String s must consist of a possibly null sequence of blanks, then an optional plus or minus sign, and finally a sequence of one or more digits. For number bases larger than 10, the digits can include a, b, c (alternately A, B, C ) which represent the digit values 10, 11, 12 The base, if given, must be in the range 2 to 36 (36 because there are 10 base ten digits and 26 letters). For example, strint ("FF", 16) = 255.
The intstr function is the inverse of strint, so for any integer i,
strint ( intstr ( i ) ) = i.


Saving a reference image.
To make a test for the turtle module you can use this page to save a reference image.
Testname:
animate:
don't animate:
import turtle t = turtle.Turtle()
Run
Save


Is Microsoft creating tomorrow's IE security holes today? | The Register
Quite an interesting piece, wondering whether Microsoft is creating problems for itself in the future with IE, with amongst other things, tight integration with the OS.
I do disagree with one or two point made though, especially "Rapid development cycles won the browser wars, and it wasn't the strong-arming or the marketing that motivated users to switch browsers, it was the features"
My memory of it was that IE wasn't that much more featureful than Netscape Navigator, and if you want to know my opinion of why Microsoft won that war it is the plain and simple fact of being bundled on the desktop when Navigator wasn't. Non-technical users do not go looking for alternate products, so long as the default one they're provided with does a reasonable job.
In fact it's telling that Firefox is gaining ground on IE, as that says to me that a percentage of Internet users no longer regard IE as doing a reasonable job.
Back to the story, I'd agree that tight OS integration is to my mind a problem for IE. I see no reason why an Operating system has to have an Internet Browser. Definately for server operating systems it seems totally redundant (although in several use cases I add that a GUI on a server is a waste of resouces).
From a security point of view having components so tightly integrated into the OS that an administrator cannot easily remove (not disable) them just increases the amount of code that needs maintained and increases the likelihood that code on the server will have an exploitable security vulnerability....


Bristol-Myers Squibb wins antitrust approval to buy Celgene on condition it sell psoriasis drug
by ReutersWASHINGTON — Bristol-Myers Squibb Co
and Celgene Corp have won U.S. antitrust
approval for their merger on condition that they sell Celgene’s
psoriasis drug Otezla, the Federal Trade Commission said in a
statement on Friday.
(Reporting by Diane Bartz;
Editing by Sandra Maler)


Featuring the fabulous Apple Click Wheel first introduced on iPod mini, iPod offers up to 12 hours of battery life. Weighs in at just 5.6 ounces. Slips into your pocket with ease. And lets you carry your entire music library with you wherever you go. iPod — available in 20GB and 40GB models for $299 and $399, respectively — continues its stand as the archetypal digital music player for Mac and Windows.
iPod (Click Wheel) models have a Click Wheel like the iPod mini, but are larger and the hold switch is on the top-right side. iPod (Click Wheel) models have a monochrome display. iPod (Click Wheel) is referred to as the fourth-generation iPod.
iPod Photo
October 2004
Why should your ears have all the fun?
A delight for the ears. A feast for the eyes. Though it’s no bigger than a pack of playing cards and weighs in at just over 6 ounces, iPod photo delivers a one-two sensory punch. Letting you carry an entire library of your favorite music — up to 15,000 songs — or enough photos — as many as 25,000 — to fill nearly 200 slide trays or cover nearly 5,000 square feet of wall space. Got a really big den?
iPod and iPod photo are now one and the same, with every white iPod boasting a full-color display. They continue to have the same controls as iPod (Click Wheel) but now all models have a color display like iPod photo—ideal for viewing album artwork and playing slideshows. These are considered fourth-generation models along with iPod (Click Wheel).


|
target/pdftable1/10.1016_j.pain.2014.09.020/tables/table1/table.png
|
| First author (year) |
Study type and quality (CONSORT/STROBE score) |
Incidence (95% CI) |
Main cancer class (chemotherapy) |
Dose (mg/m )^{2} (mean or cumulative) |
| Antonacopoulou (2009) ^{*} |
Prospective cohort |
58.8% (42.2–75.3) |
Colorectal (oxaliplatin) |
— |
| Argyriou (2006) |
Prospective cohort (18/22) |
61.5% (35.1–87.9) |
Breast (paclitaxel) |
1980 |
|
|
42.8% (16.9–68.7) |
Lung (cisplatin) |
720 |
| Argyriou (2007) [8] |
Prospective cohort (19/22) |
64% (45.2–82.8) |
Colorectal (oxaliplatin) |
1740 |
| Argyriou (2007) |
Prospective cohort (19/22) |
69.2% (44.1–94.3) |
Multiple solid (cisplatin and paclitaxel) |
126·7 |
| Argyriou (2012) |
Prospective cohort (19/22) |
83.3% (77.3–89.3) |
Colorectal (oxaliplatin) |
1646 |
| Argyriou (2013) |
Prospective cohort (20/22) |
84.5% (79.4–89.5) |
Colorectal (oxaliplatin) |
1651 |
| Attal (2009) |
Prospective cohort (19/22) |
66.6% (44.8–88.4) |
Colorectal (oxaliplatin) |
1278 |
| Baldwin (2012) |
Prospective cohort (20/22) |
67.2% (64.1–70.3) |
Breast (paclitaxel) |
— |
| Cascinu (1995) |
RCT (18/25) |
64% (45.2–82.8) |
Gastrointestinal (cisplatin) |
— |
| Cascinu (2002) |
RCT (16/25) |
78.9% (60.6–97.3) |
Colorectal (oxaliplatin) |
783 |
| Chaudhary (2008) ^{†} |
Prospective cohort (13/22) |
96.2% (89.2–103) |
Multiple myeloma (bortezomib and thalidomide) |
36 |
| Dimopoulos (2011) |
RCT (21/25) |
46.7% (41.4–52.1) |
Multiple myeloma (bortezomib) |
38·4 |
| Gandara (1995)^{‡} |
RCT (18/25) |
12.1% (5.6–18·5) |
Ovarian and lung (cisplatin) |
379 |
| Ghoreishi (2012) |
RCT (19/25) |
59.2% (40.7–77.8) |
Breast (paclitaxel) |
— |
| Glendenning (2010) ^{†} |
Cross sectional cohort (21/22) |
20.1% (15.5–24.7) |
Testicular (cisplatin and vincristine) |
400 |
| Gobran (2013) |
RCT (13/25) |
70% (53.6–86.4) |
Colorectal (oxaliplatin) |
763 |
| Ishibashi (2010) |
RCT (20/25) |
93.7% (81.9–105) |
Colorectal (oxaliplatin) |
72·8 |
| Johnson (2011) |
RCT (23/25) |
32.1% (29.1–34.9) |
Multiple myeloma (thalidomide) |
— |
|
|
19.6% (16.3–22.9) |
(Vincristine) |
— |
| Kawakami (2012) ^{†} |
Prospective cohort (14/22) |
76% (64.1–87.8) |
Lung (cisplatin and paclitaxel) |
— |
| Kemp (1996) |
RCT (19/25) |
67·5% (59.2–75.8) |
Gynecological (cisplatin) |
— |
| Krishnan (2005) |
Prospective cohort (16/22) |
50% (25.5–74.5) |
Colorectal (oxaliplatin) |
1200 |
| Lin (2006) |
Randomised trial (15/24) |
90% (71.4–108) |
Colorectal (oxaliplatin) |
1200 |
| Milla (2009) |
Randomised trial (11/24) |
92.8% (79.3–106) |
Colorectal (oxaliplatin) |
772 |
| Pace (2003) |
Randomised trial (11/24) |
85.7% (67.4–104) |
Multiple solid (cisplatin) |
420 |
| Pace (2007) |
Prospective cohort (14/22) |
92.8% (79.4–106) |
Breast (paclitaxel) |
1744 |
| Pace (2010) |
RCT (19/25) |
41.6% (21.9–61.4) |
Multiple solid (cisplatin) |
450 |
| Planting (1999) |
Randomised trial (13/24) |
13.5% (2.5–24.5) |
Multiple solid (cisplatin) |
401 |
| Plasmati (2002) |
Prospective cohort (15/22) |
96% (88.3–103) |
Multiple myeloma (thalidomide) |
18 |
| Van der Hoop (1999) |
RCT (12/25) |
41.6% (13.7–69.5) |
Gynecological (cisplatin) |
416 |
| Von Schlippe (2001) |
Prospective cohort (9/22) |
17.2% (3.4–30.9) |
Testicular (cisplatin) |
— |
| Won (2012) |
Prospective cohort (16/22) |
40.6% (30.8–50.4) |
Colorectal (oxaliplatin) |
935 |
target/pdftable1/10.1016_j.pain.2014.09.020/tables/table1/table.svg.html
|
|
target/pdftable1/10.1016_j.pain.2014.09.020/tables/table2/table.png
|
| Time of assessment (after cessation of chemotherapy) |
Prevalence (95% CI) |
Studies included |
Total no. of patients in group |
| 61 mo |
68.1% (57.7–78.4) |
Antonacopolou 2009 |
2085 |
|
|
Argyriou 2007 |
|
|
|
Argyriou 2012 |
|
|
|
Argyriou 2013 |
|
|
|
Baldwin 2012 |
|
|
|
Cascinu 1995 |
|
|
|
Cascinu 2002 |
|
|
|
Chaudhry 2008 |
|
|
|
Dimopoulos 2011 ^{*} |
|
|
|
Gandara 1995 |
|
|
|
Ghoreishi 2012 |
|
|
|
Gobran 2013 ^{*} |
|
|
|
Ishibashi 2010 |
|
|
|
Kawakami 2012 |
|
|
|
Krishnan 2005 ^{*} |
|
|
|
Lin 2006 |
|
|
|
Milla 2009 ^{*} |
|
|
|
Pace 2003 |
|
|
|
Pace 2007 ^{*} |
|
|
|
Pace 2010 |
|
|
|
Van Der Hoop 1999 |
|
|
|
Won 2012 |
|
| 3 mo |
60.0% (36.4–81.6) ^{†} |
Argyriou 2006 |
234 |
|
|
Argyriou 2007 |
|
|
|
Kemp 1996 |
|
|
|
Planting 1999 |
|
|
|
Plasmati 2007 |
|
| P6 mo |
30.0% (6.4–53.5) ^{†} |
Johnson 2011 ^{‡} |
1860 |
|
|
Attal 2009 |
|
|
|
Glendenning 2010 |
|
|
|
Von Schlippe 2001 |
|
target/pdftable1/10.1016_j.pain.2014.09.020/tables/table2/table.svg.html
|
|
target/pdftable1/10.1016_j.pain.2014.09.020/tables/table3/table.png
|
| Study type (CONSORT/STROBE) |
Main cancer class |
CIPN severity report (count by grade if given) |
CIPN assessment time points |
CIPN assessment method(s) |
| Oxaliplatin: 72.3% (95% CI = 59.7–86.8) |
|
|
|
|
| Antonacopoulou |
|
|
|
|
| Prospective cohort |
Colorectal |
NR |
Unclear |
TNSc |
| (2009) ^{*} |
|
|
|
|
| Argyriou (2007) [8] |
|
|
|
|
| Prospective cohort |
Colorectal |
Grade I (6/16) |
Baseline |
TNSc |
|
|
Grade II (8/16) |
Cycles 4, 8, 12 |
NPS |
|
|
Grade III (2/16) |
|
NCI-CTC |
| Argyriou (2012) |
|
|
|
|
| Prospective cohort |
Colorectal |
Grade I (38/125) |
Baseline |
TNSc |
|
|
Grade II (46/125) |
Cycles 3, 6 (FOLFOX) |
NPS |
|
|
Grade III (41/125) |
Cycles 4, 8 (XELOX) |
NCI-CTC |
| Argyriou (2013) ^{†} |
|
|
|
|
| Prospective cohort |
Colorectal |
Grade I (62/169) |
Baseline |
TNSc |
|
|
Grade II (46/169) |
Cycle 6, 12 (FOLFOX) |
NCI-CTC |
|
|
Grade III (61/169) |
Cycles 4, 8 (XELOX) |
|
| Attal (2009) |
|
|
|
|
| Prospective cohort |
Colorectal |
Sensory symptom counts |
Baseline |
NCI-CTC |
|
|
described as means/ |
Cycle 3, 6, 9 |
NPS |
|
|
individual |
12 ± 2 mo after chemo end |
(EORTC) QLQ-C30 |
| Cascinu (2002) |
|
|
|
|
| RCT |
Colorectal |
Grade I (4/15) |
Baseline |
NCI-CTC |
|
|
Grade II (6/15) |
Cycles 4, 8, 12 |
NPS |
|
|
Grade III (4/15) |
Within 2 wk of chemo end |
|
|
|
Grade IV (1/15) |
|
|
| Gobran (2013) |
|
|
|
|
| RCT |
Colorectal |
Grade I (7/21) |
Unclear if at baseline |
NCI-CTC |
|
|
Grade II (0/21) |
At each chemo cycle until end of chemo |
|
|
|
|
(variable no. of cycles) |
|
|
|
Grade III (14/21) |
Longer follow-up for those with CIPN (but |
|
|
|
|
denominator unclear) |
|
|
|
Grade IV (0/21) |
|
|
| Ishibashi (2010) |
|
|
|
|
| RCT |
Colorectal |
Grade I (15/15) |
Baseline |
NCI-CTC |
|
|
Grade II (1/15) |
At each chemo cycle until end of chemo |
|
|
|
Grade III (0/15) |
|
|
|
|
Grade IV (0/15) |
|
|
| Krishnan (2005) |
|
|
|
|
| Prospective cohort |
Colorectal |
NR |
No baseline |
NCI-CTC |
|
|
|
Within 1 mo of chemo end only reported |
NPS |
|
|
|
assessment |
|
|
|
|
|
TNSc |
| Lin (2006) |
|
|
|
|
| Controlled trial |
Colorectal |
Grade I (1/9) |
Baseline |
NCI-CTC |
|
|
Grade II (5/9) |
Cycles 4, 8, 12 |
NPS |
|
|
Grade III (3/9) |
Within 2 wk of end of chemo |
|
|
|
Grade IV (0/9) |
|
|
| Milla (2009) |
|
|
|
|
| Controlled trial |
Colorectal |
Grade I (0/13) |
Baseline |
NCI-CTC |
|
|
Grade II (9/13) |
Cycles 5, 9, 12 |
NES |
|
|
Grade III (4/13) |
(Some followed up longer but denominator |
|
|
|
|
unclear) |
|
| Won (2012) |
|
|
|
|
| Prospective cohort |
Colorectal |
NR |
Unclear if at baseline |
NCI-CTC |
|
|
|
At each chemo cycle until end of chemo |
NES |
|
|
|
(variable no. of cycles) |
|
| Cisplatin: 42.2% (95% CI = 21.3–63.1) |
|
|
|
|
| Argyriou (2006) ^{‡} |
|
|
|
|
| Prospective cohort |
Lung |
Reported by age group |
Baseline |
PNS |
|
|
only |
Cycles 3, 6 |
NPS |
|
|
|
3 mo after chemo end |
|
| Cascinu (1995) |
|
|
|
|
| RCT |
Gastrointestinal |
Grade I (3/16) |
Baseline |
NCI-CTC |
|
|
Grade II (10/16) |
After 9 and 15 wk of therapy |
NPS |
|
|
Grade III (2/16) |
Within 1 wk after end of chemo |
|
|
|
Grade IV (1/16) |
|
|
| Gandara (1995) |
|
|
|
|
| RCT |
Ovarian and |
Only grade P3 reported |
Unclear if at baseline |
NCI-CTC |
|
lung |
|
At each cycle until chemo end (variable no. of |
|
|
|
|
cycles) |
|
|
|
|
Study stopped early after interim analysis due |
|
|
|
|
to high toxicity in intervention group |
|
| Kemp (1996) |
|
|
|
|
| RCT |
Gynecological |
Grade I (31/81) |
Baseline |
NCI-CTC |
|
|
Grade II (35/81) |
Cycles 4, 5, 6 |
NES |
|
|
Grade III (15/81) |
Monthly after chemo for 3 mo |
|
| Pace (2003) |
|
|
|
|
| Controlled trial |
Multiple solid |
Grade I (6/12) |
Baseline |
TNSc |
|
|
Grade II (4/12) |
After 6 cycles |
NES |
|
|
Grade III & IV (2/12) |
|
|
| Pace (2010) |
|
|
|
|
| RCT |
Multiple solid |
Only grade P3 reported |
Baseline |
TNSc |
|
|
|
Every cycle for 3 cycles |
NPS |
|
|
|
1 mo after chemo end |
|
| Planting (1999) |
|
|
|
|
| Controlled trial |
Multiple solid |
Grade I (5/5) |
Baseline |
NCI-CTC |
|
|
|
Cycle 3, 6 |
NES |
|
|
|
3 mo after chemo end |
|
|
|
|
(Longer follow-up but no denominator info) |
|
| Van der Hoop (1999) |
|
|
|
|
| Controlled trial |
Gynecological |
Mean vibration threshold |
Baseline |
NES |
|
|
|
Cycles 2, 4, 6 |
|
|
|
|
End of chemo |
|
target/pdftable1/10.1016_j.pain.2014.09.020/tables/table3/table.svg.html
|
|
target/pdftable1/10.1016_j.pain.2014.09.020/tables/table4/table.png
|
| Study |
Category of risk factor reported |
Data source of study |
Sample size of study (N) |
Risk factor details |
| Argyriou (2013) |
Genetic |
Prospective cohort |
200 |
SNC4A-rs2302237 OR = 2.65 (1.15–6) |
|
|
|
|
SCN10A-rs1263292 OR = 0.39 (0.17–0.88) |
| Attal (2009) |
Clinical |
Prospective cohort |
18 |
Cold allodynia OR = 39 (1.8–817) |
|
|
|
|
Cold hyperalgesia OR = 3.9 (1.0–1.20) |
| Baldwin (2012) |
Genetic |
Prospective cohort |
855 |
FGD4-rs10771973 HR = 1.57 (1.30–1.91) |
| Dimopoulos (2011) |
Clinical |
RCT |
340 |
Baseline neuropathy HR = 1.79 (p < 0.01) |
| Glendenning (2010) |
Clinical and treatment-related |
Cross-sectional cohort |
293 |
Cisplatin dose increase OR = 1.91 (1.61–2.26) |
|
|
|
|
Carboplatin dose increase OR = 1.26 (1.04–1.52) |
|
|
|
|
Age at follow-up OR = 1.06 (1.04–1.08) |
| Johnson (2011) ^{⁄} |
Genetic |
RCT |
970 + 550 |
ABCA1-rs363717 OR = 0.71 (0.52–0.98) |
|
|
|
|
ICAM1-rs1799969 OR = 0.67 (0.44–1.03) |
|
|
|
|
PPARD-rs2076169 OR = 0.60 (0.38–095) |
|
|
|
|
SERPINB2-rs6103 OR = 0.70 (0.52–0.95) |
|
|
|
|
SLC12A6-rs7164902 OR = 0.60 (0.44–0.80) |
| Kawakami (2012) |
Clinical |
Prospective cohort |
50 |
Smoking history pack-years HR = 1.03 (1.0–1.05) |
|
|
|
|
Decreased creatinine clearance HR = 0.96 (0.92–0.99) |
| Won (2012) ^{†} |
Genetic |
Prospective cohort |
96 |
TAC1-rs10486003 |
|
|
|
|
FOXC1-rs2338 |
|
|
|
|
ITGA1-rs830884 |
|
|
|
|
ACYP2-rs843748 |
|
|
|
|
DLEU7-rs797519 |
target/pdftable1/10.1016_j.pain.2014.09.020/tables/table4/table.svg.html
|


Um, What Is a Neural Network?
It's a technique for building a computer program that learns from data. It is based very loosely on how we think the human brain works. First, a collection of software "neurons" are created and connected together, allowing them to send messages to each other. Next, the network is asked to solve a problem, which it attempts to do over and over, each time strengthening the connections that lead to success and diminishing those that lead to failure. For a more detailed introduction to neural networks, Michael Nielsen's Neural Networks and Deep Learning is a good place to start. For a more technical overview, try Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
This Is Cool, Can I Repurpose It?
Please do! We've open sourced it on GitHub with the hope that it can make neural networks a little more accessible and easier to learn. You're free to use it in any way that follows our Apache License. And if you have any suggestions for additions or changes, please let us know.
We've also provided some controls below to enable you tailor the playground to a specific topic or lesson. Just choose which features you'd like to be visible below then save refresh the page.
, orWhat Do All the Colors Mean?
Orange and blue are used throughout the visualization in slightly different ways, but in general orange shows negative values while blue shows positive values.
The data points (represented by small circles) are initially colored orange or blue, which correspond to positive one and negative one.
In the hidden layers, the lines are colored by the weights of the connections between neurons. Blue shows a positive weight, which means the network is using that output of the neuron as given. An orange line shows that the network is assiging a negative weight.
In the output layer, the dots are colored orange or blue depending on their original values. The background color shows what the network is predicting for a particular area. The intensity of the color shows how confident that prediction is.
What Library Are You Using?
We wrote a tiny neural network library that meets the demands of this educational visualization. For real-world applications, consider the TensorFlow library.
Credits
This was created by Daniel Smilkov and Shan Carter. This is a continuation of many people's previous work — most notably Andrej Karpathy's convnet.js demo and Chris Olah's articles about neural networks. Many thanks also to D. Sculley for help with the original idea and to Fernanda Viégas and Martin Wattenberg and the rest of the Big Picture and Google Brain teams for feedback and guidance.


Conscripting your kids into coding service
Kris van Rens
November 21st, 2019
So..
..probably not all of you have kids..
But..
..you might recognize something like this:
My spaceship is faster!
No! My spaceship is faster!
But my spaceship is faster still!
And my spaceship is faster still still!
My spaceship is always one hundred faster than yours!
And my spaceship is always one thousand million faster than yours!
Ad infinitum
However!
This is the perfect moment to bring up the topic of recursive functions!
Definition
A recursive function, in logic and mathematics, is a type of function or expression predicating some concept or property of one or more variables, which is specified by a procedure that yields values or instances of that function by repeatedly applying a given relation or routine operation to known values of the function.
Encyclopaedia Britannica
Recursion
Square one
long spaceship_speed(long start_speed) {
return spaceship_speed(++start_speed);
}
int main() {
return spaceship_speed(1);
}
Recursion
All the hip kids use lambdas
int main() {
auto spaceship_speed =
[&](long start_speed) {
return spaceship_speed(++start_speed);
};
return spaceship_speed(1);
}
Recursion
Whoops. C++17 to the rescue!
#include <functional>
int main() {
std::function<long(long)> spaceship_speed =
[&](long start_speed) {
return spaceship_speed(++start_speed);
};
return spaceship_speed(1);
}
Recursion
Even hipper kids do compile-time code
constexpr long spaceship_speed(long start_speed) {
return spaceship_speed(++start_speed);
}
int main() {
return spaceship_speed(1);
}
Recursion
Even hipper kids do compile-time code
constexpr long spaceship_speed(long start_speed) {
return spaceship_speed(++start_speed);
}
int main() {
return spaceship_speed(1);
}
..or do they?
Recursion
Uh-oh..
constexpr long spaceship_speed(long start_speed) {
return spaceship_speed(++start_speed);
}
template<long VALUE>
struct OmgWtf { long value_ = VALUE; };
int main() {
return OmgWtf<spaceship_speed(1)>{}.value_;
}
Recursion
Oh dear
constexpr long spaceship_speed(long start_speed) {
return spaceship_speed(++start_speed);
}
template<typename TYPE, TYPE VALUE>
struct OmgWtf { TYPE value_ = VALUE; };
int main() {
constexpr auto value = spaceship_speed(1);
return OmgWtf<decltype(value), value>{}.value_;
}
Recursion
Again, all the hip kids use lambdas
constexpr long spaceship_speed(long start_speed) {
return spaceship_speed(++start_speed);
}
template<typename TYPE, TYPE VALUE>
struct OmgWtf { TYPE value_ = VALUE; };
int main() {
const auto get_value = []() {
constexpr auto value = spaceship_speed(1);
return OmgWtf<decltype(value), value>{}.value_;
};
return get_value();
}
Recursion
IIFE to complete the mess
constexpr long spaceship_speed(long start_speed) {
return spaceship_speed(++start_speed);
}
template<typename TYPE, TYPE VALUE>
struct OmgWtf { TYPE value_ = VALUE; };
int main() {
return [] {
constexpr auto value = spaceship_speed(1);
return OmgWtf<decltype(value), value>{}.value_;
}();
}
Identifying recruitment opportunities
Multi-threading issues
Multiple children == multithreading issues
Toy-sharing hell
Toys are resources; races and deadlocks occur.

All the time.
Another one: resource overcommitment
Holidays, birthday parties anyone?
Another one: resource overcommitment
Holidays, birthday parties anyone?
I guess you get it by now.
Hey, would you please clean up your toys?
…
Hey, would you please clean up your toys?
…
HEY, now will you please clean up your toys?
…
…
Sigh.


{% extends "base.html" %} {% block title %}Authorized CloudDAV Users{% endblock %} {% block header %}Authorized CloudDAV Users{% endblock %} {% block content %}
Note:
The following users are granted access to this CloudDAV drive:


RED KIMONO (director: Walter Lang; screenwriters: Dorothy Arzner/Adela Rogers St. John; cinematographer: James Diamond; cast: Priscilla Bonner (Gabrielle Darley), Theodore Von Eltz (Frederick), Virginia Pearson (Mrs. Fontaine), Nellie Bly Baker (Clara), Mary Carr (The Matron), Emily Fitzroy (The Housekeeper), Carl Miller (Howard Blaine); Runtime: 75; 1925-silent)
Reviewed by Dennis Schwartz
This silent film from 1925 is based on a true story as reported in the Hearst newspapers by Adela Rogers St. John. It was produced by Dorothy Davenport, who liked to call herself Mrs. Wallace Reid. Her husband, Wallace, was a big silent screen star, but died two years before the film from his morphine addiction. He developed the drug habit after an accident while filming and having that drug prescribed to him. The screenplay was written by Dorothy Azner, who made it into a statement film about women being abused in a male-dominated society.
The film starts out in New Orleans and tells about the lack of love in Gabrielle Darley's (Priscilla Bonner) house, where she was a young lady.  After promising to marry her, she one day finds herself abandoned by him, and is comforted by her best friend Clara.
She goes to Los Angeles and while looking into a jewelry store, she spots Howard buying a wedding ring. Something snaps inside her when confronting Howard, as she will state in court -- "When I saw him buying the wedding ring...with the money I earned..."
At the trial, she tells her story and of her years in bondage to Howard. The sympathetic jury comes in with a not guilty verdict. Now she only wants redemption and to work off her guilt and do service to others to help restore her good name. A leading social figure and philanthropist, Mrs. Fontaine (Virginia Pearson), takes her home as a guest and gets notoriety in the papers about her good deed of helping out the unfortunate one. Mrs. Fontaine's chauffeur, Fred (Theodore Von Eltz), falls in love with her and they shyly act friendly to one another. But when Mrs. Fontaine gets all the publicity out of her that she could get, she gives her a letter of recommendation to the County Hospital to become a nurse and asks her to leave the residence. The hospital director recognizes her from the trial and refuses to hire her, so she wanders the streets of Los Angeles where she can't find work. Fred, meanwhile, had to take Mrs. Fontaine to Sacramento for a month and so she couldn't have his support.
The movie is an unbearable tearjerker, as Gabrielle is forced to return to New Orleans to get help and meets with one misfortune after another. Fred, when he learns that she went there, gets fired from his job, after giving some lip to Mrs. Fontaine for being such a phony. Unfortunately, before Fred could reach her in New Orleans, Gabrielle was being sexually attacked in the street by a burly man and when she ran from him, she got hit by a car and is taken to the hospital for a prolonged stay. Fred tries the address she should have been staying at, but finds out she never showed up there and no one knows where she is. Why she didn't tell her friends via note where she was, is beyond me.
At the hospital, when she recovers, America just entered World War 1 and the hospital is in desperate need for helpers. She becomes a cleaning woman, and one day Fred comes into the hospital in an Army uniform and they meet again. In a melodramatic scene, they embrace and he asks her to marry him, telling her in a few minutes he is shipping out to the front. She tells him that she wants to wait until after the war.
As an upshot of this true story, after the picture became very popular, the real Gabrielle Darley got upset that her name was once again public knowledge, as she wanted to forget the past and live with the good name she has since achieved. She takes Mrs. Wallace Reid, the producer, to court, and wins her law suit for infringement of her rights, and as payment she gets the luxury house Mrs. Reid's husband left her.
The film itself was unconvincing and too banal to strike a chord with me. It therefore doesn't surprise me, that, perhaps, Gabrielle wasn't as nice a person as portrayed in the film. This film is worth a look only for historical reasons, to see how melodrama was done before there were talkies.
REVIEWED ON 8/15/2000 GRADE: C-
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
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Related links: index of all rec.arts.movies.reviews reviews


Gecode::Support::StaticStack< T > Class Template Reference
[Support algorithms and datastructures]
#include <static-stack.hh>
Detailed Description
template<class T>
class Gecode::Support::StaticStack< T >
Stack with fixed number of elements.
Requires
#include "gecode/support/static-stack.hh"
Definition at line 36 of file static-stack.hh.
Public Member Functions | |
| StaticStack (const int n) | |
| Initialize for n elements. | |
| ~StaticStack (void) | |
| Release memory. | |
| void | reset (void) |
| Reset stack (pop all elements). | |
| bool | empty (void) const |
| Test whether stack is empty. | |
| T | pop (void) |
| Pop topmost element from stack and return it. | |
| T & | top (void) |
| Return element on top of stack. | |
| T & | last (void) |
| Return element that has just been popped. | |
| void | push (T x) |
| Push element x on top of stack. |
Constructor & Destructor Documentation
|
|
Initialize for n elements.
Definition at line 66 of file static-stack.hh. |
|
|
Release memory.
Definition at line 73 of file static-stack.hh. |
Member Function Documentation
|
|
Reset stack (pop all elements).
Definition at line 85 of file static-stack.hh. |
|
|
Test whether stack is empty.
Definition at line 79 of file static-stack.hh. |
|
|
Pop topmost element from stack and return it.
Definition at line 91 of file static-stack.hh. |
|
|
Return element on top of stack.
Definition at line 97 of file static-stack.hh. |
|
|
Return element that has just been popped.
Definition at line 103 of file static-stack.hh. |
|
|
Push element x on top of stack.
Definition at line 109 of file static-stack.hh. |
The documentation for this class was generated from the following file:
- gecode/support/static-stack.hh (Revision: 3188)


#include <uw-mac-DACAP-alter.h>
Class that represents a DACAP node.
Definition at line 156 of file uw-mac-DACAP-alter.h.
◆ MMacDACAP()
◆ ~MMacDACAP()
| MMacDACAP::~MMacDACAP |
( |
| ) |
|
|
virtual |
◆ backoffEndTime()
| virtual void MMacDACAP::backoffEndTime |
( |
| ) |
|
|
inlineprotectedvirtual |
◆ backoffStartTime()
| virtual void MMacDACAP::backoffStartTime |
( |
| ) |
|
|
inlineprotectedvirtual |
◆ command()
| int MMacDACAP::command |
( |
int |
argc, |
|
|
const char *const * |
argv |
|
) |
| |
|
virtual |
TCL command interpreter.
It implements the following OTcl methods:
- Parameters
-
| argc | Number of arguments in argv. |
| argv | Array of strings which are the command parameters (Note that argv[0] is the name of the object). |
- Returns
- TCL_OK or TCL_ERROR whether the command has been dispatched successfully or not.
Definition at line 444 of file uw-mac-DACAP-alter.cpp.
◆ computeTxTime()
| double MMacDACAP::computeTxTime |
( |
int |
type | ) |
|
|
protectedvirtual |
Computes the time that is needed to transmit a packet, implementing a CrLayMessage for asking the PHY layer to perform this calculation.
- Parameters
-
- Returns
- double the time needed for transmission in seconds
Definition at line 594 of file uw-mac-DACAP-alter.cpp.
◆ computeWaitTime()
| double MMacDACAP::computeWaitTime |
( |
int |
mode, |
|
|
double |
distance |
|
) |
| |
|
protectedvirtual |
Computes the T_w parameter, that is the minimum time that the sender has to wait between the reception of CTS packet and the effective transmission of data packet.
- Parameters
-
| mode | indicates whether the ACK mode or no_ACK mode is activated |
| distance | between the sender and the receiver |
- Returns
- double the value of T_w
Definition at line 633 of file uw-mac-DACAP-alter.cpp.
◆ crLayCommand()
| int MMacDACAP::crLayCommand |
( |
ClMessage * |
m | ) |
|
|
virtual |
Cross-Layer messages interpreter.
- Parameters
-
| ClMessage* | an instance of ClMessage that represent the message received |
- Returns
- 0 if successful.
Definition at line 511 of file uw-mac-DACAP-alter.cpp.
◆ deferEndTime()
| virtual void MMacDACAP::deferEndTime |
( |
double |
duration | ) |
|
|
inlineprotectedvirtual |
Sets the end time of the defer summing the duration to the actual duration of defer.
- Parameters
-
| double | duration of the defer time |
Definition at line 707 of file uw-mac-DACAP-alter.h.
◆ exitBackoff()
| void MMacDACAP::exitBackoff |
( |
| ) |
|
|
inlineprotectedvirtual |
◆ exitSleep()
| void MMacDACAP::exitSleep |
( |
| ) |
|
|
protectedvirtual |
This method erase the MAC address of the two nodes that have intiated a handshake procedure if this handshake is done.
Definition at line 694 of file uw-mac-DACAP-alter.cpp.
◆ freezeBackoff()
| void MMacDACAP::freezeBackoff |
( |
| ) |
|
|
inlineprotectedvirtual |
◆ getBackoffTime()
| double MMacDACAP::getBackoffTime |
( |
| ) |
|
|
protectedvirtual |
◆ getCtsPktsRx()
| virtual int MMacDACAP::getCtsPktsRx |
( |
| ) |
|
|
inlineprotectedvirtual |
◆ getCtsPktsTx()
| virtual int MMacDACAP::getCtsPktsTx |
( |
| ) |
|
|
inlineprotectedvirtual |
◆ getMeanDeferTime()
| virtual double MMacDACAP::getMeanDeferTime |
( |
| ) |
|
|
inlineprotectedvirtual |
◆ getQueueSize()
| virtual int MMacDACAP::getQueueSize |
( |
| ) |
|
|
inlineprotectedvirtual |
◆ getRecontendTime()
| double MMacDACAP::getRecontendTime |
( |
| ) |
|
|
protectedvirtual |
◆ getRemainingPkts()
| virtual int MMacDACAP::getRemainingPkts |
( |
| ) |
|
|
inlineprotectedvirtual |
- Returns
- Number of data packets that are remained in the data queue at the end of simulation
Definition at line 632 of file uw-mac-DACAP-alter.h.
◆ getRtsPktsRx()
| virtual int MMacDACAP::getRtsPktsRx |
( |
| ) |
|
|
inlineprotectedvirtual |
◆ getRtsPktsTx()
| virtual int MMacDACAP::getRtsPktsTx |
( |
| ) |
|
|
inlineprotectedvirtual |
◆ getTotalDeferTimes()
| virtual int MMacDACAP::getTotalDeferTimes |
( |
| ) |
|
|
inlineprotectedvirtual |
◆ getUpLayersDataPktsRx()
| int MMacDACAP::getUpLayersDataPktsRx |
( |
| ) |
|
|
inlineprotected |
◆ getWrnPktsRx()
| virtual int MMacDACAP::getWrnPktsRx |
( |
| ) |
|
|
inlineprotectedvirtual |
◆ getWrnPktsTx()
| virtual int MMacDACAP::getWrnPktsTx |
( |
| ) |
|
|
inlineprotectedvirtual |
◆ incrCtsPktsRx()
| virtual void MMacDACAP::incrCtsPktsRx |
( |
| ) |
|
|
inlineprotectedvirtual |
◆ incrCtsPktsTx()
| virtual void MMacDACAP::incrCtsPktsTx |
( |
| ) |
|
|
inlineprotectedvirtual |
◆ incrRtsPktsRx()
| virtual void MMacDACAP::incrRtsPktsRx |
( |
| ) |
|
|
inlineprotectedvirtual |
◆ incrRtsPktsTx()
| virtual void MMacDACAP::incrRtsPktsTx |
( |
| ) |
|
|
inlineprotectedvirtual |
◆ incrTotalDeferTimes()
| virtual void MMacDACAP::incrTotalDeferTimes |
( |
| ) |
|
|
inlineprotectedvirtual |
◆ incrWrnPktsRx()
| virtual void MMacDACAP::incrWrnPktsRx |
( |
| ) |
|
|
inlineprotectedvirtual |
◆ incrWrnPktsTx()
| virtual void MMacDACAP::incrWrnPktsTx |
( |
| ) |
|
|
inlineprotectedvirtual |
◆ initInfo()
| void MMacDACAP::initInfo |
( |
| ) |
|
|
protectedvirtual |
Initializes the protocol at the beginning of the simulation.
This method is called by a command in tcl.
- See also
- command method
Definition at line 525 of file uw-mac-DACAP-alter.cpp.
◆ initPkt()
| void MMacDACAP::initPkt |
( |
Packet * |
p, |
|
|
int |
pkt_type |
|
) |
| |
|
protectedvirtual |
Initializes the packet with the correct MAC source address and destination address, the correct type and size.
- Parameters
-
| Pointer | to the packet that we have to initialize |
| type | of the packet that we are going to initialize |
Definition at line 802 of file uw-mac-DACAP-alter.cpp.
◆ Phy2MacEndRx()
| void MMacDACAP::Phy2MacEndRx |
( |
Packet * |
p | ) |
|
|
protectedvirtual |
Method called when the Phy Layer finish to receive a Packet.
- Parameters
-
| const | Packet* Pointer to an object of type Packet that rapresent the packet received |
Definition at line 962 of file uw-mac-DACAP-alter.cpp.
◆ Phy2MacEndTx()
| void MMacDACAP::Phy2MacEndTx |
( |
const Packet * |
p | ) |
|
|
protectedvirtual |
Method called when the PHY layer finish to transmit the packet.
- Parameters
-
| Packet* | Pointer to an object of type Packet that rapresent the Packet transmitted |
Definition at line 864 of file uw-mac-DACAP-alter.cpp.
◆ Phy2MacStartRx()
| void MMacDACAP::Phy2MacStartRx |
( |
const Packet * |
p | ) |
|
|
protectedvirtual |
Method called when the Phy Layer start to receive a Packet.
- Parameters
-
| const | Packet* Pointer to an object of type Packet that rapresent the Packet that is in reception |
Definition at line 956 of file uw-mac-DACAP-alter.cpp.
◆ printStateInfo()
| void MMacDACAP::printStateInfo |
( |
double |
delay = 0 | ) |
|
|
protectedvirtual |
Prints a file with every state change for debug purposes.
- Parameters
-
| delay | from the call of the method and the effective write process on the file |
Definition at line 2281 of file uw-mac-DACAP-alter.cpp.
◆ queuePop()
| virtual void MMacDACAP::queuePop |
( |
bool |
flag = true | ) |
|
|
inlineprotectedvirtual |
◆ recvFromUpperLayers()
| void MMacDACAP::recvFromUpperLayers |
( |
Packet * |
p | ) |
|
|
protectedvirtual |
Receive the packet from the upper layer (e.g.
IP)
- Parameters
-
| Packet* | pointer to the packet received |
Definition at line 776 of file uw-mac-DACAP-alter.cpp.
◆ refreshState()
| virtual void MMacDACAP::refreshState |
( |
int |
state | ) |
|
|
inlineprotectedvirtual |
Refresh the state of the protocol.
- Parameters
-
| int | current state of the protcol |
Definition at line 500 of file uw-mac-DACAP-alter.h.
◆ resetStats()
| virtual void MMacDACAP::resetStats |
( |
| ) |
|
|
inlineprotectedvirtual |
◆ rxStateBackoff()
| void MMacDACAP::rxStateBackoff |
( |
Packet * |
p | ) |
|
|
protectedvirtual |
The case of reception while the protocol is in BACKOFF state is handled.
- Parameters
-
| Packet* | pointer to the packet is reception |
Definition at line 1216 of file uw-mac-DACAP-alter.cpp.
◆ rxStateIdle()
| void MMacDACAP::rxStateIdle |
( |
Packet * |
p | ) |
|
|
protectedvirtual |
The case of reception while the protocol is in IDLE state is handled.
- Parameters
-
| Packet* | pointer to the packet is reception |
Definition at line 1153 of file uw-mac-DACAP-alter.cpp.
◆ rxStateRecontendWindow()
| void MMacDACAP::rxStateRecontendWindow |
( |
Packet * |
p | ) |
|
|
protectedvirtual |
The case of reception while the protocol is in RecontendWindow state is handled.
- Parameters
-
| Packet* | pointer to the packet is reception |
Definition at line 1866 of file uw-mac-DACAP-alter.cpp.
◆ rxStateSendWarning()
| void MMacDACAP::rxStateSendWarning |
( |
Packet * |
p | ) |
|
|
protectedvirtual |
The case of reception while the protocol is in SendWarning state is handled.
- Parameters
-
| Packet* | pointer to the packet is reception |
Definition at line 1786 of file uw-mac-DACAP-alter.cpp.
◆ rxStateWaitACK()
| void MMacDACAP::rxStateWaitACK |
( |
Packet * |
p | ) |
|
|
protectedvirtual |
The case of reception while the protocol is in WaitACK state is handled.
- Parameters
-
| Packet* | pointer to the packet is reception |
Definition at line 1380 of file uw-mac-DACAP-alter.cpp.
◆ rxStateWaitCTS()
| void MMacDACAP::rxStateWaitCTS |
( |
Packet * |
p | ) |
|
|
protectedvirtual |
The case of reception while the protocol is in WaitCTS state is handled.
- Parameters
-
| Packet* | pointer to the packet is reception |
Definition at line 1302 of file uw-mac-DACAP-alter.cpp.
◆ rxStateWaitData()
| void MMacDACAP::rxStateWaitData |
( |
Packet * |
p | ) |
|
|
protectedvirtual |
The case of reception while the protocol is in WaitDATA state is handled.
- Parameters
-
| Packet* | pointer to the packet is reception |
Definition at line 1433 of file uw-mac-DACAP-alter.cpp.
◆ rxStateWaitWarning()
| void MMacDACAP::rxStateWaitWarning |
( |
Packet * |
p | ) |
|
|
protectedvirtual |
The case of reception while the protocol is in WaitWarning state is handled.
- Parameters
-
| Packet* | pointer to the packet is reception |
Definition at line 1710 of file uw-mac-DACAP-alter.cpp.
◆ rxStateWaitXAck()
| void MMacDACAP::rxStateWaitXAck |
( |
Packet * |
p | ) |
|
|
protectedvirtual |
The case of reception while the protocol is in WaitXAck state is handled.
- Parameters
-
| Packet* | pointer to the packet is reception |
Definition at line 1608 of file uw-mac-DACAP-alter.cpp.
◆ rxStateWaitXCts()
| void MMacDACAP::rxStateWaitXCts |
( |
Packet * |
p | ) |
|
|
protectedvirtual |
The case of reception while the protocol is in WaitXCts state is handled.
- Parameters
-
| Packet* | pointer to the packet is reception |
Definition at line 1482 of file uw-mac-DACAP-alter.cpp.
◆ rxStateWaitXData()
| void MMacDACAP::rxStateWaitXData |
( |
Packet * |
p | ) |
|
|
protectedvirtual |
The case of reception while the protocol is in WaitXData state is handled.
- Parameters
-
| Packet* | pointer to the packet is reception |
Definition at line 1550 of file uw-mac-DACAP-alter.cpp.
◆ rxStateWaitXWarning()
| void MMacDACAP::rxStateWaitXWarning |
( |
Packet * |
p | ) |
|
|
protectedvirtual |
The case of reception while the protocol is in WaitXWarning state is handled.
- Parameters
-
| Packet* | pointer to the packet is reception |
Definition at line 1653 of file uw-mac-DACAP-alter.cpp.
◆ setBackoffNodes()
| void MMacDACAP::setBackoffNodes |
( |
double |
node, |
|
|
double |
node_b |
|
) |
| |
|
protectedvirtual |
Saves the MAC address of another couple of nodes that is the handshake procedure in order to access the channel.
Definition at line 1064 of file uw-mac-DACAP-alter.cpp.
◆ stateBackoff()
| void MMacDACAP::stateBackoff |
( |
| ) |
|
|
protectedvirtual |
The protocol is in Backoff state.
The node hasn't received the ACK packet and the backoff timer is set up for the re-transmission of the DATA packet
Definition at line 2003 of file uw-mac-DACAP-alter.cpp.
◆ stateCTSReceived()
| void MMacDACAP::stateCTSReceived |
( |
| ) |
|
|
protectedvirtual |
◆ stateDataReceived()
| void MMacDACAP::stateDataReceived |
( |
Packet * |
data_pkt | ) |
|
|
protectedvirtual |
The protocol is in the DataReceived state.
a Data Packet is received
- Parameters
-
| Packet* | pointer to the data packet received |
Definition at line 2224 of file uw-mac-DACAP-alter.cpp.
◆ stateDeferData()
| void MMacDACAP::stateDeferData |
( |
| ) |
|
|
protectedvirtual |
The protocol is in the DeferData state.
The node has received a WRN packet or has received a CTS packet coming from other nodes. The scheduled transmission is deferred
Definition at line 2176 of file uw-mac-DACAP-alter.cpp.
◆ stateIdle()
| void MMacDACAP::stateIdle |
( |
| ) |
|
|
protectedvirtual |
◆ stateRecontendWindow()
| void MMacDACAP::stateRecontendWindow |
( |
| ) |
|
|
protectedvirtual |
The protocol is in RecontendWindow state.
In multi-hop scenario after a transmission the node enter in a recontention window before return in IDLE state
Definition at line 2269 of file uw-mac-DACAP-alter.cpp.
◆ stateSendAck()
| void MMacDACAP::stateSendAck |
( |
| ) |
|
|
protectedvirtual |
The protocol is in SendAck state.
The node is sending an ACK after the reception of a data packet (if the acknowledgement is active)
Definition at line 2247 of file uw-mac-DACAP-alter.cpp.
◆ stateSendCTS()
| void MMacDACAP::stateSendCTS |
( |
| ) |
|
|
protectedvirtual |
The protocol is in SendCTS state.
The node is sending CTS afeter the reception of a RTS packet
Definition at line 2258 of file uw-mac-DACAP-alter.cpp.
◆ stateSendData()
| void MMacDACAP::stateSendData |
( |
| ) |
|
|
protectedvirtual |
The protocol is in the SendData state.
The handshaking phase is done. The node transmit his data packet
Definition at line 2197 of file uw-mac-DACAP-alter.cpp.
◆ stateSendRTS()
| void MMacDACAP::stateSendRTS |
( |
| ) |
|
|
protectedvirtual |
The protocol is in SendRTS state.
A RTS packet is prepared to be transmitted
Definition at line 1988 of file uw-mac-DACAP-alter.cpp.
◆ stateSendWarning()
| void MMacDACAP::stateSendWarning |
( |
| ) |
|
|
protectedvirtual |
The protocol is in the SendWarning state.
The node has received a CTS or RTS packet from a third node not involved in the transmission. The node is sending the WRN packet to the sender to warning the possibility of a collision is the transmission is occuring
Definition at line 2120 of file uw-mac-DACAP-alter.cpp.
◆ stateWaitACK()
| void MMacDACAP::stateWaitACK |
( |
| ) |
|
|
protectedvirtual |
◆ stateWaitCTS()
| void MMacDACAP::stateWaitCTS |
( |
| ) |
|
|
protectedvirtual |
The protocol is in the WaitCTS state.
The node has just sent a RTS packet and now he's waiting for the answer with a CTS packet
Definition at line 2137 of file uw-mac-DACAP-alter.cpp.
◆ stateWaitData()
| void MMacDACAP::stateWaitData |
( |
| ) |
|
|
protectedvirtual |
The protocol is in the WaitData state.
The exchanging of RTS/CTS packets is done. Now the node is waiting for the data packet
Definition at line 2150 of file uw-mac-DACAP-alter.cpp.
◆ stateWaitWarning()
| void MMacDACAP::stateWaitWarning |
( |
| ) |
|
|
protectedvirtual |
The protocol is in WaitWarning state.
The node has received a CTS or RTS packet from a third node not involved in the transmission. The node is waiting the WRN packet from the receiver
Definition at line 2104 of file uw-mac-DACAP-alter.cpp.
◆ stateWaitXAck()
| void MMacDACAP::stateWaitXAck |
( |
| ) |
|
|
protectedvirtual |
The protocol is in WaitXAck state.
The node has received a data packet coming from a third node that is not involved in the transmission. Now the node is waiting for the ACK packet coming from the destinatay of the data packet (if the acknowledgement is active)
Definition at line 2077 of file uw-mac-DACAP-alter.cpp.
◆ stateWaitXCts()
| void MMacDACAP::stateWaitXCts |
( |
| ) |
|
|
protectedvirtual |
The protocol is in WaitXCts state.
The node has received a RTS packet coming from a third node that is not involved in the transmission. Now the node is waiting for the CTS coming from the node destinatary of the RTS
Definition at line 2054 of file uw-mac-DACAP-alter.cpp.
◆ stateWaitXData()
| void MMacDACAP::stateWaitXData |
( |
| ) |
|
|
protectedvirtual |
The protocol is in WaitXData state.
The node has received a CTS packet coming from a third node that is not involved in the transmission. Now the node is waiting for the data packet coming from the node destinatary of the CTS
Definition at line 2065 of file uw-mac-DACAP-alter.cpp.
◆ stateWaitXWarning()
| void MMacDACAP::stateWaitXWarning |
( |
| ) |
|
|
protectedvirtual |
The protocol is in WaitXWarning state.
The node is waiting for a Warning coming from a third node not involved in the transmission
Definition at line 2088 of file uw-mac-DACAP-alter.cpp.
◆ txAck()
| void MMacDACAP::txAck |
( |
| ) |
|
|
protectedvirtual |
◆ txCts()
| void MMacDACAP::txCts |
( |
| ) |
|
|
protectedvirtual |
◆ txData()
| void MMacDACAP::txData |
( |
| ) |
|
|
protectedvirtual |
◆ txRts()
| void MMacDACAP::txRts |
( |
| ) |
|
|
protectedvirtual |
◆ txWrn()
| void MMacDACAP::txWrn |
( |
| ) |
|
|
protectedvirtual |
◆ waitForUser()
| void MMacDACAP::waitForUser |
( |
| ) |
|
|
inlineprotectedvirtual |
Used for debug purposes.
(Permit to have a "step by step" behaviour of the protocol)
Definition at line 2318 of file uw-mac-DACAP-alter.cpp.
◆ DACAPBTimer
◆ DACAPTimer
◆ ACK_size
◆ alpha_
smoothing factor used for statistics using first order IIR filter
Definition at line 795 of file uw-mac-DACAP-alter.h.
◆ backoff_counter
| int MMacDACAP::backoff_counter |
|
protected |
◆ backoff_duration
| double MMacDACAP::backoff_duration |
|
protected |
◆ backoff_first_start
| double MMacDACAP::backoff_first_start |
|
protected |
Timestamp at which the backoff timer is started for the first time (if the timer has been freezed many times.
Definition at line 818 of file uw-mac-DACAP-alter.h.
◆ backoff_freeze_mode
| bool MMacDACAP::backoff_freeze_mode |
|
protected |
◆ backoff_pending
| bool MMacDACAP::backoff_pending |
|
protected |
true if the backoff-timer is active (i.e.
there's a backoff process)
Definition at line 756 of file uw-mac-DACAP-alter.h.
◆ backoff_remaining
| double MMacDACAP::backoff_remaining |
|
protected |
◆ backoff_start_time
| double MMacDACAP::backoff_start_time |
|
protected |
◆ backoff_timer
◆ backoff_tuner
| double MMacDACAP::backoff_tuner |
|
protected |
◆ buffer_pkts
| int MMacDACAP::buffer_pkts |
|
protected |
◆ cts_pkts_rx
| int MMacDACAP::cts_pkts_rx |
|
protected |
◆ cts_pkts_tx
| int MMacDACAP::cts_pkts_tx |
|
protected |
◆ CTS_size
◆ curr_data_pkt
| Packet* MMacDACAP::curr_data_pkt |
|
protected |
◆ curr_dest_addr
| int MMacDACAP::curr_dest_addr |
|
protected |
◆ curr_state
| int MMacDACAP::curr_state |
|
protected |
◆ defer_data
| bool MMacDACAP::defer_data |
|
protected |
◆ defer_times_no
| double MMacDACAP::defer_times_no |
|
protected |
◆ delta_D
| double MMacDACAP::delta_D |
|
protected |
Value (in m) that indicates how far we want the CTS propagates over the sender before initiate the data transmission process (it determines the T_w value)
Definition at line 776 of file uw-mac-DACAP-alter.h.
◆ delta_data
| double MMacDACAP::delta_data |
|
protected |
Dimension difference (in bytes) among data packets ( 0 if the packets have always the same dimension)
Definition at line 779 of file uw-mac-DACAP-alter.h.
◆ fout
Object that handle the file where the state transitions is written (if the print_transitions option is active)
Definition at line 840 of file uw-mac-DACAP-alter.h.
◆ has_buffer_queue
| bool MMacDACAP::has_buffer_queue |
|
protected |
◆ HDR_size
◆ info
| map< int, string > MMacDACAP::info |
|
staticprotected |
◆ last_data_id_rx
| int MMacDACAP::last_data_id_rx |
|
protected |
◆ last_data_id_tx
| int MMacDACAP::last_data_id_tx |
|
protected |
◆ last_reason
| int MMacDACAP::last_reason |
|
protected |
◆ max_backoff_counter
| double MMacDACAP::max_backoff_counter |
|
protected |
◆ max_payload
| int MMacDACAP::max_payload |
|
protected |
◆ max_prop_delay
| double MMacDACAP::max_prop_delay |
|
protected |
One way maximum propagation delay (in seconds) in the network.
Definition at line 782 of file uw-mac-DACAP-alter.h.
◆ max_tx_tries
| double MMacDACAP::max_tx_tries |
|
protected |
Maximum transmission tries for one packet before discarding the packet.
Definition at line 785 of file uw-mac-DACAP-alter.h.
◆ multihop_mode
| bool MMacDACAP::multihop_mode |
|
protected |
true if the multihop mode is active to simulate a multihop scenario with routing
Definition at line 766 of file uw-mac-DACAP-alter.h.
◆ op_mode
◆ prev_state
| int MMacDACAP::prev_state |
|
protected |
◆ print_transitions
| bool MMacDACAP::print_transitions |
|
protected |
true if the state-transitions of the protocol is printed on a file
Definition at line 762 of file uw-mac-DACAP-alter.h.
| std::queue<Packet *> MMacDACAP::Q |
|
protected |
◆ rts_pkts_rx
| int MMacDACAP::rts_pkts_rx |
|
protected |
◆ rts_pkts_tx
| int MMacDACAP::rts_pkts_tx |
|
protected |
◆ RTS_sent_time
| double MMacDACAP::RTS_sent_time |
|
protected |
◆ RTS_size
◆ rttsamples
| int MMacDACAP::rttsamples |
|
protected |
◆ RxActive
◆ session_active
| bool MMacDACAP::session_active |
|
protected |
true if a session is active (i.e.
an hand-shaking process is occurring
Definition at line 754 of file uw-mac-DACAP-alter.h.
◆ session_distance
| double MMacDACAP::session_distance |
|
protected |
◆ sleep_node_1
| int MMacDACAP::sleep_node_1 |
|
protected |
◆ sleep_node_2
| int MMacDACAP::sleep_node_2 |
|
protected |
◆ srtt
◆ start_tx_time
| double MMacDACAP::start_tx_time |
|
protected |
◆ sum_defer_time
| double MMacDACAP::sum_defer_time |
|
protected |
◆ sumrtt
◆ sumrtt2
| double MMacDACAP::sumrtt2 |
|
protected |
◆ t_min
◆ T_W_min
| double MMacDACAP::T_W_min |
|
protected |
◆ timer
◆ TxActive
◆ txsn
◆ u_data_id
◆ u_pkt_id
◆ wait_costant
| double MMacDACAP::wait_costant |
|
protected |
◆ warning_sent
| bool MMacDACAP::warning_sent |
|
protected |
◆ wrn_pkts_rx
| int MMacDACAP::wrn_pkts_rx |
|
protected |
◆ wrn_pkts_tx
| int MMacDACAP::wrn_pkts_tx |
|
protected |
◆ WRN_size
The documentation for this class was generated from the following files:


Teleplay by: Steven Rosenhaus
Story by: R. Lee Flemming, Jr.
Transcribed by: Eric Aasen
[Scene: The Hallway, Rachel walks up the stairs and knocks on Joeys door.]
Rachel: Joey? Are you in there?
[Cut to inside Monica and Chandlers, Monica, Chandler and Joey are eating breakfast.]
Joey: (hearing Rachel and jumping up with his plate) Oh God! Thats Rachel!
Monica: Joey, you have to talk to her!
Joey: No-no, I cant! I cant! Not after the other night, its just its too weird, okay? Dont tell her Im here! (Turns to run to the bathroom and his bagel falls off the plate onto the floor.) Dont eat that! (Runs to the bathroom as Rachel enters.)
Rachel: Hey!
Chandler: Hey!
Monica: Hey Rachel!
Rachel: Is Joey here?
Chandler: I dont see him. (To Monica) Do you see him?
Monica: I dont see him. Hey! Maybe hes in the sugar bowl! (Opens the sugar bowl) Joey? Nope! (Closes the sugar bowl and they both laugh.)
Rachel: Well, at least you make each other laugh.
Monica: Whats up?
Rachel: Well, I havent seen him since that night that he told me how he yknow I dont know, I think hes avoiding me. Why is that bagel on the floor?
Monica: We were playing a game.
Rachel: Ew, was Chandler naked? Sort of like a, like a ring toss kind of situation?
Monica: Sure.
Chandler: What?! No! No!
Rachel: All right. Well listen, if you see Joey will you just tell him uh tell him I miss him. (Exits and Joey enters.)
Monica: (To Joey) Okay, did you hear that?
Joey: Yeah, a naked bagel game? (Picks up his dropped bagel.) (To Chandler) Dude, I dont know. Thats a pretty small hole.
Monica: Honey, you gotta talk to her.
Joey: I cant! Yknow?
Chandler: (incredulous) I dont know what thats like?! Up until I was 25 I thought the only response to, "I love you," was, "Oh crap!"
Monica: Hello? No rejection? I got shot down at fat camp! Boy, kids are mean when theyre hungry.
Joey: All right so, so what do I do?
Monica: This is Rachel. I mean, what are you gonna do, never going to talk to her again? I mean I know its weird, its awkward, but you gotta at least try.
Joey: Yeah. Okay. (Goes to take a bite out of the previously mentioned bagel.) Whoa! (Stops.) I almost forgot this was on your
Chandler: (interrupting him) We didnt play it!!
Opening Credits
[Scene: Central Perk, Phoebe is holding a book as she, Rachel, and Monica drink some tea as Chandler looks on.]

Chandler: I didnt know you read tea leaves.
Phoebe: Oh yeah, Ive done it for years. I actually stopped because I was so accurate. Yknow, and-and yknow, one of the great joys of life is its-its wondrous unpredictability. Yknow? And also tea tends to give me the trots.
Monica: Okay, Im done. Read mine.
Phoebe: Okay. (Looks at the leaves.) Ooh, I see a ladder. (Checks the book) Which can mean either a promotion or a violent death.
Monica: (stunned) I-Im the head chef. I-I cant get promoted.
Phoebe: Uh-hmm. Uh-hmm, whos next?
Rachel: Okay, Im done. Do mine.
Phoebe: Okay. (Reads the leaves) Umm, oh! Okay, I see a circle.
Rachel: Ah.
Phoebe: Oh! (Checking the book) Which can either mean youre having a baby or youre gonna make a scientific discovery!
Rachel: Well, I have been spending a lot of time in the lab.
Chandler: What does yours say Pheebs?
Phoebe: Umm Wow, all right. (Checks the book.) Wow! Yay! Ooh, Im gonna meet a guy! And really soon! And hes gonna be the man of my dreams. Probably not the guy I had a dream about last night. (Points at Chandler.)
Ross: (entering) Hey! Has anyone seen my shirt? Its a button down, like a, like a faded salmon?
Monica: You mean your pink shirt?
Ross: Faded salmon color.
Monica: No, I-I havent seen your pink shirt.
Ross: Great! Great. Then I mustve left it at Monas. I knew it!
Chandler: Well, Im sure you get another one at Ann Taylors.
Ross: Thats my favorite shirt! Okay? I love that shirt!
Rachel: Well just ask Mona to give it back!
Ross: I dont know. I mean I-I guess I could. Its just that we didnt really end things such good terms.  (Walks away.)
Phoebe: (noticing a guy sitting by the green post looking at her) Oh wait a second you guys for the last couple weeks Ive been that guy everywhere I go. We take the same bus. We go to the same bookstore, the same dry cleaners; maybe hes the tea guy. (He gets up to leave, and smiles at Phoebe.)
Chandler: Phoebe, did you see that?! He totally checked you out! He is so cute! (Looking at his tea.) Mine has a picture of The Village People, what does that mean?
(Joey enters.)
Rachel: Hi!
Joey: Hey.
(He slowly walks to the other side of the couch and sits down at the table, an awkward silence follows.)
Rachel: Hi.
Joey: Hi.
(More awkward silence.)
Monica: Tea gives Phoebe the trots.
[Scene: Monica and Chandler's, Chandler is cooking as Rachel paces nervously.]
Rachel: So I thought Joey and I would be okay once we hung out, but its not even like we know how to be with each other anymore.
Chandler: I know its tough now, but things will get better.
Rachel: How do you know that? What if it just gets worse and worse and worse, to the point where we cant even be in the same room with each other?!
Chandler: Im not great at the advice, can I interest you in a sarcastic comment? Some cheese?
Monica: Honey, what is the Bruce Springsteen CD in the Kat Stevens case?
Chandler: Lets just say if I cant find the right CD case I just put it in the nearest one.
Monica: Okay, where is the Kat Stevens CD?
Chandler: In the James Taylor case.
Monica: Where is the James Taylor CD?

Monica: Okay. No need to panic. Deep breathes everyone. Okay umm uh, were just gonna have to spend some time and put the CDs in the right cases.
Chandler: Well, if were gonna do that we should come up with some kind of order. Yknow alphabetically or by genre?
Monica: Hmm, I dont know. We really have to talk this through.
Rachel: Oh my God!! You guys have such problems!! I feel so terrible for you!
Monica: Okay, I-Im sorry. You and Joey, your both focusing on this uncomfortable thing, what you need to do is to change the subject. Next time you see him try to get him talking about something else.
Rachel: Oh yeah. That makes sense.
Monica: Yeah, like I dont know, maybe you have a work problem that you need his advice on.
Rachel: Ooh, I can do that.
Monica: Good. (To Chandler) Uh honey, the Miami Vice soundtrack? Really?
Chandler: They were just giving those away at the store (off Monicas look) in exchange for money.
Joey: (entering) Hey!
Rachel: Hi.
Monica: Uh I really dont know what to tell you Rach, I really dont. I mean, maybe Joey can help you out with your, with your big work problem.
Rachel: What?
Monica: Yeah Joey shes Rachels got this really big work problem, and it is a head scratcher. Wow! (To Chandler) Yknow what, if were gonna make dinner were gonna have to leave. Yeah. (She and Chandler exit.)
Joey: So you uh, have a big work problem?
Rachel: Yeah its umm Yeah its uh It-its yknowIts nothing.
Joey: Huh. Okay. (Awkward silence.) So uh, I think Im gonna take off.
Rachel: YeahNo wait! Joey no wait it is. Its something. Its-its umm its my boss.
Joey: Yeah?
Rachel: Yeah, and umm my baby.
Joey: Yeah?
Rachel: My boss wants to buy my baby!
Joey: What?! Oh my-oh my God!
Rachel: I know I told you, its a really big problem.
Joey: What he wants to buy your baby?!
Rachel: Can you believe that?!
Joey: Thats crazy!
Rachel: Thats what I told him!
Joey: Okay, how did this even happen?
Rachel: Well Ill tell ya! (Pause) See uh my-my boss and his wifeThey-they cant have children. So umm, and thatwe were at the Christmas party, and he got drunk, and he said to me, "Rachel, I want to buy your baby."
Joey: Man! When you said it was a problem about your boss and the baby I figured it was something about maternity leave.
Rachel: Ohh! Yeah! (Pause) Yeah that-that wouldve been a much simpler problem.
[Scene: A newsstand, Phoebe is looking at a magazine as the guy from before walks by and picks up a newspaper.]
Phoebe: Oh hello.
Guy: Oh, its you. I see you everywhere. Im Jim, Jim Nelson.
Phoebe: Oh Jim, Jim Nelson Im Phoebe, Phoebe Buffay. We certainly have been seeing a lot of each other lately.
Jim: We have. Maybe well be seeing each other at dinner tomorrow night, say around 8 oclock?
Phoebe: Well, maybe we will. (Starts to walk away.) Oh! (She turns around and the exchange information.)
[Scene: Outside Monas Apartment, Ross is knocking on the door.]
Ross: Mona? (Theres no answer, so he starts to leave but remembers where her extra key is. He reaches atop a hall light just outside her door and grabs the key.  After dropping the key he pulls his shirtsleeve over his hand and uses the key to open the door and enter Monas apartment.) Okay, if I were a salmon shirt, where would I be? (He hears a key in the door and as it opens he dives behind the couch.)
Mona: (entering, with her date) I am so sorry I spilled wine all over your shirt.
Mona's Date: Oh, its okay.
Mona: No, its still wet. Yknow what? Let me get it out before it sets. Ooh, I have something you can wear. Here. (Hands him Rosss shirt.)
Mona's Date: Oh umm, I-I dont know if I want to wear a womans shirt.
Mona: No-no thats a mans shirt.
Mona's Date: Its awfully pink. (Ross mouths, "Its salmon!")
[Scene: Monica and Chandler's, Monica and Chandler are sorting their CDs.]
Chandler: Oh my God, honey we are so meant to be together. We both have copies of the Annie soundtrack.
Monica: Honey, both yours.
Rachel: (entering) Hey! Great advice on that Joey thing!
Monica: Yeah? The work problem?
Rachel: Oh it was perfect! I mean it really felt like he was my friend again.
Chandler: What problem did you tell him you had?
Rachel: Oh thats not important. The point is, I reallyI think everythings gonna be okay.
[Scene: Mr. Zelners Office, hes in a meeting as Joey bursts in.]
Mr. Zelner: May I help you?
Joey: Do you think you can just buy my friends baby?!
Commercial Break
[Scene: A restaurant, Phoebe and Jim are on their date.]
Phoebe: Isnt it funny how we kept running into each other? Its as if someone really wants us to be together.
Jim: Someone does. Me.
Phoebe: Oh, witty banter. Well done.
Jim: So, tell me a little bit about yourself.
Phoebe: Oh okay, well Im a masseuse, and I used to work at this place
Jim: Do you like to party?
Phoebe: I-I-I like, I like parties.
Jim: Youre wild, arent ya?
Phoebe: Yeah I guess, a little.
Jim: It aint no thing, Im wild too.
Phoebe: (a little freaked out) So! Umm, anyway I-I lived in New York, someone wildly I guess, for ummWell since I was fourteen.
Jim: Im sorry. Im staring. Its just that you have the most beautiful eyes.
Phoebe: Oh stop it.
 Hmm!!!
Phoebe: Okay.  But Im going to give you the benefit of the doubt, because it seems the universe really wants to be together. So, why dont we just start over okay? And you can just tell me about yourself.
Jim: All right.
Phoebe: Okay.
Jim: I write erotic novels, for children.
Phoebe: What?!
Jim: Theyre wildly unpopular.
Phoebe: Oh my God!
Jim: Oh also, you might be interested to know that I have a Ph.D.
Phoebe: Wow! You do?
Jim: Yeah, (looks at his crotch) a Pretty Huge
Phoebe: All right. (Gets up and walks out.)
[Scene: Monica and Chandler's, Monica, Chandler, and now Rachel are organizing CDs.]
Chandler: (singing) The sunll come out tomorrow! Bet your bottom dollar that tomorrow (The girls start laughing, and in a deep voice) therell be sun.
Joey: (entering) Hey!
Rachel: Hey! Oh Joey, honey listen, thank you for talking to my yesterday about that thing with my boss. That really meant a lot.
Joey: Not a problem.
Rachel: (worried) What?
Joey: Lets just say I took care of it.
Rachel: Whoa-whoa-whoa, lets say more!
Joey: Dont worry! Dont worry. I just told him, very nicely, "You dont go buying peoples babies, so back off!!"
Rachel: What?!
Chandler and Monica: What?!
Rachel: No! No, no-no-no Joey he doesnt want to buy my baby! I made that up!
Joey: What?! Why?!
Rachel: So that we would have something to talk about! So it wouldnt be awkward!
Joey: And you couldnt think of anything else?!
Monica: You said your boss wants to buy your baby?!
Rachel: (To Joey) I cant believe that you yelled at my boss! Im-Im gonna lose my job! What am I going to do?!
Chandler: You can always sell your baby.
Rachel: Oh Joey, I cant believe you brought my boss into this! Im gonna get fired!
Joey: You lied to me!
Rachel: Well, she told me too! (Points to Monica and Joey glares at her.)
Monica: Chandler has two copies of Annie!
[Scene: Monas Apartment, she and her date are making out as Ross flips through a magazine while lying behind the couch and sees something that he likes. Meanwhile, Monas date takes off Rosss shirt and Mona throws it on the floor. While they start making out again, Ross tries to pull the rug the shirt is on over to him, but while he does that he moves the coffee table and it bumps into the couch.]
Mona: (seeing Ross) Oh my God! Ross!!!
Ross: Hello!!
Mona: Ross, what are you doing?!
Ross: Not touching myself if that makes anyone less uncomfortable.
[Scene: Mr. Zelners Office, Rachel is knocking on the door.]
Rachel: Morning. You wanted to see me?
Mr. Zelner: Please, come in. Have a seat. (She does so.)
Rachel: Okay look Mr. Zelner
Mr. Zelner: Oh I think its best that I speak first.
Rachel: Yeah. (Motions for him to continue.)
Mr. Zelner: Ive asked Lee from human resources to be here as a witness to our conversation.
Rachel: Oh God.
Mr. Zelner: If I in any way implied that I wanted to buy your baby I am sorry. Okay? Last week when I asked you when your due date was uh, I certainly did not mean that I felt that I was due your baby. Yeah, I want to be very clear that I understand that its your baby, and it is not mine to purchase.
Rachel: Well, as long as we are clear about that. (Exits smugly.)
[Scene: A Street, Phoebe and Monica are walking down it.]
Phoebe: Oh there it is.
Monica: Thats not your regular dry cleaners.
Phoebe: I know, but that creep that I went on that date with goes to there so I have to find a new one. I also have to find a new video store, a new bank, a new adult bookstore, a new grocery store
Monica: What?!
Phoebe: A new grocery store. The universe said I was going to meet a nice guy and thats what they gave me?
 If I can meet a great guy, so can you.
Phoebe: Yeah, we both can. And we both will.
(They enter the dry cleaners.)

Monica: Are you kidding? This is where they get out stains! Okay? This is like Disneyland for me. Im-Im gonna be over here watching the dance of the clean shirts. (She points to and walks over to the electric clothes rack they have.)
Phoebe: Okay.
(A guy enters that looks suspiciously like Alec Baldwin from The Hunt for Red October, Pearl Harbor, and Beetlejuice.)
Guy: (To Phoebe) Oh, excuse me! I think you dropped s (looks at Phoebe.) Wow!
Phoebe: What?
Guy: Im sorry, its just that youre so incredibly beautiful.
Phoebe: Oh yeah well, Im sorry about that too, but what are you going to do?
Guy: I hope you dont think Im crazy but I feel like I was meant to pick this up, do you believe in that kind of thing?
Phoebe: A little. (She turns around and looks up.) (To the universe) Now youre talking.
Guy: Would you like to go out and have a cup of coffee?
Phoebe: I-I-Id love to. Let me just tell my friend.
Monica: (to the clerk) Ooh, an ink stain! Hey, can I watch how you get this out?
Phoebe: (looking around) She mustve left.
[Scene: Monica and Chandler's, Chandler is watching Joey pace nervously as Rachel enters.]
Rachel: Hi.
Joey: So? What-what-what happened?
Rachel: Its all gonna be okay. Theyre just so happy that Im not suing them that they gave me one extra month paid maternity leave. So long as I understand that the money should not construed as a down payment on this or any other child I should bear.
Chandler: Wow, Ralph Lauren is really going out of there way to show theyre not in the baby buying business.
Rachel: Chandler, can you give us a minute?

Rachel: Yeah.
Chandler: Ill be in there. (Goes into the bedroom.)
Rachel: Joey, Im really sorry that I lied to you. I was just trying to make things
Joey: I know. I know.
Rachel: It kinda worked. I mean yknow, I dont know about you buy I havent thought about our thing since all this.
Joey: Hey youre right. Yeah, its kinda been like us again a little bit.
Rachel: Yeah I know! I miss that.
Joey: Me too. I mean I havent thought at all about how I put myself out there and said all that stuff and how you didnt feel the same way about me and-and how it was really awkward.
(Awkward silence.)
Rachel: My gynecologist tried to kill me.
Closing Credits
[Scene: Ross and Rachel's, Ross is ordering something over the phone from a catalog.]
Ross: (on phone) Item J437-A, color: winterberry. (Theres a knock on the door as he hangs up the phone. He answers it to Mona.) Hi umm, listen come here, come in. (She does so.) Im so-so sorry about yesterday. I-Im really sorry. Its just that I (He picks up the pink shirt.)
Mona: (interrupting him) Listen Ross, you dont have to apologize. I understand why you were there.
Ross: You do?
Mona: Yeah, you still have feelings for me. And-and to be honest, I-I still have feelings for you. And I wish that we can work it out Ross, but we cant. Its too complicated with you and Rachel and the baby, I-I just It just wasnt meant to be.

Mona: Ross, we we have to be strong. Okay, I-Im gonna go. (She picks up the shirt Ross has just set down.) Can I? To remember you?
Ross: (laughs) No. (Takes the shirt back.)
End


Player One's hand:
Player Two's hand:
Player Three's hand:
Player Four's hand:
Down The Rabbit Hole:


@header@
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At the time of this experiment, Udacity courses currently have two options on the course overview page: “start free trial”, and “access course materials”. If the student clicks “start free trial”, they will be asked to enter their credit card information, and then they will be enrolled in a free trial for the paid version of the course. After 14 days, they will automatically be charged unless they cancel first. If the student clicks “access course materials”, they will be able to view the videos and take the quizzes for free, but they will not receive coaching support or a verified certificate, and they will not submit their final project for feedback.
In the experiment, Udacity tested a change where if the student clicked “start free trial”, they were asked how much time they had available to devote to the course. If the student indicated 5 or more hours per week, they would be taken through the checkout process as usual. If they indicated fewer than 5 hours per week, a message would appear indicating that Udacity courses usually require a greater time commitment for successful completion, and suggesting that the student might like to access the course materials for free. At this point, the student would have the option to continue enrolling in the free trial, or access the course materials for free instead. This screenshot shows what the experiment looks like.
The hypothesis was that this might set clearer expectations for students upfront, thus reducing the number of frustrated students who left the free trial because they didn’t have enough time—without significantly reducing the number of students to continue past the free trial and eventually complete the course. If this hypothesis held true, Udacity could improve the overall student experience and improve coaches’ capacity to support students who are likely to complete the course.
The unit of diversion is a cookie, although if the student enrolls in the free trial, they are tracked by user-id from that point forward. The same user-id cannot enroll in the free trial twice. For users that do not enroll, their user-id is not tracked in the experiment, even if they were signed in when they visited the course overview page.
Invariant Metrics:
Evaluation Metrics:
Metrics Not Chosen:
In order to launch the experiment, the experiment results need to align with the two goals:
These two goals correspond to the following two chosen evaluation metrics changes:
To launch the experiment, one should require to see BOTH decreased gross conversion AND increased or non-changed net conversion.
Given a sample size of 5000 cookies visiting the course overview page
Rough estimates of the baseline values for these metrics:
| Metric Name | Value |
|---|---|
| Unique cookies to view course overview page per day | 40000 |
| Unique cookies to click “Start free trial” per day | 3200 |
| Enrollments per day | 660 |
| Click-through-probability on “Start free trial” | 0.08 |
| Probability of enrolling, given click | 0.20625 |
| Probability of payment, given enroll | 0.53 |
| Probability of payment, given click | 0.1093125 |
binomSD <- function(p, n) {
return(sqrt(p * (1-p) / n))
}
# Number of clicks for the given sample
n_clicks <- 5000 * (3200 / 40000)
# Gross conversion
p_gross_conversion <- 0.20625
binomSD(p_gross_conversion, n_clicks)
## [1] 0.0202306
Gross conversion standard deviation: 0.0202. The analytic estimate would be comparable to the empirical variability, because the unit of diversion and the unit of analysis are the same: a cookie.
# Net conversion
p_net_conversion <- 0.1093125
binomSD(p_net_conversion, n_clicks)
## [1] 0.01560154
Net conversion standard deviation: 0.0156. The analytic estimate would be comparable to the empirical variability, because the unit of diversion and the unit of analysis are the same: a cookie.
## Strategy: For a bunch of Ns, compute the z_star by achieving desired alpha, then
## compute what beta would be for that N using the acquired z_star.
## Pick the smallest N at which beta crosses the desired value
# Inputs:
# The desired alpha for a two-tailed test
# Returns: The z-critical value
get_z_star <- function(alpha) {
return(-qnorm(alpha / 2))
}
# Inputs:
# z-star: The z-critical value
# s: The standard error of the metric at N=1
# d_min: The practical significance level
# N: The sample size of each group of the experiment
# Returns: The beta value of the two-tailed test
get_beta <- function(z_star, s, d_min, N) {
SE <- s / sqrt(N)
return(pnorm(z_star * SE, mean=d_min, sd=SE))
}
# Inputs:
# s: The standard error of the metric with N=1 in each group
# d_min: The practical significance level
# Ns: The sample sizes to try
# alpha: The desired alpha level of the test
# beta: The desired beta level of the test
# Returns: The smallest N out of the given Ns that will achieve the desired
# beta. There should be at least N samples in each group of the experiment.
# If none of the given Ns will work, returns -1. N is the number of
# samples in each group.
required_size <- function(s, d_min, Ns=1:20000, alpha=0.05, beta=0.2) {
for (N in Ns) {
if (get_beta(get_z_star(alpha), s, d_min, N) <= beta) {
return(N)
}
}
return(-1)
}
I will not use the Bonferroni correction during my analysis. Use an alpha of 0.05 and a beta of 0.2.
For dectecing gross conversion difference of the practical significance boundary \(d_\text{min}=0.01\), the required number of pageviews is 642475:
# Required sample size for gross conversion
pageviews_gross_conversion <- required_size(s=sqrt(0.20625*(1-0.20625)*2), d_min=0.01, alpha=0.05, Ns=1:1000000)*(40000/3200)*2
pageviews_gross_conversion
## [1] 642475
For detecting net conversion difference reaches the practical significance boundary \(d_\text{min}=0.0075\), the required number of pageviews is 679300:
# Required smaple size for net conversion
pageviews_net_conversion <- required_size(s=sqrt(0.1093125*(1-0.1093125)*2), d_min=0.0075, alpha=0.05, Ns=1:1000000)*(40000/3200)*2
pageviews_net_conversion
## [1] 679300
Because the experiment needs to be powerful enough to detect both metrics reaching practical significance boundaries, I need 679300 pageviews in total to collect to adequately power the experiment.
pageviews_required <- max(pageviews_gross_conversion, pageviews_net_conversion)
pageviews_required
## [1] 679300
I think this experiment is of low risk and the number of page views needed is fairly large, therefore diverting all traffic to the experiment can help finish the experiment in a timely fashion in 17 days.
# Number of days needed if diverting all traffic to the experiment
duration <- ceiling(pageviews_required / 40000)
duration
## [1] 17
The risk of the experiment is low, because:
# Import csv data
data_control <- read.csv('data_control.csv', row.names=1)
data_experiment <- read.csv('data_experiment.csv', row.names=1)
The meaning of each column is:
# Inputs:
# var_control: A dataframe column containing the control group data
# var_experiment: A dataframe column containing the experiment group data
# Returns: The observed fraction assgined to the control group
observed_counts_fraction <- function(var_control, var_experiment) {
return(sum(var_control) / (sum(var_control) + sum(var_experiment)))
}
# Inputs:
# var_control_numerator: A dataframe column containing the control group data numerator
# var_control_denominator: A dataframe column containing the control group data denominator
# var_experiment_numerator: A dataframe column containing the experiment group data numerator
# var_experiment_demoninator: A dataframe column containing the experiment group data denominator
# Returns: The observed proportion difference between groups
observed_proportion_difference <- function(var_control_numerator, var_control_denominator, var_experiment_numerator, var_experiment_demoninator) {
return(sum(var_experiment_numerator)/sum(var_experiment_demoninator) - sum(var_control_numerator)/sum(var_control_denominator))
}
# Inputs:
# var_control: A dataframe column containing the control group data
# var_experiment: A dataframe column containing the experiment group data
# alpha: The desired alpha level
# Returns: The 95% confidence interval for the expected fraction assigned to the control group
ci_counts_fraction <- function(var_control, var_experiment, alpha=0.05) {
p <- 0.5
z_star <- get_z_star(alpha)
SE <- sqrt(p*(1-p) / (sum(var_control) + sum(var_experiment)))
return(c(p - z_star * SE, p + z_star * SE))
}
# Inputs:
# var_control_numerator: A dataframe column containing the control group data numerator
# var_control_denominator: A dataframe column containing the control group data denominator
# var_experiment_numerator: A dataframe column containing the experiment group data numerator
# var_experiment_demoninator: A dataframe column containing the experiment group data denominator
# alpha: The desired alpha level
# Returns: The 95% confidence interval for the expected proportion difference between groups
ci_proportion_difference <- function(var_control_numerator, var_control_denominator, var_experiment_numerator, var_experiment_demoninator, alpha=0.05) {
p_pool <- (sum(var_control_numerator) + sum(var_experiment_numerator)) / (sum(var_control_denominator) + sum(var_experiment_demoninator))
z_star <- get_z_star(alpha)
SE <- sqrt(p_pool*(1-p_pool)*(1/sum(var_control_denominator) + 1/sum(var_experiment_demoninator)))
return(c(-z_star * SE, z_star * SE))
}
# Input:
# observed: The observed value
# ci: The 95% confidence interval
# Returns: A bool value indicating whether passing sanity check
pass_sanity_check <- function(observed, ci) {
return(observed>ci[1] & observed<ci[2])
}
Number of cookies
observed_n_cookies <- observed_counts_fraction(data_control$Pageviews, data_experiment$Pageviews)
observed_n_cookies
## [1] 0.5006397
ci_n_cookies <- ci_counts_fraction(data_control$Pageviews, data_experiment$Pageviews)
ci_n_cookies
## [1] 0.4988204 0.5011796
pass_sanity_check(observed_n_cookies, ci_n_cookies)
## [1] TRUE
Number of clicks
observed_n_clicks <- observed_counts_fraction(data_control$Clicks, data_experiment$Clicks)
observed_n_clicks
## [1] 0.5004673
ci_n_clicks <- ci_counts_fraction(data_control$Clicks, data_experiment$Clicks)
ci_n_clicks
## [1] 0.4958846 0.5041154
pass_sanity_check(observed_n_clicks, ci_n_clicks)
## [1] TRUE
Click-through-probability
observed_click_through_probability <- observed_proportion_difference(data_control$Clicks, data_control$Pageviews, data_experiment$Clicks, data_experiment$Pageviews)
observed_click_through_probability
## [1] 5.662709e-05
ci_click_through_probability <- ci_proportion_difference(data_control$Clicks, data_control$Pageviews, data_experiment$Clicks, data_experiment$Pageviews)
ci_click_through_probability
## [1] -0.001295655 0.001295655
pass_sanity_check(observed_click_through_probability, ci_click_through_probability)
## [1] TRUE
| Invariant Metric | Lower Bound | Upper Bound | Observed | Passed |
|---|---|---|---|---|
| Number of cookies | 0.4988 | 0.5012 | 0.5006 | Yes |
| Number of clicks | 0.4959 | 0.5041 | 0.5005 | Yes |
| Click-through-probability | -0.0013 | 0.0013 | 0.0001 | Yes |
# Inputs:
# var_control_numerator: A dataframe column containing the control group data numerator
# var_control_denominator: A dataframe column containing the control group data denominator
# var_experiment_numerator: A dataframe column containing the experiment group data numerator
# var_experiment_demoninator: A dataframe column containing the experiment group data denominator
# alpha: The desired alpha level
# Returns: The 95% confidence interval for the effective size of proportion difference between groups
ci_effective_size <- function(var_control_numerator, var_control_denominator, var_experiment_numerator, var_experiment_demoninator, alpha=0.05) {
p1 <- sum(var_experiment_numerator) / sum(var_experiment_demoninator)
p2 <- sum(var_control_numerator) / sum(var_control_denominator)
d <- p1 - p2
z_star <- get_z_star(alpha)
SE <- sqrt(p1*(1-p1)/sum(var_experiment_demoninator) + p2*(1-p2)/sum(var_control_denominator))
return(c(d - z_star*SE, d + z_star*SE))
}
# Input:
# ci: The 95% confidence interval of evaluation metric
# Returns: A bool value indicating whether the evaluation metric is statistically significant
check_statistical_significance <- function(ci) {
return(ci[1]>0 | ci[2]<0)
}
# Input:
# ci: The 95% confidence interval of evaluation metric
# dmin: The practical significane level
# Returns: A bool value indicating whether the evaluation metric is statistically significant
check_practical_significance <- function(ci, dmin) {
return(ci[1]>dmin | ci[2]<(-dmin))
}
Because both gross conversion and net conversion calculations use columns that contain rows with NA values, I will filter the data to contain only non-NA rows.
data_control_filtered <- na.omit(data_control)
data_experiment_filtered <- na.omit(data_experiment)
Gross conversion
ci_gross_conversion <- ci_effective_size(data_control_filtered$Enrollments, data_control_filtered$Clicks, data_experiment_filtered$Enrollments, data_experiment_filtered$Clicks)
ci_gross_conversion
## [1] -0.02912016 -0.01198959
check_statistical_significance(ci_gross_conversion)
## [1] TRUE
check_practical_significance(ci_gross_conversion, 0.01)
## [1] TRUE
Net conversion
ci_net_conversion <- ci_effective_size(data_control_filtered$Payments, data_control_filtered$Clicks, data_experiment_filtered$Payments, data_experiment_filtered$Clicks)
ci_net_conversion
## [1] -0.011604186 0.001856741
check_statistical_significance(ci_net_conversion)
## [1] FALSE
check_practical_significance(ci_net_conversion, 0.0075)
## [1] FALSE
| Evaluation Metric | Lower Bound | Upper Bound | Statistical Significance | Practical Significance |
|---|---|---|---|---|
| Gross conversion | -0.0291 | -0.0120 | Yes | Yes |
| Net conversion | -0.0116 | 0.0019 | No | No |
# Inputs:
# var_control_numerator: A dataframe column containing the control group data numerator
# var_control_denominator: A dataframe column containing the control group data denominator
# var_experiment_numerator: A dataframe column containing the experiment group data numerator
# var_experiment_demoninator: A dataframe column containing the experiment group data denominator
# Returns: Sign test result
sign.test <- function(var_control_numerator, var_control_denominator, var_experiment_numerator, var_experiment_demoninator) {
n_total <- length(var_control_numerator)
n_success <- sum(var_experiment_numerator/var_experiment_demoninator > var_control_numerator/var_control_denominator)
return(binom.test(n_success, n_total)$p.value)
}
# Inputs:
# p_value: The p value of a given hypothesis test
# n_tails: 1: one-tailed; 2: two-tailed
# alpha: The desired alpha level of the test
check_statistical_significance_wtih_p_value <- function(p_value, n_tails=2, alpha=0.05) {
return(p_value<alpha/n_tails)
}
Gross conversion
p_value_gross_conversion <- sign.test(data_control_filtered$Enrollments, data_control_filtered$Clicks, data_experiment_filtered$Enrollments, data_experiment_filtered$Clicks)
p_value_gross_conversion
## [1] 0.002599478
check_statistical_significance_wtih_p_value(p_value_gross_conversion)
## [1] TRUE
Net conversion
p_value_net_conversion <- sign.test(data_control_filtered$Payments, data_control_filtered$Clicks, data_experiment_filtered$Payments, data_experiment_filtered$Clicks)
p_value_net_conversion
## [1] 0.6776395
check_statistical_significance_wtih_p_value(p_value_net_conversion)
## [1] FALSE
| Evaluation Metric | p-value | Statistical Significance |
|---|---|---|
| Gross conversion | 0.0026 | Yes |
| Net conversion | 0.6776 | No |
I did not use the Bonferroni correction. The Bonferroni correction is useful to prevent false positives when there are many evaluation metrics. In this experiment, in order to be comfortable to launch the testing feature, we need to meet BOTH of the criteria:
Bonferroni correction is useful if I only require to meet ANY criteria in this experiment to launch the feature. Because I will launch the feature only when BOTH of the criteria are met, I do not need to use the Bonferroni correction.
The effective size tests results agree with the sign tests results.
I recommend not to launch the experiment.
Our first criterion is met as our data shows that gross conversion of the experiment group is practically significantly lower than that of the control group.
However, regarding our second criterion, the confidence interval of net conversion includes the negative practical significance boundary. This means it is possible that the net conversion might actually have decreased, and the second criterion is not met.
I suggest dig deeper into the data, maybe by looking at some cohorts of each group, or figure out if there are other factors that might have impacted the experiment.
I would like to suggest a follow up experiment, in which once students start free trial, the system will send the student a daily reminder email with title saying “You’ve been continuously studying for your X nanodegree for Y days. Keep being udacious and keep learning everyday.” To prevent the day number rolling back to zero, students have to come back to watch at least one lecture video or finish at least one quiz every day.
The hypothesis is that by utilizing the popular “Don’t break the chain” technique to motivate students to form a daily study habit, students are more likely to spend more time studying and feel more motivated. A motivated student is less likely to cancel his/her free trial and drop out, therefore he/she becomes a paid user eventually.
Invariant metrics:
Number of user-ids: The number of users who enroll in the free trial should be comparable between the experiment and the control group.
Evaluation metrics:
Retention: The number of user-ids to remain enrolled past the 14-day boundary (and thus make at least one payment) divided by number of user-ids to start the free trial. If the reminder email is working, students in the experiment group is less likely to cancel. We would expect the retention of the experiment group is higher than that of the control group.
Unit of diversion: user-id. Because the reminder email needs to be associated with and sent to a specific user-id.


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There are several places to eat at our Ealing site whether you want to grab a coffee to go or get lunch with friends.
• The Heart
• Feast
• lly Coffee Pod
• Go'Sasa Deli
• Freddie's Coffeehouse
• Freddie's Bar
The Heart
The Heart, located in the centre of our Ealing site, offers a wide-range of fair-trade speciality coffees and tea, drinks and meals including:
• Breakfast service: a selection of pastries and freshly made toasties.
• Lunch service: a new approach to designing your own food - including the Heavenly Burger, hand- made pizzas, soup and pasta. There is also a wide selection of sandwiches, hot jumbo sausage rolls, and jacket potatoes with a filling of the day, puddings and cold sweets.
Term hours
Monday - Thursday, 8am - 8pm.
Friday, 8am - 4pm
Saturday, 8:30am - 3:30pm
Feast
Feast serves lunch for students, staff and visitors at the University’s St Mary’s Road, Ealing site. Includ All the food is made by our catering students. They serve a variety of lunches, ing a fresh salad and pasta bar, soup, a hot menu and vegetarian options, which can be eaten in or taken away,
as well as wide selection of fresh fruit and desserts.
Students can get two courses for £5, or three for £6
Staff can get two courses for £5.50, or three for £6.50.
Term hours:
Monday - Friday, 11:45am - 1:30pm
Illy Coffee Pod
Located in the reception of St Mary's Road, the Coffee Pod welcomes you with a selection of luxury hot drinks as well as a range of sandwiches, cakes and snacks.
Term hours
Monday - Friday, 8:30am - 4pm
Freddie's Coffeehouse
Run by the UWL Students' Union, Freddie's Coffeehouse serves Starbucks coffee, fresh sandwiches, wraps and grab-and-go snacks.
Term hours
Monday - Friday, 8am - 6pm
Freddie's Bar
brFreddie's Bar, run by the UWL Students' Union, serves up fresh food and drinks in a relaxed space.
Term hours
Monday - Friday, 11am - 11pm
The Canteen
Located in the reception area, the main counter offers a selection of fair-trade coffee and tea drinks and a delicious selection of homemade meals including:
• Breakfast service will be a selection of freshly baked pastries and Breakfast toasties.
• Lunch service: a new approach to designing your own food - a new daily offer, it includes: design your own burger, hand crafted pizza, build your own soup offer, create your own pasta. In addition to the concept of the day there is a selection of sandwiches, hot jumbo sausage rolls and jacket potatoes with hot filling of the day.
Term hours
Monday - Friday, 8:30am - 5pm
Friday, 8am - 4pm
Starbucks Coffee Pod
Located in the reception area next to the cafe, the Starbucks coffee pod serves a selection of hot drinks including cappuccinos, lattés, Tazo teas and frappuccinos, as well as a range of paninis, homemade cakes and a selection of morning pastries all of which are freshly prepared.
Starbucks Coffee Pod
Located in the reception area next to the cafe, the Starbucks coffee pod serves a selection of hot drinks including cappuccinos, lattés, Tazo teas and frappuccinos, as well as a range of paninis, homemade cakes and a selection of morning pastries all of which are freshly prepared.
Term hours
Monday - Friday, 8:30am - 5pm
Friday, 8:30am - 2:30pm
Closed out of term times.
There are also vending machines offering hot and cold beverages, confectionery, crisps and snacks at both the Ealing and Brentford sites.


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This is an example exit page. Your session has been removed.
If you click back you'll be redirected back to the start page.
{% endblock %}


Katie and I decided to take advantage of the 3 day weekend over Memorial Day to backpack to Lake of the Woods. This was Finn's first backpacking trip, and a great first wilderness experience for him. We started out from the same trailhead as the Jorn Lake trip, but turned northeast by Marion Lake. The total distance of this trip was about 16.5 kilometers, since we strayed from the snow covered trail at times. This image shows a frosty Lake of the Woods.
As we left the Marion lake trailhead to The Lake of the Woods the trails were clear as far as we could see. About 1.2 kilometers in we came to the first swichtback that brings us up to Lake Ann. We came across several other hikers with fishing poles and other day hikers.
Once we got to Marion Lake the sight of snow was a litte toubling as we looked up at the plateau above the lake. It looked like we were going to have to trek over the snowpack to reach the Lake of The Woods, which is fine and even makes some aspects of the hike more fun, but there are added dangers.
Once we got to Finn's lake (an unmarked pooling of snow melt) the snowpack was more or less continues. From here on up to the lake contact with the trail as intermitent. Finn became quite useful throught this section, as he'd actually sniff out the trail and usually find it before we did.
As I was planning this trip I found that we would pass by the grave of John Swallow. The information I found about John Swallow grave was limited to the geograhpic location. At this location the trail split off to the right and would take you to the Pacific Creast Trail. We stayed right and continued on.
As we continued to trail blaze our own path we came across several dangers of the snowpack, tree wells, down trees under the snow and unseen creeks. All of these can melt the snow away from the soil first leaving thin crust of snow for you to step through. Finn also proved to be valuable here as he lead the way exposing thin tracks of ice.
We finally made it to The Lake of The Woods with wet feet. Finn was exhausted, but still found enough energy to go bury sticks and romp around the snow near camp. Katie and I took this time to indulge ourselves with some adult beverages, enjoy the scenery and set up camp.


Live. Love. Jesus. Looking for someone with the same passion for JC as I do!
Graphic Designer! I can code your website only if we get coffee sometime lol.
If you’re not just as obsessed with Game of Thrones as I am, we can’t be friends..