# sK1 illustration program sK1 is an open-source illustration program that can substitute professional proprietary software like Corel Draw or Adobe Illustrator*. Currently GNU/Linux is our main development platform, but porting on Win32 and MacOS X desktops has been scheduled. sK1 supports professional publishing features, such as CMYK color, separations, ICC color management and press-ready PDF output. # UniConvertor UniConvertor is a universal vector graphics translator. It is a command line tool which uses sK1 object model to convert one file format to another. The project is a multiplatform software and can be compiled under GNU/Linux (and other UNIX-like systems), MacOS X and Win32/64 operation systems. # CDR Explorer CDR Explorer is a research tool for CorelDraw file formats. It was used to create a CDR import filter for sK1 Editor and UniConvertor. This tool is written on Python and works under GNU/Linux, MacOS X and Win32. # WMF format parser (pymfvu) pymfvu is a small research project. This project was started to prepare new WMF/EMF filter for sK1 and Uniconvertor. The pymfvu program can open and render both Windows Metafile and Enchanced Metafile files.
Natural Earth is a public domain map dataset available at 1:10m, 1:50m, and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software.
A very common workflow is to index some data based on its embeddings and then given a new query embedding retrieve the most similar examples with k-Nearest Neighbor search. For example, you can imagine embedding a large collection of papers by their abstracts and then given a new paper of interest retrieve the most similar papers to it.
TLDR in my experience it ~always works better to use an SVM instead of kNN, if you can afford the slight computational hit
R. Schwarzenberg, L. Raithel, and D. Harbecke. (2019)cite arxiv:1904.01500Comment: NAACL-HLT 2019 Workshop on Evaluating Vector Space Representations for NLP (RepEval).
H. Yu, J. Han, and K. Chang. KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, page 239--248. New York, NY, USA, ACM Press, (2002)