walrus

* There are frequent car crossings at blind alleys and driveways, often every 20-30 feet.

* Many are "protected" by flexible posts that are easily driven over.

* When protected bike lanes run next to roads, drivers often turn across them at intersections without looking.

I generated the map with Overpass Turbo: <https://overpass-turbo.eu/s/1mT6>. To make it render nicely, check "Don't display small features as POIs" under settings. If you want to try it for a different city, drag/zoom it into view and hit "Run".

I used to ride as far to the right as possible on roads without bike lanes, but then I got hit by a driver trying to squeeze past me without changing lanes.

[1] https://github.com/qmk/qmk_firmware/tree/master/keyboards/pl...

The website Retraction Watch[1] aggregates these retractions and provides a database that you can query. Reference management software like Zotero[2] can use this to monitor your collection of papers and notify you when one is retracted.

[1] https://retractionwatch.com/

[2] https://www.zotero.org/blog/retracted-item-notifications/

1. IPython with Pandas if I expect to do any in-depth exploration/manipulation of the data.

2. A short Python script like `python -c 'import csv, sys; r = csv.reader(sys.stdin); ...' <data.csv` if it needs to run on someone else's machine.

3. https://github.com/BurntSushi/xsv if I want to quickly munge some data or extract a field.

4. Gnumeric if I want a GUI.

5. https://github.com/andmarti1424/sc-im if I want a TUI. This is closest to what you were asking for.

Someone should let FinCEN know that their definition is incorrect: https://www.fincen.gov/history-anti-money-laundering-laws

[0] https://openai.com/blog/block-sparse-gpu-kernels/

Install pyenv[0] and pyenv-virtualenv[1]. Clone the repo and set up an environment:

  git clone https://github.com/taki0112/UGATIT
  cd UGATIT
  pyenv install 3.6.10  # [2]
  pyenv virtualenv 3.6.10 UGATIT
  pyenv local UGATIT
  pyenv activate
  pip install opencv-python==4.2.0.34 tensorflow==1.14.0

  tar xf ugatit100.tar.xz
  mkdir checkpoint
  mv UGATIT_selfie2anime* checkpoint
  mkdir -p dataset/selfie2anime/{train,test}{A,B}

  python main.py --dataset selfie2anime --phase test

[0] https://github.com/pyenv/pyenv#installation

[1] https://github.com/pyenv/pyenv-virtualenv#installation

[2] Other versions may work, but this is the version mentioned in https://github.com/taki0112/UGATIT#requirements

I worked at two small ISPs, each with a fiber network spanning a few US states. They had their own databases to track this information, but as far as I know there was no central database. One of them used OSPInsight backed by a self-hosted SQL Server database. I don't know what the other place used.

According to [1], the OOM killer is a consequence of the fork syscall, and can't be removed without breaking backward compatibility.

[1] https://drewdevault.com/2018/01/02/The-case-against-fork.htm...

[1] https://twitter.com/stats385

[2] https://www.researchgate.net/project/Theories-of-Deep-Learni...

I tried to do so myself and couldn't come up with anything significantly better, but I've been writing Python for a long time and might just be stuck in a local minimum :)

I've seen this stated by a few people, but never justified. What are some reasons for not deploying PyTorch in production?

Here's roughly how it works, if I understand correctly. First, a normal male endocrine system looks roughly like this:

  1. The hypothalamus produces GnRH.
  2. In response to stimulation by GnRH, the pituitary produces LH and
     FSH.
  3. In response to FSH and LH, the testes produce sperm and
     testosterone, respectively.
  4. Testosterone inhibits the production of GnRH by the hypothalamus (a
     negative feedback mechanism). This effectively keeps testosterone
     levels regulated.

  1. The increased testosterone inhibits the production of GnRH by the
     hypothalamus.
  2. In absence of GnRH, the pituitary produces less FSH and LH.
  3. In absence of FSH, the testes produce less sperm (this is the
     contraceptive effect). In absence of LH, the testes produce less
     testosterone.

This makes me wonder: would this lead to testicular atrophy? That happens with steroidal antiandrogens and GnRH agonists/antagonists, both of which have the effect of decreasing LH and FSH. Also, I think there are cardiovascular risks associated with high testosterone.

If it was actually determined to be the fault of ZFS, what was the problem? I'd like to avoid it in my own deployment.