How do I use the variable from the outer scope of the function. I have created this simple example here  var view = {     init(){     const targetOne = document.querySelector(".targetOne");      const targetTwo = document.querySelector(".targetTwo");      var val = "outer scope";      targetOne.addEventListener('click', (e) =&gt; {       console.log('target one', val)      });     targetTwo.addEventListener('click', this.handleEvent);   },    handleEvent(e) {         console.log('targetTwo ', val);     } }  view.init();    JS fiddle link:   For the targetOne callback I can use the variable val, but when i define a function for the targetTwo callback, that variable val is not defined in the scope. I feel like I am missing something very simple here, I refreshed on scope chaining and closure but I am not able to get it
How would you explain JavaScript closures to someone with a knowledge of the concepts they consist of (for example functions, variables and the like), but does not understand closures themselves?  I have seen  given on Wikipedia, but unfortunately it did not help.
Is the universe isomorphic to a universal turing machine? I often think about problems that require an understanding of the very essence of computation and its inherent limitations. So, my questions are as followed:   Is the universe isomorphic to a universal turing machine? Is the universe isomorphic to a universal quantum computer? What evidence do we have within our universe to support an answer to such a question? What corollaries follow from a universe that is or is not isomorphic to a universal turing machine (or universal quantum turing machine)? Is this question really answerable, and what is the current literature on this subject?   I'm mostly interested in the implications and corollaries that follow from a universe that is or is not isomorphic to a turing machine (or universal quantum computer) and the insights that it would provide us with, about the nature of computation. Can an integrity check be run against a USB boot disk? EM algorithm gaussian mixtures- derivation I'm trying to make sense of a derivation I'm following from the lecture notes of Stanford's ML course.  Specifically the notes are here:   I'm particular I'm interested in the step where they are optimizing the lower bound of the log-likelihood by finding the derivative of that function w.r.t the mean (top of page 7). I'm a bit stuck on going from line 1 to line 2 in that sequence.  To copy here (using abbreviated notation for space):  $$ \nabla \mu_s \sum_{i=1}^{m}\sum_{j=1}^{k} w_j^{(i)}\log\frac{\alpha_j \exp\psi_{j}^{(i)}\phi_j}{w_j^{(i)}}  \qquad(1) $$ $$ \nabla \mu_s \sum_{i=1}^{m}\sum_{j=1}^{k} w_j^{(i)}\psi_{j}^{(i)}\qquad(2) $$ where $\psi_{j}^{(i)} =-\frac{1}{2}(x^{(i)}-\mu_j)^T\Sigma^{-1}_{j}(x^{(i)}-\mu_j)$ and $\alpha_j=\frac{1}{(2\pi)^{n/2}|\Sigma_j|^{1/2}}$.  The quantity $\mu_s$ is the mean (of a multivariate gaussian distribution) for class $s$.    In short, I'm not sure how they get from (1) to (2).  From just changing the log of (1) I see:  $$ \nabla \mu_s \sum_{i=1}^{m}\sum_{j=1}^{k} w_j^{(i)}\log \left(\alpha_j \exp\psi_{j}^{(i)}\phi_j\right) - w_j^{(i)}\log w_j^{(i)} \qquad(1) $$  To get to their form (2) my impression is that they take pre-emptively discard terms that would contain derivatives of $w_j^{(i)}$ (w.r.t. $\mu_s$).  At least if you assume that much, you get to (2).  However, as they show earlier in that PDF,  $$ w_j^{(i)} = p(z^{(i)}=j | x^{(i)}) =\frac{p(x^{(i)}|z^{(i)}=j)p(z^{(i)}=j) }{\sum_j p(x^{(i)},z^{(i)}=j) } $$ i.e. this quantity is the posterior distribution of the class labels.  Intuitively, it seems like $\partial w_j^{(i)}/ \partial \mu_s$ should be non-zero.  After all, changing the location of the mean $\mu_s$ would change your posterior probability for a particular class given the same data.  (Right?).  Just from a mechanical point of view, $\partial w_j^{(i)}/\partial \mu_j$ would seemingly have some non-zero terms since $p(x^{(i)}|z^{(i)}=j) \sim \mathcal{N}(\mu_j, \Sigma_j)$.  If I work through the algebra, I get  $$ \frac{\partial w_j^{(i)}}{\partial \mu_j} = w_j^{(i)}(1-w_j^{(i)})\frac{\partial \psi^{(i)}_{j}}{\partial \mu_j} $$  What am I missing here?  If I take the log-likelihood (without even thinking about EM algorithm),    $$ \ell = \sum_{i=1}^{m}\log \sum_{j=1}^{k}p(x^{(i)}|z^{(i)}=j)p(z^{(i)}=j) $$ and differentiate w.r.t $\mu_s$, I correctly get  $$ \mu_s^* = \frac{\sum_{i=1}^{m}w_s^{(i)}x^{(i)}}{\sum_{i=1}^{m}w_s^{(i)}} $$  so I'm comfortable with the eventual result, just not the intermediate steps. How to solve configure: error: readline library not found? I have an Ubuntu 10.04 LTS server with a minimal install that I want to compile postgres on. With the minimal install the server did not have gcc so I had to do an apt-get install gcc but now I keep getting this error from the postgres ./configure.  configure: error: readline library not found   I can't seem to find the name of the package that contains the readline library. So I have two questions.   What is the name of the package that contains readline? Is there some way given the error message above for me to find the associated package from its standard name? Pull-up and Pull-down Resistor Usage on Input or Output MCU Pins Are pull-up/down resistors (whether internal or external) only needed for MCU INPUT pins? In contrast, an MCU pin configured as an OUTPUT "knows what level it's at" because it does the driving - a "floating" MCU OUTPUT pin tied to some input of another circuit doesn't make sense, because the state of the MCU pin can only be high or low... do I have this right? Now, upon MCU bootup or failure, it may be beneficial to have a pull-up/down tied to this "MCU output to IC input" line to ensure that the input to some IC is never floating.  Maybe I just answered my own question here... pull-up/down resistors can be used on both input and output pins, depending on application? What exactly are genes, DNA and chromosomes. How do they relate to each other and what is their function? So I am new to biology and I have been reading, and searching the web, but I couldn't understand the whole framework of a cell. So as I understood:    A DNA, is a double chain of Nucleotides (A,T,G,C and a sugar) and a phosphate residue. Some parts of a DNA code a gene and some do not, exons and introns accordingly. As I understand it these the sequences of exons hold genes and the whole sequence is called genome. Now I read in the book Introduction to Computational Molecular Biology by J. C. Setubal and J. Meidanis. that I cite Each cell of an organism has a few very long DNA molecules. Each such molecule is called a chromosome. So each X shaped thing called a chromosome holds one DNA molecule?   Or is each "hand" of the chromosome one complete double helix of DNA so we have 23 pairs of such chromosomes? or is it that each we 23 pairs Xs?   Shouldn't we have just one uniquely identifiable DNA sequence? I am asking this, because I have also read that each chromosome encodes a different part of one's genome. Does this mean we have several DNA sequences?   This is really bothering me, since I can't figure out the correct structure of a cell.   EDIT: Now if each chromosome contains a pair of DNA helices. We have 46 chromosomes so overall within the cell there are 92 DNA sequences? All of which are the same? Or are they different? If they are different how do they code for different aspects of one's genome? What is the name of this castle part? How can I prove my publication list is mine? In some of the questions regarding author name, it is mentioned that people can use whatever they like, however, they must be consistent across their publications.  When it comes to being hired by a university for a tenure track position or by another principal investigator as a researcher, how can someone prove that they are the author of papers when their author name does not exactly match the one on their ID card? How can a principal investigator be sure that the publication list does actually belong to the person claiming them? Children’s books, one in space setting, the other in a jungle If $X_1, ..., X_n$ are independent identically-distributed random variables, what can be said about the distribution of $\min(X_1, ..., X_n)$ in general? Show that $y_1$ and $y_2$ are not Linearly Independent Suppose that $y_1(x)$ and $y_2(x)$ are solutions of the differential equation $y''+py'+qy=0$ on $I$. How can I show that if $y_1$ and $y_2$ vanish at the same point then they are not linearly independent?   Here is my attempt to prove the problem. Since $y_1(x)$ and $y_2(x)$ are solutions of the differential equation $y''+py'+qy=0$ on $I$, it is enough for me to show that the Wronskian of $y_1$ and $y_2$ denoted by $W(y_1,y_2)$ is zero. Now since $y_1$ and $y_2$ vanish at the same point say $p$ we have $y_1(p)=0$ and also $y_2(p)=0$.   Solving for $W(y_1,y_2)(p)$ we have: $y_1(p)y_2'(p)-y_2(p)y_1'(p)=0$ since $y_1(p)=0$ and also $y_2(p)=0$.   Am I correct? Thanks Why do I get "Permission denied (publickey)" when trying to SSH from local Ubuntu to a Amazon EC2 server? I have an instance of an application running in the cloud on Amazon EC2 instance, and I need to connect it from my local Ubuntu. It works fine on one of local ubuntu and also laptop. I got message "Permission denied (publickey)" when trying to access SSH to EC2 on another local Ubuntu. It's so strange to me.  I'm thinking some sort of problems with security settings on the Amazon EC2 which has limited IPs access to one instance or certificate may need to regenerate.  Does anyone know a solution? Lost/Deleted dpkg (package manager for Debian). How do I get it back? If $A=AB-BA$, is $A$ nilpotent? Let matrix $A_{n\times n}$, be such that there exists a matrix $B$ for which $$AB-BA=A$$  Prove or disprove that there exists $m\in \mathbb N^{+}$such  $$A^m=0,$$  I know $$tr(A)=tr(AB)-tr(BA)=0$$ then I can't.Thank you Do I need quote marks when I quote myself? How do you add a certificate authority (CA) to Ubuntu? Lighter nuclei liberate energy when undergoing fusion, heavier nuclei when undergoing fission.  What is it about the nucleus of an Iron atom that makes it so stable?  Alternatively: Iron has the greatest nuclear binding energy - but why? Warning when battery is fully charged I would like to get an alert (with sound) when my laptop battery is fully charged.  How can I do that? onChange event doesn't trigger "To your left" versus "on your left" There is a security camera on the building to your left      There is a security camera on the building on your left   Is there any real difference in meaning here? Which sounds more natural to native speakers?