ChatGPT is amazing for neurodivergent folk. When I was applying for jobs this summer, I just word vomited a stream of consciousness professional biography into a text document. I then used this as context for chatgpt to help me write cover letters and resumes. If you want to be especially clever you can also include the job posting. Just be very careful to change up some of the language otherwise it’ll smell like AI.
No. The problem is in a reduction op of some sort (sum or whatever). Since there no guarantee of the order you receive the terms for the reduction, the nondeterminism enters from order of terms reduced. Since float math isn't associative, there will be slight differences depending on the order and these can amplify quickly over a deep net.
You would have to explicitly order the terms prior to reduction but you don't always have that level of control.
I used to work at a 24 hr end user tech support call center. They didn’t use Macs, but we had a machine for the techs to use to understand what the customer is looking at. I wrote a script to sleep until late at night then start saying weird/creepy stuff to mess with the overnight crew.
I’m convinced you cannot taste single protons. Water self ionizes, so there will always be acidic species (H+, H3O+, …) way way above the concentration of single molecules.
Check out Shotr (https://shotr.cc) for mac and flameshot (https://flameshot.org/) for Linux! These apps are totally indispensable in my day-to-day and it sounds like it’ll solve your problem.
This is all correct. These simulations actually can model molecular crowding by using a diffusive propensity proportional to the particles in the adjacent cells but I don’t think it was used here. I developed this methodology in grad school, but it didn’t go any where.
Dna does not fit in a voxel. Transcription is modeled by particles fixed in space which produce transcripts at a constant rate. The mRNA is treated the same as the other discrete particles in the simulation, though they are likely a bit bigger than the voxel size.
There are two main reasons to take advantage of the Gpu in lattice microbes. It can simulate the stochastic chemical reaction and diffusion dynamics in parallel: one thread per voxel. For instance, an E. coli sized cell would have ~40000 voxels. It’s not quite embarrassing parallel, but close. Second, the simulation is totally memory bound so we can take advantage of fast gpu memory. The decision to use CUDA over OpenCL was made in like 2009 or so. Things have changed a lot since then. I don’t think anyone has the time or interest to port it over, unfortunately.
SimBioSys | ON SITE | Chicago/Champaign, IL | Full-time
SimBioSys[0] is seeking an experienced Deep Learning Engineer to work on one of the most important challenges in medicine - improving outcomes in Cancer. We are a technology company on a mission to deploy Computational Oncology to transform decision making and patient experience in Cancer Care. By virtualizing cancer, clinicians and patients are empowered with a better understanding of the disease and can assess all available options computationally to truly individualize treatment. We are seeking scientists and engineers to drive research and development of deep neural networks applied to medical imaging analysis. The role will involve implementation of methods from the current state of the art and development of novel methodologies to support the creation of 3D biophysical models individualized to a particular patient's cancer. The position will require you to independently plan and execute projects to improve and expand our core technology. You will work closely with collaborators from diverse backgrounds including clinicians, biological scientists and software engineers. Sound like something you'd like to do? Please send me an email at [email protected]
I just looked up the link to share this, but you already got to it so I’m going to sell Darknet Diaries to y’all.
This podcast series is one of my favorites. It some how manages to find a balance between being technical and accessible to the average person. Jack Resider is a great story teller. The level of care and detail they put into their research is incredible and they share the primary sources if you want to go even deeper.
If you like podcasts, give this a listen. Physical pentest episodes are my favorite.
SimBioSys | ON SITE | Chicago/Champaign, IL | Full-time
SimBioSys[0] is seeking an experienced Deep Learning Engineer to work on one of the most important challenges in medicine - improving outcomes in Cancer. We are a technology company on a mission to deploy Computational Oncology to transform decision making and patient experience in Cancer Care. By virtualizing cancer, clinicians and patients are empowered with a better understanding of the disease and can assess all available options computationally to truly individualize treatment.
We are seeking scientists and engineers to drive research and development of deep neural networks applied to medical imaging analysis. The role will involve implementation of methods from the current state of the art and development of novel methodologies to support the creation of 3D biophysical models individualized to a particular patient's cancer.
The position will require you to independently plan and execute projects to improve and expand our core technology. You will work closely with collaborators from diverse backgrounds including clinicians, biological scientists and software engineers.
Sound like something you'd like to do? Please send me an email at [email protected]