Penalized maximum likelihood estimation and variable selection in geostatistics

We consider the problem of selecting covariates in spatial linear models with Gaussian process errors. Penalized maximum likelihood estimation (PMLE) that enables simultaneous variable selection and parameter estimation is developed and, for ease of computation, PMLE is approximated by one-step sparse estimation (OSE). To further improve computational efficiency, particularly with large sample sizes, we propose penalized maximum covariance-tapered likelihood estimation (PMLE$_{\mathrm{T}}$) and its one-step sparse estimation (OSE$_{\mathrm{T}}$). General forms of penalty functions with an emphasis on smoothly clipped absolute deviation are used for penalized maximum likelihood. Theoretical properties of PMLE and OSE, as well as their approximations PMLE$_{\mathrm{T}}$ and OSE$_{\mathrm{T}}$ using covariance tapering, are derived, including consistency, sparsity, asymptotic normality and the oracle properties. For covariance tapering, a by-product of our theoretical results is consistency and asymptotic normality of maximum covariance-tapered likelihood estimates. Finite-sample properties of the proposed methods are demonstrated in a simulation study and, for illustration, the methods are applied to analyze two real data sets.Comment: Published in at http://dx.doi.org/10.1214/11-AOS919 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Can Single-Pass Contrastive Learning Work for Both Homophilic and Heterophilic Graph?

Existing graph contrastive learning (GCL) typically requires two forward pass for a single instance to construct the contrastive loss. Despite its remarkable success, it is unclear whether such a dual-pass design is (theoretically) necessary. Besides, the empirical results are hitherto limited to the homophilic graph benchmarks. Then a natural question arises: Can we design a method that works for both homophilic and heterophilic graphs with a performance guarantee? To answer this, we analyze the concentration property of features obtained by neighborhood aggregation on both homophilic and heterophilic graphs, introduce the single-pass graph contrastive learning loss based on the property, and provide performance guarantees of the minimizer of the loss on downstream tasks. As a direct consequence of our analysis, we implement the Single-Pass Graph Contrastive Learning method (SP-GCL). Empirically, on 14 benchmark datasets with varying degrees of heterophily, the features learned by the SP-GCL can match or outperform existing strong baselines with significantly less computational overhead, which verifies the usefulness of our findings in real-world cases.Comment: 20 pages, 6 figures, 9 tables. arXiv admin note: substantial text overlap with arXiv:2204.0487

Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters

We present several methods towards construction of precursors, which show great promise towards early predictions, of solar flare events in this paper. A data pre-processing pipeline is built to extract useful data from multiple sources, Geostationary Operational Environmental Satellites (GOES) and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), to prepare inputs for machine learning algorithms. Two classification models are presented: classification of flares from quiet times for active regions and classification of strong versus weak flare events. We adopt deep learning algorithms to capture both the spatial and temporal information from HMI magnetogram data. Effective feature extraction and feature selection with raw magnetogram data using deep learning and statistical algorithms enable us to train classification models to achieve almost as good performance as using active region parameters provided in HMI/Space-Weather HMI-Active Region Patch (SHARP) data files. Case studies show a significant increase in the prediction score around 20 hours before strong solar flare events

A Hierarchical Bayesian Approach to Neutron Spectrum Unfolding with Organic Scintillators

We propose a hierarchical Bayesian model and state-of-art Monte Carlo sampling method to solve the unfolding problem, i.e., to estimate the spectrum of an unknown neutron source from the data detected by an organic scintillator. Inferring neutron spectra is important for several applications, including nonproliferation and nuclear security, as it allows the discrimination of fission sources in special nuclear material (SNM) from other types of neutron sources based on the differences of the emitted neutron spectra. Organic scintillators interact with neutrons mostly via elastic scattering on hydrogen nuclei and therefore partially retain neutron energy information. Consequently, the neutron spectrum can be derived through deconvolution of the measured light output spectrum and the response functions of the scintillator to monoenergetic neutrons. The proposed approach is compared to three existing methods using simulated data to enable controlled benchmarks. We consider three sets of detector responses. One set corresponds to a 2.5 MeV monoenergetic neutron source and two sets are associated with (energy-wise) continuous neutron sources ($^{252}$Cf and $^{241}$AmBe). Our results show that the proposed method has similar or better unfolding performance compared to other iterative or Tikhonov regularization-based approaches in terms of accuracy and robustness against limited detection events, while requiring less user supervision. The proposed method also provides a posteriori confidence measures, which offers additional information regarding the uncertainty of the measurements and the extracted information.Comment: 10 page

A Graphical Model for Fusing Diverse Microbiome Data

This paper develops a Bayesian graphical model for fusing disparate types of count data. The motivating application is the study of bacterial communities from diverse high dimensional features, in this case transcripts, collected from different treatments. In such datasets, there are no explicit correspondences between the communities and each correspond to different factors, making data fusion challenging. We introduce a flexible multinomial-Gaussian generative model for jointly modeling such count data. This latent variable model jointly characterizes the observed data through a common multivariate Gaussian latent space that parameterizes the set of multinomial probabilities of the transcriptome counts. The covariance matrix of the latent variables induces a covariance matrix of co-dependencies between all the transcripts, effectively fusing multiple data sources. We present a computationally scalable variational Expectation-Maximization (EM) algorithm for inferring the latent variables and the parameters of the model. The inferred latent variables provide a common dimensionality reduction for visualizing the data and the inferred parameters provide a predictive posterior distribution. In addition to simulation studies that demonstrate the variational EM procedure, we apply our model to a bacterial microbiome dataset

A review on N-doped biochar for oxidative degradation of organic contaminants in wastewater by persulfate activation

The Persulfate-based advanced oxidation process is the most efficient and commonly used technology to remove organic contaminants in wastewater. Due to the large surface area, unique electronic properties, abundant N functional groups, cost-effectiveness, and environmental friendliness, N-doped biochars (NBCs) are widely used as catalysts for persulfate activation. This review focuses on the NBC for oxidative degradation of organics-contaminated wastewater. Firstly, the preparation and modification methods of NBCs were reviewed. Then the catalytic performance of NBCs and modified NBCs on the oxidation degradation of organic contaminants were discussed with an emphasis on the degradation mechanism. We further summarized the detection technologies of activation mechanisms and the structures of NBCs affecting the PS activation, followed by the specific role of the N configuration of the NBC on its catalytic capacity. Finally, several challenges in the treatment of organics-contaminated wastewater by a persulfate-based advanced oxidation process were put forward and the recommendations for future research were proposed for further understanding of the advanced oxidation process activated by the NBC