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6
7
8
9
10
ã
H
v
The
New
York
copyright
reserved
botanical
Garden
SMITHSONIAN
INSTITUTION
From
THE
UNITED
STATES
NATIONAL
HERBARIUM
PLANTS
OF
THE
WEST
INDIES
A.
C.
Smith,
no.
10131
March
14,
1956
Cassipourea
alba
Griseb.
I)et.
E.
C.
Leonard
New
York
Botanical
Garden
Ib231fl5
Freely
branched
tree
to
£
m.
high,
in
low
forest;
styles
pale
green.
GRENADA:
Vicinity
of
Grand
Etang;
alt.
5OO-525
m.
Collected
on
the
Smithsonian-Bredin
Caribbean
Expedition
1623982
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


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AntFileListing
- class net.matthaynes.xml.dirlist.
AntFileListing
.
Ant task interface for XML Directory Listing Class
AntFileListing()
- Constructor for class net.matthaynes.xml.dirlist.
AntFileListing
atts
- Static variable in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
C
createElement(File, int, boolean)
- Method in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Creates a new element based on the file based through.
D
DirectoryListing
- class net.matthaynes.xml.dirlist.
DirectoryListing
.
Command line interface for XML Directory Listing class.
DirectoryListing()
- Constructor for class net.matthaynes.xml.dirlist.
DirectoryListing
dateFormat
- Variable in class net.matthaynes.xml.dirlist.
AntFileListing
dateFormat
- Variable in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Date format for date attributes.
depth
- Variable in class net.matthaynes.xml.dirlist.
AntFileListing
depth
- Variable in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
How deep to recurse into directory structure.
depthControl
- Variable in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Depth control for directory recursion.
directory
- Variable in class net.matthaynes.xml.dirlist.
AntFileListing
E
excludeRE
- Variable in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
The regular expression for the exclude pattern.
excludesRegEx
- Variable in class net.matthaynes.xml.dirlist.
AntFileListing
execute()
- Method in class net.matthaynes.xml.dirlist.
AntFileListing
G
generateXmlDirectoryListing(File, OutputStream)
- Method in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Starts generation of XML directory listing.
H
hd
- Static variable in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
I
includeRE
- Variable in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
The regular expression for the include pattern.
includesRegEx
- Variable in class net.matthaynes.xml.dirlist.
AntFileListing
isExcluded(File)
- Method in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Determines if a given File shall be excluded from viewing.
isIncluded(File)
- Method in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Determines if a given File shall be visible.
L
log
- Variable in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Log4j logger
M
main(String[])
- Static method in class net.matthaynes.xml.dirlist.
DirectoryListing
myAppender
- Variable in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Log4j appender
myLayout
- Variable in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Log4j PatternLayout
N
net.matthaynes.xml.dirlist
- package net.matthaynes.xml.dirlist
O
output
- Variable in class net.matthaynes.xml.dirlist.
AntFileListing
P
parseDirectory(File, int)
- Method in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Sort all files in directory, create an element for each.
R
reverse
- Variable in class net.matthaynes.xml.dirlist.
AntFileListing
reverse
- Variable in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Reverse sort method.
S
serializer
- Static variable in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
setAttributes(File)
- Method in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Sets all attributes for the file
setAttributes(File, boolean)
- Method in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Sets all attributes for the root node
setDateFormat(String)
- Method in class net.matthaynes.xml.dirlist.
AntFileListing
Gets the dateformat attribute from the ant task
setDateFormat(String)
- Method in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Sets the date format for the class, must be valid SimpleDateFormat
setDepth(int)
- Method in class net.matthaynes.xml.dirlist.
AntFileListing
Gets the depth attribute from the ant task
setDepth(int)
- Method in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Sets the depth of the directory listing.
setDestFile(String)
- Method in class net.matthaynes.xml.dirlist.
AntFileListing
Gets the srcdir attribute from the ant task
setExcluded(String)
- Method in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Sets the exclude regular expression
setExcludesRegEx(String)
- Method in class net.matthaynes.xml.dirlist.
AntFileListing
Gets the excludesRegEx attribute from the ant task
setIncluded(String)
- Method in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Sets the include regular expression
setIncludesRegEx(String)
- Method in class net.matthaynes.xml.dirlist.
AntFileListing
Gets the includesRegEx attribute from the ant task
setReverse(boolean)
- Method in class net.matthaynes.xml.dirlist.
AntFileListing
Gets the revrse attribute from the ant task.
setSort(String)
- Method in class net.matthaynes.xml.dirlist.
AntFileListing
Gets the sort attribute from the ant task.
setSort(String)
- Method in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Sets the sort type, returns error if bad type specified.
setSortReverse(boolean)
- Method in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Sets the sort reverse attribute.
setSrcDir(String)
- Method in class net.matthaynes.xml.dirlist.
AntFileListing
Gets the srcdir attribute from the ant task
setVerbose(boolean)
- Method in class net.matthaynes.xml.dirlist.
AntFileListing
Gets the verbose attribute from the ant task
sort
- Variable in class net.matthaynes.xml.dirlist.
AntFileListing
sort
- Variable in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Sort method for the directory listing.
sortFiles(File[])
- Method in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Sorts the array of files based upon the specified sort order.
streamResult
- Static variable in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
T
tf
- Static variable in class net.matthaynes.xml.dirlist.
XmlDirectoryListing
V
verbose
- Variable in class net.matthaynes.xml.dirlist.
AntFileListing
X
XmlDirectoryListing
- class net.matthaynes.xml.dirlist.
XmlDirectoryListing
.
XML Directory Listing Class
XmlDirectoryListing()
- Constructor for class net.matthaynes.xml.dirlist.
XmlDirectoryListing
Consturctor.
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Report: China’s President Xi says HK violence threatens ‘one country, two systems’
BEIJING, Nov 14 — The unrest in Hong Kong has “seriously challenged” the “one country, two systems” principle governing the semi-autonomous city, China’s President Xi Jinping said today in comments reported by Chinese state media.
Pro-democracy protesters choked the city with barricades and rallies for a fourth straight day today, days after police shot a protester with a live round and a man expressing pro-Beijing views was set on fire.
Xi said Beijing “firmly supports” Hong Kong’s government and police and that “stopping violence and controlling chaos while restoring order is currently Hong Kong’s most urgent task,” the People’s Daily reported.
Recent actions by protesters have “seriously challenged the baseline principle of ‘one country, two systems’”, Xi said in rare comments on the violence in Hong Kong at a summit of BRICS countries in the Brazilian capital of Brasilia.
Protests have rocked Hong Kong for nearly six months.
Initially focused on opposition to a proposed extradition law, protestors’ demands have evolved into broader calls for democracy and investigations into police violence.
Hong Kong was handed back to China by the British in 1997 under a “one country, two systems” policy which gives the territory rights unseen on the mainland — freedoms protesters say are being steadily eroded by Beijing.
Clashes between protestors and police escalated after the recent death of a student who succumbed to head injuries sustained during a fall as police skirmished with demonstrators inside a car park. — AFP


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Eugene has been at Coventry Consultants for ten years. A profound connoisseur of the high-tech industry, an energy market expert and a specialist in emerging market entry strategies, Eugene is as close to an all-rounder as they come. He has had considerable success in overseeing M&As. He was involved in the Renault-Nissan alliance and facilitated negotiations between General Electric and 3M.
In his free time he enjoys eating smores, taking long walks on the beach, watching Game of Thrones with his wife Margaret and their nine kids and buying purple accessories for his two large unshaven poodles. He is also a five-time national chess champion and an accomplished sailor.
Amanda lives for the risk. With an MSc and over five years of experience in the risk management industry, her qualifications and sound sense of judgment make her ideally placed to address any and all of your firm's business risks. She is available for international projects.
Amanda nearly won the last Repocrat presidential election in her home country of Ruisdael. The influence she gained over the course of the campaign secured her an advisory position in the current government. She also enjoys playing World of Warcraft and knitting sweaters for giraffes, which she breeds passionately in her country home in Tanzania.


Monotone laugh generator.
For when someone tells a bad joke, and you want them to know it.
Chinese
Spanish
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Hello from the host !!! This page is being served by mounting the html folder containing this index.html from your local machine into the Minikube virtual machine, from there is being mounted into the container running in your pod.


| Name | Coordinates | Actions | |
|---|---|---|---|
| {{item.name}} | {{item.uri}} |
No registered task apps.
You can register task apps by going to the Apps tab.
Alternatively, using the Data Flow Shell application, you can register new apps with the app register and app import commands.


| lv. | When you grow to that level, spend points here. | |||
| Roh | Mip | Tal | Velm | |
| 41 | Innate Melee Haste | Innate Melee Def. | Mult. Targets | Group Heal. |
| 42 | Damage Inc. | Innate Casting Haste | Mult. Targets | Group Shield. | 43 | Critical Attacks | Group Haste | Incrsd. Bow Dmg. | Group Shield. |
| 44 | Innate Melee Haste | Group Haste | Mult. Targets | Innate Melee Def. | 45 | Damage Inc. | Group Haste | Mult. Targets | Group Heal. |
| 46 | Damage Inc. | Innate Melee Def. | Incrsd. Bow Dmg. | Innate Melee Def. | 47 | Critical Attacks | Innate Casting Haste | Innate Melee Haste | Group Shield. |
| 48 | Innate Melee Haste | Group Haste | Innate Melee Haste | Group Heal. | 49 | Critical Attacks | Group Haste | Innate Magic Res. | Innate Melee Def. |
| 50 | Innate Magic Res. | Group Haste | Innate Magic Res. | Innate Melee Def. |
Note: once you get to level 50, stop spending skill points. You'll see why later.
Eh? Now what? Oh, it's you. Hi. If you're completely lost, click on the header to get back to where you once belonged (if you're lost, you'll want to check the main page.) If you haven't beaten Act 3 yet, go ahead and do that.
If you aren't lost and you have beaten Act 3, then you're probably in the right place. Congrats. Well, the Haunted Woods is a pretty small chapter (at least compared to the last one), and it shouldn't take too much time to get through. Have fun with this chapter, since the next chapter (which is the last) one is all business. Say, did I mention that the next chapter is the last one?
Before you start, keep in mind that this chapter is home to those diabolical Chias I've been telling you about. Be scared. Very scared.
As soon as you land in the Haunted Woods, you'll notice a town just below you. Walk onto it, and you'll find out that it's infested with ghosts. Before you ask, no, you can't bust them. Sorry. The main tourist trap attraction here is the weapons/armor/potions store in the southwestern building.
That's that for Shadow Gulch. Head out of town and get ready to use the Fight-As-You-Go method. Open up your map to the West Haunted Woods and look for Von Roo's Castle in the northeast corner. Head on over there, and enter as soon as you get there. (We don't need to level up just yet.)
Before you start doing anything, know that you should stay on Hunting for this entire castle.  It'll help. Believe me.
Now that that's over with, take a peek at the map of the Entrance and look for the stairs in the east side of the level. There's actually two ways to get to it, but it really doesn't matter one way or the other as long as you are on Hunting mode. (Sense a theme? Thought so.) By the time you get to the stairs, get to level 41/42/43 if you aren't at that level already. Climb the stairs once you're ready.
Now you're on the Dungeon level of the castle. It's a pretty simple route to Meuka, the boss of this castle. Yeah, you aren't fighting Von Roo. Go figure. But heal yourself and walk up to Meuka to pick your nose a fight.
| Recommended Level: | 41 on Normal 42 on Evil 43 on Insane | |||||||||
| HP: | 1000 on Normal 1250 on Evil 1500 on Insane | |||||||||
| Special Abilities: |
| |||||||||
| Prizes: |
5 Vitality Potions 2 weapons/armor 2000 gold 850 NP |
You can talk to Von Roo if you like, but take the teleporter outside. Stay on Hunting mode (actually, you'll want to stay on Hunting mode for the rest of this chapter), and head east into the East Haunted Woods. You might not notice it on your map, but there's a lot of impassable ground in this swamp. Just be aware of that.
You'll notice the entrance to a Cave of Dark Things at the end of this swamp; walk over to it. Make sure you're at least level 42/43/44 before you enter.
So, as I was saying, you want to stay on Hunting mode for all of this cave and the rest of the chapter. I'm feeling lazy now so I'll stop repeating it. Anyhow, we need to grow to level 44/45/46 - yeah, two levels - before we take on the boss at the end of this cave. Just keep that in mind.
Take a peek at your map and look for the conveniently placed slime puddles. Make your way through the cave, following those puddles. Once you get to the boss at the end of the cave, you'll hopefully have at least one more level under your belt. Train (yeah, that's what you did back before you fought Zombom... good times, good times) till you get to level 44/45/46.
Once you've gained that level, walk up to the spider boss guy and proceed to beat him up.
| Recommended Level: | 44 on Normal 45 on Evil 46 on Insane | |||||||||
| HP: | 1200 on Normal 1500 on Evil 1800 on Insane | |||||||||
| Special Abilities: |
| |||||||||
| Prizes: |
2 weapon/armor 2000 gold 900 NP |
In another decidedly anticlimactic turn of events (wow, that was really flowery language), you don't bury the Grundo. You just talk to him. Anyway, equip those weapons and head west out of the cave (you're still on Hunting mode, right? That'll help.)
Once you leave the cave, start walking south on the path till the scenery changes. You'll know what I mean when you get there...
Yeah, it's all green... and happy... which is weird, considering the evil Chias I've been telling you about live pretty nearby. Anyhow, make sure you're on that mode I was telling you about earlier *cough* Hunting *cough*. If you look at the west end of the grove, you'll notice some faeries who don't look very cheery. That, of course, means they're bosses. Before we fight them, we have to grow to level 46/47/48.
As usual, there's a dungeon thingy we need to get through. It's not a dungeon, though, it's the Happy Fun Non-Haunted House. You're currently on Level 1. You need to get to the stairs to level 2, which will take you almost to the exit of the house. It's weird, yeah, but it's not like this section is normal. Keep Fighting as You Go till you get to the exit of the house. Try to gain a level in the process.
Once you get outside again, you'll notice the Brain Tree and a weird purple Bruce fella. He's actually the town, except rolled into one slightly anorexic package. Convenient. Take a look at your shopping list:
So yeah, don't hesitate to buy stuff, since there isn't another town for a while. Talk to the Brain Tree, too, he's got some useful info. If you aren't at level 46/47/48 yet, train in the house until you get to that level. Once you do, you can actually walk north from where the Bruce and the Tree are. That'll send you south (go figure) back into the Grove. Conveniently, you now know the secret word (whatever that is), so walk up to the faeries and prepare for a long, hard fight. (Don't you wish I had said that earlier?)
| Recommended Level: | 46 on Normal 47 on Evil 48 on Insane |
| Prizes: |
3 weapons/armor 2500 gold 1000 NP |
| Faeries | |||
| name | hp | special abilities | comments |
| Fire | 600 on Normal 750 on Evil 900 on Insane |
100HP Direct Damage 56HP Group Damage |
The hardest-hitting of the bunch |
| Dark | 750 on Normal 937 on Evil 1125 on Insane |
38% Group Haste 62% Slowing Mesmerization |
Dangerous because of Mesmerization |
| Water | 900 on Normal 1125 on Evil 1350 on Insane |
78HP Group Heal 150HP Heal |
The healer of the group |
| Earth | 1200 on Normal 1500 on Evil 1800 on Insane |
Critical Attacks Stunning Strikes |
A tough melee attacker |
Nice job! You've just beaten one of the hardest (if not THE hardest) bosses in the game.  Switch to Normal mode (the baddies in the Grove don't give out much EXP now) and head back to the Haunted House (yeah, it changed its name.)
I'm not sure if you noticed, but when you enter the house there are gates on either side of the house, which let you get to the guys behind the house without actually having to go through it. (Before you ask, they were opened when you talked to the Brain Tree.) So walk on back and talk to the Bruce guy again. Restock your potions (you want 20 of the Sloth's Sloth and Corrode Potions) and sell your old weapons.
You can walk north from the guys again to get back into the Grove. Once you do, get ready for our next section...
Walk over to where the Faeries were (you'll notice everything looks all evil again) and start going west. Look at your map for Hubrid's Mountain Range or something to that effect. Stay on Normal mode and head through the woods and mountains until you find yourself in the Esophagor's Swamp or the Goo Bog. Don't fight anyone or enter the fortress. Let me repeat that:
Why? Well, the baddies around here are called evil for a reason: they're ridiculously strong and attack in droves. One baddie, the Hand of Nox, can Obliterate Mipsy in two shots if she's a little hurt. Mipsy's died plenty of times against these guys on my watch. The Heart of Nox uses the odd Focus Energy ability to become just about indestructible. And the other Chias aren't much easier.
But don't worry. We'll be back soon. And yeah, we'll enter that fortress and beat up the leader of these Chias.
Pretty soon you'll reach level 50, which is a Really Big Deal. Once you grow to level 50, spend that skill point, but when you reach level 51, don't spend that skill point! We're actually going to save our skill points until we reach level 60, at which point we'll spend them.
I know what you're thinking: why? Is it cause I feel like it? Well, partially, but not really. See, weapons and armor in Faerieland (act 5) give big bonuses. I'm talking 2-3 points for 3 or more skills. Well, take a look at Tricia and Arthur.
Tricia is your standard, no-nonsense NQII player. She figured it just makes sense to spend your skill points as soon as you get them. So every time she levels up in Act 5, she spends the skill point she gets immediately. By level 60, Rohane has 15 points in Critical Attacks and Damage Increase, so those stats are maxed out.
But at level 60, she finds the Sword of Apocalypse, which gives 2 points bonus in all of Rohane's non-innate skills. Rohane's Critical Attacks and Damage Increase would go up, but they can't. So she's just wasted 4 skill points, which is a Really Big Deal. And it isn't a good idea to have wasted skill points when you're up against the toughest boss in the game.
Arthur decides to try the save-your-skill-points method, because he listened to me and I'm *ahem* awesome like that. So he stops spending skill points after level 50. He spends his skill point when he grew to level 50, but after that he saves his points until he gets to level 60. At level 60, his Rohane only has 13 points in Damage Increase and Critical Attacks, so he doesn't have the easiest time in Faerieland.
But his patience is rewarded. At level 60, he finds the Sword of Apocalypse, which gives 2 points bonus in all of Rohane's non-innate skills. Rohane's Critical Attacks and Damage Increase go to 15. Bam, no skill points wasted, it just falls neatly into place.
So now, Arthur and Tricia are on the same level, skill-wise. The only difference is that Arthur has 10 more skill points to spend, which is, again, a Really Big Deal. The last boss is so much easier for him now. Thank you, planning ahead.
The gist of it is that you should stop spending skill points once you reach level 50. You can spend those saved skill points after the 2nd-to-last-boss. I'll let you know when it's time. And now back to your regularly scheduled production.
Yeah, instead of training in the home of the barbaric Chias, we're going to train a bit here in the Goo Bog (your map might call it the Esophagor's Swamp or something like that.)  Go far enough into the swamp that you see scattered trees but not too far. If you look at your map you'll see what I mean.
You'll want to train to level 49/50/51 (yeah, 3 levels) here. Don't worry, it isn't that bad. The baddies here give incredible experience and they're far easier than those Chias I told you about earlier. So what's not to like?
Now that that's done, we can finally go back to Chia-land. Before you leave the Goo Bog, though, Switch to Normal mode. No point fighting the Chias anymore, since they don't give out much experience. Well, now head northwest out of the Goo Bog until you're in the mountain range you came through. Remember the tower in the middle of the mountains? (If not, open up your map of the Mountain Range.) Yeah, that's where we're headed.
Take a look at your map for Nox's Fortress, Level 1. Yeah, there are 9 levels of this thing. I trust you can make your way up through the 9 levels. If you run into any Chias, feel free to kill 'em. Once you make it to the top of the tower (Level 9), you'll notice an especially big Chia, who's incidentally your next boss.
| Recommended Level: | 49 on Normal 50 on Evil 51 on Insane | |||||||||||||||||||||
| HP: | 1500 on Normal 1875 on Evil 2250 on Insane | |||||||||||||||||||||
| Special Abilities: |
| |||||||||||||||||||||
| Prizes: |
2 weapons/armor 3000 gold 1000 NP |
Once you finally kill this guy off, give yourself a pat on the back. You won't have to face another boss as hard as this guy for... maybe 5 more levels. I know it isn't much, but hey.
You're really close to the end of this chapter. Take the teleporter outside the fortress, and head back to the Goo Bog. Grow to level 50/51/52 in the Goo Bog, then use your maps to get to the Esophagor in the southeast corner of the swamp. Walk on up to him and get ready to fight him.
| Recommended Level: | 50 on Normal 51 on Evil 52 on Insane | ||||||
| HP: | 2000 on Normal 2500 on Evil 3000 on Insane | ||||||
| Special Abilities: |
| ||||||
| Prizes: |
3 weapons/armor 500 gold 1500 NP |
Nice! You've just finished Act 4. All that's left is Act 5, where you'll find misguided flying cats and the huge ugly dragon at the end of the game. Head on over to ThePhoenixRising's page to get started. See you next page.


We study several kinds of matching problems between two point processes. First we consider the set of integers $\mathbb{Z}$. We assign
a color red or blue with probability 1/2 to each integer. We match each red integer to a blue integer using some algorithm and analyze
the matched edge length of the integer zero. Next we go to $\mathbb{R}^{d}$. We consider matching between two different point processes
and analyze a typical matched edge length $X$. There we see that the results vary significantly in different dimensions. In dimensions
one and two (d=1,2), even $E[X^{d/2}]$ does not exist. On the other hand in dimensions more than two (d>2), $E[\exp(cX^{d})]$ exist,
where $c$ is a constant depends on $d$ only. Next we discuss about matching problems in a finite setting, namely in a unit square.
Take $n$ red points and $n$ blue points chosen independently and uniformly in a unit square. There are $n!$ many possible matchings
between these red and blue points. We investigate the optimal average matched edge length and the minimum of the maximum matched edge
lengths, where the minimum is over all possible $n!$ matchings. There we see that the optimal average matched edge length is like $\sqrt
{\frac{\log n}{n}}$ and the min-max matched edge length is like $\frac{(\log n)^{3/4}}{\sqrt{n}}$.


Our Motto: Making Twisted.Web look like Apache since 2002
This file documents the specific configuration framework used for the
Debian package of Twisted.Web. For non-trivial options which are not
covered here, be sure to read
/usr/share/doc/twisted-doc/howto/using-twistedweb.xhtml
(in the twisted-doc
package).
The default configuration's goals are the following, in this order
The user it runs as (after binding to ports) is www-data
, and
the group is also www-data
.
By default, the configuration is to handle all hostnames to be the same --
all pointing to /var/www
. No processors
are enabled, so it
is possible to allow untrusted parties to put files in /var/www
without giving them the ability to run code as www-data
.
/doc
is pointed to /usr/share/doc/
, so all the
Debian documentation is
served by default (which means, for example, dhelp
works).
/cgi-bin
is pointed to /usr/lib/cgi-bin/
, so
third-party packages CGI
(mailman
, man2html
) are likewise served.
/users/<name>/
points to the user's
public_html
directory.
/users/<name>.twistd/
will relay requests to the user
personal server, if it exists.
/~name/
, or /~name.twistd/
, function the same as
/users/name/
or /users/name.twistd/
.
Any documents with unrecognised extension are served as
text/html
.
In order to avoid modifying the configuration files so maintainer updates
can happen smoothly, please put local configuration files in
/etc/wubwubwub/local.d
.
All .py
files in this directory are evaluated according to
lexical order.
It is recommended files will begin with two digits to set the order.
All example drop-in configuration files follow this convention.
Drop-in configuration files are evaluated inside a namespace consisting of
root
NameVirtualHost
default
File
root for names which are not virtual
hostssite
web.server.Site
, the factoryapplication
twisted.service.Application
Examples of such configuration scripts are in
/usr/share/doc/twisted-doc/examples
Note that the examples use the convention of prefixing every script name by two decimal digits. This convention, together with the fact that the scripts are read in lexicographical order, allows the order of evaluation to be easily seen (and modified, if so desired).
Since ports are often needed to be modified by programs, the port list
is kept in /etc/wubwubwub/ports
. These are strports-compatible ports:
"8080", "tcp:8080:interface=<IP_ADDRESS>" and "ssl:8080" are all valid
options, as well as more esoteric options like "unix:/foo:listen=100".
Note that if you have any ssl ports
, you should install
python2.3-pyopenssl
for them to work correctly.
In case you do want to modify the package's conffiles, note that they
have been partitioned into logical pieces.
/etc/wubwubwub/wubwubwub
is the master configuration, which loads all others. It should rarely,
if ever, be modified.
The configuration loads the following files:
/etc/wubwubwub/default
/etc/wubwubwub/root
/etc/wubwubwub/site
/etc/wubwubwub/application
/etc/wubwubwub/directories
/etc/wubwubwub/package.d
and /etc/wubwubwub/local.d
A user who wants dynamic pages should somehow make sure a personal server is
up and running. The easiest way to do this is to use the
mktap web
command
with the --personal
argument, and then start the server with
twistd2.3 -f ~/web.tap
Starting servers can be done with crontab
and a line like:
@reboot cd $HOME;twistd2.3 -f ~/web.tap
If some other program is running on port 80 Wubwubwub will be disabled
until
you change /etc/wubwubwub/disable
to be empty. You should do
this after
changing the configuration of Wubwubwub to listen on some other port, or
changing the configuration of the other server. After undisabling Wubwubwub,
run the command invoke-rc.d wubwubwub start
to start the server
manually.
wubwubwub-rproxy
exists to run Twisted on an out-of-the-way port and
prepare it
to be a reverse proxy. It will reconfigure Wubwubwub to run on another
port, and will also, optionally, drop a file in Apache2's configuration
directory to reverse proxy a named virtual host to the Wubwubwub server.
Running wubwubwub-rproxy without Apache 2 support:
wubwubwub-rproxy --port=8538
Running wubwubwub-rproxy with Apache 2 support:
wubwubwub-rproxy --port=8538 --vhost=twisted.example.com --apache-port=80
If you don't use the option to generate the Apache2 virtual host configuration,
or if you want to add more virtual host configurations, here is the template
for a file to drop in /etc/apache2/sites-available
:
<VirtualHost *> ProxyPass / http://localhost:8538/vhost/http/example.com:80/ ServerName example.com </VirtualHost>
Remember to symlink it to sites-enabled, and restart Apache2!
/usr/lib/rpy-lib
is served as a
ResourceScriptDirectory
-- anything in
it is assumed to be a ResourceScript
, and will be served as such.
If your package has a Resource
it wants to serve,
Depend:
on wubwubwub
,
and drop your ResourceScript
(which does *not* have to be named
.rpy
)
in /usr/lib/rpy-lib
. In particular if your package wants to serve
a portion
of the web site, perhaps as a different user, use
ResourcePublisher
to
listen on a socket somewhere on /var/run
, and have a
ResourceSubscriber
in a ResourceScript
which is then dropped in
/usr/lib/rpy-lib
. It is
possible to cache the ResourceSubscriber
in the registry.
For more
information about writing resources, see the using-twistedweb.xhtml
document in twisted-doc
. Note that starting and stopping your
server is still your responsibility.
/etc/wubwubwub/package.d
is designed for packages to drop
in their
configurations. For example, a package installing itself in a non-standard
location may use this to add its directory to sys.path
.
Note that files in /etc/wubwubwub/local.d
are
not deleted as part
of the purge. These file are the system administrator's creations.
If you want to get rid of them, run
rm -f /etc/wubwubwub/local.d/*
.
When the configuration changes, it is highly recommended to run
wubwubwub-confcheck
. This program, which can be run as a regular user,
will execute the configuration and check the application object
for simple mistakes. This will increase signifcantly that a
invoke-rc.d wubwubwub restart
will succeed.


This property contains the available OEM specific actions for this resource.
| string | Description |
|---|---|
| SameOriginOfCondition | Indicates the message is cleared by the other message(s) listed in the ClearingLogic object, provided the OriginOfCondition for both Events are the same. |
The MessageArg types, in order, for the message.
| string | Description |
|---|---|
| number | The parameter is a number. |
| string | The parameter is a string. |


Do or do not. There is no try.
#import <stdio.h> int main(int argc, char **argv) { printf("Hello world\n"); }


{{ range first 1 .Pages}} {{.Content}} {{end}}
Your browser doesn't support HTML5 audio. Here is a link to the audio instead.


[#assign actionTitle="关键词管理" /]
[#assign _TITLE_="关键词管理" /]
[#assign actions=[{"action":"","title":"关键词列表","url":"${baseUrl}admin/keywords.jhtml"},
{"action":"","title":"添加关键词","url":"javaScript:add()"}
]]
[#include "/plugin/cms/common/includes/header.htm"/]
[#import "/plugin/cms/base/macros/pager-macros.html" as mypager /]


Password Generator
Password Generator
Created by PATH08
Click on the button to generate password
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hello Dev blabla
And you go
Experience
Cant wait
Deliverd
For Hire
- Networking and prospecting
- Showing properties and working with home buyers
- Ensure product traceability
- Closing Deals
- Relationship development with the Parisian fashion houses and the GCC (Chanel, Dior, YSL, Hermes, Louis Vuitton)
- Customer portfolio development
- Customer care - Showroom organizing
- Preparation of major events
- Purchasing management
- Export-import management
- Creation of new marketing strategies
- Meeting and exceeding sales goals
- Generating sales leads
- Taking ownership of customer issues and resolve them
- Maintaining customer database and contribute towards the development of prospects list
- Networking and prospecting
- Showing properties and working with home buyers
- Ensure product traceability
- Closing Deals
- Relationship development with the Parisian fashion houses and the GCC (Chanel, Dior, YSL, Hermes, Louis Vuitton)
- Customer portfolio development
- Customer care - Showroom organizing
- Preparation of major events
- Purchasing management
- Export-import management
- Creation of new marketing strategies
- Meeting and exceeding sales goals
- Generating sales leads
- Taking ownership of customer issues and resolve them
- Maintaining customer database and contribute towards the development of prospects list
If you would like to get in touch with me feel free to full out this contact form. I ussualy reply within 2-5 business days. Thanks!


|
| int | pi_flash_open (struct pi_device *device) |
| | Open a flash device. More...
|
| |
| static void | pi_flash_close (struct pi_device *device) |
| | Close an opened flash device. More...
|
| |
| static int32_t | pi_flash_ioctl (struct pi_device *device, uint32_t cmd, void *arg) |
| | Control device. More...
|
| |
| static void | pi_flash_reg_set (struct pi_device *device, uint32_t reg_addr, uint8_t *value) |
| | Set flash register. More...
|
| |
| static void | pi_flash_reg_get (struct pi_device *device, uint32_t reg_addr, uint8_t *value) |
| | Get flash register. More...
|
| |
| static void | pi_flash_reg_set_async (struct pi_device *device, uint32_t reg_addr, uint8_t *value, pi_task_t *task) |
| | Set flash register asynchronously. More...
|
| |
| static void | pi_flash_reg_get_async (struct pi_device *device, uint32_t reg_addr, uint8_t *value, pi_task_t *task) |
| | Get flash register asynchronously. More...
|
| |
| static void | pi_flash_read (struct pi_device *device, uint32_t pi_flash_addr, void *data, uint32_t size) |
| | Enqueue a read copy to the flash (from flash to processor). More...
|
| |
| static void | pi_flash_program (struct pi_device *device, uint32_t pi_flash_addr, const void *data, uint32_t size) |
| | Enqueue a write copy to the flash (from processor to flash). More...
|
| |
| static void | pi_flash_erase_chip (struct pi_device *device) |
| | Erase the whole flash. More...
|
| |
| static void | pi_flash_erase_sector (struct pi_device *device, uint32_t pi_flash_addr) |
| | Erase a sector. More...
|
| |
| static void | pi_flash_erase (struct pi_device *device, uint32_t pi_flash_addr, int size) |
| | Erase an area in the flash. More...
|
| |
| static void | pi_flash_read_async (struct pi_device *device, uint32_t pi_flash_addr, void *data, uint32_t size, pi_task_t *task) |
| | Enqueue an asynchronous read copy to the flash (from flash to processor). More...
|
| |
| static void | pi_flash_program_async (struct pi_device *device, uint32_t pi_flash_addr, const void *data, uint32_t size, pi_task_t *task) |
| | Enqueue an asynchronous write copy to the flash (from processor to flash). More...
|
| |
| static void | pi_flash_erase_chip_async (struct pi_device *device, pi_task_t *task) |
| | Erase the whole flash asynchronously. More...
|
| |
| static void | pi_flash_erase_sector_async (struct pi_device *device, uint32_t pi_flash_addr, pi_task_t *task) |
| | Erase a sector asynchronously. More...
|
| |
| static void | pi_flash_erase_async (struct pi_device *device, uint32_t pi_flash_addr, int size, pi_task_t *task) |
| | Erase an area in the flash asynchronously. More...
|
| |


Encountered a "401 - Unauthorized" error while loading this page.
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Go to Jira home


The importer can turn a Wordpress export XML file into an archive (.zip) containing markdown files. The importer downloads images in your posts and stores them with the markdown files.


What have we learned
- Semantic HTML5 makes templates easier to read, and a way to help content authors form relationships between types of content
- Layouts get better, with minimal code, when using semantic containers
- CSS enables ways to style enumerated elements without creating extra classes
- CSS enables us to count the number of items in a container
- The data attribute is our friend, and helps us make CMS-friendly plugins
- The Canvas API enables client-side detection of image brightness, dominant color, and much more
Browser support
Just about everything here is supported in IE9 and up. But if you're ever curious, there's some tools


The purpose of the Price Tag Website is to be an information page on the features, benefits, and pricing for the DataTags Price Tag Software.
My ideal target audience are Appliance and Electronics stores in need of business support software that helps them focus on the part of their business that makes them money.
The goal of the website is to find and advertise the software to business owners who looking for a price tag program and to convert the interest into sales.
William Porter
50
Appliance and Electronics Retail Store Owner
Samsung smart phone with an Android OS on it. Various Window Operating Systems from OS 7 to 10. Computers have been upgraded or added as needed. Potentially Using Chrome, Firefox, Edge depending on what's loaded on which machine they are on.
He is dedicated and focused on the day to day work of selling. There are many moving parts to make it all function properly and it is a constant tug of war on his attentions. He relies on multiple different support services and staff to keep the business profitable. Sometimes he is having to follow up and inspect the work that's been done to make sure it is adequately done. Because of this he prefers simple processes to complex ones.
He has several support programs, but they require separate upkeep processes to maintain the product data, inventory, and pricing. It's hard to get help because the processes are complicated and involved. It makes employee retention important and advancement more difficult.
As a small business owner, I wear many different hats. I need my day-to-day processes to be uncomplicated and easy to manage.


At Pachamama Eco, we believe in sustainable living.
To replace every-day plastics with zero- or minimal-waste alternatives and to offer products that look and feel good, and are good for (Mother) Earth too.
Francisca Wood
<EMAIL_ADDRESS>(757) 318-0951


CLOTHES
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Parable 2016.04
Virtual Machine
-
reverse now preserves the pointer type (pointer, stack, remark)
-
+ now supports new forms: cs-s, sc-s, and cc-s
- the stack is now implemented as a single array of tuples (value, type)
- remove BC_ROUND
- remove BC_FLOW_RETURN
- BC_MEM_COPY now works with any pointer types
- added parsed_stack()
- added stack_values()
- added stack_types()
- Re-enable automatic garbage collection on OOM
- Allocate a larger array of slices when OOM
- Add | prefix for function calls
- fixed bugs in BC_MEM_FETCH and B_MEM_STORE
- line condenser no longer requires a \ to join lines
- rewrote tokenizer
- refactored stack_change_type()
- fixed BC_LOGN
- BC_SUBSLICE now remembers the type of pointer
- added support for micropython
Standard Library
-
additions
- byKey:
- cycle
- decons
- subslice<left>
- subslice<right>
- vocab{
- }vocab
-
bug fixes
-
other improvements
- round now written in parable
- removed \ from multi-line definitions
Interfaces
- PaaS: revised API
- Punga: more detailed stack display
Documentation
- added tools/gfm.py (git flavored markdown to html)
- added docs/Makefile to generate HTML from Markdown using tools/gfm.py
Build
- Use a single top-level Makefile.config
- Makefiles are no longer hard coded to update my personal environment
Tooling
- Added tools/ptags.py to generate a ctags compatible tags file


Super Normal Manifesto
by Jasper Morrison (2007)
Design, which used to be almost unknown as a profession, has become a major source of pollution. Encouraged by glossy lifestyle magazines and marketing departments, it’s become a competition to make things as noticeable as possible by means of colour, shape and surprise. Its historic and idealistic purpose, to serve industry and the happy consuming masses at the same time, of conceiving things easier to make and better to live with, seems to have been side-tracked. The virus has already infected the everyday environment. The need for businesses to attract attention provides the perfect carrier for the disease.
Design makes things seem special, and who wants normal if they can have special? And that’s the problem.
What has grown naturally and unselfconsciously over the years cannot easily be replaced. Not that old things shouldn’t be replaced or that new things are bad, just that things that are designed to attract attention are usually unsatisfactory. There are better ways to design than putting a big effort into making something look special. Special is generally less useful than normal, and less rewarding in the long term. Special things demand attention for the wrong reasons, interrupting potentially good atmosphere with an awkward presence. Preserve Normal, resist Special!


--- layout: post status: publish published: true title: "生命是一个奇迹" author: Leask author_login: leask author_email<EMAIL_ADDRESS>author_url: https://leaskh.com wordpress_id: 1706 wordpress_url: https://leaskh.com/?p=1706 date: '2011-08-12 23:09:28 +0800' date_gmt: '2011-08-12 15:09:28 +0800' categories: - Life - Music tags: [] comments: [] ---
听说哥哥老婆的肚子已经鼓起来了，12月就要诞下宝宝了。
嫂嫂发来一张彩色超声波照片，让我十分震撼。
让我想起 Céline Dion 的 MIRACLE，歌词写得很美，分享给大家。
祝天下宝宝健康成长。


Guitar Cabinets
Every Cabinet is made from your choice of either 3/4" Baltic Birtch Maple plywood, or 3/4" Pine hardwood. The coices are endless for customizing exterior coverings.
Shameless Customs, LLC.
Bass Cabinets
Every Cabinet is made from your choice of either 3/4" Baltic Birtch Maple plywood, or 3/4" Pine hardwood. The coices are endless for customizing exterior coverings.


Philosophy + JavaScript
Father
Zerg player
Humble anarchist
Developer at Thomas Cook NE
(Stockholm Office)
Fluent in C# and JavaScript
"organizations which design systems ... are constrained to produce designs which are copies of the communication structures of these organizations"
Seneca, Lucius Annaeus - Letters from a stoic
control those things that are within our power (namely our beliefs, judgments, desires , and attitudes) and be indifferent or apathetic to those things which are not in our power (namely, things external to us)
Nicolas Taleb
Always
Starting point
var aDependency = "I'm a dependency";
var module = (function (publicModule, myDependency) {
var somethingPrivate = "I'm private";
publicModule.somethingPublic = aDependency;
return publicModule;
}(module || {}, aDependency));
var output = module.somethingPublic;
output;
Repl
var aDependency = "I'm a dependency";
var myModule = function (myDependency) {
var publicModule = {};
publicModule.somethingPublic = aDependency;
return publicModule;
};
Repl
Single truth
var aTruth = function () {
var publicModule = {};
publicModule.number = 0;
publicModule.ChangeState = function(aNumber) {
publicModule.number = aNumber;
//Publish
};
return publicModule;
};
var aSubscriber = function()
{
//Subscribe to change
return {};
}
Repl
var longRunning = function()
{
window.setTimeout(function() { logger("Work done") },2000)
}
var yetAnotherLongRunning = function()
{
window.setTimeout(function() {logger("Work done") },1000)
}
longRunning();
yetAnotherLongRunning();
logger("All done!");
JsFiddler
describe("First state", function() {
it("contains spec with an expectation", function() {
expect(true).toBe(true);
});
describe("Something else is going on", function() {
it("contains spec with an expectation", function() {
expect(true).toBe(true);
});
});
});
control those things that are within our power (namely our beliefs, judgments, desires , and attitudes) and be indifferent or apathetic to those things which are not in our power (namely, things external to us)
@emilcardell


Psalms 33:11
The counsel of the LORD standeth for ever, the thoughts of his heart to all generations.


DAYLIGHT A film review by Scott Renshaw Copyright 1996 Scott Renshaw
(Universal) Starring: Sylvester Stallone, Amy Brenneman, Viggo Mortensen, Stan Shaw. Screenplay: Leslie Bohem. Producers: John Davis, Joseph M. Singer, David T. Friendly. Director: Rob Cohen. MPAA Rating: PG-13 (profanity, violence). Running Time: 115 minutes. Reviewed by Scott Renshaw.
It is one of the more trustworthy rules of popular culture that everything old must eventually become new again, and 1996 at the movies sure has looked a lot like the mid-70s. TWISTER and INDEPENDENCE DAY were less like contemporary action films than the were heirs to the disaster epics of twenty years ago, with plucky bands of one-dimensional characters trying to withstand barrages of special effects. With two volcano films and James Cameron's TITANIC on the horizon, it is beginning to feel like Irwin Allen has taken over Hollywood again, and that thought doesn't inspire much nostalgia. DAYLIGHT is a POSEIDON ADVENTURE clone with a few effectively tense moments, but it is so plodding and mechanical that it is rarely entertaining.
Sylvester Stallone stars as Kit Latura, a one-time Emergency Medical Services worker now working as a chauffeur after a tragic accident cost him his job. His training will come in handy, however, when a massive explosion involving toxic chemicals seals off an underwater commuter tunnel between Manhattan and New Jersey. With only twelve survivors trapped in the tunnel -- including struggling playwright Madelyne Thompson (Amy Brenneman), athletic gear tycoon Roy Nord (Viggo Mortensen) and transit cop George Tyrell (Stan Shaw) -- Kit attempts a risky entry through a route from which he will not be able to return. That leaves him in danger as well, though the greatest danger isn't entirely clear. While rising waters and a collapsing structure place the clock against them, the group also finds progress threatened by internal dissent, as well as by a dangerous procedure undertaken by officials on the outside which could cost everyone their lives.
DAYLIGHT begins with a couple of red-herring criminal activities which end up causing the firestorm in the tunnel, but it is indicative of the shortcomings of the script that the bad guys then become cinders rather than sticking around to make any more sparks fly.  There simply isn't enough to any of the characters to give the conflict energy; everything we know about each of them comes from a series of postage-stamp sized establishing scenes, and that's not nearly enough. With the vague bureaucratic malevolence aboveground and the muted mutineering below ground as the only antagonism in most of DAYLIGHT, the absence of a real villain becomes a distinct drawback, and results in a plot which grinds from impossible situation to yet-more-impossible situation.
That is not to say that there is no drama in DAYLIGHT, though little of it comes from the silly dialogue or thin characterizations. Surprisingly, it also doesn't come from the special effects by Industrial Light and Magic, which often have a strangely low-budget quality. The real drama comes from watching the dirty baker's dozen of our protagonists realize that their only chance at survival lies in cooperation, and from the difficult moral decisions of a crisis situation. Repeatedly, characters are forced to decide who can be saved, or whether someone is worth saving, and director Rob Cohen (DRAGONHEART) somehow makes those moments matter even though individual characters don't. When the entire group joins together to free one of them trapped under a car, the heroism of necessity gives DAYLIGHT a boost of substance.
That alone might be more than you would expect from a Sylvester Stallone action film, although Sly himself merely seems to be going through the motions. Screenwriter Leslie Bohem has handed him a character which even seems to be a barely re-worked version of his tragedy-scarred, redemption-hungry role in CLIFFHANGER, and he wanders grimly through most of the film without showing us any hint of a personality. He also has to carry DAYLIGHT on his broad shoulders, because this is one disaster film which doesn't blow its budget on putting an all-star cast in harm's way; Amy Brenneman and Viggo Mortensen are no Faye Dunaway and Fred Astaire. However, DAYLIGHT does try to follow up on one bit of disaster film trivia -- both THE POSEIDON ADVENTURE and THE TOWERING INFERNO won Best Original Song Academy Awards -- by serving up a sappy romantic ballad called "Whenever There Is Love." The female half of the duet? Donna Summer. Yes indeed, folks, it sure does feel like the mid-70s all over again.
On the Renshaw scale of 0 to 10 tunneling infernos: 5.
Visit Scott Renshaw's MoviePage http://www-leland.stanford.edu/~srenshaw Subscribe to receive reviews directly via email See details on the MoviePage
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


This referral requires charity care.
Please click here when you arrive at your patient's FS appointment for further directions.
Once your patient has charity care, please contact the CT Referrals Manager. The CT referrals manager will give you directions on how to schedule your patient for their specialty referral appointment.
Cost to EHHOP Patient: Once your patient submits documents confirming their income, Financial Services will grant you patient Charity Care and place them on a sliding scale. Most EHHOP patients are placed on Level 1. For patients with Level 1, the CT scan will cost $20 per body part regardless of whether contrast is used, and which body part is scanned. To find out the costs for patients who are not Level 1, please contact the CT scans Manager, Chiaki Nakata
Cost to Society: CT scans w/o contrast cost ~500 in new york out of pocket.
How long it takes patients to complete their appointment will depend on how soon the patients can submit all their documents confirming their income to Financial Services. One average this takes 24 days Once all documents are submitted, it takes about 1 week for a CT scan.


Ranu Singh is a student in the SMArchS Architecture and Urbanism program in the Department of Architecture at MIT. Currently working as a Research Assistant at the Aga Khan Program for Islamic Architecture, her research area focuses on current urbanism and planning issues in developing countries. Ranu completed her undergraduate studies in architecture from India after which she worked on various architecture and urban projects in India. Her publications include a book titled Temporal Rhythm in Aesthetic Experience of Architecture, published in 2015.


diff_classifier.heatmaps
¶
diff_classifier.heatmaps
¶
-
diff_classifier.heatmaps.
plot_heatmap
(prefix, feature='asymmetry1', vmin=0, vmax=1, resolution=512, rows=4, cols=4, upload=True, dpi=None, figsize=(12, 10))[source]¶ Plot heatmap of trajectories in video with colors corresponding to features.
- prefix: string
- Prefix of file name to be plotted e.g. features_P1.csv prefix is P1.
- feature: string
- Feature to be plotted. See features_analysis.py
- vmin: float64
- Lower intensity bound for heatmap.
- vmax: float64
- Upper intensity bound for heatmap.
- resolution: int
- Resolution of base image. Only needed to calculate bounds of image.
- rows: int
- Rows of base images used to build tiled image.
- cols: int
- Columns of base images used to build tiled images.
- upload: boolean
- True if you want to upload to s3.
- dpi: int
- Desired dpi of output image.
- figsize: list
- Desired dimensions of output image.
-
diff_classifier.heatmaps.
plot_histogram
(prefix, xlabel='Log Diffusion Coefficient Dist', ylabel='Trajectory Count', fps=100.02, umppx=0.16, frames=651, y_range=100, frame_interval=20, frame_range=100, analysis='log', theta='D', upload=True)[source]¶ Plot heatmap of trajectories in video with colors corresponding to features.
- prefix: string
- Prefix of file name to be plotted e.g. features_P1.csv prefix is P1.
- xlabel: string
- X axis label.
- ylabel: string
- Y axis label.
- fps: float64
- Frames per second of video.
- umppx: float64
- Resolution of video in microns per pixel.
- frames: int
- Number of frames in video.
- y_range: float64 or int
- Desire y range of graph.
- frame_interval: int
- Desired spacing between MSDs/Deffs to be plotted.
- analysis: string
- Desired output format. If log, will plot log(MSDs/Deffs)
- theta: string
- Desired output. D for diffusion coefficients. Anything else, MSDs.
- upload: boolean
- True if you want to upload to s3.
-
diff_classifier.heatmaps.
plot_individual_msds
(prefix, x_range=100, y_range=20, umppx=0.16, fps=100.02, alpha=0.01, upload=True, folder='.')[source]¶ Plot MSDs of trajectories and the geometric average.
- prefix: string
- Prefix of file name to be plotted e.g. features_P1.csv prefix is P1.
- x_range: float64 or int
- Desire x range of graph.
- y_range: float64 or int
- Desire y range of graph.
- fps: float64
- Frames per second of video.
- umppx: float64
- Resolution of video in microns per pixel.
- alpha: float64
- Transparency factor. Between 0 and 1.
- upload: boolean
- True if you want to upload to s3.
- geo_mean: numpy array
- Geometric mean of trajectory MSDs at all time points.
- geo_SEM: numpy array
- Geometric standard errot of trajectory MSDs at all time points.
-
diff_classifier.heatmaps.
plot_particles_in_frame
(prefix, x_range=600, y_range=2000, upload=True)[source]¶ Plot number of particles per frame as a function of time.
- prefix: string
- Prefix of file name to be plotted e.g. features_P1.csv prefix is P1.
- x_range: float64 or int
- Desire x range of graph.
- y_range: float64 or int
- Desire y range of graph.
- upload: boolean
- True if you want to upload to s3.
-
diff_classifier.heatmaps.
plot_scatterplot
(prefix, feature='asymmetry1', vmin=0, vmax=1, resolution=512, rows=4, cols=4, upload=True)[source]¶ Plot scatterplot of trajectories in video with colors corresponding to features.
- prefix: string
- Prefix of file name to be plotted e.g. features_P1.csv prefix is P1.
- feature: string
- Feature to be plotted. See features_analysis.py
- vmin: float64
- Lower intensity bound for heatmap.
- vmax: float64
- Upper intensity bound for heatmap.
- resolution: int
- Resolution of base image. Only needed to calculate bounds of image.
- rows: int
- Rows of base images used to build tiled image.
- cols: int
- Columns of base images used to build tiled images.
- upload: boolean
- True if you want to upload to s3.
-
diff_classifier.heatmaps.
plot_trajectories
(prefix, resolution=512, rows=4, cols=4, upload=True)[source]¶ Plot trajectories in video.
- prefix: string
- Prefix of file name to be plotted e.g. features_P1.csv prefix is P1.
- resolution: int
- Resolution of base image. Only needed to calculate bounds of image.
- rows: int
- Rows of base images used to build tiled image.
- cols: int
- Columns of base images used to build tiled images.
- upload: boolean
- True if you want to upload to s3.
-
diff_classifier.heatmaps.
voronoi_finite_polygons_2d
(vor, radius=None)[source]¶ Reconstruct infinite voronoi regions in a 2D diagram to finite regions.
- vor : Voronoi
- Input diagram
- radius : float, optional
- Distance to ‘points at infinity’.
- regions : list of tuples
- Indices of vertices in each revised Voronoi regions.
- vertices : list of tuples
- Coordinates for revised Voronoi vertices. Same as coordinates of input vertices, with ‘points at infinity’ appended to the end.


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 64
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 64
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
real_inputs = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name = 'real_inputs')
real_z = tf.placeholder(tf.float32, (None, z_dim), name = 'real_z')
learning_rate = tf.placeholder(tf.float32, name = 'learning_rate')
return real_inputs, real_z, learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False, alpha=0.2):
"""
Create the discriminator network
:param image: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
with tf.variable_scope('discriminator', reuse = reuse):
keep_prob = 0.8
layer1 = tf.layers.conv2d(images, 64, 5, strides=2, kernel_initializer=tf.contrib.layers.xavier_initializer(), padding='same')
relu1 = tf.maximum(alpha * layer1, layer1)
drop1 = tf.nn.dropout(relu1, keep_prob)
layer2 = tf.layers.conv2d(drop1, 128, 5, strides=2, kernel_initializer=tf.contrib.layers.xavier_initializer(), padding='same')
bn2 = tf.layers.batch_normalization(layer2, training=True)
relu2 = tf.maximum(alpha * bn2, bn2)
drop2 = tf.nn.dropout(relu2, keep_prob)
layer3 = tf.layers.conv2d(drop2, 256, 5, strides=2, kernel_initializer=tf.contrib.layers.xavier_initializer(), padding='same')
bn3 = tf.layers.batch_normalization(layer3, training=True)
relu3 = tf.maximum(alpha * bn3, bn3)
drop3 = tf.nn.dropout(relu3, keep_prob)
# Flatten it
flat = tf.reshape(drop3, (-1, 4*4*256))
logits = tf.layers.dense(flat, 1)
output = tf.sigmoid(logits)
return output, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train = True, alpha=0.2):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
with tf.variable_scope('generator', reuse = not is_train):
# First fully connected layer
x = tf.layers.dense(z, 7*7*256)
# Reshape it to start the convolutional stack
layer1 = tf.reshape(x, (-1, 7, 7, 256))
bn1 = tf.layers.batch_normalization(layer1, training=is_train)
relu1 = tf.maximum(alpha * bn1, bn1)
layer2 = tf.layers.conv2d_transpose(relu1, 256, 1, strides=1, padding='same')
bn2 = tf.layers.batch_normalization(layer2, training=is_train)
relu2 = tf.maximum(alpha * bn2, bn2)
layer3 = tf.layers.conv2d_transpose(relu2, 128, 5, strides=2, padding='same')
bn3 = tf.layers.batch_normalization(layer3, training=is_train)
relu3 = tf.maximum(alpha * bn3, bn3)
layer4 = tf.layers.conv2d_transpose(relu3, 128, 1, strides=1, padding='same')
bn4 = tf.layers.batch_normalization(layer4, training=is_train)
relu4 = tf.maximum(alpha * bn4, bn4)
layer5 = tf.layers.conv2d_transpose(relu4, 64, 5, strides=2, padding='same')
bn5 = tf.layers.batch_normalization(layer5, training=is_train)
relu5 = tf.maximum(alpha * bn5, bn5)
# Output layer
logits = tf.layers.conv2d_transpose(relu5, out_channel_dim, 1, strides=1, padding='same')
# 28x28x3
output = tf.tanh(logits)
return output
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
g_model = generator(input_z, out_channel_dim, is_train=True)
d_model_real, d_logits_real = discriminator(input_real, reuse=False)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*0.9))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
discriminator_loss = d_loss_real + d_loss_fake
generator_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
return discriminator_loss, generator_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
train = tf.trainable_variables()
discriminator_var = [x for x in train if x.name.startswith('discriminator')]
generator_var = [x for x in train if x.name.startswith('generator')]
update = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
generator_updates = [op for op in update if op.name.startswith('generator')]
# Optimize
with tf.control_dependencies(generator_updates):
discrimiator_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=discriminator_var)
generator_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=generator_var)
return discrimiator_opt, generator_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
_, image_width, image_height, image_channels = data_shape
real_inputs, real_z, learning_rate_in = model_inputs(image_width, image_height, image_channels, z_dim)
d_loss, g_loss = model_loss(real_inputs, real_z, image_channels)
d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
steps = 0
print_every = 10
show_every = 100
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
steps += 1
batch_images *= 2
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
_ = sess.run(d_opt, feed_dict={ real_inputs: batch_images, real_z: batch_z, learning_rate_in: learning_rate })
_ = sess.run(g_opt, feed_dict={ real_z: batch_z, learning_rate_in: learning_rate })
if steps % print_every == 0:
d_loss_train = d_loss.eval({
real_z: batch_z,
real_inputs: batch_images
})
g_loss_train = g_loss.eval({
real_z: batch_z
})
print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
"Discriminator Loss: {:.4f}...".format(d_loss_train),
"Generator Loss: {:.4f}".format(g_loss_train))
if steps % show_every == 0:
show_generator_output(sess, batch_size, real_z, image_channels, data_image_mode)
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 64
z_dim = 128
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


Go Fuzz
Workers
Corpus
Crashers
Restarts
Execs
Cover
Uptime
History
Workers
Corpus
Crashers
Restarts
Execs
Cover
Uptime


There are 25 question with an additional final question at the end.
Each question answered correctly is worth 10 points, with the final question being worth 50. (if you manage to answer the final question with time remaining it will be worth 100 points)
You will be given 2 minutes to complete this quiz.
But be careful, Answering a question incorrectly will take away 5 seconds from your time remaining.


What time of the day and day of the week works best for you?
Do you have a specific dental concern?
How did you hear about us?


Mosquito Fens [al62], swamp, in Imperia, wilderness
Last updated Turn #148
Mosquito Fens [al62], swamp, in Imperia, wilderness
------------------------------------------------------------------------
Routes leaving Mosquito Fens:
North, swamp, to Mosquito Fens [ak62], 14 days
East, swamp, to Mosquito Fens [al63], 14 days
South, swamp, to Mosquito Fens [am62], 14 days
West, swamp, to Mosquito Fens [al61], 14 days
Inner locations:
Graveyard [ac9k], graveyard, hidden, 1 day, owner:
Spirit [gx7x], spirits, number: 12
Seen here:
Stack of nazgul [ff0l], nazgul, number: 5
Stack of giants [hz5r], giants, number: 10
Stack of nazgul [as8j], nazgul, number: 2
Stack of giants [ge5s], giants, number: 8
Kosh [b8l], refugee, with one peasant


skills developed every day
Knowledge of PHP, JS and other web language
SQL language is not a secret for me
Learning of C/C++/JAVA/Python
Learning of LDAP, Crontable, Samba, encryption protocols, and the basics of hte OS
Knowledge of assembler language for microprocessor coding and robotic stuff
Learning classes in CISCO's network
Start learning how to find a client, find a project. The monitoring project. With some rules imposed by the French laws for enterprises and Junior enterprises.
Scrum master of agile scrum team, member of kanban team and worked in V cycle
This is all of my projects, school projects and personal projects. I am proud to show you.
This is what I can say about me.
Hello, I am Pierre-yves. I pass my exam of IT engineer at ISEN. I was member of CAPISEN it is the junior-enterprise of my school. I like travel. I spent two months alone in the United Kingdom, it was like a road trip across the country. I really love live in other country. I have lot of hobbies in my life, the sport is one of the most important for me and the other is the poker. Without a doubt i liked working as bartender during some summer.
I have lot of differents hobbies. I love sports. I praticed Taekwondo 10 years, i am actualy black belt. I participated to differents championship and win for the brittany championship. Actualy I stop due to an injury at my knees. I hope I will be able to continue this sport in the futur. I played football too, maybe 5/7 years.
I practice another type of sport. I practice E-sport in counter strike. I think it is a real sport not physical of course but psychological one. The competition in this game is a 5 vs 5 match. So we have to improve the communication and team work to coordinate all of the 5 players to play the best game.
One of my favourit hobbies is the poker. I really appricate this game due to the huge mathematical part and the psycological one.
I have my motorbike licence, I love to drive my motorbike when I have some time, It is really amazing.
Finally I love travel in the world, I went 2 months in the UK. I also travel across the East Europe, I went to Budapest, Vienne, Bratislava and Prague during the same trip.
In my school there is a Junior enterprise, Capisen.
I had the chance to join the team of Capisen. I was charged to find client, to talk with, to write the Specifications of theire projects. After I verify the project during his creation. This junior entreprise is like a real one in France and we where subject of all the french laws.
I worked for Decathlon, I supervise the creation of two videos for this firm. It was a real pleasure to work with an enterprise like Decathlon. I also worked with two guys for the creation of a website.
I also have differents experiences. I worked as Barman for differents opportunities. I worked for a Bar the night. From midnight to 5 AM. I was charged to serve the drinks, controle the stocks and clean the place at the end. I also did this job in differents concert, I was volunteerism for that.
My mother is farmer and I help her since my early age when I had some free times. I harvest some vegetables, I work around the cows. Finaly I do some mechanical work, like repair car/moto/tractor ...
This is all of my differents experiences (at school/at work and during my free time)
This is my first job after the school. I'm actually consultant at Arkea. Arkea is a french bank. I'm part of a DevOps team.
I was member of agile team in scrum and an other part was working in kanban. I was in charge of developpement of a website for Orange Buisness Services.
The purpose of this intership was to start an ESB for one part of the SI. I supervised in the same time the developpement of one mobile application.
I participate at one start-up weekend and i was a member of BaguetteAdvisor. This startup is now growing, and some people are still working on it. Let's look closer :)
In ISEN we have to do an international experience. I spent 1 month in United Kingdom. I visited London, Oxford, Cardiff and Bristol. Before that I went to Brighton to look for a barman job for all the summer.  So I had to do a road trip during the summer instead of working. But it was really interesting because I was oblige to live in a foreigne country during a long time by myself.
In 2014 I participed at the Startup Weekend in Brest. The purpose of this weekend is to creat a startup in 54H. During this weekend we had lot of formations in law, in Buisness plan with Antoine Dumont. During the startup Weekend I met a lot of people. It was really exigent because in only 54H we have to creat a startup, the begining of the web site, or application... My team worked on "Covoit' de Boite", we created a web site like "blablacar" but for the night.
This intership was the first real intership. His purpose was to work as an IT technician. I worked in the IT service in the hospital of Morlaix. My mission was to creat an other web site for the travel expenses of staff. The new thing for me was the utilisation of the PHPTAL. I was forced to use that language because all of other wab site of the hospital was creat by using this language.
At the end of my first year at ISEN, I had to do an association intership. The purpose of the intership was to officer a group of people. I decided to join the staff of the youth service of my town. During the intership I created some activities. Officer group during the moring and afternoon activities.
This is my timeline of Twitter, my linkedin profil and other, have a look :)


!Researcher Keplon:
It permits teleportation between the floors of the tower. I'm not sure exactly how it works, but through much trial and error I have determined how to go to the first, fifth and tenth floor.
To go to the fifth floor, you need a Blue Dimension Stone.
These are quite rare... At great personal cost I was able to secure a small box of them, but Hallate and his minions stole them away... Now they can only be obtained by killing those demons...
[npc_%objectId%_Chat 2|"Tell me more about Death Lord Hallate."]
[npc_%objectId%_Chat 0|Return]


Transcriptional regulation during myelopoiesis.
Abstract
The coordinated production of all blood cells from a common stem cell is a highly regulated process involving successive stages of commitment and differentiation. From analyses of mice deficient in transcription factor genes and from the characterizations of chromosome breakpoints in human leukemias, it has become evident that transcription factors are important regulators of hematopoiesis. During myelopoiesis, which includes the development of granulocytic and monocytic lineages, transcription factors from several families are active, including AML1/CBF beta, C/EBP, Ets, c-Myb, HOX, and MZF-1. Few of these factors are expressed exclusively in myeloid cells; instead it appears that they cooperatively regulate transcription of myeloid-specific genes. Here we discuss recent advances in transcriptional regulation during myelopoiesis.


If `group` is an instance of [`RunOrg.Group`](/people/group.htm),
then `group.List(limit,offset)` returns all the [people](/people.htm)
in the specified group, in arbitrary order. Supports `limit` and `offset`
pagination.
Returns a [promise](/concepts/promise.htm) that will resolve to an
array of [short profiles](/people/person.htm).
new RunOrg.Group("1ax69bfa335").List(10,0).then(function(list) {
for (var i = 0; i < list.length; ++i)
console.log("Found person %s.", list[i].label)
})
Returns all the [people](/people.md) in the specified group, in
arbitrary order.
# Request
This is a GET request with the following parameters:
limit | | The maximum number of people to be returned.
offset | | The number of people to be skipped.
# Response
This is a [read-only](/concepts/read-only.htm) request that always
responds `200 OK` and the following fields:
list | array | A list of [short profiles](/people/person.htm).
count | | The total number of members in the group.
GET /db/0Et4X0016om/groups/0Et9j0026rO?limit=3&offset=213
200 OK
Content-Type: application/json
{ "list" : [
{ "id" : "0SNQe00311H",
"label" : "George Sand",
"gender" : "f",
"pic" : "https://www.gravatar.com/avatar/1ed54d253636f5b33eff32c2d5573f70" },
{ "id" : "0SNQg00511H",
"label" : "Victor Hugo",
"gender" : "m",
"pic" : "https://www.gravatar.com/avatar/5a31b00f649489a9a24d3dc3e8b28061"} ],
"count" : 215 }
# Errors
## Returns `401 Unauthorized`
- if the provided token does not allow acting as the viewer.
## Returns `403 Forbidden`
- if the viewer does not have **list** access to the group.
## Returns `404 Not Found`
- if the database does not exist,
- if group `{id}` does not exist in the database.
- if the viewer does not have at least **view** access to the
group, to ensure [absence equivalence](/concepts/absence-equivalence.md).
# Access restrictions
Viewing group members requires **list** access to that group.


Let's pretend this is an e-commerce site that sells shoes.
Make a couple color selections and watch how the text, URL, and titles change as a result. Then, use the browsers
back button to navigate backwards. With no state object passed to pushState
, we become out of sync
after navigating back and forth.
You are currently viewing all color
shoes
If and when a popState event occurs, details will show below:


---
layout: default
title: Updates
page-header:
headline: "Version History"
alignment: left
---
Notable Updates
-
Shift in naming for screen-size breakpoints
-
Shift in naming for screen-size breakpoints.
-
Shift in naming for screen-size breakpoints.
Read more
Notable Updates
-
Shift in naming for screen-size breakpoints
-
Shift in naming for screen-size breakpoints.
-
Shift in naming for screen-size breakpoints.
Read more


--- layout: default title: home ---
Welcome! This site is currently under development.
Its source and any updates can be viewed here.


C
a
r
n
a
p
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s
P
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z
a
Thank you for your order
!
Delivery Details:
Name
Address
Phone Number
Submit Order
Size
Small $10
Medium $13
Large $15
Toppings!
$1 Each
Mushroom
Olives
pepperoni
Add Pizza
TOTAL
Pizza Cart:
Your Order:
Checkout


Docs about Intel® Concurrent Collections for C++
Tutorial
API Documentation
Release Notes
Getting Started
FAQ
CnC Papers and Related Links
The Concurrent Collections Programming Model
Parallel Programming For Distributed Memory Without The Pain
Performance Evaluation of Concurrent Collections on High-Performance Multicore Computing Systems
Measuring the Overhead of Intel C++ CnC over Gauss-Jordan Elimination
Segmentation for the Brain Connectome using a Multi-Scale Parallel Computing Architecture
Cluster Computing using Intel Concurrent Collections
Habanero Concurrent Collections Project at Rice University


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---
layout: post
title: "Week in Review"
date: 2015-10-25 10:59:00 +0000
categories:
tags:
image: https://s3.amazonaws.com/audreywatters/2015-10-25-olympia.jpg
imagecredits: https://www.flickr.com/photos/wonderlane/22325149486
---
Travel
- Olympia, WA and Hermosa Beach, CA.
- Upcoming week: No travel.
Hack Education
Essays Elsewhere
Speaking
- I delivered the opening keynote at NWeLearn and sat on the closing panel with Jesse Stommel.
Known Notes and Fragments
Other Projects
- I bought tickets for opening day of the new Star Wars movie.
- Therapy! Massage!
- I made a donation to my favorite NPR station, KPCC.
- I talked to folks at Safari about distributing The Monsters of Education Technology on their platform.
Viewing
- I have caught up on the new season of The Walking Dead.
Family and Friends
- Kin is back from Europe.
- I’ve got The Kid here for the weekend.


-
Stop asking bad questions. “Do you think it’s a good idea?” “Would you use this?” These questions are bad because you’re asking people to speculate about the future and people are overly optimistic and bad at predicting the future. Instead ask them about the past, then you are dealing with concrete facts, not speculations. Better questions are “When was the last time you…?”, “Did you try to search for a solution?”, “How are you currently solving the problem?”
-
Try get as concrete responses as possible. Try to avoid answers like, “I usually do…” or questions like “Do you ever…?” Try to get them to describe specific instances. Dig deeper into generic responses like “I always..” or “I never…”. Usually there are exceptions that may be interesting.
-
You should ignore basically everything that isn’t a concrete description of the past or an actual commitment. This includes compliments like “That’s a great idea!” or non-commital promises like, “I would definitely buy that”.
-
When you get a feature request, always ask dig deeper into why they want it. The real reason might surprise you.
-
Even if someone says they have a problem, it might not be a big enough problem for them to actually try to solve it. Even if they seem to hate the problem they might just want to complain, or they already have a work around that’s good enough that they won’t pay for a solution.
-
Product risk vs market risk: Talking with customers the right way can lower market risk but not product risk. Market risk is whether people want what you’re offering, product risk is whether you will be able to deliver the promises of that product. Video games have high product risk, because it’s hard to validate the product value without building a lot of it.


We are discussing the shape of the base, and I proposed to make a sphere as the base with a hole on top, so that the top part can stick into it perfectly. I thought about my previous experience of paper folding, so I built a paper ball and used it as the base.
The final object we made was quite tall, but not the tallest among all. We concluded that it may be caused by uncareful designing and scornfulness toward other teams. We considered to minimize the surface area, but wasted too much newspaper on the constructing part as the base.


Pipelines.Rd
Uses nmr_pca_outliers_robust to perform the detection of outliers
Normalize the full spectra to the internal calibrant region, then exclude that region and finally perform PQN normalization.
pipe_load_samples(samples_dir, glob = "*0", output_dir = NULL) pipe_add_metadata(nmr_dataset_rds, excel_file, output_dir) pipe_interpolate_1D(nmr_dataset_rds, axis, output_dir) pipe_exclude_regions(nmr_dataset_rds, exclude, output_dir) pipe_outlier_detection(nmr_dataset_rds, output_dir) pipe_filter_samples(nmr_dataset_rds, conditions, output_dir) pipe_peakdet_align( nmr_dataset_rds, nDivRange_ppm = 0.1, scales = seq(1, 16, 2), baselineThresh = 0.01, SNR.Th = -1, maxShift_ppm = 0.0015, acceptLostPeak = FALSE, output_dir = NULL ) pipe_peak_integration( nmr_dataset_rds, peak_det_align_dir, peak_width_ppm, output_dir ) pipe_normalization( nmr_dataset_rds, internal_calibrant = NULL, output_dir = NULL )
| samples_dir | The directory where the samples are |
|---|---|
| glob | A wildcard aka globbing pattern (e.g. |
| output_dir | The output directory for this pipe element |
| nmr_dataset_rds | The nmr_dataset.rds file name coming from previous nodes |
| excel_file | An excel file name. See details for the requirements The excel file can have one or more sheets. The excel sheets need to be as simple as possible: One header column on the first row and values below. Each of the sheets contain metadata that has to be integrated. The merge (technically a left join) is done using the first column of each sheet as key. In practical terms this means that the first sheet of the excel file MUST start with an "NMRExperiment" column, and as many additional columns to add (e.g. FluidXBarcode, SampleCollectionDate, TimePoint and SubjectID). The second sheet can have as the first column any of the already added columns, for instance the "SubjectID", and any additional columns (e.g. Gender, Age). The first column on each sheet, named the key column, MUST have unique values. For instance, a sheet starting with "SubjectID" MUST specify each subject ID only once (without repetitions). |
| axis | The ppm axis range and optionally the ppm step |
| exclude | A list with regions to be removed Typically:
|
| conditions | A character vector with conditions to filter metadata.
The
Only samples fullfilling all the given conditions are kept in further analysis. |
| nDivRange_ppm | Segment size, in ppms, to divide the spectra and search for peaks. |
| scales | The parameter of peakDetectionCWT function of MassSpecWavelet package, look it up in the original function. |
| baselineThresh | It will remove all peaks under an intensity set by
baselineThresh. If you set it to 'NULL', nmr_detect_peaks will
automatically compute an aproximate value considering baseline between 9.5
and 10.0 ppm (automatically calculation using |
| SNR.Th | The parameter of peakDetectionCWT function of MassSpecWavelet package, look it up in the original function. If you set -1, the function will itself re-compute this value. |
| maxShift_ppm | The maximum shift allowed, in ppm |
| acceptLostPeak | This is an option for users, TRUE is the default value. If the users believe that all the peaks in the peak list are true positive, change it to FALSE. |
| peak_det_align_dir | Output directory from pipe_peakdet_align |
| peak_width_ppm | A peak width in ppm |
| internal_calibrant | A ppm range where the internal calibrant is, or |
This function saves the result to the output directory
This function saves the result to the output directory
This function saves the result to the output directory
This function saves the result to the output directory
This function saves the result to the output directory
Pipeline: Filter samples according to metadata conditions
Pipeline: Peak detection and Alignment
Pipeline: Peak integration
Pipe: Full spectra normalization
If there is no internal calibrant, only the PQN normalization is done.
Other import/export functions:
files_to_rDolphin()
,
load_and_save_functions
,
nmr_data()
,
nmr_meta_export()
,
nmr_read_bruker_fid()
,
nmr_read_samples()
,
nmr_zip_bruker_samples()
,
save_files_to_rDolphin()
,
save_profiling_output()
,
to_ChemoSpec()
Other metadata functions:
nmr_meta_add()
,
nmr_meta_export()
,
nmr_meta_get_column()
,
nmr_meta_get()
Other outlier detection functions:
nmr_pca_outliers_filter()
,
nmr_pca_outliers_plot()
,
nmr_pca_outliers_robust()
,
nmr_pca_outliers()
Other peak detection functions:
nmr_baseline_threshold()
,
nmr_identify_regions_blood()
,
nmr_identify_regions_cell()
,
nmr_identify_regions_urine()
,
nmr_integrate_regions()
,
regions_from_peak_table()
,
validate_nmr_dataset_peak_table()
Other alignment functions:
nmr_align_find_ref()
,
validate_nmr_dataset_peak_table()
Other peak integration functions:
computes_peak_width_ppm()
,
nmr_identify_regions_blood()
,
nmr_identify_regions_cell()
,
nmr_identify_regions_urine()
,
nmr_integrate_regions()
,
validate_nmr_dataset_peak_table()
## Example of pipeline usage ## There are differet ways of load the dataset dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR") #excel_file <- system.file("dataset-demo", # "dummy_metadata.xlsx", # package = "AlpsNMR") #output_dir <- tempdir() ## Load samples with pipes #pipe_load_samples(dir_to_demo_dataset, # glob = "*.zip", # output_dir = "../pipe_output") ## Another way to load it #nmr_dataset <- nmr_read_samples_dir(dir_to_demo_dataset) ## Saving the dataset in a .rds file #nmr_dataset_rds <- tempfile(fileext = ".rds") #nmr_dataset_save(nmr_dataset, nmr_dataset_rds) ## Interpolation #pipe_interpolate_1D(nmr_dataset_rds, # axis = c(min = -0.5, max = 10, by = 2.3E-4), # output_dir) ## Get the new path, based in output_dir #nmr_dataset_rds <- paste(output_dir, "\", "nmr_dataset.rds", sep = "", collapse = NULL) ## Adding metadata to samples #pipe_add_metadata(nmr_dataset_rds = nmr_dataset_rds, output_dir = output_dir, # excel_file = excel_file) ## Filtering samples #conditions <- 'SubjectID == "Ana"' #pipe_filter_samples(nmr_dataset_rds, conditions, output_dir) ## Outlier detection #pipe_outlier_detection(nmr_dataset_rds, output_dir) ## Exclude regions #exclude_regions <- list(water = c(5.1, 4.5)) #pipe_exclude_regions(nmr_dataset_rds, exclude_regions, output_dir) ## peak aling #pipe_peakdet_align(nmr_dataset_rds, output_dir = output_dir) ## peak integration #pipe_peak_integration(nmr_dataset_rds, # peak_det_align_dir = output_dir, # peak_width_ppm = 0.006, output_dir) ## Normalization #pipe_normalization(nmr_dataset_rds, output_dir = output_dir)


{% extends 'base.html' %} {% block title %}Your event - {{ object.name }} - has been created{% endblock %} {% block content %}
If you are going to be using this computer sign in participants, for their security, it is highly recommended that you sign out of this session before allowing anyone to sign in. In fact, it's required.
Have fun! 🌹
{% endblock %}


Topological sort
Topological sort or topological ordering of a directed graph is a linear ordering of its vertices and only possible if and only if the graph has no directed cycles, that is, if it is a directed acyclic graph (DAG). Any DAG has at least one topological ordering. Topological sorting has many applications especially in ranking problems such as feedback arc set. The vertices of the graph may represent the tasks to be performed, and the edges may represent constraints that one task must be performed before another.
This algorithm is thus responsible for taking a directed graph and returning an array of the nodes in the order of their reference. This algorithm gives the linear order of vertices in a directed graph. The sorted graph is as following:
A topological ordering is, simply said, just a valid sequence for the tasks. In bnlearn
we can also derive the topological ordering of the graph.
Real-time Applications
Manufacturing workflows, data serialization, Critical path analysis, Sentence ordering, and context-free grammar.
Scheduling jobs that are interdependent on each other.
Operation system deadlock detection (As the algorithm decides on what task should be done before a particular task can be started.)
Lets create the underneath DAG and examine the topological ordering of 1. the entire graph, and 2. using a starting point in the graph.
Topological ordering example 1
# Import bnlearn
import bnlearn as bn
# Set Edges in graph
edges = [
('0', '1'),
('0', '2'),
('1', '2'),
('1', '5'),
('2', '3'),
('5', '3'),
('5', '4'),
('6', '1'),
('6', '5'),
]
# Make the actual Bayesian DAG
DAG = bn.make_DAG(edges, verbose=0)
# Plot
bn.plot(DAG)
# Topological ordering of the entire graph
bn.topological_sort(DAG)
# ['0', '6', '1', '2', '5', '3', '4']
# Topological ordering using starting point in graph
bn.topological_sort(DAG, '2')
# ['2', '3']
It is also possible to examine the topological ordering of your learned DAG using structure learning.
Topological ordering example 2
# Import bnlearn
import bnlearn as bn
# Import DAG
DAG = bn.import_DAG('sprinkler')
# Generate data using the DAG
df = bn.sampling(DAG, n=1000, verbose=0)
# Structure learning
model = bn.structure_learning.fit(df, methodtype='chow-liu', root_node='Wet_Grass')
G = bn.plot(model)
# Topological ordering of the entire graph
bn.topological_sort(model)


-webkit-marquee-speed
Defines the scroll or slide speed of a marquee box.
This property can either take one speed parameter (slow
, for example) or a measure of distance and a measure of time separated by a slash (/
).
-webkit-marquee-speed: fast | normal | slow;
-webkit-marquee-speed: <distance> / <time>;
The marquee moves at a fast speed.
The marquee moves at a normal speed.
The marquee moves at a slow speed.
Integer, nonnegative value.
Time unit, nonnegative value.