Or are they not local? I am trying to listen to some Logic MP3 Bounces that I dragged into Apple Music a while back. I cancelled my subscription recently, which I assume caused this message.
Does Apple Music actually store everything on the cloud? I assumed I could play my own MP3s, like how iTunes worked.
This ad popped up on my instagram feed:
Anyone tried one of these?
For $40, it’s hard to suspect it’s not some sort of scam. But it does look intriguing…
A few weeks ago I posted some silly concept art of plugging in multiple cinema HD. Well, it’s in the works.. but I’m stuck.
My first question is: in the Last picture (13), I’ve got the two 30 inch displays.. they both turned on, but the computer crashed. Hmm.
That Mac has two Radeon GPUs and I’m plugged into both DVI ports. What about those two display ports on each card? Does this mean the computer could support 6 displays?
I’ve also got 3 G5 towers, I was wondering if they could be scavenged for ram or another GPU, but I don’t think they’d fit on top of those two red radeons.
Oh, one more question -
When you have more than one of these monitors plugged in, what are you supposed to do with all the FireWire/USB jacks dangling around? You don’t try to plug in each monitor, right? What’s even the purpose of them?
Okay, thanks for any help!
I work in a neuroscience lab studying mouse brains. The current method of registering (mapping) a brain to an atlas of all its regions involves image to image warps and transformations.
Basically you “warp” your own datasets (think 3D MRI-type imaging) to an “official” mouse brain dataset.
It works okay, sometimes - but it’s not perfect.
I am currently working on a proposal for a collaboration with our university’s new AI institute. They’re interested in brains, and we are interested in machine learning.
The idea that has been brewing in my head is to combine a neural network, or a series of them, that are specialized for different areas of the brain, and specifically, trained to identify the difference between adjacent brain regions by picking up on different cytoarchitectural details (or, different brain tissue textures).
For example. The olfactory bulbs are in the front of the brain, and the hindbrain is in the back. They will never, ever be touching each other.
To me, it would seem more intuitive to give my model “rules” like this - focus on what regions would realistically be coming in contact with each other, and using these geometrical/geographical certainties to our advantage.
But I’ve had mixed reactions to this idea - some people have told me this is not really how neural networks operate. They’ve said, humans don’t get to give the network rules - it just figures these things out by itself.
Hope this makes sense, I am probably not using the right terminology for some of this stuff. I have trained some basic neuron segmentation models before, but that’s about it - and it was part of an already-established pipeline.
Thanks for any insights!
So I recently upgraded my Mac and am finally playing with Logic 12.2.
Very impressed with the stem splitter, as well as the chord ID.
I ran chord ID on a region (a bounce of my full demo), and it did a great job.
I see the chords under the region
But the session players don't seem to pay attention to these chords at all.
Do I have to apply the region chords to the global chord track? How do I do that?
Thanks.
I rescued 4 cinema displays from getting sent to pasture, and they all come with their power supplies, dongles, etc. They all work.
I love the matte displays, and they still look pretty dang good!
I want to set up a "battle station" with them, but I'm not sure what kind of Mac would be a good choice to hook them all into. I'm guessing you'd need a pretty decent GPU?
I've never had more than two monitors at a time, so if anyone has experience with something like this, I'd love to know what your setup was.
And please forgive the hastily created concept art. :P
I have biomedical 3D brain datasets in hdf5 format.
The files are almost a TB, so training a model on the full size brains is not possible.
But I was wondering, what if I could extract a small amount of chunks for the original size, just enough volume to display one single brain region.
Could I train a model of that small region, then repeat with another region, etc, then at the end somehow merge these individual models into one master model that can identify each brain region?
Also, How do resolution levels factor in? If I train something at the original scale (for reference, my HDF5 brain images contain full scale, half scale, 1/4 scale, etc.. for easy viewing), will any of what the model's learned, apply to the smaller scales? Or will they require their own separate training?
Thanks for any advice! It's probably obvious but I am a newcomer to this sort of thing!
Do you even tell them?
I just switched doctors, and I’m not sure what to tell this new one tomorrow. I don’t want to come across as a weirdo when I bring up the topic of different generic manufacturers.
But I don’t know the best way to tell them that this medicine doesn’t work the way it used to.
I’ve given up hoping for a miracle every month. Just refilled, and it’s Epic brand. I already know my month is doomed.
Maybe it’s just time to switch to something else..
Is there a better way to smooth out this blob than using Object->Path->Simplify?
The simplified version is on the right, at max settings.
Still not simple enough, though.