simpler binary classification between trivial insulators and nontrivial topological materials,three different approaches are also tested.Key characteristics influencing material topology are identified,with the maximum packing efficiency and the fraction of p valence electrons being highlighted as critical features.展开更多
文摘In this paper, we propose the test statistic to check whether the nonparametric function in partially linear models is linear or not. We estimate the nonparametric function in alternative by using the local linear method, and then estimate the parameters by the two stage method. The test statistic under the null hypothesis is calculated, and it is shown to be asymptotically normal.
文摘We propose the test statistic to check whether the nonpararnetric functions in two partially linear models are equality or not in this paper. We estimate the nonparametric function both in null hypothesis and the alternative by the local linear method, where we ignore the parametric components, and then estimate the parameters by the two stage method. The test statistic is derived, and it is shown to be asymptotically normal under the null hypothesis.
文摘Mainly due to its implementation simplicity, the non-coherent Ultra-Wide Band (UWB) receiver is attractive for lower data rate applications, which gains much attention again in recent years. In this paper, a General Likelihood Ratio Test (GLRT) based non-coherent receiver on UWB Pulse-Position-Modulation (PPM) signal in multipath channels is derived, and a novel structure is proposed as well. Subsequently, the closed-form expressions of asymptotic error-rate performance related to the non-coherent receiver are also derived and verified.
基金supported by the National Science Foundation(NSF)under PIRE grant0968296support by the National Natural Science Foundation of China(Grant Nos.11503007,91636111 and 11690021)+2 种基金partial support through the New York Space Grant Consortiumsupport by NASA through the Einstein Fellowship grant PF4-150120upport from the JPL RTD program
文摘In pulsar timing, timing residuals are the differences between the observed times of arrival and predictions from the timing model. A comprehensive timing model will produce featureless resid- uals, which are presumably composed of dominating noise and weak physical effects excluded from the timing model （e.g. gravitational waves）. In order to apply optimal statistical methods for detecting weak gravitational wave signals, we need to know the statistical properties of noise components in the residuals. In this paper we utilize a variety of non-parametric statistical tests to analyze the whiteness and Gaussianity of the North American Nanohertz Observatory for Gravitational Waves （NANOGrav） 5- year timing data, which are obtained from Arecibo Observatory and Green Bank Telescope from 2005 to 2010. We find that most of the data are consistent with white noise; many data deviate from Gaussianity at different levels, nevertheless, removing outliers in some pulsars will mitigate the deviations.
基金funding from the National Key Research and Development Program of China (Grant No. 2022YFA1403800)the National Natural Science Foundation of China (Grant No. 12188101)+2 种基金Y.H. was supported by China Scholarship Council (Grant No. 201904910878)H.W. is also supported by the New Cornerstone Science Foundation through the XPLORER PRIZEComputational resources have been provided by the supercomputing facilities of the Université catholique de Louvain (CISM/UCL) and the Consortium des Équipements de Calcul Intensif en Fédération Wallonie Bruxelles (CÉCI) funded by the Fond de la Recherche Scientifique de Belgique (F.R.S.-FNRS) under convention 2.5020.11 and by the Walloon Region.
文摘A dataset of 35,608 materials with their topological properties is constructed by combining the density functional theory(DFT)results of Materiae and the Topological Materials Database.Thanks to this,machine-learning approaches are developed to categorize materials into five distinct topological types,with the XGBoost model achieving an impressive 85.2%classification accuracy.By conducting generalization tests on different sub-datasets,differences are identified between the original datasets in terms of topological types,chemical elements,unknown magnetic compounds,and feature space coverage.Their impact on model performance is analyzed.Turning to the simpler binary classification between trivial insulators and nontrivial topological materials,three different approaches are also tested.Key characteristics influencing material topology are identified,with the maximum packing efficiency and the fraction of p valence electrons being highlighted as critical features.
