Mitigation of the Brighter-Fatter Effect in the LSST Camera

Kavli Affiliate: Stuart Marshall | First 5 Authors: Alex Broughton, Yousuke Utsumi, Andrés Plazas Malagón, Christopher Waters, Craig Lage | Summary: Thick, fully depleted charge-coupled devices (CCDs) are known to exhibit non-linear behavior at high signal levels due to the dynamic behavior of charges collecting in the potential wells of pixels, called the brighter-fatter effect […]


Continue.. Mitigation of the Brighter-Fatter Effect in the LSST Camera

Mitigation of the Brighter-Fatter Effect in the LSST Camera

Kavli Affiliate: Stuart Marshall | First 5 Authors: Alex Broughton, Yousuke Utsumi, Andrés Plazas Malagón, Christopher Waters, Craig Lage | Summary: Thick, fully depleted charge-coupled devices (CCDs) are known to exhibit non-linear behavior at high signal levels due to the dynamic behavior of charges collecting in the potential wells of pixels, called the brighter-fatter effect […]


Continue.. Mitigation of the Brighter-Fatter Effect in the LSST Camera

Mitigation of the Brighter-Fatter Effect in the LSST Camera

Kavli Affiliate: Stuart Marshall | First 5 Authors: Alex Broughton, Yousuke Utsumi, Andrés Plazas Malagón, Christopher Waters, Craig Lage | Summary: Thick, fully depleted charge-coupled devices (CCDs) are known to exhibit non-linear behavior at high signal levels due to the dynamic behavior of charges collecting in the potential wells of pixels, called the brighter-fatter effect […]


Continue.. Mitigation of the Brighter-Fatter Effect in the LSST Camera

Generating Interpretable Networks using Hypernetworks

Kavli Affiliate: Max Tegmark | First 5 Authors: Isaac Liao, Ziming Liu, Max Tegmark, , | Summary: An essential goal in mechanistic interpretability to decode a network, i.e., to convert a neural network’s raw weights to an interpretable algorithm. Given the difficulty of the decoding problem, progress has been made to understand the easier encoding […]


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Watermarking for Neural Radiation Fields by Invertible Neural Network

Kavli Affiliate: Jia Liu | First 5 Authors: Wenquan Sun, Jia Liu, Weina Dong, Lifeng Chen, Ke Niu | Summary: To protect the copyright of the 3D scene represented by the neural radiation field, the embedding and extraction of the neural radiation field watermark are considered as a pair of inverse problems of image transformations. […]


Continue.. Watermarking for Neural Radiation Fields by Invertible Neural Network