I am french and my student visa for China arrives soon to its expiration date and I only have one entry. I am planning to go traveling to Hong Kong then Thailand and come back to Beijing (to find my stuffs), and finally go back to Paris 2 days after arriving to Beijing.  Am I eligible to 72-hour visa free transit in the Beijing Capital Airport ?  I already made some reasearch, and it seems that frontier security only check the first and third country you are going. In my case it would be Hong Kong and Beijing.  Do anyone who had similar situation can tell me ?
Assuming one wants to travel through China, what are the rules for visa-free access? To be more specific:   How does the 24-hour visa free transit work? How does the 72-hour version work? What does a person ineligible for either of these programs have to do to transit China? Do the programs apply to flights only or do they also work for transit on land? Would a visitor save much hassle by using visa free transit instead of just getting a visa?      NB: this is intended as a canonical question on transiting China
JSF 2.1 ViewScopedBean @PreDestroy method is not called Is it a scam if the person only wants to deposit into my account, not make a withdrawal? A man in  west  coast  Austria wants to deposit money  into  my account.  I'm in Alabama.  I had to get an online  account.  I did that, but he says his bank manager  needs all this information:   DOB SSN    online access username  online access  password   security  question and answer   bank name   bank address   bank number  person who is on the account     their name their address    account no   routing no     OK. Is it safe  to do this or am I being scammed?  I don't want him  to be able to end up withdrawing all my money instead of depositing money. $R^2$ of 1 but RMSE > 0 Let's say we have two models trained. And let's say we are looking for good accuracy.  The first has an accuracy of 100% on training set and 84% on test set. Clearly over-fitted. The second has an accuracy of 83% on training set and 83% on test set.   On the one hand, model #1 is over-fitted but on the other hand it still yields better performance on an unseen test set than the good general model in #2.   Which model would you choose to use in production? The First or the Second and why? What is the best way to clean the sensor on a interchangeable lens camera (mirrorless or digital SLR)? I remember being taught that the correct order of adjectives in English was something along the lines of "Opinion-Size-Age-Color-Material-Purpose."  However, it's been a long time and I'm pretty sure I've forgotten a few categories (I think there were eight or nine). Can anyone fill them in? When is the image of a linear operator (between Banach spaces) closed? Let $T: X \longrightarrow Y$ be a continuous linear map between two Banach spaces.  When is $\operatorname{Ran}(T)$ a closed subspace?  What theorems are there?   Thanks :) I want to build an uncountable well-ordered set within the theory $Z^\textbf-$. So, I take $A=\omega$ (exists by infinity axiom) and define $$W:=\{(X,R)\in\mathcal{P}(A)\times\mathcal{P}(A\times A):\langle X,R\rangle\ \text{is a well-ordered set}\}$$  With this I consider the set $T:=W/\cong$ (where $\cong$ is isomorphism relation). Note that $W$ and $T$ are sets by power set axiom. So I define for each equivalence class $[x],[y]\in T$ the order  $$ [x]\leq_T[y]\Leftrightarrow \text{type}(x, R_x)\leq \text{type}(y,R_y) $$  Here, $R_x$ means the order of $x$ that make it belongs to $[x]$.   So, $T$ is well ordered by $\leq_T$ and it is uncountable.  Question: I don't pretty sure if I can build $\leq_ T$ without replacement axiom. Another doubt is, Can I take the $R_x$ without AC? Determining the dimension of span$\{AB-BA : A,B \in M_{n \times n}(\mathbb R) \}$ Let $S$ be the subspace , of  $M_{n \times n}(\mathbb R)$ (the vector space of all $n \times n$ real matrices ) , generated by matrices of the form $AB-BA$ , where $A,B \in M_{n \times n}(\mathbb R)$ , then how do we prove that $\dim S=n^2-1$ ? The only thing that I can determine is that the trace of all matrices of $S$ is $0$ . Please help Find the smallest disk in complex plane containing the eigenvalues of a matrix (NBHM 2012) Consider the given matrix  $$\left[\begin{matrix}1&amp; -2&amp; 3&amp; -2 \\1&amp; 1&amp; 0&amp; 3\\-1&amp; 1&amp; 1&amp; -1\\0&amp; -3&amp; 1&amp; 1&amp;\end{matrix}\right]$$  Find out the smallest disk like ($|z-1| &lt; r$ ) in the complex plane containing the eigenvalues of the given matrix.  One easiest method is to find out the eigenvalues from the characteristic equation of the matrix. I can do it so no problem with it.   I am looking for a method to solve the problem without the above method that can be evaluated in very short time. This type of method is possible, I believe.  Thank you for your help Disk full, du tells different. How to further investigate? Prove that $1+\frac{1}{3}+\frac{1\cdot 3}{3\cdot 6}+\frac{1\cdot 3\cdot 5}{3\cdot 6 \cdot 9}+.........=\sqrt{3}$ Prove that $$1+\frac{1}{3}+\frac{1\cdot 3}{3\cdot 6}+\frac{1\cdot 3\cdot 5}{3\cdot 6 \cdot 9}+.........=\sqrt{3}$$   $\bf{My\; Try::}$ Using Binomial expansion of  $$(1-x)^{-n} = 1+nx+\frac{n(n+1)}{2}x^2+\frac{n(n+1)(n+2)}{6}x^3+.......$$  So we get $$nx=\frac{1}{3}$$ and $$\frac{nx(nx+x)}{2}=\frac{1}{3}\cdot \frac{3}{6}$$  We get $$\frac{1}{3}\left(\frac{1}{3}+x\right)=\frac{1}{3}\Rightarrow x=\frac{2}{3}$$  So we get $$n=\frac{1}{2}$$  So our series sum is $$(1-x)^{-n} = \left(1-\frac{2}{3}\right)^{-\frac{1}{2}} = \sqrt{3}$$  Although I know that this is the simplest proof, can we solve it any other way someting like defining $a_{n}$ and then use Telescopic sum.  Thanks. I need to make my (BibTeX) references section appear in the table of contents of my LaTeX document (documentclass: article), with section numbering too.  My approach until now has been making a new section and including the bibliography (references.bib) at that point:  \section{References} \bibliography{references}   However, the final document shows both the section title that I have written and the section title that BibTeX writes, which is quite redundant and I definitely dislike.  How can I either remove BibTeX's section title, or make the BibTeX bibliography appear in the table of contents without making a new section?  If I were to make the BibTeX bibliography appear in the table of contents without making a new section, how could I assure that the section title that BibTeX writes looks exactly like sections typeset with \section? Diffeomorphism group of the unit circle I am given to understand that the group of diffeomorphisms of the unit circle, $\operatorname{Diff}(\mathbb{S}^1)$, has two connected components, $\operatorname{Diff}^+(\mathbb{S}^1)$ and $\operatorname{Diff}^-(\mathbb{S}^1)$, the diffeomorphisms that preserve or reverse the canonical (counterclockwise) orientation, respectively.  Question 1: How does one prove that?  Question 2: Given $\Phi, \Psi \in \operatorname{Diff}^+(\mathbb{S^1})$, can one construct an explicit path joining them?  I'd be satisfied already with a proof that we can join $\Psi, \Phi \in \operatorname{Diff}^+ (\mathbb{S}^1)$, without giving the path explicitly. I tried the obvious path, $t \mapsto \dfrac{t\Phi + (1-t)\Psi}{|t\Phi + (1-t)\Psi|}$, but that doesn't seem to work.  Thanks. I need a way to determine the type of an HTML element in JavaScript. It has the ID, but the element itself could be a &lt;div&gt;, a &lt;form&gt; field, a &lt;fieldset&gt;, etc. How can I achieve this? Between you and ("me" or "I")? What physical properties of materials trigger the capacitive touch screen? I have watched some youtube videos about capacitive touch screen      The videos show that a conductor(finger), in which charges can be induced, can be used to trigger the capacitive touch screen. However, I tried to wear a plastic glove to hold a metal rod to touch the screen and no detection is triggered.  I really want to know the physics theory about the capacitive sensing.  Can someone tell me what physical properties or shape trigger the capacitive touch screen?  Is there any examples that something WITHOUT conducting to human body can trigger the screen?  (P.S. I have read other posts about capacitive touch screen here and do not find a good answer so this is not a duplicate) How to extract files to another directory using 'tar' command? I thought tar archive.tar /users/mylocation would work, but it doesn't. How can I do that? Prove that $f$ analytic, $f(x) \in \mathbb{R}$ for all $x \in \mathbb{R}$ implies $f(\overline{z})=\overline{f(z)}$ Let $U\subset \mathbb{C}$ be a nonempty connected open set such that for every $z\in U$, $\overline z\in U$.  Let $f$ be analytic on $U$. Suppose $f(x)\in\mathbb R$ for every $x\in U\cap\mathbb R$. Prove that $f(\overline{z})=\overline{f(z)}$ for any $z \in U$.    By definition, I know that $f$ analytic on $U$ means that for every $z_0 \in U$, there exists $r&gt;0$ and a sequence of complex numbers $\left(a_n\right)_{n=0}^\infty$ such that $f(z)=\sum_{n=0}^\infty a_n\left(z-z_0\right)^n$ on the disc $D(z_0,r)\subseteq U$. I see that $f$ takes points without imaginary components to other points without imaginary components. But I don't see how this implies a symmetry that $f(\overline{z})=\overline{f(z)}$ for $z \in \mathbb C \setminus \mathbb R$. It seems like a very strong conclusion and I'm not sure how I would prove it.  I have also shown in the preceding question that $U\cap\mathbb R$ contains an open interval, however I am not sure if that detail is meant to be helpful to this question. Background I'm working on a time series data set of energy meter readings. The length of the series varies by meter - for some I have several years, others only a few months, etc. Many display significant seasonality, and often multiple layers - within the day, week, or year. One of the things I've been working on is clustering of these time series. My work is academic for the moment, and while I'm doing other analysis of the data as well, I have a specific goal to carry out some clustering. I did some initial work where I calculated various features (percentage used on weekends vs. weekday, percentage used in different time blocks, etc.). I then moved on to looking at using Dynamic Time Warping (DTW) to obtain the distance between different series, and clustering based on the difference values, and I've found several papers related to this. Question Will the seasonality in a specific series changing cause my clustering to be incorrect? And if so, how do I deal with it? My concern is that the distances obtained by DTW could be misleading in the cases where the pattern in a time series has changed. This could lead to incorrect clustering. In case the above is unclear, consider these examples: Example 1 A meter has low readings from midnight until 8AM, the readings then increase sharply for the next hour and stay high from 9AM until 5PM, then decrease sharply over the next hour and then stay low from 6PM until midnight. The meter continues this pattern consistently every day for several months, but then changes to a pattern where readings simply stay at a consistent level throughout the day. Example 2 A meter shows approximately the same amount of energy being consumed each month. After several years, it changes to a pattern where energy usage is higher during the summer months before returning to the usual amount. Possible Directions  I've wondered whether I can continue to compare whole time series, but split them and consider them as a separate series if the pattern changes considerably. However, to do this I'd need to be able to detect such changes. Also, I just don't know if this is a suitable way or working with the data. I've also considered splitting the data and considering it as many separate time series. For instance, I could consider every day/meter combination as a separate series. However, I'd then need to do similarly if I wanted to consider the weekly/monthly/yearly patterns. I think this would work, but it's potentially quite onerous and I'd hate to go down this path if there's a better way that I'm missing.  Further Notes These are things that have come up in comments, or things I've thought of due to comments, which might be relevant. I'm putting them here so people don't have to read through everything to get relevant information.  I'm working in Python, but have rpy for those places where R is more suitable. I'm not necessarily looking for a Python answer though - if someone has a practical answer of what should be done I'm happy to figure out implementation details myself. I have a lot of working &quot;rough draft&quot; code - I've done some DTW runs, I've done a couple of different types of clustering, etc. I think I largely understand the direction I'm taking, and what I'm really looking for is related to how I process my data before finding distances, running clustering, etc. Given this, I suspect the answer would be the same whether the distances between series are calculated via DTW or a simpler Euclidean Distance (ED). I have found these papers especially informative on time series and DTW and they may be helpful if some background is needed to the topic area: