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1. Scan-Specific Residual Convolutional Neural Networks for Fast MRI Using Residual RAKI

   https://doi.org/10.1109/IEEECONF44664.2019.9048706  

   Zhang, Chi; Hossein Hosseini, Seyed Amir; Moeller, Steen; Weingartner, Sebastian; Ugurbil, Kamil; Akcakaya, Mehmet (November 2019, 2019 53rd Asilomar Conference on Signals, Systems, and Computers)
2. Residual stress and material symmetry

   https://doi.org/10.1016/j.ijengsci.2023.104013  

   Rajagopal, KR; Wineman, A (April 2024, International Journal of Engineering Science)

   There seems to be a basic misconception in several recent papers concerning the material symmetry of bodies in configurations that are pre-stressed. In this short paper we point to the source of the error and show that the material symmetry that is possible depends on the nature of the pre-stress. We also extend the results for material symmetry which have been well known within the context of simple elastic solids to the general class of simple materials. This generalization has relevance to the material symmetry of biological solids that are viscoelastic. 

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3. Residual finiteness for central pushouts

   https://doi.org/10.1090/proc/15368  

   Chirvasitu, Alexandru (June 2021, Proceedings of the American Mathematical Society)

   null (Ed.)
4. StereoDRNet: Dilated Residual Stereo Net

   Chabra, Rohan; Straub, Julian; Sweeney, Chris; Newcombe, Richard; Fuchs, Henry (June 2020, IEEE Computer Society Conference on Computer Vision and Pattern Recognition)

   We propose a system that uses a convolution neural network (CNN) to estimate depth from a stereo pair followed by volumetric fusion of the predicted depth maps to produce a 3D reconstruction of a scene. Our proposed depth refinement architecture, predicts view-consistent disparity and occlusion maps that helps the fusion system to produce geometrically consistent reconstructions. We utilize 3D dilated convolutions in our proposed cost filtering network that yields better filtering while almost halving the computational cost in comparison to state of the art cost filtering architectures. For feature extraction we use the Vortex Pooling architecture [24]. The proposed method achieves state of the art results in KITTI 2012, KITTI 2015 and ETH 3D stereo benchmarks. Finally, we demonstrate that our system is able to produce high fidelity 3D scene reconstructions that outperforms the state of the art stereo system. 

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5. Residual Intersections and linear powers

   EISENBUD, David; HUNEKE, CRAIG; ULRICH, BERND (July 2024, Transactions of the American Mathematical Society)

   If I is an ideal in a Gorenstein ring S, and S/I is Cohen-Macaulay, then the same is true for any linked ideal I ; but such statements hold for residual intersections of higher codimension only under restrictive hypotheses, not satisfied even by ideals as simple as the ideal Ln of minors of a generic 2 × n matrix when n > 3. In this paper we initiate the study of a different sort of Cohen-Macaulay property that holds for certain general residual intersections of the maximal (interesting) codimension, one less than the analytic spread of I. For example, suppose that K is the residual intersection of Ln by 2n − 4 general quadratic forms in Ln. In this situation we analyze S/K and show that In−3(S/K) is a self-dual maximal Cohen-Macaulay S/K-module with linear free resolution over S. The technical heart of the paper is a result about ideals of analytic spread 1 whose high powers are linearly presented. 

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