Ciro Santilli @cirosantilli 35

Things actually have gotten more and more closed, e.g. of stuff getting paywalled with time:It appears that things got really bad starting in 2017, possibly when WebLearn was introduced. When things migrated to Canvas, they were closed by default, apparently with any mechanism to publish publicly.

Therefore, they managed to make things more closed than when teachers would just upload to good old ox.ac.uk/~name static websites!!

Ciro Santilli has also heard that some people in the Mathematical Institute of the University of Oxford opposed to moving away from their Moodle instance precisely because the new options did not support open publishing, so kudos to those people. But most teachers likely don't care and just do whatever is the best internally supported default.

Their "open" video material: podcasts.ox.ac.uk/ A somewhat small part is Creative Commons, but most proprietary. Despite the name "podcasts", they do contain video, it is just a relic.

podcasts.ox.ac.uk/open contains actual Creative Commons only it seems.

It does however appear that professors own their lecture notes, so there some hope maybe: governance.admin.ox.ac.uk/legislation/statute-xvi-property-contracts-and-trusts#collapse1383636

Talks: talks.ox.ac.uk/. Mathematical, Physical and Life Sciences (MPLS) subset: talks.ox.ac.uk/talks/department/id/oxpoints:23232639

It is said that you leave Oxford with either

Bibliography:

Group of students that represent students academic views about the courses.

www2.physics.ox.ac.uk/students/course-materials/symmetry-in-condensed-matter-physics# Archive: web.archive.org/web/20230804204137/https://www2.physics.ox.ac.uk/students/course-materials/symmetry-in-condensed-matter-physics Lecture notes from 2019.

Professor: Radu Coldea

Published as: arxiv.org/pdf/2304.03442.pdf Generative Agents: Interactive Simulacra of Human Behavior by Park et al.

See sections: "Example 1 - N even", "Example 2 - N odd" and "Representation in terms of sines and cosines" of www.statlect.com/matrix-algebra/discrete-Fourier-transform-of-a-real-signal

The transform still has complex numbers.

Summary:Therefore, we only need about half of $X_k$ to represent the signal, as the other half can be derived by conjugation.

"Representation in terms of sines and cosines" from www.statlect.com/matrix-algebra/discrete-Fourier-transform-of-a-real-signal then gives explicit formulas in terms of $X_k$.

NumPy for example has "Real FFTs" for this: numpy.org/doc/1.24/reference/routines.fft.html#real-ffts