相关论文: Evaluating the New Automatic Method for the Analys…

In general, most of the substances in nature exist in mixtures, and the noninvasive identification of mixture composition with high speed and accuracy remains a difficult task. However, the development of Raman spectroscopy, machine…

A unified linear algebraic approach to adaptive signal processing (ASP) is presented. Starting from just Ax=b, key ASP algorithms are derived in a simple, systematic, and integrated manner without requiring any background knowledge to the…

Motivation: Mass spectrometry-based proteomics is among the most commonly used methods for scrutinizing proteomic profiles in different organs for biological or medical researches. All the proteomic analyses including peptide/protein…

In recent years it has become common practice to divide observed transit absorption spectra by synthetic absorption spectra computed for the transit of an atmosphere-less planet. This action supposedly corrects the observed absorption…

An optical absorption spectrum constitutes one of the most fundamental material characteristics, with relevant applications ranging from material identification to energy harvesting and optoelectronics. However, the database of both…

This paper investigates the problem of selecting the embedding dimension for large heterogeneous networks that have weakly distinguishable community structure. For a broad family of embeddings based on normalized adjacency matrices, we…

Spectral algorithms leverage spectral regularization techniques to analyze and process data, providing a flexible framework for addressing supervised learning problems. To deepen our understanding of their performance in real-world…

An accurate treatment of electronic spectra in large systems with a technique such as time dependent density functional theory (TDDFT) is computationally challenging. Due to the Nyquist sampling theorem, direct real time simulations must be…

This paper addresses the problem of separating spectral sources which are linearly mixed with unknown proportions. The main difficulty of the problem is to ensure the full additivity (sum-to-one) of the mixing coefficients and…

The main purpose of Feature Subset Selection is to find a reduced subset of attributes from a data set described by a feature set. The task of a feature selection algorithm (FSA) is to provide with a computational solution motivated by a…

We introduce a new analysis of an adaptive mixture method that combines outputs of two constituent filters running in parallel to model an unknown desired signal. This adaptive mixture is shown to achieve the mean square error (MSE)…

This article explores the application of various artificial intelligence techniques to the analysis of near-infrared (NIR) spectra of paracetamol, within the spectral range of 900 nm to 1800 nm. The main objective is to evaluate the…

The finite volume algorithm for absorption correction developed by Wunch and Prewitt is examined. This algorithm is based on the numerical integration of the transmission function where three-dimensional quadratic surfaces define the sample…

Feature selection, as a critical pre-processing step for machine learning, aims at determining representative predictors from a high-dimensional feature space dataset to improve the prediction accuracy. However, the increase in feature…

We propose an experimental method for measuring bias in face recognition systems. Existing methods to measure bias depend on benchmark datasets that are collected in the wild and annotated for protected (e.g., race, gender) and…

To design semiconductor-based optical devices, the optical properties of the used semiconductor materials must be precisely measured over a large band. Transmission spectroscopy stands out as an inexpensive and widely available method for…

Spectral deconvolution is essential for extracting peak structures that encode material properties and chemical structures, but conventional automated methods often fail when spectra contain high-intensity noise or unknown background…

We introduce a machine-learning-based approach to enhance the sensitivity of optical-extreme ultraviolet (XUV) transient absorption spectroscopy. A reference spectrum is used as input to a three-layer feed-forward neural network, allowing…

Synthetic data generation is increasingly used in machine learning for training and data augmentation. Yet, current strategies often rely on external foundation models or datasets, whose usage is restricted in many scenarios due to policy…

Recently, new adaptive techniques were developed that greatly improved the efficiency of solving PDEs using spectral methods. These adaptive spectral techniques are especially suited for accurately solving problems in unbounded domains and…