One of the authors here. Thanks for your comment! While a lot of this research is theoretical and does not have immediate use cases, we have tried to summarize some of them in the last paragraph of the paper (VII. Applications of Non-Euclidean Geometry). See page 26. We present some in Chemistry and Drug Development, Structural Biology and Protein Engineering, Computer Vision, Biomedical Imaging, Recommender Systems and Social Networks and Physics.
Thanks for the feedback! This model will evolve over time, we are planning on pushing a much higher resolution soon. Skill wise we will publish a paper in the next few months.
We are currently integrating with Forecast Watch a 3rd party that analyses and compare various forecasting systems [1]. Please stay tuned until we integrate our APIs. I will be updating this thread when it is ready.
We will be adding the Celsius switch shortly. We started with F since we expected most users of the forecast to be in the bay area but it is great to see interest from non Fahrenheit users :)
Thanks for the feedback :) The highest resolution models you can currently find on windy is around 3x3 km (HRRR) and this resolution is unfortunately too big to capture fine terrain and water features. 300x300 gives you 100 times more data points to work with.
While a good set of initial conditions is indeed critical, having a smaller model is helpful for modeling micro climates such as the ones you see in the Bay Area. At this resolution you can have a much more detailed representation of relief and water, which are two of the biggest drivers behind the beautiful dynamics we observe here.
Kalman filtering is only one part of the process, and plays a critical role during the data assimilation part. Classical Kalman filtering is optimal for Gaussian-distributed linear dynamical systems, but needs tweaks for non Gaussian distributions and non linear systems.
Classical NWP models for instance will integrate the primitive partial differential equations in time and space and run various parameterizations (which can be in some cases even more expensive than integrating the primitive equations). ECMWF on their end use IFS, which is a spectral method for solving the PDEs.
The whole process of solving these models accurately has definitely been some of the most fascinating science and engineering I’ve had the pleasure to work with. It’s extremely humbling :)
We just hired someone with an h1b that was approved in premium processing in June. How likely is he to be able to come in January? He currently lives in Ukraine and will need to do his embassy appointment there.
If someone has:
- a full-time job in a foreign country (say, Germany) that sends him in the US on a 2 months mission,
- a part-time job in the US (1 day per week), which let him work from Germany but also in the US under a part-time O1 visa (accepted).
How should that person enter the US for the 2 months trip? Under an VISA waiver B or O1 part-time?
I have a question related to the Green Card process with EB2/NIW and simultaneous O1 visa.
Say, someone is on an O1 that is expiring in August 2017. That person also has a Green Card EB2-NIW process ongoing: i140 accepted in December 2016, the NVC immigration visa process is currently on hold, waiting for acceptation of a G28.
Can the O1 visa be extended or can someone apply for another non-immigrant visa while waiting for the immigration visa / Green Card?
How long do you think it would take to get the G28 accepted (it has been 3 months now), and to get the Green card in this way, given the current situation?
How long do you think it would take to be granted an extension of an O1 visa?
Bay Labs | Software Engineer | San Francisco (Union Square) | Full-time, ONSITE, http://baylabs.io, VISA, C++11, python | email: [email protected]
Bay Labs is at the forefront of bringing deep learning advances to critical unsolved healthcare problems. We have assembled an exceptional team of clinicians, engineers and scientists who are developing breakthrough technologies in cardiovascular imaging and care, addressing the largest cause of death in the US. The Bay Labs team brings diverse expertise to the problem of diagnosing and managing heart disease, with leaders in machine learning, visual neuroscience, robotics, and physics. We have teamed up with a network of world class clinical and academic advisors and made remarkable progress in a very short period of time. Current investors in Bay Labs are recognized leaders in venture capital.
1. We wish to find a versatile engineer, who can demonstrate a capability to scientifically develop, benchmark, and validate a wide variety of deep neural network architectures for the purpose of extracting clinically-relevant knowledge from medical images.
2. We are hiring to fill a role that blends data pipeline architect and site reliability engineer. Site Reliability Engineering (SRE) is what you get when you treat operations as if it’s a software problem. If you are a software engineer at heart with ops experience and want to work some craft on deep learning, this role is for you.