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Multi-scale modelling of land-use change and river training effects on floods in the
Rhine basin. Te Linde, A. H., Aerts, J. C.
J. H. & Kwadijk, J. C. J. Effectiveness of flood management measures on peak
discharges in the Rhine basin under climate change. J. Flood
Risk Manag. Hooijer, A., Klijn, F., Pedroli, G. B. M. & Van Os,
A. G. Towards sustainable flood risk management in the Rhine and
Meuse riverbasins: synopsis of the findings of IRMA-SPONGE.
Salazar, S. et al. A comparative analysis of the effectiveness of flood management measures based on the concept of “retaining water in the landscape”
in different European hydro-climatic regions. Natural Haz.
Earth Syst. Pattison, I. & Lane, S. N. The link between land-use management and fluvial flood risk: a chaotic conception? Prog.
Phys. Geogr. Earth Environ. Yang, L. et al. River networks system changes and its impact on storage and flood control capacity under rapid urbanization.
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It takes the contents of an email as a string, gets the list of words with createTable(),
and adjusts the dataset values. This function is a bit
complex, so let’s take an example. Suppose, our classifier has seen five emails.
Three of them contain “replica” and two of them contain “loans”.
Now, suppose we have an email which reads: “replica watches”.
The filter has never seen “watches” but it occurs
in the new message. So, the numerator will be equal to
one, because after the training, the filter would have seen it exactly once.
The filter has seen “loans”, but it does not occur in this message.
So, the new numerator won’t increase; it will be equal to the previous numerator.
The word “replica” occurs both in the new message and
in the previously seen messages. Thus, we must add one to the old numerator.
The learnHam() function is similar, except for the fact that it works on hammicity values.
The predict() function creates a list of words with createTable(), and calculates for
each word.
The young man listened as his friend counseled him on personal responsibility and
the Prophet’s sayings. Over the months, Siraj found himself pouring his heart out to Eldawoody, about
his financial woes and about Mano, the woman in Pakistan he had met online; he hoped to marry her soon. He was distraught when Eldawoody
confided that he was suffering from a liver disease and worried that
it was potentially fatal. Slowly, their conversations took
on a darker edge. Eldawoody complained to Siraj that the
F.B.I. Muslim who knew about nuclear engineering. They discussed the Abu Ghraib prison scandal
and online images of Muslims being tortured and killed in the wars overseas.
When Siraj saw a picture of a girl who was raped, he broke down and cried.
Eldawoody seemed to share his friend’s anger. Something had to
be done, something that would get the world to pay attention. They agreed that an attack that would hurt the United States economically would help save Muslim lives.
An obvious thing to try was to use GPT 4. However,
I misread the costs of OpenAI’s API and thought that their charges were per-token, not per 1000 tokens.
So with estimates that were off by three orders of magnitude, GPT
4 seemed a bit too expensive for a random experiment and
I used GPT 3.5 for everything. I didn’t write this post the
same way, but this experimental worked well enough that I might try it again in the
future for longer public writing. This post is nearing 8 000 words.
Over more than a decade, a handful of standards
have developed into passkeys-a plausible replacement for passwords.
They picked up a lot of complexity on the way,
and this post tries to give a chronological account of the development of the core of
these technologies. Nothing here is secret;
it’s all described in various published standards. However,
it can be challenging to read these standards and understand
how it’s meant to fit together.
Feel free to visit my website; "https://www.kenpoguy.com/phasickombatives/profile.php?id=1478374 |
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