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Ecologists interested in monitoring the effects caused by climate change are increasingly turning to passive acoustic monitoring, the practice of placing autonomous audio recording units in ecosystems to monitor species richness and occupancy via species calls. However, identifying species calls in large datasets by hand is an expensive task, leading to a reliance on machine learning models. Due to a lack of annotated datasets of soundscape recordings, these models are often trained on large databases of community created focal recordings. A challenge of training on such data is that clips are given a "weak label," a single label that represents the whole clip. This includes segments that only have background noise but are labeled as calls in the training data, reducing model performance. Heuristic methods exist to convert clip-level labels to "strong" call-specific labels, where the label tightly bounds the temporal length of the call and better identifies bird vocalizations. Our work improves on the current weakly to strongly labeled method used on the training data for BirdNET, the current most popular model for audio species classification. We utilize an existing RNN-CNN hybrid, resulting in a precision improvement of 12% (going to 90% precision) against our new strongly hand-labeled dataset of Peruvian bird species.Jacob Ayers (Engineers for Exploration at UCSD); Sean Perry (University of California San Diego); Samantha Prestrelski (UC San Diego); Tianqi Zhang (Engineers for Exploration); Ludwig von Schoenfeldt (University of California San Diego); Mugen Blue (UC Merced); Gabriel Steinberg (Demining Research Community); Mathias Tobler (San Diego Zoo Wildlife Alliance); Ian Ingram (San Diego Zoo Wildlife Alliance); Curt Schurgers (UC San Diego); Ryan Kastner (University of California San Diego) 

We propose a new nonlinear preconditioned conjugate gradient (PCG) method in real arithmetic for computing the ground states of rotational Bose--Einstein condensate, modeled by the Gross--Pitaevskii equation. Our algorithm presents a few improvements of the PCG method in complex arithmetic studied by Antoine, Levitt, and Tang [J. Comput. Phys., 343 (2017), pp. 92--109]. We show that the special structure of the energy functional $$E(\phi)$$ and its gradient with respect to $$\phi$$ can be fully exploited in real arithmetic to evaluate them more efficiently. We propose a simple approach for fast evaluation of the energy functional, which enables exact line search. Most importantly, we derive the discrete Hessian operator of the energy functional and propose a shifted Hessian preconditioner for PCG, with which the ideal preconditioned Hessian has favorable eigenvalue distributions independent of the mesh size. This suggests that PCG with our ideal Hessian preconditioner is expected to exhibit mesh size-independent asymptomatic convergence behavior. In practice, our preconditioner is constructed by incomplete Cholesky factorization of the shifted discrete Hessian operator based on high-order finite difference discretizations. Numerical experiments in two-dimensional (2D) and three-dimensional (3D) domains show the efficiency of fast energy evaluation, the robustness of exact line search, and the improved convergence of PCG with our new preconditioner in iteration counts and runtime, notably for more challenging rotational BEC problems with high nonlinearity and rotational speed. 

Abstract Ground heat flux (G₀) is a key component of the land‐surface energy balance of high‐latitude regions. Despite its crucial role in controlling permafrost degradation due to global warming,G₀is sparsely measured and not well represented in the outputs of global scale model simulation. In this study, an analytical heat transfer model is tested to reconstructG₀across seasons using soil temperature series from field measurements, Global Climate Model, and climate reanalysis outputs. The probability density functions of ground heat flux and of model parameters are inferred using availableG₀data (measured or modeled) for snow‐free period as a reference. When observedG₀is not available, a numerical model is applied using estimates of surface heat flux (dependent on parameters) as the top boundary condition. These estimates (and thus the corresponding parameters) are verified by comparing the distributions of simulated and measured soil temperature at several depths. Aided by state‐of‐the‐art uncertainty quantification methods, the developedG₀reconstruction approach provides novel means for assessing the probabilistic structure of the ground heat flux for regional permafrost change studies. 

ArcticDEM provides the public with an unprecedented opportunity to access very high-spatial resolution digital elevation models (DEMs) covering the pan-Arctic surfaces. As it is generated from stereo-pairs of optical satellite imagery, ArcticDEM represents a mixture of a digital surface model (DSM) over a non-ground areas and digital terrain model (DTM) at bare grounds. Reconstructing DTM from ArcticDEM is thus needed in studies requiring bare ground elevation, such as modeling hydrological processes, tracking surface change dynamics, and estimating vegetation canopy height and associated forest attributes. Here we proposed an automated approach for estimating DTM from ArcticDEM in two steps: (1) identifying ground pixels from WorldView-2 imagery using a Gaussian mixture model (GMM) with local refinement by morphological operation, and (2) generating a continuous DTM surface using ArcticDEMs at ground locations and spatial interpolation methods (ordinary kriging (OK) and natural neighbor (NN)). We evaluated our method at three forested study sites characterized by different canopy cover and topographic conditions in Livengood, Alaska, where airborne lidar data is available for validation. Our results demonstrate that (1) the proposed ground identification method can effectively identify ground pixels with much lower root mean square errors (RMSEs) (<0.35 m) to the reference data than the comparative state-of-the-art approaches; (2) NN performs more robustly in DTM interpolation than OK; (3) the DTMs generated from NN interpolation with GMM-based ground masks decrease the RMSEs of ArcticDEM to 0.648 m, 1.677 m, and 0.521 m for Site-1, Site-2, and Site-3, respectively. This study provides a viable means of deriving high-resolution DTM from ArcticDEM that will be of great value to studies focusing on the Arctic ecosystems, forest change dynamics, and earth surface processes. 

Abstract Metal-free electrocatalysts represent a main branch of active materials for oxygen evolution reaction (OER), but they excessively rely on functionalized conjugated carbon materials, which substantially restricts the screening of potential efficient carbonaceous electrocatalysts. Herein, we demonstrate that a mesostructured polyacrylate hydrogel can afford an unexpected and exceptional OER activity – on par with that of benchmark IrO 2 catalyst in alkaline electrolyte, together with a high durability and good adaptability in various pH environments. Combined theoretical and electrokinetic studies reveal that the positively charged carbon atoms within the carboxylate units are intrinsically active toward OER, and spectroscopic operando characterizations also identify the fingerprint superoxide intermediate generated on the polymeric hydrogel backbone. This work expands the scope of metal-free materials for OER by providing a new class of polymeric hydrogel electrocatalysts with huge extension potentials.