Not sure how rare they are, but it was rare for me
otter
joined 1 year ago
Cool :)
Thanks for sharing!
Or maybe
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Why are people willing to sell their kids to defend the above companies?
I don't really see this happening.
As for why I recommend some things, it's usually because my recommendation is better than the alternative.
Perfection is hard to find, but in the meantime I'll recommend something good over something bad.
There's this app, I don't use it often but leave it installed for this
https://github.com/MobileFirstLLC/irregular-expressions
The option spongemock LeTs YoU wRiTe LiKe ThIs
That would run face first into proprietary info and corporate classified info.
The other side of regulation should be around purchasing. Bad repairability, delays in repairs, and proprietary parts need to be more important factors when deciding on what device to buy. Either at the government level for public institutions, or at least at the corporate level for private
Format like this
So
So it sounds like you could make categories that are
- location based: sky (flying), jungle
- climate based: ice/cold, desert
- task based: tunneling animals, builder animals, nest making animals
You could also start from a list of animals and then categorize them afterwards based on what you have. As for a list, maybe by biological families or classes?
There are mock location apps that spoof your location, but it might be a lot of extra work to turn on the spoofing before opening the app each time. It also doesn't prevent leaks if it does stuff in the background.
You could also try using the mobile site?
I need to start using this one, I already use "burn that bridge" fairly often
Well, they don't all connect to the internet
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Automated cooling tower detection through deep learning for Legionnaires’ disease outbreak investigations: a model development and validation study
(www.thelancet.com)
cross-posted from: https://lemmy.ca/post/23884006
Link to full text study:
https://www.thelancet.com/journals/landig/article/PIIS2589-7500(24)00094-3/fulltext
Background Cooling towers containing Legionella spp are a high-risk source of Legionnaires’ disease outbreaks. Manually locating cooling towers from aerial imagery during outbreak investigations requires expertise, is labour intensive, and can be prone to errors. We aimed to train a deep learning computer vision model to automatically detect cooling towers that are aerially visible.
Methods Between Jan 1 and 31, 2021, we extracted satellite view images of Philadelphia (PN, USA) and New York state (NY, USA) from Google Maps and annotated cooling towers to create training datasets. We augmented training data with synthetic data and model-assisted labelling of additional cities. Using 2051 images containing 7292 cooling towers, we trained a two-stage model using YOLOv5, a model that detects objects in images, and EfficientNet-b5, a model that classifies images. We assessed the primary outcomes of sensitivity and positive predictive value (PPV) of the model against manual labelling on test datasets of 548 images, including from two cities not seen in training (Boston [MA, USA] and Athens [GA, USA]). We compared the search speed of the model with that of manual searching by four epidemiologists.
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MIT license:
It looks nice, and has extra features like tabs, tagging 7zip/archive management, cloud drives, git integration, comparing file hashes, etc.
The only issue I had was performance, it took a long time to start each time. I'm planning on trying it again sometime later