How to Replace CentOS's GRUB with Ubuntu's GRUB I can't boot into Ubuntu 12.04 just CentOS. How should I do to revert back Ubuntu GRUB?
How can I repair grub? (How to get Ubuntu back after installing Windows?) I installed Windows 7, which ate Ubuntu's boot file. When starting up the computer, it now goes straight to Windows, without giving me the option of booting Ubuntu.   How can I get Ubuntu back?
Deriving Heaviside-Feynman formula for the electric field of an arbitrarily moving charge from Lienard-Wiechert potential Disappointed to see that the 18.04 installer no longer offers the option to encrypt the home directory. According to  bug report referenced in the installer, the recommended method for encryption these days is full-disk with LUKS, or fscrypt for directories. Full-disk encryption seems a bit overkill for my needs, and all the bugs and caveats mentioned on the  don't make it a very attractive option. All I really want is to protect my home directory from someone accessing my documents, photos, etc. if my laptop were to get stolen, making fscrypt the option for me.  The  has some examples on how to set it up, but I can't find any documentation aimed at encrypting the home directory on Ubuntu. The old ecryptfs tool is still available, but after setting it up Ubuntu would sometimes freeze at the login screen.  So my question is: How do I setup fscrypt to encrypt my /home directory and decrypt when I log in? I also liked how ecryptfs allowed decrypting the folder manually (eg. from disk images).  (A similar question was posted  and was unfortunately closed for being an "off-topic" bug report. To clarify, this is not a bug report. The fact that the encrypt home directory option was removed from the installer was intentional. All I'm asking here is how to setup fscrypt.) How much longer will my 10 year old frame last? Unknown apps/processes with number only are shown in the battery screen Book about classical mechanics Automatically show connected mass storage devices on Windows desktop When creating the id attributes for HTML elements, what rules are there for the value? I have been trying to install a fresh copy of Ubuntu 14.04 (64 bit version) on my desktop PC from a USB memory stick, but I'm experiencing a problem.  Each time I attempt an install I received an  "[Errno 5] Input/output error", followed by the following message before the installer exits:  "The installer encountered an error copying files to the hard disk: [Errno 5] Input/output error This is often due to a faulty CD/DVD disk or drive, or a faulty hard disk. It may help to clean the CD/DVD, to burn the CD/DVD at a lower speed, to clean the CD/DVD drive lens (cleaning kits are often available from electronics suppliers), to check whether the hard disk is old and in need of replacement, or to move the system to a cooler environment."  In order to attempt to remedy this I have so far tried installing Ubuntu to two different hard disks, one of which is brand new, and each time I have received the same error "Errno 5" message. I have also tried installing from 3 different USB sticks using torrent and Ubuntu site iso images burned using both Unetbootin and Startup Disk Creator.  Again, these measures have proved fruitless.  The installation process varies depending of the iso I use, with the Ubuntu download page iso exiting at an earlier stage compared to the torrented iso.  The later torrent iso returns an error and exits when I am prompted for my name, computer's ID and password.  I've never experienced anything like this when installing previous versions of Ubuntu.  I'm absolutely stumped!  Does anyone have any more ideas? How can I stop the symbolic x coords to be plotted twice? Lower case "k" in Cocoa I know this is a common convention, but what does the "k" in variable names signify? (i.e. kMaxImageViewSize)  I looked in the Apple documentation on Variable names and found no mention of it.  Thanks for answering How can I prevent 'grep' from showing up in ps results? When I  search for some process that doesn't exist, e.g.  $ ps aux | grep fnord                           wayne    15745  0.0  0.0  13580   928 pts/6    S+   03:58   0:00 grep fnord   Obviously I don't care about grep - that makes as much sense as searching for the ps process!  How can I prevent grep from showing up in the results? For $u:\mathbb{R}\times [0,1]$ with boundary conditions $u(0,x)=\cos (2\pi x)$ and $u(t,0)=u(t,1)$.   Solve $u_t=u_{xx}+u_x$.  I had this on an exam and tried to write $u$ as a product of two single variate functions and convert to an ode using the usual methods but things got messy and I couldn't finish. I also tried to write $u$ as a Fourier series in $x$ with coefficients $a_k(t)$ depending on $t$, but this also didn't seem to yield anything.  Is there a more clever way to approach this, possibly with Fourier series? I heard from a friend there may be a Fourier transform method since the equation was "homogeneous in momentum space."   Thanks! Recursive time series forecasting model In a recursive forecasting model, let's say you are trying to predict sales of Target for the next month and you will append that prediction to your input and predict the month after. Basically, your target is Sales quantity, which you lagged. But let's say you have other continuous numerical features like Price which you will lag as well. As your model is making predictions on Sales_QTY, it will feed it back to sales_qty as input so you will never ran out of values for that one. So how do you deal with your other features (other than the target) in order to generate them as next months inputs in a recursive forecasting model (because eventually you will run out of them)? DO you create a sub-model and try to predict them as well? How to use wc and piping to find how many files and directories are in a certain directory? How can I use word counter (wc) and piping to count how many files or directories are in the /usr/bin directory? sudo credential caching on by default Mapping building to only one grid which contains more than half of its area using QGIS? Properly discontinuous actions and discrete groups in complete Riemannian manifolds. I was reading the article "The Geometries of 3-manifolds" by Peter Scott and in the end of page 406 he states the following:     If $G$ acts properly discontinuously on a space $X$, then $G$ is a   discrete subset of the space of all continuous functions $X \to X$   with the compact-open topology. The converse is false, in general, but   is true if $X$ is a complete Riemannian manifold and $G$ is a group of   isometries of $X$.   How do I prove the last statement?     If $X$ is a complete Riemannian manifold and   $G$ is a group of isometries of $X$ acting properly discontinuously on $X$, then $G$ is discrete.    Observation: We say a group $G$ of homeomorphisms of $X$ acts properly discontinuously on $X$ if for every compact $K \subset X$ the set $$\{g \in G: gK \cap K \neq \emptyset\}$$ is finite. I'm interested in understanding which neural network architecture is currently the state of the art (sometimes abbreviated "SOTA") with respect to standard image classification tasks such as MNIST, STLN-10 and CIFAR. This is challenging because new results are published frequently, and it can be hard to keep up. Is there a resource or website which tracks the best results for these tasks? What is the easiest way to encode a PHP string for output to a JavaScript variable?  I have a PHP string which includes quotes and newlines.  I need the contents of this string to be put into a JavaScript variable.  Normally, I would just construct my JavaScript in a PHP file, à la:  &lt;script&gt;   var myvar = "&lt;?php echo $myVarValue;?&gt;"; &lt;/script&gt;   However, this doesn't work when $myVarValue contains quotes or newlines. Perform feature normalization before or within model validation? A common good practice in Machine Learning is to do feature normalization or data standardization of the predictor variables, that's it, center the data substracting the mean and normalize it dividing by the variance (or standard deviation too). For self containment and to my understanding we do this to achieve two main things:   Avoid extra small model weights for the purpose of numerical stability. Ensure quick convergence of optimization algorithms like e.g. Conjugate Gradient so that the large magnitude of one predictor dimension w.r.t. the others doesn't lead to slow convergence.   We usually split the data into training, validation and testing sets. In the literature we usually see that to do feature normalization they take the mean and variance (or standard deviation) over the whole set of predictor variables. The big flaw I see here is that if you do that, you are in fact introducing future information into the training predictor variables namely the future information contained in the mean and variance.  Therefore, I do feature normalization over the training data and save the mean and variance. Then I apply feature normalization to the predictor variables of the validation and test data sets using the training mean and variances. Are there any fundamental flaws with this? can anyone recommend a better alternative?