None of them seems "tailored" to the job of numerical computation (mostly linear algebra algorithms) like Fortran seems to be.
C: it is mostly alright, especially since C99 with VLA and complex numbers. Yet the aliasing rules are a bit annoying, and multi-dimensional arrays, while possible, do not really feel natural.
C++: an unholy clusterfuck... I almost ended crazy trying to use it properly. With extreme discipline maybe you can get to do some work with it. But not me.
Octave: very beautiful language with a natural, concise notation for math. Excellent out-of-the-box support for sparse matrices. Shame that loops are so slow; needs a serious effort on a JIT.
lua: my favorite general-purpose language. The luajit interpreter may be one of the fastest and easiest alternatives to Fortran. I wrote a matrix product using an explicit triple loop, and it multiplied huge matrices just slightly slower than an optimized blas. Extremely impressive! I'm very sad that the project is sort of "abandoned".
python+numpy: My least favorite, but the one that I use the most... what can I say: this is a general-purpose language where you can sort of do math in it. But it is not a language tailored for math. The base language has natively strings and dictionaries (for which I have no use) but it doesn't have native multidimensional arrays of floats. I do not understand why the numeric computing community is shifting so much to python, it makes no sense in my eyes. Also, loops are insultingly slow.
Julia: may be the best of the bunch, but launching the interpreter is excruciatingly slow, and it does not suit my usage. In the time that julia uses to compile a three-line program to plot "sin(x)", you can run one hundred times the Fortran compiler on the equivalent program (or simply call gnuplot). All in all, it keeps pretty much the promise of "matlab with fast loops", which is just what I need. But I feel that it's still a bit far from being there.