walrus

Perhaps I'm being too cynical, but is this the "extend" step in "embrace, extend, extinguish"?

This was my first thought as well. It is linked to from a .gov website, though: https://www.cand.uscourts.gov/notices/facebook-privacy-settl...

It's fine as long as bike paths are physically separated from cars and are maintained to the same standard as roads. My college had a network of pedestrian/bike paths that they plowed in the winter, and I and many others had no problem biking year-round.

Here's Seattle's network of protected bike lanes: <https://i.imgur.com/vEDhqoz.png>. Note that many of them are flawed in one or more ways:

* 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".

The "trekking bike" and "safety" profiles seem to work pretty well. The safety profile produces routes I would take any time of day and the trekking bike profile produces routes I would take outside of peak road rage hours.

WTF is a bike doing in the middle of the road?

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.

What are some limitations of OpenCASCADE that are holding back CAD tools built on it?

It appears to use C3D as its CAD kernel, which is proprietary. You can't build it without a copy of C3D. I'm guessing the source will be available for inspection, but the binary will cost money.

In about:config, change browser.privatebrowsing.vpnpromourl to an empty string.

F#

The trackballs are listed as "completely open-source" on their website, but the mouse is listed as "open-source firmware". This is consistent with what's available on their GitHub page—the Altium and STEP files for the trackballs are available on their GitHub page, and the firmware for all their products (including the mouse) has been upstreamed[1].

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

A journal can publish a retraction.

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/

It depends what I'm doing, but I typically use one of the following (starting with the most frequent):

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.

Money laundering is the process of making illegally-gained proceeds (i.e. "dirty money") appear legal (i.e. "clean").

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

I'm just speculating (and haven't read the paper yet), but it may be possible to achieve similar speedups on GPUs by pruning the smallest 20% of blocks of size ≥K×K to produce block-sparse weights[0], rather than pruning the smallest 20% of weights.

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

The following instructions are for Linux or macOS. It may work on Windows too, but I'm not very experienced with Windows. No special hardware is required (you can run it on a CPU; no GPU needed).

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

My experience corroborates this.

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.

Yeah right. They'll find a way to sell it while claiming they don't share it.

It's not the case that malloc always works under Linux. It may return NULL if the memory is not available. Try it out! Write a program that allocates half your memory, fills it with 0xFF, then sleeps. Run two instances of it. The malloc will fail in the second one.

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...

I had the same issue. I disabled them in xmodmap for several years, then eventually decided I was never going to use them and cut out the rubber dome. Here's what it looks like: https://i.imgur.com/Bm6KtIN.jpg

zfec is an implementation of Reed-Solomon coding, the class of codes discussed in the first part of the slides.

They are being recorded and posted to YouTube as unlisted videos. You can find links to some of them on Twitter[1] and ResearchGate[2]. It looks like highlights of the lectures are being posted to Twitter and the full lectures are being posted to ResearchGate.

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

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

What is 'LexisNexis style querying'?

Can you give a sketch of how you would like these examples to look? Any language is fine; you can pretend that libraries for GPGPU, backprop, and gradient-based optimization already exist.

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 :)

There’s no way I’d deploy it to production.

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

No copyright infringement. They share a common ancestor: http://www.zorinaq.com/papers/md5-amd64.html

Disclaimer: I have no idea what I'm talking about.

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.

Was ZFS determined to be the problem in the root cause analysis, or was it another part of the storage system that failed? It's easy to pass the blame down to the lowest level of the system because no one wants to take the blame.

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.

The core complaint is the lack of typeclasses. If your coding style relies heavily on generics, maybe Elm isn't for you.

Is there a more polite way to go about this?

This is the usual process for submitting a large change to a project:

1. Check the issue tracker or mailing list for an existing discussion about Python 3 support. If there is one, read the discussion. If there isn't one, start the discussion. Mention that you're willing to make the changes if there is interest. Remember that supporting both Python 2 and Python 3 places a burden on all future contributors.

2. Keep the changes minimal and easy to review.

3. Review the changes yourself before sending a patch/pull request.

4. Run the test suite.

5. Test your changes in all versions of Python the software supports.

Forgetting #1 is usually the cause of tension when people submit large patches. The person who submits a patch wants to see their patch included. The authors of the project want to make sure it aligns with the goals of their project and doesn't break their software.