Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images

In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation hasn't efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address the these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient

Optimization under Uncertainty Tool for Modeling Porous Lithium-Ion Batteries

The motivation of this tool is to optimize the performance of battery based on energy output. During the manufacturing process, several parameters such as cathode thickness, the volume concentration of cathode and radius of negative active materials are subject to uncertainty. To optimize battery performance, it is significant to quantify those uncertainties through electrochemical multiscale computer simulation. Hence, this tool will focus on the optimization of the performance of lithium-ion battery under different currents. This tool will consist of a module on visualized generator of uncertainty input, an electrochemical system simulator, a visualization of output optimization module. First, the uncertainty input generator provides the option for selecting one of several statistical models for the input parameter distributions. The method of moment matching and Gauss-Hermite quadrature formula are used to simulate distribution. Simulations are performed using an existing electrochemical system simulator that in turn uses the data obtained from the uncertainty input generator to simulate energy and power, which can be considered as a black-box function. The simulation results are quantified graphically through error bar plots that visualize the impact of the uncertainties. For the optimization part, the variation and optimization of power and energy densities as a function of current density of the battery electrode are presented using GPy package and the result are obtained and plotted under uncertain input parameters. Bayesian optimization will be utilized to determine the global optimization through the black-box function. Additional work may be needed to include more of the uncertain variables in this framework

MSDH: matched subspace detector with heterogeneous noise

The matched subspace detector (MSD) is a classical subspace-based method for hyperspectral subpixel target detection. However, the model assumes that noise has the same variance over different bands, which is usually unrealistic in practice. In this letter, we relax the equal variance assumption and propose a matched subspace detector with heterogeneous noise (MSDH). In essence, the noise variances are different for different bands and they can be estimated by using iteratively reweighted least squares methods. Experiments on two benchmark real hyperspectral datasets demonstrate the superiority of MSDH over MSD for subpixel target detection

FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction

Universal Information Extraction (UIE) has been introduced as a unified framework for various Information Extraction (IE) tasks and has achieved widespread success. Despite this, UIE models have limitations. For example, they rely heavily on span boundaries in the data during training, which does not reflect the reality of span annotation challenges. Slight adjustments to positions can also meet requirements. Additionally, UIE models lack attention to the limited span length feature in IE. To address these deficiencies, we propose the Fuzzy Span Universal Information Extraction (FSUIE) framework. Specifically, our contribution consists of two concepts: fuzzy span loss and fuzzy span attention. Our experimental results on a series of main IE tasks show significant improvement compared to the baseline, especially in terms of fast convergence and strong performance with small amounts of data and training epochs. These results demonstrate the effectiveness and generalization of FSUIE in different tasks, settings, and scenarios.Comment: ACL202