Tootfinder

The answers to this question are, for D4, relative to expectations, refreshingly reasonable. I'm glad even the candidates with a track record of being quite car-brained (lookin' at you Albert Chow) at least feel the need to talk about balance and aren't all, "Yes, there is a war on cars, bicycles are ruining everything and only white people ride them" or something.

Meet the District 4 candidates: Is there a ‘war on cars’ in San Francisco?
In the Sunset and Parkside, over 88 percent of households own at least one car. Should travel by car be prioritized here in urban planning?

RE: https://hachyderm.io/@thomasfuchs/116404593670288025
The thing about the sycophantic interfaces that pretend to be humans is: whether you're nice or mean to them you lose.
If you're nice, you train your own brain to see the subservient word salad generator as a human being—and that demeans yourself and others.
If you're mean, you just make yourself angry and feel bad for no reason.
If you absolutely have to use them, be neutral and treat it as the hammer it is.

I've seen a bunch of "the CA age verification law is the best way to do a bad thing and so we shouldn't oppose compliance" takes, which others are rightly pointing out is a bad stance because it's blindingly obvious that compliance now sets the stage for compliance later and the clearly set up later is mandatory verification of age data. Even if you think that, for example, California's current "progressive" government won't go there, we're all currently seeing just how easy it is for a new government to pick up the oppressive tools the "good" government was using "restraint" with and put them to worse ends.
On the other hand, I'll freely admit that distros *do* need a way to shield themselves from liability right now. The clear (to me; IANAL) correct solution is to say on your website "don't download this OS if you're in a jurisdiction where it's not legal for us to provide it."). Assuming this does put you in the clear liability-wise, it has several positive effects:
- Stops zero people from downloading it.
- Makes it clear that your project will not collaborate with fascists/oppressive regime enjoyers.
- Means that when the next law makes verifying user ages mandatory (and/or explicitly requires using Palantir-adjacent services to do so) you've already got a strategy in place and there's no need for a "debate" in your "community" about compliance.
- Gets users more practice with "the law is malicious/needlessly bureaucratic/oppressive; let's ignore it" which to be honest people in general clearly desperately need at this point.
- Is the most effective political move if you want to resist the way things are going. Forcing the other side to explain why "California bans Linux" is good rhetorical strategy. Make *them* try to explain "well it's actually not so harmful since we let users set it themselves" and answer your follow-up "but what if next year the requirements change; I just refuse to go along with this slippery slope stuff and I'm not bothered if that means you want to *ban* me."
#AgeVerification

TIEG-Youpu Solution for NeurIPS 2022 WikiKG90Mv2-LSC
Feng Nie, Zhixiu Ye, Sifa Xie, Shuang Wu, Xin Yuan, Liang Yao, Jiazhen Peng, Xu Cheng
https://arxiv.org/abs/2603.28512 https://arxiv.org/pdf/2603.28512 https://arxiv.org/html/2603.28512
arXiv:2603.28512v1 Announce Type: new
Abstract: WikiKG90Mv2 in NeurIPS 2022 is a large encyclopedic knowledge graph. Embedding knowledge graphs into continuous vector spaces is important for many practical applications, such as knowledge acquisition, question answering, and recommendation systems. Compared to existing knowledge graphs, WikiKG90Mv2 is a large scale knowledge graph, which is composed of more than 90 millions of entities. Both efficiency and accuracy should be considered when building graph embedding models for knowledge graph at scale. To this end, we follow the retrieve then re-rank pipeline, and make novel modifications in both retrieval and re-ranking stage. Specifically, we propose a priority infilling retrieval model to obtain candidates that are structurally and semantically similar. Then we propose an ensemble based re-ranking model with neighbor enhanced representations to produce final link prediction results among retrieved candidates. Experimental results show that our proposed method outperforms existing baseline methods and improves MRR of validation set from 0.2342 to 0.2839.
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