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splittingTimes

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I worked in that field doing numerical many-body simulations of electron dynamics interacting with their environment in solid state devices like quantum dots, graphene, photonic waveguides & cavities , etc.

You would start from a Lagrangian formulation of the classical interaction, let's say Light-Matter, that would yield for example the Schrodinger and Maxwell equations. Following a Legendre transformation (there a post on the HN front page the other day on that) you end up with a so-called Hamilton operator from which you can derive a (huge) set of coupled differential equations which you then solve.

Here, if you wanted to increase temporal accuracy, it typically leads simply to longer calculation times.

We also tried a different approach using Feynman's path integrals and boy did that explode numerically. We optimized our programs to the point where everything was reduced to work on bits, but to no avail it was numerically unstable and the memory consumption when through the roof the longer or more accurate you wanted to make the simulation.

So, I would argue that NO, Feynman does not make it easier per se.

However, other groups made it work somehow.

As a starting point you can check that paper and it's references from the introduction section.

https://doi.org/10.1002/pssb.201000842

Spotify Is Screwed 3 years ago

Asking myself the same question. With the previous layoffs they were around 7500 people. That is mind boggling.

What do the all do all day??

For writing documentation: AsciiDoc [1] as fileformat.

For publishing documentation / to build the web site: Antora [2].

AsciiDoc has a bit more features compared to Markdown which allows for a richer and more pleasant presentation of the docs.

Antora allows you to have the project documentation in the actual project repositories. It then pulls the docs from all the different repos together to build the site. This also allows you to have the released product versions go in-synch with the docs versions. Antora builds each version of the product as part of one site. The reader can explore different product versions or navigate between pages across versions.

===

[1] https://asciidoc.org/

[2] https://antora.org/

IBM did research back in the 90s on perceptually-based colormaps and how to best represent various types of data within the color dimensions of luminescence, saturation and hue [1]. For example, they found that,

(1) Hue was not a good dimension for encoding magnitude information, i.e. rainbow color maps are bad.

(2) The mechanisms in human vision responsible for high spatial frequency information processing are luminance channels. If the data to be represented have high spatial frequency, use a color map which has a strong luminance variation across the data range.

(3) For interval and ratio data, both luminance- and saturation-varying color maps should produce the effect of having equal steps in data value correspond to equal perceptual steps, but the first will be most effective for high spatial frequency data variations and the second will be most effective for low spatial frequency variations.

===

[1] the original link got removed from IBMs website. Back in the day it was under

https://www.research.ibm.com/people/l/lloydt/color/color.HTM

A pdf copy is here:

https://github.com/frankMilde/interesting-reads/blob/master/...

In total, yes it was. Working for some years in a field, having the time afforded to emerse yourself into a subject and deeply think about it, calculating yourself into frustrating dead ends and also into successes, writing papers, going to conferences presenting your results, have scientific exchanges with peers, write applications for research grants, sit in committees, work as a referee for scientific journals, teaching students, then writing a coherent and compelling thesis and finally defending it against & having a scientific discussion on eye-to-eye level with your supervisors will shape your character... a lot.

You might wanna skip that postdoc thing though if you know you don't wanna stay in academia (and trust me, you don't).

Exactly my experience back in the days doing the mandatory "advanced experimental physics laboratory semester" where you had to do like 14 vastly different experiment of the caliber described in that post in the course of one semester on old equipment that would break during the experiment, with less than motivated PhD students or post grads as teachers. Of the 14 experiments only two worked and we got the expected results.

This experience drove me right into theoretical physics and writing computer simulations of electron dynamics and light-matter interactions in confined semiconductors (quantum dots, graphene and the like). That was fun.

Now I am working on medical device software development, as the other stuff does not pay the bills.

Great article on the topic. Biggest takeaway is how they added linters and link checkers to the toolchain.

We currently try to establish something similar on our end using AsciiDoc [1] as fileformat, Antora [2] to build the site and hosting on Azure storage. AsciiDoc has a bit more features compared to Markdown which allows for a richer presentation of the docs.

Biggest difference is that Linode has the docs in a separate repository. Not sure if it is a limitation of their toolchain or a deliberate decision.

Antora allows you to have the project documentation in the actual project repositories. It then pulls the docs from all the different repos together to build the site. This also allows you to have the released product versions go in-synch with the docs versions. Antora builds each version of the product as part of one site. The reader can explore different product versions or navigate between pages across versions.

===

[1] https://asciidoc.org/

[2] https://antora.org/

Mentioned in the text is IBM, which did research back in the 90s on perceptually-based colormaps and how to best represent various types of data within the color dimensions of luminescence, saturation and hue [1]. For example, they found that,

(1) Hue was not a good dimension for encoding magnitude information, i.e. rainbow color maps are bad.

(2) The mechanisms in human vision responsible for high spatial frequency information processing are luminance channels. If the data to be represented have high spatial frequency, use a color map which has a strong luminance variation across the data range.

(3) For interval and ratio data, both luminance- and saturation-varying color maps should produce the effect of having equal steps in data value correspond to equal perceptual steps, but the first will be most effective for high spatial frequency data variations and the second will be most effective for low spatial frequency variations.

===

[1] the original link got removed from IBMs website. Back in the day it was under

https://www.research.ibm.com/people/l/lloydt/color/color.HTM

A pdf copy is here:

https://github.com/frankMilde/interesting-reads/blob/master/...

You can load models from your computer. In this case the model won't be uploaded to any web server, the entire process happens in your browser.

Could I somehow embed this as a preview feature in an other application (which is completely under my control)?

Let's say I am working on a gallery application, but not for photos/image files, but for 3D files. Could I ember a browser/html page render and use this online 3D viewer there?

Not trying to take away from it, but it's scope seems elementary. Maybe I missed it, but did not see any more advanced operations. Wider adoption might be in the cards if at least some of the following are present:

Mesh analysis (how many manifolds, how many holes, self-Intersection detection, flipped triangles, etc)

Mesh healing algorithms to fix above artifacts

Boolean operations

Laplacian Deformation / smoothing

Offset calculation

Sewing meshes together

Mesh tagging to guide certain operations

Mesh matching

Convex Hull computation

"So although the researchers believe these eight signals resemble what a technosignature is expected to look like, they can't confidently say any or all of the signals originate from extraterrestrial intelligence. The scientists would have needed to detect the same signals multiple times, and this repetition didn't appear during brief follow-up observations by the Green Bank Telescope."

The new dataset contains a staggering 3.32 billion celestial objects — arguably the largest such catalog so far [...]

The Milky Way Galaxy contains hundreds of billions of stars, glimmering star-forming regions, and towering dark clouds of dust and gas.

So our best data set of the milky way shows 310^9 objects. How do we know then, that the are x100*10^9 objects in total?

If true, is staggering that all this data represents less than 1% of all there is in the milky way alone.

I am as ever amazed by Alan Kay's ability to comprehensively remember events, interactions with people and thoughts he had from 40 or 50 years ago and still have such detail.

I cannot remember all the interactions and conversation topics I had two weeks ago or explain in detail how an issue got fixed or what its root cause was. It feels to me as if things happen so fast today, that my brain drops old information the moment I consider it done to make room for all the new information.

Is there a trick or technique to it [1] or is his brain wired differently with higher neuroplasticity?

=== [1] I do use bullet journaling for example. It helps to stay on top of the gazillion things that need my attention, but feel it makes matters worse in the regard as I offload stuff and my brain knows it can forget about it ever more.

I agree with most of the sentiments in the article and am always keen on learning new meaningful, i.e. not individual but high-level measures. The notion of "measuring something is better than nothing" is not helpful, I feel. You not measure what is just easy to measure, but what is actually imporant and gives real business insight. This, as the article states, is typically very hard to define and execute.

Also, I would widen the perspective of "CTO needs to provide data to CEO" / engineering effectivness a bit, as this is not the most important thing you need to know. Ultimately, as a company you wanna know if your business is going in the right direction or not. You want to spend more money on things & activities that add to the value of your product offering in the eyes of a customer so that the customer will pay for it and you want to spend less money on things customers will not pay for. But what are the value-adding activities and which are non-value-adding? When will a customer buy your product or service?

For me, this sets a certain order of importance of what things to measure/quantify to answer the following questions:

1. Deliver customer value. Are we building the right product?

2. Generate business value. Can we generate revenue?

3. High value product. Do we keep quality high and build the right next features?

4. Code quality. Are we building the product right so it is maintainable and exensible?

5. Team chemistry. Is the team aligned on the goal and healthy in their interactions and spirit?

6. Process efficiency. Is success repeatable?

===

(1) Answering that should be not too hard as you can measure any kind of customer feedback on your products, be it youtube likes, alpha tester feedback, support calls, surveys.

(2) Once you know you have a product customers want and like, you need to know is the customer facing part of your organization (marketing, sales, training & education and support) connecting to said customer, so that they can sell and actually generate revenue?

This is much harder to get data for. You can measure the number of licenses, lost and new customers, or the trends in the business volume of services, but what does that tell you about the ability of your organization to be able to sell a good product to customers? I am not sure here.

(3) is to reflect on the business success (ROI) on new features that you implement. Do you keep building a high value product? Are we effective in communicating the new features value proposition to our customers? There is soo much to measure here.

Feature level: Measure via BI the business impact of new features, how often are they used? Measure the resources needed to deliver a feature. Measure number of support cases / bugs reported per feature. Measure the estimated time vs the taken time for a feature.

Quality of the overall product: Measure the yearly mean number of support cases per week. Measure the yearly mean number of bug reports per week. Measure the yearly mean number of crash reports per week. Measure number of major field incidences per year.

Do we make the customer feel cared about after the sale? Measure lead time to resolve support calls. Measure customer ratings of support calls.

(4) Code quality like compiler/sonar warnings or test coverage are the easy measures, but might not give you insight. I prefer to look more highlevel again at the product quality which ties into (3), like How often do tickets come back from testing to development? Measure number of regressions reported by customers after a release. Measure number of (real) hotfixes needed after a release.

Answering the more fundamental questions of code quality like "how easy is it to add new features" is a bit more difficult. I have not found good measures yet.

For (5) totally agree with the article, you should never measure how many lines of code were produced, how many tickets closed or the like. The question of team happiness is most important for team work. Ther are good tools for that like OfficeVibe or Glint.

Measure the employee turnover. Measure the number of uninterrupted hours of work time / total time present at work per week. Measure the mean Office Vibe score. Many further answers could be drawn off of Office vibe: Are people at ease, having a good time and enjoying interactions with their peers? Is there no sense that single individuals try to succeed in spite of the efforts of those around them? Was the work a joint product? Was everybody proud of its quality? Do they take enjoyment in their work? Is there trust and mutual esteem among the peers?

For (6) you want to know: Are our processes such that success are repeatable?

On a company level: Measure number of botched releases / roll backs. Measure number of failed audits per year. Measure number of open CAPAs per year.

Per Team level: How long are compile/ CICD times? How long do code reviews lie around before taken up? How easy is on-boarding of new employees? etc

I just wanted to add other storage challenges to your list, but upon googling for it, I had to learn that something called liquid organic hydrogen carrier exists:

"Hydrogen oil – LOHC – has two great advantages compared to compressed and liquid hydrogen:

The hydrogen is stored in oil, LOHC, and there is substantially less free hydrogen on board the ship, which reduces the risk. The hydrogen oil can be transported in conventional tanks, about the same way as for diesel today, and therefore has substantially lower transport costs than compressed and liquid hydrogen."

https://greenshippingprogramme.com/pilot/infrastructure-for-...