I was trying to boot Xubuntu, Ubunto, and many other variants, initally on BIOS my USB stick  was never detected to boot from, so after reading online I found out that I need a (bootia32.efi) to be placed in ISO Root - EFI/BOOT, I copied the (bootia32.efi) to that directory and my USB Sitck was detected in BIOS menu, now selecting to boot from the USB  Stick, brings up the menu of installation to chose from, I tried all options in that boot screen such as (boot to Xubunut without installing, Installing Xubntu, etc) and I was only presented with a black screen that dont seem to do anything, and of course I manually edited my BIOS to turn off secure boot, and placing the USB stick on top of the boot order, after digging further am starting to suspect that in EFI/BOOT I would need a (grubia32.efi) since am running windows 10 with x86 processor and a 32bit which does't support x64 grub, can anyone help on how to get a grubia32.efi so I can see if it boots? thanks a lot in advance!
I have a computer (Asus T200TA) with 2G RAM and 32-bit UEFI [Yep, what a wonderful idea to put 32-bit UEFI!]. I have two options:   Install a 64-bit system, boot manually and install grub-efi-ia32. Drawback: 2G is not much, systems with such low RAM are said to run better on 32-bit systems. Install a 32-bit version. (but from my state of knowledge it's not trivial to boot)   Which option would be better?
Shouldn't this be a pretty straightforward operation? However, I see there's neither a size() nor length() method. I would like to know how to make this effect of merged circle selection. Here are images to illustrate:     Basically I'm looking for this effect:    How the merge effect of the circles can be achieved ? I didn't found any explanation concerning this effect. I know that to project those texture I can develop a decal system but I don't know how to create the merging effect.  If possible, I'm looking for purely shaders solution. The PS3 version of Portal 2 comes with a code to link it to a Steam account. Apparently this allows you to play on both the PC and PS3 with one purchase.  Can this be used to play co-op between the two platforms? By taking courses, I have learned topic A. Through self-studying and discussion with others, I have explored topic B.    I also study topic A outside the classroom. What I want to emphasis is that topic B is not in my curriculum, but I study it on my own. Extracurricular activities can include both activities contributed to academic study although outside the normal curriculum, but also activities not contributed to academic study such as sports, volunteer work, etc. I want to emphasis the former activities. Do you say "outside classes", when mentioning study by yourself, i.e.      Outside classes, I have explored B.      If it were you, how would you rewrite the first quote for the same       meaning?    Thanks! Add new user and add meta at once Is there a bulletproof way to know a file has been successfully decrypted? I am wondering whether there is a bulletproof way to know if one is successful in decrypting a file.   As an example, say I encrypt a text file and protect it with a 3 letter password. I use brute force to decrypt it, but two different passwords lead to a file that contains English intelligible text. Is there a way for me to know which one is the original one? In other words, is there an infallible way to know I've entered the password correctly? A non-trivial normal subgroup N of a finite group G or order $p^n$ is such that $N\cap Z(G)\neq \{ 1\}$ Let $N\neq 1$ be a normal subgroup of G, where G has order $p^n$, $p$ prime, $n\geq 1$.  I need to show that $Z(G)\cap N\neq \{1\}$.  I have tried to do it this way: Suppose $|N|=p^m$, $m&lt;n$. Since $N\cap Z(G)$ is normal in $Z(G)$, in both cases ($N\cap Z(G)=\{1\}$ and $N\cap Z(G)\neq\{ 1\}$), I used the Second Group Isomorphisms Theorem to get:  $G/N\cong Z(G)/N\cap Z(G)$  so $|G/N| = |Z(G)/N\cap Z(G)|$  Since N is normal in G, $|G/N|=p^{n-m}$, with $n-m&gt;0$. So $|Z(G)/N|\cap Z(G)|=p^{n-m}\Rightarrow [Z(G):N\cap Z(G) ]=p^{n-m}$  and this means that $N\cap Z(G)\neq \{1\}$. Truncated 200GB table but disk space not released If $\mathbb f$ is analytic and bounded on the unit disc with zeros $a_n$ then $\sum_{n=1}^\infty \left(1-\lvert a_n\rvert\right) \lt \infty$ I'm going over old exam problems and I got stuck on this one. Suppose that $\mathbb{f}\colon \mathbb{D} \to \mathbb{C}$ is analytic and bounded. Let $\{a_n\}_{n=1}^\infty$ be the non-zero zeros of $\mathbb{f}$ in $\mathbb{D}$ counted according to multiplicity. Show that  $$ \sum_{n=1}^\infty \left( 1 - \left|a_n \right|\right)\lt\infty $$  I can understand that $\left|a_n \right|$ goes to $1$ since zeros are isolated, but it doesn't help showing the series is convergent. Any help will be appreciated! Microsoft Exchange 2013 is not receiving internet email Is there a math basis for what I observe in simulations? I am trying to run the following model:         I(week^2):mutation_status +         (week + I(week^2) | subject_id) ,          data = sim_dat)  This is the output I get from this model. The correlation between week and I(week)^2 is rather high (-0.95) and I was curious to know how I can change my data simulation code to lower that value. I am also having issues with convergence and would love to get thoughts on how to avoid issues regarding convergence. Thank you! Linear mixed model fit by REML. t-tests use Satterthwaite's method [lmerModLmerTest]  Formula: fetal_weight ~ week + mutation_status + week:mutation_status + I(week^2) +  I(week^2):mutation_status + (week + I(week^2) | subject_id) Data: sim_dat  REML criterion at convergence: 114  Scaled residuals:      Min      1Q  Median      3Q     Max  -1.9343 -0.3980 -0.0075  0.4168  1.9928   Random effects:  Groups     Name        Variance  Std.Dev. Corr         subject_id (Intercept)   1.51524  1.2309                          week         61.01832  7.8114   0.16                   I(week^2)   496.20236 22.2756  -0.19 -0.95  Residual                 0.05892  0.2427              Number of obs: 100, groups:  subject_id, 20  Fixed effects:                            Estimate Std. Error       df t value       Pr(&gt;|t|)     (Intercept)                  3.1331     0.4226  17.9961   7.413 0.000000713699 *** week                        -1.2465     3.5210  18.0053  -0.354          0.727     mutation_statusY             0.5061     0.5977  17.9961   0.847          0.408     I(week^2)                   34.5706    10.8146  18.0084   3.197          0.005 **  week:mutation_statusY       -1.0697     4.9795  18.0053  -0.215          0.832     mutation_statusY:I(week^2) 202.5521    15.2942  18.0084  13.244 0.000000000101 *** --- Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  Correlation of Fixed Effects:             (Intr) week   mttn_Y I(w^2) wk:m_Y week        -0.160                             muttn_sttsY -0.707  0.113                      I(week^2)    0.145 -0.966 -0.103               wk:mttn_stY  0.113 -0.707 -0.160  0.683        mtt_Y:I(^2) -0.103  0.683  0.145 -0.707 -0.966  Code for data simulation: set.seed(123)  # set the seed J &lt;- 20  # number of individuals (clusters) cs &lt;- 5  # number of time points (cluster size) gam &lt;- c(0, 0.75, 1.34)  # fixed effects G &lt;- matrix(c(1.75, 0, 0,               0, 0.0125, 0,               0,0,0.0625),              nrow = 3)  # random effect variances (G-matrix) sigma2 &lt;- 0.08  mutation_status &lt;- rep(0:1, each = 5) mutation_status &lt;- rep(mutation_status, times = 10)  X &lt;- cbind(1, seq_len(cs),             (seq_len(cs))^2)  # for each individual X &lt;- X[rep(seq_len(cs), J), ]  X &lt;- X[rep(seq_len(cs), J), ]  X[,2] &lt;- X[,2]*0.05 X[,3] &lt;- X[,3]*0.05  # repeat each row cs times pid &lt;- seq_len(J)  # individual id pid &lt;- rep(pid, each = cs)   # Generate person-level (lv-2) random effects uj &lt;- lmf::rmnorm(J, mean = rep(0, 2), varcov = G)  # Generate repeated-measure-level (lv-1) error term eij &lt;- rnorm(J * cs, sd = sqrt(sigma2))  # Compute beta_j's betaj &lt;- matrix(gam, nrow = J, ncol = 3, byrow = TRUE) + uj  # Compute outcome: y &lt;- rowSums(X * betaj[pid, ]) + eij + mutation_status*X[ , 3]*10 + 3  # Form a data frame sim_dat &lt;- tibble(y, time = X[ , 2], pid, mutation_status) sim_dat &lt;- sim_dat[,c(3,2,4,1)] colnames(sim_dat) &lt;- c(&quot;subject_id&quot;, &quot;week&quot;, &quot;mutation_status&quot;, &quot;fetal_weight&quot;) sim_dat$week_2 &lt;-sim_dat$week*5 + 15  sim_dat$mutation_status &lt;- as.factor(ifelse(sim_dat$mutation_status == 0, &quot;N&quot;, &quot;Y&quot;)) sim_dat$subject_id &lt;- as.factor(sim_dat$subject_id ) #Model Building How do Stack Overflow questions appear in Google search results in seconds? How can I set up dual monitors with nVidia? If $(X_n)$ is i.i.d. and $ \frac1n\sum\limits_{k=1}^{n} {X_k}\to Y$ almost surely then $X_1$ is integrable (converse of SLLN) Let $(\Omega,\mathcal F,P)$ be a finite measure space.  Let $X_n:\Omega \rightarrow \mathbb R$ be a sequence of iid r.v's  I need to prove that if:  $ n^{-1}\sum _{k=1}^{n} {X_k} $ converges almost surely to $Y$ then all $X_k$ have expectation.  If I understand correctly then $X_k$ has expectations means $X_k$ is in $\mathcal L^1(\Omega)$.  And I know that on finite measure space Converging in expectations is converging in $\mathcal L^1(\Omega)$ and it's stronger than almost sure convergence.  And I know that from linearity of expectation even if one of the sequence is not in $\mathcal L^1(\Omega)$ then $Y$ is not in $\mathcal L^1(\Omega)$.  How do I continue? How to delete all events on many dates all at once but not the whole Calendar in Google Calendar? How to delete all events on many dates all at once but not the whole Calendar in Google Calendar? I tried to click on a date that got an event to delete and dragged my mouse to 2 days after that date, but then I always see "create an Event". I thought I could press backspace or delete to delete all those events.  What I did so far = deleting event one by one :( How to use siunitx with non numerical values? I understand that something like \SI{5}{\meter\per\second} defines a certain space between the number (5) and units (m/s). What should I use if instead of a numerical quantity I want a variable: R m/s? \SI{R}{\meter\per\second} fails because it expect a number in first parameter, and R \si{\meter\per\second} or $R$ \si{\meter\per\second} show different space.  Which is the correct use?  \documentclass{standalone} \usepackage{siunitx} \sisetup{per-mode=symbol} \begin{document} \begin{tabular}{r} \SI{5}{\meter\per\second}\\ $R$ \si{\meter\per\second}\\ R \si{\meter\per\second}\\ \end{tabular} \end{document} Intuitive meaning of Globally Hyperbolic I am been studying differential geometry and spacetime and I keep coming across the term . I am having a hard time coming up with an intuitive understanding of this idea. What is an intuitive meaning of globally hyperbolic.  My background is mostly mathematics at a higher undergraduate level. How do you use plural or singular form of an adjective? What is the problem that gets worse after you try to solve it? Example sentence - This problem is a _____ which gets complicated every time you try to solve it.   Is there such a word that represents a problem which will become worse if/when one tries to solve it?  Note - After seeing the comments and answers I'd like to add that this is not a can of worms or Pandora's box because it doesn't create a new problem but just worsens.   Example: Magic leaves protecting a fruit by enclosing it. Every time a leaf is touched, all the leaves close together further, that is more tight than before.