conv looks like the hardest. s←1+(⍴⍵)-⍴⍺ is the result shape, number of subarrays with the length of ⍺ that will fit in ⍵ in each dimension. Looks like (⍳⍴⍺){s↑⍺↓⍵}¨⊂⍵ is missing the ¨; the inner function s↑⍺↓⍵ drops ⍺ elements (left argument) and then takes the first s, so it gets a length-s window of ⍵. This is called on each possible index into ⍺, together with the whole of ⍵, so it ends up getting all such windows. Presumably s is expected to be larger than ⍴⍺ so this is the more efficient way to slice things. ⊃+/,⍺× multiplies by ⍺ and sums, ravelling with , before applying +/ to collapse the dimensions and sum them all at once. Each element is an array of shape s, so summing them gives a result of shape s. There you go, multidimensional convolution!