Copyright Bruce Wilcox<EMAIL_ADDRESS>www.brilligunderstanding.com
Revision 2/18/2018 cs8.1
’‘’There’s more than one way to skin a cat’’’. A problem often has more than one solution. This is certainly true with ChatScript. The purpose of the Practicum series is to show you how to think about features of ChatScript and what guidelines to follow in designing and coding your bot.
Bots that require the user learn an English command set to function are only slightly better than a GUI. Humans want computers to talk in human language, not visa versa. So our job is to understand as many of the ways a user can say things as possible. This is done using patterns.
Patterns are the lifeblood of ChatScript. The rest of ChatScript may be considered just programming, but patterns blend into art and linguistic skillsets. Pattern syntax is described in the beginners and advanced ChatScript manuals and reprised in the Pattern Redux manual. This manual is not going to cover all of them. It is here to help you make practical decisions about which pattern element to use when.
Generally speaking, there is rarely any value in making a pattern choice based on speed or memory considerations. The engine is already highly optimzed for executing rules. My chatbot Rose has around 9,000 responders of 13,500 rules. So she is, in a sense, largely an FAQ bot able to answer questions like “how old is your mother”, “where do you live”, and “what is your favorite fruit.” She averages executing 1500-2000 rules per volley, which takes her around 13 milliseconds. And half or more of that time is spent in the NLP pipeline preparing the input. So optimizing patterns rarely makes sense.
The script compiler reads your script, confirms it is legal, and
subtley adjusts it for faster execution. Your patterns consist of
elements. These include ordinary words (brain
), concepts
(~animals
), user variables (‘$’), match variables (’_‘),
factsets (’@‘), comparisons (_0>5
), and various special
characters like’{‘,’<<‘,’)‘,’!’ and others). The engine expects
each token to be uniformly spaced by a separating blank, so however you
space your code, the compiler adjusts it appropriately for execution.
Because the engine uses the lead character of an element to branch to
code handling it, it sometimes adds a prefix code to what you wrote. If
you ever look at a trace, you will see them, as well as
accelerators.
u: MYLABEL ( $test<5) ok. ==> 00y u: 9MYLABEL ( =7$test<5 ) ok.
The 00y tells CS how many characters to jump ahead to reach the end
of the entire rule. The 9 in front of MYLABEL tells how many characters
to skip over to reach the actual pattern. Inside the pattern, because we
did a comparison, that is prefixed with =
and the 7 tells
CS how many characters to skip over to find the comparison operator to
use.
The time it takes to execute a pattern is roughly a constant times
how many pattern elements it has to execute to succeed or fail. There is
no difference in time between matching a word (brain
),
matching a phrase of 4 words ("life is a bowl"
), and
matching against a thousand members of a concept
(~animals
). Executing things like !
and
<
and {
are only slightly faster because
they don’t need to do a dictionary lookup.
The NLP pipeline takes an incoming sentence from the user and adjusts
it in various ways. It will try to fix spelling and capitalization. So
if you really want to see what your patterns are trying to match, you
should use :tokenize
to tell you what CS did to the
input.
Additionally, CS will mark memberships of words in various concepts, only some of which are shown below.
My dog eats steak.
~pronoun_possessive ~main_subject ~main_verb ~main_object
~noun_singular ~verb_present ~noun_singular
~pets ~kindergarten ~food
We have parts of speech, role in sentence, classifcations, age learned, etc. You can match ANY of these in a pattern. Or use them like this:
u: (_~main_subject _0?~pets) Do you have a '_0?
which finds the main subject and determines if it is a pet animal.
Compared to patterns, marking is slow, consisting of half or more of the entire cpu time. But it means that you can execute lots of patterns very rapidly.
What actually happens during pattern matching is that the matcher has two things to look at. It walks your pattern, element by element. Each element is processed in turn, without looking ahead to what the next element is. It either succeeds or fails (or sometimes caches holding data). It also has the sentence, which it walks, keeping track of where it is in the sentence (the position pointer).
It processes the pattern element using the current position and
direction in the sentence (while normally it walks forward it can also
walk backward). So when it sees a word
, it looks from the
current position and direction to see if it can find your word. If it
can’t, it fails. If it can, it sees if it meets current constraints on
how far from where we are it can be. For a simple
u: (I like worms)
when we process I
we find it
at the earliest moment in the sentence. The pattern is as though it were
u: (< * I like worms)
. Having found I
, it
looks for like
. If the input were
I really like worms
then it would find like
at
word 3, which is a gap of 2 from the original I
. The
allowed gap is only 1 (words in sequence), so the match fails. Had the
pattern been u: (I *~2 like worms)
, then the legal gap is 2
and the match would succeed. The new position pointer in the sentence is
3 (like
), moving forwards, and the next pattern element
would hunt for worms
. It would find worms
at
position 4, which is a legal change, so the pattern matches.
While looking at the pattern is always moving forwards through it,
the direction of movement through the sentence, which defaults to
forwards, can go backwards if @_n-
is used. Thereafter it
moves backwards until changed by @_n+
.
If the pattern element being processed specifies a wildcard, like
*~2
, the system stores the legal gap and moves on to the
next token. It is not legal for that to also be a gap (else the system
could not decide how to map the words to the two tokens). Similarly, if
your pattern is u: ( a *~2 {small} *~2 gap)
that would also
not be legal (though not detected by the compiler at present), because
if {small}
is not matched, your pattern becomes
u: (a *~2 *~2 gap)
.
Whenever the first real element is found, the system remembers where.
If the pattern later fails, it is allowed to unhook that match and try
to rematch it later in the sentence. This is the cheap equivalent of
exploring all possible match paths, which for efficiency the system does
not do. Therefore given an input of
I want snakes and I want cookies
and a pattern of:
u: ( I want ~food)
the system finds I
at word 1 and sets the position
pointer and the start pointer to 1. Then hunts for want
,
finding it at word 2 legally. And hunts for a food
concept
and finds it at position 7 and that is an illegal gap. It is allowed to
restart the pattern 1 word later than the initial match. So is “unbinds”
the starting I
, and hunts for another, finding it at word
5. Finds want
at word 6 and a ~food at word 7 and therefore
matches.
It CANNOT match input of I want snakes and want cookies
because it finds word 1 (I
) and word 2 (want
)
fails ~food
because the gap is wrong. It DOES NOT change to
retrying want
which would actually be found at word 5 and
succeed with ~food for word 6. That is a full recovery mechanism like
you would find in Prolog but is too expense here. It merely fails
instead, because it only uproots the starting match.
Some initial tokens can preclude moving the start of matching for
another round. Tokens like <
and @_3+
if
first in the pattern will mandate the pattern starts in a specific
position in the sentence.
Whenever the system sees a wildcard, it can save the gap for 1 round to see what happens. If the next token is a start token (paren, bracket, squiggle), this can be saved across that recursive call to the pattern matcher. Otherwise it must be resolved on the next pattern element.
For all bots, there are two things they have to do. They “understand meaning” and they carry out action. The action part is usually easy. The hard part is understanding meaning. Since bots are not truly intelligent, understanding all meaning is not possible. Instead, bots “hunt” for specific meanings they expect. They do this with patterns.
Since bots do not “understand” meaning, it is inevitable you will either underspecify or overspecify their patterns. If you underspecify, the bot is subject to false positives. It thinks something means something but it doesn’t.
u: (want ~food) I want food too.
The above patterns looks for a meaning about wanting food. But it
will accept I dont want meat
as matching. It is too
general.
u: (I want meat) I want food too.
Correspondingly the above pattern is too specific and misses tons of reasonable inputs.
Machine Learning (ML) is at an advantage in that it will always
memorize the exact specific training input and may generalize from that.
I say may, because it may not. ML trained on
I have a '97 Audi
may completely not recognize
I have a 1997 Audi
.
So your task in ChatScript is to strike the appropriate balance between too general and too specific. There will always be sentences the bot screws up as a consequence.
Patterns should be written in the correct case. Proper names should be in uppercase, normal words in lower case. Do not try to handle capitalization for the start of a sentence. Likewise when you define a concept, capitalize correctly. This allows CS to echo back the capitalization specified in the concept set when memorizing a use of it, rather than what the user typed. The compiler will warn you at times if it thinks your capitalization is wrong.
concept: ~carmakes (ford dodge)
#! I have a ford.
u: (_~carmakes) You have a _0. ==> You have a ford.
CS will spit back the wrong casing above. But if you change the contents of the concept set to uppercase names, then it will spit back the correct casing regardless of what the user typed.
The other thing to avoid is writing contractions in your patterns.
u: (what's the weather )
THIS IS AWFUL! First, because CS normally takes that input and
converts it into what is the weather
, the pattern cannot
match. Worse, it adds what's
into the dictionary as a legal
word (foolishly it trusts you). So when the NLP pipeline sees the word
what's
, it leaves it alone as legal, instead of fixing it
to the expanded form what is
. Your pattern will work, but
this will break a lot of patterns that were correctly written. You won’t
believe how many chat sentences begin with What is...
We can distinuish 3 kinds of bots: FAQ, command and control, and conversational. The patterns needed for these bots can vary a lot.
The command and control bot maps a user’s request for an action to be
taken into actually performing that action. It is similar to an FAQ bot
in not initiating a conversation, except that often it needs
entities
(critical pieces of information) to carry out the
request. E.g., What is the weather in Seattle
is a request
for weather report (intent) with the entity of location (Seattle). If
the user merely said what is the weather
, the bot might
reply with where?
. But that’s the limit of its initiative
in the conversation. Once the request is processed, it returns to idle
just like the FAQ bot.
A good starting point for building patterns for these bots is to use
Fundamental Meaning
. This is done by taking a sample input
and discarding all the words you can that will still allow an average
high schooler to understand what is being requested. It’s a form of
pidgin English. E.g.
Please tell me what the weather will be in Seattle tomorrow.
You can discard a lot of words. Please
is merely
politeness. tell me
reduces to just tell
because the user is talking to the bot so directing the bots output back
to the user is not necessary, it is assumed. tell John
would be different.
Once you have distilled the input to its fundamental meaning, you can then broaden it with synonyms.
tell + describe explain discourse speak
weather + rain snow hot cold temperature icy
seattle + (probably has no synonyms)
tomorrow + in/after 1 day, specific date, day of week
And other than the imperative verb, you can probably shuffle the order of the rest of the words.
tell tomorrow seattle weather
These steps will help you generate patterns that detect a lot of inputs quickly.
ChatScript has an advantage over ML in that it has a dictionary and pre-existing concepts. This is analogous to having hundreds or thousands of training sentences available which don’t have to be specified.
The FAQ bot maps a user’s request for information to an answer. Usually the answer is precanned text. The bot sits idle until the request; it answers the question; and then returns to idle.
An FAQ bot needs to determine the intent and usually doesn’t care
about trying to figure out any entities. With a limited repetoire of
questions it can answer, it may be enough to merely detect relevant
keywords. For example, if the FAQ has a question about
what hours are you open
, then a pattern that just listed
keywords around that might be sufficient. E.g.,
u: ([ hour open available close "what time" lock unlock when ])
We're open 9-5, 7 days a week.
The above covers a lot of ground without being very picky. Of course
it might match inappropriately as well.
Are you open to suggestions?
. But people talking to an FAQ
bot are generally looking for information, and not as likely to wander
sideways.
A conversational bot is part FAQ bot and part initiator of conversation. If the user asks it a personal question (or any other question), the bot is expected to have an answer (even if it is silly or a quibble) and then the bot should turn around and ask the user something and engage in conversation. If the user is silent for a while, the bot might initiate a gambit to get the conversation flowing again.
 Do you?
And off we go into a conversation.
Since there are multiple ways for a user to express an intent, it follows that you will have to write multiple patterns. One way to do this is in multiple rules.
u: TELLNAME (what be you name) My name is Rose.
u: WHATCALL (what be you call) ^reuse(TELLNAME)
In this simple example, I use ^reuse in the second rule. This insures that if I change the answer in TELLNAME, it automatically changes for all other rules that ^reuse it. Of course, in this example one could combine the rules into:
u: TELLNAME (what be you [name call]) My name is Rose.
but that will never hold up to all patterns. For example:
u: (who be you) ^reuse(TELLNAME)
won’t instantly combine. But in fact, I recommend combining them as follows:
u: TELLNAME ([
(what be you [name call])
(who be you)
])
My name is Rose.
This has the advantage of faster execution, smaller code, clear immediate visualization of the patterns and response, least wait in stepping thru code in the debugger. And I recommend each pattern is on a separate line as shown above. I even prefer the response on a separate line from the pattern. It means if you use the debugger to “step in”, you see it move to a new line. Clearer.
A primary rule of thumb for patterns is that they really should be
easy to read. For an experienced CS programmer, it should be obvious
what you are trying to do. Patterns with nested values of
[]
and ()
and {}
can be very hard
to read. Rather than doing that, it is better to split them into
separate patterns. The following is NOT clear.
u: ( you *~2 [ ([fine hot foxy sexy] look) (look [fine hot foxy sexy]) ] )
and would be clearer if split into two patterns nested inside a pattern:
u: ([
( you *~2 [fine hot foxy sexy ] look )
( you *~2 look [fine hot foxy sexy ] )
])
So let’s assume that generally you will write your patterns as collections of patterns, and not as ^reuse() calls.
Additionally, you should avoid wrapping pattern sections onto new lines.
u: ([
( you *~2 [fine hot
foxy sexy ]
look )
( you *~2 look [fine hot
foxy sexy ] )
])
The above are harder to read/understand when run onto multiple lines.
Furthermore, for every pattern line, you should supply a sample input
intended for it. This makes it clearer to a human reader what you are
trying to do AND the CS :verify
command to test your
patterns to see if they work. A form of unit test.
#! You have a foxy look.
#! You look sexy.
u: ([
( you *~2 [fine hot foxy sexy ] look )
( you *~2 look [fine hot foxy sexy ] )
])
*
vs *~n
Most patterns will work when the user provides only a small amount of
input. When they type in paragraph-long sentences, using the
unrestricted wildcard *
has a much higher false detection
rate.
u: ( I * ~like * you) I like you too.
The above rule works fine on input like I like you
, but
is silly if the input is
I like meat but I really loathe you
. The problem can be
alleviated by using shorter range wildcards. My favorites are
*~2
and *~3
. *~2
is good for
skipping noun descriptors. It allows for a determiner and an adjective
or an adverb and an adjective.
u: (I * ~like *~2 ~noun)
And *~3
is good for close control of word use:
u: (I *~3 ~like *~3 you)
You don’t expect many words to arise between the subject
I
and the verb ~like
. Nor between the verb and
its object.
<< >>
vs *~nb
Just as there are problems with the unrestricted wildcard
*
, the any-order construct
<< xxx yyy zzz >>
has similar issues. Of course
it does, because it acts like this
xxx < * yyy < * zzz
. One solution is to use the
restricted range bidirectional wildcard. This is effective when
searching around a particular word, looking for something close before
or close after. It avoids having to write two patterns to do the
job.
u: ( bank *~3b off-shore) # safe
u: ( << bank off-shore >>) # unsafe
Both can match I have an off-shore bank account
as well
as my bank account is off-shore
. But the unsafe one matches
We launched our kayak from the banks of the ocean and ended up far off-shore
.
[ xxx yyy]
A concept is a list of words or phrases. So is [ ]
in
the middle of a pattern. Does it matter which you use? Absolutely. On a
single use basis, the concept takes more memory (irrelevant) but is
faster to match. A [ ]
walks the list, trying to match each
word in order. A concept matches all at once as a single operation. It
doesn’t matter if the concept consists of thousands of members, it
matches as fast as a single word. Furthermore, the concept will match
the earliest occurence of any word. []
will match in the
order of words found in the list. So if an earlier word is found late in
the sentence, too bad. Given input:
I like your soul next to my shoe
, this pattern:
u: (I * [ my your])
will match to my
, even though your is earlier in the
sentence. This works out if the pattern is:
u: (I *~2 [ my your])
because the limitation of *~2 will cause my
to be found
too late, so it will be rejected and the next choice your
will work.
But it’s easy to become confused about what matches when you use
[]
. So concepts are nominally better, clearer. And when
used more than once, you only have to edit the concept, not multiple
rules. Odds are when you change []
in a rule, you will
forget to edit other places using the same words.
The problem with using concepts is a) you move the code non-local to the rule so it is no longer obvious what the matching criteria is and 2) you have to create names for your concepts. So while concepts are “better”, they can be more tedious to use.
This behavior of []
is an efficiency measure. The engine
does not do a full search of all possible paths (like Prolog would do)
because that is too expensive and rarely pays off. It at best only looks
ahead to the next token in the pattern. This allows it to suspend a
wildcard for a brief moment.
There are lots of consequences of looking for words in
[ ]
in order. One is that you should put your rarest, most
significant words first. Another is that if you really want to find the
earliest occurence of the collection, make a concept out of them and
search for the concept instead. That is guaranteed to find the earliest
occurence.
"xxx yyy"
vs
(xxx yyy)
Both forms mean a contiguous sequence of words must match. But there are differences. For the quoted form, it matches all words in a single element. So it is faster than all elements within a (). At the top level, separate words are clearer than a quoted phrase.
u: (I like you) # clearer
u: ("I like you") # slightly less clear
Whenever you add a nesting level, patterns are harder to understand. So at nested levels, quoted forms are easier to read than ones in ().
u: ([ "I like you" testing]) # clearer
u: ([ ( I like you) testing] # less clear
Things that mitigate against using quoted phrase are the following. 1) You are limited to 5 words in a quoted phrase. 2) A quoted phrase can only match the literal user input or the entirely canonical form.
If the user input is “I loved toys”, you can write a quoted phrase for “I love toy” (all canonical) or “I loved toys” (exactly given). You cannot write a pattern like this:
u: ("I love toy") # matchable
u: ("I loved toys") # matchable
u: ("I love toys") # unmatchable
because the system only detects the original user input OR the canonical form of the input. It does NOT detect a mixture of original and canonical.
When you use individual words, you can select for each whether to be
canonical or not because you can put '
in front of
each.
When your phrase incorporates only canonical forms of its words, it
can match any form of all of those words. When your phrase has some
non-canonical words or it is quoted with '
, it can only
match what the user actually typed (original input).
!
 It is handy when you are
trying to align your pattern to the position pointer in a sentence. It
is, however, completely meaningless if nested inside [ ]
or
<< >>
.
u: ANYORDER( << find {testing available} green)
u: ANYONE( [ find {testing available} green ])
In ANYORDER, since finding words in {}
is optional, it
matters not whether they are found or not. So why include them?
In ANYONE, you are already trying to find one of the words in
[ ]
. So saying that you can use the words in
{}
adds nothing over merely writing
u: ANYONE( [ find testing available green ])
<< >>
vs < *
Some patterns depend on finding multiple things in any order. The
usual pattern for this is << xxx yyy zzz >>
.
But sometimes CS imperfectly cannot handle certain patterns this way.
For example CS 8.0 does not allow !<< xxx yyy >>, even
though it should. But there is a workaround. You can do
( xxx < * yyy < * zzz)
to achieve the same effect.
That is, find an element anywhere, go to start, find another element
anywhere, go to start, find another element anywhere.
@_n+
and
@_n-
for local contextA lot of times when I find a basic match, I want to subject it to various constraints. It is faster and clearer to find the basic essential keyword, and then look around it for context. Using @_n+ and @_- allows you to jump to where the primary match occured, and then check the local context either testing forwards or testing backwards.
u: (_baby) ^refine()
a: (@_0+ [carriage formula]) # not about a baby, ignore
a: (@_0- maybe) # title of a movie, ignore
a: () # We have detected a real baby
^setindex()
vs
_10 = _0
When you match something and check the local context around it using ^refine(), a problem arises when you want to match another piece of data during refinement. Each rule starts memorizing at _0, and you risk clobbering what you have already memorized.
u: (_~food) ^refine()
a: (@_0- _~number)
If you are looking for a count before the matched food, you destroy your food as you memorize the number. Two solutions exist. One is to copy the original _0 to a different variable.
u: (_~food) _10 = _0 ^refine()
a: (@_0- _~number)
The other is to alter the starting memorization index in your pattern.
u: (_~food) ^refine()
a: (^setwildcardindex(_1) @_0- _~number)
Of the two choices, I generally prefer the _10 = _0
approach, because I do it at the outermost level rule, so dont have to
repeat it on multiple rejoinder rules, it takes less typing, and I
consider _0
to be a highly volatile variable that any
function I call might destroy also.
^mark()
The normal engine preparation on your sentence is to mark all words (and their canonical forms) with what concepts they belong in.
You can supplement these marks any time you want. For example:
u: (_you) if (^original(_0) == u) {^mark(u _0)} ^retry(RULE)
The texting
substitutions file will change
u
in input into you
. But suppose you want to
detect the actual u
. The above is such a way. It matches
the changed form, checks to see if the original was actually a
u
and marks it in that location. Thereafter the following
rule will match.
u: (u) Found the letter u.
But marking does not make the marked value appear at that position in the sentence.
u: (_you) if (^original(_0) == u) {^mark(beer _0)} ^retry(RULE)
Had we done the above, we cannot expect this to grab the word
beer
.
u: (_beer) I found _0.
The pattern will match, but the output will be
I found you
.
You can mark using any word, fake word, concept, or fake concept. There is no restriction.
^unmark()
Not only can you add markings but you can also remove them. This is handy when trying to glean data that is context sensitive.
If we want to detect blood as a body part and it is in the concept
~bodypart
we might use a rule like this:
u: (_~bodypart) Found _0.
But maybe not all occurences of blood
are valid. If the
following rule occurs earlier, it prevents faulty gleaning.
u: (_blood pressure) ^unmark(~bodypart _0) ^retry(RULE)
There are actually 2 ways you can unmark. You can unmark a specific
word or concept, or you can unmark the entire word. Unmarking the entire
word using *
means CS acts as though it is not in the
sentence at all. Given input I have low blood pressure
, the
following is what happens:
u: (_blood pressure) ^unmark(* _0) ^retry(RULE)
u: (_*) '_0 -- this prints out `I have low pressure.`
Removing the entire word is perhaps a bit drastic, and you may want to reinstate it later. The simplest way to do that is this sequence:
u: (_*) _10 = _0 -- memorize the entire sentence location
u: (_blood pressure) ^unmark(* _0) ^retry(RULE)
u: () ^mark(* _10) -- refresh all hidden words
^replaceword(word _n)
You can already mark and unmark words, which is what is used for pattern matching. But the word itself in the sentence is what is retrieved when memorizing a word. You can change the word itself just by providing the word you want used and the location in the sentence (as a match variable). Replacing a word does not make it visible to pattern matching. It is merely what will be retrieved (for both original and canonical).
This is handy, for example, for making it easy to see what was used to create an interjection. If the mark on a word in ~emogoodbye, Then
u: (_~emogoodbye)
$_tmp = ^original(_0)
^replaceword($_tmp _0)
will make it so when you do this in later patterns:
u: (_~emogoodbye) _0 is now the original text
^unmark and ^mark can be used to “correct” the behavior of standard
CS substitutions that you may not want but are unwilling to remove from
CS release files. Just detect the substitution result, check the
original, and mark and unmark accordingly. For example, “have a nice
day” is substituted into ~emogoodbye
. So you can’t normally
see it in a pattern as (have a nice day)
. But … you can
find it anyway.
u: (_~emogoodbye)
$_tmp = ^original(_0)
$_tmp1 = ^"have a nice day"
if ($_tmp == $_tmp1) {}
And when CS interjection splitting is on (by default), you sometimes
get words split over sentence boundaries, where pattern matching can’t
see them. You can compensate by setting variables in the earlier
sentence.  I don't care.
^Unmark() is also useful for gleaning data from paired chunks a
sentence. In English one might say if xxx then yyy
or
begins xxxx ends xxx
. You can mask off sentence fragments
to perform special gleaning like this:
u: (_* from _* to _*) _10 = _0 _11 = _1 _12 = _2
^unmark(* _0) # hide prior to from
^unmark(* _2) # hide after to
^respond(~gleantopic) # go find data in remainder
$data1 = $$tmpdata # save what we learned
^mark(* _2) # restore to data
^unmark(* _1) # hide from data
^respond(~gleantopic) # go find data in remainder
$data2 = $$tmpdata # save what we learned
^mark(* _0) # restore prior to from
^mark(* _1) # restore from data
While all rules can have patterns, you can even use pattern matching
inside outputmacros or rule output. You tell the IF
statement you want to use pattern syntax like this:
if (PATTERN $x<5 _~mainsubject) {}
The reality is that outputmacros (functions) can act like rules and rules can act like functions (but you have to pass arguments as globals).
It is sometimes handy to have a common rule used for rejoinders from multiple places, or to hold a common output. This can be done using a pattern that cannot match. The most obvious is:
s: MYCOMMON (?) Here is common output.
a: REJOINDER1(how) I dont know how
a: REJOINDER2(when) I dont know when
...
u: (some pattern) ^reuse(MYCOMMON) # say what MYCOMMON says
u: (some other pattern) ^setrejoinder(OUTPUT MYCOMMON)
I have this output, and my rejoinder will be handled by a common place.
#! Where do i live
u: Q1( where do i live ?) On the moon?
#! I think bottle deposits are good for the earth.
u: Q2( [bottle can] (deposits are good)) I recycle.
#! Among the fruit that I like are apples.
u: Q3( << [bananas apples] are fruit >> ) Fruit are tasty.
#! How much does the apple cost?
#! What is the price of a banana?
u: Q4([
(how much *~5 cost)
(what be *~5 price)
!(price of liberty)
])
Q1 uses I
in lower case. And the rule is overly
specific. And why use ?
in the pattern when you could
change the rule to ?:
.
Q2 has useless interior ( )
so it is not the clearest.
If you thought ( deposits are good)
should have been
changed to " deposits are good"
, you missed the clearest
answer. You don’t need to use " "
at the top level of a
pattern.
Q3 detects bananas are fruit
and
examples of fruit are bananas and pineapple
but not
the banana is a fruit
. To do that you need to make banana
singular in the pattern (along with apple
) and change
are
to the more canonical be
. Of course
limiting us to bananas and apples is ridiculous given that CS has the
in-built concept ~fruit
. You should type in your sample
word like this: :prepare banana
and see what existing
concepts cover it.
Q4 has a negative inside the [ ]
alternatives so it will
match for almost all inputs. It should be moved first, before the
[
. And probably you could combine the other elements into a
single pattern while generalizing further and still being clear.
u: Q4 (!(price of liberty)
["how much" "what be"] *~5 [cost price fee]
)
Fluency in pattern construction requires a limber mind, able to imagine how to broaden a match while not accepting wrong inputs. But it will enable your bots to seem amazingly human!


Import functions:
import { closest, matches } from '/dom/module.js';
All functions that take more than one parameter are partially applicable.
The library has a number of scripts for cheekily extending the default behaviour of DOM elements, declared via 'behaviour' attributes.
No style is given to make these behaviours do anything visual
however. Behaviours, largely, add and remove classes, how that looks
is up to your CSS. This is deliberate: by seperating style
and behaviour many different styles of interactive –
a menu, a dialog,
a tooltip – may share, for example,
popable
behaviour.


Leverage agile frameworks to provide a robust synopsis for high level overviews. Iterative approaches to corporate strategy foster collaborative thinking to further the overall value proposition. Organically grow the holistic world view of disruptive innovation via workplace diversity and empowerment.
Strategy
Iterative approaches to corporate strategy foster collaborative thinking to further the overall value proposition.
How We Work
Iterative approaches to corporate strategy foster collaborative thinking to further the overall value proposition.
Places We Work
Iterative approaches to corporate strategy foster collaborative thinking to further the overall value proposition.
Collaboration
Iterative approaches to corporate strategy foster collaborative thinking to further the overall value proposition.
Let's Work Together
Leverage agile frameworks to provide a robust synopsis for high level overviews. Iterative approaches to corporate strategy foster collaborative thinking to further the overall value proposition. Organically grow the holistic world view of disruptive innovation via workplace diversity and empowerment.


--- layout: page category: spec title: "Selection API" ---
| Current Status: | Working Draft (WD) |
|---|---|
| Canonical URI: |
http://www.w3.org/TR/selection-api
|
| Abstract: | This document is a preliminary draft of a specification for the Selection API and selection related functionality. It replaces a couple of old sections of the HTML specification, the selection part of the old DOM Range specification. This document defines APIs for selection, which allows users and authors to select a portion of a document or specify a point of interest for copy, paste, and other editing operations. |
Selection APIUpdates:


Atm_Assign_t*
Atm_AssignEventAlloc(
Var_Variable_t* var
)
- The function allocates an event assignment.
- Side Effects None
- See Also
Atm_AssignFree
Atm_Assign_t*
Atm_AssignForallAlloc(
Var_Variable_t* var,
Var_Variable_t * indexVar,
Atm_Expr_t* expr
)
- The function allocates a forall assignment.
- Side Effects None
- See Also
Atm_AssignFree
void
Atm_AssignFree(
Atm_Assign_t* assign
)
- This function frees an assignment.
- Side Effects None
Atm_Assign_t*
Atm_AssignIndexAlloc(
Var_Variable_t* var,
Atm_Expr_t * indexExpr,
Atm_Expr_t* expr
)
- The function allocates an index assignment.
- Side Effects None
- See Also
Atm_AssignFree
Atm_Expr_t **
Atm_AssignReadExprAddress(
Atm_Assign_t * assign
)
- Returns the address of expr on the right hand side of assignment.
- Side Effects None
- See Also
Atm_AssignReadType
Atm_AssignReadVariable
Atm_Expr_t *
Atm_AssignReadExpr(
Atm_Assign_t * assign
)
- Returns the expr on the right hand side of assignment.
- Side Effects None
- See Also
Atm_AssignReadType
Atm_AssignReadVariable
Atm_Expr_t*
Atm_AssignReadIndexExpr(
Atm_Assign_t * assign
)
- Returns the index expr of an index assignment.
- Side Effects None
- See Also
Atm_AssignReadIndexVar
Var_Variable_t*
Atm_AssignReadIndexVar(
Atm_Assign_t * assign
)
- Returns the index variable of a forall assignment.
- Side Effects None
- See Also
Atm_AssignReadIndexExpr
Atm_AssignType
Atm_AssignReadType(
Atm_Assign_t * assign
)
- Returns the type of an assignment.
- Side Effects None
- See Also
Atm_AssignReadVariable
Atm_AssignReadExpr
Var_Variable_t*
Atm_AssignReadVariable(
Atm_Assign_t * assign
)
- Returns the variable on the left hand side of the
assignment.
- Side Effects None
- See Also
Atm_AssignReadType
Atm_AssignReadExpr
Atm_Assign_t*
Atm_AssignStdAlloc(
Var_Variable_t* var,
Atm_Expr_t* expr
)
- The function allocates a standard assignment.
- Side Effects None
- See Also
Atm_AssignFree
void
Atm_AtomAddAwaitVar(
Atm_Atom_t * atom,
Var_Variable_t* var
)
- [Add the given variable to the await variable list.
The variable now becomes a property of the atom.
The user should not free it
- Side Effects awaitVarList is modified.
- See Also
Atm_AtomReadAwaitVarList
void
Atm_AtomAddControlVar(
Atm_Atom_t * atom,
Var_Variable_t* var
)
- [Add the given variable to the controlled variable list.
The variable now becomes a property of the atom.
The user should not free it
- Side Effects ctrlVarList in atom is modified.
- See Also
Atm_AtomReadControlVarList
void
Atm_AtomAddInitCommandList(
Atm_Atom_t * atom,
lsList cmdlist
)
- Add the given init command to the atom. If an init command list
is already present it is freed and cmdList is put in its place.
- Side Effects The old init command list will be freed. The content of
the original init command list will not be freed. The user should free
them before calling this function.
- See Also
Atm_AtomReadInitCmdList
void
Atm_AtomAddReadVar(
Atm_Atom_t * atom,
Var_Variable_t* var
)
- [Add the given variable to the read variable list.
The variable now becomes a property of the atom.
The user should not free it
- Side Effects None
- See Also
Atm_AtomReadReadVarList
void
Atm_AtomAddUpdateCommandList(
Atm_Atom_t * atom,
lsList cmdList
)
- Add the given update command list to the atom.
The update command now becomes a property of the atom.
The user should not free it
- Side Effects The old update command list will be freed. The content of
the original update command list will not be freed. The user should free
them before calling this function.
- See Also
Atm_AtomReadUpdateCmdList
void
Atm_AtomAddWaitCommandList(
Atm_Atom_t * atom,
lsList cmdList
)
- Add the given wait command list to the atom.
The wait command now becomes a property of the atom.
The user should not free it
- Side Effects None
- See Also
Atm_AtomReadWaitCmdList
Atm_Atom_t*
Atm_AtomAlloc(
char * name
)
- The function allocates an atom with the given name. name should
not be NULL. A copy of name is made. Hence, it is the caller's responsibility
to free name.
- Side Effects None
- See Also
Atm_AtomFree
void
Atm_AtomChangeName(
Atm_Atom_t * atom,
char * newname
)
- This function changes the name of an atom. It is an
error to call this function with a NULL atom name.
- Side Effects The old name will be freed automatically.
void
Atm_AtomCounterReset(
)
- Reset the global atom counter.
- Side Effects None
- See Also
Atm_AtomNewName
Atm_Atom_t *
Atm_AtomDup(
Atm_Atom_t * atom,
char * newAtmName,
st_table * variableMappingTable
)
- This function duplicates an atom, with a new atom name,
and put it in the atom table of the newModule. This function assumes that
the newModule has been created, and that all the variables associated with
the atom (ie. controlled, read and awaited variables) are already present in
the newModule. This function also updates the pointer from the variables to
the atoms. It is an error to call this function with an NULL
newAtmName, NULL atom or NULL newModule.
- Side Effects None
- See Also
Var_VariableDup
Mdl_ModuleDup
Mdl_ParComp
void
Atm_AtomFree(
Atm_Atom_t * atom
)
- This function frees an atom.
- Side Effects None
- See Also
Atm_AtomAlloc
char*
Atm_AtomNewName(
)
- The function controls a global variable which is a counter
that counts the number of intermediate
module generated. The counter is also used to generate a
name for the modules. For example, if the counter
counts 14, the generated module name will be ATM14.
It should be used in conjuction with Mdl_NamePrefixWithString
to prefix the module name before the atm name.
- Side Effects None
lsList
Atm_AtomObtainReadAwaitVarList(
Atm_Atom_t * atom
)
- The function returns a list of variables that are both
read and awaited by atom.
- Side Effects None
- See Also
Atm_AtomReadReadVarList
Atm_AtomReadAwaitVarList
lsList
Atm_AtomObtainReadOrAwaitVarList(
Atm_Atom_t * atom
)
- The function returns a list of variables that are both
read and awaited by atom.
- Side Effects None
- See Also
Atm_AtomReadReadVarList
Atm_AtomReadAwaitVarList
Atm_AtomType
Atm_AtomReadAtomType(
Atm_Atom_t* atom
)
- The function returns the atom type for the atom
(Atm_Event_c, Atm_Lazy_c, Atm_Normal_c).
- Side Effects None
lsList
Atm_AtomReadAwaitVarList(
Atm_Atom_t * atom
)
- Returns the list of await variables for the atom.
The user should not free or modfy this list in any
way
- Side Effects None
- See Also
Atm_AtomAddAwaitVar
lsList
Atm_AtomReadControlVarList(
Atm_Atom_t * atom
)
- Returns the list of control variables for the atom.
The user should not free or modfy this list in any
way
- Side Effects None
- See Also
Atm_AtomAddControlVar
Atm_Cmd_t *
Atm_AtomReadDefaultInitCommand(
Atm_Atom_t * atom
)
- Returns the default init guarded command of the atom.
- Side Effects None
- See Also
Atm_AtomReadDefaultUpdateCommand
Atm_Cmd_t *
Atm_AtomReadDefaultUpdateCommand(
Atm_Atom_t * atom
)
- Returns the default update guarded command of the atom.
- Side Effects None
- See Also
Atm_AtomReadDefaultInitCommand
lsList
Atm_AtomReadInitCommandList(
Atm_Atom_t * atom
)
- Returns the list of init commands for the atom.
The user should not free or modfy this list in any
way.
- Side Effects None
- See Also
Atm_AtomAddInitCommand
char*
Atm_AtomReadName(
Atm_Atom_t* atom
)
- Returns a copy of the name of the given atom. The user
should not free the returned string.
- Side Effects None
int
Atm_AtomReadPostorder(
Atm_Atom_t* atom
)
- This function returns the preorder of an given atom.
The first atom in the preordering is 0. By default,
every atom has a preorder value -1.
- Side Effects None
- See Also
Atm_AtomReadPreorder
Atm_AtomSetPreorder
Atm_AtomSetPostorder
int
Atm_AtomReadPreorder(
Atm_Atom_t* atom
)
- This function returns the preorder of an given atom.
The first atom in the preordering is 0. By default,
every atom has a preorder value -1.
- Side Effects None
- See Also
Atm_AtomReadPostorder
Atm_AtomSetPreorder
Atm_AtomSetPostorder
lsList
Atm_AtomReadReadVarList(
Atm_Atom_t * atom
)
- Returns the list of read variables for the atom.
The user should not free or modfy this list in any
way
- Side Effects None
- See Also
Atm_AtomAddReadVar
lsList
Atm_AtomReadUpdateCommandList(
Atm_Atom_t * atom
)
- Returns the list of update commands for the atom.
The user should not free or modfy this list in any
way.
- Side Effects None
- See Also
Atm_AtomAddUpdateCommand
lsList
Atm_AtomReadWaitCommandList(
Atm_Atom_t * atom
)
- Returns the list of wait commands for the atom.
The user should not free or modfy this list in any
way
- Side Effects None
- See Also
Atm_AtomAddWaitCommandList
void
Atm_AtomSetAtomType(
Atm_Atom_t* atom,
Atm_AtomType t
)
- Given a pointer to an atom_atom_t, an integer indicating
the type of the atom (0 for unspecified, 1 for event atom,
and 2 for lazy atom), the functions sets the the
atomtype field.
- Side Effects None
void
Atm_AtomSetDefaultInitCommand(
Atm_Atom_t * atom,
Atm_Cmd_t * cmd
)
- This function sets the default init command of atom.
- Side Effects None
- See Also
Atm_AtomSetDefaultUpdateCommand
void
Atm_AtomSetDefaultUpdateCommand(
Atm_Atom_t * atom,
Atm_Cmd_t * cmd
)
- This function sets the default update command of atom.
- Side Effects None
- See Also
Atm_AtomSetDefaultInitCommand
void
Atm_AtomSetPostorder(
Atm_Atom_t* atom,
int postorder
)
- This function sets the preorder of an given atom.
- Side Effects None
- See Also
Atm_AtomReadPostorder
Atm_AtomSetPreorder
Atm_AtomReadPreorder
void
Atm_AtomSetPreorder(
Atm_Atom_t* atom,
int preorder
)
- This function sets the preorder of an given atom.
- Side Effects None
- See Also
Atm_AtomReadPostorder
Atm_AtomReadPreorder
Atm_AtomSetPostorder
Atm_Cmd_t*
Atm_CmdAlloc(
Atm_Expr_t* guard,
lsList assignList
)
- Given a guard and a list of assigments, the routine
returns a pointer to a new guarded command.
- Side Effects None
- See Also
Atm_CmdFree
void
Atm_CmdFree(
Atm_Cmd_t* cmd
)
- The function frees a guarded command.
- Side Effects None
lsList
Atm_CmdReadAssignList(
Atm_Cmd_t * command
)
- The function returns the list of assignments of a guarded
command.
- Side Effects None
- See Also
Atm_CmdReadGuard
Atm_Expr_t *
Atm_CmdReadGuard(
Atm_Cmd_t * command
)
- This function returns the guard expression of a guarded
command.
- Side Effects None
- See Also
Atm_CmdReadAssignList
int
Atm_End(
Tcl_Interp * interp,
Main_Manager_t * manager
)
- Ends the atm package.
- Side Effects None
Atm_Expr_t*
Atm_ExprAlloc(
Atm_ExprType exprtype,
Atm_Expr_t* leftchild,
Atm_Expr_t* rightchild
)
- This routine returns a pointer to a new Atm_Expr_t tree,
which is built from the two child expressions,
leftchild and rightchild. The expr_type is set to the
desired type. It retuns NIL(Atm_Expr_t) if fails. It is
the user's responsibility to free the tree after use.
Note that the following expr_type only has one child.
- Side Effects None
- See Also
Atm_ExprFreeExpression
void
Atm_ExprFreeExpression(
Atm_Expr_t* expr
)
- This function frees an expression recursively.
- Side Effects None
void
Atm_ExprPrintExpression(
Atm_Expr_t* expr
)
- This function prints the given expression in infix
notation.
- Side Effects None
- See Also
Atm_ExprTypeConvertToString
Atm_Expr_t *
Atm_ExprReadLeftChild(
Atm_Expr_t * expr
)
- Reads the left child of an expression.
- Side Effects None
- See Also
Atm_ExprReadRightChild
Atm_Expr_t *
Atm_ExprReadRightChild(
Atm_Expr_t * expr
)
- Reads the right child of an expression.
- Side Effects None
- See Also
Atm_ExprReadLeftChild
Atm_ExprType
Atm_ExprReadType(
Atm_Expr_t * expr
)
- Returns the type of an expression.
- Side Effects None
Atm_Expr_t *
Atm_ExprSubstituteConstantForDummyVar(
Atm_Expr_t * e,
Var_Variable_t * var,
int c,
boolean isEnum
)
- This function is useful for manipulating forall
assignments. It substitutes a constant c for the dummy variable var everywhere
in the expression on the right hand side of the assignment. The function
returns the new expression.
- Side Effects None
char*
Atm_ExprTypeConvertToString(
Atm_ExprType type
)
- This function returns a string containing the operator
corresponding to an expression type.
- Side Effects None
- See Also
Atm_ExprPrintExpression
int
Atm_Init(
Tcl_Interp * interp,
Main_Manager_t * manager
)
- Initializes the atm package.
- Side Effects None
void
Atm_NumConstExprConvertToBitvectorConstExpr(
Atm_Expr_t * expr,
int numBits
)
- The function expects the argument expression to have all
its children of type Atm_IfThenElseFi_c or Atm_NumConst_c. It modifies the
leaf Atm_NumConst_c expression to a bitvector with numBits which when
interpreted as an integer has value equal to the value of the numerical
constant in the old expression.
- Side Effects expr is modified.
- See Also
Atm_NumConstExprConvertToRangeConstExpr
void
Atm_ReinitTypeCheck(
Var_TypeManager_t* typeManager
)
- optional
- Side Effects required
- See Also
optional
int
Atm_Reinit(
Tcl_Interp * interp,
Main_Manager_t * manager
)
- Reinitializes the atm package.
- Side Effects None
boolean
Atm_TypeCheckNew(
Var_Type_t * type,
Atm_Expr_t ** expr
)
- Given a type "type" and an atmExpr_t * "expr", this function
checks if "type" is contained in the allowable types of "expr".
In general an expression may allow multiple types. For example,
the number constant "1" may be interpreted as a range constant,
an integer, a nat or the boolean constant "true".
Upon success typechecking, the routine does the following
two things in order: 1. It resolve the types of expression.
In the previous example, it may turn the node "1" into a
boolean constant node "true". 2. It then simplifies the
expression by evaluating the sub-expression containing
only constants. For example, the sub-expression "true | false",
will be evaluted to "true".
- Side Effects required
- See Also
optional


Getting to Know Me a Little Bit Better!
Hello! Currently a Digital Media Undergraduate Student at UMD College Park, I strive to continuously develop my artistic vision, whether it be on paper or laptop. The goal: to make it into the art industry.
Once a person in the medical field, I decided to follow my passion for art and went towards my Bachelor's in Digital Media. Life is too short and seeing an opportunity to educate myself as an artist, I took it. While pursuing a Bachelor's, I spend my time outside of academia taking inspiration from the world around me. Examples include visiting art museums, watching tv shows, and building DIY miniature housekits. Hayao Miyazaki (renowned co-founder/director of Studio Ghibli Inc.) would be listed as my top artistic inspiration, being a big fan of the Studio Ghibli films.
Art Mediums I Have Dabbled In
Acrylic Paint • Oil Paint • Charcoal • Graphite • Ink • Oil Pastel • Alcohol Marker • Paper Crafting • Gliding • Print Trading • Cyanotype • Casting • Maya • Unity • Adobe CC
Note: To see these mediums in practice, please visit the gallery!


Pretty Pictures!
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Canyon Pictures:
Water Scenes
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Validating image by there width and height before upload
Width:
Height:


My name is Shivam Missar, I am a first year student, studying software engineering.I thought I'd chose this course because I'm really intrested in coding and how it applies to real life stuff.My main intreset in software engineering is web Development/web design I really like these topics because there is so much to do with it and some much to learn.I'm a really hard working student, I like to do tasks, I like to be kept busy.I believe by working hard, the end result is satisfying.I'm very optiumistic I like to try out new things.I had no background/history for coding but yet I decided to choose to take this course as it sounded like a skill I need but as well as having an intrest for computers.So yeah thats me.
In my spare time I like to focus and develop my skills in areas that I'm in weak in such as programming.I like practice what I learn so that I become stronger in that area.On the other hand, I like to take pictures of nature, you can find my social media page in the footer a .I also enjoy playing videa games, I like to play first person shooter games, sports games and some story based games.I'm huge fan of football and love playing and watching it!
Email<EMAIL_ADDRESS>Linkedin: Shivam Missar


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Before Change
if observation_and_action_constraint_splitter is not None:
observation, mask = observation_and_action_constraint_splitter(
observation)
observation = tf.cast(observation, dtype=self._dtype)
if self._add_bias:
// The bias is added via a constant 1 feature.
observation = tf.concat([
observation,
After Change
if observation_and_action_constraint_splitter is not None:
observation, mask = observation_and_action_constraint_splitter(
observation)
observation = tf.nest.map_structure(lambda o: tf.cast(o, dtype=self._dtype),
observation)
global_observation, arm_observations = self._split_observation(observation)
if self._add_bias:
// The bias is added via a constant 1 feature.


{% extends base_page_template %} {% load static wagtailcore_tags %} {% block body_class %}{{ block.super }} template-searchresults{% endblock %} {% block title %}{{ title }}{% endblock %} {% block breadcrumbs %}
You've been unsubscribed from the job alert.
{% elif status == STATUSES.STATUS_LINK_EXPIRED %}Sorry, we could not find a matching job alert subscription.
{% endif %}


Fault Injection for Service Fabric
#Fault Injection for Service Fabric
Customers of the PartsUnlimited website have complained of the site becoming unresponsive when there’s a big sale on. This loss in availability has a direct impact on sales. You have been tasked with ensuring high availability and scalability for the site.
Prerequisites
-
Visual Studio 2015 Update 3
-
An active Azure subscription
Tasks Overview
-
Prepare the PartsUnlimited solution to work with Service Fabric
-
Deploy the Service Fabric package to an Azure Service Fabric Cluster.
-
Run some tests against the cluster.
###Task 1: Prepare the PartsUnlimited solution to work with Service Fabric
Service Fabric requires an executable. Our first step involves ensuring the PartsUnlimited can publish the exe we require.
Step 1. Open up PartsUnlimited.sln in Visual Studio.
Step 2. Locate the project.json file under the PartsUnlimitedWebsite project.
Step 3. We need to remove the ‘platform’ property of Microsoft.NETCore.App. Check out the sample below.
Note: The platform property is used when you want to build portable apps that can be executed by the .NET Core shared runtime (via ‘dotnet run myapp’). Service Fabric requires a standalone app. Standalone apps have an executable file with the runtimes embedded in the application itself. This should be added at the begining of the .json file.
{
"dependencies": {
"Microsoft.NETCore.App": {
"version": "1.0.0"
}
...
}
}
Step 4. Now we want add the desired runtime so it will build an executable. In the same project.json file add the following to the root of the json file. This should be located at the end of the file, after the scripts section.
Note: The runtime you use here is important. If you want to solution to work locally and you’re not using Windows 10 - use one of the following:
Windows 7: win7-x64
Windows 8: win8-x64
Windows 8.1: win81-x64
Windows 10: win10-x64
{
...
"runtimes": {
"win10-x64": {}
}
...
}
Step 5. Right click on the PartsUnlimitedWebsite and select ‘Publish’
Step 6. Create a new profile called ‘FileSystem’ then select ‘Next’
Step 7. Select the file system publish method.
Step 8. Select a location you will remember for when we need to package the application.
Step 9. Set the target runtime to the one you added to the project.json file earlier. Also, select the option to delete all existing files prior to push. This will ensure that future publish actions will provide a clean code set when we come to deploy the application. Then click on publish.
Service fabric expects a specific folder structure to work correctly. This is not generated automatically from the output of the Visual Studio publish profile. We will therefore have to create this manually.
|-- WebSite
|-- WebApp
|-- Code
...
|-- PartsUnlimitedWebsite.exe
...
|-- Config
|-- Settings.xml
|-- Data
|-- ServiceManifest.xml
|-- ApplicationManifest.xml
Now lets grab all the published files we created before and put them in the Code folder as seen in the directory structure above.
The other two important files are ServiceManifest.xml and ApplicationManifest.xml.
ServiceManifest.xml should look something like this:
<?xml version="1.0" encoding="utf-8"?>
<ServiceManifest xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" Name="WebApp" Version="<IP_ADDRESS>" xmlns="http://schemas.microsoft.com/2011/01/fabric">
<ServiceTypes>
<StatelessServiceType ServiceTypeName="WebApp" UseImplicitHost="true">
<Extensions>
<Extension Name="__GeneratedServiceType__">
<GeneratedNames xmlns="http://schemas.microsoft.com/2015/03/fabact-no-schema">
<DefaultService Name="WebAppService" />
<ServiceEndpoint Name="WebAppTypeEndpoint" />
</GeneratedNames>
</Extension>
</Extensions>
</StatelessServiceType>
</ServiceTypes>
<CodePackage Name="code" Version="<IP_ADDRESS>">
<EntryPoint>
<ExeHost>
<Program>PartsUnlimitedWebsite.exe</Program>
<WorkingFolder>CodePackage</WorkingFolder>
<ConsoleRedirection FileRetentionCount="5" FileMaxSizeInKb="2048"/>
</ExeHost>
</EntryPoint>
</CodePackage>
<Resources>
<Endpoints>
<Endpoint Name="WebAppTypeEndpoint" Protocol="http" Port="5000" UriScheme="http" Type="Input" />
</Endpoints>
</Resources>
</ServiceManifest>
ApplicationManifest.xml should look something like this:
<?xml version="1.0" encoding="utf-8"?>
<ApplicationManifest xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" ApplicationTypeName="WebAppType" ApplicationTypeVersion="1.0" xmlns="http://schemas.microsoft.com/2011/01/fabric">
<ServiceManifestImport>
<ServiceManifestRef ServiceManifestName="WebApp" ServiceManifestVersion="<IP_ADDRESS>" />
</ServiceManifestImport>
<DefaultServices>
<Service Name="WebAppService">
<StatelessService ServiceTypeName="WebApp" InstanceCount="1">
<SingletonPartition />
</StatelessService>
</Service>
</DefaultServices>
</ApplicationManifest>
Settings.xml needs to exist but for now it can just be an empty file as we don’t require any configuration values at the moment.
Something to note - if you run this locally you want to make sure that the instance count is 1. This is because it’s not possible to host multiple web applications on the same port. When we deploy this to a cluster we will not have this problem.
Now we’re ready to deploy locally. Fire up PowerShell as an administrator and type the following:
We want to connect to the service fabric cluster.
Connect-ServiceFabricCluster localhost:19000
Now we’re going to point to the folder structure we created (with the app inside the code section).
Copy-ServiceFabricApplicationPackage -ApplicationPackagePath 'C:\Users\user\Desktop\WebSite' -ImageStoreConnectionString 'file:C:\SfDevCluster\Data\ImageStoreShare' -ApplicationPackagePathInImageStore 'Apps\WebApp'
Now we want to register the application type.
Register-ServiceFabricApplicationType -ApplicationPathInImageStore 'Apps\WebApp'
Now we’re going to create the new application.
New-ServiceFabricApplication -ApplicationName 'fabric:/WebApp' -ApplicationTypeName 'WebAppType' -ApplicationTypeVersion 1.0
Open a web browser and navigate to http://localhost:19080/
We can drill down on the cluster and see our active node.
Now navigate to http://localhost:5000 to check to see if the PartsUnlimited website is active.
###Task 2: Deploy the Service Fabric package to an Azure Service Fabric Cluster. Now that we have the application working locally, let’s deploy to an Azure Service Fabric Cluster.
Step 1. First things first, let’s create a Service Fabric container for our PartsUnlimited site.
Navigate to the Azure portal and select ‘New’ -> https://portal.azure.com
Step 2. Search for ‘Service Fabric Cluster’ then select the top option.
Step 3. Select the ‘Create’ button at the bottom of the ‘Service Fabric Cluster’ window.
Step 4. Fill out the basics required. Note: ensure you add a unique cluster name as some common ones may already be taken.
Select a secure username and password combination.
Select the subscription and resource group that applies to you’re PartsUnlimited HOL.
Step 5. Here we only have one node type (the PartsUnlimited website). We want to give this node type a name and allocate a machine size. We can set the initial VM scale as well.
Add the custom endpoint of 5000 as this is the default port that the app is hosted on.
Step 6. For demo purposes we will use the ‘Unsecure’ setting as there are quite a few steps involved in setting this up in Azure. If you want more information visit https://azure.microsoft.com/en-us/documentation/articles/key-vault-get-started/.
It is strongly advised to use the secure mode in production.
Step 7. Check over the settings one last time and ensure everything looks OK. Once you have validated the settings select ‘Create’.
Step 8. On the Azure dashboard, we can now see that the Service Fabric Cluster is being set up. This may take some time.
After it has sucessfully deployed you should see the following on the dashboard.
Step 9. Select the PartsUnlimited cluster. Then copy the Service Fabric Explorer link and paste it in your browser of choice.
Step 10. This is the Azure Service Fabric Explorer.
Step 11. OK, now Azure is ready we need to push up the PartsUnlimitedWebsite solution. Open up PowerShell in administrator mode.
Note: If you want more than one instance of the application, quickly navigate to your package folder. In ApplicationManifest.xml change the instance count to 5.
Firstly we want to connect to the cluster.
Connect-ServiceFabricCluster [your-server-cluster-address].[location].cloudapp.azure.com:19000
Step 12. Now we want to upload the application package to the cluster.
Copy-ServiceFabricApplicationPackage -ApplicationPackagePath 'C:\local\location\of\package' -ImageStoreConnectionString 'fabric:imagestore' -ApplicationPackagePathInImageStore 'Apps\WebApp'
Step 13. Then we want to register the application type in our cluster.
Register-ServiceFabricApplicationType -ApplicationPathInImageStore 'Apps\WebApp'
Step 14. Now we want to create a new Service Fabric application.
New-ServiceFabricApplication -ApplicationName 'fabric:/WebApp' -ApplicationTypeName 'WebAppType' -ApplicationTypeVersion 1.0
Step 15. Go back to the Service Fabric dashboard and check to see if the application has deployed successfully.
You can also view the application via the following url structure -> <yourClusterName>.<yourRegion>.cloudapp.azure.com:5000
###Task 1: Run some tests against the cluster.
Now let’s cause some mayhem. Service Fabric provides a ‘chaos’ scenario that will causes faults across the entire Service Fabric cluster.
“The chaos test scenario generates faults across the entire Service Fabric cluster. The scenario compresses faults generally seen over months or years to a few hours. The combination of interleaved faults with a high fault rate finds corner cases that would otherwise be missed.”
Step 1. Run the following commands in PowerShell.
Firstly we want to connect to the cluster (note you may already be connected)
Connect-ServiceFabricCluster [your-server-cluster-address].[location].cloudapp.azure.com:19000
Now for the core of the script. Specify a desired time to run (the below example will run for 10 minutes)
$timeToRun = 10
$maxStabilizationTimeSecs = 180
$concurrentFaults = 3
$waitTimeBetweenIterationsSec = 60
Invoke-ServiceFabricChaosTestScenario -TimeToRunMinute $timeToRun -MaxClusterStabilizationTimeoutSec $maxStabilizationTimeSecs -MaxConcurrentFaults $concurrentFaults -EnableMoveReplicaFaults -WaitTimeBetweenIterationsSec $waitTimeBetweenIterationsSec
Step 2. While the test scenario is running you should be able to see errors occurring in the Service Fabric Explorer.
Step 3. If we want to run some specific failures you can get a list of possible testability actions from here -> https://azure.microsoft.com/en-us/documentation/articles/service-fabric-testability-actions/
Let’s simulate a Service Fabric cluster node failure by stopping a node. To do this we will need to find the name of a node to test.
Now that we have the node name of “_website_1” we can run our test.
Step 4. Run this sample in PowerShell
Connect-ServiceFabricCluster [your-server-cluster-address].[location].cloudapp.azure.com:19000
Stop-ServiceFabricNode -NodeName "_website_1"
Step 5. If you want to turn the node back on use the following command.
Start-ServiceFabricNode -NodeName "_website_1"
Step 6. We can try another test scenario included in the Service Fabric SDK. This is the ‘failover test scenario’.
“The failover test scenario targets a specific service partition instead of the entire cluster, and it leaves other services unaffected. The scenario iterates through a sequence of simulated faults in service validation while your business logic runs. A failure in service validation indicates an issue that needs further investigation. The failover test induces only one fault at a time, as opposed to the chaos test scenario, which can induce multiple faults.”
Connect-ServiceFabricCluster [your-server-cluster-address].[location].cloudapp.azure.com:19000
$timeToRun = 10
$maxStabilizationTimeSecs = 180
$waitTimeBetweenFaultsSec = 10
$serviceName = "fabric:/WebApp/WebAppService"
Invoke-ServiceFabricFailoverTestScenario -TimeToRunMinute $timeToRun -MaxServiceStabilizationTimeoutSec $maxStabilizationTimeSecs -WaitTimeBetweenFaultsSec $waitTimeBetweenFaultsSec -ServiceName $serviceName -PartitionKindSingleton
We can see in the portal the actual instances being tampered with in the Service Fabric Explorer.
In this lab, you learned about how to get up and running with Service Fabric. You then learned how to test your Service Fabric cluster to ensure the availability of your application. We tried scenarios that compress faults generally seen over months or years to a few hours.


The Alphabet story begins in 2017 at Czech Life Sciences University. Mayur Sanpurkar and Yevhenii Samoilov students there, were assigned to do a project in study block. They agreed about nearly everything during the first meeting and struck a partnership. Working from their dorm rooms, they created a website called Alphabet that provides services like search, youtube,drive and cloud storage.
Since the beginning, our goal has been to develop services that significantly improve the lives of as many people as possible. Not just for some. For everyone.


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--- title: Rev. P. Hanock Kindergarden. date: 2012-06-23 comments: true layout: post categories: - namibia ref_url: ---
Our second stop of the day yesterday was at the Rev. P. Hanock Kindergarden.
This 'school' was a metal building that was a maximum of 12 feet by 12 feet and barely tall enough for Josh Martin to stand in. This school housed 24 students, about half of which had chairs to sit in. The students were prepared for us to come, and gave us a concert and a number of demonstrations of their ability to count, and name the colors and the shapes. It was really impressive and they were all so eager to show off their knowledge. From the very beginning they instill in these learners the importance of knowledge and going to school. "Knowledge is our future" they sing.
In addition to this small classroom there are outhouse facilities donated by Luther alums Orv and Cathy Johnson. The Luther connections here in Namibia are many and strong. It is almost heartbreaking to see what a relatively small amount of money by American standards can accomplish over here. We left the kindergarden some blocks and a soccer ball. The look on the children's faces when they emerged from the building to kick the ball around was another of those moments that make you realize what this trip is all about.


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Policy on reimnbuirsement of travel expenses- PDF
Policy on reimbursement of symposium expenses- PDF
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CSZ representative report 2008
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Drake Development on macOS¶
This page contains information that may be useful to people developing on macOS.
Note that the applications, tools, and libraries listed below are simply those that some have found useful. They should not be interpreted as mandatory.
Table of contents
Git Tools¶
gitk¶
The version of Git that comes with Xcode may not include gitk, a GUI-based Git repository browser. A workaround is to get a newer version using Homebrew:
brew update brew install git
If gitk does not start with an unknown color name "lime"
error, upgrade your version of Tcl/Tk:
brew cask install tcl
References:
SourceTree¶
SourceTree is a free Git and Mercurial client for Windows and macOS made by Atlassian.
(Notes coming soon)


Work Day Scheduler
A simple calendar app for scheduling your work day
9AM
10AM
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12PM
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2PM
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NEWS.md
find_data()
and prediction.lm()
to check for correct behavior in the presence of missing data (na.action
) and subset
arguments. (#28)margex
, borrowed from Stata’s identically named data.summary(prediction(...))
now reports variances of average predictions, along with test statistics, p-values, and confidence intervals, where supported. (#17)prediction_summary()
which simply calls summary(prediction(...))
.stats::poly()
rather than just poly()
in model formulae. (#22)prediction.glmnet()
method for “glmnet” objects from glmnet. (#1)prediction.merMod()
gains an re.form
argument to pass forward to predict.merMod()
.prediction.glmML()
method for “glimML” objects from aod. (#1)prediction.glmQL()
method for “glimQL” objects from aod. (#1)prediction.truncreg()
method for “truncreg” objects from truncreg. (#1)prediction.bruto()
method for “bruto” objects from mda. (#1)prediction.fda()
method for “fda” objects from mda. (#1)prediction.mars()
method for “mars” objects from mda. (#1)prediction.mda()
method for “mda” objects from mda. (#1)prediction.polyreg()
method for “polyreg” objects from mda. (#1)prediction.speedglm()
and prediction.speedlm()
methods for “speedglm” and “speedlm” objects from speedglm. (#1)prediction.bigLm()
method for “bigLm” objects from bigFastlm. (#1)prediction.biglm()
and prediction.bigglm()
methods for “biglm” and “bigglm” objects from biglm, including those based by "ffdf"
from ff. (#1)build_datalist()
. The function now returns an an at_specification
attribute, which is a data frame representation of the at
argument.prediction.gam()
is now prediction.Gam()
for “Gam” objects from gam. (#1)prediction.train()
method for “train” objects from caret. (#1)at
argument in build_datalist()
now accepts a data frame of combinations for limiting the set of levels.prediction()
methods gain a (experimental) calculate_se
argument, which regulates whether to calculate standard errors for predictions. Setting to FALSE
can improve performance if they are not needed.build_datalist()
gains an as.data.frame
argument, which - if TRUE
- returns a stacked data frame rather than a list. This argument is now used internally in most prediction()
functions in an effort to improve performance. (#18)summary.prediction()
method to interact with the average predicted values that are printed when at != NULL
.prediction.knnreg()
method for “knnreg” objects from caret. (#1)prediction.gausspr()
method for “gausspr” objects from kernlab. (#1)prediction.ksvm()
method for “ksvm” objects from kernlab. (#1)prediction.kqr()
method for “kqr” objects from kernlab. (#1)prediction.earth()
method for “earth” objects from earth. (#1)prediction.rpart()
method for “rpart” objects from rpart. (#1)mean_or_mode.data.frame()
and median_or_mode.data.frame()
methods.prediction.zeroinfl()
method for “zeroinfl” objects from pscl. (#1)prediction.hurdle()
method for “hurdle” objects from pscl. (#1)prediction.lme()
method for “lme” and “nlme” objects from nlme. (#1)prediction.merMod()
.prediction.plm()
method for “plm” objects from plm. (#1)CONTRIBUTING.md
to reflect expected test-driven development.prediction.mnp()
method for “mnp” objects from MNP. (#1)prediction.mnlogit()
method for “mnlogit” objects from mnlogit. (#1)prediction.gee()
method for “gee” objects from gee. (#1)prediction.lqs()
method for “lqs” objects from MASS. (#1)prediction.mca()
method for “mca” objects from MASS. (#1)prediction.glm()
method. (#1)category
argument to prediction()
methods for models of multilevel outcomes (e.g., ordered probit, etc.) to be dictate which level is expressed as the "fitted"
column. (#14)at
argument to prediction()
methods. (#13)mean_or_mode()
and median_or_mode()
S3 generics.mean_or_mode()
and median_or_mode()
where incorrect factor levels were being returned.prediction.princomp()
method for “princomp” objects from stats. (#1)prediction.ppr()
method for “ppr” objects from stats. (#1)prediction.naiveBayes()
method for “naiveBayes” objects from e1071. (#1)prediction.rlm()
method for “rlm” objects from MASS. (#1)prediction.qda()
method for “qda” objects from MASS. (#1)prediction.lda()
method for “lda” objects from MASS. (#1)find_data()
now respects the subset
argument in an original model call. (#15)find_data()
now respects the na.action
argument in an original model call. (#15)find_data()
now gracefully fails when a model is specified without a formula. (#16)prediction()
methods no longer add a “fit” or “se.fit” class to any columns. Fitted values are identifiable by the column name only.build_datalist()
now returns at
value combinations as a list.prediction.nnet()
method for “nnet” and “multinom” objects from nnet. (#1)prediction()
methods now return the value of data
as part of the response data frame. (#8, h/t Ben Whalley)find_data()
methods for "crch"
and "hxlr"
. (#5)prediction.glmx()
and prediction.hetglm()
methods for “glmx” and “hetglm” objects from glmx. (#1)prediction.betareg()
method for “betareg” objects from betareg. (#1)prediction.rq()
method for “rq” objects from quantreg. (#1)prediction.gam()
method for “gam” objects from gam. (#1)prediction()
and find_data()
methods for "crch"
"hxlr"
objects from crch. (#4, h/t Carl Ganz)prediction()
and find_data()
methods for "merMod"
objects from lme4. (#1)seq_range()
function from margins to prediction.build_datalist()
function from margins to prediction. This will simplify the ability to calculate arbitrary predictions.prediction.svm()
method for objects of class "svm"
from e1071. (#1)prediction.polr()
when attempting to pass a type
argument, which is always ignored. A warning is now issued when attempting to override this.mean_or_mode()
and median_or_mode()
functions, which provide a simple way to aggregate a variable of factor or numeric type. (#3)prediction()
methods for various time-series model classes: “ar”, “arima0”, and “Arima”.find_data()
is now a generic, methods for “lm”, “glm”, and “svyglm” classes. (#2, h/t Carl Ganz)


In bid to repair ties, Japan and South Korea agree to summit next month
The decision to return to the table was announced at a Group of 20 meetings and came a day after Seoul made a last-minute decision to stick to a critical intelligence-sharing deal with Japan.
Japan and South Korea agreed on Saturday to hold formal talks next month, taking a step towards improving relations strained by decades of acrimony over their wartime past and exacerbated by a simmering trade dispute.
The decision to return to the table was announced at a Group of 20 (G20) meeting and came a day after Seoul made a last-minute decision to stick to a critical intelligence-sharing deal with Japan. The dramatic reversal - after months of worsening relations - was later hailed as a "breakthrough" by South Korea.
The dispute has its roots in a decades-old disagreement over compensation for South Korean labourers forced to work at Japanese firms during World War Two. It has deepened this year, and upended trade after Japan curbed exports of materials critical to making the semiconductors that are a pillar of the South Korean economy.
"We bought time for intense discussions, but there's not much time left for us," South Korea's foreign minister, Kang Kyung-wha, told reporters after meeting with her Japanese counterpart, Toshimitsu Motegi, at a gathering of G20 foreign ministers in the central Japanese city of Nagoya.
Motegi had earlier said that he wanted to discuss the issue frankly.
"I aim to hold a candid exchange of views on the matter of labourers from the Korean peninsula, which is the core problem, and other bilateral issues," Motegi told reporters in Nagoya.
South Korea made a last-minute decision on Friday to stick with its General Security of Military Information Agreement (GSOMIA) intelligence-sharing pact with Japan. The agreement was set to expire at midnight on Friday and South Korea had earlier indicated it would let it lapse.
The decision was welcomed by Washington. The United States has pressured its two allies to set aside their feud and maintain the pact, seen as linchpin of trilateral security cooperation.


| DOSE: T+ 0:00 |
10 mg |
insufflated |
2C-T-7 |
(powder / crystals) |
| T+ 0:45 |
1 repeated |
smoked |
Cannabis |
|
I got some T7 a couple weeks ago and tried it once orally, 20mg went down about an hour after dinner. We got fairly high, very mellow and pleasent, sex was interesting, sleep was easy, light visuals, nothing very profound though. So I took the inititive to try a new way, snorting it. At about 10am I bump 10mg's of 99.9% pure 2-CT-7. It doesn't hurt much, but my body knows it just hit something DIFFERENT, I cringe. Not quite sure if I did the right thing at this point, I was looking for a nice mellow, mildly visual time. My body knows this will be more.
Within 5 minutes I throw up, I half-expected nausea as the reports state that as a possibility, so I write it off as an unpleasantness that I have to deal with and not worry about. I ended up peuking three times allthough it really wasn't that bad, pretty easy really. Well about this time is when I start getting launched out of reality, cold shivers for a little while, extreme stimulation of the skin, not knowing if I will be ok... damn intense.
At about 30-45 minutes after taking the line, the T7 starts to mellow out, turns DAMN pleasent. Beautiful colors, great patterns, no dark spots, no body noise, very comfortable.  Anyways, the herb was wonderful, I love getting stoned when I am tripping, and T7 lets you get BAKED, it really helps out the visuals by amping the colors and deepening the trails.
 My stomach felt great and everything digested as normal. Music is wonderful, I have no urge to do more although I am having a fabulous trip. Great mood enhancement. The effects left quickly at about the 5 hour mark with snorting this compound, eating it lasted about 10... all in all this is a wonderful drug that NEEDS to be RESPECTED.
All my friends who have tried this since have bumped it too, about 1 in 4 get nausea and all say it is very much worth the expierience. Remember, treat these compound with respect, they are very strong, and the dose/response curve is VERY sharp, so be smart, and invest in a milligram scale online. It will make you and your friends feel much safer, cause it is. Peace people.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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Georgia Tech vs. North Carolina St. prediction, line: Take the fave
by Howie KussoyThe Post’s Howie Kussoy, aka The Pigskin Profit, takes a shot on Thursday night’s college football game between the Georgia Tech Yellow Jackets and the visiting North Carolina State Wolfpack:
GEORGIA TECH (-2½) over North Carolina State: I’m going to be nice (I’m due) and ignore the 55 points the Wolfpack allowed to Clemson. Excluding the loss to the defending champs, N.C. State has still given up an average of 41 points during the rest of its four-game losing streak.


{% extends "base-with-no-nav.html" %} {% block content %} {% include "_nav2.html" %}
Dan Brown
Chapter 5 - Title chapter
While in Paris, Harvard symbologist Robert Langdon is awakened by a phone call in the dead of the night. The elderly curator of the Louvre has been murdered inside the museum, his body covered in baffling symbols. As Langdon and gifted French cryptologist Sophie Neveu sort through the bizarre riddles, they are stunned to discover a trail of clues hidden in the works of Leonardo da Vinci—clues visible for all to see and yet ingeniously disguised by the painter.
Continue reading


Archiva provides a user interface for querying audit logs. The current implementation only covers upload/deploy actions but will include other actions such as artifact delete, repository configuration and more in the future.
Previous: Integrity Reports | Audit Log Report | Next: Maintenance


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Of all of David Lynch's films, I find myself ranking "Blue Velvet" on a lower meter. It's a good, blazingly original film that is also wholly uneven, obscene and tasteless to the core. The latter terms are not meant as a critique - Lynch at his best is always obscene and tasteless. But I also find "Blue Velvet" to be underwhelming, as say compared to "Eraserhead" (his greatest film) or "Lost Highway." It's just that their themes are more complex and disturbing than this perverse take on suburbia.
"Blue Velvet" was released back in 1986 and was highly controversional for its time, mainly due to graphic scenes of torture and sex. There was also much talk about the sequence where Isabella Rosselini (Ingrid Bergman's daughter) is naked and publicly embarrassed, while teeth marks and cigarette burns cover her entire body. There is no question that Lynch has always had trouble protraying women in a positive light, but the sequence is pure misogyny serving no purpose in the entire film. Rosselini also endures several beatings by Dennis Hopper as a helium-sniffing psycho. There are also numerous close-up shots of ants and cockroaches littering the screen as if they are aware of something beyond our knowledge.
The story revolves around a murder mystery that is never quite explained or resolved. Kyle MacLachlan plays a college student visiting his picture-postcard hometown, Pemberton, when one day, he discovers a severed human ear on someone's lawn. He contacts the police and then decides to investigate on his own. This all leads to the apartment of a distraught singer (played by Rosselini), who is always singing "Blue Velvet" at a nightclub. Dennis Hopper plays the deranged killer who tortures her, and the scenes between the two of them are as startling and effective as any other scene in the film.
As Kyle veers further into this S & M world with the help of a policeman's daughter (Laura Dern) things get much weirder especially when Dean Stockwell shows up as a Kabuki transvestite. Both Stockwell and Hopper must hold the record for spouting more obscenities on film than Eddie Murphy.
"Blue Velvet" is a fascinating, intriguing film but it is not completely successful. The elements of the mystery are so warped that I gave up after a while trying to follow it. It is true that some of Lynch's later work was more confounding and that it did not bother me much, but this mystery is actually more conventional and, dare I say, realistic so I did expect to be able to follow it. The performances are mostly shouting matches, especially between Hopper and Rosselini. Kyle MacLachlan and Laura Dern merely react than act, except for one exquisitely subtle scene at a cafe. Plus, the uplifting ending negates the darkness that preceded it, though I did like the shot of the bird with a wasp in its mouth.
For whatever strange reason I cannot comprehend, I still liked "Blue Velvet" because nobody has ever produced or directed anything like it prior to its release. There isn't anything you can easily compare it to. It is definitely Lynch's wildest endeavor and, somehow, strangely compelling.
For more reviews, check out JERRY AT THE MOVIES at http://buffs.moviething.com/buffs/faust/
E-mail me with any questions, comments or complaints at<EMAIL_ADDRESS>or at<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Contract No
{{trans.contractNumber}}
Program
{{trans.programName}}
Surplus Amount: {{surplus_amount | currency}}
Remaining Amount: {{remaining_amount | currency}}
| Program Delivery Expenditure | Amount | Justification / Details |
|---|---|---|
| {{line.expense_name}} | ||
| Total | {{total_amount | currency}} |
Should you decide not to allocate the surplus from previous year's contract to any of the
above expenditure, please kindly return a cheque with the surplus amount payable to:
Ministry of Finance c/o Contract Administration, VSCP
Please kindly main cheque to:
302 – 815 Hornby Street
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Warning: Nothing links to this page
The DWR + Jaxer integration makes Java access easy for JavaScript developers using Aptana Jaxer, an Ajax server which embeds the Mozilla browser engine to provide server-side JavaScript, DOM, CSS, plus features typical of app servers for other languages.
What's in it for DWR and Java users? You can see this as a way to cleanly separate a presentation layer written in Ajax (at both the client and the server) from a back-end business logic layer written in Java such that the Java layer is not directly exposed to the Internet.
What's in it for Jaxer users? You can now tap Java objects directly from their server-side JavaScript code. DWR handles the type conversions and marshalling while letting the developer use the same Java object names and APIs, except of course from JavaScript.
Of course you can also use (Jaxer + DWR) and DWR in parallel to do the above, plus connect to DWR's reverse Ajax capabilities directly from the browser.
Much of the complexity of DWR configuration is to restrict what classes the Internet can access. When DWR is fronted by Jaxer, we don't need to expose DWR to the Internet. Effectively the client becomes trusted, so we can remove some of the configuration steps. There is for example no need for a dwr.xml file any more - The exported classes can be configured directly by the script. Clearly it's important that your DWR server is not accessible from the Internet and is properly fire-walled.
Synchronous communication over the Internet is generally regarded as a bad idea because it freezes the client while the server is responding. In the single-threaded JavaScript environment, this can be a significant glitch in user interface. Inside a corporate LAN, latencies are significantly reduced, and with Jaxer the UI is not visible while the rendering is happening.
There are 4 files available from the download area. 2 for use in an existing setup, and 2 as a demo to get started.
Files to add to an existing project:
If you are familiar with plain DWR, you don't need dwr.xml or any annotation configuration.
Demo files:
If you want an easy way to see what DWR+Jaxer does, start by deploying this file to a Java server and by deploying demoRemoting.zip to a Jaxer server. See below for more detailed instructions.
1. In your Aptana_Jaxer/public directory, create a directory called demoRemoting, then download and unzip demoRemoting.zip, into this directory.
You should now have 2 extra files:
2. Next, download demoServer.war and deploy it into your application server.
3. Start the application server and browse to http://localhost:8081/demoRemoting.
If you get an error message like this:
Jaxer error processing request: JavaScript Error: "Jaxer.dwr is undefined"
Then you are running against a version of Jaxer that does not contain the Jaxer/DWR integration. The preferred solution is to upgrade Jaxer, however if that fails you can also make a small edit to demoRemoting/index.html. See the comments in that file.
If your application server is not on the same machine, or is running on a different port, then you will need to edit Jaxer.dwr.pathToDwrServlet to point at the deployment of a web application containing jaxer-dwr.jar. See demoRemoting/index.html (around line 16) for more details.


| tbl_survival.survfit {gtsummary} | R Documentation |
Please use tbl_survfit.
Function takes a survfit
object as an argument, and provides a
formatted summary of the results
## S3 method for class 'survfit' tbl_survival( x, times = NULL, probs = NULL, label = ifelse(is.null(probs), "{time}", "{prob*100}%"), level_label = "{level}, N = {n}", header_label = NULL, header_estimate = NULL, failure = FALSE, missing = "-", estimate_fun = NULL, ... )
x |
A survfit object with a no stratification
(e.g. |
times |
Numeric vector of times for which to return survival probabilities. |
probs |
Numeric vector of probabilities with values in (0,1) specifying the survival quantiles to return |
label |
String defining the label shown for the time or prob column.
Default is |
level_label |
Used when survival results are stratified.
It is a string defining the label shown. The input uses
glue::glue notation to convert the string into a label.
The default is |
header_label |
String to be displayed as column header.
Default is |
header_estimate |
String to be displayed as column header of the Kaplan-Meier
estimate. Default is |
failure |
Calculate failure probabilities rather than survival probabilities.
Default is |
missing |
String indicating what to replace missing confidence
limits with in output. Default is |
estimate_fun |
Function used to format the estimate and confidence
limits. The default is |
... |
Not used |
A tbl_survival
object
The level_label
is used to modify the stratum labels. The default is
level_label = "{level}, N = {n}"
. The quantities in the curly
brackets evaluate to stratum-specific values. For example, in the trial
data set, there is a column called trt
with levels 'Drug A' and 'Drug B'.
In this example, {level}
would evaluate to either 'Drug A' or 'Drug B'
depending on the stratum. Other quantities available to print are:
{level}
level of the stratification variable
{level_label}
label of level for the stratification variable
{n}
number of observations, or number within stratum
{n.event.tot}
total number of events (total across stratum, if applicable)
{n.event.strata}
total number of events within stratum, if applicable
{strata}
raw stratum specification from survfit
object
Example 1
Example 2
Daniel D. Sjoberg
Other tbl_survival tools:
inline_text.tbl_survival()
# Example 1 ---------------------------------- library(survival) fit1 <- survfit(Surv(ttdeath, death) ~ trt, trial) tbl_strata_ex1 <- tbl_survival( fit1, times = c(12, 24), label = "{time} Months" ) # Example 2 ---------------------------------- fit2 <- survfit(Surv(ttdeath, death) ~ 1, trial) tbl_nostrata_ex2 <- tbl_survival( fit2, probs = c(0.1, 0.2), header_estimate = "**Months**" )


[% page.title = "Perl 5.18.4 Release Announcement" %]
We hereby announce perl-5.18.4, the fourth stable release of version 18 of Perl 5.
You will soon be able to download Perl 5.18.4 from your favorite CPAN mirror or find it at:
https://metacpan.org/release/RJBS/perl-5.18.4/
SHA1 digests for this release are:
69c34558a0a939a7adbbc1de48c06ea418d81e27 perl-5.18.4.tar.bz2 3ae1af872d5bf89f35b1e8d69be9098028fb158d perl-5.18.4.tar.gz
You can find a full list of changes in the file "perldelta.pod" located in the "pod" directory inside the release and on the web.
Perl 5.18.4 represents approximately 9 months of development since Perl 5.18.2 and contains approximately 2,000 lines of changes across 53 files from 13 authors.
Perl continues to flourish into its third decade thanks to a vibrant community of users and developers. The following people are known to have contributed the improvements that became Perl 5.18.4:
Daniel Dragan, David Mitchell, Doug Bell, Father Chrysostomos, Hiroo Hayashi, James E Keenan, Karl Williamson, Mark Shelor, Ricardo Signes, Shlomi Fish, Smylers, Steve Hay, Tony Cook.
The list above is almost certainly incomplete as it is automatically generated from version control history. In particular, it does not include the names of the (very much appreciated) contributors who reported issues to the Perl bug tracker.
Many of the changes included in this version originated in the CPAN modules included in Perl's core. We're grateful to the entire CPAN community for helping Perl to flourish.
For a more complete list of all of Perl's historical contributors, please see the AUTHORS file in the Perl source distribution.
The next major stable release of Perl 5, version 22.0, should appear in May 2015.


M T Casey
Unafffiliated
I retired from Defense Finance & Accounting Service on 30/Mar/2017 with a total of 15 years, 09 months of federal service; I have also worked part-time the past 2 years with a company called Argus Event Staffing, LLC, as an usher and security staff for sporting, theatrical, music, and major events in the metro Denver area; and I worked part-time for 3-4 months for Kelly Services USA, LLC, scoring standardized tests from middle school students in several states.
I have an Associate of Applied Science degree in Medical Assisting from Anchorage Community College, University of Alaska; and I have a Bachelor of Science degree in Accounting from University of Colorado, Colorado Springs.
80238
I have no experience in any of these kinds of activities for any group in Colorado
When I lived in Anchorage, in the 1970's, I worked on a campaign for an attorney friend who was running, for what I can't even remember; I think it was a local office and all I did was telephone calling. I have not contributed, money or time, to any campaigns or political organizations since 10-Nov-2015
I have not belonged to any political, civic, or community organizations since 10-Nov-2015
I have contributed next to nothing to political happenings in the United States as an adult, other than to vote. However, I am interested in redistricting to help create a more level field for electing representatives to our state and federal congresses and legislatures. When I heard info on the radio this morning about the redistricting commissions needing applicants, I thought this might be a civic duty/responsibility that I could contribute to.
I am a reasonable person who can consider both sides of a situation; and I can make up my own mind, based on factual evidence. I'm from a large family with political and religious leanings at both ends of the spectrum, and with an age difference between the oldest and the youngest that precludes them from having much of anything in common. As a middle child, I love finding anything my siblings might have in common and pointing it out to them, so they have at least one thing they can talk about and relate to together. I am a good listener and I like fairness.
I have enough experience and education to help me figure out when something sounds fishy or too good to be true, and I trust my 'gut instinct' to keep my integrity intact. I'm not afraid to be honest with myself or others, and over the past several years, with the help of a life coach, I have developed a softer approach to dealing with others I don't agree with.
female


Whac-a-mole
Test your skill.How many boxes(moles) can you check(hit) in 30 seconds?
Start Game / Stop Game
Time:
play
Score:
Instruction:
Click on the radio buttons(moles) as they are selected randomly by the computer.
1 point per hit, minus 1 point per misss.


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