To_Mars_and_Beyond

Assuming the God doesn't exist, there may still be truth to the Biblical story of the Deluge. It seems unlikely to me that the ancients would have observed the sea level changes, so we need to rule out a global explanation. The stories of the Deluge, found in many cultures, could originate from a localized flood. One options is a legendary spring flood. Another one is a Tsunami. I personally find this the most convincing explanation.

The world energy consumption is some 150,000 TWh. The average solar irradiance at the surface level is 163 W/m^2. Assuming, quite arbitrarily, that we can harvest 10% of that by using the photovoltaics aka. solar cells, we can calculate how much solar panel would be needed to satisfy the world energy demand. The answer is 1.05 million square kilometres. While that sounds like a lot, it must be compared to the total surface area of the Earth, 510 million square kilometres. It may also be compared to the the area of the Australian desert, 1.37 million sq-km, or the area of the Saharan desert, 9.2 million sq-km. So, is the future solar? Isn't solar energy the silver bullet to climate change? Topics to discuss: Has anybody suggested offshore solar? In which countries is the solar energy really a feasible and economic option? How much do you expect the price of the solar panels to decrease and/or the efficiency to increase? Opinions welcome.

Which shows that you're a professor! 😄 Seriously though, I think you can make justified generalizations between different professional groups. Mine are just based on personal observations but you might do this in a more scientific way... Anyway, my favorite group to work with are in fact medical doctors. They're like "do what you want, just give me the estimate by Monday". That's probably because they don't see any geek as their competitor, and that's why they can afford to be nice. Why O why did I ever leave the hospital...? Another explanation is that they know how real mistakes look like - so they don't bitch about italics and Oxford commas. Which is one thing I find super frustrating about nerds in general, not just professors.

I have never known a nice professor. Or maybe I have known some, but I have never worked with one. This rant comes to my mind because I'm right now waiting for an email from a professor. I wrote him two days ago and I'm super certain that he's read my email, because frankly, academics refresh their email five times a day (because they find their career more interesting than anything on the smartphone). He's not the first professor or PI to do so. I've known a couple who have done the same. They just don't answer emails because a) the person annoys them, b) they find the person uninteresting, c) some sadistic reason. I find this really f*** irritating. When I was a kid, I was thought that you can say yes or no, depending on what you think. And this is of course just the peak of the iceberg. Typically, people who make it to the professor level backstab, twist the rules, lie and conspire all the time. It seems that the real motivation for most of them is to get just more and more money. But if they are that way, why are they in the academia in the first place? I wonder if other people have similar experiences?

In my books P(E)=1 means infinite odds. 1) I'm ok with that. 2) To me is that we have a frequentist estiamate P(E)^=1 and we admit that we don't believe in our own estimate. 3) To me is just lazy thinking.

Oh but yes. So you still can change individual components of the photon's velocity. So you would have vx^2 + vy^2 + vz^2 = c^2. That eats up one degree of freedom, but you get an extra one from the change of frequency. Nice.

Yeah nice. The blurring of spectral lines is a good demonstration that HUP is real. (Had I thought about this before my spectroscopy lab, it would have been one of the great revelations of my life, like seeing Andromeda or human karyotype for the first time!) Non-quantum uncertainty associated with certain classical phenomena... Do you mean turbulence? Chaotic systems?

Save your efforts, @studiot, I have seen too many Bayes textbooks. I'd be happy to hear your three interpretations of probability. In statistics, you have typically the two explanations: A frequentist interpretation means that if you repeat the same experiment infinitely many times you will get a proportion P (so think of your coin toss, you'd always get heads). A subjectivist interpretation means that probability is our measure of ignorance. In Kolmogorov axioms, probability is just a measure defined on some abstract space and it induces measures on all variables of interest, be they vectors or scalars. So probability is just some number, and there's no point in asking what it really is or how it relates to the real world. Are these your three interpretations?

Let us imagine we have a huge classical body moving in a vacuum and we want to measure its position and velocity. Is it better to bombard this body with electrons or photons? My uneducated guess from a layman: With electrons because they can convoy more information than photons. Both electrons and photons can be observed at specific places at the sensor (or bubble chamber or whatever), but electrons will change their velocity as a result of the collision. So you have 3d data: change of x velocity, change of y velocity, change of z velocity. But photons only change their frequency as a result of the collision. So you have only 1d data! Am I right?

Thank you guys! This is awesome! So measurement effect and HUP are two distinct phenomena. If what my teacher said was true, you couldn't actually measure a classical system precisely. And that seems unlikely.

This is true. In extremely small samples, you can't do classical aka. frequentist analysis. Another example is a coin toss. If you toss a coin only once and get heads, classical statistics says that the probability of heads is 1.00 and there's no uncertainty to it. But are this kind of examples really very realistic? Who would do serious statistical analysis from 1, 2 or 3 observations? And let us think of your New York example again... As you say, there's no data, so aren't you just analyzing your prior? I'd say this is more sort of probability, not statistics. As both of you say, @Prometheus and @studiot, both Bayesian and classical statistics work on probability and are consistent with Kolmogorov axioms. The difference in nutshell: Bayesian statistics: Parameter has a prior distribution p(a). After observing data, it has a posterior distribution p(a|y). Classical statistics: The parameter has a true value a*. We can calculate from data an estimator a^ which tends to the true value, subject to certain assumptions. In practice, the estimator a^ will have some distribution p(a^). We work with this distribution in just the same way as we would with the posterior p(a|y), i.e. we calculate confidence intervals. My point is that calculating the classical confidence intervals is much faster and more reliable than doing the MCMC to analyze the posterior.

Yes you can, for a girl. A girl has two X chromosomes, one inherited from the mum and one from the father.

At school, we were teached that you can understand Heisenberg's uncertainty principle by considering the measurement event: To measure the particle, you have to interact with it, and this is typically done by throwing a photon to it. Even a single photon will give some momentum to the particle, and thus, the state of the particle is not same anymore, and thus, you can never measure it exactly. I have become to question this explanation. Let us think in purely classical terms. We have a target particle with disposition x (a 3d vector) and velocity v (also a 3d vector). So, we have six unknowns. We poke it with a photon whose disposition changes by dx (a 3d vector) and the photon's wavelength changes by df (a 3d vector). So, we have six equations. Problem solved??? Or, maybe just use two photons? My question: Aren't there sufficient degrees of freedom in particle collisions to solve the state of the target particle exactly? Was my physics teacher kidding me?

Supposing you are not trolling: There's a measure called the kinship coefficient. It is defined as the probability that a gene sampled from person A is of the same origin as the gene sampled from the same location of person B's genome. A few examples: You have kinship 50% with yourself, assuming that your parents are completely unrelated. (You either pick the same gene copy twice or two different ones, so it's 50/50.) You have kinship 25% with your mum. To get the gene of same origin, you have to pick you maternal gene copy (50% chance) and you have to pick the copy your mum has given you from her genome (also a 50% chance). You have 25% kinship with your bro. Say, you pick your paternal gene copy from your genome. It's a 50% chance that you pick the paternal copy also from your bro, and it's a 50% chance that your dad has given the same copy to the both of you. So, you see that it turns out a very interesting probability exercise. I'm sure you can work it out for the inbred family you mention. (Inbreeding is way more common than people believe.) Drawing the pedigree will help!

All I remember from my freshman biology is that aging is caused by the exhaustion of telomeres. So there's a number of N cell divisions each cell line can go by and then it stops dividing, and this is a safeguard against cancer. So, if you want to live old, you should either have few cell divisions (slow metabolism, little food) or long telomeres by genetic heritage. So, in this sense aging is not a genetic function. It's not like you turn on some aging genes, and therefore, you can't avoid it by methylating the 'aging genes'. However, I may be wrong. There may be some genes that affect the telomerase enzyme and the epigenetic modification of these genes could hinder aging, up to a certain limit. But I'd still say that the number of cell divisions is the most significant factor. (One thing I hate about biology is that there's an exception to everything and the 'truth' presented in the college textbooks is never the full story. I guess some people find this adorable?)