How does lifelink affect indestructible creatures?
Does a creature with Lifelink provide lifegain equal to its power, or the defender's toughness?
Proving $\sqrt{2}+\sqrt[3]{3}$ is algebraic. Is it possible to choose which OS will be loaded using a hardware switch? Can someone please give me some intuition as to when to choose either SVM or LR? I want to understand the intuition behind what is the difference between the optimization criteria of learning the hyperplane of the two, where the respective aims are as follows:     SVM: Try to maximize the margin between the closest support vectors LR: Maximize the posterior class probability    Let's consider the linear feature space for both SVM and LR.  Some differences I know of already:   SVM is deterministic (but we can use Platts model for probability score) while LR is probabilistic. For the kernel space, SVM is faster (stores just support vectors) "The antipodal map of $S^2$ is the isometry $\bar m$ sending each point $P$ to its opposite point $-P$" (from textbook)  Show that antipodal points remain antipodal under any isometry of $S^2$. In other words, for each isometry f of $S^2$, if $P$ and $P'$ are antipodal, then so are $f(P)$ and $f(P')$  I am unsure how I can define the $P$ and $P'$ so that they are opposites of each other in the antipodal map, and am I supposed to apply the traditional isometries (translation, reflection and rotation) to the points? I don't understand this problem. Ubuntu 18.10 gnome extensions stop working after hibernation After upgrading to 18.10 I am experiencing very strange behaviour of gnome extensions. After my laptop goes into hibernation they all switch off and enabling them back on  doesn't help: nothing changes and when I refresh the page they are disabled again.   The only way to turn them back on is to reinstall them. Do we have $\lim_{h\to 0^+}\int_0^\infty|f(t+h)-f(t)|dt=0$? Let $f:\mathbb{R^+}\to\mathbb{R}$ be an integrable function ($f\in L^1(\mathbb{R}^+,\mathbb{R})$). Do we have $$\lim_{h\to 0^+}\int_0^\infty|f(t+h)-f(t)|dt=0$$ ? How can we prove it ? Below is an attempt at a proof of the following problem. Any feedback would be greatly appreciated. Thx!  Let $E$ be a set and $O_n = \{x: d(x, E) &lt; \frac{1}{n}\}$. Show  If $E$ is compact then $m(E) = lim_{n \rightarrow \infty} m(O_n)$.  This is false for $E$ closed and unbounded or $E$ open and bounded.   Note that all sets are real and measurable refers to Lebesgue measurable.  Suppose $E$ is compact. Then $m(E) &lt; \infty$. Also, since each $O_n$ is an open set of $\mathbb{R}^d$, $m(O_n) &lt; \infty$. If either of the following is true, we have that $lim_{n \rightarrow \infty}m(O_n)=m(E)$: $O_n \nearrow E$ $O_n \searrow E$ Now  $\bigcap_{n=1}^{\infty} O_n = \{ x:d(x,E) = 0\}$. That is, $x \in \bigcap_{n=1}^{\infty}O_n \iff \exists n&gt; \frac{1}{\epsilon},\ n \in \mathbb{N} \iff x\in \{x \mid d(x,E) =0\}$. Then we have that $\bigcap_{n=1}^{\infty}O_n = E$ and $O_n \searrow E$. Thus $ \ m(E) = lim_{n \rightarrow \infty} m(O_n)$. Suppose $E$ is closed and unbounded. Let $E = \{(x,y): y = 2 \}$ and $O_n = \{(x,y): d(x,E) &lt; \frac{1}{n}\}$. Since $E$ is a line in $\mathbb{R}^2$, $m(E) = 0$. Also, since the measure of a rectangle in $\mathbb{R}^d$ is its volume, $m(O_n) = \left| O_n \right| = \infty$, since the rectangle $O_n$ is unbounded. It is therefore apparent that $m(E) \not = lim_{n \rightarrow \infty} m(O_n)$. Suppose $E$ is open and bounded. Let $E=(0,1)$ and $O_n = \{x \mid d(x,E) &lt; \frac{1}{n}\}$. $\mathbb{R}-E$ is closed and $O_n \in \mathbb{R}-E$. Since $\mathbb{R}-E$ contains all its limit points $lim_{n \rightarrow \infty}O_n = O \in \mathbb{R}-E$. Thus $lim_{n \rightarrow \infty} m(O_n) \neq m(E)$. All chromium extensions throw errors since update to 13.10 How to Disavow Bad Backlinks Automatically? DOS commands for Epson dot matrix not working Our Epson dot matrix printer is not printing on the following DOS command.:  Type text.txt&gt;prn   But it does print perfectly on:  open TEXT.txt&gt;File&gt;Print    What is the issue here? Let $S$ be an arbitrary amount and define the relation $R \subseteq \mathcal{P}(S) \times \mathcal{P}(S)$ so that $(A,B) \in R$ if and only if $A \supseteq B$. Here $\mathcal{P}(S)$ is a spelling for the power set to $S$. Show that $(\mathcal{P}(S),R)$ is a partially ordered quantity. Using --exclude with the du command This is probably something basic but I'm not able to make it work. I'm trying to use DU to get a total size of files minus certain directories. I need to exclude one specific directory called uploads but not every directory called uploads. For example, my file structure looks a bit like this:  /store   /uploads     /junk_to_ignore     /more_junk_to_ignore   /user_one     /uploads   /user_two   I can run the following command:   du -ch --exclude=uploads*    and it gives me the file size minus all the "uploads" directories. However, in trying to exclude certain directories (and all its sub-directories) I fail. I've tried variations of:  du -ch --exclude=./uploads* du -ch --exclude='/full/path/to/uploads/*'   but can't seem to figure it out. How do I exclude a specific directory? 'A / One / At least one student entered the room.' Are these the same? (truth-conditionally) I just wonder if the two following sentences are truth-conditionally the same. Sentence 1 essentially means there existed a student who entered the room, and this situation includes two, three or more students entering the room. Hence, I believe these two sentences are truth-conditionally the same. Are they?        A student entered the room.   At least one student entered the room.      How about        One student entered the room      I gather this is different, as sentence 3 only means one student, not more and not fewer?  Any and all insights would be welcome. I have a dataset for which I have multiple sets of binary labels. For each set of labels, I train a classifier, evaluating it by cross-validation. I want to reduce dimensionality using principal component analysis (PCA). My question is:  Is it possible to do the PCA once for the whole dataset and then use the new dataset of lower dimensionality for cross-validation as described above? Or do I need to do a separate PCA for every training set (which would mean doing a separate PCA for every classifier and for every cross-validation fold)?  On one hand, the PCA does not make any use of the labels. On the other hand, it does use the test data to do the transformation, so I am afraid it could bias the results.  I should mention that in addition to saving me some work, doing the PCA once on the whole dataset would allow me to visualize the dataset for all label sets at once. If I have a different PCA for each label set, I would need to visualize each label set separately. What package should I use to typeset units? Ubuntu 20.04 interface font too small after restart, even with high scaling factor I have read enough threads on QQplots here to understand that a QQplot can be more informative than other normality tests. However, I am inexperienced with interpreting QQplots. I googled a lot; I found a lot of graphs of non-normal QQplots, but no clear rules on how to interpret them, other than what it seems to be comparison with know distributions plus "gut feeling".   I would like to know if you have (or you know of) any rule of thumb to help you decide for non-normality.  This question came up when I saw these two graphs:    I understand that the decision of non-normality depends on the data and what I want to do with them; however, my question is: generally, when do the observed departures from the straight line constitute enough evidence to make unreasonable the approximation of normality?  For what it's worth, the Shapiro-Wilk test failed to reject the hypothesis of non-normality in both cases. Can I create a user-specific hosts file to complement /etc/hosts? Is it possible to add a list of hosts that are only specific to a certain user? Perhaps a user-specific hosts file?  This mechanism should also complement the entries in the /etc/hosts file. Can I switch the User-Agent header for only a single web site? I'm sure its a simple question and I suppose it was asked many times, but I just can't figure it out from other answers, sorry.  I use recent versions of PostgreSQL and MySQL. I have 2 tables:  CREATE TABLE authors (     id INT,     name VARCHAR )  CREATE TABLE posts (     id INT,     author_id INT,     text VARCHAR,     date DATE )   I need to select one most recent post for each author. Thanks!  UPDATE  Thanks, both links provide answers to my question with some exception. All the following queries give the same result (which one is the most efficient btw?) The issue is when there is more than one post from the same author with the same date. Then the returned result set contains all such posts. How should I modify these queries to return exactly one post per author?  SELECT p1.* FROM posts p1 LEFT JOIN posts p2 ON p1.author_id = p2.author_id AND p1.date &lt; p2.date WHERE p2.author_id IS NULL ORDER BY p1.author_id;  SELECT p1.*  FROM posts p1 INNER JOIN (   SELECT author_id, MAX(date) AS max_date   FROM posts   GROUP BY author_id) p2   ON p1.author_id = p2.author_id AND p1.date = p2.max_date ORDER BY p1.author_id;  SELECT * FROM posts p1 WHERE date = (SELECT MAX(p2.date)               FROM posts p2               WHERE p1.author_id = p2.author_id) ORDER BY author_id;  SELECT * FROM (     SELECT author_id, MAX(date) date     FROM posts GROUP BY author_id ) p1 INNER JOIN posts p2 USING (author_id, date) ORDER BY author_id;