diff --git a/Julia100Exercises.jl b/Julia100Exercises.jl
index 4451773..a84775c 100644
--- a/Julia100Exercises.jl
+++ b/Julia100Exercises.jl
@@ -6,14 +6,14 @@
 #
 # In order to generate this file:
 # 1. Clone the repository (Or download).
-# 2. Activate the project.
+# 2. Activate and instantiate the project.
 # 3. Run:
 # ```Julia
 # using Literate;
 # Literate.markdown("Julia100Exercises.jl", name = "README", execute = true, flavor = Literate.CommonMarkFlavor());
 # ```
 #
-# **Remark**: Tested with Julia `1.7.2`.  
+# **Remark**: Tested with Julia `1.8.2`.  
 #
 # **To Do**:
 # 1. Reevaluate the difficulty level of each question.
@@ -143,7 +143,7 @@ mA[:, [1, end]] .= 1;
 mA[[1, end], :] .= 1;
 mA
 
-# An alternative way:
+# An alternative way (Different dimensions):
 
 mA = ones(4, 5);
 mA[2:(end - 1), 2:(end - 1)] .= 0;
@@ -153,10 +153,10 @@ mA[2:(end - 1), 2:(end - 1)] .= 0;
 mA = zeros(4, 5);
 mA[[LinearIndices(mA)[cartIdx] for cartIdx in CartesianIndices(mA) if (any(cartIdx.I .== 1) || cartIdx.I[1] == size(mA, 1) || cartIdx.I[2] == size(mA, 2))]] .= 1;
 
-# By Tomer Arnon (https://github.com/tomerarnon):
+# By [Tomer Arnon](https://github.com/tomerarnon):
 
-numRows = 4;
-numCols = 5;
+numRows = 5;
+numCols = 4;
 mA = Int[ii ∈ (1, numRows) || jj ∈ (1, numCols) for ii in 1:numRows, jj in 1:numCols];
 
 # ## Question 016
@@ -467,7 +467,7 @@ mX, mY = MeshGrid(vX, vY); #<! See https://discourse.julialang.org/t/48679
 #+
 @show mY
 
-# By Tomer Arnon (https://github.com/tomerarnon):
+# By [Tomer Arnon](https://github.com/tomerarnon):
 
 mXY = [(ii, jj) for ii in 0:0.25:1, jj in 0:0.25:1]; #<! Also `tuple.(0:0.25:1, (0:0.25:1)')`
 
@@ -503,7 +503,7 @@ vA = rand(10);
 
 vA[argmin(abs.(vA .- inputVal))]
 
-# By Tomer Arnon (https://github.com/tomerarnon):
+# By [Tomer Arnon](https://github.com/tomerarnon):
 
 function ClosestValue(vA::Vector{T}, inputVal::T) where{T <: Number}
     return argmin(y -> abs(y - inputVal), vA);
@@ -647,7 +647,7 @@ end
 
 vA[closeSecondIdx] == vA[sortperm(abs.(vA .- inputVal))[2]]
 
-# By Tomer Arnon (https://github.com/tomerarnon):
+# By [Tomer Arnon](https://github.com/tomerarnon):
 
 vA[partialsortperm(abs.(vA .- inputVal), 2)]
 
@@ -1223,7 +1223,7 @@ end
 
 mB
 
-# By Tomer Arnon (https://github.com/tomerarnon):
+# By [Tomer Arnon](https://github.com/tomerarnon):
 
 mB = reverse!(reduce(hcat, digits.(vA, base = 2, pad = 8))', dims = 2);
 
@@ -1334,8 +1334,8 @@ vI = [all(vA .== round.(vA)) && sum(vA) == sumVal for vA in eachrow(mA)];
 # ## Question 100
 # Compute bootstrapped `95%` confidence intervals for the mean of a 1D array `vA`. Namely, resample the elements of an array with replacement `N` times, compute the mean of each sample and then compute percentiles over the means. (★★★)
 
-numTrials   = 1000;
-numSamples  = 100;
+numTrials   = 10000;
+numSamples  = 1000;
 μ           = 0.5;
 
 vA = μ .+ randn(numSamples);
diff --git a/README.md b/README.md
index 43c30aa..498283b 100644
--- a/README.md
+++ b/README.md
@@ -6,14 +6,14 @@ A set of introductory exercises for Julia. Based on [100 NumPy Exercises](https:
 
 In order to generate this file:
 1. Clone the repository (Or download).
-2. Activate the project.
+2. Activate and instantiate the project.
 3. Run:
 ```Julia
 using Literate;
 Literate.markdown("Julia100Exercises.jl", name = "README", execute = true, flavor = Literate.CommonMarkFlavor());
 ```
 
-**Remark**: Tested with Julia `1.7.2`.
+**Remark**: Tested with Julia `1.8.2`.
 
 **To Do**:
 1. Reevaluate the difficulty level of each question.
@@ -30,6 +30,16 @@ using Tullio;
 using StaticKernels;
 ````
 
+````
+[ Info: Precompiling UnicodePlots [b8865327-cd53-5732-bb35-84acbb429228]
+WARNING: method definition for centered at D:\Applications\Programming\JuliaWin\userdata\.julia\packages\GeometryBasics\3PqdK\src\primitives\rectangles.jl:515 declares type variable N but does not use it.
+WARNING: method definition for volume at D:\Applications\Programming\JuliaWin\userdata\.julia\packages\GeometryBasics\3PqdK\src\meshes.jl:209 declares type variable FT but does not use it.
+WARNING: method definition for volume at D:\Applications\Programming\JuliaWin\userdata\.julia\packages\GeometryBasics\3PqdK\src\meshes.jl:209 declares type variable VT but does not use it.
+WARNING: method definition for volume at D:\Applications\Programming\JuliaWin\userdata\.julia\packages\GeometryBasics\3PqdK\src\meshes.jl:221 declares type variable FT but does not use it.
+WARNING: method definition for volume at D:\Applications\Programming\JuliaWin\userdata\.julia\packages\GeometryBasics\3PqdK\src\meshes.jl:221 declares type variable VT but does not use it.
+
+````
+
 ## Question 001
 Import the `LinearAlgebra` package under the name `LA`. (★☆☆)
 
@@ -45,7 +55,7 @@ println(VERSION);
 ````
 
 ````
-1.7.2
+1.8.2
 
 ````
 
@@ -58,16 +68,16 @@ vA = Vector{Float64}(undef, 10)
 
 ````
 10-element Vector{Float64}:
- 1.335846132e-314
- 9.473510105e-315
- 9.473550895e-315
- 9.47399579e-315
- 1.335904171e-314
- 1.3359041947e-314
- 1.3359042184e-314
- 9.473992946e-315
- 9.47399326e-315
- 9.47399358e-315
+ 0.0
+ 1.314224183e-315
+ 1.314224183e-315
+ 0.0
+ 1.314248214e-315
+ 1.31427351e-315
+ 0.0
+ 1.314248214e-315
+ 1.314106556e-315
+ 0.0
 ````
 
 Which is equivalent of
@@ -78,16 +88,16 @@ vA = Array{Float64, 1}(undef, 10)
 
 ````
 10-element Vector{Float64}:
- 1.5e-323
- 1.6e-322
- 6.95331311286057e-310
- 4.94e-322
- 4.94e-321
- 1.235e-321
- 1.0e-322
- 1.5e-323
- 1.6e-322
- 2.0e-323
+ 0.0
+ 1.314248214e-315
+ 1.314106556e-315
+ 0.0
+ 1.314248214e-315
+ 1.314106556e-315
+ 0.0
+ 1.314248214e-315
+ 1.314106556e-315
+ 0.0
 ````
 
 ## Question 004
@@ -289,12 +299,12 @@ mA = randn(2, 2, 2)
 ````
 2×2×2 Array{Float64, 3}:
 [:, :, 1] =
-  0.757423  0.90535
- -1.42309   1.20069
+ -1.34961   -0.225955
+  0.116267   1.03802
 
 [:, :, 2] =
- -0.325565  -0.341087
- -0.169036  -0.13069
+  0.737672  2.2642
+ -0.839961  0.218133
 ````
 
 ## Question 013
@@ -306,7 +316,7 @@ minVal = minimum(mA)
 ````
 
 ````
-0.22387297955883545
+0.02305480092860468
 ````
 
 ````julia
@@ -314,7 +324,7 @@ maxVal = maximum(mA)
 ````
 
 ````
-0.9681837108020949
+0.9708814560256962
 ````
 
 Using `extrema()` one could get both values at once:
@@ -331,7 +341,7 @@ meanVal = mean(randn(30))
 ````
 
 ````
-0.10430740159495046
+-0.04218053798839749
 ````
 
 ## Question 015
@@ -352,7 +362,7 @@ mA
  1.0  1.0  1.0  1.0
 ````
 
-An alternative way:
+An alternative way (Different dimensions):
 
 ````julia
 mA = ones(4, 5);
@@ -366,11 +376,11 @@ mA = zeros(4, 5);
 mA[[LinearIndices(mA)[cartIdx] for cartIdx in CartesianIndices(mA) if (any(cartIdx.I .== 1) || cartIdx.I[1] == size(mA, 1) || cartIdx.I[2] == size(mA, 2))]] .= 1;
 ````
 
-By Tomer Arnon (https://github.com/tomerarnon):
+By [Tomer Arnon](https://github.com/tomerarnon):
 
 ````julia
-numRows = 4;
-numCols = 5;
+numRows = 5;
+numCols = 4;
 mA = Int[ii ∈ (1, numRows) || jj ∈ (1, numCols) for ii in 1:numRows, jj in 1:numCols];
 ````
 
@@ -384,13 +394,14 @@ mB
 ````
 
 ````
-6×7 Matrix{Float64}:
- 0.0  0.0  0.0  0.0  0.0  0.0  0.0
- 0.0  1.0  1.0  1.0  1.0  1.0  0.0
- 0.0  1.0  0.0  0.0  0.0  1.0  0.0
- 0.0  1.0  0.0  0.0  0.0  1.0  0.0
- 0.0  1.0  1.0  1.0  1.0  1.0  0.0
- 0.0  0.0  0.0  0.0  0.0  0.0  0.0
+7×6 Matrix{Float64}:
+ 0.0  0.0  0.0  0.0  0.0  0.0
+ 0.0  1.0  1.0  1.0  1.0  0.0
+ 0.0  1.0  0.0  0.0  1.0  0.0
+ 0.0  1.0  0.0  0.0  1.0  0.0
+ 0.0  1.0  0.0  0.0  1.0  0.0
+ 0.0  1.0  1.0  1.0  1.0  0.0
+ 0.0  0.0  0.0  0.0  0.0  0.0
 ````
 
 ## Question 017
@@ -530,10 +541,10 @@ mA .= (mA .- mean(mA)) ./ std(mA) #<! Pay attention that `@.` will yield error (
 
 ````
 4×4 Matrix{Float64}:
-  1.20607    -1.13748    0.823965   0.850322
- -0.0389568  -0.163611   0.926198  -0.9654
-  0.0334464   0.969688  -1.95059   -0.81439
-  0.323745   -1.55141    0.780498   0.707899
+ -1.0589     0.696722    0.474169   0.0807478
+  1.33922    1.23329    -0.559528  -1.24305
+ -0.652489   0.919517    1.16875   -1.49035
+ -0.0565761  0.0550669   0.711533  -1.61812
 ````
 
 ## Question 023
@@ -551,7 +562,7 @@ sMyColor = sColor(rand(UInt8, 4)...)
 ````
 
 ````
-Main.##300.sColor(0x7f, 0xe0, 0x77, 0x8d)
+Main.var"##312".sColor(0xb0, 0xae, 0x35, 0x81)
 ````
 
 ## Question 024
@@ -563,8 +574,8 @@ mA = rand(2, 4) * randn(4, 3)
 
 ````
 2×3 Matrix{Float64}:
- -1.69224     0.207454  -0.348213
- -0.0328024  -0.194292  -0.171239
+ -1.47402   -1.10919   -1.22996
+ -0.745242   0.188422  -3.19746
 ````
 
 ## Question 025
@@ -577,14 +588,14 @@ map!(x -> ((x > 3) && (x < 8)) ? -x : x, vA, vA)
 
 ````
 8-element Vector{Int64}:
-  8
- -5
-  9
-  3
  -6
+  3
+  1
+ -4
+  3
+ -4
  -5
- 10
- -7
+ -4
 ````
 
 Julia allows Math like notation as well (See `Q0027`):
@@ -596,14 +607,14 @@ map!(x -> 3 < x < 8 ? -x : x, vA, vA)
 
 ````
 8-element Vector{Int64}:
- 10
-  9
-  8
-  8
+ -7
+ -6
  -4
   1
+ -6
  10
   8
+ -5
 ````
 
 Using logical indices one could use:
@@ -644,9 +655,9 @@ vZ .^ vZ
 
 ````
 3-element Vector{Int64}:
- 10000000000
-       46656
-        3125
+ 387420489
+ 387420489
+     46656
 ````
 
 ````julia
@@ -658,7 +669,7 @@ end
 ````
 
 ````
-MethodError(<<, (2, [10, 6, 5]), 0x0000000000007d24)
+MethodError(<<, (2, [9, 9, 6]), 0x0000000000007f1f)
 
 ````
 
@@ -676,9 +687,9 @@ false
 
 ````
 3-element Vector{Complex{Int64}}:
- 0 + 10im
+ 0 + 9im
+ 0 + 9im
  0 + 6im
- 0 + 5im
 ````
 
 ````julia
@@ -687,9 +698,9 @@ vZ / 1 / 1
 
 ````
 3-element Vector{Float64}:
- 10.0
-  6.0
-  5.0
+ 9.0
+ 9.0
+ 6.0
 ````
 
 ````julia
@@ -748,16 +759,16 @@ map(x -> x > 0 ? ceil(x) : floor(x), vA)
 
 ````
 10-element Vector{Float64}:
- -1.0
   1.0
- -2.0
  -1.0
  -1.0
  -1.0
- -3.0
- -3.0
- -2.0
   2.0
+ -1.0
+  2.0
+  2.0
+  2.0
+  1.0
 ````
 
 ## Question 030
@@ -771,13 +782,9 @@ vA[findall(in(vB), vA)]
 ````
 
 ````
-6-element Vector{Int64}:
-  2
- 10
-  2
- 10
-  8
-  8
+2-element Vector{Int64}:
+ 7
+ 5
 ````
 
 ## Question 031
@@ -819,9 +826,9 @@ println("Tomorrow: $(today() + Day(1))");
 ````
 
 ````
-Yesterday: 2022-04-08
-Today: 2022-04-09
-Tomorrow: 2022-04-10
+Yesterday: 2022-10-26
+Today: 2022-10-27
+Tomorrow: 2022-10-28
 
 ````
 
@@ -878,8 +885,8 @@ mA .= ((mA .+ mB) .* (.-mA ./ 2))
 
 ````
 2×2 Matrix{Float64}:
- -0.412244  -0.467578
- -0.180105  -0.272612
+ -0.35038   -0.685683
+ -0.510546  -0.444504
 ````
 
 Using the dot macro:
@@ -903,9 +910,9 @@ floor.(mA)
 
 ````
 3×3 Matrix{Float64}:
- 2.0  2.0  4.0
- 3.0  1.0  1.0
- 4.0  2.0  3.0
+ 0.0  0.0  4.0
+ 2.0  3.0  4.0
+ 0.0  3.0  3.0
 ````
 
 Option 2:
@@ -916,9 +923,9 @@ round.(mA .- 0.5) #<! Generates -0.0 for numbers smaller than 0.5
 
 ````
 3×3 Matrix{Float64}:
- 2.0  2.0  4.0
- 3.0  1.0  1.0
- 4.0  2.0  3.0
+  0.0  0.0  4.0
+  2.0  3.0  4.0
+ -0.0  3.0  3.0
 ````
 
 Option 3:
@@ -929,9 +936,9 @@ mA .÷ 1
 
 ````
 3×3 Matrix{Float64}:
- 2.0  2.0  4.0
- 3.0  1.0  1.0
- 4.0  2.0  3.0
+ 0.0  0.0  4.0
+ 2.0  3.0  4.0
+ 0.0  3.0  3.0
 ````
 
 Option 4:
@@ -942,9 +949,9 @@ mA .- rem.(mA, 1)
 
 ````
 3×3 Matrix{Float64}:
- 2.0  2.0  4.0
- 3.0  1.0  1.0
- 4.0  2.0  3.0
+ 0.0  0.0  4.0
+ 2.0  3.0  4.0
+ 0.0  3.0  3.0
 ````
 
 ## Question 037
@@ -1018,16 +1025,16 @@ sort(vA) #<! Use `sort!()` for inplace sorting
 
 ````
 10-element Vector{Int64}:
+  1
   3
   3
-  5
-  7
-  7
+  4
+  4
+  4
   7
   8
   9
  10
- 10
 ````
 
 ## Question 041
@@ -1050,7 +1057,7 @@ MySum(vA)
 ````
 
 ````
-48.537414353199296
+51.34991384741698
 ````
 
 ## Question 042
@@ -1085,16 +1092,16 @@ mB = [ConvToPolar(vX) for vX in eachrow(mA)]
 
 ````
 10-element Vector{Vector{Float64}}:
- [1.2198102844886598, 0.8691819574941603]
- [0.6172554595719763, 0.0866188511436483]
- [1.2598174331024279, 0.8511111052915155]
- [0.41518042921108855, 0.0221993255448236]
- [0.2951530478912789, 0.965721549303624]
- [0.7286702913050966, 0.44049202220018413]
- [1.236749583652066, 0.7202678911408135]
- [0.9202604422817915, 0.48747648762350454]
- [0.46875488383598934, 0.09208196173364079]
- [1.2090934232620685, 0.761937024298385]
+ [0.9505364569557584, 0.031648622825633764]
+ [0.704392126760974, 0.5147716023358581]
+ [0.29679995764188105, 1.201649642883764]
+ [0.637435149299262, 0.02987122275134911]
+ [0.933745980010277, 0.7244055260446777]
+ [0.9187482092583683, 0.36651502794834323]
+ [1.0164226188075605, 0.9581226557842126]
+ [0.5946171056720952, 1.4392243895565575]
+ [0.7889136757232136, 0.2964469880257975]
+ [1.044698619009772, 0.8754548962664869]
 ````
 
 In order to have the same output size:
@@ -1119,16 +1126,16 @@ vA
 
 ````
 10-element Vector{Float64}:
- -1.851561592589276
+  0.15593661212531384
+  0.8177229521444311
+ -1.4850097210409072
   0.0
- -0.9081462686181941
- -1.1229524974071992
- -1.0371745175120426
-  0.5239827523226771
- -0.10376274726301882
- -0.7086576853798374
-  0.15431667813077293
-  0.36597231741158176
+  0.8552383451908633
+  1.476127811761641
+  0.2407698166125247
+ -0.6363509339561092
+ -0.5514594178114751
+  0.8756142494364301
 ````
 
 General solution:
@@ -1173,7 +1180,7 @@ mX, mY = MeshGrid(vX, vY); #<! See https://discourse.julialang.org/t/48679
  1.0   1.0   1.0   1.0   1.0
 ````
 
-By Tomer Arnon (https://github.com/tomerarnon):
+By [Tomer Arnon](https://github.com/tomerarnon):
 
 ````julia
 mXY = [(ii, jj) for ii in 0:0.25:1, jj in 0:0.25:1]; #<! Also `tuple.(0:0.25:1, (0:0.25:1)')`
@@ -1191,11 +1198,11 @@ mC = 1 ./ (vX .- vY')
 
 ````
 5×5 Matrix{Float64}:
-   4.34948     1.92686   3.41342  1.34913  1.36163
- -37.7124      3.80878  27.373    2.06275  2.09212
-   2.48487     1.44612   2.1483   1.0944   1.10261
-  -3.36296  -120.634    -4.26788  4.67403  4.82757
-  -2.7321    -12.9953   -3.30066  6.88295  7.22115
+ 21.646    3.23455  34.1025   3.79522  -3.10044
+ 12.1532   2.89647  15.2886   3.33807  -3.49101
+  5.11271  2.18076   5.59545  2.422    -5.77562
+ 27.6005   3.34229  51.6618   3.94442  -3.00751
+  2.36374  1.45768   2.46193  1.56164  18.4072
 ````
 
 ## Question 048
@@ -1245,7 +1252,7 @@ print(mA);
 ````
 
 ````
-[0.6826122845982936 0.5192965452164663 0.9860952682441186; 0.978788699489747 0.9102486524515176 0.8120342833140729; 0.9140200702682315 0.8115435431147521 0.055947672943274185]
+[0.6807535316053605 0.849791747148149 0.32690265736495716; 0.9773227682466125 0.5276250185081583 0.9071015474523568; 0.3192975731085147 0.009594822627075117 0.21726819354916904]
 ````
 
 ## Question 050
@@ -1259,10 +1266,10 @@ vA[argmin(abs.(vA .- inputVal))]
 ````
 
 ````
-0.4164882658950231
+0.5023858297186229
 ````
 
-By Tomer Arnon (https://github.com/tomerarnon):
+By [Tomer Arnon](https://github.com/tomerarnon):
 
 ````julia
 function ClosestValue(vA::Vector{T}, inputVal::T) where{T <: Number}
@@ -1273,7 +1280,7 @@ ClosestValue(vA, inputVal)
 ````
 
 ````
-0.4164882658950231
+0.5023858297186229
 ````
 
 ## Question 051
@@ -1306,11 +1313,11 @@ mD #<! Apply `sqrt.()` for the actual norm
 
 ````
 5×5 Matrix{Float64}:
- 0.0        0.529849  0.533383     0.085191   0.0674761
- 0.529849   0.0       0.539715     0.302772   0.219417
- 0.533383   0.539715  1.11022e-16  0.212654   0.401994
- 0.085191   0.302772  0.212654     0.0        0.0375171
- 0.0674761  0.219417  0.401994     0.0375171  0.0
+ -4.44089e-16  0.135171   0.729876     0.724544   0.525945
+  0.135171     0.0        0.539868     0.255352   0.136451
+  0.729876     0.539868  -1.38778e-17  0.403845   0.814356
+  0.724544     0.255352   0.403845     0.0        0.135001
+  0.525945     0.136451   0.814356     0.135001  -4.44089e-16
 ````
 
 ## Question 053
@@ -1324,11 +1331,11 @@ vB = reinterpret(Int32, vA); #<! Creates a view
 
 ````
 5-element reinterpret(Int32, ::Vector{Float32}):
-  983
- 7644
- 8121
- 5973
- 7461
+ 8898
+ 1815
+ 6821
+ 6523
+ 9953
 ````
 
 The above is equivalent of:
@@ -1372,23 +1379,23 @@ end
 
 ````
 1
-0.9213084845535593
+0.7028850425818378
 2
-0.6918722219874133
+0.9169645026836734
 3
-0.8725707935840159
+0.887675432103485
 4
-0.5958441953220581
+0.6988267487719038
 5
-0.10790298709330681
+0.7143993871851885
 6
-0.9589979316827606
+0.8303140449034555
 7
-0.8263364553480945
+0.43387559304673373
 8
-0.6899584045881396
+0.13163225199507878
 9
-0.2375216575503002
+0.6590727215475687
 
 ````
 
@@ -1448,7 +1455,7 @@ mean(mA, dims = 1)
 
 ````
 1×3 Matrix{Float64}:
- 0.22697  0.0207947  -0.247765
+ 0.261517  -0.0218978  -0.23962
 ````
 
 ## Question 059
@@ -1463,9 +1470,9 @@ mA[sortperm(mA[:, colIdx]), :]
 
 ````
 3×3 Matrix{Float64}:
- 0.732867  0.0802497  0.304318
- 0.738193  0.711796   0.771238
- 0.162079  0.7315     0.426529
+ 0.870197  0.229455  0.462788
+ 0.812345  0.61344   0.0990058
+ 0.271644  0.722126  0.182457
 ````
 
 Using `sortslices()`:
@@ -1483,7 +1490,7 @@ any(all(iszero.(mA), dims = 1))
 ````
 
 ````
-true
+false
 ````
 
 ## Question 061
@@ -1497,7 +1504,7 @@ vA[sortperm(abs.(vA .- inputVal))[2]]
 ````
 
 ````
-0.5602543551990966
+0.5947029703004421
 ````
 
 Alternative way (More efficient)
@@ -1528,14 +1535,14 @@ vA[closeSecondIdx] == vA[sortperm(abs.(vA .- inputVal))[2]]
 true
 ````
 
-By Tomer Arnon (https://github.com/tomerarnon):
+By [Tomer Arnon](https://github.com/tomerarnon):
 
 ````julia
 vA[partialsortperm(abs.(vA .- inputVal), 2)]
 ````
 
 ````
-0.5602543551990966
+0.5947029703004421
 ````
 
 ## Question 062
@@ -1549,7 +1556,7 @@ sum(aVal + bVal for aVal in vA, bVal in vB)
 ````
 
 ````
-11.428935841059424
+11.149909948305584
 ````
 
 ## Question 063
@@ -1575,8 +1582,8 @@ println(vA);
 ````
 
 ````
-[4, 8, 4, 7, 3]
-[4, 8, 5, 8, 4]
+[1, 6, 2, 6, 2]
+[3, 7, 2, 6, 2]
 
 ````
 
@@ -1600,9 +1607,9 @@ println("vF: $vF");
 ````
 
 ````
-vX: [5, 4, 5, 5, 1, 1, 3, 2, 4, 4]
-vI: [14, 2, 5, 14, 13, 15, 6, 2, 10, 2]
-vF: [0.0, 10.0, 0.0, 0.0, 5.0, 3.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 1.0, 10.0, 1.0]
+vX: [5, 4, 3, 5, 2, 5, 2, 5, 5, 1]
+vI: [3, 2, 6, 11, 12, 10, 5, 11, 12, 2]
+vF: [0.0, 5.0, 5.0, 0.0, 2.0, 3.0, 0.0, 0.0, 0.0, 5.0, 10.0, 7.0]
 
 ````
 
@@ -1619,7 +1626,7 @@ print("Number of Unique Colors: $numColors");
 ````
 
 ````
-Number of Unique Colors: 971022
+Number of Unique Colors: 970711
 ````
 
 Another option:
@@ -1630,7 +1637,7 @@ print("Number of Unique Colors: $numColors");
 ````
 
 ````
-Number of Unique Colors: 971022
+Number of Unique Colors: 970711
 ````
 
 Simpler way to slice a pixel:
@@ -1641,7 +1648,7 @@ print("Number of Unique Colors: $numColors");
 ````
 
 ````
-Number of Unique Colors: 971022
+Number of Unique Colors: 970711
 ````
 
 ## Question 067
@@ -1655,8 +1662,8 @@ sum(reshape(mA, (2, 2, :)), dims = 3)
 ````
 2×2×1 Array{Float64, 3}:
 [:, :, 1] =
- 1.41862  1.08303
- 1.99375  1.80971
+ 1.817    1.16234
+ 2.44291  2.32796
 ````
 
 ## Question 068
@@ -1688,9 +1695,9 @@ println("vF: $vF");
 ````
 
 ````
-vX: [4, 3, 3, 4, 5, 2, 3, 5, 4, 4]
-vI: [1, 6, 3, 15, 13, 5, 5, 14, 7, 13]
-vF: [4.0, 0.0, 3.0, 0.0, 2.5, 3.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.5, 5.0, 4.0]
+vX: [5, 5, 4, 2, 1, 1, 3, 5, 3, 2]
+vI: [14, 7, 8, 10, 5, 10, 14, 2, 7, 13]
+vF: [0.0, 5.0, 0.0, 0.0, 1.0, 0.0, 4.0, 4.0, 0.0, 1.5, 0.0, 0.0, 2.0, 4.0]
 
 ````
 
@@ -1707,10 +1714,10 @@ vD = [dot(mA[ii, :], mB[:, ii]) for ii in 1:numDiagElements]
 
 ````
 4-element Vector{Float64}:
- 2.3046457089354355
- 1.5321878288670419
- 2.3611837835729648
- 1.6720347573355088
+ 1.8937792321469207
+ 1.035584608236753
+ 2.0251852803210024
+ 2.2065505485118653
 ````
 
 Alternative way:
@@ -1721,10 +1728,10 @@ vD = reshape(sum(mA[1:numDiagElements, :]' .* mB[:, 1:numDiagElements], dims = 1
 
 ````
 4-element Vector{Float64}:
- 2.3046457089354355
- 1.5321878288670419
- 2.3611837835729643
- 1.6720347573355088
+ 1.8937792321469207
+ 1.035584608236753
+ 2.0251852803210024
+ 2.2065505485118653
 ````
 
 ## Question 070
@@ -1769,25 +1776,25 @@ mA .* mB #<! Very easy in Julia
 ````
 5×5×3 Array{Float64, 3}:
 [:, :, 1] =
- 0.0253066  0.0578434  0.245036  0.10566   0.128313
- 0.538787   0.027032   0.113935  0.166183  0.049351
- 0.490902   0.0289957  0.197145  0.262019  0.170993
- 0.791761   0.418461   0.232067  0.205582  0.0501827
- 0.61709    0.126556   0.147224  0.544224  0.0811581
+ 0.492335  0.301617  0.0158155   0.411494  0.0331824
+ 0.555254  0.148368  0.111992    0.366332  0.192798
+ 0.104442  0.249306  0.0138448   0.018588  0.721434
+ 0.665075  0.136419  0.00305929  0.430373  0.208216
+ 0.290075  0.642668  0.519127    0.511527  0.151719
 
 [:, :, 2] =
- 0.0552786  0.0941616  0.557706   0.0096415  0.440917
- 0.66762    0.0529537  0.0127874  0.368139   0.269553
- 0.389103   0.0246526  0.551285   0.28712    0.0644469
- 0.747566   0.127425   0.0884657  0.130286   0.00610839
- 0.512197   0.191283   0.254354   0.528399   0.0966842
+ 0.234647   0.447608  0.000903852  0.00299349  0.0626502
+ 0.0159529  0.164814  0.0807125    0.27415     0.180858
+ 0.103613   0.291711  0.00759939   0.013771    0.11713
+ 0.314552   0.210101  0.00966737   0.343302    0.377711
+ 0.129541   0.642804  0.833245     0.372239    0.816422
 
 [:, :, 3] =
- 0.174052   0.0633047   0.0290718  0.0477022  0.461449
- 0.43145    0.044583    0.0397083  0.522961   0.291885
- 0.479104   0.00164765  0.643211   0.0190869  0.0849537
- 0.0142636  0.889647    0.0569704  0.0136173  0.0202209
- 0.358161   0.499842    0.526666   0.396949   0.0894064
+ 0.774094  0.181579   0.0151706   0.26963    0.0364216
+ 0.167574  0.0016081  0.0130672   0.823491   0.398857
+ 0.062678  0.40945    0.00790564  0.0141378  0.468662
+ 0.344903  0.18419    0.00697708  0.487335   0.504131
+ 0.118821  0.0130137  0.803091    0.754898   0.0470187
 ````
 
 ## Question 072
@@ -1801,8 +1808,8 @@ println(mA);
 ````
 
 ````
-UInt8[0x8a 0x39; 0x08 0x5d; 0xa9 0x3f]
-UInt8[0x08 0x5d; 0x8a 0x39; 0xa9 0x3f]
+UInt8[0xc7 0x2f; 0xbf 0x8f; 0xac 0x2a]
+UInt8[0xbf 0x8f; 0xc7 0x2f; 0xac 0x2a]
 
 ````
 
@@ -1817,36 +1824,36 @@ mC = unique(mC)
 
 ````
 30-element Vector{Vector{Int64}}:
- [8, 61]
- [61, 74]
- [8, 74]
- [11, 22]
- [11, 76]
- [22, 76]
- [40, 94]
- [40, 84]
- [84, 94]
- [23, 25]
- [14, 25]
- [14, 23]
- [0, 34]
- [34, 95]
- [0, 95]
- [5, 87]
- [5, 35]
- [35, 87]
- [45, 79]
- [45, 93]
- [79, 93]
- [42, 88]
- [72, 88]
- [42, 72]
- [33, 50]
- [19, 33]
- [19, 50]
- [33, 59]
- [33, 40]
- [40, 59]
+ [52, 86]
+ [23, 86]
+ [23, 52]
+ [53, 88]
+ [53, 95]
+ [88, 95]
+ [4, 31]
+ [4, 24]
+ [24, 31]
+ [34, 80]
+ [34, 99]
+ [80, 99]
+ [19, 69]
+ [19, 21]
+ [21, 69]
+ [38, 74]
+ [36, 74]
+ [36, 38]
+ [18, 20]
+ [20, 48]
+ [18, 48]
+ [0, 23]
+ [23, 77]
+ [0, 77]
+ [23, 42]
+ [42, 75]
+ [23, 75]
+ [20, 69]
+ [7, 69]
+ [7, 20]
 ````
 
 ## Question 074
@@ -1956,7 +1963,7 @@ println("Min Distance: $minDist");
 ````
 
 ````
-Min Distance: 0.17359580885397907
+Min Distance: 0.42811311783896533
 
 ````
 
@@ -1980,11 +1987,11 @@ end
 ````
 ┌ Warning: Assignment to `minDist` in soft scope is ambiguous because a global variable by the same name exists: `minDist` will be treated as a new local. Disambiguate by using `local minDist` to suppress this warning or `global minDist` to assign to the existing global variable.
 └ @ string:9
-The minimum distance from the 1 -th point: 0.49924754495719714
-The minimum distance from the 2 -th point: 0.9121971956924372
-The minimum distance from the 3 -th point: 0.45582837870387133
-The minimum distance from the 4 -th point: 0.19236584214073
-The minimum distance from the 5 -th point: 0.04344746636278687
+The minimum distance from the 1 -th point: 0.33333028326949365
+The minimum distance from the 2 -th point: 0.09596286876613971
+The minimum distance from the 3 -th point: 0.15987404532559377
+The minimum distance from the 4 -th point: 0.09922388894592074
+The minimum distance from the 5 -th point: 0.0701461047753776
 
 ````
 
@@ -2071,7 +2078,7 @@ vB[argmax(dropdims(sum(vA .== vB', dims = 1), dims = 1))] #<! The input to `argm
 ````
 
 ````
-3
+1
 ````
 
 Comparing bits:
@@ -2109,9 +2116,9 @@ mB = [mA[ii:(ii + winReach), jj:(jj + winReach)] for ii in 1:(numRows - winReach
 
 ````
 3×3 Matrix{Matrix{Int64}}:
- [7 3 8; 7 1 2; 7 8 4]  [3 8 2; 1 2 9; 8 4 5]  [8 2 5; 2 9 9; 4 5 3]
- [7 1 2; 7 8 4; 9 2 3]  [1 2 9; 8 4 5; 2 3 4]  [2 9 9; 4 5 3; 3 4 4]
- [7 8 4; 9 2 3; 6 1 7]  [8 4 5; 2 3 4; 1 7 5]  [4 5 3; 3 4 4; 7 5 8]
+ [9 1 1; 3 1 2; 9 4 6]  [1 1 4; 1 2 2; 4 6 2]  [1 4 7; 2 2 1; 6 2 2]
+ [3 1 2; 9 4 6; 4 2 1]  [1 2 2; 4 6 2; 2 1 7]  [2 2 1; 6 2 2; 1 7 1]
+ [9 4 6; 4 2 1; 2 9 8]  [4 6 2; 2 1 7; 9 8 1]  [6 2 2; 1 7 1; 8 1 4]
 ````
 
 ## Question 085
@@ -2149,7 +2156,7 @@ mA
 ````
 
 ````
-2×2 Main.##300.SymmetricMatrix{Int64}:
+2×2 Main.var"##312".SymmetricMatrix{Int64}:
  0  5
  5  0
 ````
@@ -2222,22 +2229,22 @@ vB
 
 ````
 16-element Vector{Float64}:
- 3.748234822363809
- 3.171870981384324
- 3.321254139347196
- 3.8413572656931247
- 3.9208525141288866
- 5.394935729742489
- 2.5354535893285357
- 5.677131588719307
- 4.3908979507404
- 3.3855542151777165
- 3.167629585929154
- 5.099521098536441
- 3.8636829563584847
- 4.730649745212458
- 3.717628033685399
- 4.132300388500083
+ 4.354231620225193
+ 3.734856822720345
+ 3.297606250613403
+ 4.256409222766379
+ 3.2941384563138993
+ 3.776165923653995
+ 3.600353929303568
+ 4.518180088610547
+ 3.6679777425586186
+ 4.6420262282688345
+ 4.296040312870272
+ 5.1547123230902825
+ 3.0214360614973477
+ 3.202315920090961
+ 4.669003591708878
+ 3.3257368418156616
 ````
 
 ## Question 088
@@ -2317,9 +2324,9 @@ vA[partialsortperm(vA, 1:numValues, rev = true)]
 
 ````
 3-element Vector{Float64}:
- 0.9308144744273673
- 0.9121778853679466
- 0.9030546045293627
+ 0.8713271882275592
+ 0.8486847944348448
+ 0.8445471427655805
 ````
 
 ## Question 090
@@ -2408,12 +2415,12 @@ vD = [all(vC) for vC in eachcol(mC)]
 
 ````
 8-element Vector{Bool}:
- 1
- 1
  0
  1
  1
  1
+ 0
+ 0
  1
  1
 ````
@@ -2460,16 +2467,16 @@ vD = [maximum(vA) != minimum(vA) for vA in eachrow(mA)]
 
 ````
 10-element Vector{Bool}:
+ 1
+ 0
  1
  1
  1
  1
  1
- 0
  1
  1
  1
- 0
 ````
 
 ## Question 095
@@ -2492,19 +2499,19 @@ mB
 
 ````
 10×8 Matrix{Bool}:
- 1  1  1  0  0  0  0  0
- 1  1  0  0  0  0  1  0
- 1  0  0  0  1  1  0  1
- 1  1  0  1  0  0  1  1
- 1  1  0  0  0  1  0  0
+ 0  0  1  0  1  1  1  1
+ 1  1  0  1  1  1  0  0
+ 0  0  0  0  1  0  1  0
+ 1  1  1  0  0  1  0  1
+ 0  1  0  1  0  1  0  1
+ 0  0  0  1  0  1  1  1
+ 1  0  0  0  1  0  1  0
  1  1  0  1  1  1  0  0
- 0  1  1  1  1  1  1  1
- 0  1  0  0  0  0  0  0
- 0  0  1  1  0  0  1  1
- 0  1  1  0  0  1  1  0
+ 0  0  1  1  1  1  0  0
+ 1  0  0  0  0  1  0  0
 ````
 
-By Tomer Arnon (https://github.com/tomerarnon):
+By [Tomer Arnon](https://github.com/tomerarnon):
 
 ````julia
 mB = reverse!(reduce(hcat, digits.(vA, base = 2, pad = 8))', dims = 2);
@@ -2647,21 +2654,21 @@ hP
 
 ````
       ┌────────────────────────────────────────┐             
-   10 │                                    ...:│ Samples     
-      │                                   .:   │ Interpolated
-      │                                ..:'    │             
-      │                             ...:       │             
-      │                           :''          │             
-      │                        ..:'            │             
-      │                      .:'               │             
-      │                    ..:                 │             
-      │                   .:                   │             
-      │                ..:'                    │             
-      │             .:''                       │             
-      │          .:''                          │             
-      │        :''                             │             
-      │       :'                               │             
-    0 │...:''''                                │             
+   10 │                                     ..:│ Samples     
+      │                                 ...''  │ Interpolated
+      │                              .:''      │             
+      │                             .:         │             
+      │                            .:          │             
+      │                        .:'''           │             
+      │                       :'               │             
+      │                    ..:'                │             
+      │                  :''                   │             
+      │              ....:                     │             
+      │            ..:                         │             
+      │        :''''                           │             
+      │     .:''                               │             
+      │   .:'                                  │             
+    0 │..:'                                    │             
       └────────────────────────────────────────┘             
        0                                     10              
 ````
@@ -2682,8 +2689,8 @@ vI = [all(vA .== round.(vA)) && sum(vA) == sumVal for vA in eachrow(mA)];
 Compute bootstrapped `95%` confidence intervals for the mean of a 1D array `vA`. Namely, resample the elements of an array with replacement `N` times, compute the mean of each sample and then compute percentiles over the means. (★★★)
 
 ````julia
-numTrials   = 1000;
-numSamples  = 100;
+numTrials   = 10000;
+numSamples  = 1000;
 μ           = 0.5;
 
 vA = μ .+ randn(numSamples);
@@ -2693,8 +2700,8 @@ quantile(tM, [0.025, 0.975])
 
 ````
 2-element Vector{Float64}:
- 0.2629943920259447
- 0.6666867801452737
+ 0.4620914889349693
+ 0.5830619321705541
 ````
 
 ---