The sequence flanking the N-terminus of the CLV3 peptide is critical for its cleavage and activity in stem cell regulation in Arabidopsis

The "Shakespeare Authorship Question"—regarding the identity of the poet-playwright—has been debated for over 150 years. Now, with the growing list of signatories to the "Declaration of Reasonable Doubt," the creation of a Master's Degree program in Authorship Studies at Brunel University in London, the opening of the Shakespeare Authorship Research Studies Center at the Library of Concordia University in Portland, and the release of two competing high-profile books both entitled Shakespeare Beyond Doubt, academic libraries are being presented with a unique and timely opportunity to participate in and encourage this debate, which has long been considered a taboo subject in the academy.https://journal.lib.uoguelph.ca/index.php/perj/article/view/280

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs). Conditional GANs have enabled a variety of applications, but the results are often limited to low-resolution and still far from realistic. In this work, we generate 2048x1024 visually appealing results with a novel adversarial loss, as well as new multi-scale generator and discriminator architectures. Furthermore, we extend our framework to interactive visual manipulation with two additional features. First, we incorporate object instance segmentation information, which enables object manipulations such as removing/adding objects and changing the object category. Second, we propose a method to generate diverse results given the same input, allowing users to edit the object appearance interactively. Human opinion studies demonstrate that our method significantly outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing.Comment: v2: CVPR camera ready, adding more results for edge-to-photo example

Implicit Warping for Animation with Image Sets

We present a new implicit warping framework for image animation using sets of source images through the transfer of the motion of a driving video. A single cross- modal attention layer is used to find correspondences between the source images and the driving image, choose the most appropriate features from different source images, and warp the selected features. This is in contrast to the existing methods that use explicit flow-based warping, which is designed for animation using a single source and does not extend well to multiple sources. The pick-and-choose capability of our framework helps it achieve state-of-the-art results on multiple datasets for image animation using both single and multiple source images. The project website is available at https://deepimagination.cc/implicit warping/Comment: To be published at NeurIPS 202