Drop trailing singleton slice dimensions.

The effect of this commit is the following:

    julia> M = rand(4,3)
    4x3 Float64 Array:
     0.246766  0.445467   0.625134
     0.121681  0.524432   0.0826616
     0.159059  0.0579328  0.261074
     0.92314   0.132067   0.202765

    julia> M[1,:]
    1x3 Float64 Array:
     0.246766  0.445467  0.625134

    julia> M[:,1]
    4-element Float64 Array:
     0.246766
     0.121681
     0.159059
     0.92314

    julia> M[1,1]
    0.2467660488808927

    julia> T = cat(3,rand(4,3),rand(4,3))
    4x3x2 Float64 Array:
    [:, :, 1] =
     0.673916  0.650694   0.583733
     0.398368  0.0546743  0.624825
     0.212863  0.345038   0.44051
     0.828705  0.615862   0.951405

    [:, :, 2] =
     0.742772    0.531061  0.300308
     0.343641    0.339739  0.0563449
     0.00311445  0.158575  0.478734
     0.544428    0.589634  0.746131

    julia> T[1,:,:]
    1x3x2 Float64 Array:
    [:, :, 1] =
     0.673916  0.650694  0.583733

    [:, :, 2] =
     0.742772  0.531061  0.300308

    julia> T[:,1,:]
    4x1x2 Float64 Array:
    [:, :, 1] =
     0.673916
     0.398368
     0.212863
     0.828705

    [:, :, 2] =
     0.742772
     0.343641
     0.00311445
     0.544428

    julia> T[:,:,1]
    4x3 Float64 Array:
     0.673916  0.650694   0.583733
     0.398368  0.0546743  0.624825
     0.212863  0.345038   0.44051
     0.828705  0.615862   0.951405

    julia> T[1,:,1]
    1x3 Float64 Array:
     0.673916  0.650694  0.583733

    julia> T[:,1,1]
    4-element Float64 Array:
     0.673916
     0.398368
     0.212863
     0.828705

    julia> T[1,1,1]
    0.673916474154618

Note that non-cell vectors now print as columns so
that they look as similar as possible to column
matrices, which is how they behave.

Work still needs to be done on SubArray and DArray
to make their behaviors match this.
added analytics tracking code
Fix to general tensor case in 0cd86dcda00a7a41c55952677de43ad6a466527f.

The intention was to drop trailing dimensions indexed with a scalar,
not trailing dimensions indexed with length-one abstract vectors.
fixing top-level loading behavior to be consistent with REPL
closes #424
also reverting the CWD changing behavior in loading that Stefan didn't like
Merge branch 'master' of github.com:JuliaLang/julia
faster vcat(::BitMatrix)
Some code cleanup/refactoring in bitarray
bitarray: more code polish (nicer del(Range1))
bitarray: fixed reshape
bitarray: efficient reverse
bitarray: exposed generalized copy_to
bitarray: drop trailing singleton slice dimensions
Merge branch 'master' into local_blas
Merge pull request #699 from carlobaldassi/bitarray

Bitarray update
filename fix in parser
Merge branch 'master' of github.com:JuliaLang/julia
Added Debian shootout pidigits.jl in a new 'shootout' directory in the examples; it is a straighforward rewrite of the Python version available on the shootout. Also had to modify the bigint.jl module and corresponding gmp_wrapper.c in order to make 'divmod' and 'lshift' available. Without the printouts (commented out in the code), the speed is on par with Python -- use the timeit macro now available in timing.jl (also in shootout directory) and adapted from perf.jl. Also moved fasta.jl to that new shootout directory.
Deleted the fasta.jl file from examples, because it moved to examples/shootout.
Took into account Pao's comments https://github.com/JuliaLang/julia/pull/693 and cleaned the code. Also: added some documentation for pidigits ; added an option to print the intermediate digits as requested by the shootout, leaving it as a non-default option because printing is so slow in Julia compared to Python...
Removed absolute path in bigint.jl and some path related comments in pidigits.jl