liuhenry

Lithos Carbon | Full-Stack Engineer, Data Scientist/Engineer, and Applied Statistician | Hybrid (SF Bay Area, Seattle) and US Remote | Full-time

Hey everyone — we're a startup that's scaling a form of durable carbon dioxide removal called enhanced rock weathering. In addition to the critical efforts around decarbonization and emissions mitigations, you might have heard of efforts to scale carbon removal to mop up the remainder. Even so, we need to create an entirely new industry capable of removing tens of billions of tons of CO2 within the next decade.

Our bet at Lithos is that the best chance of success is by leveraging three industries already capable of this scale: agriculture, mining, and transportation. Rock weathering is a key part of the Earth's geologic carbon cycle, and has acted as a crucial "global thermostat" to keep Earth habitable — but it normally takes millennia. By enhancing this process, we can speed up the reaction to have meaningful effects within human timescales.

On a day-to-day basis, this involves planning, coordinating, tracking, and optimizing a distributed physical supply chain. We're looking for full-stack engineers, data scientists, and applied statisticians to build the next iteration of our platform under 10x operational growth, and design what will become the "operating system" for enhanced weathering at scale. There's a diverse set of challenges spanning interactive geospatial tooling, map and document digitization, laboratory sample management, logistical routing and optimization, and scientific modeling.

We are hiring for Senior-level roles in:

- Full-Stack Engineering

- Data Science & Data Engineering

- Applied Statistics

If you have experience of any kind in farming, commercial agriculture, geospatial data / GIS, farm management software, building tools for semi-technical power users (think Excel/Airtable/Google Earth), or working on offline applications, please mention this in your email - we'd love to hear about it!

You can reach out to us directly via hn [at] lithoscarbon.com

Lithos Carbon | Full-Stack Engineer, Data Scientist, and Applied Statistician | Hybrid (SF Bay Area, Seattle) | Full-time

Hey everyone — we're a startup that's scaling a form of durable carbon dioxide removal called enhanced rock weathering. In addition to the critical efforts around decarbonization and emissions mitigations, you might have heard of efforts to scale carbon removal to mop up the remainder. Even so, we need to create an entirely new industry capable of removing tens of billions of tons of CO2 within the next decade.

Our bet at Lithos is that the best chance of success is by leveraging three industries already capable of this scale: agriculture, mining, and transportation. Rock weathering is a key part of the Earth's geologic carbon cycle, and has acted as a crucial "global thermostat" to keep Earth habitable — but it normally takes millennia. By enhancing this process, we can speed up the reaction to have meaningful effects within human timescales.

On a day-to-day basis, this involves planning, coordinating, tracking, and optimizing a distributed physical supply chain. We're looking for full-stack engineers, data scientists, and applied statisticians to build the next iteration of our platform under 10x operational growth, and design what will become the "operating system" for enhanced weathering at scale. There's a diverse set of challenges spanning interactive geospatial tooling, map and document digitization, laboratory sample management, logistical routing and optimization, and scientific modeling.

We are hiring for Senior-level roles in:

- Full-Stack Engineering

- Data Science

- Applied Statistics

If you have experience of any kind in farming, commercial agriculture, geospatial data / GIS, farm management software, building tools for semi-technical power users (think Excel/Airtable/Google Earth), or working on offline applications, please mention this in your email - we'd love to hear about it!

You can reach out to us directly via hn [at] lithoscarbon.com

Lithos Carbon | Founding Full-Stack Engineer and Data Scientist | Hybrid (SF Bay Area, Seattle) and US Remote | Full-time

Hey everyone — we're a startup that's scaling a form of durable carbon dioxide removal called enhanced rock weathering. In addition to the critical efforts around decarbonization and emissions mitigations, you might have heard of efforts to scale carbon removal to mop up the remainder. Even so, we need to create an entirely new industry capable of removing tens of billions of tons of CO2 within the next decade.

Our bet at Lithos is that the best chance of success is by leveraging three industries already capable of this scale: agriculture, mining, and transportation. Rock weathering is a key part of the Earth's geologic carbon cycle, and has acted as a crucial "global thermostat" to keep Earth habitable — but it normally takes millenia. By enhancing this process, we can speed up the reaction to have meaningful effects within human timescales.

On a day-to-day basis, this involves planning, coordinating, tracking, and optimizing a distributed physical supply chain. We're looking for founding full-stack engineers and data scientists to build the next iteration of our platform under 10x operational growth, and design what will become the "operating system" for enhanced weathering at scale. There's a diverse set of challenges spanning interactive geospatial tooling, map and document digitization, laboratory sample management, logistical routing and optimization, and scientific modeling.

Feel free to reach out to me at henry [at] lithoscarbon.com or check out https://careers.lithoscarbon.com/

Which definition do you mean? SPX is a cap-weighted index, but that's very different from "50% of capital outperforms the index".

I don’t think the parent response is correct, though, if by profit (colloquially) we go by earnings that could be distributed to shareholders.

Sure, gross profit would not include either of those expenses. But operating profit would subtract research and development (e.g. the salaries and bonuses of the engineers working on the next process node) and depreciation on new factory (i.e. the cost of the new factory spread out over its useful lifetime - though it doesn’t include a factory under construction). Then you subtract interest and taxes to get to net profit. And TSMC has net profit margins of 39% in 2020!

> everyday materials usually obey the Pauli exclusion principle

In the everyday case, this is pretty much always in reference to electrons (which are fermions). Even when we're talking about atoms or solids, the effects of the PEP are due to electrons: https://en.wikipedia.org/wiki/Pauli_exclusion_principle#Appl...

The quantum-mechanical wavelength of everyday whole atoms is much smaller than their physical size, so they behave as classical particles (and the PEP doesn't really apply). In contrast, electrons have wavelengths large enough that they exhibit macroscopic quantum mechanical effects in everyday scenarios.

> Since it's only a even/odd difference, I would expect that roughly half materials form composite bosons, and the other half form composite fermions, but this doesn't seem to be the case.

Every element has bosonic and fermionic isotopes. Neutral atoms have equal numbers of protons and electrons, so any neutral atom with an odd number of neutrons is a composite fermion, and any neutral atom with an even number of neutrons is a composite boson.

The ELI15 explanation is that at these temperatures, quantum mechanical effects take over and the atoms themselves become indistinguishable from each other. They no longer have a definite position in space and experimentally act as matter waves [1].

This diagram is for bosons, but the top two boxes still apply for fermions (Bose-Einstein condensates are different but closely related): https://condensed-matters-cmcdt.wp.st-andrews.ac.uk/2019/02/...

The process is 1) cool until the de Broglie wavelength is larger than the typical interparticle distance and 2) compress.

If you did 2) before getting 1) then you would risk forming a classical liquid/solid. More info on the process: [2].

[1] https://en.wikipedia.org/wiki/Matter_wave [2] https://www.uni-muenster.de/Physik.AP/Demokritov/en/Forschen...

Right, quantum jumps as used here is actually a specific technical term. It seems like the parent comment takes it to be a lay analogy, but it’s from a formulation called quantum trajectory theory [1]. Rather, the result is about the electrons in an atom jumping between energy levels and experimentally observing a continuous evolution.

[1] https://en.m.wikipedia.org/wiki/Quantum_Trajectory_Theory

I'm trying to understand the concern but most of my worry around "historical financial data" would be actual transaction-level information, not rough summary figures to estimate a financial plan.

I just played around with the site myself and I didn't see anywhere that it imports data, plus the tagline on the homepage says "never ask to link your financial accounts"?

Isn't this essentially the same risk profile as like FIREcalc [1], or the NYT Rent or Buy calculator [2], or some portfolio analysis tool [3], are you saying that those shouldn't be online calculators either?

[1] https://firecalc.com

[2] https://www.nytimes.com/interactive/2014/upshot/buy-rent-cal...

[3] https://www.portfoliovisualizer.com

I grew up in the US and (unfortunately) this was the first time I'd heard of such a concept. I did some research and "prison furloughs" became a key political issue in the 1988 US presidential election [1]. The federal prison system now only allows non-emergency furloughs for inmates if they are within 2 years of their release/parole date, and who did not commit a violent crime.

Prison furlough programs existed in all 50 states in the 1980s, and "almost 10 percent of state and federal prisoners received a furlough in 1987". [2][3]

[1] https://en.wikipedia.org/wiki/Revolving_Door_(advertisement)

[2] https://www.nytimes.com/1988/10/12/us/study-says-53000-got-p...

[3] https://www.themarshallproject.org/2015/05/13/willie-horton-...

I understand what you're saying, but honest question: why is it helpful to add this caveat?

It's ~£500 payments per month (which covers both interest and some of the principal). The total interest comes out to £1,473.

I'm sympathetic to this point, but won't it continue to be a problem as long as airlines still allow pets?

The DOT ruling cites an industry statistic that 784k pets and 751k ESAs were transported in 2017 [1]. I can certainly believe that it's grown more lopsided since then, but it still seems that a significant number of flights would have animals on them after this rule takes effect.

https://www.transportation.gov/sites/dot.gov/files/2020-12/S...

It's worth noting that the Department of Transportation final ruling devotes a section to economic impact [1], and estimates a ~$55M increase in fees paid by passengers traveling with ESAs to airlines.

> The current policy amounts to a price restriction which requires that airlines forgo a potential revenue source, as airlines are currently prohibited from charging a pet fee for transporting emotional support animals

> Removing the current requirement that carriers must transport emotional support animals free of charge will allow market forces (i.e., carriers as producers and passengers as consumers) to set the price for air transportation of emotional support animals as pets. This provision will allow carriers to charge passengers traveling with emotional support animals (dogs and other accepted species on board of an aircraft) with pet transportation fees. This represents a transfer of surplus from passengers to airlines, and does not have implications for the net benefits calculation of the final rule."

[1] https://www.transportation.gov/sites/dot.gov/files/2020-12/S...

Logically, the "surcharge for potential damage" should be a security deposit, or fines and penalties against specific behavior.

You'd have the same issues with tenants that were excessively noisy, or held parties and spilled alcohol in the hallways, or scratched doors/walls/floors from moving bikes, furniture, etc. So it seems inconsistent to apply the nonrefundable fee only to the case of pets.

The risk to PayPal is that Visa/MC no longer allow PayPal to transact on their network if aggregate chargebacks exceed some percentage.

I don’t know how the exact details iron out but this was a concern when I worked on a payments platform. PayPal enables transactions for merchants who don’t have a relationship directly with Visa/MC and thus are themselves responsible at some level.

Fair - I don't own a car myself, but the US DOT reports 24.4 mpg in 2018 for cars, SUVs, vans, and light trucks shorter than 121 inches. (This is an estimate of vehicles on the road, not 2018 model year.)

If we use a 2020 model year for passenger cars of ~40mpg and a more conservative 11,500 miles per year, it comes out to 1.1kW.

US Department of Transporation says the avg. American drives 13.5k miles per year.

Using an average mpg of 21.5 mpg (if the average age of a car is 12 years), this comes to ~628 gallons of gas per year.

EPA uses 33.7 kWh per gallon for ~21,160 kWh in a year. Divide by 360 * 24 and you get 2.5 kW continuous, so it seems plausible.

EU numbers based on [1] come out to ~1 kW for driving?

[1] https://www.odyssee-mure.eu/publications/efficiency-by-secto...

Something I always find pretty mind-blowing is that a physical thin lens can also perform a Fourier transform.

This (generally) only applies to coherent light, so it's not something we're used to everyday, but it forms the basis of a lot of laser-based optical technologies. Combined with some other effects you can do "all-optical" signal processing [1,2], or generate create dynamic, "programmable" holograms [3]

[1] https://ieeexplore.ieee.org/document/686739

[2] https://ieeexplore.ieee.org/document/6648413

[3] https://en.wikipedia.org/wiki/Computer-generated_holography

This doesn't quite get to ELI5 but hopefully is understandable to an HN audience (please feel free to correct if I've gotten anything incorrect):

"Topological" and "magnetic" are two separate concepts (see below). Phase just refers to "phase of matter" - it's used here because a material has different properties depending on the external environment (like heating a magnet beyond a certain point means it's no longer magnetic).

Topological typically refers to a "topological band structure". If you know some band theory: a material can be classed as an insulator, conductor, or semiconductor based on its band gap. A topological material is "mostly" an insulator, but has a special set of conductive channels on the surface.

Remember that an electron has this additional property of spin ("up" or "down"). The topological material is furthermore unique in that these conducting channels only conduct electrons with a particular spin. The channels are also "topologically protected", so they are immune to material imperfections and have dissipation-less conductance (kind of "superconducting" in laymen's terms, but this has a different specific meaning in physics).

Normally these topological materials are not magnetic (the topological effect is closely related to magnetic ordering), but this paper shows evidence of both, and also demonstrates that the electronic properties can be modulated by applying external magnetic field.

So combining all of this, an application of interest in computing is "spintronics", where electronics use spin current rather than charge current. Dissipation-less conductance means much more power efficient, and spin isn't volatile like charge (HDD vs RAM). Topological materials are also being explored as a promising hardware platform for quantum computing.

You're both correct but talking about different situations.

The original post is saying that some people may discover they've contributed too much to their IRA after doing the tax calculations (say, if they just contribute the max $6k now).

If this happens, you're subject to a 6% penalty unless you withdraw the excess along with "net attributable" earnings. These earnings are subject to the 10% early withdrawal penalty in addition to the normal income tax, and are calculated as a prorated portion of the entire IRA growth.

Full derivation:

https://physicspages.com/pdf/Griffiths%20QM/Griffiths%20Prob...

At least 10cm in any dimension (i.e. a 10cm diameter sphere, or a 10cm x 10cm x 10cm cube).

https://docs.fcc.gov/public/attachments/FCC-19-81A1.pdf

You've actually stumbled upon the central premise of this discovery.

If you finely divide a magnet into tiny magnets and suspend the particles in a liquid, you get... a normal ferrofluid. It's magnetic in the presence of an external field, but will lose its magnetization once that field is removed. This is because permanent magnetism (i.e. ferromagnetism) is a bulk property.

Every atom has a magnetic moment, but they are normally randomly aligned and thus the macroscopic field cancels out. It's only when these moments are aligned that a macroscopic magnetic field arises. Permanent magnets have the requisite crystalline structure for this to happen. As you chop it up, this macroscopic organization is destroyed.

If you get into the details, what they've done is to use the surface tension of the oil-in-water to "jam" an outer layer of magnetic particles and prevent them from rotating, thus preserving their magnetic alignment. This, in turn, is apparently enough to keep the free-floating, unjammed particles inside the droplet aligned as well, thus turning the entire droplet into a magnet. Pretty interesting, because without the membrane, none of the ferrofluid is magnetic, but with the membrane, all of it is.

In this case, Google makes the above content and a bunch of related materials intentionally public: https://rework.withgoogle.com/guides/

I had this impression as well, but there's something else going on. If you run the numbers, at T=293, there's a negligible fraction above 5 km/s. The atmospheric loss models typically use an exosphere temperature of T=1000, but again, negligible fraction above 10 km/s.

However, there were some contradictions I couldn't immediately understand. This source [1] says "Only about one in a million helium atoms is lost from Earth via Jeans’ escape.". But it also cites a plot from [2] showing the rule-of-thumb for Jeans' escape: 1/6th the escape velocity vs the RMS thermal velocity. In this case, He has an RMS velocity of 2.5 km/s at T=1000, which is greater than 1.9 km/s.

[1] https://geosci.uchicago.edu/~kite/doc/Catling2009.pdf [2] http://geosci.uchicago.edu/~kite/doc/Catling_and_Kasting_ch_...

10 milliKelvin is the temperature range that allows the usage of commercial RF equipment in the ~5GHz range to control the qubits.

The relationship between the two is essentially Energy = k_B * temp = h * freq, where k_B is the Boltzmann constant and h is Planck's constant.

If the temperature was higher than 10 milliKelvin, then thermal fluctuations could have comparable energy to your control signals and thus cause unintended electronic state transitions.

It's a set of course notes from MIT 6.055 - "The Art of Approximation in Science and Engineering". They have been edited and updated into a book as well:

https://ocw.mit.edu/courses/electrical-engineering-and-compu...

For most interplanetary missions, Earth's gravitational pull on spacecraft can be modeled as a sphere of influence of about 1 million km.

https://en.wikipedia.org/wiki/Sphere_of_influence_(astrodyna...

A loose analogy is that a lab notebook in this context is more like a courtroom transcript than a personal journal. It's a primary record of experimental data and procedures. Also there can be multiple people running a single experiment and recording data in a common lab notebook.