Main

processing priority

4

site type

3 (personal blog or private political site, e.g. Blogspot, Substack, also small blogs on own domains)

review version

11

html import

20 (imported)

Events

first seen date

2024-10-07 18:51:17

expired found date

-

created at

2024-10-07 18:51:17

updated at

2026-01-13 17:20:06

Domain name statistics

length

24

crc

48889

tld

2211

nm parts

0

nm random digits

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nm rare letters

0

Connections

is subdomain of id

69893241 (blogspot.com)

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replaced with id

0

related id

-

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dns alternative id

0

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0 (unclassified, or currently active)

Subdomains and pages

deleted subdomains

0

page imported products

0

page imported random

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page imported parking

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Error counters

count skipped due to recent timeouts on the same server IP

0

count content received but rejected due to 11-799

0

count dns errors

0

count cert errors

0

count timeouts

0

count http 429

0

count http 404

0

count http 403

0

count http 5xx

0

next operation date

-

Server

server bits

server ip

-

Mainpage statistics

mp import status

20

mp rejected date

-

mp saved date

-

mp size orig

157266

mp size raw text

42934

mp inner links count

43

mp inner links status

20 (imported)

Open Graph

title

F# and Data Mining

description

image

site name

author

updated

2026-01-12 11:21:12

raw text

F# and Data Mining F# and Data Mining Monday, January 11, 2016 Improve cache performance: matrix multiplication as an example It is surprising to see mul1() is 10 times slower than mul2(). Mul2: By using j as the inner loop, C[i][j] & B[k][j] have good cache hits, while A[i][k] is a constant during the inner loop. Mul1: In the inner loop, C[i][j] has a constant address, and A[i][k] has good cache hits; however B[k][j] is doomed to cache misses. There are other methods to optimize matrix multiplication, but this one should be the most significant. reference slide: https://www.cs.duke.edu/courses/fall06/cps220/lectures/PPT/lect12.pdf #include #include #include using namespace std; const int N = 2000; double A[N][N], B[N][N], C[N][N]; void fill() { for (int i=0; i

Text analysis

redirect type

0 (-)

block type

0 (no issues)

detected language

1 (English)

category id

AI [en] (229)

index version

2025123101

spam phrases

0

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0

text cyrillic

0

text characters

30483

text words

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text unique words

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text paragraphs

77

text words per sentence

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text matched phrases

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text matched dictionaries

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RSS

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32 (unknown)

rss found date

2024-10-07 18:51:19

rss size orig

460213

rss items

25

rss spam phrases

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rss detected language

1 (English)

inbefore feed id

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inbefore status

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Sitemap

sitemap status

40 (completed successful import of reports.txt file to table in_pages)

sitemap review version

2

sitemap urls count

48

sitemap urls adult

0

sitemap filtered products

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sitemap filtered videos

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sitemap found date

2024-10-07 18:51:18

sitemap process date

2024-10-07 18:51:19

sitemap first import date

-

sitemap last import date

2025-06-14 02:55:14