How to handle players who take a long time to take their turn?
How can I speed up D&D 3.5e combat?
I'm planning to fly from Ulaanbaatar to Melbourne via Seoul (and Kuala Lumpur).  However, can I actually enter South Korea using this visa waiver? I'm hoping to meet a friend there for a few hours before flying. Also, would traveling via Malaysia affect this policy?  Transit tourists bound for another country Eligible countries  Applicable to all countries (save for Macedonia, Cuba, Syria, Sudan and Iran) that are not granted visa-free entry into Korea.  Applicable to…  Those with a visa (excluding Japanses Group Visa, excluding e-visa) or a re-entry permit for U.S., Japan, Canada, Australia, or New Zealand (5 countries) and traveling to any one of those countries through Korea.  Those traveling from any one of the five countries mentioned above, whose final destination is not Korea.  However, please note that the first bullet points are not applicable to those travelling to/from Guam and Saipan, which are unincorporated territories of the United States.   Requirements  Must have a confirmed onward flight ticket for departure within 30 days after entering Korea, in addition to having no record of criminal offence in the five countries mentioned above. Is there a streaming API for JSON? Is DOM the only way to parse JSON? As far as I understand, one of the main claimed problems with initializing e.g. a feed-forward neural network (with several $\text{tanh}$ or $\text{ReLU}$ layers) with $W=0$ is that it doesn't break "network symmetry", meaning, backpropagation would propagate the same error through all such units (i.e. "nudging all weights in the same direction"). This is, I presume, undesirable because we would not be learning "different" calculations through different paths of the network.  However, I'm confused why that even matters in this case given the fact that if $W$ ever drops to 0, we will effectively be propagating no gradients at all through the network, since W=0 would multiply all errors from the output and prevent any learning.  Put another way, even if $W=0$ does not break network symmetry (wasting calculations and paths in the network) is it correct to say that if $W=0$ (e.g. by initialization) we are effectively killing gradients in a neural network, and thus no learning can take place? Gmail, how to forward email to group using a filter I extracted simple example:  require 'pp' x = 1..3 pp x.map do |i| {:value =&gt; i, :double =&gt; (i*2)} end pp x.map { |i| {:value =&gt; i, :double =&gt; (i*2)} }  pp(x.map do |i| {:value =&gt; i, :double =&gt; (i*2)} end) pp(x.map { |i| {:value =&gt; i, :double =&gt; (i*2)} })   I am wondering why first pp produces:  [1, 2, 3]   While all the oders are giving:  [{:value=&gt;1, :double=&gt;2}, {:value=&gt;2, :double=&gt;4}, {:value=&gt;3, :double=&gt;6}]   I assume it has something to do with operator precedence. Where can I find good explanation? Is the expression      All women who are mothers of daughters are here.   correct? I mean every woman who has at least one daughter is here. Today a student asked me $\int \ln (\sin x) \, dx.$ Calculate the integral $$\int \ln (\sin x) \, dx.$$ Wikipedia lists the following pseudocode for HMAC:  function hmac (key, message)     if (length(key) &gt; blocksize) then         key = hash(key) // keys longer than blocksize are shortened     end if     if (length(key) &lt; blocksize) then         // keys shorter than blocksize are zero-padded ('∥' is concatenation)          key = key ∥ [0x00 * (blocksize - length(key))]      end if      // Where blocksize is that of the underlying hash function        o_key_pad = [0x5c * blocksize] ⊕ key      // Where ⊕ is exclusive or (XOR)     i_key_pad = [0x36 * blocksize] ⊕ key       // Where '∥' is concatenation        return hash(o_key_pad ∥ hash(i_key_pad ∥ message))  end function   Why are they there?  How were they chosen?  It seems to me that  $hash(key + hash(message))$ (where + is concatenation) would suffice.    Obviously I'm missing something.  What is it? Bag-of-Words for Text Classification: Why not just use word frequencies instead of TFIDF? A common approach to text classification is to train a classifier off of a 'bag-of-words'. The user takes the text to be classified and counts the frequencies of the words in each object, followed by some sort of trimming to keep the resulting matrix of a manageable size.  Often, I see users construct their feature vector using TFIDF.  In other words, the text frequencies noted above are down-weighted by the frequency of the words in corpus.  I see why TFIDF would be useful for selecting the 'most distinguishing' words of a given document for, say, display to a human analyst.  But in the case of text categorization using standard supervised ML techniques, why bother downweighting by the frequency of documents in the corpus?  Will not the learner itself decide the importance to assign to each word/combination of words?  I'd be grateful for your thoughts on what value the IDF adds, if any. Opening files from version 2.8 in 2.79 What is the difference between the memory usage report in System Monitor and the one by free? Just ran the latest batch of updates on 20.04 (Xubuntu), and now I'm getting a GRUB error: symbol 'grub_calloc' not found  I'm dropped into the 'grub rescue' shell, but have no idea what to do there that might be useful. To me, 'symbol not found' implies some sort of build error with the grub package, but I don't really know how grub works. I noticed that this update also included 'firmware', not sure if that could be related. Is my best bet just to boot from a live CD and see if I can roll back the update to grub somehow? Edited to add: OK, thanks to lots of people ! Here's what I think I now understand.  On 'non-UEFI' systems, grub is installed in two separate parts. The first, most basic, part is the part that is started on bootup. But for most of it's functionality, it needs the second part. These parts must be aligned - neither part must require any functionality from the other part which is not actually there. The visible, run-time problem occurs when these parts are not aligned, and the function grub_calloc is not supplied. It's not 100% clear to me if grub_calloc belongs in the second, larger part or the first. I would have expected the second, but the grub build system is a work of considerable art, so I don't know :).  The root cause of the problem is that the grub update has not ensured that both parts have been updated. Ideally, failure to do this should cause grub installation to fail, and the system should be reverted to a safe state. This does not happen. This is actually still a bit of a mystery to me. All that the update needs to do by default is put each part where the current parts are, because obviously that worked. If the install locations/drives are configuration - driven, and one of these locations can't be reached, then somehow a mismatch has arisen between that configuration data and reality. This might not show up as a problem as long as no new dependency was introduced between the parts.   All flavours of solution involve reinstalling grub to ensure that the two parts are aligned. It's not actually necessary to go back to the previous version ( although that will work ), because it's not the grub runtime per se that is broken. There are numerous ways to achieve this, depending on your environment, but running the Boot-repair live disk worked for me. It may be useful, for the purpose of avoiding such a misalignment in future, to ensure that the grub installer on your system is configured to install to the correct devices. This update resolves some important bugs (). If you have reverted grub to resolve this problem, be aware that you are exposed to these issues. Fuzzy graphics after upgrading to Ubuntu 20.04 I have recently upgraded Ubuntu 19.10 to latest LTS Ubuntu 20.04. After updating, I get serious artifacts on my screen. Every time I reboot (or log-in), my screen is fuzzy or scrambled (picture attached):     This is temporary. The fuzzy screen goes away when I change the background. But it reappears the moment I reboot or log back in.   I have Intel HD Graphics 520 (Skylake GT2).  The output of lspci -nn |grep -E 'VGA|Display' is:  00:02.0 VGA compatible controller [0300]: Intel Corporation Skylake GT2 [HD Graphics 520] [8086:1916] (rev 07)   Update:   On enabling Wayland in Ubuntu 20.04 and logging in to, these artifacts are however cured.   This is okay for now, but I am not sure about Wayland as I have not yet used it before.   Is there a way to correct the graphic issue with normal Ubuntu startup? Topical example:  'There was a scuffle outside the local pub. There appears to be no connection with religious extremists.'  ...but now you're wondering if there was. It's a nasty tactic used to raise fear and doubt - deliberately calling attention to something by explicitly denying it.  What's the term for this? Here's an example:  Double d = (1/3); System.out.println(d);   This returns 0, not 0.33333... as it should.  Does anyone know? Is there a constructive way to exhibit a basis for $\mathbb{R}^\mathbb{N}$? Assuming the Axiom of Choice, every vector space has a basis, though it can be troublesome to show one explicitly. Is there any constructive way to exhibit a basis for $\mathbb{R}^\mathbb{N}$, the vector space of real sequences? Proving that $\sum\limits_{k=0}^{n} {{m+k} \choose{m}} = { m+n+1 \choose m+1 }$ I have to prove that:     $$\sum_{k=0}^{n} {{m+k} \choose{m}} = { m+n+1 \choose m+1 }$$   I tried to open up the right side with Pascal's definition that: $$ { n \choose k} = {n-1 \choose {k}} + {n-1 \choose {k-1}}$$  Here is what I came up with, and I am sure it is wrong because it does not equal the left side:  $$ {m+n+1 \choose m+1} = {m+n \choose m+1} + {m+n \choose m} = ... ={m+n \choose m+1} + {m+n-1 \choose m} + {m+n-2 \choose m-1} + ... + {m \choose m+1} = \sum_{k=0}^{n} {m+k \choose m+k-n+1 }  $$  Which, again, probably is wrong because it is not equal $\sum_{k=0}^{n} { m+k \choose m}$. Any help is appreciated Convergence of $\sum_{n=1}^\infty\frac{n}{(n+1)!}$ Can someone give an explanation using the definition of convergence in partial sum to show how the above infinite sum converges to 1?  Thanks No more than 10,000 results are visible in search If you make a search for something that returns more than 10,000 items in result. For example, search for  return 161,338 results on MSE. But if you select a page where 10,000+ result should be shown:        In the edge case  shows zero results:        But this is actual for any page where per_page * page_number is more than 10,000.  Though this does not happen for Stack Overflow in English. It looks like all other sites are affected.  Related bug report on ruSO.meta: Convert IEnumerable to DataTable