Search for: All records

As blue intensity (BI) methods are increasingly employed to generate temperature-sensitive tree-ring records around the globe, the influence of intra-site variation in elevation on climate-growth relationships for BI parameters remains largely unresolved. Here, we develop six latewood blue intensity (LWBI) chronologies along an elevational gradient for two montane conifer species, Abies concolor var. concolor (Gordon & Glend.) Lindl. Ex Hilderb and Picea engelmannii Parry ex Engelm., growing in the arid southwestern United States. In this first documented study to examine the climate response of LWBI from A. concolor, we find positive, significant (p < 0.05) correlations between the LWBI chronology from the highest elevation plot and spring–summer temperatures (April–August, r > 0.46). Moreover, the positive temperature response of A. concolor is generally stronger and more temporally stable than for P. engelmannii across varying seasonal windows. In comparing the differences in climate response across species and elevation, we document distinct clinal relationships between the temperature response of LWBI for A. concolor, where both the strength and temporal stability of the positive temperature signal increases with elevation. Meanwhile, the mid-elevation P. engelmannii demonstrate the highest climate sensitivity. As such, our findings contribute to a more comprehensive understanding of how elevation influences the type and strength of the climatic information embedded within the LWBI parameter from arid, montane conifers growing near their historical range margins. 

Accurate instrument segmentation in the endoscopic vision of minimally invasive surgery is challenging due to complex instruments and environments. Deep learning techniques have shown competitive performance in recent years. However, deep learning usually requires a large amount of labeled data to achieve accurate prediction, which poses a significant workload. To alleviate this workload, we propose an active learning-based framework to generate synthetic images for efficient neural network training. In each active learning iteration, a small number of informative unlabeled images are first queried by active learning and manually labeled. Next, synthetic images are generated based on these selected images. The instruments and backgrounds are cropped out and randomly combined with blending and fusion near the boundary. The proposed method leverages the advantage of both active learning and synthetic images. The effectiveness of the proposed method is validated on two sinus surgery datasets and one intraabdominal surgery dataset. The results indicate a considerable performance improvement, especially when the size of the annotated dataset is small. All the code is open-sourced at: https://github.com/HaonanPeng/active_syn_generator 

Two graduate-level courses were designed to advance creative, interdisciplinary teamwork among graduate students. Over three years, the two courses underwent three iterations largely focused on refinements to teamwork, which led to high-quality student products. This design case presents the three course iterations, how course design decisions were made, and the kind of results that were achieved. The paper concludes with reflections for designing higher education courses focused on creativity, interdisciplinarity, and teamwork. 

Strong electron correlation plays an important role in transition-metal and heavy-metal chemistry, magnetic molecules, bond breaking, biradicals, excited states, and many functional materials, but it provides a significant challenge for modern electronic structure theory. The treatment of strongly correlated systems usually requires a multireference method to adequately describe spin densities and near-degeneracy correlation. However, quantitative computation of dynamic correlation with multireference wave functions is often difficult or impractical. Multiconfiguration pair-density functional theory (MC-PDFT) provides a way to blend multiconfiguration wave function theory and density functional theory to quantitatively treat both near-degeneracy correlation and dynamic correlation in strongly correlated systems; it is more affordable than multireference perturbation theory, multireference configuration interaction, or multireference coupled cluster theory and more accurate for many properties than Kohn–Sham density functional theory. This perspective article provides a brief introduction to strongly correlated systems and previously reviewed progress on MC-PDFT followed by a discussion of several recent developments and applications of MC-PDFT and related methods, including localized-active-space MC-PDFT, generalized active-space MC-PDFT, density-matrix-renormalization-group MC-PDFT, hybrid MC-PDFT, multistate MC-PDFT, spin–orbit coupling, analytic gradients, and dipole moments. We also review the more recently introduced multiconfiguration nonclassical-energy functional theory (MC-NEFT), which is like MC-PDFT but allows for other ingredients in the nonclassical-energy functional. We discuss two new kinds of MC-NEFT methods, namely multiconfiguration density coherence functional theory and machine-learned functionals.