all retailers should mark tarriff charges, just as sales tax should be clear.
It is just unfortunate that it will be much harder to mark intermediate good costs - such as for an item that is assembled in the USA but which has 45% of its parts from out of the country.

I agree that CS students should be exposed to different languages. For a true introductory class where the student are not expected to have programmed before you want a language that minimizes boiler plate and which is easy to read and understand.

Python was designed for this purpose by Guido and works well in this context. It has the additional advantage of a good standard library and broad ecosystem so that it is fun and rewarding for newbies. It is easy to introduce different paradigms and data structures.

Additional languages can be introduced as needed: C, lisp, prolog, SQL, haskell, Go, ocaml, Lean4, erlang, rust all have things to illustrate/teach.

Java is probably the worse for newbies as its advantages donâ(TM)t really start to pay off when acting as a programmer collaborating with others in large software projects. The newbie ends up paying the price of lots of boilerplate without getting any of the advantages.

I'll second this. It great to have additional energy storage technologies to compete with the current lithium standard.
Natron Energy claims that their sodium batteries are not susceptible to thermal runaway or fire after being punctured. So they should reduce the fire-risk. So potentially they could be used for home batteries that last longer and are less likely to cause a fire in your house. (A worry for me, as someone who as seen a desk fire from a lithium laptop battery.)

reading both the original tweet the FDA and the advisory it links to it the FDAâ(TM)s message sounds appropriate.
The tweet is directed at lay-people and is informal and very short, but it does not say anything technically incorrect.

The advisory [1] clarifies some of the questions/complaints that some of the posters have raised:

> the FDA has received multiple reports of patients who have required medical attention, including hospitalization, after self-medicating with ivermectin intended for livestock.

Thus pointing to the main issue endangering patients is the diversion of medicine for livestock (or fish in one case I think) to human use.

The advisory also states explicitly that ivermectin is packaged and dosed for human use and that if you (the patient) is prescribed ivermectin, then you should fill the prescription at a legitimate pharmacy (for humans) and take it exactly as directed by your doctor.

Of note, ivermectin can be a useful medicine in humans. The most dramatic example I can think of is that it is used in preventing river blindness [2] which caused by parasitic infection and which can be a major cause of blindness in some places.

Of course ivermectin has side-effects, some serious. Also, like many medicines it can be a poison in excessive doses.

Regarding the conspiracy theories:

All doctors I know (and the FDA too) would have loved for it to be an effective treatment for covid-19. Multiple trials were done at with money, effort and patientâ(TM)s health put on the line. I believe the FDA even gave emergency authorizations for these trials. [can someone check this]

Unfortunately, it turned out that ivermectin didnâ(TM)t work to improve patients health in any of the well designed trials.

Doctors can use medicines âoeoff labelâ . But the ethics and practice of such off-label use varies widely based upon the context. It is a complicated issue.

This article by Furey and Wilkins in the AMA Journal of Ethics [3] had a nice summary statement:

> Off-label prescribing is a common and legal practice in medicine. This practice is justified when scientific evidence suggests the efficacy and safety of a medication for an indication for which it does not have FDA approval and when the practice is supported by expert consensus or practice guidelines. Through shared decision making, patients and families are equal partners in clinical decision-making processes, which can help a physician carefully weigh risks and benefits of a given treatment according to the patientâ(TM)s unique circumstances.

References:
[1] fda advisory that the tweet links to : https://www.fda.gov/consumers/consumer-updates/why-you-should-not-use-ivermectin-treat-or-prevent-covid-19

[2] river blindness (onchocerciasis) https://www.cdc.gov/parasites/onchocerciasis/treatment.html

[3] Furey and Wilkins. 2016. Prescribing âoeOff-Labelâ: What Should a Physician Disclose? AMA Journal of Ethics https://journalofethics.ama-assn.org/article/prescribing-label-what-should-physician-disclose/2016-06#:~:text=Off%2Dlabel%20prescribing%20is%20a,expert%20consensus%20or%20practice%20guidelines.

Abstract

Big data and large-scale machine learning have had a profound impact on science and engineering, particularly in fields focused on forecasting and prediction. Yet, it is still not clear how we can use the superior pattern-matching abilities of machine learning models for scientific discovery. This is because the goals of machine learning and science are generally not aligned. In addition to being accurate, scientific theories must also be causally consistent with the underlying physical process and allow for human analysis, reasoning, and manipulation to advance the field. In this paper, we present a case study on discovering a symbolic model for oceanic rogue waves from data using causal analysis, deep learning, parsimony-guided model selection, and symbolic regression. We train an artificial neural network on causal features from an extensive dataset of observations from wave buoys, while selecting for predictive performance and causal invariance. We apply symbolic regression to distill this black-box model into a mathematical equation that retains the neural networkâ(TM)s predictive capabilities, while allowing for interpretation in the context of existing wave theory. The resulting model reproduces known behavior, generates well-calibrated probabilities, and achieves better predictive scores on unseen data than current theory. This showcases how machine learning can facilitate inductive scientific discovery and paves the way for more accurate rogue wave forecasting

Tomorrow and Tomorrow and Tomorrow is one of my favorite reads of the last year.

Recommend for you if you like old video games, Hamlet, stories of collaboration and non-romantic love and musings about the nature of creativity and art.

Want a GPU accelerated terminal and not a GUI interface to the command line?
Might try kitty. https://sw.kovidgoyal.net/kitty/

This is what I think of when I think: "Terminal emulator innovation"

What a terrible inflammatory title for an article. There are many measures of employment/unemployment because each one of the available ones is incomplete. The one cited is useful and historically fairly consistent so it is possible to make some meaningful comparisons from year to year. At least it is more useful than many other cited numbers like the Dow.

As an example, here are 6 measures for the USA: https://www.bls.gov/news.release/empsit.t15.htm