At first I made a simple array just inside the .js file and wrote the function to make list items from it. Then clicking on freshly created li elements should do something.  //This code works  dic[0] = "decir"; dic[1] = "querer"; dic[2] = "poder";   $(document).ready(     function verbsarray() {          for (var i = 0; i &lt; dic.length; i++) {                       var verbo = dic[i];                                          verbos += "&lt;li class='h_li'&gt;" + verbo + "&lt;/li&gt;\n";         };                   $('ul.h_list').html(verbos);                        });      $(".h_li").click(function() {     alert("it works!");     //Dollar finds .h_li here }   Since that was a prototype, then I developed that function to take items not from the static array, but from loaded JSON. Function parses the needed items and again makes list items from them.  But the other function (that looks for clicked &lt;li class="h_li"&gt;) now doesn't work...  //this doesnt work :-(   function verbos_from_json () {      $.getJSON('verbos.json', function verbsarray(vjson) {         for (var i = 0; i &lt; vjson.data.length; i++) {                    verbo = vjson.data[i].verb;                                      verbos += "&lt;li class='h_li'&gt;" + verbo + "&lt;/li&gt;\n";         };                $('ul.h_list').html(verbos);         });          };  $(".h_li").click(function() {     alert("it works?.. no"); }
I have a bit of code where I am looping through all the select boxes on a page and binding a .hover event to them to do a bit of twiddling with their width on mouse on/off.  This happens on page ready and works just fine.  The problem I have is that any select boxes I add via Ajax or DOM after the initial loop won't have the event bound.  I have found this plugin (), but before I add another 5k to my pages with a plugin, I want to see if anyone knows a way to do this, either with jQuery directly or by another option.
Counter example for $(A \times B) \cap (C \times D) = (A \cap C ) \times (B \cap D)$ Which is correct? - "Guess, what it is?" or "Guess, what is it?" I want to know which sentence is correct. If i say     Guess, what it is?   or     Guess, what is it?   which one is correct? My headphones are not playing voices, but only the music I installed Ubuntu 14.04.1 on one of my laptops and now I want distribute that exact installation to a few of my friends. I tried making an iso with Gparted on a live session but it makes a 500 GB file (I've only used around 8 GB) .   Thanks in Advance ! How to make ttf-mscorefonts-installer package download fonts, after it says it installed? When I install "Installer for Microsoft TrueType core fonts" (ttf-mscorefonts-installer) from the Software Center on 14.04, it gives a positive response--but after this I get a download error. It seems that the files actually providing the fonts are never retrieved. Is there any way to resolve this? I know that it can be done with graphics packages like TikZ, but it seems to be a bit of an overkill. And for those not already familiar with the TikZ system, learning it can be a bit of a barrier. So my question is     Assuming the advanced graphics capabilities are not necessary, and I just need a bare-bones flow chart (which should be implementable just using the picture environment), is there a LaTeX package for simply making a flow chart? Proving $\frac{1}{n+1} + \frac{1}{n+2}+\cdots+\frac{1}{2n} &gt; \frac{13}{24}$ for $n&gt;1,n\in\Bbb N$  To solve it I used induction but it is leading me nowhere my attempt was as follows:  Lets assume the inequality is true for $n = k$ then we need to prove that it is true for $k+1$  so we need to prove $\frac1{k+2} + \frac1{k+3}+\cdots+\frac1{2(k+1)} &gt; 13/24$  I don't know where to go from here please help. How to prove $\lim\limits_{n \to \infty} (1+\frac1n)^n = e$? Mathematics needed for string theory I'm interested in cutting edge string theory studied by research physicist. I'm wonder what  mathematics is needed and how far am I in terms of mathematics background needed and how much more mathematics do I need in order to study it properly and reach my goal. My mathematics level is at calculus/Linear algebra. What causes Nova's Triple Tap to go on a short cooldown? This isn't about the level 20 ultimate talent.  There is a brief "arming phase" where Nova locks onto her target before shooting three bullets.  When something happens to her target during that phase (allied Devouring Maw, Divine Shield, etc.), sometimes Triple Tap is placed on the full 100 second cooldown, other times it is placed on a much shorter 10 second cooldown.  What triggers this 10 second cooldown?   Is this a timing issue?  I believe if she is interrupted after the shots start going out, Triple Tap is always put on the full cooldown.  Maybe I'm just seeing the ability get blocked right as the first shot goes out? Is this ability-specific?  Does stunning Nova have a different effect than a Devouring Maw or Void Prison on her target?  And is that different from a Divine Shield/Sanctification on the target? Is this determined by whether the line to the target was interrupted by an ally or an enemy hero?  For example, is there a difference between an allied Void Prison and an enemy Void Prison on the target? Divisor function asymptotics Define $\tau_{r}(n) = \sum_{d_1...d_r = n}1$.   One exercise in a book on sieve theory asked for an elementary proof by induction of the fact that   $$\sum_{n\le x}\tau_r(n) = \frac{1}{(r - 1)!}x(\ln x)^{r - 1} + O\left(x(\ln x)^{r - 2}\right)$$  The base case $r = 2$ is easy with reversing the order of summation.  The only other progress that I made was the fact that $\sum_{n\le x}\tau_{r}(n) = \sum_{d\le x}\left\lfloor\frac{x}{d}\right\rfloor\tau_{r- 1}(d)$, but I don't know how to proceed. Is there a Stack Exchange site for both technical and behavioural interview questions? Where I could ask questions about, for example:   Sample questions and solutions, experiences, or seeking advice for interviews A place where I could ask interview questions and what the best way to answer it (not just a correct way or the most efficient).   What to do in a certain situation such as negotiation, unfair interviews, etc.   Experiences interviewing at so-and-so company. How can I solve "Be aware that removing the lock file is not a solution and may break your system"? I did: sudo apt-get update  I got this: Reading package lists... Done E: Could not get lock /var/lib/apt/lists/lock. It is held by process 1131 (packagekitd)  N: Be aware that removing the lock file is not a solution and may break your system.  E: Unable to lock directory /var/lib/apt/lists/  I used to just remove the lock file, but I've never seen that note before (did it break my system?)?? What do I do? Neg Binomial and the Jeffreys' Prior I'm trying to obtain the Jeffreys' prior for a negative binomial distribution. I can't see where I go wrong, so if someone could help point that out that would be appreciated.  Okay, so the situation is this: I am to compare the prior distributions obtained using a binomial and a negative binomial, where (in both cases) there are $n$ trials and $m$ successes. I get the right answer for the binomial case, but not for the negative binomial.   Let's call the Jeffreys' prior $\pi_J(\theta)$. Then,  $$ \pi_J(\theta)\propto [I(\theta)]^{1/2}. $$  Under the regularity conditions (fulfilled as we are dealing with the exponential family),  $$ I(\theta)=-E\left(\frac{\partial^2 \log L(\theta|x)}{\partial \theta^2}\right) $$ where for the negative binomial $n$ is $x$ in the above expression (the total number of successes $m$ is fixed, $n$ is not). The distribution --I think-- is  $$ p(m|\theta)\propto\theta^m(1-\theta)^{n-m} $$ since $\theta$ is defined as the probability of success and $m$ is the number of successes. This is also the likelihood, since $m$ is a scalar and not a vector. Hence,  $$ L(\theta|n)\propto\theta^m(1-\theta)^{n-m}\\ \log L(\theta|n)=m\log\theta +(n-m)\log (1-\theta)\\ \frac{\partial\log L(\theta|n)}{\partial \theta}=\frac{m}{\theta}-\frac{n-m}{1-\theta}\\ \frac{\partial^2\log L(\theta|n)}{\partial \theta^2}=-\frac{m}{\theta^2}-\frac{n-m}{(1-\theta)^2} $$ so the Fisher information is  $$ I(\theta)=-E\left(\frac{\partial^2\log L(\theta|n)}{\partial \theta^2}\right)=\frac{m}{\theta^2}+\frac{E(n)-m}{(1-\theta)^2}=\frac{m}{\theta^2}+\frac{\frac{m\theta}{1-\theta}-m}{(1-\theta)^2}\\ =\frac{m(1-\theta)^2+\frac{m\theta^3}{(1-\theta)}-m\theta^2}{\theta^2(1-\theta)^2}=\frac{m(1-2\theta)+\frac{m\theta^3}{(1-\theta)}}{\theta^2(1-\theta)^2}\\ =\frac{m(1-2\theta)(1-\theta)+m\theta^3}{\theta^2(1-\theta)^3}=\frac{m(1-3\theta+2\theta^2+\theta^3)}{\theta^2(1-\theta)^3}\\ \propto\frac{1-3\theta+2\theta^2+\theta^3}{\theta^2(1-\theta)^3} $$  This, however, does not give me the correct answer. The correct answer is  $$ \pi_J(\theta)\propto \frac{1}{\theta(1-\theta)^{1/2}} $$ which means that the Information I get should be   $$ I(\theta)=\frac{1}{\theta^2(1-\theta)} $$ since the prior should be proportional to the square root of the information.   Can anyone find any mistakes? I wouldn't be surprised if I screwed something up with the set up of the distribution (successes vs failures with their respective probabilities, etc).   I used the expected value from  and I know the correct answer from . which analysis fit the best for the data? I am trying to work on the data of my master thesis. I have to create a model, but I don't know which analysis perform. My data look like this     number ID sp ab alt SWdiv     1 A1 4 6 630 0.25802965   2 A2 9 16 750 0.29235324   3 A3 8 14 850 0.411620203   4 A4 9 22 950 0.444079553   5 A5 2 12 1050 0.294855765   6 A6 13 40 1130 0.298451014   7 B1 2 2 750 0.275329152   8 B2 5 6 850 0.043352118   9 B3 14 16 950 0   10 B4 17 58 1050 0.230358688   11 B5 7 9 1150 0.298759725   12 B6 8 68 1250 0.012192474   13 C1 12 20 830 0.467542842   14 C2 13 19 930 0.454480722   15 C3 6 30 1030 0.464584038   16 C4 7 14 1130 0.448191833   17 C5 5 15 1250 0.052045275   18 C6 2 7 1315 0.051767368   19 D1 3 3 650 0.294855541   20 D2 11 17 750 0.291717599   21 D3 9 46 850 0.084284864   22 D4 8 57 950 0.088574421   23 D5 4 32 1050 0.160873283   24 D6 13 22 1150 0.297638962   25 D7 6 22 1250 0.290733158   26 E1 11 23 800 0.250878932   27 E2 8 8 900 0.418021272   28 E3 10 13 1000 0.132159303   29 E4 5 10 1100 0.058487554   30 E5 5 15 1200 0.250310822   31 E6 10 19 1350 0.01600807   32 F1 6 20 800 0.462497319   33 F2 15 54 900 0.376552904   34 F3 6 13 1000 0.298264791   35 F4 6 13 1100 0.394736812   36 G1 14 22 830 0.391455956   37 G2 11 16 930 0.282922551   38 G3 5 10 1030 0.282398234   39 G4 10 15 1130 0.153502726   40 G5 13 18 1230 0.051586706   41 H1 10 11 700 0.3009609   42 H2 7 14 800 0.579228102   43 H3 5 11 900 0.528108703   44 H4 20 48 1000 0.28689587   45 H5 13 18 1100 0.264542772   46 H6 10 20 1250 0.29928462   47 I1 12 13 535 0.35326876   48 I2 3 5 635 0.273947657   49 I3 18 30 720 0.467208221   50 I4 16 188 835 0.276669711   51 I5 7 69 935 0.000867067   52 L1 6 10 435 0.36910895   53 L2 7 20 606 0.236700524   54 L3 14 33 697 0.111132519   55 L4 17 42 797 0.296757045   56 L5 5 7 910 0.27539552   57 L6 10 15 1000 0.29748451   58 L7 6 11 1101 0.276755556     58 sample points each has two value: altitude and diversity index related to habitat heterogeneity. What I sample are different insects and I have two values: sp = species (total number of different species for each sample point) and ab = abundance (total number of insects for each sample point). My question are are the species richness or the abundance affected by the altitude or the habitat heterogeneity? which is the best analysis to perform? and why? thank you for your help. When we say something in specified operating system, which preposition should I use?  e.g.     I play games {in/on/under} Windows. Can Cycles physically mix colours? Prove $\frac{1+a^{2}}{1+b+c^{2}} +\frac{1+b^{2}}{1+c+a^{2}} +\frac{1+c^{2}}{1+a+b^{2}} \geq 2$ For positive integers Numbers  $a, b, c $ prove that  $$\frac{1+a^{2}}{1+b+c^{2}} +\frac{1+b^{2}}{1+c+a^{2}} +\frac{1+c^{2}}{1+a+b^{2}} \geq  2$$ This inequality above take long time to prove it and still couldn't complete it.  How I can prove this inequality?  Any hint will help. Thanks I'd like to prove a nice property of a stochastic integral with respect to Brownian motion.   Let $(H_t)_{t\geq0}$ be a progressive and bounded process that is continuous at $0$ and $B$ a standard Brownian motion. Then  $\frac{1}{B_{\varepsilon}}\int_{0}^{\varepsilon}H_s\mathbb{d}B_s\rightarrow H_0$ as $\varepsilon\rightarrow0$ in probability.   Anyone got some hints ? I'm really puzzled and I don't know where to start.   EDIT_2.0. Applying Ito-Isometry might be a bit tricky. This Brownian Motion in the denominator kinda troubles me :/ Book about space alien cats