Main

related bits

0

processing priority

3

site type

0 (generic, awaiting analysis)

review version

11

html import

20 (imported)

Events

first seen date

2024-11-21 05:46:37

expired found date

-

created at

2024-11-21 05:46:37

updated at

2024-11-21 05:46:40

Domain name statistics

length

22

crc

56358

tld

86

nm parts

0

nm random digits

0

nm rare letters

0

Connections

is subdomain of id

87719371 (github.io)

previous id

0

replaced with id

0

related id

-

dns primary id

0

dns alternative id

0

lifecycle status

0 (unclassified, or currently active)

Subdomains and pages

deleted subdomains

0

page imported products

0

page imported random

0

page imported parking

0

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

10320

mp size raw text

4219

mp inner links count

0

mp inner links status

1 (no links)

Open Graph

title

description

image

site name

author

updated

2026-02-24 22:34:22

raw text

Non-Probability Sampling Network for Stochastic Human Trajectory Prediction | Project Page Non-Probability Sampling Network for Stochastic Human Trajectory Prediction Inhwan Bae , Jin-Hwi Park and Hae-Gon Jeon* Computer Vision and Pattern Recognition 2022 Project Paper Code Abstract Capturing multimodal natures is essential for stochastic pedestrian trajectory prediction, to infer a finite set of future trajectories. The inferred trajectories are based on observation paths and the latent vectors of potential decisions of pedestrians in the inference step. However, stochastic approaches provide varying results for the same data and parameter settings, due to the random sampling of the latent vector. In this paper, we analyze the problem by reconstructing and comparing probabilistic distributions from prediction samples and socially-acceptable paths, respectively. Through this analysis, we observe that the inferences of all stochastic models are biased towar...

Text analysis

redirect type

0 (-)

block type

0 (no issues)

detected language

1 (English)

category id

Pozostałe (16)

index version

1

spam phrases

0

Text statistics

text nonlatin

0

text cyrillic

0

text characters

3455

text words

583

text unique words

251

text lines

27

text sentences

25

text paragraphs

5

text words per sentence

23

text matched phrases

0

text matched dictionaries

0

RSS

rss path

rss status

1 (priority 1 already searched, no matches found)

rss found date

-

rss size orig

0

rss items

0

rss spam phrases

0

rss detected language

0 (awaiting analysis)

inbefore feed id

-

inbefore status

0 (new)

Sitemap

sitemap path

sitemap status

1 (priority 1 already searched, no matches found)

sitemap review version

2

sitemap urls count

0

sitemap urls adult

0

sitemap filtered products

0

sitemap filtered videos

0

sitemap found date

-

sitemap process date

-

sitemap first import date

-

sitemap last import date

-