gabeiscoding
Joined 18 years ago
Joined 18 years ago
I have a side-by-side rasterized image showing the difference here:
https://github.com/gaberudy/epaper-calendar/blob/main/docs/f...
Linux insists on doing some freetype font thickening, giving the output a random thick-line look. If anyone knows more tricks to disable this or influence the anti-alias font rendering behavior, let me know!
But now I can look forward to their leap over to “social audio”. First Twitter spaces (where I would sometimes chime in live) and now the Discord-hosted On the Metal.
Most podcasts are sports commentary. These guys are full-contact in the game. I love it. Keep it up Bryan!
It was ages ago, and has since been archived on google code :)
[0] https://code.google.com/archive/p/seam-carving-gui/ [1] https://github.com/esimov/caire
The original paper was discussed on slashdot and back at that time I was inspired to build a little GUI around an open source algorithm implementation to play with my Qt skills.
It allows you to shrink, expand and "mask out" regions you don't want touch etc.
Still available on Google Code archive:
I've been learning rust recently. As a learning exercise, I compared the robin-hood hashing of std::collections::HashMap in rust to his klib/khash he mentions in this article , and then tried various hash functions to try and match his performance:
https://github.com/gaberudy/hash_test
No dice, his hash table is smaller and faster.
My next step is to try and implement his data structure and hashing functions directly in rust and see if I can get it to near-C performance...
He's a great speaker, and a cool guy and tackles some of the most interesting (at least to hear about) science in genomics
[1] https://www.dropbox.com/s/sfg6rdmgxjwdpil/Mason_NEB_talk_AGB... [2] https://twitter.com/mason_lab/status/964151387687972864
Not as fancy as photoshop I'm sure, but does have the ability to paint a mask of regions to keep / remove to aid the algorithm and get the desired result. Multi-threaded too!
It looks to be solving the same problems as DNAnexus, Seven Bridges, BaseSpace etc as a way to wrap open source tools in more user-friendly ways.
But it's orchestrating the production of smaller set of data that still needs the next step of human interpretation, report writing, family-aware algorithms and most complex annotations (the problem space Golden Helix is in).
In other words, the automatable bits that is not the hard part that I mentioned in my blog post.
Ultimately, the "hard part" about genomics is not big-data requiring Spanner and BigTable to get anything done. I actually wrote a blog post about this this week:
http://blog.goldenhelix.com/grudy/genomic-data-is-big-data-b...
Both BAM and VCF files can be hosted through a plain HTTP file-server and be meaningfully queried through their BAI/TBI indexes. Visualization tools like our GenomeBrowse or the Broad's IGV can already read S3 hosted genomic files directly without having an API layer and very efficiently (gzip compressed blocks of binary data). So, I see the translation of the exact same data into API-only accessible storage system, where I can't download the VCF and do quick and iterative analysis on it more of a downside that plus.
Disclaimer: I build variant interpretation software for NGS data at Golden Helix. Our customers are often small clinical labs who size of data and volume are not driving them to the cloud.
Nanopores are no where near the throughput and accuracy of Illumina's sequencing by synthesis tech, and if there is a pathway to challenge Illumina's position, it will be extremely complex, iterative and _long_.
Meanwhile Illumina is amassing a billion dollar war chest and is adding its own complex and iterative improvements to its platform (two-color detection, longer and longer reads, higher cluster density), maintaining its market lead.
As much as the analogy to microprocessor manufacturing and Moore's law is alluring, the messy stuff of biology and single molecule chemical manipulation and sensor detection is unlikely to obediently follow the same innovation curve.