I've researched lots of places and still cannot wrap my head around the cross product.   The closest thing I have to an understanding  of the cross product is that its a measure of how orthogonal two vectors are.  But there are still lots of things about the cross product I dont understand.  Why do we make the product vector perpendicular to the plane formed by the multiplying vectors? How does that relate to the determinant of a 3x3 matrix?  I get that this is a very vague and general question, but If someone could help me understand why the cross product is defined the way it is and whats its meaning, or refer me to a good source, which again, I've tried reading several explanations, that would be highly appreciated. I'm happy to clear up anything that is unclear about my question
I don't think I ever understood the rationale behind this.  I get that the dot product $\mathbf{a} \cdot  \mathbf{b} =\lVert \mathbf{a}\rVert \cdot\lVert \mathbf{b}\rVert \cos\theta$ is derived from the cosine rule. (Do correct me if I'm wrong.)  However, I never really understood why $\mathbf{a} \times  \mathbf{b} =\lVert \mathbf{a}\rVert \cdot\lVert \mathbf{b} \rVert \sin\theta$. Is there a proof for this?  In addition, how does the vector cross product lead to orthogonal vectors?  I remember learning that they do and I know how to solve equations using the respective formulas, but I never got why is it so.
Today, after returning from vacations, I started my QGIS 1.7 and got this terrible message:    Help, anyone? I write about  that shows a big probability mass. It's a web-based interactive visualization of topics estimated using Latent Dirichlet Allocation that is built using a combination of R and D3; the default for scaling the set of inter-topic distances defaults to Principal Components, but other algorithms are also enabled.    This is the way that it finally shows with some data I found online and used to understand Latent Dirichlet Allocation topic modeling. As far as I understand, this graph is ideal to give out final names to each topic according to the frequency or relevance of each term given the topic but I've got two questions:   1. What would you simply say about topic 8 position on the graph? Meaning that I can't tell what each square represents in this graph, I can't tell in plain words what's the difference between topic 1 and 10, or topic 8 and 3, given their position on this graph?   2. What can you say about the big gray line in comparison with the red filling when lambda is 0? I think I get that when lambda is 1, it's meaning about plain frequency of the term in the whole corpus vs the frequency of the terms in that topic, but I'm not sure I get it with putting more weight in the ratio from red to gray: what is the gray line saying about that term and how is red contrasting?  You can get this for a model named lda_model$plot() found .   I am looking for the main idea at least. If there is info I need to add please tell me. The height of a string in a gravitational field in 2-dimensions is bounded by $h(x_0)=h(x_l)=0$ (nails in the wall)  and also $\int_0^l ds= l$. ($h(0)=h(l)=0$, if you take $h$ as a function of arc length) .  What shape does it take?  My try so far: minimise potential energy of the whole string,  $$J(x,h, \dot{h})=\int_0^l gh(x) \rho \frac{ds}{l}=\frac{g \rho }{l}\int_0^l h(x) \sqrt{1+\dot{h}^2} dx$$  With the constraint  $$\int_0^l \sqrt{1+\dot{h}^2} dx- l=0$$ If it helps, it's evident that $\dot{h}(\frac{l}{2})=0$.  Generally, this kind of equation is a case of a constrained variational problem, meaning that the integrand in  $$\int_0^l \frac{g \rho }{l}h(x) \sqrt{1+\dot{h}^2} +\lambda(\int_0^l \sqrt{1+\dot{h}^2} dx- l)dx$$  Must satisfy the Euler Lagrange equation. The constraint must also be satisfied.  But, in truth, by this point I am clueless. $\lambda$ is worked through $\nabla J  = \lambda \nabla(\int_0^l \sqrt{1+\dot{h}^2} dx- l)$. I have tried this , but get nonsensical answers.  Is this method the best? If so, in what ways am I going about it wrongly thusfar? Integral $\int_{0}^{\infty}e^{-ax}\cos (bx)\operatorname d\!x$ I want to evaluate the following integral via complex analysis $$\int\limits_{x=0}^{x=\infty}e^{-ax}\cos (bx)\operatorname d\!x \ \ ,\ \ a &gt;0$$  Which function/ contour should I consider ? I'm going to install Linux on Lenovo B5400, which contains:   500GB Hybrid SSHD with a 8 GB SSD and 500 GB HDD   Designing partitions for my 2 Linux distros was quite easy before, when I had only HDD. I always had:   10GB for Linux distro (mounted as "/") 10GB for another distro (again "/"; for another distro; for testing purposes or to assure continuity when switching distros) 2GB for swap (used for both distros; no need to separate) 300GB+ for "/home" (used for both distros; different usernames eliminates possible conflicts)   It's simple, isn't it? I would like to keep this approach, but I know that SSD has its own specificity - it prefers to be read, continuous writing may affect its life span. I don't want to kill SSD in a year, so I would like to move some system partitions to HDD in order to protect SSD. As far as I know /tmp is one of often read&amp;written locations. How much space it needs? What else should be placed on HDD?  Or: how to design partitions to achieve exactly the same effect and to keep SSD as safe as possible?  I'd be grateful for your help. Distribution of number of Bernoulli trials to a given number of successes In the simple linear regression case $y=\beta_0+\beta_1x$, you can derive the least square estimator $\hat\beta_1=\frac{\sum(x_i-\bar x)(y_i-\bar y)}{\sum(x_i-\bar x)^2}$ such that you don't have to know $\hat\beta_0$ to estimate $\hat\beta_1$  Suppose I have $y=\beta_1x_1+\beta_2x_2$, how do I derive $\hat\beta_1$ without estimating $\hat\beta_2$? or is this not possible? Gaps between arrows and labels Segmentation Fault when attempting to print value in C I used the following code to list my java code, but the it looks like there's too much space between words. What can I do?  % source code listing \usepackage{listings} \usepackage{color} \usepackage{xcolor}  \usepackage{caption}  % citation style \usepackage{apacite} \DeclareCaptionFont{white}{\color{white}} \DeclareCaptionFormat{listing}{\colorbox{gray}{\parbox{\textwidth}{#1#2#3}}}  \begin{document} \begin{lstlisting}[label=some-code,caption=bind to service call] bindService(new Intent(this, Recognizer.class), recConnection, Context.BIND_AUTO_CREATE); \end{lstlisting} \end{document}   The snapshot of what I got. The code not completely shown, and space too wide, in function bindService. When should I use exposure compensation? When should I use exposure compensation, rather than ISO, shutter speed or aperture? Do I really need to encode '&' as '&amp;'? Saleforce Too many future calls: 11 Access to our computer (not only to the internet) needs to be restricted for the accounts of my kids (7, 8) until they are old enough to manage this by themselves. Until then we need to be able to define the following:   the hours of the day when computing is o.k. (e.g. 5 - 9 pm) the days of the week when computing is not o.k. (e.g. mondays to fridays) the amount of time allowed per day (e.g. 2 hours)   In 11.10 all of the following that used to do the job don't work any more:   : for > 11.10 no more available through the ppa. : command line alternative, but from 11.10 removed from the repositories. : Looks great but repeatedly crashes to force restarting X-server. So we can't use or recommed this program at the moment.   Are there any other alternatives? How can we make First letter Capital while using trigonometric functions. eg: \cos (60^\circ) gives simple cos (all in small). What is needed is 'Cos' (with initial capital). For italics in mactex i used \it but it didn't italicized 'cos' only the number '60'. So how to do that? Preservation of ideals in localized rings Refer to localization of rings in Lang's Algebra p. 108. In particular, let $A$ be a commutative ring, $S$ a subset of $A$ that is a submonoid of the multiplicative monoid structure of $A$, containing the identity. Then  $S^{-1}A$ is the set of elements $\frac{a}{s}$ with $a \in A$ and $s \in S$.  We have $\frac{a_1}{s_1}=\frac{a_2}{s_2}$ if there exists $\bar{s} \in S$ such that $\bar{s}(s_2a_1 - s_1 a_2)=0$. Now consider the injection of $A$ into $S^{-1}A$ given by $x \mapsto \frac{x}{1}$. This is a ring homomorphism. Let $J$ be an ideal of $A$. Ring homomorphisms preserve ideals and so the injection of $J$ into $S^{-1}A$, call it $J^{*}$, will be an ideal. My problem is that i don't see why for any $\frac{a}{s} \in S^{-1}A$ and any $\frac{j}{1} \in J^{*}$ we will have that $\frac{aj}{s}$ will be in $J^{*}$, i.e. why there must exist an element $\kappa \in J$ such that $\frac{aj}{s}=\frac{\kappa}{1}$. Which versions of C++ redistributables can I remove? I have a number of Microsoft Visual C++ 2005 and 2008 Redistributable and I would like to know which ones are safe to remove, because there are actually more than 10 installed on my computers.   Microsoft Visual C++ 2005 ATL Update kb973923 - x64 8.0.50727.4053 Microsoft Visual C++ 2005 Redistributable Microsoft Visual C++ 2005 (x64) Microsoft Visual C++ 2008 ATL Update kb973924 - x64 9.0.30729.4148 Microsoft Visual C++ 2008 Redistributable - x64 9.0.30729.17 Microsoft Visual C++ 2008 Redistributable - x86 9.0.30729.17 Microsoft Visual C++ 2008 Redistributable - x86 9.0.30729.4148 Microsoft Visual C++ 2008 Redistributable - x86 9.0.30729.4974 Microsoft Visual C++ 2010 x64 Runtime - 10.0.30319   It could be that these versions are all different, but a confirmation of that is fine for me as well. Thanks in advance.  PS. I'm using an x64 machine one windows 7. Testing whether two regression coefficients are significantly different (in R ideally) If this is a duplicate question, please point to the right way, but the similar questions I've found here haven't been sufficiently similar. Suppose I estimate the model $$Y=\alpha + \beta X + u$$  and find that $\beta&gt;0$. However, it turns out that $X=X_1+X_2$, and I suspect $\partial Y/\partial X_1 \ne \partial Y / \partial X_2$, and in particular, that $\partial Y/\partial X_1 &gt; \partial Y / \partial X_2$. So I estimate the model $$Y=\alpha + \beta_1 X_1 + \beta_2 X_2 +u$$and find significant evidence for $\beta_1,\beta_2&gt;0$. How can I then test whether $\beta_1&gt; \beta_2$? I considered running another regression $$Y=\alpha +\gamma(X_1 - X_2) + u$$ And testing whether $\gamma&gt;0$. Is this the best way?  Also, I need to generalize the answer to many variables, i.e. suppose we have $$Y=\alpha + \beta^1 X^1 + \beta ^2 X^2 + \dots + \beta^nX^n + u$$ where for each $j=1,\dots,n$, $X^j=X_1^j+X_2^j$, and I would like to test for each $j$ whether $\partial Y/\partial X_1^j \ne \partial Y / \partial X_2^j$.  By the way, I am primarily working in R. Map composer issue Is the set of polynomials of degree less than or equal to $n$ closed? This question is in relation to the space $C(I)$, $I = [a, b]$. Define $P_n =\{ a_0+\dots+a_nx^n \mid a_i \in \mathbb{R}\}$ (any or all $a_i$ could be zero); clearly $P_n \subset C(I)$. The norm I'm using is $\lVert f\rVert_I = \sup_I |f(x)|$. Is $P_n$ closed under $\lVert\cdot\rVert_I$?   I am almost sure the answer is "yes", but I can't seem to prove it. My first instinct was to biject  $P_n$ to $\mathbb{R}^{n+1}$, using coefficients as coordinates, and prove that sequences of degree-$n$ polynomials converge to degree-$n$ polynomials, but I can't prove that the metric $\lVert\cdot\rVert_I$ is equivalent to the standard metric on $\mathbb{R}^{n+1}$. Next, intuitively given a function $f \in C(I), \notin P_n$ I should be able to find some $\epsilon &gt; 0$ such that there are no low-degree polynomials "nearby", then that function was not a limit point of $P_n$ so $P_n$ is closed. Again I have no idea how to prove this. What should I do?