So I just bought a used Sony vaio with the ubuntu operating system. I disabled my admit account thinking that when I powered up it would just go to the home screen. Now I can't log in. It recognizes my user name but not the password. How can I get passed the log in screen?
I'm trying to install the  IDE in my Ubuntu and in the process it's asking for my root password which I don't remember. I know my  user password. How can I recover my root password?
How do you setup a TeX document to self publish a book online? I am a casual TeX user. Mostly I have used it to type up some reports/homeworks for class in my graduate engineering work. In the past I have used document type "article". And then kept all the defaults.  I need to self publish something online. At the highest level I would like to know: How do you setup a TeX document to self publish a book online?  Some more low level questions:   Is there a document type for "book" and what is its name? I am looking at Amazon Createspace. : filetype=pdf, trim size, margins (gutter and outside margins). All of these various trim size, margins gutter/outside can be adjusted in the Tex document?   Has anyone seen any good TeX templates for Amazon Createspace? If you could demonstrate how to tweak a specific "book" document type that would be very helpful also.    Other than the above questions, please feel free to offer any other sort of help since I am completely new to this process (advice, links to good tutorials, links to wikibooks). There are no interfaces on which a capture can be done Ethernet device not managed I am using Ubuntu 16.10 and recently I have not been able to connect to internet using Ethernet. The network manager shows device not managed. The WiFi network is working fine.      I've tried the solution from this questionn  with no use.  My /etc/network/interfaces file:  # interfaces(5) file used by ifup(8) and ifdown(8) auto lo iface lo inet loopback   My /etc/NetworkManager/NetworkManager.conf file:  [main] plugins=ifupdown,keyfile,ofono dns=dnsmasq  [ifupdown] managed=true   Output of nmcli d:  DEVICE  TYPE      STATE      CONNECTION  wlp2s0  wifi      connected  eduroam     enp8s0  ethernet  unmanaged  --          lo      loopback  unmanaged  -- I just attempted to fix a typo on a question on cooking.stackexchange.com and saw this     Oops! Your edit couldn't be submitted because:         Your edit summary must be at least 10 characters   This edit is less than 6 characters; surely more can be improved with your edit?      So, misspelled words have to just be left misspelled?  That could prevent the post from being found in a search. Why does $\lim_{n \to\infty}a_{n+1} = \lim_{n\to\infty}a_n$? Assume that $\{a_n\}$ is a convergent sequence. How to use the definition of a limit of a sequence to prove that $\lim\limits_{n \to\infty}a_{n+1} = \lim\limits_{n\to\infty}a_n$? Does option "-h" for "ls" display size in MB or MiB and is it consistent over Unix-based systems? Creating a responsive menu system for a site with a lot of content I am currently creating a website which has a lot of content - text. For this I need a serious menu structure with 5-10 menu items per menu and at least 15 menu-blocks per main menu item.   Further it has to be accessible via mobile.   Till now I have used panels and added menus inside the panels. But mobile isn't to happy about that and the list-names in the menus overlap the content.   How would you go about solving this issue? I trust you experts here! :-) Detecting device for debugging (ADB) does not work How to understand the drawbacks of K-means K-means is a widely used method in cluster analysis. In my understanding, this method does NOT require ANY assumptions, i.e., give me a dataset and a pre-specified number of clusters, k, and I just apply this algorithm which minimizes the sum of squared errors (SSE), the within cluster squared error.  So k-means is essentially an optimization problem.  I read some material about the drawbacks of k-means.  Most of them say that:   k-means assumes the variance of the distribution of each attribute (variable) is spherical;  all variables have the same variance; the prior probability for all k clusters is the same, i.e., each cluster has roughly equal number of observations;   If any one of these 3 assumptions are violated, then k-means will fail.  I could not understand the logic behind this statement. I think the k-means method makes essentially no assumptions, it just minimizes the SSE, so I cannot see the link between minimizing the SSE and those 3 "assumptions". Given two sets$$A=\{1,3,5\}\quad,\quad B=\{1,3,8\}.$$ Then I compute the $A\setminus B=\{5\}.$   But my book$^\dagger$said: ... . Also, observe that $x\notin A\setminus B$ does not mean that $x\notin A\lor x\in B$. Why?  I don't know how to explain the Why, please give me some example.    $^\dagger$, second edition, page $4$.  EDIT: My intuition tells me that it does mean that. I guess there is some special case about null-set or about the restriction of the universal set. Prove $(x+y) \text{ mod } n = ((x \text{ mod } n)+(y \text{ mod } n)) \text{ mod } n,\,$ and product analog For all $n \in \mathbb{N}$, $n &gt; 1$ and $x,y \in \mathbb{Z}$ prove that      \begin{equation}   (x+y) \text{ mod } n   = ((x \text{ mod } n)+(y \text{ mod } n))\text{ mod } n. \end{equation}   Would this be a proof if I say:  Let $x = x' \text{ mod } n$ and let $y=y' \text{ mod } n$. Then  \begin{equation}   x+y = x'+y' \text{ mod } n. \end{equation}  Now, if we look at the current thing we want prove, this will follow:  \begin{equation}   x+y \text { mod } n = (x \text{ mod } n + y \text{ mod } n) \text{ mod } n. \end{equation}    I'm not sure if this is allowed or will count as a "proof" at all. Maybe you can show me some other ways of doing this, more precise ones? I'm trying to solve this problem from Hatcher 3.3.24.  Let $M$ be a closed connected 3-manifold, and write $H_1(M, \mathbb{Z})$ as $\mathbb{Z^r} \oplus T$ where $T$ is torsion. Show that $H_2(M, \mathbb{Z})$ is $\mathbb{Z^r}$ if $M$ is orientable or $\mathbb{Z^{r-1}} \oplus \mathbb{Z_2}$ if $M$ is not orientable. In particular $r \geq 1$ if $M$ is not orientable. Using exercise 6 construct examples showing there are no other restrictions on the homology groups of closed 3-manifolds. (In the nonorientable case consider the manifold $N$ obtained from $S^2 \times I$ by identifying $S^2 \times \{0\}$ with $S^2 \times \{1\}$ via a reflection of $S^2$.)  I think I've solved the first part, here's my proof:   First note the following theorem: If $M$ is a closed connected and orientable n-manifold then $H_n(M, \mathbb{Z}) = \mathbb{Z}$ and $Tors(H_{n-1}(M, \mathbb{Z})) = 0$. If nonorientable, then $H_n(M, \mathbb{Z}) = 0$, $Tors(H_{n-1}(M, \mathbb{Z})) = \mathbb{Z_2}$.  Now note by Poincare duality and UCT we have that $H_2(M) \cong H^1(M) \cong \mathbb{Z^r} \oplus T_0 \cong \mathbb{Z^r}$.   In the nonorientable case consider the exact sequence:  $0 \longrightarrow H_2(M) \longrightarrow H_1(M) \longrightarrow H_0(M) \longrightarrow 0$  But we know $H_1(M) = \mathbb{Z^r} \oplus Z_2$ from the earlier theorem and $H_0(M) = \mathbb{Z}$.   Therefore $H_2(M) \cong ker(\mathbb{Z^r} \oplus Z_2 \rightarrow \mathbb{Z}) \cong \mathbb{Z^{r-1}} \oplus \mathbb{Z_2}$.   For the rest of the question I'm quite lost. Proving : $ \bigl(1+\frac{1}{n+1}\bigr)^{n+1} \gt (1+\frac{1}{n})^{n} $ My processor is 64-bit - does that mean I need the amd64 image? Help me understand Support Vector Machines I understand the basics of what a Support Vector Machines' aim is in terms of classifying an input set into several different classes, but what I don't understand is some of the nitty-gritty details. For starters, I'm a bit confused by the use of Slack Variables. What is their purpose?  I'm doing a classification problem where I've captured pressure readings from sensors I've placed on the insole of a shoe. A subject will sit, stand, and walk for a couple of minutes while pressure data is recorded. I want to train a classifier to be able to determine whether a person is sitting, standing or walking and be able to do that for any future test data. What classifier type do I need to try? What is the best way for me to train a classifier from the data I've captured? I have 1000 entries for sitting, standing and walking (3x1000=3000 total), and they all have the following feature vector form. (pressurefromsensor1, pressurefromsensor2, pressurefromsensor3, pressurefromsensor4) Windows 10 won't accept Firefox as the default browser Grabbing the first [x] characters for a string from a pipe Solve the equation $28^x = 19^y+87^z$ in integers Possible Duplicate:           My name is Greg — this is Greg 's post.  If my name ended with an 's', I am aware of the proper apostrophe usage (James&nbsp;→ James'), but how should this be pronounced?  Phonetically, am I Jameses best friend or James best friend? Let's say you have a typical web app and with a file configuration.whatever. Every developer working on the project will have one version for their dev boxes, there will be a dev, prod and stage versions. How do you deal with this in source control? Not check in this file at all, check it with different names or do something fancy altogether?