Periwinkle (C. Roseus) & Cannabis
| DOSE: |
repeated |
smoked |
Periwinkle |
(plant material) |
| |
repeated |
smoked |
Cannabis |
(plant material) |
My experience log, with Madagascar Periwinkle (leaves, avg. 2in in length or smaller, fresh-dried, with Cannabis, amount will update as I go along).
Copyright Allen S.
12:34AM- Migraine, so I decided to smoke some pot to ease it. Recently read through online books and read on two instances where M.Periwinkle (Catheranthus Roseus spp.) was used as an extreme hallucinations, and thought I would try them out in a combination to observe the effects =^.^=
12:35AM- One periwinkle leaf ground mixed with a 1cmx1cm bud of pot in a glass bowl.
12:38AM- Wondering what the effects will be like, I hesitantly take the first hit. Smoke is kinda harsh, flavored oddly, but not unpleasently, mixes well with pot. Still harsh initially. Held for about 15 seconds. Immediately feel headrush, probably from migraine or holding my breath so long =\.
12:39 AM- Putting on some music for now, no effects yet besides odd pleasing tingling in my head.
12:40AM- Take another hit. Whatever chemical is in C. Roseus seems to be an expectorant, besides the inole alkaloid (The hallicinogens). No effects yet, not even from pot.
12:42 AM- Sounds from music sound more impressive, defined and pleasent. Chest feeling pulsating, and there's a very light haze everywhere I look that is very pleasent.
12:44 AM- Sound is very pleasent, slight euphoria, took third hit. Motion is pleasing, an causes not-unpleasent feelings indeed =D
12:46AM- Soda[DR PEPPER DIET CAFFEINE FREE] is ingested orally. Delicious. Simply delicious.
12:47- Rap doesn't go good on this. Everything BUT.O_O
12:50- Wow, its really been ten minutes? HOLY SHIT. Temperal sense is greatly distorted, other senses greatly intensefied to a fabulous level. Pulsanting sensation all across body. Visual trails of light and from objects moving in front of Ion emitting devices (ergo. TVs and flourecent things and glowsticks) its awesome. Moving and typing and mental coordination is greatly increased. I haven't made a typo yet high, and normally when I tpye all that would show is OIAFHOPWFHPInso[tj230- if I was high and I'd be trying to say like quack or some shit.
12:53 AM- Just finished typing previos sentence ^_^ Music is amazing. Any sensation or movement triggers ecsstacy like bliss, but not feeling any of the stupor of pot or downers or heavy drugs, This is amazing and nice. Decreased pain threshold ^_^ Niiiiiice. Music is feeling.
12:54, yeah I type pretty fast right now. Speed is increased to the point that time feels distorted, very good vocational skills. =D
12:55AM- Took four more hits, 8 hits total.
12:56- Packing a second leaf and a bit of sprinkled but to reload the bowl! ^_^ Increased muscle refelx. Barely any pot buz so far so I think this is all from the Periwkinle thus far or increased effectiveness because of it.
12:57- Music causes some very intense and vivid mental images right now, but I feel as if I'm actually there. but I'm still aware of my surroundings. This is pretty cooool. TAKIN NOTHER HIT HAO KTHX@!@
12:58AM- Took another fresh hit, was very tasty but harsh very harsh.Felt another great rush, confirmed that it's the Periwinkle. Images getting stronger, feel pleasntly warm waves down back.
12:59- I felt as if it were 1999.  Like I believed it.
12:60AM LULZ 1:00AM- Music is very nice =D MSN MESSAGE BRB KTHX
1:07AM- Very very vivid aural hallucinations that I know are deff not part of the song, like mental images of bleeding corpses to britney spears' toxic O.O And the screaming and rifles anre prevalent.
107.5AM-CLAMS.
1:09-I feel giddy, extreemly euphotric, excstacy like sensory, mild visuals and aural hallucinations (sounds) Muscle twtitches in legs but mmmmthey feel good. It feels nice to move and do things....and music is fantastic,all kinds, cept rap, it makes me violent on this apparently.>.>;
1:10AM- I'm bisexual.    Time is definatly distorted, I'm physically going faster then the world.  But apparently satan's actually my ashtray, some heair gel, my caddy is a green juice cup and and the prostitutes were my leg.
1:18 AM- What the fuck......?
!:18 AM- Awesome I'm the little line cursor on the computer and I'm literally shitting these letters out. Man this is haaaaaaaaaaaaaard freekin work.   See what I meant about pot typing ^ lolz.
1:25 AM -Smoke tastes like clouds =)~~~)
5656565655656565565656556565655656563528959879853987987189735897137983189538973189718978935198798798792109840419209814092710962097AM-I don't think so Mr. Hallucination.
1:28AM=Packed a nice full bowl of just C.Roseus, tasted okay but still harsh. Feelings of it rush back in euphoria.
 Breathing feels lovely. 1:31:AM.  Highly reccommended, it hath mine full supporting. I'm getting a special high from this alone.  KTHNXXXXXXXXXXXXXXX.
Moooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo:136AMoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooopiggy. That's a feeling.
6total large leaves of croseus used. likeing it muchos.
Not a lot of pot used so far and I feel baked as shit, it's a good filler ^_^
It grows native through all of florida natuarlly.
1:41AM.4.5bowls packed.
Visuals every so often. Confusion. Awesome. Feels motiony, Superb feeling.
1:44AM I bent over and I could physically feel my brain.  It's odd, they look like skittles. Its hilarious.
1:45AM I AM THE NARRAROTOR, BEHOLD!,THE POWER OF DELETE! F CK YOU! GO DELETE, O.
See.
I smoked some dehydrated aphids that time >.o 147AM,16 hits, 11 leaves used [theyre small they burn quick]1/11th of a gram marijuana mmoked. Glass pipe. _loo0p_ O_OIt feels powerful to move ^_^.
PENCIL SHARPERNOR!
1:30. OMG I just saw the world in a grain of sand, and heaven in a wildflower...I just held infinity in the palm of my hand, and spent eternity literally in an hour.
That was odd as hell.
1:51. It took one minute to type thjat last sentece. Dont belive the thirty.
Having increased diffuculty with typing. not with dexterity but with speed. Its like their going faster then they keyboard wants to let them.
O_O MOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOSPasm.
I'mma keep these in here. letters are doubles and rainbows are wafting off them as I type.

A:53AM-1*...yeas
2:22Am I'm sobner again, but that was fun. Think I'll take another ride after I finish this up.
I liked it alot, I'm about to do it again. No hangover, and no real craving, I just thought it was really fun.
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.


Kenneth Johnson
Team Leader Extraordinary, soon to be Lambda Grad!
Mckay Wrigley
The BackEnd Wizard who makes the magic happen.
Pearl Paris
React All-Star who makes the app be snappy.
Tyler Lippe
User Interface maker from the New California Republic.
Aaron Harbaugh
React master who is getting that app to do work!
Andy Dillon
Front Ender who is taking the website to another level.
York Cook
Just trying to stay one jump ahead of being Flexed.


FEMALE PERVERSIONS A film review by Scott Renshaw Copyright 1997 Scott Renshaw
(October) Starring: Tilda Swinton, Amy Madigan, Karen Sillas, Laila Robins, Dale Shuger, Paulina Porizkova, Clancy Brown. Screenplay: Susan Streitfeld and Julie Hebert. Producer: Mindy Affrime. Director: Susan Streitfeld.  Reviewed by Scott Renshaw.
Some people have facial features which seem to come from another era; Tilda Swinton has features from another world. Her almost translucent skin, high cheekbones and wide blue eyes conspire to give her a faintly alien appearance, intelligent and curious but isolated. Those striking, androgynous features made her perfect for the gender-bending title role in 1993's ORLANDO. In a different way, they also make her perfect for the role of FEMALE PERVERSIONS' protagonist Eve Stephens. In Susan Streitfeld's compelling character study, Eve's psyche becomes the battleground between a hundred different social images of womanhood. Watching that battle in Swinton's face is, quite simply, electrifying.
Eve is an up-and-coming Southern California prosecutor, up-and-coming enough that the governor is considering her for a seat on the Court of Appeals. An impending judgeship should make Eve a very confident woman, but instability seems to define every aspect of her life. She carries on sexual relationships simultaneously with a man (Clancy Brown) and a woman (Karen Sillas); a strained relationship with her sister Madelyne (Amy Madigan) becomes even more complicated when Madelyne is arrested for shoplifting. As her interview with the governor approaches, Eve is forced to confront insecurities inspired by years of mixed messages about what it means to be a woman.
FEMALE PERVERSIONS takes its title the non-fiction text by Dr. Louise J. Kaplan in which she defines "perversions" as deviations from strict societal proscriptions on female behavior, sexuality and fantasy. It's a great jumping-off point for a sociological study of modern American femininity, but Streitfeld shows a peculiar and ill-advised devotion to her source. The film's most awkward conceit involves the periodic appearance of quotes from the book in odd locations -- embroidered into pillow-cases, as advertising on billboards and in magazines -- offering modestly insightful epigrams like "Perverse scenarios are about desperate need." It's a Peter Greenaway type of stunt from a rookie director (Streitfeld is a rehabilitated...sorry, _retired_ Hollywood agent) which tosses generalities into the path of individual characters.
Those characters, and Eve's interactions with them, really are the "plot" of FEMALE PERVERSIONS. Each one feels like something which began as a case study but grew into a unique and fully-realized individual.  The performances -- by Amy Madigan, Karen Sillas, even Paulina Porizkova -- are uniformly superb, taking snapshots of different women coping with life and giving them unexpected vitality.
We see them all through Eve's eyes, eyes which watch herself in a news conference after winning a big case and can only see the lipstick smeared on her teeth. Swinton takes on the incredibly challenging role of a woman living multiple lives -- she has to seem plausible as a competent and effective professional who exposes herself in her lover's office, and who flies into a disgusted rage at her fumbled response to an interview question about her marital status. Over the course of FEMALE PERVERSION, Eve's confidence waxes and wanes, her remarkable face alternately displaying confidence and deep feelings of inadequacy about her physical appearance. It is a performance so rich and detailed that it feels like part of a documentary.
Streitfeld stumbles when she reduces Eve's complex psychology to the result of one childhood trauma. FEMALE PERVERSION is about much more than one woman's issues with Mommy and Daddy; Eve's story deals with the state of being a woman as much as it deals with a specific woman. Give credit to Tilda Swinton for bringing life to an archetype, for giving a face to the struggles of modern women.
On the Renshaw scale of 0 to 10 feminine mystiques: 8.
Visit Scott Renshaw's MoviePage http://www-leland.stanford.edu/~srenshaw Subscribe to receive reviews directly via email See details on the MoviePage
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Passed, failed or broken, skipped or incomplete tests.
"Unknown" executor means either the test was not executed by Selenium node at all (@noBrowser tests) or the executing node cannot be determined.
If you see all tests executed by the "unknown" executor, then probably your Selenium server does not provide this information (it is supported only by Selenium server running in grid mode).


Extinction Rebellion activists wearing gas masks block central London road
The protesters, outside Leicester Square Tube station, are demanding the next government tackles air pollution in the capital.
Climate change activists wearing gas masks blocked a central London road to demand the next government tackles “deadly levels of air pollution” in the capital.
Six Extinction Rebellion protesters dressed in hi-vis suits glued their hands to yellow breeze-blocks in the middle of Cranbourn Street, outside Leicester Square Tube station.
The demonstration, called The Air We Grieve, involved 25 cement blocks which the activists said represents the number of Londoners who die each day as a result of air pollution.
A van parked sideways next to them was emblazoned with a sign reading: “Air Pollution Kills 25 Londoners Each Day”.
Police later arrived in the street, which protesters said has “dangerously high levels of air pollution”.
A Metropolitan Police spokesman said: “Police are aware of a small number of protesters outside Leicester Square Underground station.
“Officers are in attendance.”
One protester, teacher Rosamund Frost, said: “We are here demanding action on illegal levels of toxic air in our communities, our schools and our streets.
“In February 2017, the European Commission issued a ‘final warning’ to the UK over illegal levels of air pollution.
“In May that year, after the Government failed to take decisive action, they were taken to Europe’s highest court, the European Court of Justice.
“The Government have neglected to tackle fatal levels of air pollution. How can we put our trust in them to address the broader climate and ecological emergency?”
Meanwhile, other Extinction Rebellion protesters were entering their fourth week on hunger strike outside political party headquarters in Westminster.
The strikers, including 76-year-old grandfather Peter Cole, have sent a letter to Prime Minister Boris Johnson, inviting him to discuss the climate and ecological emergency with them on Monday.


we both work in web operations @ mozilla
architecture
load balancers
databases
async jobs
caching
self service
paas
cloud
clusters
admin node
web nodes
databases nodes
some clusters shared
Zeus (now Stingray)
software solution
in front of nearly everything
Only ONE is active for writes in the Load Balancer.
Write to master, but only read from slaves.
DATABASES = {
...
'slave': {
'ENGINE': 'django.db.backends.mysql',
'NAME': 'mozillians_org',
'USER': 'mozillians',
'PASSWORD': 'YoUt#!nk+h1$izR3@l?',
'HOST': 'generic-ro-zeus',
'PORT': '3306',
},
}
SLAVE_DATABASES = ['slave']
No virtualization in production.
AWESOME DBA's are AWESOME!
+query optimation like code reviews.
Jan 10, 2013 -> Jan 13, 2013 inclusive.
Jan 10, 2013 -> Jan 13, 2013 inclusive.
We chose Puppet, but there are other great options like: Chef & CFEngine
to become platform engineers!
everyone has access to the graphs, real time.
With Kibana, everyone has access to the logs.
yup, real time.
everyone has access to exception tracking.
you guessed it, real time!


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--- layout: post title: Using Git DVC in conjuntion with a legacy SCM tool tags: - Git - Software Configuration Management - Software Development - Work Practices status: publish type: post published: true meta: email_notification: '1276742863' jabber_published: '1276742863' _edit_last: '451289' --- Often developers get a limited choice on the Version Control (VC) or Software Configuration management tool they have to use when working on a project. However even when the Git (the Distributed Version Control tool) does not interface with the project tool there is value in using Git for a personal workflow. This can work as follows.


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Understanding Fees
The payment fee depends on the donation method. You can either assume the cost or allow the nonprofit to pay the fee. By default the nonprofit will assume the cost.
Here is fee table to give you a good sense of the process:||Dwolla:|$10 and under = free to send/receive money|$10.01 and over = flat fee of 25¢||Credit Card:|2.9% + 30¢ for any transaction.||Some participating organizations have sponsors that help cover the costs of processing fees.


Titanic Project
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| fn_fmls {rlang} | R Documentation |
fn_fmls()
returns a named list of formal arguments.
fn_fmls_names()
returns the names of the arguments.
fn_fmls_syms()
returns formals as a named list of symbols. This
is especially useful for forwarding arguments in constructed calls.
fn_fmls(fn = caller_fn()) fn_fmls_names(fn = caller_fn()) fn_fmls_syms(fn = caller_fn()) fn_fmls(fn) <- value fn_fmls_names(fn) <- value
fn |
A function. It is lookep up in the calling frame if not supplied. |
value |
New formals or formals names for |
Unlike formals()
, these helpers throw an error with primitive
functions instead of returning NULL
.
call_args()
and call_args_names()
# Extract from current call: fn <- function(a = 1, b = 2) fn_fmls() fn() # fn_fmls_syms() makes it easy to forward arguments: call2("apply", !!! fn_fmls_syms(lapply)) # You can also change the formals: fn_fmls(fn) <- list(A = 10, B = 20) fn() fn_fmls_names(fn) <- c("foo", "bar") fn()


| I l@ve RuBoard |
The next major built-in type is the Python string —an ordered collection of characters, used to store and represent text-based information. From a functional perspective, strings can be used to represent just about anything that can be encoded as text: symbols and words (e.g., your name), contents of text files loaded into memory, and so on.
You've probably used strings in other languages too; Python's strings serve the same role as character arrays in languages such as C, but Python's strings are a higher level tool. Unlike C, there is no char type in Python, only one-character strings. And strictly speaking, Python strings are categorized as immutable sequences— big words that just mean that they respond to common sequence operations but can't be changed in place. In fact, strings are representative of the larger class of objects called sequences; we'll have more to say about what this means in a moment, but pay attention to the operations introduced here, because they'll work the same on types we'll see later.
Table 2.4 introduces common string constants and operations. Strings support expression operations such as concatenation (combining strings), slicing (extracting sections), indexing (fetching by offset), and so on. Python also provides a set of utility modules for processing strings you import. For instance, the string module exports most of the standard C library's string handling tools, and the regex and re modules add regular expression matching for strings (all of which are discussed in Chapter 8).
Empty strings are written as two quotes with nothing in between. Notice that string constants can be written enclosed in either single or double quotes; the two forms work the same, but having both allows a quote character to appear inside a string without escaping it with a backslash (more on backslashes later). The third line in the table also mentions a triple-quoted form; when strings are enclosed in three quotes, they may span any number of lines. Python collects all the triple-quoted text into a multiline string with embedded newline characters.
Rather than getting into too many details right away, let's interact with the Python interpreter again to illustrate the operations in Table 2.4.
Strings can be concatenated using the + operator, and repeated using the * operator. Formally, adding two string objects creates a new string object with the contents of its operands joined; repetition is much like adding a string to itself a number of times. In both cases, Python lets you create arbitrarily sized strings; there's no need to predeclare anything in Python, including the sizes of data structures.[5] Python also provides a len built-in function that returns the length of strings (and other objects with a length):
[5] Unlike C character arrays, you don't need to allocate or manage storage arrays when using Python strings. Simply create string objects as needed, and let Python manage the underlying memory space. Internally, Python reclaims unused objects' memory space automatically, using a reference-count garbage collection strategy. Each object keeps track of the number of names, data-structures, etc. that reference it; when the count reaches zero, Python frees the object's space. This scheme means Python doesn't have to stop and scan all of memory to find unused space to free; it also means that objects that reference themselves might not be collected automatically.
% python >>> len('abc') # length: number items 3 >>> 'abc' + 'def' # concatenation: a new string 'abcdef' >>> 'Ni!' * 4 # like "Ni!" + "Ni!" + ... 'Ni!Ni!Ni!Ni!'
Notice that operator overloading is at work here already: we're using the same operators that were called addition and multiplication when we looked at numbers. Python is smart enough to do the correct operation, because it knows the types of objects being added and multiplied. But be careful; Python doesn't allow you to mix numbers and strings in + and * expressions: 'abc' + 9 raises an error, instead of automatically converting 9 to a string. As shown in the last line in Table 2.4, you can also iterate over strings in loops using for statements and test membership with the in expression operator:
>>> myjob = "hacker" >>> for c in myjob: print c, # step though items ... h a c k e r >>> "k" in myjob # 1 means true 1
But since you need to know something about statements and the meaning of truth in Python to really understand for and in, let's defer details on these examples until later.
Because strings are defined as an ordered collection of characters, we can access their components by position. In Python, characters in a string are fetched by indexing—providing the numeric offset of the desired component in square brackets after the string. As in C, Python offsets start at zero and end at one less than the length of the string. Unlike C, Python also lets you fetch items from sequences such as strings using negative offsets. Technically, negative offsets are added to the length of a string to derive a positive offset. But you can also think of negative offsets as counting backwards from the end (or right, if you prefer).
>>> S = 'spam' >>> S[0], S[-2] # indexing from front or end ('s', 'a') >>> S[1:3], S[1:], S[:-1] # slicing: extract section ('pa', 'pam', 'spa')
In the first line, we define a four-character string and assign it the name S. We then index it two ways: S[0] fetches the item at offset from the left (the one-character string 's'), and S[-2] gets the item at offset 2 from the end (or equivalently, at offset (4 + -2) from the front). Offsets and slices map to cells as shown in Figure 2.1.
The last line in the example above is our first look at slicing. When we index a sequence object such as a string on a pair of offsets, Python returns a new object containing the contiguous section identified by the offsets pair. The left offset is taken to be the lower bound, and the right is the upper bound; Python fetches all items from the lower bound, up to but not including the upper bound, and returns a new object containing the fetched items.
For instance, S[1:3] extracts items at offsets 1 and 2, S[1:] gets all items past the first (the upper bound defaults to the length of the string), and S[:-1] gets all but the last item (the lower bound defaults to zero). This may sound confusing on first glance, but indexing and slicing are simple and powerful to use, once you get the knack. Here's a summary of the details for reference; remember, if you're unsure about what a slice means, try it out interactively.
Fetches components at offsets (the first item is at offset zero)
Negative indexes mean to count from the end (added to the positive length)
S[0] fetches the first item
S[-2] fetches the second from the end (it's the same as S[len(S) - 2])
Extracts contiguous sections of a sequence
Slice boundaries default to zero and the sequence length, if omitted
S[1:3] fetches from offsets 1 up to, but not including, 3
S[1:] fetches from offsets 1 through the end (length)
S[:-1] fetches from offsets up to, but not including, the last item
Later in this chapter, we'll see that the syntax used to index by offset (the square brackets) is also used to index dictionaries by key; the operations look the same, but have different interpretations.
Remember those big words—immutable sequence? The immutable part means that you can't change a string in-place (e.g., by assigning to an index). So how do we modify text information in Python? To change a string, we just need to build and assign a new one using tools such as concatenation and slicing:
>>> S = 'spam' >>> S[0] = "x" Raises an error!
>>> S = S + 'Spam!' # to change a string, make a new one >>> S 'spamSpam!' >>> S = S[:4] + 'Burger' + S[-1] >>> S 'spamBurger!' >>> 'That is %d %s bird!' % (1, 'dead') # like C sprintf That is 1 dead bird!
Python also overloads the % operator to work on strings (it means remainder-of-division for numbers). When applied to strings, it serves the same role as C's sprintf function: it provides a simple way to format strings. To make it go, simply provide a format string on the left (with embedded conversion targets—e.g., %d), along with an object (or objects) on the right that you want Python to insert into the string on the left, at the conversion targets. For instance, in the last line above, the integer 1 is plugged into the string where the %d appears, and the string 'dead' is inserted at the %s. String formatting is important enough to warrant a few more examples:
>>> exclamation = "Ni" >>> "The knights who say %s!" % exclamation 'The knights who say Ni!' >>> "%d %s %d you" % (1, 'spam', 4) '1 spam 4 you' >>> "%s -- %s -- %s" % (42, 3.14159, [1, 2, 3]) '42 -- 3.14159 -- [1, 2, 3]'
In the first example, plug the string "Ni" into the target on the left, replacing the %s marker. In the second, insert three values into the target string; when there is more than one value being inserted, you need to group the values on the right in parentheses (which really means they are put in a tuple, as we'll see shortly).
Python's string % operator always returns a new string as its result, which you can print or not. It also supports all the usual C printf format codes. Table 2.5 lists the more common string-format target codes. One special case worth noting is that %s converts any object to its string representation, so it's often the only conversion code you need to remember. For example, the last line in the previous example converts integer, floating point, and list objects to strings using %s (lists are up next). Formatting also allows for a dictionary of values on the right, but since we haven't told you what dictionaries are yet, we'll finesse this extension here.
|
% |
String (or any object's print format) |
%X |
Hex integer (uppercase) |
|
%c |
Character |
%e |
Floating-point format 1[6] |
|
%d |
Decimal (int) |
%E |
Floating-point format 2 |
|
%i |
Integer |
%f |
Floating-point format 3 |
|
%u |
Unsigned (int) |
%g |
Floating-point format 4 |
|
%o |
Octal integer |
%G |
Floating-point format 5 |
|
%x |
Hex integer |
%% |
Literal % |
[6] The floating-point codes produce alternative representations for floating-point numbers. See printf documentation for details; better yet, try these formats out in the Python interactive interpreter to see how the alternative floating-point formats look (e.g., "%e %f %g" % (1.1, 2.2, 3.3)).
As previously mentioned, Python provides utility modules for processing strings. The string module is perhaps the most common and useful. It includes tools for converting case, searching strings for substrings, converting strings to numbers, and much more (the Python library reference manual has an exhaustive list of string tools).
>>> import string # standard utilities module >>> S = "spammify" >>> string.upper(S) # convert to uppercase 'SPAMMIFY' >>> string.find(S, "mm") # return index of substring 3 >>> string.atoi("42"), `42` # convert from/to string (42, '42') >>> string.join(string.split(S, "mm"), "XX") 'spaXXify'
The last example is more complex, and we'll defer a better description until later in the book. But the short story is that the split function chops up a string into a list of substrings around a passed-in delimiter or whitespace; join puts them back together, with a passed-in delimiter or space between each. This may seem like a roundabout way to replace "mm" with "XX", but it's one way to perform arbitrary global substring replacements. We study these, and more advanced text processing tools, later in the book.
Incidentally, notice the second-to-last line in the previous example: the atoi function converts a string to a number, and backquotes around any object convert that object to its string representation (here, `42` converts a number to a string). Remember that you can't mix strings and numbers types around operators such as +, but you can manually convert before that operation if needed:
>>> "spam" + 42 Raises an error
>>> "spam" + `42` 'spam42' >>> string.atoi("42") + 1 43
Later, we'll also meet a built-in function called eval that converts a string to any kind of object; string.atoi and its relatives convert only to numbers, but this restriction means they are usually faster.
Finally, we'd like to show you a few of the different ways to write string constants; all produce the same kind of object (a string), so the special syntax here is just for our convenience. Earlier, we mentioned that strings can be enclosed in single or double quotes, which allows embedded quotes of the opposite flavor. Here's an example:
>>> mixed = "Guido's" # single in double >>> mixed "Guido's" >>> mixed = 'Guido"s' # double in single >>> mixed 'Guido"s' >>> mixed = 'Guido\'s' # backslash escape >>> mixed "Guido's"
Notice the last two lines: you can also escape a quote (to tell Python it's not really the end of the string) by preceding it with a backslash. In fact, you can escape all kinds of special characters inside strings, as listed in Table 2.6; Python replaces the escape code characters with the special character they represent. In general, the rules for escape codes in Python strings are just like those in C strings.[7] Also like C, Python concatenates adjacent string constants for us:
[7] But note that you normally don't need to terminate Python strings with a \0 null character as you would in C. Since Python keeps track of a string's length internally, there's usually no need to manage terminators in your programs. In fact, Python strings can contain the null byte \0, unlike typical usage in C. For instance, we'll see in a moment that file data is represented as strings in Python programs; binary data read from or written to files can contain nulls because strings can too.
>>> split = "This" "is" "concatenated" >>> split 'Thisisconcatenated'
And last but not least, here's Python's triple-quoted string constant form in action: Python collects all the lines in such a quoted block and concatenates them in a single multiline string, putting an end-of-line character between each line. The end-of-line prints as a "\012" here (remember, this is an octal integer); you can also call it "\n" as in C. For instance, a line of text with an embedded tab and a line-feed at the end might be written in a program as python\tstuff\n (see Table 2.6).
>>> big = """This is ... a multi-line block ... of text; Python puts ... an end-of-line marker ... after each line.""" >>> >>> big 'This is\012a multi-line block\012of text; Python puts\012an end-of-line marker\012after each line.'
Python also has a special string constant form called raw strings, which don't treat backslashes as potential escape codes (see Table 2.6). For instance, strings r'a\b\c' and R"a\b\c" retain their backslashes as real (literal) backslash characters. Since raw strings are mostly used for writing regular expressions, we'll defer further details until we explore regular expressions in Chapter 8.
\newline |
Ignored (a continuation) |
\n |
Newline (linefeed) |
\\ |
Backslash (keeps one \) |
\v |
Vertical tab |
\' |
Single quote (keeps ') |
\t |
Horizontal tab |
\" |
Double quote (keeps ") |
\r |
Carriage return |
\a |
Bell |
\f |
Formfeed |
\b |
Backspace |
\0XX |
Octal value XX |
\e |
Escape (usually) |
\xXX |
Hex value XX |
\000 |
Null (doesn't end string) |
\other |
Any other char (retained) |
Now that we've seen our first composite data type, let's pause a minute to define a few general type concepts that apply to most of our types from here on. One of the nice things about Python is that a few general ideas usually apply to lots of situations. In regard to built-in types, it turns out that operations work the same for all types in a category, so we only need to define most ideas once. We've only seen numbers and strings so far, but they are representative of two of the three major type categories in Python, so you already know more about other types than you think.
When we introduced strings, we mentioned that they are immutable sequences: they can't be changed in place (the immutable part), and are ordered collections accessed by offsets (the sequence bit). Now, it so happens that all the sequences seen in this chapter respond to the same sequence operations we previously saw at work on strings—concatenation, indexing, iteration, and so on. In fact, there are three type (and operation) categories in Python:
Numbers support addition, multiplication, etc.
Sequences support indexing, slicing, concatenation, etc.
Mappings support indexing by key, etc.
We haven't seen mappings yet (we'll get to dictionaries in a few pages), but other types are going to be mostly more of the same. For example, for any sequence objects X and Y:
X + Y makes a new sequence object with the contents of both operands.
X * N makes a new sequence object with N copies of the sequence operand X.
In other words, these operations work the same on any kind of sequence. The only difference is that you get back a new result object that is the same type as the operands X and Y (if you concatenate strings, you get back a new string, not a list). Indexing, slicing, and other sequence operations work the same on all sequences too; the type of the objects being processed tells Python which flavor to perform.
The immutable classification might sound abstract, but it's an important constraint to know and tends to trip up new users. If we say an object type is immutable, you shouldn't change it without making a copy; Python raises an error if you do. In general, immutable types give us some degree of integrity, by guaranteeing that an object won't be changed by another part of a program. We'll see why this matters when we study shared object references later in this chapter.
| I l@ve RuBoard |


Gold prices in India hit 5-month high amid growing demand
The country's gold imports fell for the fourth straight month, while demand in China remained muted.
Physical gold demand climbed in India this week, with sellers charging premiums for the first time in five-and-a-half months as jewellers took advantage of a price dip and lower imports squeezed supply.
Dealers charged a premium of up to $1.50 an ounce over official domestic prices this week, compared with discounts of up to two dollars an ounce last week. The domestic price includes a 12.5 percent import tax and a 3 percent sales tax.
More:
Gold futures were trading around 37,978 rupees per 10 grams on Friday, down from a record 39,885 rupees touched in September.
India's gold imports in October fell a third from a year earlier, dropping a fourth straight month.
"Anticipating a correction in prices, jewellers were not buying for the last few months. Now, they have to buy (to restock) after Diwali sales," said a Mumbai-based dealer with a bullion-importing bank.
Earlier this month, Indians celebrated the Dhanteras and Diwali festivals, when demand peaks.
In top consumer China, premiums of $4 - $4.50 an ounce were quoted, little changed from last week's four - five-dollar mark-ups.
"Demand in Shanghai is getting pressured because the economy is not doing well. People do not have enough money to buy the expensive metal," said Samson Li, a Hong Kong-based precious metals analyst at Refinitiv GFMS.
Hong Kong and Singapore demand steadied after a dip in global benchmark prices, which have fallen over seven percent from a six-year high of $1,557 an ounce hit in early September.
In Hong Kong, gold was sold at a premium of $0.30-$0.50 an ounce versus $0.40-$0.50 last week. Violent protests in the financial hub have deterred buyers recently.
"Demand in Hong Kong is steady as spot prices fell, but that is being offset by the protests as people are not shopping - not only jewellery but anything for that matter," said Peter Fung, head of dealing at Wing Fung Precious Metals.
Sellers in Singapore charged premiums of $0.50-$0.60 an ounce over the benchmark, versus $0.50-$0.80 last week.
"While physical gold demand is not at the high levels we saw between July and September, we're still seeing a good volume purchased by clients who see the current price dip as a buying opportunity," Silver Bullion sales manager Vincent Tie said.
Japanese sellers saw demand improve after prices fell below $1,450 an ounce, a Tokyo-based trader said, with a premium, of about $0.50 an ounce, being charged for the first time since early June.


{{ title }}
{% if success %} {% trans question_title=question.title, question_url=question.get_absolute_url() %} You will no longer receive email updates for this question: {{ question_title }} {% endtrans %} {% else %} {% trans question_title=question.title, question_url=question.get_absolute_url() %} We could not unsubscribe you from this question: {{ question_title }}. Please make sure you copied and pasted the unsubscribe link correctly. {% endtrans %} {% endif %}


First Sidebar Link
Second Sidebar Link
Dropdown
arrow_drop_down
First
Second
Third
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Material
First Sidebar Link
Second Sidebar Link
Dropdown
First
Second
Third
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menu


src/react/help-panel.js
var React = require('react');
var AutoComposerLogic = require('../autocomposer-logic');
var AcLogic = new AutoComposerLogic.AutoComposerLogic();
/**
* React Component representing the Help/Info Panel
*/
class HelpPanel extends React.Component {
constructor(props) {
super(props);
this.state = {
chordArray: AcLogic.getChordDictionary(true)
}
}
buildChordDictionary() {
var dictionaryItems = [];
var ctr = 1;
this.state.chordArray.forEach(function(chordString) {
dictionaryItems.push(
{chordString}
);
ctr++;
});
return(

{dictionaryItems}

);
}
render() {
var lowestPitch = AcLogic.DEFAULT_LOWER_LIMIT;
var highestPitch = AcLogic.DEFAULT_UPPER_LIMIT;
var lowestPitchAccomp = AcLogic.ACCOMPANIMENT_LOWER_LIMIT;
var highestPitchAccomp = AcLogic.ACCOMPANIMENT_UPPER_LIMIT;
var lowestPitchBass = AcLogic.BASS_LOWER_LIMIT;
var highestPitchBass = AcLogic.BASS_UPPER_LIMIT;
if(this.props.isShown) {
return (

Help!

How to use this web app:

Enter a chord progression in the text box.

Click the Generate button.

Squeal in delight, as the promised melodies are shown on the screen.

Click a melody.

Hit the  button!

Other features:

You can download the loaded music by clicking the  button. The melody will be saved as a MIDI file, containing the generated melody and accompaniment.

You can also save the score as an image. Just right click it and download!

Chord Dictionary

These are the chords you can use for this application:

{this.buildChordDictionary()}

Technical Info

Smoothness = the distance between notes in the melody (in semitones), all added together

Range = distance between the lowest note and the highest note (in semitones)

The MIDI instruments for the voices are:

Violin

Piano

Bass (plucked)

Melody range is {lowestPitch} to {highestPitch}

Accompaniment range is {lowestPitchAccomp} to {highestPitch}

Bass range is {lowestPitchBass} to {highestPitchBass}

Melodies are filtered/sorted by a few rules:

Only the 100 smoothest melodies are shown

Range must be no greater than one octave

Only one repetition occurs in the melody

);
} else {
return null;
}
}
}
module.exports = HelpPanel;


Properties
Optional answer_id
answer_id: number
Optional answer_ids
answer_ids: number[]
Optional author_id
author_id: number
can_edit
can_edit: boolean | number
can_report
can_report: boolean | number
can_share
can_share: boolean | number
can_vote
can_vote: boolean | number
closed
closed: boolean | number
disable_unvote
disable_unvote: boolean | number
end_date
end_date: number
is_board
is_board: boolean | number
multiple
multiple: boolean | number
owner_id
owner_id: number
question
question: string
Current user's answer ID


Product Returns
Reflecting Returns and Credits Memos in AvaTax
When you’re calculating tax on returns or credit memos through the API, there are a few things to keep in mind:
- Avalara Avatax makes no direct association to the original invoice. That’s handled by you and your application.
- Send us negative extended sale amounts (qty * price) on your line items. This will make us calculate negative liability. Quantity should always be a positive value and price should always be negative to ensure that the amount is negative.
- Keep an eye on your DocCode. You can use the same DocCode as your original invoice exactly once if you send the return with DocType ReturnInvoice, but make sure you think about multiple returns associated with the same invoice.
- Dates. You want to report the return in the period in which it was processed, but it may have calculated tax in a previous period (which had different tax rates). To handle this, send the DocDate as the date of return processing, and use TaxOverride.TaxDate to send the date of the original invoice.
Return Invoice or Credit Memo processing is largely a business practice that the developer and the business manager need to map out prior to coding or moving forward to a production environment. A thorough understanding of the API’s GetTaxRequest members and document states is key to developing an architecture suitable for processing returns in your development.
For the example provided below, we will process a return invoice on the following sales invoice, as seen on the Admin Console:
With the following line items:
As you address returns processing, keep in mind that:
- The document in question has (in this example) been committed, and the tax remitted to the appropriate jurisdiction.
- There may be multiple lines in the document.
- We need to work towards the eventual outcome of a complete or partial refund.
- Call GetTax with a duplicate of the document (invoice) you want to process returns on:
- Using the same invoice number AND DocType of ReturnInvoice or
- With a new invoice number (DocCode) with the original invoice number passed in the reference number field, or
- Re-use the original document’s invoice number with a “.1” or other change added to it.
Note: you can use a DocCode once per DocType. A DocStatusError will result if you attempt to commit a second document with the same DocType and DocCode
- Set the DocDate to the date when the return is being processed (not the date of the original order).
- Set the TaxOverride.TaxDate to reflect the original order date (this will override the date used for tax calculation so that the same taxes are calculated as were on the original order).
- Set TaxOverride.TaxOverrideType to TaxDate
- Set TaxOverride.TaxDate to the original order date
- Set TaxOverride.Reason to some string – this is an audit message for the TaxOverride.
NOTE: Pass only line items being returned -- do not include the line items that will not be returned unless all items are returned). In this example, we are returning line items 1 and 3.
- Set the Amt property to a negative dollar amount on the line items (always leave the Qty as a positive number)
- Call GetTax with a duplicate of the document (invoice) you want to process returns on:
Once sent to the AvaTax web service, the tax engine will return negative tax amounts on the line items based on the TaxDate specified. If no TaxDate is set, the document date will be used to calculate tax. Taxable Amounts on a Return Invoice show negative amounts equal to the items returned.
Out of the 6 items originally processed, 2 items have been reversed on your tax reporting –- in this case $29.10 appears essentially as a “credit” on the current month’s tax liability.


Syllabus
This document and others linked within it should be your PRIMARY source for understanding the expectations of this course. Be sure to read it carefully. You must contact the instructor for clarification if you receive information from any another source that is in contradiction to what is provided below.
Schedule
For lab, lecture and office hours please check the course Google Calendar For hw, lab and exam schedule, please check the assignment calendar
Grading
Grade breakup by evaluation component
- Lecture and section partcipation: 2%
- Homeworks: 8%
- Lab assignments: 20%
- Projects: 20%
- Midterm Exams: 20%
- Final Examination : 30%
Thus attendance is required, and reading the assigned readings is required.
Grade assignment policy
| A+ | 97-100 | A | 93-97 | A- | 90-93 |
| B+ | 87-90 | B | 83-87 | B- | 80-83 |
| C+ | 77-80 | C | 73-77 | C- | 70-73 |
| D+ | 67-70 | D | 63-67 | D- | 60-63 | F | Below 60 |
Curving: The grade scale above represents the minimum letter grade you will be assigned—at the instructor’s discretion, the grading scale may be altered in the students’ favor if this will be better reflect the students’ mastery of the material. Thus, if there is a "curve", it will be applied at the end, not to individual assignments.

A+ grades: These may be awarded to the very best performing students in the class—but the cutoff for A+ grades will be determined at the end of the course at the discretion of the instructor (there is no pre-determined cutoff—an average of 97 or more doesn’t guarantee you an A+ grade.) Everything is cumulative—that’s just how CS is.
Finally—note that just as in a math class, everything we do builds on all the work that came before. So, everything is cumulative—so, you can’t afford to miss any classes unless absolutely necessary. Miss two lectures in a 10-week two-lecture per week course, and you’ve already skipped exactly 10% of the course—it wouldn’t be surprising if your performance (i.e. final grade) in the course dipped by a similar amount. # Resources ## Required Resources * Required textbook: Michael Main and Walter Savitch. Data Structures and Other Objects Using C++ (4th edition), Addison-Wesley, 2011. * Recommended textbook: Problem Solving with C++, Walter Savitch, Edition 9. Available for purchase at the UCSB book store * Peer Instruction : iClickers: The course lectures will follow a Peer Instruction format, a teaching model which places stronger emphasis on classroom discussion and student interaction. As part of this you will need to own an iClicker register it on the Gauchospace. Be sure to register your clicker by the end of the first week. ## What you should know to be ready for CS24
Here’s the list of a few important things you’ll need to know to be ready for CS24.
- A few of the basic data types of C++, including at least, int, double, char, bool, string
- The basic control structures of C++ (if/else, while, for etc.)
- Defining functions in C++, and passing parameters to functions in three different ways (by value, by pointer, and by reference)
- Scope and lifetime of variables in C++
- The use of “const” with parameters to functions
- Using arrays in C++, and C-strings (null-terminated character arrays)
- How arrays are passed to functions, and the relationship between arrays and pointers
- Defining and working with structs in C++
- Using structs to create singly linked lists where the space for the list nodes is allocated on the heap
- The difference between space allocated on the stack (e.g. local variables) and space allocated on the heap (with the new and delete operators)
- Converting from binary to decimal, octal, and hex, and back again—and how this relates to how C++ programs store various kinds of data in memory.
- The basic principles of recursion, and some idea of when a recursive solution is appropriate.
Course objectives
- Students learn object-based programming techniques: abstraction and encapsulation.
- Students learn to specify, implement and apply lists, trees and other data structures.
- Students are exposed to complexity analysis, and learn to distinguish algorithms and data structures on the basis of efficiency.
- Students learn (and practice) to produce better programs more quickly and with less stress.


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runtime.gobuf.lr (field)
13 uses
runtime (current package)
heapdump.go#L351: lr = gp.sched.lr
panic.go#L1153: gp.sched.lr = 0
proc.go#L1904: gp.sched.lr = 0
proc.go#L3450: _g_.sched.lr = 0
runtime2.go#L331: lr uintptr
stack.go#L947: traceback(morebuf.pc, morebuf.sp, morebuf.lr, morebuf.g.ptr())
stack.go#L966: "\tmorebuf={pc:", hex(morebuf.pc), " sp:", hex(morebuf.sp), " lr:", hex(morebuf.lr), "}\n",
stack.go#L967: "\tsched={pc:", hex(gp.sched.pc), " sp:", hex(gp.sched.sp), " lr:", hex(gp.sched.lr), " ctxt:", gp.sched.ctxt, "}\n")
stack.go#L970: traceback(morebuf.pc, morebuf.sp, morebuf.lr, gp)
stack.go#L976: thisg.m.morebuf.lr = 0
stack.go#L1016: "\tmorebuf={pc:", hex(morebuf.pc), " sp:", hex(morebuf.sp), " lr:", hex(morebuf.lr), "}\n",
stack.go#L1017: "\tsched={pc:", hex(gp.sched.pc), " sp:", hex(gp.sched.sp), " lr:", hex(gp.sched.lr), " ctxt:", gp.sched.ctxt, "}\n")
traceback.go#L123: lr0 = gp.sched.lr


Class: Aws::MediaPackageV2::Types::Segment
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-mediapackagev2/lib/aws-sdk-mediapackagev2/types.rb |
Overview
The segment configuration, including the segment name, duration, and other configuration values.
Constant Summary
Instance Attribute Summary
-
#encryption ⇒ Types::Encryption
rw
The parameters for encrypting content.
-
#include_iframe_only_streams ⇒ Boolean
rw
When selected, the stream set includes an additional I-frame only stream, along with the other tracks.
-
#scte ⇒ Types::Scte
rw
The SCTE configuration options in the segment settings.
-
#segment_duration_seconds ⇒ Integer
rw
The duration (in seconds) of each segment.
-
#segment_name ⇒ String
rw
The name that describes the segment.
-
#ts_include_dvb_subtitles ⇒ Boolean
rw
By default,
::Aws::MediaPackage
excludes all digital video broadcasting (DVB) subtitles from the output. -
#ts_use_audio_rendition_group ⇒ Boolean
rw
When selected,
::Aws::MediaPackage
bundles all audio tracks in a rendition group.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#encryption ⇒ Types::Encryption (rw)
The parameters for encrypting content.
# File 'gems/aws-sdk-mediapackagev2/lib/aws-sdk-mediapackagev2/types.rb', line 1845
class Segment < Struct.new( :segment_duration_seconds, :segment_name, :ts_use_audio_rendition_group, :include_iframe_only_streams, :ts_include_dvb_subtitles, :scte, :encryption) SENSITIVE = [] include Aws::Structure end
#include_iframe_only_streams ⇒ Boolean
(rw)
When selected, the stream set includes an additional I-frame only
stream, along with the other tracks. If false, this extra stream is
not included. ::Aws::MediaPackage
generates an I-frame only stream from the
first rendition in the manifest. The service inserts
EXT-I-FRAMES-ONLY tags in the output manifest, and then generates
and includes an I-frames only playlist in the stream. This playlist
permits player functionality like fast forward and rewind.
# File 'gems/aws-sdk-mediapackagev2/lib/aws-sdk-mediapackagev2/types.rb', line 1845
class Segment < Struct.new( :segment_duration_seconds, :segment_name, :ts_use_audio_rendition_group, :include_iframe_only_streams, :ts_include_dvb_subtitles, :scte, :encryption) SENSITIVE = [] include Aws::Structure end
#scte ⇒ Types::Scte (rw)
The SCTE configuration options in the segment settings.
# File 'gems/aws-sdk-mediapackagev2/lib/aws-sdk-mediapackagev2/types.rb', line 1845
class Segment < Struct.new( :segment_duration_seconds, :segment_name, :ts_use_audio_rendition_group, :include_iframe_only_streams, :ts_include_dvb_subtitles, :scte, :encryption) SENSITIVE = [] include Aws::Structure end
#segment_duration_seconds ⇒ Integer
(rw)
The duration (in seconds) of each segment. Enter a value equal to,
or a multiple of, the input segment duration. If the value that you
enter is different from the input segment duration, ::Aws::MediaPackage
rounds segments to the nearest multiple of the input segment
duration.
# File 'gems/aws-sdk-mediapackagev2/lib/aws-sdk-mediapackagev2/types.rb', line 1845
class Segment < Struct.new( :segment_duration_seconds, :segment_name, :ts_use_audio_rendition_group, :include_iframe_only_streams, :ts_include_dvb_subtitles, :scte, :encryption) SENSITIVE = [] include Aws::Structure end
#segment_name ⇒ String
(rw)
The name that describes the segment. The name is the base name of the segment used in all content manifests inside of the endpoint. You can't use spaces in the name.
# File 'gems/aws-sdk-mediapackagev2/lib/aws-sdk-mediapackagev2/types.rb', line 1845
class Segment < Struct.new( :segment_duration_seconds, :segment_name, :ts_use_audio_rendition_group, :include_iframe_only_streams, :ts_include_dvb_subtitles, :scte, :encryption) SENSITIVE = [] include Aws::Structure end
#ts_include_dvb_subtitles ⇒ Boolean
(rw)
By default, ::Aws::MediaPackage
excludes all digital video broadcasting
(DVB) subtitles from the output. When selected, ::Aws::MediaPackage
passes
through DVB subtitles into the output.
# File 'gems/aws-sdk-mediapackagev2/lib/aws-sdk-mediapackagev2/types.rb', line 1845
class Segment < Struct.new( :segment_duration_seconds, :segment_name, :ts_use_audio_rendition_group, :include_iframe_only_streams, :ts_include_dvb_subtitles, :scte, :encryption) SENSITIVE = [] include Aws::Structure end
#ts_use_audio_rendition_group ⇒ Boolean
(rw)
When selected, ::Aws::MediaPackage
bundles all audio tracks in a rendition
group. All other tracks in the stream can be used with any audio
rendition from the group.
# File 'gems/aws-sdk-mediapackagev2/lib/aws-sdk-mediapackagev2/types.rb', line 1845
class Segment < Struct.new( :segment_duration_seconds, :segment_name, :ts_use_audio_rendition_group, :include_iframe_only_streams, :ts_include_dvb_subtitles, :scte, :encryption) SENSITIVE = [] include Aws::Structure end


Homesick for Habitat: Zoos You Shouldn't Go To
Zoos have been treating animals poorly. If we treated humans like this would
be illegal... this should be too. Unlike animal sanctuaries, the zoo’s focus
is not on what’s best for the animals.
List of zoos that are the most dangerous to wild caught animals:
1.Topeka Zoo Kansas
2.The Greater Wynnewood Exotic Animal Park
3.Tregembo Animal Park
4.Suncoast Primate Sanctuary
5.Three Bears General Store
6.Sea World
7.Waccatee Zoo
8.Alligator Adventure and Barefoot Landing’s T. I. G. E. R. S. Preservation Station
9.Pittsburgh Zoo and Conservation Center
10.Natural Bridge Zoo
11. Oklahoma City Zoo


Hi everyone! My name is Vlad and I am pleased to meet everyone.
I'm 25, married. Originally, I come from a county called Moldova, which is located in Eastern Europe. I've lived in U.S. for almost 10 years. I'm now located in Concord, CA(about 30 minutes away from SF).
I'm a big soccer fan. Also, know a lot about hockey and used to play it myself back in the day.Really enjoy studying history.Like both cats and dogs.
This is where I get my daily sports info:


The presence of dense tree canopy is important to creating safe and pleasant streets. Consequently, urban designers in cities aspire to densify tree coverage. To do so they need to prioritize areas in need for new trees.
To detect different tree canopy levels across NYC a street tree coverage indicator was developed using NYC's open data. The Diameter at Breast Height (DBH) for each tree in NYC was used as an estimation of the a tree crown diameter. To get a street level score DBH’s were summed over for each street segment, and then divided by the street segment length. In the map, the scores are displayed using five equal probabilities to represent “very low” to “very high” street tree coverage groups.
Street designers are usually informed on which streets need capital investments using methods like surveys and observations. In addition, most cities have limited resources that they are able to allocate for street improvements. A data-driven approach to street design, such as this, could lead to planning safer streets and a more equitable allocation of funds between the city’s neighborhoods.
Using this street tree coverage metric planners and designers can accurately understand the distribution of tree canopy in the city. They can decide where to plant new trees and prioritize areas with low/very low tree coverage.
The street tree coverage was developed in order to demonstrate how big data can help support current public space research methods. This work was presented at the 2019 D4GX conference along with the Using Big Data to Support Public Space Research book chapter outline co-wrote by Dr. Kristen Day and myself. The design of this poster was partly done as part of my Data Vis & Info Aesthetics Parsons class project.


|
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| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |
| Interface Summary | |
|---|---|
| Key | An interface supported by all data objects that hold the unique value of the key of an entity, e.g. |
| Loadable | |
| Message | A message to be handled by the policy manager. |
| MessageHandler | The interface specify the handling of a message. |
| RDQLQueryQualifier | An interface that specifies style of the rdql query to be generated. |
| SupportsRDQLQuery | An interface that specifies a method to generate an rdql query subset. |
| VariableGenerator | An interface that specifies a variable generator (gensym style). |
| Class Summary | |
|---|---|
| AbstractMessage | |
| Jena | A class defining convenience functions to manipulate the Jena Triple store. |
| KeyImpl | |
| MessageError | |
| NotImplementedHandler | |
| RDF | A class containing the names of relationships and concepts serialised in the RDF triple store. |
| Exception Summary | |
|---|---|
| RegistryException | Reports an exception or set of exceptions from the server-side to the user. |
| StoreException | A exception denoting an inconsistency of the datamodel in the store. |
|
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Muffy
is wanted in 38 states for racketeering but has a heart of gold.
Hugo
is an ardent devotee of full contact football and competitive drooling. He sleeps 23 hours a day.
Princess
dreams of being a professional stylist and enjoys practicing at home. Don't touch her hair, its part of her style.
Rambo
is a very high energy animal who needs a great deal of love and attention. Sleeps one hour a day and consumes at least 2 pounds of kibble every day.
Rattikins
is a snuggler, easily scared by loud noises, and is very communicative. Such a good boy.


public class ReplicatedEnvironmentStats
extends java.lang.Object
implements java.io.Serializable
The statistics are logically grouped into four categories. Viewing the
statistics through toString()
displays
the values in these categories, as does viewing the stats through the RepJEMonitor
mbean. Viewing the stats with toStringVerbose()
will provide more detailed
descriptions of the stats and stat categories.
The current categories are:
| Modifier and Type | Method and Description |
|---|---|
long |
getAckWaitMs()
The total time in milliseconds that the master spent waiting for the
Durability.ReplicaAckPolicy to be satisfied during successful transaction
commits. |
long |
getLastCommitTimestamp()
The commit timestamp of the last committed transaction on the master, or
0 if not known or this node is not the master.
|
long |
getLastCommitVLSN()
The VLSN of the last committed transaction on the master, or 0 if not
known or this node is not the master.
|
int |
getNFeedersCreated()
The number of Feeder threads since this node was started.
|
int |
getNFeedersShutdown()
The number of Feeder threads that were shut down, either because this
node, or the Replica terminated the connection.
|
long |
getNMaxReplicaLag()
The lag (in VLSNs) associated with the replica that's farthest behind in
replaying the replication stream.
|
java.lang.String |
getNMaxReplicaLagName()
The name of the replica that's farthest behind in replaying the
replication stream.
|
long |
getNProtocolBytesRead()
The number of bytes of Replication Stream read over the network.
|
long |
getNProtocolBytesWritten()
The number of Replication Stream bytes written over the network.
|
long |
getNProtocolEntriesWrittenOldVersion()
Returns the number of messages containing log entries that were written
to the replication stream using the previous log format to support
replication to a replica running an earlier version during an upgrade.
|
long |
getNProtocolMessageBatches()
The number of Replication Stream message batches written to the network.
|
long |
getNProtocolMessagesBatched()
The number of Replication Stream messages that were written as part
of a message batch instead of being written individually.
|
long |
getNProtocolMessagesRead()
The number of Replication Stream messages read over the network.
|
long |
getNProtocolMessagesWritten()
The total number of Replication Stream messages written over the
network.
|
long |
getNReplayAborts()
The number of abort records which were replayed while the node was in
the Replica state.
|
long |
getNReplayCommitAcks()
The number of commit log records that needed to be acknowledged to the
Master by this node when it was a Replica.
|
long |
getNReplayCommitNoSyncs()
The number of commitNoSync() calls executed when satisfying transaction
commit acknowledgment requests from the Master.
|
long |
getNReplayCommits()
The number of commit log records that were replayed by this node when
it was a Replica.
|
long |
getNReplayCommitSyncs()
The number of commitSync() calls executed when satisfying transaction
commit acknowledgment requests from the Master.
|
long |
getNReplayCommitWriteNoSyncs()
The number of commitNoSync() calls executed when satisfying transaction
commit acknowledgment requests from the Master.
|
long |
getNReplayGroupCommitMaxExceeded()
The number of group commits that were initiated due the
max group size being
exceeded. |
long |
getNReplayGroupCommits()
The number of group commit operations.
|
long |
getNReplayGroupCommitTimeouts()
The number of group commits that were initiated due to the
group timeout
interval being exceeded. |
long |
getNReplayGroupCommitTxns()
The number of replay transaction commits that were part of a group
commit operation.
|
long |
getNReplayLNs()
The number of data records (creation, update, deletion) which were
replayed while the node was in the Replica state.
|
long |
getNReplayNameLNs()
The number of NameLN records which were replayed while the node was in
the Replica state.
|
long |
getNTxnsAcked()
The number of transactions that were successfully acknowledged based
upon the
Durability.ReplicaAckPolicy policy associated with the
transaction commit. |
long |
getNTxnsNotAcked()
The number of transactions that were not acknowledged as required by the
Durability.ReplicaAckPolicy policy associated with the transaction commit. |
long |
getProtocolBytesReadRate()
Bytes read throughput, in terms of bytes received from the replication
network channels per second.
|
long |
getProtocolBytesWriteRate()
Bytes written throughput, in terms of bytes written to the replication
network channels per second.
|
long |
getProtocolMessageReadRate()
Incoming replication message throughput, in terms of messages received
from the replication network channels per second.
|
long |
getProtocolMessageWriteRate()
Outgoing message throughput, in terms of message written to the
replication network channels per second.
|
long |
getProtocolReadNanos()
The number of nanoseconds spent reading from the network channel.
|
long |
getProtocolWriteNanos()
The number of nanoseconds spent writing to the network channel.
|
long |
getReplayElapsedTxnTime()
The total elapsed time in milliseconds spent replaying committed and
aborted transactions.
|
long |
getReplayMaxCommitProcessingNanos()
The maximum time taken to replay a transaction commit operation.
|
long |
getReplayMinCommitProcessingNanos()
The minimum time taken to replay a transaction commit operation.
|
long |
getReplayTotalCommitProcessingNanos()
The total time spent to replay all commit operations.
|
java.util.SortedMap<java.lang.String,java.lang.Long> |
getReplicaDelayMap()
Returns a map from replica node name to the delay, in milliseconds,
between when a transaction was committed on the master and when the
master learned that the transaction was processed on the replica, if
known.
|
java.util.SortedMap<java.lang.String,java.lang.Long> |
getReplicaLastCommitTimestampMap()
Returns a map from replica node name to the commit timestamp of the last
committed transaction that was processed on the replica, if known.
|
java.util.SortedMap<java.lang.String,java.lang.Long> |
getReplicaLastCommitVLSNMap()
Returns a map from replica node name to the VLSN of the last committed
transaction that was processed on the replica, if known.
|
java.util.SortedMap<java.lang.String,java.lang.Long> |
getReplicaVLSNLagMap()
Returns a map from replica node name to the lag, in VLSNs, between the
replication state of the replica and the master, if known.
|
java.util.SortedMap<java.lang.String,java.lang.Long> |
getReplicaVLSNRateMap()
Returns a map from replica node name to a moving average of the rate, in
VLSNs per minute, that the replica is processing replication data, if
known.
|
java.util.Map<java.lang.String,java.lang.String> |
getTips() |
long |
getTotalTxnMs()
The total time in milliseconds spent in replicated transactions.
|
long |
getTrackerLagConsistencyWaitMs()
The total time (in msec) for which a Replica held back a
Environment.beginTransaction(Transaction,TransactionConfig)
operation to satisfy the TimeConsistencyPolicy . |
long |
getTrackerLagConsistencyWaits()
The number of times a Replica held back a
Environment.beginTransaction(Transaction,TransactionConfig)
operation to satisfy the TimeConsistencyPolicy . |
long |
getTrackerVLSNConsistencyWaitMs()
The total time (in msec) for which a Replica held back a
Environment.beginTransaction(Transaction,TransactionConfig)
operation to satisfy the CommitPointConsistencyPolicy . |
long |
getTrackerVLSNConsistencyWaits()
The number of times a Replica held back a
Environment.beginTransaction(Transaction,TransactionConfig)
operation to satisfy the CommitPointConsistencyPolicy . |
long |
getVLSNRate()
A moving average of the rate replication data is being generated by the
master, in VLSNs per minute, or 0 if not known or this node is not the
master.
|
java.lang.String |
toString()
Returns a string representation of the statistics.
|
java.lang.String |
toStringVerbose() |
public int getNFeedersCreated()
public int getNFeedersShutdown()
getNFeedersCreated()
public long getNMaxReplicaLag()
public java.lang.String getNMaxReplicaLagName()
public java.util.SortedMap<java.lang.String,java.lang.Long> getReplicaDelayMap()
public java.util.SortedMap<java.lang.String,java.lang.Long> getReplicaLastCommitTimestampMap()
public java.util.SortedMap<java.lang.String,java.lang.Long> getReplicaLastCommitVLSNMap()
public java.util.SortedMap<java.lang.String,java.lang.Long> getReplicaVLSNLagMap()
public java.util.SortedMap<java.lang.String,java.lang.Long> getReplicaVLSNRateMap()
public long getNTxnsAcked()
Durability.ReplicaAckPolicy
policy associated with the
transaction commit.public long getNTxnsNotAcked()
Durability.ReplicaAckPolicy
policy associated with the transaction commit.
These transactions resulted in InsufficientReplicasException
or
InsufficientAcksException
.public long getTotalTxnMs()
getAckWaitMs()
.public long getAckWaitMs()
Durability.ReplicaAckPolicy
to be satisfied during successful transaction
commits.getTotalTxnMs()
public long getLastCommitVLSN()
public long getLastCommitTimestamp()
public long getVLSNRate()
public long getNReplayCommits()
public long getNReplayCommitAcks()
NReplayCommits
statistic, if the Durability
policy used by transactions on the master calls for transaction commit
acknowledgments and the Replica is current with respect to the Master.public long getNReplayCommitSyncs()
public long getNReplayCommitNoSyncs()
public long getNReplayCommitWriteNoSyncs()
public long getNReplayAborts()
public long getNReplayNameLNs()
public long getNReplayLNs()
public long getReplayElapsedTxnTime()
public long getNReplayGroupCommitTimeouts()
group timeout
interval
being exceeded.public long getNReplayGroupCommitMaxExceeded()
max group size
being
exceeded.public long getNReplayGroupCommitTxns()
public long getNReplayGroupCommits()
public long getReplayMinCommitProcessingNanos()
public long getReplayMaxCommitProcessingNanos()
public long getReplayTotalCommitProcessingNanos()
public long getNProtocolBytesRead()
If the node has served as both a Replica and Master since it was first started, the number represents the sum total of all Feeder related network activity, as well as Replica network activity.
public long getNProtocolMessagesRead()
If the node has served as both a Replica and Master since it was first started, the number represents the sum total of all Feeder related network activity, as well as Replica network activity.
public long getNProtocolBytesWritten()
If the node has served as both a Replica and Master since it was first started, the number represents the sum total of all Feeder related network activity, as well as Replica network activity.
public long getNProtocolMessagesBatched()
getNProtocolMessagesWritten()
getNProtocolMessageBatches()
public long getNProtocolMessageBatches()
getNProtocolMessagesBatched()
public long getNProtocolMessagesWritten()
If the node has served as both a Replica and Master since it was first started, the number represents the sum total of all Feeder related network activity, as well as Replica network activity.
public long getProtocolReadNanos()
If the node has served as both a Replica and Master since it was first started, the number represents the sum total of all Feeder related network activity, as well as Replica network activity.
public long getProtocolWriteNanos()
If the node has served as both a Replica and Master since it was first started, the number represents the sum total of all Feeder related network activity, as well as Replica network activity.
public long getProtocolMessageReadRate()
If the node has served as both a Replica and Master since it was first started, the number represents the message reading rate over all Feeder related network activity, as well as Replica network activity.
public long getProtocolMessageWriteRate()
If the node has served as both a Replica and Master since it was first started, the number represents the message writing rate over all Feeder related network activity, as well as Replica network activity.
public long getProtocolBytesReadRate()
If the node has served as both a Replica and Master since it was first started, the number represents the bytes reading rate over all Feeder related network activity, as well as Replica network activity.
public long getProtocolBytesWriteRate()
If the node has served as both a Replica and Master since it was first started, the number represents the bytes writing rate over all Feeder related network activity, as well as Replica network activity.
public long getNProtocolEntriesWrittenOldVersion()
public long getTrackerLagConsistencyWaits()
Environment.beginTransaction(Transaction,TransactionConfig)
operation to satisfy the TimeConsistencyPolicy
.public long getTrackerLagConsistencyWaitMs()
Environment.beginTransaction(Transaction,TransactionConfig)
operation to satisfy the TimeConsistencyPolicy
.public long getTrackerVLSNConsistencyWaits()
Environment.beginTransaction(Transaction,TransactionConfig)
operation to satisfy the CommitPointConsistencyPolicy
.public long getTrackerVLSNConsistencyWaitMs()
Environment.beginTransaction(Transaction,TransactionConfig)
operation to satisfy the CommitPointConsistencyPolicy
.public java.lang.String toString()
toString
in class java.lang.Object
public java.lang.String toStringVerbose()
public java.util.Map<java.lang.String,java.lang.String> getTips()
Copyright (c) 2002, 2017 Oracle and/or its affiliates. All rights reserved.


19 August 2013
At the adaptTo() Apache Sling conference I gave a talk about RESTFul Transactions with Apache Sling.
REST and transactions is an oxymoron.
Trying to force a stateless model into something per definition stateful concept is most likely wrong. However, almost every developer using Apache Sling was at least once faced with the question or requriement to use transactions in his application.
Most engineers are familiar with the theory and usage of transaction working in an old fashion J2EE environment. When transitioning to a modern REST application framework like Sling, in most cases the solution is to use hibernate or similar tools, to leave and violate almost all REST principles.
In this Session we will talk about transactions in general, explain the different kind of transactions and especially how to deal with services layers in conjunctions with Apache Sling. Various approaches, to help J2EE developers to transition fully to a RESTful world, will be explained.
In this Article I want to cover following Questions:
This is the questions of all questions, why do we have to write about transactions? There has been an signifitant shift in web business. I am the classical year 2k kind of guy. Fancy, nice but this is no longer the case, And the projects: ppl ask spring etc.
Not only digital representation of your print flyers Or business cards Remeber big jpgs?
Once you make money, it is getting serious , remember subdomains? Forums. Or guestbook. ? You can’t do that anymore! Before j2ee applications in house was important for business, now we web developer took over
Your Website IS THE Application
An input message to a computer system that must be dealt with as a single unit of work
1
2
3
4
5
begin transaction
debit checking account
credit savings account
update history log
commit transaction
Late 70s: Jim Gray defined the semantic of reliable transactions
“James nicholas” Gray One single unit. No error and consistent, either transaction si complete without any errors or nothing will be done and you have to rollback the changes in the system
Early 80s: Andreas Reuter and Theo Härder defined the acronym “ACID” in their paper “Principles of Transaction-Oriented Database Recovery
Atomoicity: “all or nothing” – on error, roll back! Consistency: transaction will bring the data from one valid state into another valid state. “it is alwayx the appliaction programmer is repsonsible that it is right” Isolation: two ppl might read and don’t know that there r transactions Durability: once it is committed it will remain so. Power loss, crash, errors.
How do tools like for instance Hybernate deal with Transactions?
1
2
3
4
5
6
7
8
9
10
11
12
13
Session sess = factory.openSession();
Transaction tx = null;
try {
tx = sess.beginTransaction();
//do some working
...
tx.commit();
} catch (RuntimeException e) {
if(tx != null) tx.rollback();
throw e;
} finally {
sess.close();
}
REST and transaction is an oxymoron.
Atomoicity: “all or nothing” – on error, roll back! Consistency: transaction will bring the data from one valid state into another valid state. “it is alwayx the appliaction programmer is repsonsible that it is right” Isolation: two ppl might read and don’t know that there r transactions Durability: once it is committed it will remain so. Power loss, crash, errors.
Resource: /home/senol/profile
Begin transaction change interests Commit transaction
Copy the node /home/senol/profile
to /home/senol/transactions/profile/*
code example1:
1
2
3
4
5
6
<form action="/home/senol/transactions/profile/*" method="POST">
<input type="hidden" name="@CopyFrom" value="/home/senol/profile">
<input type="hidden" name="status" value="begin">
<input type="hidden" name=":replace" value="true">
<input type="hidden" name=":redirect" value="/hoe/senol/transactions/profile/*.html">
</form>
Update the node with a regular post request
1
2
3
4
<form action="/home/senol/transactions/profile/1_13800000477631" method="POST">
<input type="hidden" name="status" value="inprogress">
<input type="hidden" name="interest" value="my interests">
</form>
We have multiple solutions to actually perform the commit
.
1
2
3
<form action="/home/senol/transactions/profile/1_13800000477631" method="POST">
<input type="hidden" name="status" value="commit">
</form>
1
2
3
4
5
6
<form action="/home/senol/profile" method="POST">
<input type="hidden" name="@CopyFrom" value="/home/senol/transactions/profile/1_13800000477631">
<input type="hidden" name="status" value="updated">
<input type="hidden" name=":replace" value="true">
<input type="submit" value="save / commit">
</form>
Have a look at the Workflow Launcher!


Kolibri HTML5App testing ground
Any issues will show up below in all caps:
INLINE SCRIPTS FAILED TO EXECUTE
INLINE STYLES DO NOT WORK!
READING STATIC RESOURCE FAILED!
If the following iframe is empty, then nested IFrames are broken:
PROBLEM using postMessage in iframe


Pompeii Consulting
IT Consulting | IT Management
Information Security and Controls
11 July 2016
Business Intelligence
11 July 2016
Strategic Value of Information Technology
11 July 2016
Data and Information Management
11 July 2016
Future
Page 5 of 5
Past


@* * Copyright 2017 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import uk.gov.hmrc.play.views.html.helpers._ @import uk.gov.hmrc.cbcrfrontend.AppConfig @import uk.gov.hmrc.cbcrfrontend.views.html._ @import helper._ @import tags._ @(asideBusiness: Html, phaseBannerBeta: Html, form:Form[_])( implicit request: Request[_], messages: Messages) @sidebarContent = { @asideBusiness } @fullNameErrors = @{form.error("fullName").isDefined} @agencyBusinessNameErrors = @{form.error("agencyBusinessName").isDefined} @jobRoleErrors = @{form.error("jobRole").isDefined} @contactPhoneErrors = @{form.error("contactPhone").isDefined} @emailErrors = @{form.error("email").isDefined} @main_template("Country by Country Reporting", sidebarLinks = Some(sidebarContent)) {


Documentation of the EmailClassificatieSoftware package¶
Hello! This is the documentation for the ‘EmailClassificatieSoftware’ package for Dutch Text Classification (the research accompanying this package was focussed on email classification). This package contains several often-used and state-of-the-art models for text classification, which can be trained on Dutch text. It also contains some utility classes and functions that can be used for preparing and anonymizing training data. For the neural networks using word embeddings, the word embeddings developed for Dutch by Stephan Tulkens and Chris Emmery and Walter Daelemans [TED16] in are used.
Indices and tables¶
References¶
- DCLT18
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
- TED16
Stephan Tulkens, Chris Emmery, and Walter Daelemans. Evaluating unsupervised dutch word embeddings as a linguistic resource. In Nicoletta Calzolari (Conference Chair), Khalid Choukri, Thierry Declerck, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, and Stelios Piperidis, editors, Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016). Paris, France, may 2016. European Language Resources Association (ELRA).


Test-Work
Academy
SERVICES
PRODUCT CATEGORIES
Accessories
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Romantic
Sport
Toys
SALE
Accessories
Alcohol
Art
Books
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COMPANY
LIBRARY
CONTACT US
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Kristina Dam Oak Table With White Marble Top
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My dragons are bisbehaving again. Unbelievable!


genes line APL petal Fig. flowering Supplementary expression gene floral lines rapeseed apetalous napus using development putative early RNA-seq .-fold Arabidopsis protein DEGs analysis Brassica {border-style flower APL, PL, Table px;} #ffffff;} characteristic involved Plant RNA S). expressed Data APL. PL. QTLs performed showed time underlying up-regulated changes compared data identified significantly transition BnaCgD), Sci genes, genetic inflorescences levels photoperiod regulating study, used young (logFC = .) Biol CIs probably reads regulatory responsible terms BnaAgD) biosynthesis down-regulated group molecular number rape vernalization (logFC = −.) (q = .E-) basis development, displayed genome plants possibly px; respectively, transcription {font-family AP, However, addition, candidate controlling font-size generated implying initiation oilseed primordia quantitative regulation study thaliana (logFC = .), BnaAgD LFY LINK MADS-box PDgr SNPs acid activity" angiosperm biological collected combination correlation development. double; enriched evolutionary functioning governing high morphogenesis napus. normal patterns previous qRT-PCR quality replicates samples APETALA Arabidopsis, Based BnaAgD), China FLOWERING Kit Nature Pearson Science USA). assay assays associated basic border-color border-width cellular character component considered count detected differential fold functions higher mapping meristem napus, normally obtained organs pathways petalled phenotypic present processes regulators related required shown similar solid; specifically stages stamen suggested transcriptional variety verified vital work {font-style {font-weight ‘Holly’ Bn-A-p, Cell Chen DAG Development FLC Floral Furthermore, Gene Genetics Jiangsu LEAFY Molecular No. PISTILLATA QTL Rapeseed Subsequently, Wang aggregated based bold calculated categories comparative complex consistent containing courier; crucial duplication em; function function’ growth high-throughput homologs identification identity leaves level located main map million morphogenesis, mutants numbers organ origination package pathway potential pt;} regulate regulates results sequencing software studies total, up-regulation upstream ‘AH’ ‘biological ‘molecular


Hand Digit Classifier
One
Two
Three
Four
Five
One Digit Images:
Two Digit Images:
Three Digit Images:
Four Digit Images:
Five Digit Images:
Train Network
Once training is complete, click 'Start Predicting' to see predictions, and 'Stop Predicting' to end. Once you are happy with your model, click 'Download Model' to save the model to your local disk.
Start Predicting
Stop Predicting
Click to Download the model in JSON Format
Download Model


Data Structures
More coming here soon.
Algorithms
Sorting
Bubble sort
Quicksort
Contributing
Check out the project
on GitHub!


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one |
19/09/2021 |
When Text is too large for the cell, ellipsis will be shown. |
| 3 |
zero |
zero |
29/09/2021 |
a |
| 99 |
five |
five |
25/10/2021 |
When Text is too large for the cell, ellipsis will be shown. |
| 99 |
five |
five |
31/10/2021 |
When Text is too large for the cell, ellipsis will be shown. |
| 99 |
seven |
seven |
17/11/2021 |
When Text is too large for the cell, ellipsis will be shown. |
| 3 |
nine |
nine |
21/11/2021 |
a |


Status Overview
Broker
Controller
Battery
Canopen
Broker server settings
Battery Values
| Parameter | Current Value |
|---|---|
| Cell Voltage High | xxxx V |
| Cell Voltage Avarage | xxxx A |
| Cell Voltage Low | xxxx V |
| BMS Error Battery 1 | LED |
| BMS Error Battery 2 | LED |
| TS On | LED |
Maxon Epos current state
Device control
Switch On
Enable Operation
PID settings
Sinamics inverter current state
Device control
Switch On
Enable Operation
VOF Settings
Log messages received
Last 15 messages only| Topic | Payload | Time | QOS |
|---|


Why Ideathon 2.0
- MHRD’s initiative to promote innovation
- Top three teams will be awarded with cash prize worth 18K
- Top three teams will get an opportunity to pitch their ideas at the state and national level and may also get a chance to visit Canada or South Korea for their product development
Guidelines
- Create your account in our web portal.
- Pay 50INR To UPI ID: anju.marina.lobo@oksbi and Submit your UPI Transaction ID while submitting your Idea
- Ideas With No Associated Payment Will Not Be Evaluated
- Add your "username" in the description box of the UPI transaction page. This will help us to identify your faster.
- Download Ideathon_2.0.docx file in which instructions are given.
- Follow the instructions and the prescribed format.
- Create and upload a PDF file to the website.(MAXIMUM FILE SIZE IS 500 kB)
Other Information
- The uploaded ideas will be screened to go to the second round.
- In second round, the selected teams will have to present their idea in front of the jury panel.
FAQ
- Anyone can participate (irrespective of stream)
- Interbranch teams are allowed
- Maximum 3 members in a team
- Intercollegiate teams are not allowed.


The moodleR package contains functions and algorithms to access Moodle\(^{TM}\) data and perform reporting and/or learning analytics based in these data. For more information about Moodle go to www.moodle.org. For more information about \(\chi^\color{red}2\) (chi square) Laboratories and the work we do in learning analytics, visit our website: www.chi2labs.com.
To install the latest stable release from CRAN use:
install.packages("moodleR")
To install the latest development version:
devtools::install("chi2labs/moodleR")
After which the package is attached in the usual way.
When connecting to a moodle database moodleR will look for database information in a config file (config.yml). The fields that need to be included can be seen in the snipped below:
default:
moodleR:
user: "root"
password: "root"
dbname: "moodle38"
host: "localhost"
port: 3306
Once the connection information available you can get a connection to the database by calling mdl_get_connection()
, with the use_cache
set to false:
mdl_con <- mdl_get_connection(use_cache = FALSE)
The reason for use_cache = FALSE
will be explained below.
The moodleR package allows you to create a cache of the most relevant data on your local computer.
There are several reasons you would want to cache data locally rather than operating directly on a moodle database:
The moodleR package provides a method moodle_cache
which allows for local caching of the data.
When you run the moodle_cache
function moodleR will create a local directory mdl_cache
in your working directory. In this directory an sqlite database, mdl_cache.sqlite
, will be created to contain the cached tables.
The output should look something like this:
#> Gettng Moodle DB connection
#> Gettng sqlite connection
#> Directory ' /home/sasha/chi2/moodleR/mdl_cache' used by default.
#> Set config variable in moodleR:mdl_cache_dir to override.
#> This message is displayed once per session.
#> Filename 'mdl_cache.sqlite' used by default.
#> Set config variable in moodleR:mdl_cache_dir to override.
#> Cache file created: /home/sasha/chi2/moodleR/mdl_cache/mdl_cache.sqlite
#> Downloading course table
#> Joining, by = "categoryid"
#> Caching course table
#> Downloading discussion posts table
#> Caching discussion posts table
#> Downloading user table
#> Caching user table
#> Downloading grades table
#> Caching grades table
#> Downloading config table
#> Downloading log table
#> Caching log table
#> Downloading roles table
#> Caching role table
#> Downloading enrolments
#> Caching enrolments


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--- title: "3.3 — Entrepreneurship, Profit, and Prices — Class Content" citeproc: false bibliography: ../../static/bib/references.bib csl: ../../static/bib/chicago-syllabus-no-bib.csl output: blogdown::html_page: template: ../../pandoc/toc-title_html.template toc: true editor_options: chunk_output_type: console ---
Today we discuss how markets work in real life, and what prices actually do, and why we need them to coordinate economic activity.
This is hands down my favorite lecture to give every semester, and arguably the most important one, since we focus on reading “between the lines” of the fictional models we construct (supply and demand, perfect competition, etc.) and what happens in real world markets. All too often, people learn the models of basic economics and make one of the following mistakes:
I will argue, over the remainder of the semester, that despite the flaws in these models, they are still useful for understanding and explaining how the real world works.
The essence of this lecture is very subtle, but very important. Hence, I have tried to give you some well-known readings
Knight’s (1920) book is the most famous discussion of the perfect competition model, and how real world competition differs from the model because of uncertainty, which gives rise to the possibility of positive profit in the real world.
Hayek (1945) is one of the most famous papers in economics, arguing how the economy is not a constrained optimization problem, and that central planning faces an enormous knowledge problem, as prices are needed to tap into the fragmented, tacit knowledge dispersed across individuals (that no single person or entity can know) to coordinate the behavior of millions of people.
Alchian (1950) argues that the behavioral assumption of firms maximizing profits is incorrect, because there is no way anything can be maximized in a world of uncertainty (see Knight). It is better to view market competition in evolutionary terms, firms survive simply by earning positive profits (rather than maximum profits) by best adapting to the competitive environment — whether by intention and skill, or by sheer luck: “Even in a world of stupid men there would be profits.”
The video lecture by Gerd Gigerenzer provides a more modern approach to understanding how to model the world under uncertainty: that it is impossible to optimize, but instead to use heuristics. This stands as a strong criticism of behavioral economics (which focuses on the supposed irrationality of decision-making).
Similarly, Smith’s (2003) Nobel Prize article discusses this.
Finally, the two videos “What if There Were No Prices” and “Mises vs. Marx” are the best way to understand what was known as the “Socialist Calculation Debate” in the 1920s-1940s: can we centrally plan an economy? I cover this debate more extensively in two of my other classes (see links to ECON 317 and ECON 452).


Roberto Santana and Unai Garciarena
Department of Computer Science and Artificial Intelligence
University of the Basque Country
D. O. Hebb. The organization of behavior: A neuropsychological theory. Psychology Press. 1949.
H. Wang, B. Raj, and E. P. Xing. On the Origin of Deep Learning. arXiv preprint arXiv:1702.07800. 2017.
\( \Delta w_i \): change in synaptic weights between pre-synaptic and post-synaptic neurons.
\( \eta \): learning rate
\(x_i\): activation of pre-synaptic neuron $i$
\( y \): post-synaptic neuron reponse.
The update is proportional to the product of the activation and the response.
F. Rosenblatt. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6):386. 1958.
F. Rosenblatt. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6):386. 1958.
F. Rosenblatt. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6):386. 1958.
F. Rosenblatt. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6):386. 1958.
where \( d^j \) is the desired output for sample \( x^j \)
H. Wang, B. Raj, and E. P. Xing. On the Origin of Deep Learning. arXiv preprint arXiv:1702.07800. 2017.
H. Wang, B. Raj, and E. P. Xing. On the Origin of Deep Learning. arXiv preprint arXiv:1702.07800. 2017.
At epoch \(t\), predictions are made as:
\[
y^j(t) = \begin{cases} 1, & \mbox{if } \sum_{i=1}^{n+1} w_i(t) x^j_i \geq 0 \, \forall j \\
0, & \mbox{otherwise}
\end{cases}
\]
The error of the prediction is computed as:
\[
MSE(t) = \frac{1}{N} \sum_{j=1}^N \left ( d^j-y^j(t) \right )^2
\]
Weights are updated as:
\[
w_i(t+1) = w_i(t) + \left ( d^j -y^j (t) \right ) x_{j}^{i},
\]
F. Rosenblatt. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6):386. 1958.