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  1. Abstract

    Automated experimentation has yielded data acquisition rates that supersede human processing capabilities. Artificial Intelligence offers new possibilities for automating data interpretation to generate large, high-quality datasets. Background subtraction is a long-standing challenge, particularly in settings where multiple sources of the background signal coexist, and automatic extraction of signals of interest from measured signals accelerates data interpretation. Herein, we present an unsupervised probabilistic learning approach that analyzes large data collections to identify multiple background sources and establish the probability that any given data point contains a signal of interest. The approach is demonstrated on X-ray diffraction and Raman spectroscopy data and is suitable to any type of data where the signal of interest is a positive addition to the background signals. While the model can incorporate prior knowledge, it does not require knowledge of the signals since the shapes of the background signals, the noise levels, and the signal of interest are simultaneously learned via a probabilistic matrix factorization framework. Automated identification of interpretable signals by unsupervised probabilistic learning avoids the injection of human bias and expedites signal extraction in large datasets, a transformative capability with many applications in the physical sciences and beyond.

     
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  2. Abstract

    Ecological distance‐based spatial capture–recapture models (SCR) are a promising approach for simultaneously estimating animal density and connectivity, both of which affect spatial population processes and ultimately species persistence. We explored how SCR models can be integrated into reserve‐design frameworks that explicitly acknowledge both the spatial distribution of individuals and their space use resulting from landscape structure. We formulated the design of wildlife reserves as a budget‐constrained optimization problem and conducted a simulation to explore 3 different SCR‐informed optimization objectives that prioritized different conservation goals by maximizing the number of protected individuals, reserve connectivity, and density‐weighted connectivity. We also studied the effect on our 3 objectives of enforcing that the space‐use requirements of individuals be met by the reserve for individuals to be considered conserved (referred to as home‐range constraints). Maximizing local population density resulted in fragmented reserves that would likely not aid long‐term population persistence, and maximizing the connectivity objective yielded reserves that protected the fewest individuals. However, maximizing density‐weighted connectivity or preemptively imposing home‐range constraints on reserve design yielded reserves of largely spatially compact sets of parcels covering high‐density areas in the landscape with high functional connectivity between them. Our results quantify the extent to which reserve design is constrained by individual home‐range requirements and highlight that accounting for individual space use in the objective and constraints can help in the design of reserves that balance abundance and connectivity in a biologically relevant manner.

     
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