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  1. ; ; ; ; ; ; ; ; ; ; et al ( , JOM)
    Abstract 316L stainless steel (316L SS) is a flagship material for structural applications in corrosive environments, having been extensively studied for decades for its favorable balance between mechanical and corrosion properties. More recently, 316L SS has also proven to have excellent printability when parts are produced with additive manufacturing techniques, notably laser powder bed fusion (LPBF). Because of the harsh thermo-mechanical cycles experienced during rapid solidification and cooling, LPBF processing tends to generate unique microstructures. Strong heterogeneities can be found inside grains, including trapped elements, nano-inclusions, and a high density of dislocations that form the so-called cellular structure. Interestingly, LPBFmore »316L SS not only exhibits better mechanical properties than its conventionally processed counterpart, but it also usually offers much higher resistance to pitting in chloride solutions. Unfortunately, the complexity of the LPBF microstructures, in addition to process-induced defects, such as porosity and surface roughness, have slowed progress toward linking specific microstructural features to corrosion susceptibility and complicated the development of calibrated simulations of pitting phenomena. The first part of this article is dedicated to an in-depth review of the microstructures found in LPBF 316L SS and their potential effects on the corrosion properties, with an emphasis on pitting resistance. The second part offers a perspective of some relevant modeling techniques available to simulate the corrosion of LPBF 316L SS, including current challenges that should be overcome.« less
    Free, publicly-accessible full text available April 1, 2023
  2. ; ; ; ; ; ; ; ; ; ; et al ( , Journal of Materials Science & Technology)
    Free, publicly-accessible full text available January 1, 2023
  3. ; ; ; ( , Electric Power Systems Research)
    Full Text Available 
  4. ; ; ; ; ; ; ; ( , The Astrophysical Journal)
    Free, publicly-accessible full text available September 1, 2022
  5. ; ; ; ; ; ( , Geophysical Research Letters)
    Full Text Available 
  6. ; ; ; ( , Journal of Geophysical Research: Atmospheres)
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  7. ; ; ; ; ( , IEEE Conference on Computer Vision and Pattern Recognition)
    How to effectively represent camera pose is an essential problem in 3D computer vision, especially in tasks such as camera pose regression and novel view synthesis. Traditionally, 3D position of the camera is represented by Cartesian coordinate and the orientation is represented by Euler angle or quaternions. These representations are manually designed, which may not be the most effective representation for downstream tasks. In this work, we propose an approach to learn neural representations of camera poses and 3D scenes, coupled with neural representations of local camera movements. Specifically, the camera pose and 3D scene are represented as vectors andmore »the local camera movement is represented as a matrix operating on the vector of the camera pose. We demonstrate that the camera movement can further be parametrized by a matrix Lie algebra that underlies a rotation system in the neural space. The vector representations are then concatenated and generate the posed 2D image through a decoder network. The model is learned from only posed 2D images and corresponding camera poses, without access to depths or shapes. We conduct extensive experiments on synthetic and real datasets. The results show that compared with other camera pose representations, our learned representation is more robust to noise in novel view synthesis and more effective in camera pose regression.« less
    Accepted Manuscript Full Text Available
  8. ; ; ; ; ; ; ; ; ; ; et al ( , Nature Communications)
    Abstract Spin-valley locking in monolayer transition metal dichalcogenides has attracted enormous interest, since it offers potential for valleytronic and optoelectronic applications. Such an exotic electronic state has sparsely been seen in bulk materials. Here, we report spin-valley locking in a Dirac semimetal BaMnSb 2 . This is revealed by comprehensive studies using first principles calculations, tight-binding and effective model analyses, angle-resolved photoemission spectroscopy measurements. Moreover, this material also exhibits a stacked quantum Hall effect (QHE). The spin-valley degeneracy extracted from the QHE is close to 2. This result, together with the Landau level spin splitting, further confirms the spin-valley lockingmore »picture. In the extreme quantum limit, we also observed a plateau in the z -axis resistance, suggestive of a two-dimensional chiral surface state present in the quantum Hall state. These findings establish BaMnSb 2 as a rare platform for exploring coupled spin and valley physics in bulk single crystals and accessing 3D interacting topological states.« less
    Free, publicly-accessible full text available December 1, 2022
  9. ; ; ; ; ; ; ; ( , IEEE Transactions on Dependable and Secure Computing)
    Accepted Manuscript Full Text Available
  10. ; ; ; ; ; ; ; ( , Proceedings of the 17th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), 2021)
    Accepted Manuscript Full Text Available