sohamsankaran

We believe that we can scale up the process and bring the price down, so hopefully we can work with insurers and governments to make this possible, especially for patients who have no approved therapies available to them.

You could use it for in vivo CAR-T, for example, as well as for gene therapies.

That is the hope, but we'll have to see what the clinical immunogenicity results show!

Thank you! Australia is very fast -- they have a decentralized system of institutional and private ethics committees to approve trials, rather than the regulator deciding centrally, though the regulator retains the right to halt any trial at any time. They also have a generous tax credit for startups doing R&D there.

I've written about the Australian clinical trial system and the reforms I think India needs to accelerate our slow and archaic framework twice recently:

1) https://www.infinitesunrise.com/p/we-must-accelerate-clinica...

2) https://economictimes.indiatimes.com/opinion/et-commentary/a...

I'm the founder of PopVax -- here's some context on this vaccine:

PVX-001 is a broadly-protective COVID-19 vaccine candidate intended to protect against new variants of SARS-CoV-2 even as the virus mutates. It is built on our RNA-encoded virus-like particle (VLP) architecture to elicit a broad & potent immune response.

Our lipid nanoparticle (LNP) formulation, based on our own novel ionizable lipid PVXL-150, keeps the vaccine stable at 2-8 °C, regular refrigerator temperatures, for at least 9 months, rather than the -20 or -80 °C required by the first-generation mRNA vaccines approved for COVID-19. This will make it far easier to distribute the vaccine in developing countries.

The vaccine was largely funded by Vitalik Buterin's scientific investment fund Balvi -- in collaboration with them, we've made a commitment to making this vaccine open-source. Once the Phase I trial of PVX-001 concludes, we will open-source the design and manufacturing information needed for others to produce and build on it, and we will not enforce our intellectual property rights against anyone who uses it to make vaccines against any betacoronavirus -- the viral genus that includes SARS-CoV-2, SARS-CoV, and MERS-CoV.

We are doing this because SARS-CoV-2 and its close cousins remain a substantial pandemic risk. In just the past few decades, they have caused one major epidemic (SARS), one close call (MERS), and one global pandemic that killed millions. The designs developed after the original SARS outbreak gave the first generation of COVID-19 vaccines a crucial head start in 2020. We want humanity to have an even better head start next time: a broadly-protective, refrigerator-stable vaccine that any scientist, vaccine producer, or member of the public, located anywhere, can improve, adapt to their needs, and manufacture without having to worry about IP.

From painful experience this is true, especially the parts Rachit wrote to personally call me out.

I'm one of the authors of this article -- happy to answer questions if anyone has any.

This is great! If you're not a Delta Chat user and someone tries to message you, can you receive and respond to it as an email?

Gandi has been my registrar for years -- they're solid, high-integrity folks. Hope they manage to resolve this without too much damage.

Pashi (YC W20) | REMOTE / US / India | Full-time | Compiler engineer | https://pashi.com

Pashi is building the operating system for manufacturing. We already work with some of the largest manufacturers in the world, and our software is being brought to bear against some of the thorniest problems that plague the most mission-critical high-volume production processes ever constructed.

We're currently a very small, very technical team spread across India, Europe, and the US, and we're looking for folks with strong backgrounds in programming languages and compilers to help us build better programming abstractions for physical processes.

Email [email protected] with a paragraph-length description of any relevant experience if you're interested.

This has been obvious for months -- Romer has been yelling from the rooftops about it -- and yet no major western govt save the Brits have attempted to even try this strategy. Madness.

As it turns out, you can build most distributed protocols on top of an API which only allows you to append to and read from a shared log -- for example, if you want to do consensus across a number of actors, you can just have each one write their proposed value and the current epoch to the log, and the value that is written into the earliest log slot wins. In general, shared total orderings are equivalent to consensus -- this is clear when you consider that State Machine Replication (Paxos, Raft) is all about ensuring that all actors perform the same set of commands in the same order. If you're looking for a more detailed exposition of the shared log abstraction and its uses, you might benefit from reading the Corfu (https://www.cs.yale.edu/homes/mahesh/papers/corfumain-final....), Tango (http://www.cs.cornell.edu/~taozou/sosp13/tangososp.pdf), and FuzzyLog (https://www.usenix.org/system/files/osdi18-lockerman.pdf) papers from the lead author that precede this one.

Netflix gained ground in original television by funding bold new stuff that nobody else would (Orange Is The New Black, BoJack). They've now becomes slaves to their data despite the fact that the signals they rely on are hopelessly confounded. It's possible that they've hit upon the right strategy, but I both suspect and hope that isn't true.

Reference? AFAIK this was proposed by Wipro in a white paper but was never implemented.

Starting an independent project or two in an area you haven't played in before might be both fun and useful. I have a somewhat long list of ideas I don't have the time to do if you're looking for inspiration -- feel free to email me if you want 'em (soham at soh dot am).

Hi folks, I'm the host of Segfault, which is a podcast about computer science research. In this episode, Cornell Professor and former Facebook AI Researcher Bharath Hariharan joins me to discuss what got him into Computer Vision, how the transition to deep learning has changed the way CV research is conducted, and the still-massive gap between human perception and what machines can do.

Happy to answer any questions about the stuff we talked about, and would appreciate any feedback you might have.

FYI, in addition to the audio, there's a complete transcript available on the page as well.

Ethan also writes about machine learning at https://honestyisbest.com/kernels-of-truth each week -- his most recent piece there (https://honestyisbest.com/kernels-of-truth/2020/Jul/14/facia...) has a neat explanation of how convolutional neural networks (CNNs) work.

This is a test of a potential bug in the HN API, please ignore.