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val ifft : Complex.complex vector - > Complex.complex vector Compute the one-dimensional inverse Fourier Transform using the Cooley-Tukey FFT algorithm . val idft2d : Complex.complex Array2.array - > Complex.complex Array2.array Compute the two-dimensional inverse Fourier Transform using the ( glacially slow ) naive algorithm . Jenrette 's style is clearly not limited to architecture and antiques , however ; his words are friendly and inviting , as if he is chatting with you over tea . A particular receptor will not be able to bind to all pathogens ; the more complementary the structures of the epitope and the lymphocyte receptor , the more likely it is that a bond will occur . The higher the affinity of a B-cell for pathogens present , the more likely it is that the B-cell will clone ; B-cells end up competing for available pathogens , with the highest affinity B-cells being the " fittest " and hence replicating the most . Any Committee member may ask follow-up questions of a candidate . val idft : Complex.complex vector - > Complex.complex vector Compute the one-dimensional inverse Fourier Transform using the ( glacially slow ) naive algorithm . These routines are likely to be as good as anything you can download , so use them . val dft2d : Complex.complex Array2.array - > Complex.complex Array2.array Compute the two-dimensional Fourier Transform using the ( glacially slow ) naive algorithm . If a bond is very likely to occur , then many receptors will bind to pathogen epitopes , resulting in a high affinity for that pathogen ; if a bond is unlikely to occur , then few receptors will bind to epitopes , and the lymphocyte will have a low affinity for that pathogen . There are millions of lymphocytes , so what we have is a system of highly distributed detection , where there is no centralised control , and little , if any , heirarchical control .