I can't do "sudo apt-get update" (malformed entry 54 in sources.list)
E: Malformed entry 54 in list file /etc/apt/sources.list (Component)
Harmonic Motion What does the second column in the output of 'ls -n' mean? Topology induced by seminorms and initial topology Let's say we have a family of seminorms $(\rho_\alpha)_{\alpha \in A}$ on a vector space $V$.  There are two ways to topologize $V$ using those seminorms:   We define topology $\mathcal S$ by a subbasis consisting of sets of the form $$ B_{x, \alpha, \epsilon} =\{v \in V : \rho_\alpha(v - x) &lt; \epsilon \} $$ for some $\epsilon &gt; 0, x \in V, \alpha \in A$. We define topology $\mathcal I$ as the smallest topology in which the seminorms are continuous.   I've showed that the seminorms are continuous in $\mathcal S$, and that  implies $\mathcal I \subseteq \mathcal S$. I'm having trouble with the other direction. It is clear to me that $B_{0, \alpha, \epsilon} = \rho_\alpha^{-1}  ((-\epsilon, \epsilon ))$, so it's enough to show that vector addition is continuous in respect to $\mathcal I$, but I don't know how to do that. I use object != null a lot to avoid . What is an alternative to: if (someobject != null) {     someobject.doCalc(); } What's the most efficient way to concatenate strings? How to solve: -bash: : cannot execute binary file I have a file named b1 that contains assembly language. I am trying to execute the file on OSX using   $ ./b1   I get the following error:   -bash: ./b1: cannot execute binary file   I've looked at other similar questions, but to no avail. I'm definitely using a 64 bit architecture.   Here are output of relevant info:  $ file b1 b1: ELF 32-bit LSB executable, Intel 80386, version 1 (SYSV), dynamically linked (uses shared libs), for GNU/Linux 2.6.18, not stripped $ uname -a Darwin Anthonys-MacBook-Pro.local 13.0.0 Darwin Kernel Version 13.0.0: Thu Sep 19 22:22:27 PDT 2013; root:xnu-2422.1.72~6/RELEASE_X86_64 x86_64   Edit:   Ran as b1 which ended up terminating with:  /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/as: fatal error in /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/../libexec/as/x86_64/as How to solve $\frac 1{1.2.3}+\frac 1{2.3.4}+\frac 1{3.4.5}+...$ As my question says,   $$\frac 1{1.2.3}+\frac 1{2.3.4}+\frac 1{3.4.5}+...$$  As far as $\frac 1{1.2}+\frac 1{2.3}+\frac 1{3.4}+...$ is concerned, we can write it as $\frac 1{n-1} - \frac 1{n}$ What can we do here? What are the fundamentals of implementing hook_menu()?  I would like to see the basics covered in a single question, to avoid having to answer the same similar but different questions over and over. Regex: match everything but specific pattern Is it possible to remove a terminal command? For example, if there was a command call lookup (not an alias), how would I go about removing it? How exactly does a "random effects model" in econometrics relate to mixed models outside of econometrics? I used to think that "random effects model" in econometrics corresponds to a "mixed model with random intercept" outside of econometrics, but now I am not sure. Does it?  Econometrics uses terms like "fixed effects" and "random effects" somewhat differently from the literature on mixed models, and this causes a notorious confusion. Let us consider a simple situation where $y$ linearly depends on $x$ but with a different intercept in different groups of measurements:  $$y_{it} = \beta x_{it} + u_i + \epsilon_{it}.$$  Here each unit/group $i$ is observed at different timepoints $t$. Econometricians call it "panel data".    In mixed models terminology, we can treat $u_i$ as  a fixed effect or as a random effect (in this case, it's random intercept). Treating it as fixed means fitting $\hat \beta$ and $\hat u_i$ to minimize squared error (i.e. running OLS regression with dummy group variables). Treating it as random means that we additionally assume that $u_i\sim\mathcal N(u_0,\sigma^2_u)$ and use maximum likelihood to fit $u_0$ and $\sigma^2_u$ instead of fitting each $u_i$ on its own. This leads to the "partial pooling" effect, where the estimates $\hat u_i$ get shrunk toward their mean $\hat u_0$.  R formula when treating group as fixed:    y ~ x + group R formula when treating group as random:   y ~ x + (1|group)  In econometrics terminology, we can treat this whole model as a fixed effects model or as a random effects model. The first option is equivalent to the fixed effect above (but econometrics has its own way of estimating $\beta$ in this case, called "within" estimator). I used to think that the second option is equivalent to the random effect above; e.g. @JiebiaoWang in his highly upvoted answer to  says that      In econometrics, the random-effects model may only refer to random intercept model as in biostatistics    Okay --- let us test if this understanding is correct. Here is some random data generated by @ChristophHanck in his answer to  (I put  for those who do not use R):    @Christoph does two fits using econometrics approaches:    fe &lt;- plm(stackY~stackX, data = paneldata, model = "within") re &lt;- plm(stackY~stackX, data = paneldata, model = "random")   The first one yields the estimate of beta equal to -1.0451, the second one 0.77031 (yes, positive!). I tried to reproduce it with lm and lmer:  l1 = lm(stackY ~ stackX + as.factor(unit), data = paneldata) l2 = lmer(stackY ~ stackX + (1|as.factor(unit)), data = paneldata)   The first one yields -1.045 in perfect agreement with the within estimator above. Cool. But the second yields -1.026, which is miles away from the random effects estimator. Heh? What is going on? In fact, what is  plm even doing, when called with model = "random"?  Whatever it is doing, can one somehow understand it via the mixed models perspective?  And what is the intuition behind whatever it is doing? I read in a couple of econometrics places that random effects estimator is a weighted average between the fixed effects estimator and the "between" estimator which is more or less regression slope if we do not include group identity in the model at all (this estimate is strongly positive in this case, around 4.) E.g. @Andy :     The random effects estimator then uses a matrix weighted average of the within and between variation of your data. [...] This makes random effects more efficient[.]   Why? Why would we want this weighted average? And in particular, why would we want it instead of running a mixed model? Find the nilpotents in $\mathbb{Z}_3[x]/(x^2-1)$ Marketing Cloud IP Warming - is it critical that we send up to the weekly limits by domain? SQL comma-separated row with Group By clause How can I make an X-Y scatter plot with histograms next to the X-Y axes? Is it standard to say $-i \log(-1)$ is $\pi$? I typed $\pi$ into Wolfram Alpha and in the short list of definitions there appeared  $$ \pi = -i \log(-1)$$  which really bothered me. Multiplying on both sides by $2i$:  $$ 2\pi i = 2 \log(-1) = \log(-1)^2 = \log 1= 0$$  which is clearly false.  I guess my error is $\log 1 = 0$ when $\log$ is complex-valued.  I need to use $1 = e^{2 \pi i}$ instead.  So my question: is it correct for WA to say $\pi = -i \log(-1)$?  Or should be they specifying "which" $-1$ they mean?  Clearly $ -1 = e^{i \pi}$ is the "correct" value of $-1$ here. I'm trying to test a page that has an input but really its a drop down list. The user enters text and then a dropdown appears based on what the user inputted. They are groups that are in the database. If I look for these elements before typing anything they dont exist but once I start typing they do exist.  The problem is I cant just type the whole name and then press enter or return because an option has to be selected from the list.  I can press arrow down to get the focus on the item from the list but then if I say enter it doesnt select, it sends that enter to the original element and nothing gets selected.  I can find the select element:  var ddList = driver.FindElement(By.CssSelector(".ui-autocomplete-input")); ddList.SendKeys("Admins");   This is the first time the list appears in the html.  It looks like this:  &lt;li class="ui-menu-item" role="presentation"&gt; Deriving the conditional distributions of a multivariate normal distribution We have a multivariate normal vector ${\boldsymbol Y} \sim \mathcal{N}(\boldsymbol\mu, \Sigma)$. Consider partitioning $\boldsymbol\mu$ and ${\boldsymbol Y}$ into  $$\boldsymbol\mu = \begin{bmatrix}  \boldsymbol\mu_1 \\  \boldsymbol\mu_2 \end{bmatrix} $$ $${\boldsymbol Y}=\begin{bmatrix}{\boldsymbol y}_1 \\  {\boldsymbol y}_2 \end{bmatrix}$$  with a similar partition of $\Sigma$ into $$  \begin{bmatrix} \Sigma_{11} &amp; \Sigma_{12}\\ \Sigma_{21} &amp; \Sigma_{22} \end{bmatrix} $$ Then, $({\boldsymbol y}_1|{\boldsymbol y}_2={\boldsymbol a})$, the conditional distribution of the first partition given the second, is  $\mathcal{N}(\overline{\boldsymbol\mu},\overline{\Sigma})$, with mean $$ \overline{\boldsymbol\mu}=\boldsymbol\mu_1+\Sigma_{12}{\Sigma_{22}}^{-1}({\boldsymbol a}-\boldsymbol\mu_2) $$ and covariance matrix $$ \overline{\Sigma}=\Sigma_{11}-\Sigma_{12}{\Sigma_{22}}^{-1}\Sigma_{21}$$  Actually these results are provided in Wikipedia too, but I have no idea how the $\overline{\boldsymbol\mu}$ and $\overline{\Sigma}$ is derived. These results are crucial, since they are important statistical formula for deriving Kalman filters. Would anyone provide me a derivation steps of deriving $\overline{\boldsymbol\mu}$ and $\overline{\Sigma}$ ? Thank you very much! How to get exact camera view? Combinatorial proof of an identity with squared binomial coefficients Is it true that $\sum_kk\binom{n}{k}^2=n\binom{2n-1}{n-1}$? (I proved it using generating functions). Could you help me to prove it combinatorially? please