MatteoFrigo

A lawyer for the movement gave a press conference today, saying approximately the following. Because it is complicated for GoFundMe to distribute funds to individual truckers, GFM is exercising heightened due diligence, as they should. Meanwhile, the movement is setting up a nonprofit company, with all the proper auditing and accounting mechanisms in place. Once everything is ready, GFM will distribute the money to a trust fund managed by the company, and truckers will issue claims against the fund.

Edit: link: https://rumble.com/vtz6fg-freedom-convoy-2022-press-conferen... starts around 5:00

I think you have answered your own question. If you want to know "how do you make people feel welcome and accepted in your classroom", just ask that. Tell people to write a statement of pedagogy or something, where they discuss the things you care about.

Your problem is the DEI label. Your DEI office is working hard to convince the world that all whites are racists, which leads to pearls like this one which just came out today: https://alex-hanna.medium.com/on-racialized-tech-organizatio... I was not on the receiving end of the rants of this particular person, but similar expressions of deluded ontology happened all the time. You appreciate that calling one's colleagues racist all the time is not conducive to a good working relationship.

Maybe DEI was a good thing in academia years back. That horse has sailed. In the same way as people have redefined racism from KKK to "anything that leads to unequal outcomes", DEI is now associated with "the world is racist", "everybody is a white supremacist", "white fragility", and all this stuff. The message that your DEI office sends is pretty clear.

I wish you to be able to retain control over the choice of your colleagues forever.

> The "ideology" is actually that diversity and inclusion are important to think about at all!

(I appreciate you taking the time for this discussion.)

Yes, I fully agree that this is an ideology. This is where the problem is.

You presented (elsewhere) several examples of bad behavior that happens in a student body (racial slurs, sexual harassment, etc.) I think everybody agrees that this behavior needs to be "managed" and the professor needs to at the very least be prepared when this happens. One way to handle this situation is to offer incoming professors a pamphlet describing various situations likely to happen and suggestions on how to deal with them.

You cross the ideological boundary when you start labeling these issues as DEI, and this is where the animosity starts, including animosity from me.

What's so bad with using the DEI label? It's just a name, right? What's in a name? Stat rosa pristina nomine, nomina nuda tenemus, and all that.

The problem is that by now DEI is an industry that has convinced half the American public that the other half is racist, sexist, homophobic, etc. Under the name of DEI, I have been subject to all sort of indoctrination claiming that I am inherently racist, that I am unconsciously believing that women suck at math and I need to do something to fix this problem, etc.

The effect of this nonsense on workplaces is disastrous. You have black people here on HN saying, "I was getting along with everybody and now everybody thinks I am a diversity hire". I have been called a racist for suggesting that we (a cloud provider) have no right to know what customers are doing with the services we sell them. (I am racist because customers may use such services for "racist" causes.) Companies have explicit DEI goals, and I have seen at least one VP committing to meet with two "minoritized" employees per quarter in order to meet his bar for support for DEI. I can hardly think of anything more offensive than effectively saying "you are a black woman, I don't give a crap about you, but I need to spend 30m with you to get my bonus". And don't get me started with the impact of all this nonsense on hiring.

The bullshit level around women in tech is particularly ridiculous. Everybody believes that "science" proves that there is a bias against hiring women, and the DEI training provided this paper by Zingales et al. as a proof: https://www.pnas.org/content/pnas/111/12/4403.full.pdf From a bullshit toy example where a bunch of students are asked to simulate hiring women for a simple math task, everybody is now inferring nefarious behavior in the real world. What's really funny about this paper is how they choose the specific task: "Although there is some evidence of a sex difference in mathematics performance (5, 6), which is shrinking over time (7), there is no sex disparity in performance on an arithmetic task such as ours (8)." [See Discussion section] So what these guys are really saying is: women can't do complicated math [5, 6], so we pick the trivial task of adding a bunch of numbers, which [8] proves that even women can do. The very article that complains about bias is predicated upon the fact that the bias is, in fact, justified and grounded in science.

I don't think I have to say that I regard all of the above as complete bullshit, both the paper and all its references. In my professional experience, the women I have interacted with were on average better than the dudes. But this is the nonsense that is sold as DEI these days. It doesn't solve any problems, and it creates needless tensions.

You seem to be under the impression that DEI is just a way to learn to deal with difficult situations. If you really believe this, maybe look outside your department?

With all due respect, I think you are misreading the situation. Some people at MIT disinvited Prof. Abbot. The authors of the article are those who opposed this action and are trying to establish rules to prevent the situation from happening again.

You are free to trust whomever you want, of course. FWIW, I am on your side, I know some of the people who signed the doc, and I don't see anything nefarious going on.

Do you have a reference to the 20% number in DK?

The reason why I am asking is that the percentage of people ever infected in the US hovers around 80% (see https://covidestim.org/ and click on a few random states). So either 80% is wrong, 20% is wrong, or there is something really interesting going on.

Would you mind stating which institution you work for, so I can recommend that my kids avoid it like the plague?

I want teachers who can answer questions about Fourier transforms, not teachers who can "manage a diverse classroom". Really, "manage"?

Does the graph of excess mortality in Denmark at https://euromomo.eu/graphs-and-maps make you feel better? It looks like they have been at the historical norm for a while.

Yours is a pathetic rhetorical attempt to reduce a complex issue to your narrow-minded ontology. You are like the dude who, during the plague of 1630 in Italy, was arguing that Aristotle had said that everything is either a substance or an accident (where "accident" has a technical meaning that I won't discuss here). He then argued that the plague cannot be a substance, and it cannot be an accident either, and therefore the plague does not exist. QED.

Small minds discuss people. Great minds discuss ideas.

You said "covid kills about 2% of those who get it". Are you taking issue with the best available estimate of how many people "got it"? If not, your statement would imply that covid killed 5M people in the US, which is almost all the deaths in the US in the past two years.

See https://covidestim.org/ for a quantification of your "feeling". Summary: 80% of the population in California and New York state have been infected. Other states are in a similar range, but I was unable to find a USA-wide figure so I refrain from making a more general statement.

The 2% number is an overestimate. For the US over two years we had about 800K dead and 250M infected (ballpark assuming that 80% holds everywhere), which yields about .3% death probability per individual ever infected. For context, a bit less than 1% of the entire population dies each year (2,854,838 died in the US in 2019 https://www.cdc.gov/nchs/products/databriefs/db395.htm#secti...), so about 6M people would have died in two years anyway even without covid.

Your claim is empirically false. The total number of excess deaths was 81K in 2017, 158K in 2018, 101K in 2019. (You need to click a few buttons to enable data from past years.) The reason is that the sinusoidal amplitude is fairly small and is explicitly calibrated not to capture the flu. Look for example at the 2019-2020 winter graph: the empirical data matches the model until covid kicks in. For some reason, people die more in winter irrespective of the flu, and the sinusoidal component attempts to capture this effect.

Stepping back from the details for a minute: this is a 15-year long joint effort of public health authorities in Europe, with the explicit goal "to design a routine public health mortality monitoring system aimed at detecting and measuring, on a real-time basis, excess number of deaths related to influenza and other possible public health threats across participating European Countries." In terms of data analysis, their model is very simple, comprising a constant, a linear trend term for population growth, and a sine-wave term for non-flu seasonality. Do you really think that they would be so incompetent as to use a model that explicitly removes the flu, which is the very effect that they are trying to capture? Give them some credit, they know what they are doing.

> Data is deseasonalized, which does not mean what you wrote.

You are correct that I oversimplified a complex situation. https://euromomo.eu/how-it-works/methods/ describes their method, especially section "Hypothesis".

Their model includes a sinusoidal seasonal component, as you noted. However, what I said is also true. From the Hypothesis section, third bullet:

"Parts of Spring and Autumn are less likely to be influenced by additional processes leading to excess deaths and the main pattern of mortality can therefore be modelling using only those periods, resulting in a baseline being the number of deaths expected when no particular process increases mortality."

As is clear from the bullet that precedes the text that I quoted, "processes leading to excess deaths" means winter infections and summer heat waves. So my description, while incomplete, is hopefully not too misleading. For completeness, I should mention that they also adjust for population growth. (You can't just do 2020-avg(2015-2019) because the total population is different.)

> Immunity deniers

Let's not do this. When this mess is over we'll all want to work and break bread together again, and that's much more important than whatever any of us happened to believe or say in the fog of war.

Either way, there is no point in vaccinating the unvaccinated cohort that you mentioned.

If you are right, most of them have natural immunity and the data proves that this is already better than the vaccine, so what's the point? They are already doing ok, and vaccination would then move them to the vaccinated column and count their immunity as a success for the vaccine, which is unscientific and dishonest.

See https://covidestim.org/us for an update to your "too few" statement. The number of previously infected people is at least 70% in all US states I have looked at (80% in NY and CA).

While not directly comparable, an official report from the UK [1, Figure 3] estimated that effectively 100% of blood donors were either vaccinated, previously infected, or both. This was in September. Blood donors are not a representative sample of the population, but it's not hard to believe that by now effectively everybody (in the UK) has some kind of immunity.

[1] https://assets.publishing.service.gov.uk/government/uploads/...

Your ~half estimate is more like 70-80% these days. See https://covidestim.org/us

(This makes your case stronger, of course.)

In case you haven't seen it before, https://euromomo.eu/graphs-and-maps has graphs of excess data for most of Europe (plus Israel) by age groups.

I find it a great resource to cross-check the misinformation of the mainstream media. For example, I challenge anybody to look at the 0-14 and 14-44 graphs and tell me when covid actually occurred.

Note that excess is not defined as a deviation from a typical year, but as a deviation from a typical year when no diseases are circulating. The goal of the euromomo website is to detect things like flu outbreaks, so the number of excess deaths is usually greater than 0, capturing the flu, heat waves, and presumably other disasters.

> The effect exists but the data shows it does not matter.

You may have been right at a time where almost nobody had a previous infection. However, estimates by reputable sources https://covidestim.org/us show that a large percentage of people have been infected by covid at least once. The data are by state, I see ~75% for MA, 80% for CA and NY, and you can pick your favorite state.

At this point it is more correct to say that the vaccine does not matter, and immunity by prior infection is the dominant effect. Either that, or the Yale school of public health, the Harvard school of public health, and the Stanford Medicine institutes who run the covidestim.org site are full of shit.

https://covidestim.org/us has estimates on the percentage of people ever infected by state. I looked at MA, NY, and CA, and they all hover around 80% (with error bars). I can't find a cumulative US estimate.

I have no idea how they came up with the estimate, but the site is maintained by reputable institutions, so I am inclined to believe these numbers unless proven otherwise.

I don't quite understand this reasoning. If the transplant is going to shut off the patient's immune system anyway, what's the point of the vaccine given that they are going to undo it shortly afterwards? There must be some nuance that I am missing.

One proxy for the context that you are seeking is https://euromomo.eu/graphs-and-maps

That site reports weekly excess death numbers for most of Europe and Israel as well. Excess death appears to be defined not as excess from an average year, but as excess from an average period where there are no flu or other diseases circulating. This makes sense because the goal of collecting these data is to know quickly whether there are breakouts of diseases, and thus the baseline must be "a typical year without flu" rather than "a typical year". Thus, the numbers give you some idea of the impact of covid compared with the flu, at least in terms of deaths. Occasionally you can see other phenomena such as the impact of heat waves in the summer. I understand that the baseline is adjusted for population growth.

In the area covered it looks like there are about 70000 baseline deaths per week, or roughly 3.5M deaths for a year. There were about 100K excess deaths in 2017 and 2019, about 160K excess deaths in 2018, 400K in 2020 and 350K in 2021. Eyeballing, the current wave seems to be comparable to the 2017-2018 winter episode.

I find the graphs by age very interesting, especially the 0-14 range.

> hundreds of kids too

I keep hearing this statement, but I find it hard to reconcile it with hard data. For example, credible numbers from Europe https://euromomo.eu/graphs-and-maps show that the excess mortality in the 0-14 age range was actually negative in 2020, and in a normal range in 2021 (slightly higher than 2017 and 2018 but lower than in 2019). Certainly by looking at the weekly 0-14 chart it is impossible to say that there is anything nefarious going on.

The US CDC site was harder to navigate, but last time I looked, it painted a similar picture.

The built-in SIP client is gone without warning in Android 12.

I found out the hard way after wondering why the phone wasn't ringing anymore.

Don't you want to divide J by (J/cal) to get cal (as opposed to multiply)?

2.0e23J = 4.78e+22 cal

It's hard to tell exactly what the "TV" meant, but the two statements "50% of positives were admitted for non-covid reasons" and "50% of patients are positive" are not equivalent.

For example, consider a hospital with 100 patients of which 90 are negative and 10 are positive. Of the 10 positive, 5 were admitted for covid and 5 for some other reason. Thus, 10% of all patients are positive, but the "TV" statement still holds.

That being said, the number of people who have had covid appears to be much larger than people seem to think. https://covidestim.org/us/MA estimates that 73% +- 20% of the MA population was infected at some point before Jan 8 2022. Like everything else, take all estimates with a grain of salt, but I have no reason to doubt those numbers.

You are totally correct that in Paxos the proposer is supposed to "carry-forward" the value proposed by somebody else. In this view, which is how everybody understands Paxos, Paxos is an algorithm for consensus.

However, the proposer can also modify the value proposed by somebody else, and it turns out that this is a perfectly valid algorithm for implementing a distributed read-modify-write. This variant does more than just consensus---it basically behaves like a fault-tolerant memory with an atomic update operation (think compare-and-swap or load-linked/store-conditional). In his blog post, GP also mentions that paxos is a read-modify-write transaction, but perhaps I read too much into what he is saying. Either way, this RMW variant is correct, I have verified it exhaustively with TLA+, and it is used in a few production systems that I have implemented.

The difficulty is now to say exactly what this RMW variant does. I was hoping that GP (or anybody else) may have some insights.

Your view that Paxos is an algorithm for read-modify-write is a really deep insight. Once you adopt this point of view, things like Raft are just RMW updates of a log. (I like to think of Paxos as the fault-tolerant version of the load-linked/store-conditional pair of instructions.)

However, there is a difficulty in expressing exactly what RMW-Paxos does, because it may end up "modifying" a value that was never "committed".

For example, assume that I have three acceptors storing a replicated counter. The proposer executes phase-1, increments the value received from the acceptor with the highest ballot, and sends the value to all acceptors in phase-2. Consider now an execution where all acceptors initially hold 0. The proposer manages to store a new value 1 in one acceptor, and then crashes. Thus, 1 was not committed and a reader may conclude that the consensus value is still 0. Now a new proposer arrives, receives (1, 0, 0) from the three acceptors, chooses 1 as the newest value, and writes (2, 2, 2) to all acceptors. Thus "2" is now committed, and the commit of the "2" retroactively commits the "1". Thus, the condition for being committed is no longer "there exists a phase-2 quorum ...", but it is more complicated and depends on future history.

I have verified with TLA+ that this algorithm does indeed implement an atomic read-modify-write operation, but only under a complicated notion of being "committed" that boils down to either having a phase-2 quorum, or one of the successor operations having a phase-2 quorum.

Did you ever figure out a simpler way to say this?

I wouldn't call 1/3 "generous". https://covidestim.org/ has some estimates of the fraction of people ever infected by US state. I can't seem to find an US aggregate figure, but by clicking on a few random states they all seem to be at least 50%. California (the most populous state) is around 60% even before omicron.

Your estimate is likely off by a factor of two.

I don't know about AWS, but both Google Cloud and Oracle Cloud maintain at least a high level history of past outages. See https://status.cloud.google.com/summary and https://ocistatus.oraclecloud.com/history

https://covidestim.org/ provides estimates of the percentage of previously infected individuals by US state. I happen to be in Massachusetts, for which they estimate around 50% previously infected, with error bars. So a bit lower than South Africa, but not too far in the big scheme of things.

The UK provides some estimates of seroprevalence in blood donors (thus a skewed sample): https://www.gov.uk/government/publications/covid-19-vaccine-... (Figure 3). These estimates aren't quite what you are looking for, but one test is sensitive to previous infection and estimates 20% previously infected, whereas another test is sensitive to (infection OR vaccine) and estimates ~100%. Basically, in that population, pretty much everybody has some kind of antibodies.

To answer your question, I would say that SA doesn't look particularly unique.