Yes, this. A lot of work in this field is missing from that timeline. Just circa 2010-2020, Les Loew's VCell 3D PDE approaches, Faeder et al.'s BioNetGen / ODE work, Luthey-Schulten Shulten's grid based cell models, the Pittsburgh supercomputing center's 3D monte-carlo MCell, the image-based deep learning models at the Allen Institute for Cell Science...
It's nice to see the idea of virtual cells make a comeback now, though the meaning seems to have shifted to transciptomics-based transformer / gpu-powered models (which have issues[0]), it's a fun field / problem, but I think it will make better progress if we take advantage of all the varied computational work that has come before.
[0] Benchmarking Transcriptomics Foundation Models for Perturbation Analysis : one PCA still rules them all https://arxiv.org/abs/2410.13956
it's uncreative/tired but at least to the point. Too many papers are confused / opaque agglomerations of a year's worth of research shoehorned into a paper. At least with these you can fairly easily assess whether the claim is supported or not.
While I was in grad school I got bored sitting behind a computer all day, and my wife and I decided to build a tiny house on a trailer as a way of venting our pent-up DIY urges. We'd just build it in our spare time. LOL.
We started in the late summer of 2013, with a trailer and no plans and a stack of construction books from the library.
Cut to spring 2016, having spent every single weekend and most evenings since (in zero degree winters and brutal Pittsburgh summers) sweating and swearing, really pushing the "divorce cabin" line, and having legitimate discussions late at night about the benefits of burning it all to the ground, where my wife, eight month pregnant, is trying to finish the trim work before I submit my dissertation and we tow it across the country.
The way the article captures the "not knowing what we were getting into" / tiny things that delay you to death / stressed out / losing friends / doing absolutely nothing else with your life / so over budget it hurts / final elation at success is absolutely perfect.
We only made it across the finish line because living in Pittsburgh on a grad student stipend is actually, well, livable, and I could do that while my wife worked pretty much full time on our housing boondoggle.
The main learning experience coming out of it was that you should absolutely pay how ever many thousands of dollars it costs for a good set of plans from someone who's done this before. Learning smaller tasks like framing and roofing etc is easy. Stitching it all together into a plan that you're arguing about because neither of you have any idea what you're doing, all while you're wasting precious daylight is _hard_. We would have finished at least a year sooner if we just had plans to follow.
All that said, building a place to live in was super rewarding (as others have said) type II fun.
We still have it, it's beautiful, and I have not yet burned it down.
Allen Institute for Cell Science | ONSITE Seattle | Full-time | software engineer / ML / computer vision | https://alleninstitute.org/
The modeling team at the Allen institute for Cell Science is hiring for two software engineering positions -- a data generalist and a ML / computer vision specialist:
The Allen Institute for Cell Science aims to impact the entire cell science community. Our goal is to advance understanding of cell behavior in its normal, pathological, and regenerative contexts. Our multidisciplinary team will generate novel cellular reagents, data, models and databases that are informed by and open to scientists around the world. We will produce unique dynamic, visual databases and cellular models that integrate information and data across cellular and molecular sciences.
Allen Institute for Cell Science | software engineer / ML / computer vision | ONSITE Seattle | Full-time
The modeling team at the Allen institute for Cell Science is hiring for two software engineering positions -- a data generalist and a ML / computer vision specialist:
The Allen Institute for Cell Science aims to impact the entire cell science community. Our goal is to advance understanding of cell behavior in its normal, pathological, and regenerative contexts. Our multidisciplinary team will generate novel cellular reagents, data, models and databases that are informed by and open to scientists around the world. We will produce unique dynamic, visual databases and cellular models that integrate information and data across cellular and molecular sciences.
1. All of the above. The label free tool in particular gives you such a big free lunch at the microscope that the combination of it and good visualization has the potential to massively impact research workflows.
2. We are very interested in how cells change as they divide, differentiate, age, are perturbed by their environment, etc. We study cells in culture right now -- getting good images of in vitro cells from multicellular organisms is way harder. So yes it would absolutely be useful. I don't know if we're going to tackle it ourselves, but one of our core missions is to lay the groundwork for the community to take our tools and run with them -- it's a big win for us if we can bring previously unfeasible research within the realm of the possible.
3. I am a Bayesian at heart, so modeling uncertainty is something that I'm always thinking about. It's high on my list of priorities to do something along these lines.
4. Image similarity is a hard problem. At the end of the day, metrics only get you so far and the proof is in the pudding. Unfortunately there is no ground-truth data to test against -- the probabilistic model was constructed exactly because we can't measure where everything is all at the same time. Some things we do to convince ourselves that we're on track is to see that the variation in the imputed predictions and the actual data are statistically similar, and to see if experts are confounded in differentiating the outputs of our models from actual data. You can read more here https://www.biorxiv.org/content/early/2017/12/21/238378
That's at least close to true. It might be better to think of it as a smoothed "segmentation" of a 3D image, i.e. some algorithm decided what pixels are officially part of e.g. the mitochondria set, and outlined them. That could be based on level sets or seeded watershed or whatever else works well.
There are some alternate visualizations here http://www.allencell.org/3d-cell-viewer.html of data that came off of our microscopes that we also use to visualize our models in house but wasn't;t included in the video. It's hard to visualize varying density 3D data in 2D -- there's no one good way to do it, especially on the fly over the web -- but if you have any feedback about what would be more informative / easier to understand, let us know.
Characterizing cellular variation is exactly what we're interested in, e.g when and why are the mitts clustered around the nuclear vs not. Lots of images in our data have them packed around the nucleus -- you can look at the localizations from our microscopes here (select the Tom20 tag): http://www.allencell.org/3d-cell-viewer.html here. You can also grab the raw data (including bright field images e.g. what you would "see" in the microscope) here http://www.allencell.org/data-downloading.html#DownloadImage...
just saw this; I'm part of the computational modeling team that worked on this -- can try to field any questions or find more qualified people to do so.
- a few of the questions were very good, and either spoke to key high level concepts, or were specific while being language agnostic. (e.g which one of these layers wouldn't you need, why wouldn't this type of classifier work on this data).
- too many of the questions were hyper-focused on the minutiae of word embeddings, tensor flow syntax, SQL queries, and recommender schemes.
- many of the questions were constructed vaguely enough that "I don't know" would be the technically correct answer even though I don't think that was what you were going for.
metadata: recent PhD with serious grad courses in ML and working in DL/CV for the past year using a non-tensorflow framework (PyTorch).
specious extrapolations aside, the plot itself is deceptive -- exponential growth in a log-linear plot should be a straight line, not one accelerating upward super-exponentially.
Sedgewick's 1978 paper[0] on implementing quicksort has some interesting hand optimizations of the assembly code -- loop rotating, unrolling, etc. I wonder if modern compilers do the same?
Zee's QFT in a Nutshell is a very readable, high-level view of what's going on in QFT. Griffiths' book is his one text I haven't read, but I love his others so much I can't help but second the recommendation.
Right, but it's the 'every once in a while" part that I wonder about, i.e. the variance of that distribution. Maybe you could argue that since the Maxwell-Boltzmann distribution is narrower the lower its mean, if kT is small then it's an exponentially tiny effect.
I've always been curious: I understand that h.nu from cell phone radiation isn't big enough to ionize (say) DNA, but since the molecules in our body are in thermal equilibrium, can't h.nu + kT get it done every once in a while?
[edited because using an asterisk to denote multiplication was a bad idea]