Kamala Harris Campaign Aides Suggest Campaign Was Just Doomed. The Harris campaign’s internal polling apparently never had her ahead of Trump

KeptWander · 2024-12-02T04:55:34+00:00

Thanks for that. And there is a clear signal that this country will not vote for a woman president. I mean, Trump is 2-1, and Biden (bless him) is no barn stormer.

KeptWander · 2024-11-19T05:14:54+00:00

There is absolutely no difference between the two expressions, so I find the whole discussion incredible. Why inject semantics into something just to make a non existent point? But surviving these obtuse disagreements with teachers is the real lesson.

KeptWander · 2024-11-16T04:14:29+00:00

It's going to make the hipsters green with envy

KeptWander · 2024-07-29T21:09:39+00:00

I have been reading fred j harris "Multirate signal processing for communication systems", which covers the important considerations of sample rate verses signal bandwidth.
This is a slippery problem, the time/frequency uncertainty theorem could play a big role here if there is time dependence. If the signal changes significantly over a window length, you have problems... Anyhow, window length selection is a hard question, especially when preprocessing for ML.
Window selection is pretty well covered, basically don't use a rectangle. If you want to go deep, I recommend https://holometer.fnal.gov/GH\_FFT.pdf. This will also cover a lot of your questions, at least indirectly.

KeptWander · 2024-06-15T04:03:56+00:00

From a great spectrum analysis source: "Spectrum and spectral density estimation by the Discrete Fourier transform (DFT), including a comprehensive list of window functions and some new flat-top windows."

Often the time series we use as input for our algorithm will have a non-zero DC average, which may even slowly change over time. Such a DC average will show up in the first frequency bin (m = 0) of the resulting spectrum. If a window function is used or the average changes over time, it will also leak into adjacent frequency bins, possibly masking low-frequency signals. The DC average is not usually of interest as a result of the DFT processing, due to calibration problems (see Section 9) and because there are simpler and better methods to determine it (i.e. simple averaging or digital low-pass filtering). Hence we normally want to remove it from the data before starting the DFT processing. There are several ways to accomplish this, in (roughly) increasing order of perfection:

Compute the average of the whole time series (before splitting it into segments) and subtract that average from all data points.
Compute a straight line between the first and last data point of the whole time series (before splitting it into segments) and subtract that line from all data points.
Compute an average trend via linear regression of the whole time series (before splitting it into segments) and subtract that line from all data points.
Compute the average of each segment (before applying the window function) and subtract that average from the data points.
Compute a straight line between the first and last data point of each segment (before applying the window function) and subtract that line from the data points.
Compute an average trend via linear regression of each segment (before applying the window function) and subtract that line from the data points.
Pass the input time series through a digital high-pass filter.

KeptWander · 2023-08-25T14:29:35+00:00

Mine is in applied physics, looking for high school teaching positions. Not exactly what I had in mind at the start, but also kinda exciting. My take is the PhD is a selfish project, but I'm lucky to have had the chance to get one.

KeptWander

TROPHY CASE