Obtaining an Australian visa by a Russian citizen while in the US in F1 status

I’m a Russian citizen living in the US in F1 status. My F1 visa expires in December 2019. First of all, is it possible to apply for an Australian visa with my status? I haven’t found any explicit reference to it in the official FAQ (of the Australian embassy in the US). Secondly, assuming it is possible to obtain a visa for me, if I go to Australia in August for a couple of weeks, are there any requirements on how long my US F1 visa should be valid upon returning to the US? I wasn’t able to find such information in any official sources either. (E.g. if you apply for a EU visa with F1 status, then there are such kind of requirements.)

Cleaning a column obtaining last, first name so I can filter it from my data frame

I’m stumped. My issue is that I want to grab specific names from a given column. However, when I try and filter them I get most of the names except for a few, even though I can clearly see their names in the original excel file. I think it has to do what some sort of special characters or spacing in the name column. I am confused on how I can fix this.

I have tried using excels clean() function to apply that to the given column. I have tried working an Alteryx flow to clean the data. All of these steps haven’t helped any. I am starting to wonder if this is an r issue.

surveyData %>% filter(`Completed By` == "Spencer,(redbox with whitedot in middle)Amy")  surveyData %>% filter(`Completed By` == "Spencer, Amy")  

in r the first line had this redbox with white dot in between the comma and the first name. I got this red box with white dot by copy the name from the data frame and copying it into notepad and then pasting it in r. This actually works and returns what I want. Now the second case is a standard space which doesn’t return what I want. So how can I fix this issue by not having to copy a name from the data frame and copy to notepad then copying the results from notepad to r, which has the redbox with a white dot in between the comma(,) and first name.

Expected results is that I get the rows that are attached to what ever name I filter by.

What is the deterministic time complexity of obtaining the set of distinct elements?

Consider a sequence $ s$ of $ n$ integers (let’s ignore the specifics of their representation and just suppose we can read, write and compare them in O(1) time with arbitrary positions). What’s known about the worst-case time complexity of producing a sequence of all distinct elements in $ s$ , in any order?

By randomized hashing, one can do this in expected $ O(n)$ . On the upper bound side, one may sort the elements, then produce the output in a single pass by only copying elements which differ from their predecessors to the output – for a total time of $ O(n \log(n))$ .

But can one do better than $ O(n \log(n) )$ deterministically?

Note: This is sort-of a “remove duplicates” problem, but since the order is not preserved I’m not sure it should be called that.

Obtaining the dual variables of an optimisation problem with second order cone constraints solved using Gurobi

I’m solving an optimisation problem with linear and second order cone constraints, in other words the problem is convex and should have dual variables. I’m using Julia with JuMP to formulate the optimisation problem and I’m solving the problem with Gurobi. From the JuMP documentation, it should be possible to obtain the dual variables using the function dual. However I obtain NaNs when I do this while a linear version of my problem returns the duals as expected. Can anyone explain to me why this is and possible work arounds?

Obtaining a lower bound on the expectation using the Sudakov-Fernique inequality

In my work I wish to obtain a lower bound for the term below, independent of the vector $ x$ . Here the expectation is taken over $ h$ , a standard random Gaussian vector of length $ n$ . The vector $ x$ is fixed. The minimum is taken over all $ \{i_1,\dots,i_L\} \in \{1,\dots,n\}$ . Can this be done using the Sudakov-Fernique inequality? $ $ \mathbb{E}_{h} \min _{i_{1}, \ldots, i_{L}}\left[\sum_{j\neq i_1,\dots,i_L}h_j\mathrm{sign}(x_j^*)\right]. $ $

Using Girsanov Theorem Backwards?/ Obtaining Radon-Nikodym Derivative

On page 112/133 of Den Hollanders book on Large Deviations he wants to calculate the R.N derivative between two path measures : one is the path measure of the solution to an SDE $ dX_t=H(X_t)dt+dW_t$ and the other is the path measure induced by the Brownian Motion $ W_t$ appearing in that SDE.

He does this through Girsanov, it seems like some kind of reverse Girsanov theorem, I can’t understand it. Any ideas welcome.

SSL certificates limit reached for pcsuite.net www.pcsuite.net. Please wait before obtaining another SSL

I am trying to renew my a site SSL, While I am using LetsEncrypt SSL Certificate for 3 years. Now says SSL certificates limit reached for pcsuite.net www.pcsuite.net. Please wait before obtaining another SSL. Now how much would I hove to wait to get another SSL certificate? As my all other sites are down right now.

Obtaining generator matrix and first-passage time distribution for CTMC?


I have a model of a biological process described by two ODEs as follows: $ $ \dot{X_1} = (\beta_1-d-1)X_1 + 2X_1^2 – X_1^3 + dX_2$ $ $ $ \dot{X_2} = (\beta_2-d-1)X_2 + 2X_2^2 – X_2^3 + dX_1$ $

I want to analyze the stochastic version of this system using an appropriate underlying mechanistic process. My choice of representation is a chemical reaction network as follows:

$ $ X_1 \overset{\beta_1}{\rightharpoonup} 2X_1 $ $ $ $ X_1 \overset{1}{\rightharpoonup} \emptyset $ $ $ $ 2X_1 \overset{4}{\rightharpoonup} 3X_1 $ $ $ $ 3X_1 \overset{6}{\rightharpoonup} 2X_1 $ $ $ $ X_2 \overset{d}{\rightharpoonup} X_1 $ $ $ $ X_2 \overset{\beta_2}{\rightharpoonup} 2X_2 $ $ $ $ X_2 \overset{1}{\rightharpoonup} \emptyset $ $ $ $ 2X_2 \overset{4}{\rightharpoonup} 3X_2 $ $ $ $ 3X_2 \overset{6}{\rightharpoonup} 2X_2 $ $ $ $ X_1 \overset{d}{\rightharpoonup} X_2 $ $

Following the procedure in Section 5.3.6 of Edward Allen’s Modeling with Ito Stochastic Differential Equations, we can formulate a system of SDEs for the above model using the chemical reaction network. This allows for a noise vector that is derived from first principles, i.e. not tagged on in an ad-hoc manner to account for observed phenomenology.

I’ve been working with numerical simulations of this system for a while now. I’ve also surveyed a ton of literature for tools to derive analytical results. However, analytical progress is very slow (due to the cubic nonlinearities within a multi-dimensional system).


  1. Is there a way to obtain the infinitesimal generator matrix for the continuous-time Markov chain associated with this stochastic process? If so, how?

  2. How can first-passage time distributions be obtained analytically, or via numerical estimates?