Why don't we breathe nitrogen when it makes up most of the air?

Question: Why don't we breathe nitrogen while it makes most of the air?
Why do we always tend to breathe oxygen, not hydrogen and nitrogen?
Answer: Animals use oxygen as a chemical energy source because oxygen gas can react with many other compounds to form oxides, which releases energy and happen spontaneously.
Both carbon and nitrogen can be made to react with oxygen, but otherwise they are pretty inert.  So of all the gasses in the air present at over a fraction of a percent, oxygen is the only one we can use for energy.
Nitrogen gas itself (N2) is incredibly chemically inert; N2 requires energy input into it to react chemically.  Biometabolism relies upon a chemical release of energy.
If we had ammonia gas (NH3) in our air it would be a great redox source of energy... taking energy from the ammonia could produce N2.   N2 takes a lot of work put into it to get the nitrogen out for other uses.
Hydrogen (and sulfer) are both possible substitutes for oxygen in the role of redox energy source, but are normally pretty small components of our environment.  On another planet they might well be the basis of biometabolism.
Of course the fact that plants can use carbon dioxide to fix carbon is a different case of biology using a gas out of the air.  Its the defining quality of plants!
The energetics of using CO2 is endothermic - it requires energy input. They have to use sunlight to get the energy to utilize this energy and its very costly energetically.  Animals can afford to move and grow because they use oxygen while they eat plants.
Comment: I wouldn't call oxygen an "energy source". It's an electron acceptor.  But the rest of the first sentence, "because oxygen gas can react with many other compounds to form oxides, which releases energy" is spot on. +1
Comment: @shigeta, superb answer. I know you tried to keep the answer simple for our benefit, but could you please edit the answer to include the relevant scientific concepts (an equation perhaps). What does *inert* mean in chemistry? Does it mean for example that a chemical reaction involving an inert element/compound has a higher *activation energy*? Gracias.
Comment: @shigeta, ok, arigato gozaimus. A quick question. What makes a molecule reactive as opposed to inert? Is it the electron configuration? Also *endothermicity* is a good explanation.
Comment: Sure.  @AgentSmith its about the electron configuration around the nucleus of a given charge.  this is one of the foundational bases of chemistry.  some chemicals react, others do not.  why is a pretty deep question... far beyond a single answer here. https://www.coursera.org/learn/intro-chemistry this course is free at the moment
Comment: try doing without it for a few minutes and see what happens to your energy :) combustion engines, forest fires and thermite reactions could also be called 'electron transfer' reactions. People falling off of tall buildings could also be called 'release of potential energy events' but also i wouldn't expect the use of language to conform to freshman chemistry just because there is different language for the same thing.
Comment: ok @AgentSmith i made an effort though discoursing into freshman chemistry is something i typically try to avoid
Answer: I'd argue that we do "breathe" all those gases. Air that we inhale (at sea level) is about 78% N$_2$, 20.9% O$_2$, 1% argon, and smaller percentages of CO$_2$, neon, methane, etc. So all those gases are going into the lungs with every breath in.
We take up oxygen preferentially because we have hemoglobin to bind O$_2$. When hemoglobin binds the oxygen, it upsets the balance and pulls more oxygen across the alveolar membrane. This is aided by pulmonary circulation which carries the blood away. Here's a demo of the diffusion process.
Comment: I would argue that your argumentation isn't correct. Saying we have hemogobin to transport oxygen is like saying we have amino acids to build proteins. Your answer describes how the oxygen gets transported but not what the actual goal of this transport is. This is mainly because  oxygen is extremely electronegative and therefore important to form water  during oxidative phosphorylation
Comment: Nitrogen dissolved in the blood and pressurized during an underwater dive can, during a return to the surface, bubble out of the blood, just like the release of pressure opening a bottle of a carbonated drink causes bubbling.  In human divers, it can causes the painful  and potentially lethal condition called [decompression sickness or "the bends"](http://en.wikipedia.org/wiki/Decompression_sickness).
Comment: Completely agree with @RickBeeloo,  this answer gets the causality all wrong. We don't breathe O2 *because* we have hemoglobin --- we have hemoglobin because we breathe O2!
Comment: You seem to be saying that nitrogen does not get absorbed into the blood. Are you, in fact, saying that? I came here because I am trying to learn whether humans can survive with trace quantities or even no nitrogen in the atmosphere.
Answer: Nitrogen is much less reactive than oxygen.  Indeed, if I haven't totally forgotten my long-ago chemistry courses, most chemical reactions involving N2 are energy-consuming.  Thus you get nitrogen compounds produced by lightning, in auto engines, and other places where there's a lot of energy to spare.
Oxygen reactions, OTOH, are energy-producing.  You might think instead of fire: most organic stuff will burn (if dried), but it only combines with the oxygen in the air, not the nitrogen.
PS: Indeed, many nitrogen compounds take so much energy to create that they are explosives.  Ammonium nitrate, nitroglycerin, trinitrotolulene (TNT), even the potassium nitrate (saltpeter) used to make gunpowder.
Comment: You can focus on the fact that dinitrogen (N₂) is very stable and is not a good electron acceptor. Many anaerobic organisms can use metal ions as electron acceptors instead of oxygen.
Answer: The bond in oxygen molecules is high energy, and ready to undergo an energy-yielding reaction with other molecules like sugar.
The bond in nitrogen not chemical useful to us...other organisms use energy to "fix" nitrogen to make energy rich nitrogen compounds that we can use.
Answer: The other answers seem to be missing the role of oxygen in oxidative phosphorylation, as an organism with aerobic metabolism we use oxygen for its electronegativity. Basically, as we break down glucose energy is released in the form of free electrons, these are "transported" for use in the oxidative phosphorylation to create new ATP which is our main form of energy storage. Oxygen eagerly accepts these free electrons at the last step of the oxidative phosphorylation and binds with H+ to form H2O. See here.
So without oxygen there would be an accumulation of electrons, stopping the oxidative phosphorylation and forcing us to break down glucose in the (less efficient) anaerobic manner.
Answer: There are two parts to this answer, and several answers have addressed one or both aspects but I figured I'd put it all in one place.
1) We use oxygen for a purpose that nitrogen is chemically useless for
2) While there is a different purpose we might want to use nitrogen for, it is something that is difficult to evolve (only bacteria have done it) and we can manage without.
Explanations:
1) We use oxygen because our metabolism uses it for energy. Our metabolism derives chemical energy from the breakdown of complex carbon molecules; this doesn't happen on its own and you need very reactive molecules to interact with those complex carbon molecules and break them down. All organisms do this step-by-step, using successive "electron acceptors" to basically strip electrons off of simpler-and-simpler molecules and thus break them down. Molecular oxygen is the most reactive molecule and greedy electron acceptor out there, and allows organisms that use it to get the most energy possible out of a given carbohydrate. That's why aerobic respiration is so useful, and that's what we use oxygen for. Molecular nitrogen has completely different chemical properties; it isn't that electronegative (i.e. greedy for electrons) at all. There are other molecules that can be used as electron acceptors, and are used in various forms of anaerobic respiration: nitrate, sulfate, carbon dioxide... but molecular nitrogen isn't one of them.
For various kinds of anaerobic respiration, see :
https://en.wikipedia.org/wiki/Microbial_metabolism
2) There IS a purpose for which one could use molecular nitrogen, which is to use it to build nitrogen-based molecules that our body depends on - like DNA, RNA and proteins, which basically do everything in a living organism. No organism uses molecular nitrogen as a source for these; it's much easier to use organic nitrogen compounds like nitrates and ammonia. It can seem silly that such compounds are so limiting, when nitrogen makes up most of the atmosphere! This is less of an issue for carnivores since we get all of our nitrogen needs from eating nitrogen-filled animals, but it's a huge issue for plants. The need for such compounds (and, to a lesser extent, phosphates) is why agriculture needs fertilizer. So why can very very few organisms break down molecular nitrogen ? Because it is a very stable molecule; if you've done chemistry you might know that the two nitrogen atoms in the nitrogen molecule are connected by a triple bond, which is very strong and hard to break. This may be a big reason why the metabolism to break that bond evolved only in bacteria, and all Eukaryotes get by using the bacteria themselves (nitrogen-fixing plants), absorbing nitrogen-filled organisms (carnivores, carnivorous plants - it's the reason they're carnivorous!) or getting by on the organic nitrogen that naturally occurs in the ground thanks to nitrogen-fixing bacteria.
https://en.wikipedia.org/wiki/Nitrogen_fixation
As an aside, the fertilizer humans make uses the Haber process, which converts molecular nitrogen in the atmosphere to ammonia. If you look at the Wikipedia page by the way you'll get an idea of how hard it is to break that triple bond, between the catalysts and the high temperatures and pressures... But through that process you could argue that humanity as a species does "breathe" nitrogen.
https://en.wikipedia.org/wiki/Haber_process
So basically, tl;dr:
1) we don't need to breathe nitrogen
2) if we did our bodies still wouldn't because it's really hard to do; no eukaryote does it, except maybe humans themselves but only through technology.
Answer: As others have pointed out, we do breathe atmospheric nitrogen but we cannot do anything useful with it.
The problem is that the triple-bonded N2 is very unreactive and almost all animals and plants cannot convert it to anything else. Humans do not have the ability to reduce it to NH3, for example, but is would be great if we could.
All forms of life need nitrogen. It is necessary to make protein and DNA, for example. It is also very plentiful. N2 constitutes about 78% of air by volume, but it is in a form (N2) not usable by most forms of life. And therein lies the problem: in order to use atmospheric nitrogen, it needs to be 'fixed', ie converted to a form available to 'normal' metabolic transformation.  This is usually taken to mean that N2 needs to be converted to NH3.  Very few forms of life have the ability to do this.  The 'fixation' of N2 is also a great industrial problem.
As outlined by Rozenn Keribin, the first artificial process to successfully 'fix' N2 is the Haber-Bosch process, which was developed in the early part of the 20th century, and uses a metal catalyst and high pressures to achieve the following transformation:

N2 + 3 H2 → 2 NH3

This process remains the main industrial source of NH3 today, and is a 'mainstay' of the fertilizer industry. During the First World War, it was also an source of NH3 for the production of ammunition by Germany. For his work in this area, Haber was awarded the 1918 Nobel prize in Chemistry, a (controversial) honour that has surely stood the test of time.
Biological N2 fixation is an amazing story, and I'll restrict the discussion to an amazing enzyme: nitrogenase. This enzyme catalyzes the ATP-dependent reduction of N2 to NH3
(I will not deal with  leguminous plants, which can also 'fix' N2 using a symbiotic relationship with bacteria in root nodules, as I do not know enough about it).

In nature, [the] ability to fix N2 is restricted to a small
  but diverse group of diazotrophic microorganisms that contain the enzyme nitrogenase (Burgess & Lowe, 1996)

One type of nitrogenase (that contains molybdenum) catalyzes the following reaction:

N2 + 8 H+ + 8 e- + 16 ATP → 2 NH3 + H2 + 16 ADP + 16 Pi

Let's analyze this one: that's an eight-electron reduction that uses 16 ATPs just to make two  NH3 from one N2 !
Nitrogenases may be classified into 3 general types depending on metal content: Molybdenum nitrogenase, vanadium nitrogenase and iron-only nitrogenase. (All forms contain iron).
Biological nitrogen fixation was discovered by Martinus Beijerinck and t Hermann Hellriegel.  In addition, Beijerinck discovered that tobacco mosaic disease was caused by a virus.  Neither received a Nobel prize.
Ref
Burgess, B,K. & Lowe, D. J. (1996) Mechanism of Molybdenum Nitrogenase
Chem. Rev. 96, 2983−3011
Answer: Basically when air fills our alveoli, by the process of diffusion, only oxygen in the air is taken into the blood stream while the other gases along with the waste CO2 is exhaled. So you do breathe in nitrogen, but it is exhaled as it is by the body. The whole process of the respiratory system is explained here with diagrams.
Comment: Nitrogen and CO2 are also taken in. All gases dissolve and diffuse across the alveolar membrane


Move a card from one board to another board without compromising existing comments

Question: When I transferred a card from one board to another it loses all the comments made up to that point.  It still shows "7 comments were made on this card" in the icon at the top but the only comment that actually shows is the comment that it was transferred from another board.
Answer: In ACTIVITY you can find "X transferred this card from XXX". Click on XXX to read the original comments.
It is not comfortable, but it is the only way I found to have a look at these lost comments.


Bayesian and frequentist reasoning in plain English

Question: How would you describe in plain English the characteristics that distinguish Bayesian from Frequentist reasoning?
Comment: Perhaps some of you good folks could also contribute an answer to a question about Bayesian and frequentist interpretations that is asked over at [philosophy.stackexchange.com](http://philosophy.stackexchange.com/questions/8275/interpret-bayesian-probability-as-frequentist-probability).
Comment: [This question](https://stats.stackexchange.com/questions/21439/estimating-probability-of-success-given-a-reference-population) about drawing inferences about an individual bowl player when you have two data sets - other players' results, and the new player's results, is a good spontaneous example of the difference which my answer tries to address in plain English.
Comment: I have provided an answer to this question on another thread, [Bayesian vs frequentist interpretations of probability](https://stats.stackexchange.com/questions/31867/bayesian-vs-frequentist-interpretations-of-probability/503079#503079).
Answer: Here is how I would explain the basic difference to my grandma:
I have misplaced my phone somewhere in the home. I can use the phone locator on the base of the instrument to locate the phone and when I press the phone locator the phone starts beeping.
Problem: Which area of my home should I search?
Frequentist Reasoning
I can hear the phone beeping. I also have a mental model which helps me identify the area from which the sound is coming. Therefore, upon hearing the beep, I infer the area of my home I must search to locate the phone.
Bayesian Reasoning
I can hear the phone beeping. Now, apart from a mental model which helps me identify the area from which the sound is coming from, I also know the locations where I have misplaced the phone in the past. So, I combine my inferences using the beeps and my prior information about the locations I have misplaced the phone in the past to identify an area I must search to locate the phone.
Comment: It should be pointed out that, from the frequentists point of view, there is no reason that you can't incorporate the prior knowledge *into* the model. In this sense, the frequentist view is simpler, you only have a model and some data. There is no need to separate the prior information from the model.
Comment: I like the analogy. I would find it very useful if there were a defined question (based on a dataset) in which an answer was derived using frequentist reasoning and an answer was derived using Bayesian - preferably with R script to handle both reasonings. Am I asking too much?
Comment: The simplest thing that I can think of that tossing a coin n times and estimating the probability of a heads (denote by p). Suppose, we observe k heads. Then the probability of getting k heads is:

P (k heads in n trials) = (n, k) p^k (1-p)^(n-k)

Frequentist inference would maximize the above to arrive at an estimate of p = k / n.

Bayesian would say: Hey, I know that p ~ Beta(1,1) (which is equivalent to assuming that p is uniform on [0,1]). So, the updated inference would be:

p ~ Beta(1+k,1+n-k) and thus the bayesian estimate of p would be

p = 1+k / (2+n)

I do not know R, sorry.
Comment: As was commented already in 2010, from the frequentists point of view, there is no reason that you can't incorporate the prior knowledge into the model. Here an example of explicitly using informative priors in ferquentist reasoning: Using prior knowledge in frequentist tests. figshare.
https://doi.org/10.6084/m9.figshare.4819597.v3
 See also alternative definitions in other answers below.
Comment: @BYS2 The programming language called R.
Comment: The example is nice but should really start at the beginning;  Suppose you have no data ("no beeps"), could you make a probabilistic inference? Yes, you can, says the Bayesian, because you have prior knowledge about where you usually leave your phone (very likely) - but no, you cannot if you are a frequentist, since only data are random. -- It is here (I find) that one sees the "beauty" and consistency of Bayesian reasoning, because probabilistic inference without new data IS natural and the Bayesian nicely integrates how new data (beeps) should influence the inference.
Comment: @user28 - when you say "I do not know R", what are you referring to with the letter 'R'?
Comment: @user28  As a comment on your comment, if $n = 3$, then the frequentist would estimate $p = 0$ (respectively $p = 1$) upon seeing a result of $k = 0$ heads (respectively $k = 3$ heads), i.e., the coin is two-headed or two-tailed.  The Bayesian estimates $1/5$ and $4/5$ respectively do allow for the possibility that it is a somewhat less biased coin.
Comment: I recently posted a question about a Bayesian example that is similar to this one but slightly different in an interesting way.  A fisherman is lost at sea and the Coast Guard searches for him with a model and then updates the model as they conduct searches... so they are combining what their model predicts based on ocean currents/wind with the "prior information" of where they already searched. http://stats.stackexchange.com/q/119952/25734
Comment: @Farrel - the recent question at http://stats.stackexchange.com/questions/21439/estimating-probability-of-success-given-a-reference-population/21466#21466 and my answer in two parts (unintentionally) is a nice simple example of this.  It would be fairly easy to knock together an example dataset and R script showing the two approaches.
Comment: It should be mentioned that which model is chosen depends on the application. If a strict accuracy is desired, without caring about side effects, then Bayesian model should be used. If you want to keep a 'fair' distribution when taking actions based on inferencing, then the frequentist model may be necessary. For instance, it may be true that taking into account race for determining someone's guilt of a crime may increase accuracy in a probabilistic guess, it also creates greater false negatives and false positives for different races. Effects from actions taken will effect the distribution.
Answer: Tongue firmly in cheek:
A Bayesian defines a "probability" in exactly the same way that most non-statisticians do - namely an indication of the plausibility of a proposition or a situation.  If you ask them a question about a particular proposition or situation, they will give you a direct answer assigning probabilities describing the plausibilities of the possible outcomes for the particular situation (and state their prior assumptions).
A Frequentist is someone that believes probabilities represent long run frequencies with which events occur; if needs be, they will invent a fictitious population from which your particular situation could be considered a random sample so that they can meaningfully talk about long run frequencies.  If you ask them a question about a particular situation, they will not give a direct answer, but instead make a statement about this (possibly imaginary) population.  Many non-frequentist statisticians will be easily confused by the answer and interpret it as Bayesian probability about the particular situation.
However, it is important to note that most Frequentist methods have a Bayesian equivalent that in most circumstances will give essentially the same result, the difference is largely a matter of philosophy, and in practice it is a matter of "horses for courses".
As you may have guessed, I am a Bayesian and an engineer. ;o)
Comment: As a non-expert, I think that the key to the entire debate is that people actually reason like Bayesians. You have to be trained to think like a frequentist, and even then it's easy to slip up and either reason or present your reasoning as if it were Bayesian. "There's a 95% chance that the value is within this confidence interval." Enough said.
Comment: The key also is to think about what kind of lobbying has the statistics of the 20th century be called "classical" while the statistics that Laplace and Gauss have started to use in the 19th century are not...
Comment: Maybe I've been doing frequentist work too long, but I'm not so sure the Bayesian viewpoint is always intuitive. For example, suppose I am interested in a real world parameter of interest, such as average height of a population. If I tell you "there is a 95% chance the parameter of interest in the my credible interval", and then follow up with a question of "If we created 100 such intervals for different parameters, what proportion of them would we expect to contain the real values of the parameter?", the fact that the answer is **not** 95 must be confusing to some people.
Comment: @CliffAB but why would you ask the second question?  The point is they are different questions, so it is unsurprising that they have different answers.  The Baysian can answer both questions, but the answer may be different (which seems reasonable to me).  The frequentist can only answer one of the questions (due to the restrictive definition of probability) and hence (implicitly) uses the same answer for both questions, which is what causes the problems.  A credible interval is not a confidence interval, but a Bayesian can construct *both* a credible interval and a confidence interval.
Comment: My comment was in response to Wayne's; the idea that people "naturally" think in a Bayesian context, as it's easier to interpret a credible interval. My point is that while it's simpler to construct the right interpretation of a credible interval (i.e. less of a word soup), I think the non-statistician is just as likely to be confused about what that *really* means.
Comment: @CliffAB, ah, I sort of see, however if you think that way because you have been doing frequentist work too long, that rather suggests that your mode of thinking about probability is not your natural one, but one that you have learned and become accustomed to.  I don't think I agree with the conclusion, the usual misunderstanding of a confidence interval is precisely that of interpreting it as a credible interval, i.e. an interval likely to contain the true value with a given confidence.  Likewise the p-value fallacy arises from interpreting a frequentist test in a Bayesian way.
Comment: My point is more that I think only a statistician (Frequentist or Bayesian) would think that the statements "if X happens 1 in 100 times, the probability X happens is 1/100" and "probability is a measure of uncertainty" are not compatible statements. So it gets really tough to say one interpretation is more "natural" than the other. I think most people don't naturally think there is a distinction.
Comment: "As you may have guessed..." I actually thought that you were a frequentist, hahahaha!
Comment: @CliffAB the two statements are not incompatible within a Bayesian definition ( along run frequency is a reasonable way of representing an uncertain degree of belief where a long run frequency exists), but they are under a frequentist one, which rather makes my point that the Bayesian framework is more natural.  Note I am a "horses for courses" man, so there are some things more easily explained in frequentist terms than Bayesian ones.
Answer: Very crudely I would say that:
Frequentist: Sampling is infinite and decision rules can be sharp. Data are a repeatable random sample - there is a frequency. Underlying parameters are fixed i.e. they remain constant during this repeatable sampling process.
Bayesian:  Unknown quantities are treated probabilistically and the state of the world can always be updated. Data are observed from the realised sample. Parameters are unknown and described probabilistically. It is the data which are fixed.
There is a brilliant blog post which gives an indepth example of how a Bayesian and Frequentist would tackle the same problem. Why not answer the problem for yourself and then check?
The problem (taken from Panos Ipeirotis' blog):

You have a coin that when flipped ends up head with probability $p$ and ends up tail with probability $1-p$. (The value of $p$ is unknown.)
Trying to estimate $p$, you flip the coin 100 times. It ends up head 71 times.
Then you have to decide on the following event: "In the next two tosses we will get two heads in a row."
Would you bet that the event will happen or that it will not happen?
Comment: The *big* problem with that blog post is it does not adequately characterize what a non-Bayesian (but rational) decision maker would do.  It's little more than a straw man.
Comment: Since $0.71^2=0.5041$, I would regard this as close enough to an even bet to be prepared to go modestly either way just for fun (and to ignore any issues over the shape of the prior).  I sometimes buy insurance and lottery tickets with far worse odds.
Comment: At the end of that blog post it says "instead of using the uniform distribution as a prior, we can be even more agnostic. In this case, we can use the Beta(0,0) distribution as a prior. Such a distribution corresponds to the case where any mean of the distribution is equally likely. In this case, the two approaches, Bayesian and frequentist give the same results." which kind of sums it up really!
Comment: [Beta(.5, .5)](https://en.wikipedia.org/wiki/Beta_distribution) doesn't look at all like an appropriate prior for the _p_ of a coin. (I guess that's supposed to be an ["uninformative" prior](https://en.wikipedia.org/wiki/Prior_probability#Uninformative_priors) but it's not uninformative in the everyday sense, rather it's "opinionated" that p is near 0 or 1, an opinion that seems wrong.)
Comment: @tdc: the Bayesian (Jeffreys) prior is Beta(0.5, 0.5) and some would say that it is the only justifiable prior.
Comment: @mcb -- precise.
Comment: What does 'sharp' mean?
Answer: Let us say a man rolls a six sided die and it has outcomes 1, 2, 3, 4, 5, or 6. Furthermore, he says that if it lands on a 3, he'll give you a free text book.
Then informally:
The Frequentist would say that each outcome has an equal 1 in 6 chance of occurring. She views probability as being derived from long run frequency distributions.
The Bayesian however would say hang on a second, I know that man, he's David Blaine, a famous trickster! I have a feeling he's up to something. I'm going to say that there's only a 1% chance of it landing on a 3  BUT  I'll re-evaluate that beliefe and change it the more times he rolls the die. If I see the other numbers come up equally often, then I'll iteratively increase the chance from 1% to something slightly higher, otherwise I'll reduce it even further. She views probability as degrees of belief in a proposition.
Comment: I think the frequentist would (verbosely) point out his assumptions and would avoid making any useful prediction. Maybe he'd say, "Assuming the die is fair, each outcome has an equal 1 in 6 chance of occurring. Furthermore, if the die rolls are fair and David Blaine rolls the die 17 times, there is only a 5% chance that it will never land on 3, so such an outcome would make me doubt that the die is fair."
Comment: Couldn't the frequentist use a hypothetical David Blaine dice model and not necessarily a uniform fair dice model?
Comment: So would "likelihood" (as in MLE) be the frequentist's "probability"?
Answer: Just a little bit of fun...
A Bayesian is one who, vaguely expecting a horse, and catching a glimpse of a donkey, strongly believes he has seen a mule.
From this site:
http://www2.isye.gatech.edu/~brani/isyebayes/jokes.html
and from the same site, a nice essay...
"An Intuitive Explanation of Bayes' Theorem"
http://yudkowsky.net/rational/bayes
Comment: In which case, the wouldn't the frequentist be one who knows the ratio of donkey, mule and horse populations, and upon observing a pack of mules starts to calculate the p-value to know as to whether there has been a statistically significant increase in the population ratio of mules.
Comment: This doesn't answer the question at all.
Answer: In plain english, I would say that Bayesian and Frequentist reasoning are distinguished by two different ways of answering the question:
What is probability?
Most differences will essentially boil down to how each answers this question, for it basically defines the domain of valid applications of the theory.  Now you can't really give either answer in terms of "plain english", without further generating more questions.  For me the answer is (as you could probably guess)
probability is logic
my "non-plain english" reason for this is that the calculus of propositions is a special case of the calculus of probabilities, if we represent truth by $1$ and falsehood by $0$.  Additionally, the calculus of probabilities can be derived from the calculus of propositions.  This conforms with the "bayesian" reasoning most closely - although it also extends the bayesian reasoning in applications by providing principles to assign probabilities, in addition to principles to manipulate them.  Of course, this leads to the follow up question "what is logic?"  for me, the closest thing I could give as an answer to this question is "logic is the common sense judgements of a rational person, with a given set of assumptions" (what is a rational person? etc. etc.).  Logic has all the same features that Bayesian reasoning has.  For example, logic does not tell you what to assume or what is "absolutely true".  It only tells you how the truth of one proposition is related to the truth of another one.  You always have to supply a logical system with "axioms" for it to get started on the conclusions.  They also has the same limitations in that you can get arbitrary results from contradictory axioms.  But "axioms" are nothing but prior probabilities which have been set to $1$.  For me, to reject Bayesian reasoning is to reject logic.  For if you accept logic, then because Bayesian reasoning "logically flows from logic" (how's that for plain english :P ), you must also accept Bayesian reasoning.
For the frequentist reasoning, we have the answer:
probability is frequency
although I'm not sure "frequency" is a plain english term in the way it is used here - perhaps "proportion" is a better word.  I wanted to add into the frequentist answer that the probability of an event is thought to be a real, measurable (observable?) quantity, which exists independently of the person/object who is calculating it.  But I couldn't do this in a "plain english" way.
So perhaps a "plain english" version of one the difference could be that frequentist reasoning is an attempt at reasoning from "absolute" probabilities, whereas bayesian reasoning is an attempt at reasoning from "relative" probabilities.
Another difference is that frequentist foundations are more vague in how you translate the real world problem into the abstract mathematics of the theory.  A good example is the use of "random variables" in the theory - they have a precise definition in the abstract world of mathematics, but there is no unambiguous procedure one can use to decide if some observed quantity is or isn't a "random variable".
The bayesian way of reasoning, the notion of a "random variable" is not necessary.  A probability distribution is assigned to a quantity because it is unknown - which means that it cannot be deduced logically from the information we have.  This provides at once a simple connection between the observable quantity and the theory - as "being unknown" is unambiguous.
You can also see in the above example a further difference in these two ways of thinking - "random" vs "unknown".  "randomness" is phrased in such a way that the "randomness" seems like it is a property of the actual quantity.  Conversely, "being unknown" depends on which person you are asking about that quantity - hence it is a property of the statistician doing the analysis.  This gives rise to the "objective" versus "subjective" adjectives often attached to each theory.  It is easy to show that "randomness" cannot be a property of some standard examples, by simply asking two frequentists who are given different information about the same quantity to decide if its "random".  One is the usual Bernoulli Urn: frequentist 1 is blindfolded while drawing, whereas frequentist 2 is standing over the urn, watching frequentist 1 draw the balls from the urn.  If the declaration of "randomness" is a property of the balls in the urn, then it cannot depend on the different knowledge of frequentist 1 and 2 - and hence the two frequentist should give the same declaration of "random" or "not random".
Comment: It's too contested what it actually is, and too culturally specific.  "Common sense" is short hand for whatever is the perceived sensible way of doing things in this particular culture (which all too often looks far from sensible to another culture in time and space), so referring to it in a definition ducks the key questions.  It's particularly unhelpful as part of a definition of logic (and so, I would argue, is the concept of a "rational person" in that particular context - particularly as I am guessing your definition of a "rational person" would be a logical person who has common sense!)
Comment: He can't provide one, his argument is that _there is no universal definition_, only culturally-specific ones. Two people from different cultural backgrounds (and that includes different styles of statistical education) will quite possibly have two different understandings of what is sensible to do in a given situations.
Comment: This answer has nuggets of goodness (how's that for plain English?), but I don't believe (how's that for being a Bayesian!) that the following statement is true: "For if you accept logic... you must also accept Bayesian reasoning". For instance, if you think instead of translating the abstract theory of the mathematics into the real world, you'll find that the axiomatic approach can be consistent with both Frequentist and Bayesian reasoning! Arguably, Kolmogorov in the first case, and, say, Jeffreys in the second. In essence, it's the theory of probability that's logic; not its interpretation.
Comment: I'd be interested if you could rewrite this without the reference to common sense.
Comment: @PeterEllis - What's wrong with common sense?  We all have it, and it is usually foolish not to use it...
Comment: I fail to understand why using common sense is bad.  using your definition of it, why would we not want to do what is sensible at the time?  And what is the "key questions" that are being dodged?  you say common sense has no well defined meaning, and then go and provide one!
Comment: I like the last 3 paragraph, especially the last paragraph. This is also how I eventually convince myself that probability is a subjective belief, i.e., different people have different information set about an event and hence, they may have different belief about the likelihood of the occurrence of that event. I used that same example, the ball draw from urn with two people
Comment: This answer implies Kolmogorov probability is necessarily Bayesian but it is not necessarily.
Answer: The Bayesian is asked to make bets, which may include anything from which fly will crawl up a wall faster to which medicine will save most lives, or which prisoners should go to jail. He has a big box with a handle. He knows that if he puts absolutely everything he knows into the box, including his personal opinion, and turns the handle, it will make the best possible decision for him.
The frequentist is asked to write reports. He has a big black book of rules. If the situation he is asked to make a report on is covered by his rulebook, he can follow the rules and write a report so carefully worded that it is wrong, at worst, one time in 100 (or one time in 20, or one time in whatever the specification for his report says).
The frequentist knows (because he has written reports on it) that the Bayesian sometimes makes bets that, in the worst case, when his personal opinion is wrong, could turn out badly. The frequentist also knows (for the same reason) that if he bets against the Bayesian every time he differs from him, then, over the long run, he will lose.
Comment: What is the fundamental difference between a big box and a big rulebook? I cannot understand the analogy.
Comment: "over the long run, he will lose" is ambiguous. I assume 'he' is the bayesian here? Wouldn't they equal out over the long long run - the bayesian could learn and change his personal opnion until it matches the actual (but unknown) facts.
Comment: I need a "plain english" explanation of WHY if you bet against the Bayesian every time he differs from the frequentist that you would LOSE.  I will send $50 via paypal to the preferred charity of the first person to send an explanation that I can understand.
Comment: I think there are quite a few false dichotomies here - bets / reports as a metaphor for predictions / inferences. Bayesians and frequentists do both. That frequentist inference is truly agnostic is another fallacy. That frequentists control type 1 error and Bayes estimates are optimal for MSE broaden the picture so much, I can't imagine how you can describe one as "winning" or "losing" - they are merely two different tools for solving mostly the same problems, and the answers are often the same in well done designs.
Answer: In reality, I think much of the philosophy surrounding the issue is just grandstanding.  That's not to dismiss the debate, but it is a word of caution.  Sometimes, practical matters take priority - I'll give an example below.
Also, you could just as easily argue that there are more than two approaches:

Neyman-Pearson ('frequentist')
Likelihood-based approaches
Fully Bayesian

A senior colleague recently reminded me that "many people in common language talk about frequentist and Bayesian. I think a more valid distinction is likelihood-based and frequentist. Both maximum likelihood and Bayesian methods adhere to the likelihood principle whereas frequentist methods don't."
I'll start off with a very simple practical example:
We have a patient.  The patient is either healthy(H) or sick(S).  We will perform a test on the patient, and the result will either be Positive(+) or Negative(-).  If the patient is sick, they will always get a Positive result.  We'll call this the correct(C) result and say that
$$ P(+ | S ) = 1 $$
or
$$ P(Correct | S) = 1 $$
If the patient is healthy, the test will be negative 95% of the time, but there will be some false positives.
$$ P(- | H) = 0.95 $$
$$ P(+ | H) = 0.05 $$
In other works, the probability of the test being Correct, for Healthy people, is 95%.
So, the test is either 100% accurate or 95% accurate, depending on whether the patient is healthy or sick.  Taken together, this means the test is at least 95% accurate.
So far so good. Those are the statements that would be make by a frequentist.  Those statements are quite simple to understand and are true.  There's no need to waffle about a 'frequentist interpretation'.
But, things get interesting when you try to turn things around.  Given the test result, what can you learn about the health of the patient?  Given a negative test result, the patient is obviously healthy, as there are no false negatives.
But we must also consider the case where the test is positive.  Was the test positive because the patient was actually sick, or was it a false positive?  This is where the frequentist and Bayesian diverge.  Everybody will agree that this cannot be answered at the moment.  The frequentist will refuse to answer.  The Bayesian will be prepared to give you an answer, but you'll have to give the Bayesian a prior first - i.e. tell it what proportion of the patients are sick.
To recap, the following statements are true:

For healthy patients, the test is very accurate.
For sick patients, the test is very accurate.

If you are satisfied with statements such as that, then you are using frequentist interpretations.  This might change from project to project, depending on what sort of problems you're looking at.
But you might want to make different statements and answer the following question:

For those patients that got a positive test result, how accurate is the test?

This requires a prior and a Bayesian approach. Note also that this is the only question of interest to the doctor.  The doctor will say "I know that the patients will either get a positive result or a negative result.  I also now that the negative result means the patient is healthy and can be send home.  The only patients that interest me now are those that got a positive result -- are they sick?."
To summarize: In examples such as this, the Bayesian will agree with everything said by the frequentist.  But the Bayesian will argue that the frequentist's statements, while true, are not very useful; and will argue that the useful questions can only be answered with a prior.
A frequentist will consider each possible value of the parameter (H or S) in turn and ask "if the parameter is equal to this value, what is the probability of my test being correct?"
A Bayesian will instead consider each possible observed value (+ or -) in turn and ask "If I imagine I have just observed that value, what does that tell me about the conditional probability of H-versus-S?"
Comment: Do you mean `For sick patients, the test is NOT very accurate.` you forget the NOT?
Comment: It's very accurate in both cases, so no I did not forget a word.  For healthy people, the result will be correct (i.e. 'Negative') 95% of the time.  And for sick people, the result will be correct (i.e. 'Positive') 95% of the time.
Comment: I think the "weakness" in maximum likelihood is that it assumes a uniform prior on the data whereas "full Bayesian" is more flexible in what prior you can choose.
Comment: To complete the example, suppose 0.1% of the population is sick with disease D that we're testing for: this is not our prior. More likely, something like 30% of patients who come to the doctor and have symptoms matching D actually have D (this could be more or less depending on details such as how often a different sickness presents with the same symptoms). So 70% of those taking the test are healthy, 66.5% get a negative result, and 30%/33.5% are sick. So given a positive result, our posterior probability that a patient is sick is 89.6%. Next puzzle: how did we know 70% of test-takers have D?
Answer: Bayesian and frequentist statistics are compatible in that they can be understood as two limiting cases of assessing the probability of future events based on past events and an assumed model, if one admits that in the limit of a very large number of observations, no uncertainty about the system remains, and that in this sense a very large number of observations is equal to knowing the parameters of the model.
Assume we have made some observations, e.g., outcome of 10 coin flips. In Bayesian statistics, you start from what you have observed and then you assess the probability of future observations or model parameters. In frequentist statistics, you start from an idea (hypothesis) of what is true by assuming scenarios of a large number of observations that have been made, e.g., coin is unbiased and gives 50% heads up, if you throw it many many times. Based on these scenarios of a large number of observations (=hypothesis), you assess the frequency of making observations like the one you did,  i.e.,frequency of different outcomes of 10 coin flips. It is only then that you take your actual outcome, compare it to the frequency of possible outcomes, and decide whether the outcome belongs to those that are expected to occur with high frequency. If this is the case you conclude that the observation made does not contradict your scenarios (=hypothesis). Otherwise, you conclude that the observation made is incompatible with your scenarios, and you reject the hypothesis.
Thus Bayesian statistics starts from what has been observed and assesses possible future outcomes. Frequentist statistics starts with an abstract experiment of what would be observed if  one assumes something, and only then compares the outcomes of the abstract experiment with what was actually observed. Otherwise the two approaches are compatible. They both assess the probability of future observations based on some observations made or hypothesized.
I started to write this up in a more formal way:
Positioning Bayesian inference as a particular application of frequentist inference and vice versa. figshare.
http://dx.doi.org/10.6084/m9.figshare.867707
The manuscript is new. If you happen to read it, and have comments, please let me know.
Comment: I like this way of putting things very much. It is abstract but succint and clear. Thanks!
Answer: I would say that they look at probability in different ways.  The Bayesian is subjective and uses a priori beliefs to define a prior probability distribution on the possible values of the unknown parameters.  So he relies on a theory of probability like deFinetti's.  The frequentist see probability as something that has to do with a limiting frequency based on an observed proportion.  This is in line with the theory of probability as developed by Kolmogorov and von Mises.
A frequentist does parametric inference using just the likelihood function. A Bayesian takes that and multiplies to by a prior and normalizes it to get the posterior distribution that he uses for inference.
Comment: +1 Good answer, but it ought to be emphasized that the Bayesian approach and Frequency approach differ with respect to their _interpretation_ of probability. Kolmogorov, on the other hand, provides an _axiomatic foundation_ for the theory of probability, which _does not require an interpretation_ (!) like those employed by the Bayesian or Frequentist. In a sense, the axiomatic system has a life of its own! From Kolmogorov's six axioms alone, I don't think it's possible to say that his axiomatic system is either Bayesian or Frequentist, and, could, in fact, be consistent with both.
Answer: The simplest and clearest explanation I've seen, from Larry Wasserman's notes on Statistical Machine Learning (with disclaimer: "at the risk of oversimplifying"):

Frequentist versus Bayesian Methods

In frequentist inference, probabilities are interpreted as long run frequencies. The goal is to create procedures with long run frequency guarantees.
In Bayesian inference, probabilities are interpreted as subjective degrees of belief. The goal is to state and analyze your beliefs.

What's tricky is that we work with two different interpretations of probability which can get philosophical. For example, if I say "this coin has a 1/2 probability of landing heads", what does that mean? The frequentist viewpoint is that if we performed many coin flips, then the counts ("frequencies") of heads divided by the total number of flips should more or less get closer and closer to 1/2. There is nothing subjective about this which can be viewed as a good thing, however we can't really perform infinite flips and in some cases we can't repeat the experiment at all, so an argument about limits or long run frequencies might be in some ways unsatisfactory.
On the other hand, the Bayesian viewpoint is subjective, in that we view probability as some kind of "degree of belief", or "gambling odds" if we specifically use de Finetti's interpretation. For example, two people may come into the coin flipping experiment with different beliefs about what they believe about the coin (prior probability). After the experiment which has collected data/evidence and the people have updated their beliefs in accordance with Bayes' theorem, they leave with different ideas of what the posterior probability of the coin is, and both people can justify their beliefs as "logical"/"rational"/"coherent" (depending on the exact flavor of Bayesian interpretation).
In practice, statisticians can use either kind of methods as long as they are careful with their assumptions and conclusions. Nowadays Bayesian methods are becoming increasingly popular with better computers and algorithms like MCMC. Also, in finite dimensional models, Bayesian inference may have same guarantees of consistency and rate of convergence as frequentist models.
I don't think there is any way around really understanding Bayesian and frequentist reasoning without confronting (or at least acknowledging) the interpretations of probability.
Answer: I've attempted a side-by-side comparison of the two schools of thought here and have more background information here.
Answer: The way I answer this question is that frequentists compare the data they see to what they expected. That is, they have a mental model on how frequent something should happen, and then see data and how often it did happen. i.e. how likely is the data they have seen given the model they chose.
Bayesian people, on the other hand, combine their mental models.  That is, they have a model based on their previous experiences that tells them what they think the data should look like, and then they combine this with the data they observe to settle upon some ``posterior'' belief. i.e., they find the probability the model they seek to choose is valid given the data they have observed.
Comment: So, in other words, a frequentist looks at $P(data | model)$ whereas a Bayesian looks at $P(model | data)$...?
Comment: sorta. Bayesians essentially do a P(model|data) $\prop$ P(data|model)P(model), where P(model) is the prior.  The more I learn about this, the more my answer feels inadequate.  For ex, a hallmark of frequentist stats is maximum likelihood estimator, which is essentially given the data ive seen, which model parameters make what I saw most likely.  Bayesians also want this, but they calculate the model by integrating over all values of the parameter based on some prior distribution of it.  Frequentists pick a model parameter such that what they saw was most likely.
Answer: In short plain English as follows:

In Bayesian, parameters vary and data are fixed

In Bayesian, $P(\theta|X)=\frac{P(X|\theta)P(\theta)}{P(X)}$ where $P(\theta|X)$ means parameters vary and data are fixed.

In frequentist, parameters are fixed and data vary

In frequentist, $P(\theta|X)=P(X|\theta)$ where $P(X|\theta)$ means parameters are fixed and data vary.
References:

https://stats.stackexchange.com/a/513020/103153
https://math.stackexchange.com/a/2126820/351322


Can you save garlic powder that has clumped due to moisture?

Question: Every time I have garlic powder around, it always ends up forming into a hard sticky lump. Is there any way to reconstitute this into a usable form? Placing it in warm water seems to take forever and it never really dissolves.
Comment: It wouldn't dissolve, because it's ground up plant fiber with a little aromatic oil in it -- you'd end up with a paste at best, but most likely a watery, garlicky concoction you could totally cook with. To store, protect from humidity in an air-tight container in the fridge or freezer.
Comment: @goblinbox : fridge or freezer might make things worse unless it was devoid of air -- any air in the container would have moisture that would condense when it was chilled, adding moisture to the spices each time you used it.
Comment: Especially true in humid places like the Midwest. On the other hand, I live in Minnesota and my garlic powder, stored open and on the shelf, isn't clumped. Spice behavior mystery!
Comment: Reworded the title, "reconstitute" sounds as if you are trying to create a garlic puree out of the powder.
Answer: I've had this happen with other types of spices as well. I put it in the blender or coffee grinder to loosen it up again. Works well for me.
Comment: If it's really sticky try putting it in a dehydrator first.
Answer: You should make a beurre monte.
You need 4 tablespoons of water, and 1/2 cup of cubed butter (about 1 tablespoon per cube).
Bring the water to a boil and just as it does reduce the heat to a low-medium temperature, and start whisking in the butter cubes, whisk constantly and add the cubes one at a time.
Once you see the butter start to emulsify add in 4 tablespoons of the garlic powder, and continue adding butter until the entire 1/2 cup of cubed butter is used)
Strain the mixture through wet cheesecloth into a bowl.  Now you have garlic infused beurre monte.
Use it how need after that - it will be very strongly flavoured, use it sparingly.  You can refrigerate it if you like, if it looses the emulsification just soak up the water with some paper towels.
Comment: Also since its been passed through cheese cloth all the grainy garlic bits are removed as well.

Of course I'd suggest just buying some pureed garlic in a jar and keeping that in your fridge as an ultimate fix to the dry garlic issue
Answer: I have tried pouring oil in the garlic powder bottle/jar. And you have instant garlic oil for your noodles, roasts etc. Goes long way as oil acts like a preservative. How is that?
Comment: Oil doesn't act as a preservative, it only saves you from aerobic bacteria. Your chance of botulism is probably lower than with fresh garlic, but the combination still sounds dangerous.
Answer: Take the cap off the bottle of garlic powder. Put it in the microwave next to a cup of water. Run the microwave on defrost or thaw. Check the garlic powder after about 40 seconds. The garlic powder will loosen. Run microwave a little longer if needed.
Comment: I would say this is exacerbating the issue by adding more moisture to the powder. It is a short term solution but will have a negative impact in the long term. The powder is likely to mould if then kept any longer


Corrupted Grail Fragments inside Sakura, and Illya's heart

Question: Do the corrupted Grail fragments that Zokuen synthesised with his worms which were later placed in Sakura have a direct connection with Angra Mainyu inside the Grail? I know the fragments are the remnants of the Lesser Grail of the 4th HGW and are corrupted because Angra used the Lesser Grail to activate the Greater Grail and unleash his curses but can he control the fraagments after they were implanted inside the worms? And what about Illya's heart which is the Lesser Grail of the 5th war?
Answer:
Do the corrupted Grail fragments that Zokuen synthesised with his worms which were later placed in Sakura have a direct connection with Angra Mainyu inside the Grail?

yes, in the Visual Novel close to the end

 after Sakura realizes the love Rin has for her she starts trying to resist. when Shirou arrives after defeating Alter Saber he finds the "ribbons" that make up Dark Sakura's dress are now being used to wrap her up and restrain herr. since at this point Dark Sakura has already pruged all of Zouken's worm from her body the only force that could do this is Angra Mainyu, further enforced by the fact that they dispelled when Shirou stabbed Sakura with the traced Rule Breaker, severing the contract between her and Angra Mainyu

however she probably need to submit to Angra Mainyu as

 Dark Sakura doesn't appear until after she kills Shinji (to tried to rape and blackmail her) and realizes she was The Shadow

and up to then she didn't even want to fight in the war
however this is only the case because Sakura is a Master. in Fate and Unlimited Blade Works, as she gives it up to Shinji and in both cases Rider is considered defeated without Sakura's identity as her True Master being revealed one can assume that she had fully given up being a Master in these 2 routes while in Heaven's Feel she hadn't and it's only in this route does the Shadow appear. also during the period between the 4th and 5th wars Angra Mainyu didn't do anything with Sakura.

And what about Illya's heart which is the Lesser Grail of the 5th war?

supposedly uncorrupted. given that the main source of the corruption is the Greater Grail and the Einzbern knew of it's state (as it was Ilya who revealed Angra Mainyu's existence to Rin and Shirou) it would not be hard for them to create an uncorrupted lesser grail, however this would not stop them connecting to the corrupted Greater Grail as Irisviel's lesser grail connected to it, so i'm not sure how the Einzberns planned to get around Angra Mainyu to get the 3rd Magic (or maybe they would just accept the destruction because all they care about is reclaiming it)

Angra used the Lesser Grail to activate the Greater Grail and unleash his curses

actually he didn't. if he could his wish to be born could have been done without anyone getting in the way, but while he is not manipulating Kotomine he did need him to wish for his birth, and by his own will Kotomine wants Angra Mainyu to be born.
just like in Fate and Unlimited Blade Works, the connection between the lesser grail and greater grail is the reason for the black mud to be spilling out and in Fate/Zero the fire caused by the spilled contents is hinted to be because Kotomine, before dying, wished for humanity to die aswell

In Kirei's last moments, the Holy Grail appears to him in his mind. Filled with hatred and envy for Kiritsugu, Kirei reaches out and touches the cursed artifact. He prays for an end to humanity before his death and his wish is granted in the form of the fire.

Source: Avenger (Fate/Stay Night) > Role > Fate/Zero (6th paragraph)


What is a good Version Control GUI Client for Linux?

Question: I find there are too few for SVN, so I might switch to another. Right now I'm using RabbitVCS, an SVN nautilus integration. Very similar to tortoiseSVN but with bugs/beta/alpha feel. I need something much more stable, more features, and good for SOLO development. There will be no other team members in the future, so that needs to be taken into consideration.
Answer: Try SmartSVN
Answer: Subclipse for Ecplise isn't bad. There's also the prospect of using KDESVN, if you use KDE. Another suggestion would be using git-svn and maybe checking out some of their clients?
You might want to post to stackoverflow or search there for better answers.


OSX 10.6.7 VPN client only connects once

Question: I'm experiencing an odd problem when connecting from a MacBook Pro to a macMini runing OS X server.The client will only let me connect to the server once. If I disconnect, then I have to recreate the vpn adapter in the client. Anyone else seeing this?
Answer: I noticed the same issue on 10.6.8. I logged into VPN via the native IPSec VPN client, logged out, and could not log back in. I then switched to a 4.9.xx version of Cisco VPN Client, but was not able to log into the same VPN.
My friend tried the same but with a different VPN and was able to connection/disconnect/connect.
I think your issue is coming from either your computer's VPN settings, or more likely the VPN server settings.
============ UPDATE ================
Long after writing the my first response, I just wanted to confirm the root cause of the issue I was experiencing. The server side of the VPN server did not allow the same user to log-in within a 5 minute interval. I bet this is common, and therefore might be the same issue you are experiencing.


How do I change the field of view in the Crysis 2 Demo?

Question: I want to change Field of view in Crysis 2 demo to make it wider. Is there a console like in Counter-Strike where I can type a command, or is there another way to do it?
Answer: You sure can!  Heres how:
Open : //// C:Program Files/Electronic Arts/Crytek/Crysis 2/Demobin32/Crysis2Launcher.ini

Add the two lines :

Code:
con_restricted = 0
cl_fov = 80  (where this is your field of view setting)

another link
Answer: Create a shortcut for Crysis2Demo.exe; in its properties write your commands:
E:\Games\Crysis2Demo\bin32\Crysis2Demo.exe +cl_fov=85+r_DrawNearFoV=85 +pl_movement.power_sprint_targetFov=85 +g_skipIntro=1 +r_Glow=0

Answer: If you don't want to mess around with the nitty-gritty file details, there is a GUI-based launcher for Windows: http://www.incrysis.com/forums/viewtopic.php?id=33008.
It works for regular Crysis 2, at least.


prototype.js or jQuery for new projects?

Question: Should I use prototype.js or jQuery for a new project? Rails comes with prototype.js but jQuery seems the library of choice for the rest of the world. Is prototype.js still under active development? It seems a little bit dusty ...
Answer: Nothing against prototype but it does not have the amount of plug-ins or nearly as large of a community as jQuery does.
For jQuery there is already a plug-in for almost anything, so I end up writing a lot less js.
Answer: It's mostly a matter of preference. Some people don't like how Prototype extends objects, but for basic use they are fairly equivalent.
Prototype is still in active development - they put out a release candidate for the next release just a couple months ago.
Answer: The reasons I use jQuery over any other library:
Fast and Lightweight
jQuery continues to increase the performance of their library even in of minor-revision-level releases. When they released 1.4.2, the speed of the library was greatly increased even from 1.4.1, as shown in an Ajaxian blog post regarding its release. Performance analysis of 1.4 compared to 1.3 was done by the jQuery team in the release announcement for 1.4, showing that 1.4 had been heavily optimized in the areas of its most frequent use and that performance had also increased across the board.
Popular
jQuery is served by 48 of the top 2000 websites in the Alexa directory, compared to Prototype's 6, MooTools's 3, and Dojo's 1.
Built With's Javascript Distrobution indicate that jQuery is used by over 40% of their indexed sites. Built With's jQuery Usage Statistics show 32% of the Top 10,000 sites, 23% of the top 100,000 sites, and 15% of the top 1,000,000 sites in their index using jQuery. Compare this to Prototype's 5%, 4%, and 2% respectively in those categories. Also notice that while jQuery's usages are all trending steadily upward, Prototype's usages are all trending downward.
Actively (and Intelligently) Developed
In March, jQuery posted on its blog Microsoft to Expand its Collaboration with the jQuery Community. From the post:
The jQuery Project is excited to announce that Microsoft is expanding its support of the jQuery JavaScript Library through new initiatives, to include code contributions, product integration, and the allocation of additional resources.

...

Microsoft will also ship a current release of the jQuery JavaScript Library in both Visual Studio 2010 and ASP.NET MVC as well as continue to host current versions of the library on the Microsoft CDN.
Microsoft is now included in the community of active contributors to the jQuery codebase. Microsoft has some very smart developers. Microsoft also financially contributes to jQuery. Microsoft has a lot of money. They are also giving jQuery instant exposure to any developer with Visual Studio. Microsoft is also working on ASP.NET to specifically make jQuery's integration better:
 Also learn how ASP.NET AJAX was designed to work seamlessly with jQuery, how you can create ASP.NET AJAX controls directly from jQuery and how you can use the jQuery library in ASP.NET AJAX code.
More on Microsoft's use of jQuery can be found in Scott Guthrie's ASP.NET blog post jQuery and Microsoft.
jQuery's source code is hosted publicly on GitHub. This is a plus for me personally since I prefer Git to other SCM systems.
jQuery's Sizzle selector engine is great. And it's great because jQuery isn't working on it alone. In a blog post announcing the release of v1.3 early last year, they basically said "We know we're not smarter than everybody else and we're more interested in advancing the community by working with smart people than in saying we're better than they are."
One thing that became very obvious during the development of the new engine: We wanted to be able to collaborate on it with other libraries and developers. We saw an opportunity for some solid collaboration with some of the best JavaScript developers - the result of which will help users of all libraries. For this reason we made sure that Sizzle was able to work completely standalone (no dependencies).

Additionally, as a sign of good faith and willingness to collaborate, we've released the source code to Sizzle to the Dojo Foundation. We wanted a common meeting ground where everyone would be able to work together and under which there would be a clear long-term copyright holder.

Right now we're working with Prototype, Dojo, Yahoo UI, MochiKit, and TinyMCE (and many others) on Sizzle, honing it to perfection.
Mobile Support
In the YayQuery podcast, Episode 18 (mp3), John Resign outlines the plans for jQuery's mobile support. The philosophy is that the primary jQuery library should be compatible with mobile devices, instead of writing a dedicated mobile version. One of the many benefits of this is that as your primary library is used on general as well as mobile websites, it is more likely to become cached, and mobile-specific websites can use the same cached version as non-mobile websites. John also explains exactly which phones and operating systems are planned for support.
jQuery can be used with PhoneGap to create native applications using web technologies for mobile devices. It also has a plugin named JQTouch for building native-like web apps for the iPhone and other mobile browsers. This means all those shortcuts you have with jQuery translate to other platforms, decreasing the learning curve.
Active in the Development Community
John Resig is an active speaker. He includes not only major conferences and events but has also appeared on podcasts like YayQuery. He has also written two books: Javascript Ninja and Pro Javascript, both about pure Javascript skills not dedicated jQuery.
Answer: Use whichever one fits your specifications the best. jQuery is a great library under active development with a great (and large) community following and is my personal library of choice, but don't let that deter you from using Prototype if it is the right tool for the job.
Answer: It's really a matter of preference. I tend towards jQuery, personally. It's well documented, has lots of good plugins, and makes my life a whole lot easier. I've heard some complaints from the Prototype people about its size and performance (sorry, I don't remember their specific problem) but both of those aspects have been getting better and better.
I would try both out and see which one is a better fit for you.
Answer: It really depends on which you prefer. However, jQuery does seem to have a bigger following, so resources for that might be easy to come by.
I think you can get both hosted from Google's servers, and jQuery hosted from MS's servers, which may come into your consideration.
Answer: I believe jQuery hosted via Google CDN opens up both.
Answer: It is a matter of preference obviously. I have found jquery is a little but more difficult to understand for newbies since the chaining is not really intuitive in particular when asynchronous race conditions occur. However, jquery certainly has more plugins but that can also be a disadvantage when too many plugins are automatically loaded.
Answer: While on the surface it's based on your preference, consider looking at the libraries, plugins, and samples of things that you want to use jQuery or Prototype to accomplish. For example, with a new Rails project all AJAX is done using prototype by default. Also if you want to use jQuery UI or jQTouch (mobile) I would recommend jQuery.


96 honda accord 4 cylinder intermittently overheats

Question: my honda accord (96) overheats SOMETIMES, and only at a standstill. on the freeway it is fine. I have changed the thermostat, the radiator cap the switch and bled the system. I have had the head chk and there isn't any visible leaks and the antifreeze needs to be added may be every 2 days some times the reservoir for the antifreeze gets full only SOMETIMES there is a smell that comes through the car when i accelerate. even on the freeway it smells bad. Could a loose cover on my catalytic converter be causing the overheat. any ideas will be appreciated.
Comment: Have you checked that the cooling fan is operating - as on the freeway the forward movement of the car will be forcing cooling air through the radiator cooling it, which does not happen at standstill.
Comment: Electric fan is not working!
Answer: You should sort out all problems you have with your car:

Antifreeze leaking. Do a pressure test for the cooling system, or find some other way to find where it's leaking. It may be leaking internally to the cylinders or externally. Please check oil for presence of antifreeze and antifreeze for presence of oil.
Overheating. This may be a faulty sensor (the sensor that controls the fan may be different from the sensor that show the temperature on the dashboard). It may also be a faulty fan or a faulty wiring to the fan, or a failed fuse, etc. If you know which pin gets the positive voltage and which gets the negative voltage, you can test the fan by connecting it directly to the battery.

I'm not yet convinced that your problems are connected. Overheating may be caused by a faulty fan or fan control especially considering it overheats only at standstill. Antifreeze disappearing is due to an internal or external leak.
I don't believe catalytic converter cover would cause overheating of the engine. A faulty catalytic converter or catalytic converter connected to faulty engine that doesn't burn all fuel could overheat itself, but I have never heard the cat causing overheating of the engine. Perhaps a clogged cat could lead to engine trouble, but a catalytic converter cover? No.


If you know a family member who is much more qualified and capable in mathematics than you are, should find another field of specialisation?

Question: Imposter Syndrome and depression: I feel pretty depressed that I am not as "qualified" as a close family member. In particular, this family member has undergraduate and graduate (PhD) degrees from Ivy League schools in mathematics. Whenever I try to solve a problem, this family member does it in half the time. Does this mean that I should try and find another field to specialize in?
Comment: No way. Cedric Villani could solve any research problem I face in a fraction of the time - that doesn't mean I should give up a research career :-) I think as part of life, one has to accept that there are people better than you, but as you are doing research in mathematics you should realise you are one of the brightest set of people in the world
Comment: Science and mathematics are not a competition. I actually get very excited and happy when I find other smart and capable people working in my field (and not just my field), or when I read about how someone else has solved some hard problem or done some great research.
Comment: *"If you compare yourself to others you may become vain or bitter for always there will be greater and lesser persons than yourself"* - Max Ehrmann
Comment: Two arguments against finding another field: 1. there are many math problems, so you won't necessarily be competing against people like your family member in everything you do, and 2. problem-*finding* is in some ways as valuable as problem-solving, and the people who are great at one are not necessarily great at the other.
Comment: Related: https://academia.stackexchange.com/q/2219/19607 and https://academia.stackexchange.com/q/11765/19607 (and many others)
Comment: @Wildcard I think you are confusing my comment as a swipe at other disciplines when in fact it is just a comment to reassure a depressed mathematician
Comment: @101: "Science and mathematics are not a competition." True, that's not their goal.  However, in our society, being a professional research mathematician (I restrict to mathematics because it's what I know: I *am* a professional research mathematician) is a highly compet**itive** enterprise from start to finish.  I certainly wish it were not so competitive, but underplaying the competitive aspects doesn't seem helpful to me.
Comment: The question is whether you have good ideas in mathematics, not whether you have the fastest logical mind. Also some people who are the brightest become "trapped" and creatively sterilized by the pressure to perform.
Comment: @MikeMiller, that's a rather complacent attitude.  If your sole measure of brightness is "ability to do advanced mathematics," then and only then would those doing research in mathematics *uniformly* be each of them "of the brightest set of people in the world."  That seems more than slightly dismissive of other even more crucial types of intelligence, such as the ability to coordinate data and **action**, or the ability to effectively lead people and administrate so as to make planning become an actuality.
Comment: Imagine what would happen if everyone thought like this: there would always be only one researcher in every field. The best one. They would be lonely, helpless and incredibly overworked individuals which knew that altough they are the best, every effort in bringing forth their field is futile, as it will always go very slowly. There is no-one there to discuss things, no-one there to take some load, no-one to share thoughts with, no-one to have another idea. Research would be doomed if we would only let the very best do it (even if we maybe allow the 10 best).
Comment: There is always someone better. Even Villani is a nobody when compared to, say, Euler or Hilbert. He isn't a Serre either.
Comment: @101010111100 Indeed, very true.  Unfortunately, many students get caught up in the game of gathering accolades or highest marks or best 'brand name degree'.  When they get to research or the real-world, they apply the same game.
Comment: I feel this quite a lot, as I don't work in a university (or an academic institution) and find myself unfavourably comparing myself as a failure compared to other PhD qualified researchers.  A technique I do that seems to help me (a bit) is to have a print out list of the published work I have done, the ones I am currently working and the growing list of what is coming up.  I am sure many here will think that is a monumentally stupid technique (and that I am an idiot for doing it), but strangely - it works (for me at least).
Comment: @WIldcard people skills are not intelligence. Intelligence is intelligence. I do agree however that there are some other highly intelligent fields- certain parts of literature have highly intelligent people in a different way, and philosophy has people of both/either of literary and conceptual ability in high degrees.
Comment: As someone once shared with me: ["There is nothing noble in being superior to your fellow man; true nobility is being superior to your former self." - Hemingway](https://academia.stackexchange.com/a/87673/68075).
Comment: How far along are you?
Comment: Try and gradually shift your focus away from external motivators and into internal motivators. The excitement of discovery, figuring stuff out and talking about exciting things in research together with others. Try and disregard much of the superstarery as kind of a dramatic theatre to get the kids excited about the field (which it honestly kind of is, but don't tell the kids, it could break the magical motivation that could help make them great one day!).
Comment: Is he smarter than you or just more nervous? There are plenty of decaffeinated options that taste just as good as the real thing.
Comment: I think the change of title makes this far too specific and personal a problem, whereas a question on "Imposter Syndrome and Depression" (albeit sparked by comparison to a family member) will have more relevance and value to (and be found by more) future readers.
Comment: @Saturnus, do you do research and papers for a private company or on your own?
Comment: @TeacherKSHuang  Yeah but people don't want to be noble; they want to be badasses.
Comment: To the OP: Heh, I see Insulin69's comment and I'd like to add that the two are not mutually exclusive. Bruce Wayne, Wolverine, etc. https://goodmenproject.com/featured-content/3-ways-to-be-a-noble-badass-alnpt/. What do you think, @Insulin69 :D?
Comment: @TeacherKSHuang Oh yes; I agree. Perhaps I am being a little coarse.
Comment: I know of a graduate student in logic at Rutgers that dropped out after meeting Shelah.
Answer: Research in math (and I assume in general) is hard. It doesn't get better over time, but rather we seem to get used to the accompanying depression and angst.
When I feel down, I try to remind myself that I do math because I enjoy it, not because I want to be the best at it, and for some reason people continue to pay me money to do it. If they stop, I'll figure out something else. In the meantime, I get to think about problems that I love, even if the process of that thinking isn't always fun.
The above is also hard. There are still rough days. It helps me to know that other people, including really smart people doing great math, experience the same feelings.
If you want to switch fields, go ahead. But in general, any field will have someone (seemingly) faster than you - this is not personal, we all feel like this.

[Picture Credit: David Whittaker]
I would recommend reflecting upon why you are in mathematics in the first place. If you're in it because you love doing it, does it matter if someone else is a bit faster? There is plenty of mathematics to go around.
Comment: You single-handedly got my upvote with your convincing picture.
Comment: Disclaimer: I didn't make the picture. I saw it on Facebook at some point, and for this answer, found it here https://www.psychologytoday.com/blog/happy-trails/201511/feeling-fraud
Comment: +1 for the answer, but I think the reference to the picture should be inside the post.
Comment: This seems to be the real source: https://twitter.com/rundavidrun/status/587671657193455616
Comment: @SolarMike Well, most orchestras likely have an entire line of trombonists... might be more accurate to say "Only one plays the Bass Drum", or "Only one plays the Bassoon"... since that's more likely in practice... ;-P
Comment: The radii of the circles are far too consistent for "reality" (although nicely, the overlapping regions are varied), but the overall point is still valid.
Comment: This is by far the best answer.
Comment: One of my lecturers years ago told me that we all can be in the orchestra, but only one blows through the trombone...
Comment: I'm nowhere near being capable of playing a musical instrument, but the point was well meant and absorbed. I did + 1 for the answer above though.
Comment: Just curious, is that the "Gamespy" icon in your picture?
Comment: Superb graphic.
Comment: @Anoplexian I'm pretty sure that's the old Incognito Mode icon from Chrome.
Answer: Even if other people have amazing lunches, you'd still want to have your own, even if less amazing, simply because you're hungry.
Answer: Do you love research and/or math itself?  Do you love solving problems and learning about new things and finding out how things work?  If not, then I'm afraid your motivation is misdirected.
As you progress through life, you will meet many more people that are much smarter than you.  I teach students who are much smarter than me and I wish I had their brain.  Some colleagues/peers are simply out of this world.
However, there is a place for you.  There are many research areas and countless opportunities to contribute.  If it's not in math, then it's in other areas; there are many areas/problems waiting for new tools and perspectives.  This means it is all hands on deck.  The scientific community is small relative to the world's population.  We are motivated to generate and verify new knowledge.  If not us, who else?
Go back to basics, and follow your heart and interests.  Keep moving and try to stop comparing; otherwise, it's like spending too much time in an art gallery and not doing any work :)
Answer: Math can be brutal to your self-esteem. If you are a poet of average skills you could (perhaps) convince yourself that you are an under-appreciated genius with as much raw talent as a winner of the Nobel Prize in literature. In mathematics, if you are mathematician of average talent, you can't plausibly convince yourself that you are even remotely as talented as a winner of the Fields Medal.
If you get to that stage you have to ask yourself what is more important -- your ego or your love of mathematics. If the former, by all means switch to another field, perhaps one with more subjective criteria where you can convince yourself that you are the best. If the latter -- keep with what you are doing.
Many mathematicians end up teaching undergraduates. If that is the career path you end up following, the knowledge that you are not a world-class research mathematician (if in fact you are not) can help give you the humility that you need to e.g. not lose patience with students who can't seem to understand the chain rule. What you are feeling now about your family member is similar to what some of your students will feel about you. This can help you empathize with such students. If you know what it is like to struggle with a concept that some people find obvious you will be in a better position to help others who are in a similar state.
Answer: No it does not. There will always be someone better than you and I in whatever we do, but that does not mean you should give up. Family issues are hard because it's so easy to compare yourself to a relative, especially a close one. However, that does not mean you should give up.
Comment: There are a handful of people who are actually the best in the world at something, such as the world chess champion. The remaining 7.5 billion of us just have to get used to other people being better than us in even our chosen field.
Comment: @Patricia: What you say is true, and as a logically minded person, I appreciate you pointing it out.  However, from what I know about the psychology of "elite performance," the people who are *in the running* for being the world's best at X are most often hyper-aware of their competition and have an eye out at all times for how far they are ahead.  Even being superior enough to only compete with yourself does not imply angst-free days: the struggle to exceed one's past accomplishments is overall very positive...but it's certainly not carefree and can lead to feelings of inferiority.
Answer: Although my field of study is not Math, I can relate to you because I had a experience that was very similar to yours. Just like you, I felt unqualified in my studies and just like you, I had thought about changing to another field and even giving up on trying hard. Eventually, I changed my field of study but not because I wanted to be perfect or better than others in it but because I came to the conclusion that if I want to spend my time and life on something, it should be something that I enjoy doing. I decided to focus on enjoying what I do and trying to improve myself over time rather than comparing myself to other people. For me, it is a fact that almost in any aspect of life, there are people that are better than me. As a matter of fact, there are people in my new field that are better than me (I have close interactions with some of them on a daily basis) and well, that's life.
Also, it is good to remember a well-known quote from Thomas Edison:

Genius is 1% inspiration and 99% perspiration

I have studied biographies of many prominent scientists and artists and two personality traits that I have found very common among them are that they enjoyed their work and they worked hard. In my opinion, these are the qualities that you should concentrate on. If you truly enjoy Mathematics, then just enjoy it. For a good biography, I encourage you to read the wonderful memoir of Issac Asimov, I, Asimov.
By the way, I hope that by 'feeling depressed', you do not mean that you are suffering from clinical depression (depression is always an alarming word for me). It is quite normal to feel disappointed about your qualities sometimes but, if you feel depressed about other significant aspects of your life too, please also consider seeking clinical help.
Comment: It is 99% hard work by EXPERIENCE. But it is not 99% hard work by FREQUENCY in the general population.
Answer:
Does this mean that I should try and find another field to specialize in?

In any field you choose to specialize in you will encounter someone -- probably multiple people -- like your family member who have better training and probably more talent than you. That makes me wonder why you assume the premise that it is especially bad to have a family member who is that super-talented person? Won't it be just as frustrating to have such a person as your supervisor or your colleague? In which case, isn't the conclusion that your only hope is to retire to a desert island and not attempt to do anything meaningful with your life, so that you never have to compare yourself with someone who is better than you at what you do?
The answer of course is that the premise is incorrect. You should aspire to choose a profession that brings you the highest level of personal satisfaction. If math is that area, it would be a shame to choose a different area for the sole purpose of avoiding professional jealousy between you and a family member, when in all likelihood you will encounter many other people likely to arouse similar feelings of jealousy in you. Better to learn to deal with the jealousy, accept that you are not the smartest person on earth, and find joy and meaning in your ability to make humble but useful contributions to mathematical knowledge. Good luck!
Answer: you don't say how far along you are. If you haven't finished a first degree in maths and he has a PhD from a good university then he ought be at least twice as fast as you. In fact, if he is only twice as fast as you, you are doing rather well.
A more interesting question to ask, is how is his career going? I have seen plenty of people with PhDs from top name institutions have trouble making a career in maths. If you really truly believe that you have less talent and he is having trouble in the job market, it's probably a sign that you should specialize in something more employable.
Comment: That's just ridiculous. How can an undergraduate beat a tenured professor? I'm sure he's not competing with you in any sense.
Comment: My experience contradicts the first sentence. When I was in my first two years of undergrad I was besting the tenured faculty at my university; these weren't Harvard PhDs but they weren't exactly slouches.
Answer:
Whenever I try to solve a problem, this family member does it in half the time. Does this mean that I should try and find another field to specialize in?

If you and this relative are unable to put a bit of academic distance between yourselves, for example by refraining from tackling the same math problem (and then comparing your results and your speed), then I imagine you would probably be happier not studying in the same field.  It sounds as though you may be setting yourself up repeatedly for this type of comparison, which you then get discouraged about.
Answer: In a way I can relate to this experience. My siblings are both in your situation. I am and have been the "smart" guy in my family. My siblings were very often compared to me by teachers, friends and family members and used to be asked why they can't be as "good" as I was. That got them depressed and made them give up on things they wanted to achieve because they believed what they were told, that they "have no talent". If I ever regret something in life, is that I didn't pay attention to that and didn't as much as tell them what exactly talent was.
Because, here is the thing. I have no particular talent in anything. I only know that if I work hard enough, smart enough, and I keep myself healthy, I can acquire many talents. How to learn something is a whole science, and I don't plan to discuss it here. All I'm saying, you can get a PhD, a successful career, even if you seem much less competent at the beginning than your peers. In fact, I was surprised myself how many of my PhD colleagues who seemed to struggle with just about everything, ended up making important contributions to the research fields they happened to specialize in.
I also think I should mention that even if you don't know whether you'd like or not a certain research field, the beginning is not the moment to decide on that. It's as if you'd decide not to marry someone because you don't like their nose. But, if you stay long enough to know them and you realize they treat you well and have many qualities, I doubt you'd care so much about the nose. Same with the research. Once you reach a certain level of understanding, you begin to like the field.
If I were to do a PhD again, I'd choose a field that gives me enough job opportunities, enough challenge, and problems worth solving. I'd not factor other's talents in this decision.
Answer: Einstein struggled a lot more with mathematics than a whole lot of his contemporaries, particularly mathematicians.  David Hilbert was able to retrace (and do the finishing touches Einstein was still working on) a significant amount of Einstein's General Relativity work in a small fraction of the time Einstein needed and made a paper which he later retracted since he saw it as credit-stealing.
Einstein needed a lot of time to get where he wanted to but he had the advantage of knowing for sure where he wanted to get.  That produced more tangible results than brilliant minds without his aim.
So no, the mere speed and skill with which one of your relatives (what does it matter that he is a relative anyway?) works with mathematics does not tell much about what mathematics can do with and in your life.
I mean, if he is a relative, that should be a good omen, shouldn't it?  That makes it likely that your genes are not entirely remote from your interests.
Comment: "Einstein struggled a lot more with mathematics than a whole lot of his contemporaries, particularly mathematicians." Einstein was a physicist, so yes, some mathematicians were better at math.  The idea that he "struggled" with math -- more so than anyone who does advanced math struggles with it, I mean -- seems to have no factual basis: see e.g. http://content.time.com/time/specials/packages/article/0,28804,1936731_1936743_1936758,00.html, in which we learn Einstein had mastered calculus by 15.  Hilbert was by the way the most brilliant mathematical mind of the time.
Comment: Einstein's IQ was reckoned around 160 -- in other words, probably not the very highest among physicists (having a high IQ can help you in academia, but only up to a certain threshold, and Einstein's IQ was well above that threshold) but surely enough to make him quick-witted among any reasonable cohort.  "Einstein needed a lot of time to get where he wanted to" Again, not really -- he is famous for his *annus mirabilis*, in which he wrote four amazing papers.  He was 26 at the time.  For what it's worth, I agree with the last two paragraphs much more.
Comment: @PeteL.Clark the sentence "Einstein needed a lot of time to get where he wanted to" seems to refer to the period 1908-1915 during which Einstein was working on developing general relativity, so I think user72889 has a valid point here. With that said, I agree with you that it's misleading to claim Einstein "struggled" with math - rather, he "struggled" to develop a revolutionary new theory of gravity that made use of sophisticated mathematical tools that were understood by only a handful of mathematicians at the time. Not quite a great basis for comparing OP's situation to Einstein's.
Comment: @PeteL.Clark It is hard to say IQ does not help above the threshold. For instance, Von Neumann obviously benefited from his remarkable abilities. Even though he was perhaps not the best mathematician of all.
Answer: On the contrary this should encourage you to go into the same field as your family member.  The best way to improve at something is to surround yourself with people who are more talented than you are.
When you have a problem that you are working on (or even better after you have solved it) you can discuss the problem with your family member about how they would have solved and how you could solve it better in the future.  You also have the benefit of having casual conversations about things tangentially related to your professional interests.
I think that key thing here is that you already have a network of highly skilled people in your field.  This is an extraordinary advantage and you should harness it.
Comment: There is a problem with this answer: It does not take into account the personality of OP and the interpersonal skills of the family member (which are both not stated in the question). For (an extreme) example, if the family member was Sherlock Holmes who would always tell me (directly or indirectly) how stupid I am, then I would not be comfortable working with him. Unfortunately, I met many people who have a Holmes-like attitude.
Answer: This may be a cause to move into a different sub-field than him... Many people want the glory and to feel they have done something. There is no reason to think this cannot be done within mathematics.
But on the thing of comparision, do you have better questions than him? Do you have novel approaches? These are the things that matter. Also when you talk of his ability to solve problems more quickly, are you talking about elementary problems that take mere minutes? Does he betray a deeper understanding than you?
Comment: +1 for sub-field. There must be an area within math that doesn't interest him/her but could be loved by you. Something special. Some niche.
Answer: Absolutely not!
You should only consider a change in a specialisation if you choose to, not due to a feeling of being forced - the latter, in all likelihood, would not be sustainable.  Explore your specialisation to see if you can identify and capture a niche area that grabs your interest.
When I feel a similar feeling, I look at a list I prepared of what I have accomplished within the topic, what I have going at the moment and the exciting prospects that I have planned.  Doesn't always work, but does help most times - there is no magic solution though.
One thing to consider is engaging your family member in a collaborative project, work together and even learn from them. Good chances are that he'll learn off you at the same time.
Perhaps, there is an opportunity to be mentored by your family member - benefit from his knowledge and skills.
As a saying goes "chase the dream, not the opposition" - but without the opposition, as you are not in competition with your family member.
Answer: I think that mathematics research is about being more creative than being fast. Being faster is more likely to help you in math competitions, examinations. But you will need different qualities for mathematical research.