Brute force attack on my servers ports burning through bandwidth
How do I deal with a compromised server?
$X$ is a Hausdorff space and $\sim$ is an equivalence relation.  If the quotient map is open, then $X/{\sim}$ is a Hausdorff space if and only if $\sim$ is a closed subset of the product space $X \times X$.  Necessity is obvious, but I don't know how to prove the other side. That is, $\sim$ is a closed subset of the product space $X \times X$ $\Rightarrow$ $X/{\sim}$ is a Hausdorff space. Any advices and comments will be appreciated. Insert a full page image How can I insert a full page image?  No blank space left in the page.  Bonus:  How can I add some text over the image? Energy conservation in General Relativity I understand that energy conservation is not a rule in general relativity, but I'd like to know under what circumstances it can still be possible. In other words, when is it possible to associate a potential energy to the gravitational field, so that the energy is constant in the evolution of the system?  Here are some examples, is there a convenient way to define energy in these scenarios?   Just a system of gravitational waves. A point mass moving in a static (but otherwise arbitrary) space-time. Equivalent (if I'm not mistaken) to a test mass moving in the field of a second much larger mass, the larger mass wouldn't move. Two rotating bodies of similar mass.   Overall, I'm trying to understand what keeps us from associating a potential energy to the metric. When we break the time translation symmetry of a system by introducing an electromagnetic field, we can still conserve energy by defining an electromagnetic potential energy. Why can't we do the same when we break TT symmetry by making space-time curved? Requires installation of untrusted packages? Call Servlet and invoke Java code from JavaScript along with parameters Which singular names ending in “s” form possessives with only a bare apostrophe? Many questions already ask about this topic ( , , etc.) and their answers vary, but they always give exceptions to the apostrophe-s rule, for example:      6.24 The general rule for the possessive of nouns covers most proper nouns, including most names ending in sibilants."   Examples they give include Kansas’s, Ross’s land, and Jones’s reputation. Exceptions include Jesus’  and Moses’.  Which names does this apply to?  Is the Aeneas’  form correct, or is it Aeneas’s instead? Entity Query field condition for Webform submissions I'm trying to query webform submissions based on a field value. The field is called "ticket_for". Somethings I tried and do not work:      -&gt;condition('ticket_for',$adnummer)     -&gt;condition('webform_submission_data:ticket_for',$adnummer,'CONTAINS') ...   What kind of condition should I be using in a query like this?  $query = \Drupal::entityQuery('webform_submission') -&gt;condition('webform_id', 'ticket') -&gt;condition('entity_id', $eventID) -&gt;condition('webform_submission_data:ticket_for',$adnummer,'CONTAINS'); How to write conditional equations with one-sided curly brackets Summary table with a field that shows components of each summarized record Algebra of LDA. Fisher discrimination power of a variable and Linear Discriminant Analysis Apparently,     the Fisher analysis aims at simultaneously maximising the   between-class separation, while minimising the within-class   dispersion. A useful measure of the discrimination power of a variable   is hence given by the diagonal quantity: $B_{ii}/W_{ii}$.     I understand that the size (p x p) of the Between (B) and Within-Class (W) matrices are given by the number of input variables, p. Given this, how can $B_{ii}/W_{ii}$ be a "useful measure of the discrimination power" of a single variable? At least two variables are required to construct the matrices B and W, so the respective traces would represent more than one variable.  Update: Am I right in thinking that $B_{ii}/W_{ii}$ is not a trace over a trace, where the sum is implied, but the matrix element $B_{ii}$ divided by $W_{ii}$? Currently that is the only way I can reconcile the expression with the concept. In France the higher education is like this:   3 years of "Licence" 2 years of "Master" 3 years of "Doctorat"   But what about in the United States?  I heard that they have things called "undergraduate", "graduate" and "postgraduate". How many years do each of those take? And what are the equivalencies between these and the "Licence - Master - Doctorat"?  Note: If the answer to this question depends on the field of studies, then the field is physics. You should choose your Display Name when signing up Variable with colon dash ${VAR:-} Open Windows Terminal as admin with WIN+R Is it possible to do vector calculus in Mathematica? With a macro such as:  \def\naive{na\"{\i}ve}   I find that the space which I would hope to follow it, is absent. So, with  the \naive approach   I get     the naïveapproach   in the output. How do I bring back the space? Help me understand Bayesian prior and posterior distributions In a group of students, there are 2 out of 18 that are left-handed. Find the posterior distribution of left-handed students in the population assuming uninformative prior. Summarize the results. According to the literature 5-20% of people are left-handed. Take this information into account in your prior and calculate new posterior.    I know the beta distribution should be used here. First, with $\alpha$ and $\beta$ values as 1? The equation I found in the material for posterior is   $$\pi(r \vert Y ) \propto r^{(Y +−1)} \times (1 − r)^{(N−Y +−1)} \\ $$  $Y=2$, $N=18$  Why is that $r$ in the equation? ($r$ denoting the proportion of left-handed people). It is unknown, so how can it be in this equation? To me it seems ridiculous to calculate $r$ given $Y$ and use that $r$ in the equation giving $r$. Well, with the sample $r=2/18$ the result was $0,0019$. The $f$ should I deduce from that?  The equation giving an expected value of $R$ given known $Y$ and $N$ worked better and gave me $0,15$ which sounds about right. The equation being $E(r | X, N, α, β) = (α + X)/(α + β + N)$ with value $1$ assigned to $α$ and $β$. What values should I give $α$ and $β$ to take into account prior information?  Some tips would be much appreciated. A general lecture on prior and posterior distributions wouldn't hurt either (I have vague understanding what they are but only vague) Also bear in mind I'm not very advanced statistician (actually I'm a political scientist by my main trade) so advanced mathematics will probably fly over my head. The comments in  say that I shouldn't wait until low battery charge level before I charge, because that reduces battery life (this is contrary to my understanding before that the faster we charge (without waiting for battery to discharge) the worse it will be for battery life). My question is, when I should generally start charging to avoid this effect? 20%?   Interesting question: If we always limit the usage of the battery only 20% and above, won't we, in effect, reduces our battery capacity to 80%, because we will only use the battery in 20%-100% state? The "Very Low Quality" (VLQ) flag reason is really annoying me.   It is the analog to the mommy mommy flag (which my 1.5-year-old child raises quite frequently). It provides no real clue as to what the flagger really wants us to do with the post.   Does she want us to close the question?  Does she want us to edit it into shape?  Does she want us to delete the question?  Does she want us to ban the user?  Does she want us to jump around and have a party?    Should we get rid of this flag reason?   Is it just me? Should and can we replace it with something better?     EDIT I accepted Jeff's answer. The tougher wording has heavily decreased the amount of very low quality flags. We now see only about 25% of the amount we used to and they are usually valid. Additionally, we now only allow very low quality on posts with a score of 0 or lower. I know that for continuous variable $P[X=x]=0$.   But i can't visualize that if $P[X=x]=0$, there is infinite number of possible $x$'s. And also why do their probabilities get infinitely small ?