orting

> -I don’t know whether their policy differs from country to country

Would not be surprised if it did. I remember one rental in the Netherlands where the english text said free parking, but the dutch text said parking available. The problem was AirBnBs translation and they refunded my parking expenses.

A month is not a well defined unit. There are four different month units: 28, 29, 30, 31 days. If you do calculations respecting this there will be no problem.

Complex numbers and vectors are not a good analogy.

This is a good point. From the article "Once the students were selected, the researchers then administered the Major Field Test in Computer Science, an exam that was developed by the U.S. Educational Testing Service and is regularly updated. The exam was translated for the students in China and Russia."

It seems reasonable to expect a test written for a specific education system (either implicitly or explicitly) will be biased against others.

On the other hand, it is not that surprising that the US is a the top. It is a pretty big country with a long tradition for high quality education and a lot of funds going into CS departments. However, I will be surprised if this is not changing towards more dominance by India and China, because both countries are focusing a lot of resources in this area.

I am not criticizing the study. I am highlighting that the conclusion that the paper arrives at i the correct one: That it warrants large scale studies.

This work is a prime candidate for being misrepresented as showing that this stem cell treatment is effective for age related health issues.

There are 30 participants in the phase 2 trial. There are two treatment groups (100M and 200M) with different dose and one placebo group. Each group has 10 participants.

None of the treatment groups showed adverse effects.

There is a difference between asking "Are there any adverse effects?" and "are there positive effects for parameter 1 to n"? If you ask the second kind of question and do not correct for multiple hypothesis testing, you will make many errors.

The small dose treatment group (100M) showed improvement in many parameters vs placebo, whereas the other (200M) showed improvement in fewer parameters vs placebo. Since no corrections where made for testing, this only tell us that there where no statistically significant adverse effects.

As I noted initially, I think it is interesting. Once we have seen the results of a couple of large studies, we can talk about the effects of this treatment.

The phase 2 study primarily looked at safety. There was no correction for multiple hypothesis testing on the efficacy endpoints. So it seems that the only conclusion that is warranted is that the study shows no adverse health effects and that "larger clinical trials are warranted to establish the efficacy of hMSCs in this multisystem disorder." as they state in the conclusion.

It is interesting if it works, but lets wait for the next phase before assuming it does.

Basically we can represent any signal as an infinite sum of sinusoids. If you know about Taylor expansion of a function, then you know that the first order term is the most important, then the second and so on. Same principle with the sinusoids. So if we remove the sinusoids with very high frequency we remove the terms with least information.

The first thing I noticed was the ringing, which is an artifact of low-pass filtering so it's a nice opportunity to go into problems with that kind of filtering. Other than that I think it was an ok teaser that gives an idea of how compression is done and what the trade-offs are.

As with a lot of "simple" math, the trick is to actually write it down and calculate it, because our intuition (at least for some of us) is often not the best when it comes to this kind of calculation.

In this case we write down the contingency table. Assuming that the test perfectly detects what we are looking for we find

True positives: 1

False positive: 5% of 1000 = 50

True negative: 949

False negative: 0

Chance of disease given positive results = 1/51 = 1.96%

Forcing a nation is what war is about, and it rarely works out very well.

Negotiation is about finding a solution to a problem that leaves all parties better off if they follow the solution than if they don't. It is not always easy and sometimes coercion, in the form of sactions within EU and UN, is used to make one party realize what is best for them - but this also tends to work out not very well.

Not forcing people to do what you want is often a more succesful way of getting what you need.

There is a lot of stuff the human eye cant see that is very interesting. One of the challenges in medical imaging is getting accurate labelling of images. For many labellings we see large inter- and intra-observer variability. We have both the problem that humans see something that is not interesting and miss something that is interesting.

I currently work on estimating emphysema extent in CT lung scans. Emphysema can be very diffuse and it is not possible to label individual pixels, so instead we try to learn the local emphysema pattern from a global label. Neural networks are interesting for this problem because the learn the features, but it is also a "problem" because the features might not make physically sense, which could make it hard to transfer the model and convince clinicians that they should use it.

> It is not a question of if but when

You can say that about almost anything, and the world is still full of factory workers.

As a PhD student in medical imaging, you must also know that getting fully automating segmentation methods to work to the standard required in the clinic is really hard. And once you solve it for one clinic you will likely not be able to transfer the trained model to another clinic, because scan parameters, patients and workflow are different.

But when we solve the segmentation task, I think most radiologist will clap their hands and move on.

Stating it as "thousands of patient images a day" is misleading. It would be the same as saying you inspected "thousands of parts each day". As the radiologist further down notes, CT scans contains many slices.

While computers don't get tired, they also have a really hard time solving stuff like annotation tasks automatically. One thing is getting a good enough general performance, another is to never make critical errors. I see a huge potential for ML approaches in health care, but primarily as an aid for the health care professionals and not as a full replacement.

I am curious. Could you highlight some of the technical problems with Firefox?