Ciro Santilli @cirosantilli 37

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Documentation generator  Updated 2025-05-21  +Created 1970-01-01

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Does Stockfish use the GPU?  Updated 2025-05-21  +Created 1970-01-01

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As of 2023, apparently does not use deep learning nor GPUs:

www.reddit.com/r/chess/comments/u5012z/can_stockfish_141_utilize_my_gpu_in_addition_to/

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Double pendulum  Updated 2025-05-21  +Created 1970-01-01

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OpenShot  Updated 2025-05-21  +Created 1970-01-01

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Ubuntu 20.10 crash...:

  exceptions:ERROR Unhandled Exception
Traceback (most recent call last):
  File "/usr/bin/openshot-qt", line 11, in <module>
    load_entry_point('openshot-qt==2.5.1', 'gui_scripts', 'openshot-qt')()
  File "/usr/lib/python3/dist-packages/openshot_qt/launch.py", line 97, in main
    app = OpenShotApp(argv)
  File "/usr/lib/python3/dist-packages/openshot_qt/classes/app.py", line 218, in __init__
    from windows.main_window import MainWindow
  File "/usr/lib/python3/dist-packages/openshot_qt/windows/main_window.py", line 45, in <module>
    from windows.views.timeline_webview import TimelineWebView
  File "/usr/lib/python3/dist-packages/openshot_qt/windows/views/timeline_webview.py", line 42, in <module>
    from PyQt5.QtWebKitWidgets import QWebView
ImportError: /usr/lib/x86_64-linux-gnu/libQt5Quick.so.5: undefined symbol: _ZN4QRhi10newSamplerEN11QRhiSampler6FilterES1_S1_NS0_11AddressModeES2_, version Qt_5_PRIVATE_API

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Systems biology  Updated 2025-05-21  +Created 1970-01-01

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Studies biology from a more global point of view, if putting all little pieces of an organism make up the final biological function.

Some key activities:

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TempleOS  Updated 2025-05-21  +Created 1970-01-01

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The OS that the Gods ordered be made.

One is reminded of Ulillillia, see also: www.youtube.com/watch?v=9-79yOZ13qg The Story of Ulillillia by Atrocity Guide (2019)

We are all somewhere in the spectrum.

Video 1.

I like elephants and God likes elephants

. Source.

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Ciro Santilli's hardware / Backpacks  Updated 2025-05-21  +Created 1970-01-01

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2024-04: got two backpacks "for free" with the Lenovo reward points from buying the Lenovo ThinkPad P14s gen4 amd, not bad, that was already cheap and now I got some extra swag:

Lenovo Select Targus 16" Sport Backpack: www.lenovo.com/gb/en/p/accessories-and-software/cases-and-bags/backpacks/gx41l44751
Lenovo Select Targus 16" Mobile Elite Backpack: www.lenovo.com/gb/en/p/accessories-and-software/cases-and-bags/backpacks/gx41l44752. Measured weight: 1070 g

The sport backpack has a fatal flaw: no strap to hold laptop in place, wo it just tumbles back and forth as you walk.

Neither of them have very good padding below the laptop, but at least the Elite one has a slightly elevated inner bag which woudl likely help a lot in case of a drop.

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Discrete Fourier transform  Updated 2025-05-21  +Created 1970-01-01

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Input: a sequence of

N

complex numbers

x_{k}

Output: another sequence of

N

complex numbers

X_{k}

such that:

x_{n} = \frac{1}{N} \sum_{k = 0}^{N - 1} X_{k} e^{i 2 π \frac{kn}{N}}

(1)

Intuitively, this means that we are braking up the complex signal into

N

sinusoidal frequencies:

$X_{0}$ : is kind of magic and ends up being a constant added to the signal because $e^{i 2 π \frac{kn}{N}} = e^{0} = 1$
$X_{1}$ : sinusoidal that completes one cycle over the signal. The larger the $N$ , the larger the resolution of that sinusoidal. But it completes one cycle regardless.
$X_{2}$ : sinusoidal that completes two cycles over the signal
...
$X_{N - 1}$ : sinusoidal that completes $N - 1$ cycles over the signal

and is the amplitude of each sine.

We use Zero-based numbering in our definitions because it just makes every formula simpler.

Motivation: similar to the Fourier transform:

compression: a sine would use N points in the time domain, but in the frequency domain just one, so we can throw the rest away. A sum of two sines, only two. So if your signal has periodicity, in general you can compress it with the transform
noise removal: many systems add noise only at certain frequencies, which are hopefully different from the main frequencies of the actual signal. By doing the transform, we can remove those frequencies to attain a better signal-to-noise

In particular, the discrete Fourier transform is used in signal processing after a analog-to-digital converter. Digital signal processing historically likely grew more and more over analog processing as digital processors got faster and faster as it gives more flexibility in algorithm design.

Sample software implementations:

numpy.fft, notably see the example: numpy/fft.py

Figure 1.
DFT of $2 sin (t) + cos (4 t)$ with 25 points
. This is a simple example of a discrete Fourier transform for a real input signal. It illustrates how the DFT takes N complex numbers as input, and produces N complex numbers as output. It also illustrates how the discrete Fourier transform of a real signal is symmetric around the center point.

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DNA microarray  Updated 2025-05-21  +Created 1970-01-01

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Can be seen as a cheap form of DNA sequencing that only test for a few hits. Some major applications:

gene expression profiling
single-nucleotide polymorphism: specificity is high enough to detect snips

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DNA mutation type  Updated 2025-05-21  +Created 1970-01-01

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Massive open online course  Updated 2025-05-21  +Created 1970-01-01

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MOOCs are a bad idea. We don't want to simply map the pre-computer classroom to the Internet. The Internet allows, and requires, fundamentally new ways to do things. More like Stack Overflow/Wikipedia. More like OurBigBook.com.

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