wxnx's comments

It is unfortunate that the only way to get through the regulatory process is copious $ - but it does work. If this project were better funded, it would likely have gotten through.

There is an entire political party representing something like half the population of the US dedicated to shrinking the regulatory apparatus, including the FDA. That doesn't sound like career security to me.

My perception is that the parent commenter is referring at least partly to "subcriminal" or nearly subcriminal behaviour. There is/was nowhere to "put" this kind of individual when they come from these settings, for a whole host of reasons, ranging from: something like "it's your neighbour's mentally ill kid, you really gonna call the cops for THAT?" to "what cops?".

School shooters and "crazy people acting violently" are probably incredibly rare by comparison. Sub/urban environments probably reduce the impact of the mentally ill on their surrounding neighbours in a whole host of ways, in fact -- in no small part because treatment is much less available in rural areas. (There's more of them in a smaller area and they're more visible and increasingly less criminalized, so people get the idea that poverty and health issues of that variety are modern, urban problems.)

Keywords in the literature around this include "community impact" or "community health", but it's not my area.

I really want to know this, too.

This is cool. I hooked up Llama to an open-source TTS model for a recent project and there was lots of fun engineering that went into it.

On a different note:

I think the most useful coding copilot tools for me reduce "manual overhead" without attempting to do any hard thinking/problem solving for me (such as generating arguments and types from docstrings or vice-versa, etc.). For more complicated tasks you really have to give copilot a pretty good "starting point".

I often talk to myself while coding. It would be extremely, extremely futuristic (and potentially useful) if a tool like this embedded my speech into a context vector and used it to as an additional copilot input so the model has a better "starting point".

I'm a late adopter of copilot and don't use it all the time but if anyone is aware of anything like this I'd be curious to hear about it.

Prior evidence suggests that sedentary behaviour increases blood biomarkers of cardiometabolic dysfunction.

They studied a hunter-gatherer tribe in Tanzania and found that despite having similar patterns of sedentary behaviour, their blood biomarkers of cardiometabolic dysfunction were much lower than those in industrialized nations. The authors posit that one reason for this is that sedentary behaviour in this group of individuals does not involve furniture -- rather, it usually involves a "deep squat", and the authors show that in this position the muscles are much more engaged than when someone sits in a chair.

This is consistent with evidence that breaking up periods of sitting with movement is good for you.

Their open-access paper talks about some evolutionary context for this hypothesis [1].

[1] 10.1073/pnas.1911868117

Eh, with one confirmed injury, I'm comfortable calling it "conceivable". The testimony of the injured worker in that case even reflects the fact that a spiked tree does not guarantee injury or death to the operator -- indeed, first there seems to be substantial damage to the machinery, and only then, a risk to the operator [1]:

> Alexander later filed a lawsuit against Louisiana-Pacific claiming that the band saw had been weakened from previous strikes with nails, but that he was forced to work with the saw or face dismissal.

Tree spiking is one example of this. It's so common as to be illegal in the United States [1]. Unfortunately, without strict worker protections, it's conceivable that workers would be the ones to pay for their employers' (or their upstream suppliers') illegal actions.

[1] https://en.wikipedia.org/wiki/Tree_spiking

IANAL, so I genuinely can't answer that, but in this particular case I'd hope so, yes. More generally, I suspect circumstance dictates. Prosecutors' responsibilities under Brady do seem to be a topic of conversation in the context of "Big Data Policing" [1].

[1] https://www.uclalawreview.org/wp-content/uploads/securepdfs/...

I think there's good evidence that by the time it reaches common use, it will be used that way.

Consider ShotSpotter, which uses an array of microphones in an urban environment to detect gunshots (and often then deploy officers to the location) [1]:

> A ShotSpotter expert admitted in a 2016 trial, for example, that the company reclassified sounds from a helicopter to a bullet at the request of a police department customer, saying such changes occur “all the time” because “we trust our law enforcement customers to be really upfront and honest with us.”

In this case, it seems like it's more like "evidence laundering" - a cop found a bullet (presumably through legitimate means) and would like to use the ShotSpotter results as additional evidence that the shooting took place, and so requests a re-classification of the audio recording. Even in this case, where the parallel evidentiary construction is presumably legitimate, one can imagine the problem - a jury may put more stock in a ShotSpotter result than the cop's testimony about a bullet. But in this case, the ShotSpotter "result" is due precisely to that testimony.

Never mind the fact that ShotSpotter microphones are powerful enough to pick up loud conversations [2]:

> The apparent ability of ShotSpotter to record voices on the street raises questions about privacy rights and highlights another example of how emerging technologies can pose challenges to enforcing the law while also protecting civil liberties.

Predictive policing will require large-scale data collection, and policing institutions don't seem to always use it the way we'd want them to.

[1] https://www.aclu.org/news/privacy-technology/four-problems-w...

[2] https://www.southcoasttoday.com/story/news/crime/2012/01/11/...

Nope, you're thinking of regression coefficients, where you'd be correct that usually the null hypothesis is $\beta = 0$. In this case, what's being reported are odds ratios, so the null hypothesis would be that OR = 1.

The parent comment's point is that although the reported effect is significant at $\alpha = 0.05$ (the usual "95% CI" you mentioned), there are other problems that render their test of this hypothesis less than valid.

Any evidence for them being a fad? I definitely remember my parents' generation having dedicated one-on-one meetings with their managers on a regular basis, regardless of what field they were working in (skilled labourers, but none of them in tech).

Even in Western medicine (at the research stage, at least), that is not necessarily true in patients suffering from certain conditions.

There is some evidence (in the sense of evidence-based medicine) that a low-FODMAP (fermentable *saccharides) diet reduces symptoms in patients with irritable bowel syndrome. [1]

As far as vegetables go, according to one site high-FODMAP vegetables include alliums and artichokes. [2]

It is worth noting that the authors of the linked review paper caution that it is unknown whether a low-FODMAP diet may have long-term adverse effects.

[1] doi:10.2147/CEG.S86798

[2] https://www.monashfodmap.com/about-fodmap-and-ibs/high-and-l...

Yeah, that's a fair point, and I agree. I don't think it's ideal.

Hm, in what way do you find that DVC requires creating new branches for experiment tracking?

I find the following workflow works well, for example:

1. Define steps depending on a `config.yml`.

2. Run an initial experiment (with an initial config) and commit the results.

3. Update config (preserving the alternate config and using symlinks from `config.yml` to various new configs if necessary), re-run, and commit.

4. Results are then all preserved in your git history.

A timely study, certainly.

It's important to note that risk factors for mortality overlap significantly with the risk factors for other serious adverse events (see e.g. disability status [1], or HELPP syndrome [2]). In a sense, mortality (although fortunately rare as noted in other comments) is therefore a useful proxy for other severe, potentially life-changing adverse pregnancy-related medical events which are much more common than just plain death. (As an analogy, something like 10-15% of heart attacks are fatal, but many more are debilitating.)

[1] doi:10.1001/jamanetworkopen.2021.38414

[2] doi:10.1111/1471-0528.16225

I wrote some Python code recently that uses a similar data structure (Futures instead of Promises, without knowing necessarily about the data structure's use in JavaScript). It wasn't really for caching.

I modeled mine on the Django ASGI reference library's server implementation, which uses the data structure for maintaining references to stateful event consumers. Exception handling is done with a pre-scheduled long-running coroutine that looks at the map.

I'm curious about your second point -- why exactly do things get bad with high tail latency? Is it only a weakness of the data structure when used for caching? I'm having trouble picturing that.

Thanks for your comment. I'm also a computational biologist by training, but have never worked in the private sector.

I'm familiar with the idea of re-purposing drugs for rare disease treatments (most of my adjacent work has been in very early-stage academic research), but I'm curious about the financials here. Could some of the financial risk here be minimized by aggregating multiple groups of patients, all suffering from different rare diseases? From what I know about the process, the answer is yes, but I'd be curious to hear from somebody closer to the process.

Sure, but to what end? The posture of the current government of Ukraine is quite pro-EU, and the people of Ukraine tend to agree (although I suppose one could argue that these surveys are biased?).

Sorry, I hit submit before realizing it might seem rhetorical. I've updated it to clarify that no, it was not purely rhetorical, even though I doubt the veracity of the claims in the article.

What would be the motivation for American/European officials fabricating a story about the Russian military escalating the war in Ukraine?

I don't think this article is credible, but my question above is asked in good faith. There do seem to be informed people who doubt the veracity of American claims of increasing Russian hostility.