I am an Egyptian student and willing to visit Switzerland for less than 90 days. I hold a Portuguese residence permit (due to being a student, which I renew every year). I usually have no problems traveling to Schengen area countries. My question is, if I intend to visit Switzerland for less than 90 days, would I need any kind of authorization?
I have requested for a French long term (Type D) student visa from a consulate in India. It's a national Type D visa and not a Schengen visa. My question is that can we still go to other Schengen countries on this visa? If not then can we get a Schengen visa from a consulate in France?
I am running Ubuntu 16.10 on a MacbookPro and keys 49 and 94 are inverted... I resolved this problem with xmodmap, but this won't apply permanently. After some research I found xmodmap is no longuer use by Ubuntu but xkb instead. So I searched in the configuration of xkb and I found in the config file /usr/share/X11/xkb/keycodes/macintosh this lines :   xkb_keycodes "badmap" {     &lt;TLDE&gt; =  94;     &lt;LSGT&gt; =  49; };  xkb_keycodes "goodmap" {     &lt;TLDE&gt; =  49;     &lt;LSGT&gt; =  94; };   This is exactly my problem, this two keys are inverted. So I wondering if I can't change my layout configuration with the param badmap.    The ouput of setxkbmap -query -v 10  Setting verbose level to 10 locale is C Trying to load rules file ./rules/evdev... Trying to load rules file /usr/share/X11/xkb/rules/evdev... Success. Applied rules from evdev: rules:      evdev model:      pc105 layout:     fr,fr,us variant:    mac,oss, Trying to build keymap using the following components: keycodes:   evdev+aliases(azerty) types:      complete compat:     complete symbols:    pc+fr(mac)+fr(oss):2+us:3+inet(evdev) geometry:   pc(pc105) rules:      evdev model:      pc105 layout:     fr,fr,us variant:    mac,oss, I am kind of confused on the terminology of &quot;space&quot;. From  I am getting that  In mathematics, a space is a set (sometimes called a universe) with some added structure.  And from topological and metric spaces, I know that we a space is a a tuple of a set and a structure, e.g. $(X, \tau)$, $(X, d)$, where $\tau$ and $d$ are a topology and a metric respectively. On the other hand, in machine learning the term &quot;features space&quot; is used a lot for sets like $\mathbb R^n$, e.g.  and although it often refers to the set underlaying set itself, not the tuple of set plus the added structure. In fact, I have done the same in a previous publication (which the peer-reviewers accepted), but I would like to be both correct and precise in what I research, write, and submit. Is it just that machine learning researchers are imprecise in their terminology? Is it just an ? I think, what some people are doing, might be they consider a space to be a set, which is somewhat structured, instead considering the set with the structure (so, the tuple), to be the space.  I am not sure how to think about this. How do I deal with this in my writing? Especially since, what happens a lot is that I need subsets and element out of the underlying sets of all kinds of spaces.  Of course it would greatly help if there was a general name for a set that is the underlying set of a space, , but it seems that there is no dedicated name for such a set. Let's say there was a features space $(X, \cdot)$, (where I am not even sure, what structure we would add). Then it would be great to have a name for the set, let's call it an asdf. So we could say the feature asdf.  Afterthoughts: What makes it worse for me is that it seems ( and ) I am not even using the word &quot;structure&quot;, right, since it is about operations, so we are talking about an &quot;algebraic structure&quot;, while &quot;geometric space&quot; is... something else...? So apparently a &quot;vector space&quot; is actually not a &quot;geometric space&quot;, but an &quot;algebraic structure&quot;. I can understand that point, but the language gets even more confusing. Sync +- 70 000 records from CSV into drupal I have a CSV file with approx. 70.000 records that I want to import into Drupal 7. Once this is done the client will provide a new CSV file on a daily base that has to sync the updated rows to Drupal.   I wrote a import with  and the  module, but in both cases it takes like forever to import the 70.000 rows. Is there any other solution to import the data faster in Drupal 7? How can I pass a class member function as a callback? I'm using an API that requires me to pass a function pointer as a callback. I'm trying to use this API from my class but I'm getting compilation errors.   Here is what I did from my constructor:  m_cRedundencyManager-&gt;Init(this-&gt;RedundencyManagerCallBack);   This doesn't compile - I get the following error:      Error  8 error C3867: 'CLoggersInfra::RedundencyManagerCallBack': function call missing argument list; use '&amp;CLoggersInfra::RedundencyManagerCallBack' to create a pointer to member   I tried the suggestion to use &amp;CLoggersInfra::RedundencyManagerCallBack - didn't work for me.   Any suggestions/explanation for this??  I'm using VS2008.  Thanks!! Can comments be used in JSON? I looked around but did not find that anyone has asked this before, but what are the fonts that are standard/recommended while writing academic reports/papers? I'm messing around with python for the first time.  I'm making my own changes to the interface, just because..  How can I move the text inside the buttons down and to the left? The left image is what I have now and the right one it's what I want to do..  Please have in mind that I have zero python knowledge. But I'm willing to learn.. Prove that $(a + b + c)^{13}$ is divisible by $abc$ if $b|a^3$, $c|b^3$ and $a|c^3$. Custom text color in C# console application? The interaction of TeX and Lua can be quite delicate at times.  While the former has category codes and tokens, the latter has escape sequences and datatypes.  Mixing the disparate concepts of these two worlds can lead to unexpected behaviour which favours common errors, known as gotchas. In the community wiki answer below you can find an overview of the common mistakes that people make when getting to grips with LuaTeX. I need to print an image at a certain size. What dimensions and resolution should I use? When I watch the news I've noticed that the Gallup polls for things like presidential elections have [I assume random] sample sizes of well over 1,000.  From what I remember from college statistics was that a sample size of 30 was a "significantly large" sample.  It was made to seem that a sample size over 30 is pointless due to diminishing returns. Formulae for Catalan's constant. Some years ago, someone had shown me the formula (1). I have searched for its origin and for a proof. I wasn't able to get true origin of this formula but I was able to find out an elementary proof for it.  Since then, I'm interested in different approaches to find more formulae as (1).  What other formulas similar to ($1$) are known?  Two days ago, reading the book of Lewin "Polylogarithms and Associated Functions" I was able to find out formula (2).  $\displaystyle \dfrac{1}{3}C=\int_0^1 \dfrac{1}{x}\arctan\left(\dfrac{x(1-x)}{2-x}\right)dx\tag1$   $\displaystyle \dfrac{2}{5}C=\int_0^1 \dfrac{1}{x}\arctan\left(\dfrac{\sqrt{5}x(1-x)}{1+\sqrt{5}-\sqrt{5}x}\right)dx-\int_0^1 \dfrac{1}{x}\arctan\left(\dfrac{x(1-x)}{3+\sqrt{5}-x}\right)dx\tag2$   $C$ being the Catalan's constant.  I have a proof for both of these formulae.  My approach relies on the following identity:  For all real $x&gt;1$,  $\displaystyle \int_0^1 \dfrac{1}{t} \arctan \left (\dfrac{t(1-t)}{\frac{x+1}{2}-t}\right) dt=\int_1^{\frac{\sqrt{x}+1}{\sqrt{x}-1}}\dfrac{\log(t)}{1+t^2}dt$ XGBoost Loss function Approximation With Taylor Expansion As an example, take the objective function of the XGBoost model on the $t$'th iteration:  $$\mathcal{L}^{(t)}=\sum_{i=1}^n\ell(y_i,\hat{y}_i^{(t-1)}+f_t(\mathbf{x}_i))+\Omega(f_t)$$  where $\ell$ is the loss function, $f_t$ is the $t$'th tree output and $\Omega$ is the regularization. One of the (many) key steps for fast calculation is the approximation:  $$\mathcal{L}^{(t)}\approx \sum_{i=1}^n\ell(y_i,\hat{y}_i^{(t-1)})+g_tf_t(\mathbf{x}_i)+\frac{1}{2}h_if_t^2(\mathbf{x}_i)+\Omega(f_t),$$  where $g_i$ and $h_i$ are the first and second derivatives of the loss function.   What I'm asking for is convincing arguments to demystify why the above approximation works:  1) How does XGBoost with the above approximation compare to XGBoost with the full objective function? What potentially interesting, higher-order behavior is lost in the approximation?  2) It's a bit hard to visualize (and depends on the loss function) but, if the loss function has a large cubic component, then the approximation will likely fail. How is it that this doesn't cause problems for XGBoost? I am currently trying to establish a clear picture of pure/mixed/entangled/separable/superposed states. In the following I will always assume a basis of $|1\rangle$ and $|0\rangle$ for my quantum systems. This is what I have so far:   superposed: A superposition of two states which a system $A$ can occupy, so $\frac{1}{\sqrt{2}}(|0\rangle_A+|1\rangle_A)$ seperable: $|1\rangle_A|0\rangle_B$ A state is called separable, if its an element of the (tensor)product basis of system $A$ and $B$ (for all possible choices of bases) entangled: $\frac{1}{\sqrt{2}}(|0\rangle_A|1\rangle_B+|1\rangle_A|0\rangle_B)$ is not a state within the product basis (again for all possible bases). mixed state: Is a statistical mixture, so for instance $|1\rangle$ with probability $1/2$ and $|0\rangle$ with probability $1/2$ pure state: Not a mixed state, no statistical mixture   I hope that the above examples and classifications are correct. If not it would be great if you could correct me. Or add further cases, if this list is incomplete.   On wikipedia I read about      Another way to say this is that while the von Neumann entropy of the whole state is zero (as it is for any pure state), the entropy of the subsystems is greater than zero.   which is perfectly fine. However I also read on wikipedia a criterion for :     Another, equivalent, criterion is that the von Neumann entropy is 0 for a pure state, and strictly positive for a mixed state.   So does this imply that if I look at the subsystems of an entangled state, that they are in a mixed state? Sounds strange... What would be the statistical mixture in that case?  Moreover I also wanted to ask, whether you had further illustrative examples for the different states I tried to describe above. Or any dangerous cases, where one might think a state of one kind to be the other? Radio antenna producing waves in the visible spectrum If a radio could produce waves in the visible light spectrum, what would the result be?  This is a thought experiment that I've pondered for a few years now.  I realize there are a few/many real-world constraints, but if we lifted these constraints for the sake of thought, what could we expect?  Personally, I don't see why we wouldn't observe visible light emitting from the antenna, disregarding any light from Blackbody Radiation. I was having a discussion with a friend of mine about some normal group properties and then came up with the question "if G is not commutative, then is there always a subgroup that is not a normal subgroup?" It's probably more easy to solve this in the following form:$$\forall H \leq G : H  \lhd G \Rightarrow \forall a,b \in G : ab=ba$$ My question is, can anybody give a proof, or a counter-example (because I don't think it holds) of this theorem? Thanks! Neural networks output probability estimates? Suppose my training data contains ~100 variables, and each example is tagged as "success" or "failure".  I understand how a neural network can be used to try and predict success vs failure based on the variables.  However I am interested in the neural network outputting the posterior probability rather than success or failure. In fact, I evaluate the efficacy of the NN based on how accurate the probabilities are (eg. AUROC over the entire dataset) rather than % of cases with correct prediction.  Are NN's the right tool for the job here and if so how do you structure the NN to output this?  (NOTE: I'm a ML newbie!) Proof for triangle inequality for vectors Generally,the length of the sum of two vectors is not equal to the sum of their lengths. To see this consider the vectors $u$ and $v$ as shown below.  By considering $u$ and $v$ as two sides of a triangle, we can see that the lengths of the third side is $\| u + v \|$ and we have $\| u + v \| \leq \|u\| + \|v\|$.  Under what circumstance equality occurs and how can one prove that? error C2143: syntax error : missing ';' before 'type'