36 karma · joined February 11, 2012
I have resorted to partnering with a law firm, who, for a large cut of any revenue, will do all the IP work and "marketing" (i.e. contacting legal departments at companies that might be interested in the algorithm). This is not ideal, but is so far the only path presented that may help me recoup wages lost by not working full-time for several years. I figure if I retain control of the IP (and make money through licensing), I can make sure scientists and researchers have free usage rights.
If the IP thing works, I can hopefully continue independent research. If it works well, I hope to self-fund more research without the IP shenanigans. Otherwise, it is back to full-time employment.
A pre-release payment directly addresses the issue of piracy. Piracy just doesn't exist if the content isn't out there.
You should start a "media label" then. Take a cut of the threshold payment for (1) vetting and reviews of unreleased art, (2) distribution costs of the digital media, and (3) legal assistance for artists against copyright trolls.
The idea that $10 for a digital copy of an album that is already on youtube (or a friend's harddrive) should be a viable business model is weird to me in this day and age.
I have recently been wondering about a threshold-based "media economy" where creators don't actually show us anything (except for clips or samples or low-res versions, etc) until they are guaranteed a certain amount of income. It's basically kickstarter. A musician makes an album, goes on kickstarter and asks for $10,000 to release it. Once $10k is reached, the songs go up on a server, or are released on bandcamp, spotify, or any of the usual channels. Additional money beyond the threshold can be made, but it will be as difficult as it is now. But they have already reached $10k (set by them) so everyone can feel good that the musician has earned what they feel they deserve.
I'm sure there are many problems with this. For one, many artists aren't creating just for money. They want to show us their creations, and with a threshold, they would have to hold back until it is reached (in the case of musicians, they might not even be able to play a new song at a show until the threshold is reached, b/c smartphones).
There may be a critical mass problem, too. If two artists are similar and one releases immediately while the other waits for the threshold payment, the latter may drift into obscurity. There must be some allure to the withholding, though?
What other problems kill this approach?
Could it work for open source software, too? Make your thing, don't share it. Demo it, ask for the release payment, then put it on github.
The basic idea is this. For a time-stretch factor of, say, 2x, the frequency spectrum of the stretched output at 2 sec should be the same as the frequency spectrum of the unstretched input at 1 sec. The naive algorithm therefore takes a short section of signal at 1s, translates it to 2s and adds it to the result. Unfortunately, this method generates all sorts of unwanted artifacts.
Imagine a pure sine wave. Now take 2 short sections of the wave from 2 random times, overlap them, and add them together. What happens? Well, it depends on the phase of each section. If the sections are out of phase, they cancel on the overlap; if in phase, they constructively interfere.
The phase vocoder is all about overlapping and adding sections together so that the phases of all the different sine waves in the sections line up. Thus, in any phase vocoder algorithm, you will see code that searches for peaks in the spectrum (see _time_stretch code). Each peak is an assumed sine wave, and corresponding peaks in adjacent frames should have their phases match.
Often there is a price paid in brevity, but I believe it is worth it. It may seem annoying to propagate a click explicitly through 5 parent components just to sum clicks into a count widget, but as soon as a short circuit is made, you've created a graph, and you lose the ability to isolate GUI sub-trees for testing/debugging.
The resulting format has simple compression parameters and will be optimized for time-stretching/pitch-shifting. The format is really nothing special; it is based on a sinusoids + noise model. The novelty is in the analysis algorithm, which I think identifies sinusoids particularly well, avoiding common difficulties like the Gibbs phenomenon [0], which leads to "smearing" of transients when time-stretching.
[0] https://www.youtube.com/playlist?list=PLrxfgDEc2NxZJcWcrxH3j...
That makes sense. So in the CASP competition, when teams are given a sequence, do their algorithms do something like the following?
1. Search database for homologs of given sequence 2. Look at MSA and correlated mutations of homologs 3. Look for similar correlated mutations in given sequence
I imagine 1-3 could somehow be embedded in a NN after training on a protein database.
> What do you mean by "proximal"? Close in space, or similar in structure?
I mean close in space.
I understand that similar sequences may fold similarly (although as length increases, I highly doubt it, but IDK). I'm talking about aligned sub-sequences within one chain and their ultimate distance from each other in the final structure. Co-evolution suggests that aligned sub-sequences are also proximal. But manufactured chains did not evolve, therefore the assumption is no longer useful.
It is a JSON parser in C without heap allocations. The query language is piddly, but the tool can be useful for grabbing a single value from a very large JSON file. I don't have time for it, but someone could fork and make it a real deal.