> The authors say that the 570 megapixel Dark Energy Camera (DECam) may be useful for follow-up observations.
I was curious what kind of resolution you'd have at this distance but not sure I did the math right. The camera has a resolution of 0.27"/pixel[1] which is 0.000075 degrees.
Then to get size at 500AU -> tan((pi/180) * 0.000075)(500 149597870700)
~98megameters, which is like 8 earth diameters. Is this right?
In astronomy how is image registration done? Is there some sort of astronomic dead reckoning system? Or is it just image based with some kind of Homography technique?
Most images are registered by finding the location of known stars from a high resolution catalog such as GAIA. So you fit centroids of all stars in an image, then do a search of Gaia sources in that location and do some sort of linear or higher order polynomial mapping of pixel space to the celestial sphere reference coordinates ICRF.
Astrometry.net is a service that does an approximate version of this on the web.
I am glossing over many details here, but this is roughly what happens.
Basically all observations of solar system objects besides the big planets are going to be unresolved point sources. IE: We don't have telescopes which can resolve most objects into an image. An example, Hubble famously had to image a ton to get a grainy, ~12 pixel approximation of Pluto. We can do a lot with measurements of points, even reconstruct 3d models if we have enough data.
Resolving power is related to the PSF (Point Spread Function) size. The PSF is the image on your detector if you have an infinitely small point source. A quick google search says that DECam has a PSF of at least an arcsecond (atmosphere is probably causing issues for that). Which means anything smaller than an arcsecond is going to be unresolvable.
However, you still want pixels smaller than your PSF, since PSFs are typically gaussian-ish, having a bunch of measurements within the gaussian allows you to estimate the center accurately. This is vital for Astrometry (the measurement of position).
I think that's about right, but the real purpose isn't resolving spatial details on the planet (which you correctly found isn't possible), but to find the planet at all—to distinguish a point of light out of background noise. A fine angular resolution is still helpful, for SNR reasons: smaller pixels contain less noise, wheras the signal pixel contains the same amount of signal!
Anyone remember when you could buy a physical copy of Netscape Navigator off a store shelf? I personally feel in this weird position where I don't want to pay for software and expect it for free but maybe not fully grasping that "free" software (particularly from for-profit companies) still comes at a cost. Trying to make myself more open to paying for good software again. Unfortunately the subscription model everyone is moving to doesn't make that easier.
Slight tangent but I've wondered about something similar to this, has there been much initiative to make games that directly boot on modern PC hardware? So not load a full OS but just go directly to the game. Similar to older generation gaming consoles. It should be possible, granted if you want to stay simple, things like wifi, bt, GPU would be hard to utilize without modern drivers, but a keyboard and mouse should be fairly doable as they seem to have some sort of default BIOS access? (probably wrong terms there but hopefully my point is understandable)
I don't know if it's been much used but it is known and works. I was doing this early on in my x86-16 assembler experiments but ended up using DOS as a program launcher for an easier emulator to use than qemu (dosbox-staging).
The big limits if you don't want to get into disk IO, is 512 bytes or less since you're basically running your program as a master boot record. To get more you'll need to load some LBAs from disk which yes there is an interrupt for and osdev has even better stuff.
Other than that, the difference between a .com file (usual limit 64kb single segment) and an MBR style bootable program is pretty minimal
The quote of LTO tape being much less prone to read failures (10^-20) vaguely reminded me of an old article stating that something like 50% of tape backups fail. I'm not in that side of the industry so can't really comment as to if there is some missing nuance.
Last year my company read in excess of 20,000 tapes from just about every manufacturer and software vendor. For modern, LTO/3592/T10000 era tapes the failure rate we see is around 0.3%.
Most of these failures are due to:
cartridges being dropped or otherwise physically deformed such that they do not fit into the drives anymore.
cartridges getting stuck in drives and destructive extraction being required.
Data was never written correctly in the first place.
The only exception to this rule that we have seen is tapes written with LTFS. These tapes have a 20 fold higher incidence of failure, we believe because reading data back, as if it was a HDD, causes excessive wear.
Anyone claiming 50% failure rates on tapes has no idea what they are talking about, are reading back tapes from the 1970s/80s or have a vested interest in getting people away from tape storage.
They're not saying the failure rate of tapes is 50%. They're saying if you survey attempts to do data restores from tape then 50% of the time not all the requested data is found.
I can't claim the same volumes you can but I did handle tape backups and recovery for a mid sized business for a few years. We only had one tape failure in my tenure but we had plenty of failed recoveries. We had issues like the user not knowing the name and location of the missing file with enough detail to find it, or they changed the file 6 times in one day and they need version 3 and the backup system isn't that granular.
Those are just the issues after the system was set up and working well. Plenty of people set a backup system running, never check the output, and are disappointed years later to learn the initial config was wrong.
Long story short 50% failure of tapes is ludicrous but 50% failure of recovery efforts is not.
The read failures are also attributed to other parts of the system, which for the end user still end up in failed reads. The author links to a sales PDF from Quantum.
e.g. the robot dies, the drive dies, the cartridge dies, the library bends, the humidity was too much.. multiplied by each library, robot, drive and cartridge your data is spread across.
Or, a fun little anecdote, the cleaner had access to the server room and turned off the AC of the server room, most disk drives failed, and the tapes melted inside the robots.
I've done a lot of broad research on exercise in recent years. While there is certainly a dose dependent relationship with results, it's amazing how little you can do to get "some" results. A few stats from studies or articles I've seen in the past:
- Just twelve minutes of exercise is correlated with positive health improvements
- As little as two minutes of walking after eating has a positive improvement in blood sugar
Anyone have more context on this? I've never thought of a repl as a design tool. Does he mean loading your app in a repl, calling functions manually and then manually swapping them out in real time?
Wonder what it would take to add a simple algorithm to this. Part of what makes short media apps (dangerously) addictive is that they eventually learn what you like and feed you more of that. An app like this with such an algo could help with the stickiness (and presumably get us away from the other apps at least for a little bit). "Oh this person likes science stuff, let's feed them more, oh they specifically like stuff related to quantum mechanics, let's place a summary paragraph from a related page topic in there."
On one hand I am thinking about what a very basic algorithm would like (maybe even just categories I might do) and maybe how it would make people happy.
On the other hand, I'm not sure exactly the details of wikipedia's api TOS. Also as it stands this website is entirely in the frontend at the moment, and I'm enjoying just scaffolding out what I can with limited a more limited set of tools to speak.
I realize now the suffix "tok" implies a crazy ML algo that is trained every single movement, click, tap, and pause you make, but I don't think I really want that.
It should be possible to keep this all front-end, even with some basic algorithm for the searches - just use localStorage. That keep things simple and resolve privacy concerns, as people own their data and can delete them any time.
Probably? I have no frame of reference, I've never done giant distributed systems before. I just noticed that earlier version had some slowdowns but I think I was just improperly fetching the images ahead of time.
You are basically allowed to do whatever as long as it doesn't cause an operational issue, you dont have too many requests in-flight at one time , and you put contact info in the user-agent or Api-User-Agent header. (Adding a unique api-user-agent header is probably the most important requirement, since if it does cause problems it lets operations team easily see what is happening)
I think the wiktok thing is exactly the sort of thing wikimedia folks hope people will use the api to create.
About the “Tok” suffix, I also think that while it has the algorithm connotations, it also has been used a lot to describe communities that have formed on TikTok. For example, BookTok (where some bookstores have started to pay attention to how people on TikTok can make some books popular again seemingly on a whim) or WitchTok.
browser-store and cookies, among other tools, provide nice front-end-only persistent storage for holding things like recommendation weights/scoring matrices. maybe a simple algorithm that can evaluate down from a few bytes stored in weights might be all the more elegant.
For each 10 seconds of reading, increment the tags on the current article as "favoured". Then, poll randomly from those tags for the next recommended article. Add some logarithms of division to prevent the tags from infinite scaling.
Can you tell that YouTube reels engineers? Because their Algo is a disaster where I'm only fed Sopranos and NBA content. I don't hate it, but god I have so many subscriptions (civil aviation, personal finance, etc) that I never ever see on my feed.
I misspelt the last sentence a bit. I meant division "or" logarithm.
Basically, we have an unbounded counter that is gonna start breaking things. So we need to normalize it to a percentage score (by dividing it by the total favoured count across all tags), or pass it through a logarithm to bound it.
This approach only works if all content is accurately tagged, which works basically nowhere on the internet except Wikipedia.
The relatedness of articles is already baked in with blue wiki links too. So it shouldn't be too hard to make something that just looks for neighbors.
Now, something that learns that if you like X you might like Y, even if they are disconnected. Is closer to the dystopic ad maximizing algorithm of TikTok et al.
That's what I was thinking this might have already. Maybe this could get insights from the articles linked from the ones you like too? Sort of like https://www.sixdegreesofwikipedia.com/
This would eventually collapse to people reading articles they do not actually like (i.e. get happiness from reading), I think, maybe tragic history facts or something like that?
The truth of social media harm is that it's more about humans than the algorithms themselves. Humans just tend to engage more with negative emotions. Even IRL we tend to look for intrigue and negative interactions, just look at the people who stay with toxic partners even with no financial ties, or even friend groups who turn into dysfunctional gossip fests. The only way to avoid this is by actively fighting against this tendency, and having no algorithm at all in an application helps.
Since its text, especially text with links to other articles, there is no need for tags.
If I had a clue how to do this (sorry, just a neuroscientist), I would probably create "communities" of pages on a network graph and weight the traversal across the graph network based on pages that the person liked (or spend X time on before).
it starts with sourcing - finding a massive set of interesting pages, then going through and giving them tags. planning on adding this to my web discovery app as well: https://moonjump.app/
As a dev I recently sent my first AWS support request. Received a non useful response featuring factually incorrect statements about their own platform. Replied to support ticket, no reply. Sent email to two AWS reps, never got a reply.
Though often conflated, there is a difference between introversion (preference towards less external stimuli) and shyness (fear of social judgement). You may be a shy extrovert.
Also I can totally relate to the hyper fixation on "what does this person think if me right now with everything I say?" I wish I had a simple super effective answer but I think it just takes small improvements over time. In general, if you're willing to show a reasonable level of mutual interest in the other person, they will reciprocate and with practice this can be trusted such that the anxiety reduces. Also accept that some people will just not be interested or necessarily like you and that's ok, but hard at first for sure, especially if you've faced social rejection earlier in life.
I was curious what kind of resolution you'd have at this distance but not sure I did the math right. The camera has a resolution of 0.27"/pixel[1] which is 0.000075 degrees. Then to get size at 500AU -> tan((pi/180) * 0.000075)(500 149597870700) ~98megameters, which is like 8 earth diameters. Is this right?
[1]: https://www.darkenergysurvey.org/the-des-project/instrument/...
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