We've released an updated version of Evernote for Mac, version 6.9.1, to resolve this.įortran users will be happy to hear there are no reported issues with FTranProjectBuilder These issues have been reported to Apple, but in the meantime, we recommend that you DO NOT upgrade to macOS Sierra.Įvernote a bug in some versions of Evernote for Mac that can cause images and other attachments to be deleted from a note under specific conditions. Through our testing, we discovered some issues with the EndNote PDF viewer. In preparation for Apple's release of macOS Sierra on September 20, we have been testing various versions of EndNote. DEVONthink has also been updated for Sierra.ĮndNote From Endnote (Thomson Reuters) version 7: Message for Mac user planning to update to Sierra: copy/paste, everything without issues”.ĬrystalMaker “We are pleased to confirm that all our latest software runs fine on macOS “Sierra”, as well as OS X 10.11 “El Capitan”, 10.10 “Yosemite”, and earlier.”ĬYLview app launcher (icon on the desktop or the dock) does not work need to start using “Terminal”ĭataDesk Data Desk 8 for Mac runs on OS X 10.7 up to 10.12ĭEVONagent has been updated to version 3.9.5 to support Sierra, in addition this update brings support for Qwant. One user reports “ChemDraw 15 is working fine for me. 2.3.x uses ARCĬhemDraw the official line is that it is not supported, even under El Capitan there were reports of copy/paste issues. You could note too that 2.0 through 2.2.x will never work because 10.12 removed support for garbage collected applications. When I compiled a similar lists for Yosemite and El Capitan they proved very popular with 13,000 page views, I hope this page is similarly useful.īBEdit version 11.6.2 and newer are compatible, recommend against using earlier versionsīrainsight requires version 2.3.3 for full compatibility with 10.12 Sierra. I’ll update the list regularly and feel free to send in information. Whilst there are many sites that track the compatibility on common desktop applications, it is often difficult to find out information about scientific applications. However, as they don’t have access to the Gaussian source code they can't check it. ![]() GXXforrtran is available on GitHub In theory, it should work for a standard Linux or Mac system. In addition, this package also allows Gaussian03 to be built on a case-insensitive file system (such as when using Mac OS X, cygwin or a FAT32 drive) by overriding the behaviour of “cp” and “gau-cpp” such that they don’t cause problems when used by Gaussian’s build scripts on non case-sensitive file systems. ![]() This emulation is sufficient to allow packages such as Gaussian03, that would otherwise require a commercial compiler, to be built using open source tools. This package provides a “pgf77” script that emulates the Portland Group’s PGI fortran 77 compiler, instead using the Free Software Foundation’s GNU gfortran compiler instead. There is an interesting post on the NextMove Software blog, Just what you wanted for Christmas – a compiler for Gaussian. We show a significant improvement over using synthetic images, and achieve state-of-the-art results on the MPIIGaze dataset without any labeled real data.Įverytime I mention Fortran there is an uptick in the site views so I know there are plenty of readers with an interest in Fortran on a Mac. We quantitatively evaluate the generated images by training models for gaze estimation and hand pose estimation. We show that this enables generation of highly realistic images, which we demonstrate both qualitatively and with a user study. We make several key modifications to the standard GAN algorithm to preserve annotations, avoid artifacts and stabilize training: (i) a 'self-regularization' term, (ii) a local adversarial loss, and (iii) updating the discriminator using a history of refined images. We develop a method for S+U learning that uses an adversarial network similar to Generative Adversarial Networks (GANs), but with synthetic images as inputs instead of random vectors. To reduce this gap, we propose Simulated+Unsupervised (S+U) learning, where the task is to learn a model to improve the realism of a simulator's output using unlabeled real data, while preserving the annotation information from the simulator. However, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions. ![]() With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. ![]() Apple have published some of their artificial intelligence research, arXiv:1612.07828
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