Highlights

European beech trees more than 1,500 kilometers apart all drop their fruit at the same time in a grand synchronization event now linked to the summer solstice.

From England to Sweden to Italy — across multiple seas, time zones and climates — somehow these trees “know” when to reproduce. But how?

Their analysis of over 60 years’ worth of seeding data suggests that European beech trees time their masting to the summer solstice and peak daylight.

The discovery of the genetic mechanism that governs this solstice-monitoring behavior could bring researchers closer to understanding many other mysteries of tree physiology.

So it’s easy to see why masting trees synchronize their seed production. Understanding how they do it, however, is more complicated. Plants usually synchronize their reproduction by timing it to the same weather signals.

Then the team stumbled across a clue by accident. One summer evening, Bogdziewicz was sitting on his balcony reading a study which found that the timing of leaf senescence — the natural aging process leaves go through each autumn — depends on when the local weather warms relative to the summer solstice. Inspired by this finding, he sent the paper to his research group and called a brainstorming session.

It’s the first time that researchers have identified day length as a cue for masting. While Koenig cautioned that the result is only correlational, he added that “there’s very little out there speculating on how the trees are doing what they’re doing.”

If the solstice is shown to activate a genetic mechanism, it would be a major breakthrough for the field. Currently, there’s little data to explain how trees behave as they do. No one even knows whether trees naturally grow old and die, Vacchiano said. Ecologists struggle just to study trees: From branches to root systems, the parts of a tree say very little about the physiology of the tree as a whole. What experts do know is that discovering how trees sense their environment will help them answer the questions that have been stumping them for decades.

Alice Evans is diving into a new Econ paper.

Ingrid Haegele finds that junior men are more likely to apply for promotions, primarily due to a greater desire for team leadership.

OG

How We Built the Internet

Metadata

• Author: Anna-Sofia Lesiv
• Category: article
• URL: https://every.to/p/how-we-built-the-internet

Highlights

The internet is a universe of its own.

The infrastructure that makes this scale possible is similarly astounding—a massive, global web of physical hardware, consisting of more than 5 billion kilometers of fiber-optic cable, more than 574 active and planned submarine cables that span a over 1 million kilometers in length, and a constellation of more than 5,400 satellites offering connectivity from low earth orbit (LEO).

“The Internet is no longer tracking the population of humans and the level of human use. The growth of the Internet is no longer bounded by human population growth, nor the number of hours in the day when humans are awake,” writes Geoff Huston, chief scientist at the nonprofit Asia Pacific Network Information Center.

As Shannon studied the structures of messages and language systems, he realized that there was a mathematical structure that underlied information. This meant that information could, in fact, be quantified.

Shannon noted that all information traveling from a sender to a recipient must pass through a channel, whether that channel be a wire or the atmosphere.

Shannon’s transformative insight was that every channel has a threshold—a maximum amount of information that can be delivered reliably to a sender.

Kleinrock approached AT&T and asked if the company would be interested in implementing such a system. AT&T rejected his proposal—most demand was still in analog communications. Instead, they told him to use the regular phone lines to send his digital communications—but that made no economic sense.

What was exceedingly clever about this suite of protocols was its generality. TCP and IP did not care which carrier technology transmitted its packets, whether it be copper wire, fiber-optic cable, or radio. And they imposed no constraints on what the bits could be formatted into—video text, simple messages, or even web pages formatted in a browser.

David Clark, one of the architects of the original internet, wrote in 1978 that “we should … prepare for the day when there are more than 256 networks in the Internet.”

Fiber was initially laid down by telecom companies offering high-quality cable television service to homes. The same lines would be used to provide internet access to these households. However, these service speeds were so fast that a whole new category of behavior became possible online. Information moved fast enough to make applications like video calling or video streaming a reality.

And while it may have been the government and small research groups that kickstarted the birth of the internet, its evolution henceforth was dictated by market forces, including service providers that offered cheaper-than-ever communication channels and users that primarily wanted to use those channels for entertainment.

In 2022, video streaming comprised nearly 58 percent of all Internet traffic. Netflix and YouTube alone accounted for 15 and 11 percent, respectively.

At the time, Facebook users in Asia or Africa had a completely different experience to their counterparts in the U.S. Their connection to a Facebook server had to travel halfway around the world, while users in the U.S. or Canada could enjoy nearly instantaneous service. To combat this, larger companies like Google, Facebook, Netflix, and others began storing their content physically closer to users through CDNs, or “content delivery networks.”

Instead of simply owning the CDNs that host your data, why not own the literal fiber cable that connects servers from the United States to the rest of the world?

Most of the world’s submarine cable capacity is now either partially or entirely owned by a FAANG company—meaning Facebook (Meta), Amazon, Apple, Netflix, or Google (Alphabet).

Google, which owns a number of sub-sea cables across the Atlantic and Pacific, can deliver hundreds of terabits per second through its infrastructure.

In other words, these applications have become so popular that they have had to leave traditional internet infrastructure and operate their services within their own private networks. These networks not only handle the physical layer, but also create new transfer protocols —totally disconnected from IP or TCP. Data is transferred on their own private protocols, essentially creating digital fiefdoms.

SpaceX’s Starlink is already unlocking a completely new way of providing service to millions. Its data packets, which travel to users via radio waves from low earth orbit, may soon be one of the fastest and most economical ways of delivering internet access to a majority of users on Earth. After all, the distance from LEO to the surface of the Earth is just a fraction of the length of subsea cables across the Atlantic and Pacific oceans.

What is next?

Incantations

Metadata

• Author: Jos Visser
• Category: rss
• URL: https://josvisser.substack.com/p/incantations

Highlights

The problem with incantations is that you don’t understand in what exact circumstances they work. Change the circumstances, and your incantations might work, might not work anymore, might do something else, or maybe worse, might do lots of damage. It is not safe to rely on incantations, you need to move to understanding.

We can best view the method of science as the use of our sophisticated methodological toolbox

Metadata

• Author: OUP Academic
• Category: article
• URL: https://academic.oup.com/pnasnexus/article/3/4/pgae112/7626940?login=false

Highlights

Scientific, medical, and technological knowledge has transformed our world, but we still poorly understand the nature of scientific methodology.

scientific methodology has not been systematically analyzed using large-scale data and scientific methods themselves as it is viewed as not easily amenable to scientific study.

This study reveals that 25% of all discoveries since 1900 did not apply the common scientific method (all three features)—with 6% of discoveries using no observation, 23% using no experimentation, and 17% not testing a hypothesis.

Empirical evidence thus challenges the common view of the scientific method.

This provides a new perspective to the scientific method—embedded in our sophisticated methods and instruments—and suggests that we need to reform and extend the way we view the scientific method and discovery process.

In fact, hundreds of major scientific discoveries did not use “the scientific method”, as defined in science dictionaries as the combined process of “the collection of data through observation and experiment, and the formulation and testing of hypotheses” (1). In other words, it is “The process of observing, asking questions, and seeking answers through tests and experiments” (2, cf. 3).

In general, this universal method is commonly viewed as a unifying method of science and can be traced back at least to Francis Bacon's theory of scientific methodology in 1620 which popularized the concept

Science thus does not always fit the textbook definition.

Comparison across fields provides evidence that the common scientific method was not applied in making about half of all Nobel Prize discoveries in astronomy, economics and social sciences, and a quarter of such discoveries in physics, as highlighted in Fig. 2b. Some discoveries are thus non-experimental and more theoretical in nature, while others are made in an exploratory way, without explicitly formulating and testing a preestablished hypothesis.

We find that one general feature of scientific methodology is applied in making science's major discoveries: the use of sophisticated methods or instruments. These are defined here as scientific methods and instruments that extend our cognitive and sensory abilities—such as statistical methods, lasers, and chromatography methods. They are external resources (material artifacts) that can be shared and used by others—whereas observing, hypothesizing, and experimenting are, in contrast, largely internal (cognitive) abilities that are not material (Fig. 2).

Just as science has evolved, so should the classic scientific method—which is construed in such general terms that it would be better described as a basic method of reasoning used for human activities (non-scientific and scientific).

An experimental research design was not carried out when Einstein developed the law of the photoelectric effect in 1905 or when Franklin, Crick, and Watson discovered the double helix structure of DNA in 1953 using observational images developed by Franklin.

Direct observation was not made when for example Penrose developed the mathematical proof for black holes in 1965 or when Prigogine developed the theory of dissipative structures in thermodynamics in 1969. A hypothesis was not directly tested when Jerne developed the natural-selection theory of antibody formation in 1955 or when Peebles developed the theoretical framework of physical cosmology in 1965.

Sophisticated methods make research more accurate and reliable and enable us to evaluate the quality of research.

Applying observation and a complex method or instrument, together, is decisive in producing nearly all major discoveries at 94%, illustrating the central importance of empirical sciences in driving discovery and science.

How much are your 9's worth?

Metadata

• Author: Ross Brodbeck
• Category: rss
• URL: https://hross.substack.com/p/how-much-are-your-9s-worth

Highlights

All nines are not created equal. Most of the time I hear an extraordinarily high availability claim (anything above 99.9%) I immediately start thinking about how that number is calculated and wondering how realistic it is.

Human beings are funny, though. It turns out we respond pretty well to simplicity and order.

Having a single number to measure service health is a great way for humans to look at a table of historical availability and understand if service availability is getting better or worse. It’s also the best way to create accountability and measure behavior over time…

… as long as your measurement is reasonably accurate and not a vanity metric.

Cheat #1 - Measure the narrowest path possible.

This is the easiest way to cheat a 9’s metric. Many nines numbers I have seen are various version of this cheat code. How can we create a narrow measurement path?

Cheat #2 - Lump everything into a single bucket.

Not all requests are created equal.

Cheat #3 - Don’t measure latency.

This is an availability metric we’re talking about here, why would we care about how long things take, as long as they are successful?!

Cheat #4 - Measure total volume, not minutes.

Let’s get a little controversial.

In order to cheat the metric we want to choose the calculation that looks the best, since even though we might have been having a bad time for 3 hours (1 out of every 10 requests was failing), not every customer was impacted so it wouldn’t be “fair” to count that time against us.

Building more specific models of customer paths is manual. It requires more manual effort and customization to build a model of customer behavior (read: engineering time). Sometimes we just don’t have people with the time or specialization to do this, or it will cost to much to maintain it in the future.

We don’t have data on all of the customer scenarios. In this case we just can’t measure enough to be sure what our availability is.

Sometimes we really don’t care (and neither do our customers). Some of the pages we build for our websites are… not very useful. Sometimes spending the time to measure (or fix) these scenarios just isn’t worth the effort. It’s important to focus on important scenarios for your customers and not waste engineering effort on things that aren’t very important (this is a very good way to create an ineffective availability effort at a company).

Mental shortcuts matter. No matter how much education we try, it’s hard to change perceptions of executives, engineers, etc. Sometimes it is better to pick the abstraction that helps people understand than pick the most accurate one.

Data volume and data quality are important to measurement. If we don’t have a good idea of which errors are “okay” and which are not, or we just don’t have that much traffic, some of these measurements become almost useless (what is the SLO of a website with 3 requests? does it matter?).

What is your way of cheating nines? ;)

Some article from the past ;)

Why We Love Music

Metadata

• Author: Jill Suttie
• Category: article
• URL: https://greatergood.berkeley.edu/article/item/why_we_love_music

Highlights

Using fMRI technology, they’re discovering why music can inspire such strong feelings and bind us so tightly to other people.

“A single sound tone is not really pleasurable in itself; but if these sounds are organized over time in some sort of arrangement, it’s amazingly powerful.”

There’s another part of the brain that seeps dopamine, specifically just before those peak emotional moments in a song: the caudate nucleus, which is involved in the anticipation of pleasure. Presumably, the anticipatory pleasure comes from familiarity with the song—you have a memory of the song you enjoyed in the past embedded in your brain, and you anticipate the high points that are coming.

During peak emotional moments in the songs identified by the listeners, dopamine was released in the nucleus accumbens, a structure deep within the older part of our human brain.

This finding suggested to her that when people listen to unfamiliar music, their brains process the sounds through memory circuits, searching for recognizable patterns to help them make predictions about where the song is heading. If music is too foreign-sounding, it will be hard to anticipate the song’s structure, and people won’t like it—meaning, no dopamine hit. But, if the music has some recognizable features—maybe a familiar beat or melodic structure—people will more likely be able to anticipate the song’s emotional peaks and enjoy it more. The dopamine hit comes from having their predictions confirmed—or violated slightly, in intriguing ways.

On the other hand, people tend to tire of pop music more readily than they do of jazz, for the same reason—it can become too predictable.

Her findings also explain why people can hear the same song over and over again and still enjoy it. The emotional hit off of a familiar piece of music can be so intense, in fact, that it’s easily re-stimulated even years later.

“Musical rhythms can directly affect your brain rhythms, and brain rhythms are responsible for how you feel at any given moment,” says Large.

“If I’m a performer and you’re a listener, and what I’m playing really moves you, I’ve basically synchronized your brain rhythm with mine,” says Large. “That’s how I communicate with you.”

He points to the work of Erin Hannon at the University of Nevada who found that babies as young as 8 months old already tune into the rhythms of the music from their own cultural environment.

“Liking is so subjective,” he says. “Music may not sound any different to you than to someone else, but you learn to associate it with something you like and you’ll experience a pleasure response.”

Interesting findings

Sorry for the Downtime, had not been paying enough attention to the change log and made wrong assumptions that the site does not work because up pictrs update.

But it was wrong port on the docker template!

Startling differences between humans and jukeboxes

Metadata

• Author: Adam Mastroianni
• Category: article
• URL: https://www.experimental-history.com/p/startling-differences-between-humans?utm_source=substack&utm_medium=email

Highlights

Similarly, when you survey people about what motivates them at work, they go “Feeling good about myself! Having freedom, the respect of my coworkers, and opportunities to develop my skills, learn things, and succeed!” When you survey people about what motivates others, they go, “Money and job security!” In another survey, people claimed that they value high-level needs (e.g., finding meaning in life) more than other people do.3

I’m saying “people” here as if I wasn’t one of them, but I would have agreed with all of the above. It was only saying it out loud that made me realize how cynical my theory of human motivation was, and that I applied it to everyone but myself. Yikes!

It’s not that hard to give people skills. It’s way harder to give them interests.

The best way to use incentives, then, is to:

1. find the people who already want what you want

2. help them survive

in a department where an internal survey revealed low morale among the graduate students. A town hall was convened to investigate the issue. The students knew that one of the biggest problems was that a handful of professors terrorize and neglect their underlings, and the fastest way to fix this would be to put those faculty members on an ice floe and push it out to sea. This, of course, was difficult to bring up (some of those faculty members were in the room), and so instead we talked about minor bureaucratic reforms like whether there should be some training for advisors, or whether bad advisors should have fewer opportunities to admit students. Nobody could name who these mysterious bad advisors were, of course, so even these piddling suggestions went nowhere.

If you hire someone based on the shininess of their CV and then hope that, somehow, the employee handbook will show them how to also be a good person and not just a prolific paper-producer, you’re going to end up with a department full of sad graduate students.

If you’re writing a constitution or a code of conduct, by all means, do a good job. But if you’re counting on something like “SUBSECTION 3A: Being evil is not allowed” to stop people from being evil, or if you think Roberts Rules of Order are going to turn an insecure despot into an enlightened ruler, well, strap in for some Dark Ages and some bad improv.

discovering your inner motivations takes time and experience, and we gum up the process with lots of strong opinions about what should motivate us.

If you believe that people need to be treated like jukeboxes or secret criminals, you are accepting the behaviorist premise that we need to put people inside a giant [operant conditioning chamber](https://en.wikipedia.org/wiki/Operant_conditioning_chamber#:~:text=An%20operant%20conditioning%20chamber%20(also,graduate%20student%20at%20Harvard%20University.) that dispenses food pellets for good behavior and electric shocks for bad behavior.

Instead of Oscars.