Uploaded December 2020 | Updated September 2026, 2 days ago
In this video I talk about an earlier paper of mine that was derived from my dissertation. The idea of this work was to characterize all densely defined multiplication operators for the specific space called the Polylogarithmic Hardy space. Here I give the basic outline of the paper, and then dive into some basic definitions and one theorem.
Correction: In the intro, I got the index wrong for the sum in the zeta function. For each prime we should start the index there at 0 to include the constant function 1 in the series, and the terms should be 1/p^(ns). This gives 1/(1-(1/p)^s) for the last term. How embarrassing!
Another Correction: A multiplication operator, M_phi, is bounded if phi * g is in the Hilbert space for all *g* (not phi).
Another another: in the definition of the polylogarithmic kernel, we should have (w bar * z)^n
Music:
Come 2gether by Ooyy
Let yourself go by Ooyy
Thunderbird by Ooyy
Guardians + Tek by Craig Hardgrove
In this video I talk about an earlier paper of mine that was derived from my dissertation. The idea of this work was to characterize all densely defined multiplication operators for the specific space called the Polylogarithmic Hardy space. Here I give the basic outline of the paper, and then dive into some basic definitions and one theorem.
Correction: In the intro, I got the index wrong for the sum in the zeta function. For each prime we should start the index there at 0 to include the constant function 1 in the series, and the terms should be 1/p^(ns). This gives 1/(1-(1/p)^s) for the last term. How embarrassing!
Another Correction: A multiplication operator, M_phi, is bounded if phi * g is in the Hilbert space for all *g* (not phi).
Another another: in the definition of the polylogarithmic kernel, we should have (w bar * z)^n
Music:
Come 2gether by Ooyy
Let yourself go by Ooyy
Thunderbird by Ooyy
Guardians + Tek by Craig Hardgrove

![Getting caught with facial recognition - How SVDs are used in Facial Recognition Software
This video builds on the SVD concepts of the previous videos, where I talk about the algorithm from the paper Eigenfaces for Recognition. These tools are used everywhere from law enforcement (such as tracking down the rioters at the Capitol) to unlocking your cell phone.
Followup Video: https://youtu.be/WnsKGBy1PXQ
Music:
Come 2gether by Ooyy
Guardians + Tek by Craig Hardgrove
Images:
Lectern Photo by Win McNamee/Getty Images (Used under Fair Use for an educational video)
Face Data obtained from http://www.databookuw.com/ the readme states
This data is modified from the Extended Yale Face Database B. If using this data, please also cite the following two papers:
[1] A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, From few to many: Illumination cone models for face recognition under variable lighting and pose, IEEE Trans. Pattern Anal. Mach. Intell., 23 (2001), pp. 643–660.
[2] K. Lee, J. Ho, and D. J. Kriegman, Acquiring linear subspaces for face recognition under variable lighting, IEEE Trans. Pattern Anal. Mach. Intell., 27 (2005), pp. 684–698.
Link to website: vision.ucsd.edu/content/extended-yale-face-database-b-b Getting caught with facial recognition - How SVDs are used in Facial Recognition Software](https://i.ytimg.com/vi/b-5jg5VvUEo/mqdefault.jpg)








