I upgraded to Ubuntu 19.04 from 17.10 and now I do not have anything on my desktop at all. I am running this on Virtual Box 6. I know enough about Unix to be dangerous, I just use it for developing websites.    From looking around various articles I have tried these commands as suggested but nothing has worked, any ideas?  From SSH I have tried these:  sudo apt-get install --reinstall ubuntu-desktop sudo apt install --reinstall gnome-shell-extension-desktop-icons sudo apt install --reinstall gnome
Yesterday I upgraded my Ubuntu 18.10 to Ubuntu 19.04, and after the restart, the trash icon and my Desktop files have disappeared from the UI. If I go to the Desktop folder from my personal folder, I can see the files, but my Desktop is empty.  How can I show my files in the Desktop again?
Assigning point IDs to respective start and end attributes of polyline? I have two shapefiles, Point and Polyline. I'm trying to populate my polyline layer with the point data ID (systemID) where the lines start to where they end; eg. create two columns (from and to). The lines already have flow direction assigned to them. I am using ArcHydro tools but Arcpy is also fine.  In ArcHydro I have tried using Attribute Tools > Generate From/To Nodes for lines but it creates values for To and From that are not from my point data. My other alternative was using Feature to Vertices and joining the result to my point data table to get the systemID. But then I would have to update the polyline layer as well. I am sure there is a easier way of doing it.  Note: not all the lines have points at the vertices, blank values are fine for them. For a vector space $V$ and a linear operator $f : V \to V$, under what conditions does $V = \ker(f) \oplus \operatorname{im}(f)$?  Is it always true, or only in special cases?  Edit: $V$ is finite dimensional. How can I obtain a Cauchy distribution from two standard normal distributions? I am interested in      Let $X\sim N(0,1), Y \sim N(0,1)$ independently. Show $\frac{X}{X+Y}$   is a Cauchy random variable.   My work:  $f_{X,Y}(x,y)=\frac{1}{2\pi} e^{\frac{-1}{2}(x^2+y^2)}, -\infty&lt;x,y&lt;\infty$ by independence  Let $U=\frac{X}{X+Y},V=X+Y$. (Is there a better $V$ to choose for this bivariate transformation?)  Then, $X=UV, Y=V - UV$. So, $|J|=V$.  $f_{U,V}(u,v)=f_{X,Y}(uv,v-uv)|J|=\frac{v}{2\pi}e^{\frac{-v^2}{2}(2u^2-2u+1)},-\infty&lt;u&lt;\infty$  $f_U(u)=\frac{1}{2\pi}\int_{-\infty}^{\infty}ve^{\frac{-v^2}{2}(2u^2-2u+1)}dv$. Let $y=\frac{v^2}{2}(2u^2-2u+1)$, so $dy=v(1+2u^2-2u)dv$. Then,  $f_U(u)=\frac{1}{2\pi(2u^2-2u+1)}\int_0^{\infty}e^{-y}dy=(\pi[(\frac{u-1/2}{2})^2+1])^{-1}, -\infty&lt;u&lt;\infty$,   which is not exactly a Cauchy distribution. Where did I mess up? More importantly, how would you proceed in solving this problem? Solving the matrix equation $AP=PB$, where $A$ and $B$ are similar Currently I'm stuck with this problem:     The following matrices are similar to each other.      $$A=\begin{bmatrix}-3&amp;0\\8&amp;4\end{bmatrix},\qquad B=\begin{bmatrix}-4&amp;4\\-2&amp;5\end{bmatrix},\qquad C=\begin{bmatrix}k&amp;1\\m&amp;-2\end{bmatrix}.$$      How do you find the matrix $P$ such that $AP = PB$?       And how can you calculate the values of $k$ and $m$?      Thank you in advance. How bias arises in using maximum likelihood to determine the variance of a Gaussian? While reading , I ran into the following explanation for why there is an error in estimating the variance of Gaussian distribution using Maximum Likelihood. On ,      Illustration of how bias arises in using maximum likelihood to   determine the variance of a Gaussian. The green curve shows the true   Gaussian distribution from which data is generated, and the three red   curves show the Gaussian distributions obtained by fitting to three   data sets, each consisting of two data points shown in blue, using the   maximum likelihood results (1.55) and (1.56). Averaged across the   three data sets, the mean is correct, but the variance is   systematically under-estimated because it is measured relative to the   sample mean and not relative to the true mean.   I'm pasting figure 1.15 and the required equations (1.55, 1.56) here for convenience.  $\mu_{ML} = \frac{1}{N}\sum_{n=1}^{N}x_n$   $\sigma_{ML}^2 = \frac{1}{N}\sum_{n=1}^{N}(x_n - \mu_{ML})^2$    I can imagine how maximum likelihood estimation would look pictorially. However, I'm not able to understand the figure or the explanation. I would appreciate if someone could label the figure or explain what points are considered in each part of the figure and why the curve could look like that in each case.  I looked up , and I'm able to understand why there is a factor of $\frac{N-1}{N}$ between the true $\sigma^2$ and $\sigma_{MLE}^2$.  ${\rm I\!E}[\sigma_{MLE}^2]= (\frac{N-1}{N})\sigma^2 $ Use Yaml for MongoMapper Config Proof of the equality $\sum\limits_{k=1}^{\infty} \frac{k^2}{2^k} = 6$ 4 GB of RAM installed but only 2.97 GB usable. What gives? Possible Duplicate:        I just bought a used MacBook on  that came installed with 4 GB of . The first thing I did was installing Windows&nbsp;7 using . Here's where it gets weird. When I finally booted it up in Windows&nbsp;7, I checked the Computer properties, and it said that there were 4&nbsp;GB of RAM, but only 2.97&nbsp;GB is usable. Why is that? Is there anything I can do about this? get UTC time in PHP Consider the Fibonacci numbers $F(0) = 0; F(1)=1; F(n) = F(n-1) + F(n-2)$.  Prove by induction that for all $n&gt;0$,  $$F(n-1)\cdot F(n+1)- F(n)^2 = (-1)^n$$  I assume $P(n)$ is true and try to show $P(n+1)$ is true, but I got stuck with the algebra. How do we reach $P(n+1)$ from $P(n)$? Also, strong induction may be used instead. How to placate a very angry priest? So whilst going to rescue the Moth Priest 'Dexion Evicus', I think I accidentally might have hit him, not sure how. Anyhow the bearded maniac has taken this as a personal attack and refuses to accept a yield. Because he's so angry with me he'll just not quit, but being a quest NPC he can't die, nor can my companion Mjoll. So aside from the hilarious infinity battle, I thought, fair enough I'll talk to him whilst he's 'downed' and lo, I progressed the quest.  So I go back to the Dawnguard fortress, and he's there in the middle shouting murder and attacking everyone in the fortress! Clearly his rage knows no bounds.    Is there a way to forcibly pacify him without interring him? Or do I need to roll back and do over? My Windows folder takes up too much space - how can I reduce it? In typical PCs the Size of Windows Folder is 6 to 8 GB but in my Case its 25GB. How can I delete the unwanted files from the Windows Folder (which, in turn, will optimize my C Drive size).  My system is Windows 7 with 60 GB in C Drive with 59 GB occupied. What do viruses do? There are those big green spiked things that my friends call viruses. I don't know what they do and I want to know. Do the viruses do something to the device you are playing on? Are they good or bad? Blender Cycles Bake Not Looking Like Object in Rendered View I have a couch in Blender that looks like this in rendered view.    I baked it with a combined texture and this is what it came out to be.    This is what it looks like added to the mesh.    Why does the view with the baked image not look exactly like the rendered view?  Does it have something to do with my mesh and faces?  This is my mesh.    This has never happened to me before as I baked a couple things before this which came out perfectly. OpenVPN restriction Ive set up an OpenVPN server at my place and handed out a certificate to a client who has an openvpn client at his place.  I need require that he should be able to connect to the vpn server only from one IP address (his current one).  How can I do this? If $\mid a_{jj}\mid \gt \sum_{i \neq j} \mid a_{ij} \mid$ then vectors $a_1,\dots ,a_n \in \mathbb{R}^n$ are linearly indendent. Let $a_1,\dots ,a_n \in \mathbb{R}^n$ with $a_i = (a_{i1}, \dots , a_{in})$.  Show that when $\mid a_{jj}\mid \gt \sum_{i \neq j} \mid a_{ij} \mid \; \forall \; j\in \{1,\dots ,n\}$, then $a_1,\dots ,a_n$ are linearly independent.  I want to show that the matrix \begin{matrix} a_{11} &amp; a_{21} &amp; \cdots &amp; a_{n1} \\ a_{12} &amp; a_{22} &amp; \cdots &amp; a_{n2} \\ \vdots &amp; &amp; \ddots &amp; \\ a_{1n} &amp; a_{2n} &amp; \cdots &amp; a_{nn} \end{matrix} can be transformed into an upper triangular matrix with all entries in the main diagonal $\neq 0$.  For the cases $n=1$ and $n=2$ this is easy. However, I fail to show this for the general case; the problem being that the tranformations mess up the coefficients such that I can't use the assumption on the $a_{jj}$'s anymore.  I'm pretty sure that this has already been solved somewhere on here, but I don't know how to search for it. This problem is bugging me for some time.     $f:(X,\mathcal{T}) \to (Y,\mathcal{T}')$ is a continuous bijection where $(X,\mathcal{T})$ is compact and $(Y,\mathcal{T}')$ is $T_2$ (i.e. Hausdorff and $T_1$). Then show that $f$ must be a homeomorphism.   So far I have failed to understand any relation between the compactness of domain and $T_2$-ness of the range. I tried in vain to prove that $f(U)$ is open in $(Y,\mathcal{T}')$ whenever $U$ is so in $(X,\mathcal{T})$. I know $f$ makes $X$ and $Y$ respectively $T_2$ and compact and hence makes both of them $T_4$ spaces. But that is not coming to any use. I would be glad if someone helps me with a solution. Thank you. passing values between forms (winforms) Why is there no overload of Interlocked.Add that accepts Doubles as parameters? Proof that any self-complementary graph has to have $4k$ or $4k+1$ vertex, for some $k \in \mathbb N$