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Energy-Based Generative Model Proposed for Signal Unmixing and Curve Resolution
A new arXiv preprint introduces EB-gMCR, an energy-based generative approach for separating the individual component signals contained in a single mixed measurement, such as a chemical reaction mixture or tissue sample. The method aims to recover both each component's profile and its concentration from data where only the weighted sum is observed. It is positioned as a tool for signal unmixing and multivariate curve resolution tasks in chemistry and related fields.