2020
2019
-
@misc{
title=Solving Partial Assignment Problems using Random Clique Complexes at Computational Topology Workshop, Dalian University of Technology and in Hebei Normal University, China.
authors =
year=2019
pdf=
journal=
} -
@misc{
title=On Graph Kernels and Embeddings at Computational Topology Workshop, Dalian University of Technology and in Hebei Normal University, China.
authors =
year=2019
pdf=
journal=
}
2018
-
@misc{
title=Solving Partial Assignment Problems using Random Clique Complexes at Topology Seminar, Shinshu University, Japan.
authors =
year=2018
pdf=
journal=
}
2017
-
@misc{
title=Works using Geometry and Algebraic Topology at IBM Research, New Delhi, India.
authors =
year=2017
pdf=
journal=
}
2016
-
@misc{
title=New Lower and Upper Distance Bounds for Shortest Distance Queries on Terrains at VLDB 2016, New Delhi, India.
authors =
year=2016
pdf=
journal=
} -
@misc{
title=Moving Object Databases at Big Data Workshop held at IIT Mandi, India.
authors =
year=2016
pdf=
journal=
}
2015
-
@misc{
title=Distance Bounds for Spatial Queries on Polyhedral Surfaces at Department of Computer Science and Engineering, IIT Bombay, India
authors =
year=2015
pdf=
journal=
} -
@misc{
title=Scalable Machine Learning Library Related Research at Amazon, Berlin, Germany.
authors =
year=2015
pdf=
journal=
}
2014
-
@misc{
title=Finding Better Distance Bounds for Shortest Surface Paths on 3D Terrains at Department of Computer Science and Engineering, Chalmers University of Technology, Sweden.
authors =
year=2014
pdf=
journal=
}
2013
-
@misc{
title=Building Accurate 3D Spatial Networks to Enable Next Generation Intelligent Transportation Systems at IEEE International Conference on Mobile Data Management (MDM) 2013, Milan, Italy.
authors =
year=2013
pdf=
journal=
}


During this quiz, you may not access or view any other materials, either course notes, previous homework submissions or any online material; nor may you attempt to communicate with any person other than the instructor.
Write a program that prompts the user to enter a series of integer values terminating in 0. When the input is done, report whether there were more positive than negative values ("Positive"), more negative than positive values ("Negative"), or an equal number of positive and negative values ("Equal"). Note that we do not want the sum of the numbers, just a comparison of the relative count of positive vs. negative numbers.
Your program should run like the examples shown below:
Enter number: 2 Enter number: -35 Enter number: 7 Enter number: 0 Positive
In the first example (above): there are two positive numbers (2,7) and one negative number (-35), so there are more positive numbers than negative, as indicated in the output.
Enter number: 0
Equal
Enter number: -3 Enter number: -10 Enter number: -5 Enter number: 0 Negative
Note: this program is given as a quiz, part of an ongoing assessment of your progress in the class. It may be graded differently from homework assignments. Athene will provide an estimated score, but your final score will be determined by the instructor upon reviewing your work; the final score may be higher or lower than the automated score.


A channel to discuss trading.  I mean wtf was he thinking?  Please help”
5
dukeleto10-May-19 02:57 AM
I am reading this CHAIN thing
good mother mary and joseph
is this a troll?
ryman11210-May-19 02:57 AM
Its real
Gordon Komodo10-May-19 02:58 AM
should have let "the friend" bought kmd instead(edited)
ryman11210-May-19 02:58 AM
Got 40 links for 400000
Hollowman900010-May-19 02:59 AM
“Thank you for contacting Binance Support!
Place a 12 gauge muzzle into your mouth and make sure it has solid contact against the back of your throat. Pull trigger. Wait for restart.


| print.survfit {survival} | R Documentation |
Print number of observations, number of events, the restricted mean survival and its standard error, and the median survival with confidence limits for the median.
## S3 method for class 'survfit' print(x, scale=1, digits = max(options()$digits - 4,3), print.rmean=getOption("survfit.print.rmean"), rmean = getOption('survfit.rmean'),...)
x |
the result of a call to the |
scale |
a numeric value to rescale the survival time, e.g.,
if the input data to survfit were in days,
|
digits |
Number of digits to print |
print.rmean,rmean |
Options for computation and display of the restricted mean. |
... |
for future results |
The mean and its variance are based on a truncated estimator. That is, if the
last observation(s) is not a death, then the survival curve estimate does not
go to zero and the mean is undefined.
There are four possible approaches to resolve this, which are selected by the
rmean
option.
The first is to set the upper limit to a constant, e.g.,rmean=365
.
In this case the reported mean would be the expected number of days, out
of the first 365, that would be experienced by each group. This is
useful if interest focuses on a fixed period.
Other options are "none"
(no estimate), "common"
and
"individual"
.
The "common"
option uses the maximum time for all curves in the
object as a common upper limit for the auc calculation.
For the "individual"
options the mean is computed as the area
under each curve,
over the range from 0 to the maximum observed time for that curve.
Since the end point is random, values for different curves are not
comparable and the printed standard errors are an underestimate as
they do not take into account this random variation. This option is
provided mainly for backwards compatability, as this estimate was the
default (only) one in earlier releases of the code.
Note that SAS (as of version 9.3) uses the integral up to the last
event time of each individual curve; we consider this the worst
of the choices and do not provide an option for that calculation.
The median and its confidence interval are defined by drawing a horizontal line at 0.5 on the plot of the survival curve and its confidence bands. The intersection of the line with the lower CI band defines the lower limit for the median's interval, and similarly for the upper band. If any of the intersections is not a point, then we use the smallest point of intersection, e.g., if the survival curve were exactly equal to 0.5 over an interval.
x, with the invisible flag set to prevent printing.
(The default for all print functions in R is to return the object
passed to them; print.survfit complies with this pattern. If you want to
capture these printed results for further processing, see the
table
component of summary.survfit
.)
The number of observations, the number of events, the median survival
with its confidence interval, and optionally the
restricted mean survival (rmean
) and its standard error, are printed.
If there are multiple curves, there is one line of output for each.
Miller, Rupert G., Jr. (1981). Survival Analysis. New York:Wiley, p 71.
summary.survfit
, quantile.survfit


Jamal Moon
My Github Page
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C++/Java Programmer
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Ray is a D.Eng Student at the Academy for Engineering and Technology at Fudan University. He worked in Robot Manipulation Lab for one year. Date to January 2019, It’s the first time he touched the robot on MoveItDay China. After short-term learning to manipulate a 6-DOF robot arm. He learned that he still has a long way to go. On later October, he took part in ROScon2019 at Macau, the stories about researchers and developers inspired him to move forward. Even now he struggled with his work and research, he still remembers conversations with his friends Lentin and Archu, which means a lot to him.
D.Eng in Electronics and Information, 2018
Fudan University
MA in Computer Technology, 2015
University of Shanghai for Science and Technology
BA in Software Engineering, 2011
Jiangsu University
70%
60%
55%
75%
55%
65%
Worked with the project leader to implement a face recognition algorithm on DMS(driver monitoring system) device, including data clean, model training and algorithm evaluation
Responsibilities include:
A demo of Robot Grasp by UR5


Boris Johnson casts fresh doubt on '£100billion' HS2 rail project saying it will cost 'an awful lot of money'
by James Tapsfield, Political Editor For MailonlineBoris Johnson today cast fresh doubt on the HS2 rail project - suggesting it will cost 'north of £100billion'.
The PM hinted at a rethink on the massive scheme - which would affect his own Uxbridge constituency - saying the government would review whether funding could be 'better spent'.
Interviewed on LBC radio, Mr Johnson insisted he is not 'temperamentally hostile to big infrastructure projects'.
But he said: 'If you come in and there's a project of north of £100billion probably, you have to ask yourself, it's only responsible to the taxpayer to ask whether it's being sensibly spent and that funding is being prioritised right.'
Presenter Nick Ferarri pointed out that the government's current estimate for the project costs was £88billion.
Mr Johnson said: 'Looking at how these things go, it probably will come in at over £100billion, that's my guess.
'But at the moment, you're right, it's £88billion. That is still an awful lot of money.'
An independently-led government review recommended last month that ministers commit to the full proposed network connecting London, Birmingham, Manchester and Leeds.
The leaked conclusions admitted that it is 'not affordable' within the £56billion budget set in the 2015 government spending review.
The new estimate of £88billion is likely to be increased again, the report noted.
In September, Transport Minister Grant Shapps said the first phase of the railway between London and Birmingham would be delayed by up to five years.
That section of the line was due to open at the end of 2026 but it could now be between 2028 and 2031 before the first trains run on the route.
The second phase has been delayed. That route - from Birmingham to Manchester - was due to open in 2032-33 but that has been pushed back to 2035-2040.
HS2's top line speed is about 250mph, though most trains will only run at up to 225mph - still among some of the fastest in the world.
Mr Johnson also refused to back off his famous vow to 'lie down in front of the bulldozers' to prevent Heathrow expansion.
'I don't see much sign of any bulldozers yet,' he said. 'I would have to find some way of honouring that promise.'


interface Subsystem
The Subsystem interface represents a physical component of a robot. These can be a drivetrain, a claw, etc. They should be registered at some point.
abstract fun initHardware(): Unit
This method should initialize the hardware of the component. This is typically getting all the hardware devices from hardware map. |
|
abstract fun periodic(): Unit
This method is called by Scheduler.periodic as long as the subsystem is registered. |


To save you time, we've already compared inlets PRO to VPNs and managed SaaS tunnels.
If you have a question or want to learn more, feel free to contact us


To trigger tagging use the following triggers:
Source code is available at https://github.com/nwwatson/trix-experiments


Amanda
Amanda, our AI assistant, isn't like other smart assistants, we offer extreme customizability and personalization with every step along the way. It was crucial to us that our entire ecosystem of products is easy to use, setup, and operate. This very ideology reflects in our product lineup and is recognized as one of the key elements in their design.


{% extends "base.html" %} {# TODO: I don't understand why I have to load it again =/ #} {% load datetime_duration %} {% block content %}


The Dreamlife of Angels (R) ** Starring Elodie Bouchez, Natacha Regnier Directed by Erick Zonca Year Released: 1998 A Review by Frankie Paiva
Being entertained is the purpose that all of us go to the movies. The motion pictures that fill the screens in your local multiplex shows great adventures and wonderful romance about five times a day for your viewing pleasure. Thatâ€™s why it is a rare thing when a film comes along that threatens to be a movie about the normal (or not normal it is up for you to decide) lives that people live. We are not talking about only people in America but all around the world. The French import The Dreamlife of Angels by first time directed Erick Zonca is just this type of tale. Nothing fancy, no elaborate anything, but still full of raw, pure emotion. Films like these are rarely, (if ever), made in the United States. How will US audiences take a movie that wonâ€™t excite them or make them feel happy coming out of the theater? That seems like a question for this movieâ€™s box office and word of mouth to answer.
Isa (Bouchez) has just begun working at a sewing factory in Lille when she meets Marie. Marie isnâ€™t the type of person to open up to anyone, let alone a stranger, but the two begin to form a bond of friendship. She invites her to move into the flat that she is watching over for a mother and daughter who are in a coma. The girls run about the streets looking for their ideal man and a good sum of money.
Isa is obviously much more sensitive and has a much greater amount of empathy than Marie and begins to visit the girl whom the flat belongs to in the hospital. One day when Marie tries to shoplift a leather jacket out of a store, a rich club owner named Chris (Gregoire Colin) rescues her from the police.  Distancing herself from Isa, she continues to see the man that has blinded her with feelings of love. The girls find themselves distancing from one another as their friendship turns into hatred.
The Dreamlife of Angels could be re-titled â€œWhy You Should Go To College and Get A Good Job.â€? The girls do many things in desperation of getting money and their lives are anything but dreamy. Most of the events showcased in the film range somewhere between sad, extremely depressing, or just plain horrible. The acting, on the other hand, is superb, Elodie Bouchez is wonderful as Isa and has some very emotion scenes that showcase her acting talent. Natacha Regnier is good as Marie and she does an excellent job with her tough girl attitude that occupies most of her character. Some of this Dreamlife looks like it was shot with a home video camera, but it turns out to be OK because the filming doesnâ€™t matter as much as the character development does. The film is sluggish (the 113 minute running time could easily be shortened to about 95 minutes) and slow in many parts. So I cannot quite recommend it if you are looking for a fun little movie to watch on a Saturday night. But for those that are looking for a film with a message that is (somewhat) hard to get, (I am sure that there are some people like that out there somewhere) rent The Dreamlife of Angels, it gets ** stars.
The Young-Uns: The film contains a sex scene with graphic movement. There are three separate shots featuring bare breasts, a couple is shown in bed after sex, and there is some strong language. The film is also drenched with emotional tension. Good Age 15 & Up
A Review by Frankie Paiva The 12 Year-Old Movie Reviewer E-Mail me at<EMAIL_ADDRESS>Visit my website at http://www.homestead.com/teenagemoviecritic/mainpage.html
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


SECRETS AND LIES A film review by Martin Rich Copyright 1996 Martin Rich
'Secrets and Lies', the latest film from Mike Leigh, is a complex story built around a simple theme. It asks how effectively we can live behind secrets, and looks at how little we might actually know about others in our own family. Its message, if it has one, is that openness helps - even if it's painful, and that even when the truth is ugly we're better for knowing it.
It centres around three households in very different parts of London: a young, black, single woman working as an optometrist, an upwardly mobile photographer and his wife, and a mother and her road-sweeper daughter living in a tiny, run-down, terraced house. All are connected, though to say why would be to give away too much of the film.
Like all Mike Leigh's films, this one was built up using improvisation. But its plot has a direction and a coherence which belie the method used to create it. There are points where Mike Leigh plays tricks on the audience, sometimes by involving us in the deceit that's at the centre of the film, sometimes by presenting us with scenes which may, or may not, be significant in the rest of the narrative.
Interestingly, the strongest dynamics in the film are between the women; even though the photographer is an important part, he acts as a foil to the other characters' arguments, most effectively while serving the meat from a barbecue. All the performances and characterisations are good - and the actors have resisted the temptation to develop into caricatures. Marianne Jean-Baptiste, as Hortense, the optometrist with the incredibly mobile face, and Brenda Blethyn as Cynthia, the nervous single mother both excel, but Timothy Spall as the photographer, and Phyllis Logan as his prim, obsessive, wife are also good.
Throughout the film there are incidents which touch on the themes of truth and honesty, moments where you wonder whether characters are telling the truth or not. What about the photographer's client who ascribes her facial injuries to the seat belt breaking in a car accident (she needs the picture to claim compensation)? And do we believe Hortense's friend who brags in the kitchen about her sexual exploits?
'Secrets and Lies' opens this week in the UK, and the interest that it generated at Cannes should ensure a reasonable international release. At a preview this weekend, the audience broke into a spontaneous round of applause as the credits rolled, applause which was well deserved.
-- Martin Rich City University Business School<EMAIL_ADDRESS>http://www.city.ac.uk/~sf309/home.html
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


--- layout: post title: Do Pro Golfers Feed off of their playing partners? Part 2 (I was wrong!) excerpt: I revisited my analysis from last year and discovered that my earlier conclusion is invalid. ---
In part one of this post (here), I explored whether there is a statistically significant correlation between the performance of players playing in the same grouping on the PGA Tour. I found evidence that there is a statistically significant correlation by comparing the within group variation of performance to the between group variation with ANOVA and the Kruskal-Wallis H-test. I also ran a non-parametric permutation test which also provided strong evidence for correlation of performance within a group.
In this part, I am going to go back over my assumptions and make my analysis more robust by controlling for a possible lurking variable. Then, if the evidence is still strong that there is an effect, I will explore some of the questions that I posed at the beginning of part 1: does this effect exist especially at certain tournaments? On certain courses? For certain players? During certain times? Under certain circumstances?
I will start by fastforwarding through all the data cleaning and validation steps that I performed in part one. I have made a script which performs all the transformations (motivated in part 1) and writes out a clean dataframe.
import pandas as pd
pd.options.display.max_columns = 999
import numpy as np
import warnings
import matplotlib.pyplot as plt
%matplotlib inline
## read in data and take a look at it
round_data = pd.read_csv('../data/FeedOffEffect/clean_data.csv')
round_data.head(3)
To remind people what the last 4 columns are:
Exp_StrokesGained is 0 for the first round since we had no past experience with which to produce an expectation (the data are ordered chronologically).
## data ends with the last tournament of 2016 - the tour championship
round_data.tail(3)
## how many rounds in the data?
len(round_data)
In part 1, the tests for statistical significance that I ran all assumed the the samples - the results in each group - were independent. I realized that if I did not control for skill of each player, the samples would not be independent because of the way the groupings are done - players that have done well in previous rounds are grouped together. This makes the samples of unadjusted performance not independent. To control for skill, I computed an expected performance for each golfer using his past performance and the new result variable I considered was his actual result minus his expected result.
An analogy is if we looked at whether performance on a standardized test was linked to which teacher you had at a particular school. For this we'd consider the samples to be each teacher's students and we'd look at whether the within group variation was less than what you'd expect from random chance. But what if the classes were formed by considering students' aptitude? Then the samples would no longer be independent. The correct for this, you could form a prediction of each student's performance on the test using data from before the classes were formed and use that prediction to control for inherent aptitude. This is what I did for the golfers.
I've since realized that there might be an additional lurking variable that could invalidate the assumption of independence of the samples - the difficulty of the conditions varying throughout a particular round. Since all the players in a group play under the same conditions, it stands to reason that the players' performances would vary less than you'd expect according to chance (when compared against all other groups on the same day) as the difficult of the conditions can vary throughout the day. For example, the afternoon tends to be windier than the mornings which can make the conditions more difficuly in the afternoon.
To continue the earlier analogy, let's say the students sit next to their classmates in a big auditorium. Let's say there is a distraction at one end of the room which continues thoughout the test. This would make the conditions more difficult for the classes who are sitting closest to the distraction. If the distraction impacted performance especially for certain classes (those sitting closest), the within group variation would be less than you'd expect according to chance. In other words, the samples are no longer independent because of a lurking variable.
I would like to control for this potential lurking variable and see if the effect that I found in part 1 holds up. It would be great to have really detailed weather data for each of these rounds, but I am not sure of a reliable, granular source. Instead I will use the data from within each round and use Tee_Time to fit a model that predicts the Net_Result variable. I'll then subtract the prediction from each model from the Net_Result, to obtain an outcome that I believe will be more independent than Net_Result.
Since the relationship between Tee_Time and Net_Result within each round might be non-linear, I will use a natural spline to fit the model, fitting the degrees of freedom using cross-validation.
## Define a class for a Natural Spline
## https://web.stanford.edu/~hastie/Papers/ESLII.pdf#page=163
class NaturalSpline():
def __init__(self,n_folds=6):
self.n_folds = n_folds
def get_mat(self,x,df):
notches = np.percentile(x,np.linspace(0,100,df+2)[1:-1])
X = np.column_stack(([1.0]*len(x),x))
def d(x,e_k,e_K):
def pos(x,e):
x_ = np.copy(x)
x_[x<=e] = 0.0
x_[x>e] = (x_[x>e]-e)**3
return x_
return (pos(np.copy(x),e_k) -
pos(np.copy(x),e_K))/(e_K-e_k)
for i in range(1,len(notches)-1):
X = np.column_stack((X,d(np.copy(x),notches[i],
notches[-1]) -
d(np.copy(x),notches[i-1],
notches[-1])))
return X
def score(self,X,y,n_iterations=50):
from sklearn.model_selection import KFold
from sklearn.linear_model import LinearRegression
model = LinearRegression(fit_intercept=False)
scores = []
for seed in np.random.randint(0,10000,size=n_iterations):
cv = KFold(n_splits=self.n_folds,shuffle=True,
random_state=seed)
for train,test in cv.split(X,y):
model.fit(X[train],y[train])
scores.append(model.score(X[test],y[test]))
return np.mean(scores)
def fit(self,x,y,dfs=range(3,10)):
scores = []
num_unique_x = len(np.unique(x))
if num_unique_x<min(dfs):
raise ValueError('x needs to have at least ' +\
'%d unique values' % (min(dfs),))
if len(x)<self.n_folds:
raise ValueError('x needs to have length of at ' +\
'least %d' % (self.n_folds,))
for df in dfs:
if df<=num_unique_x:
X = self.get_mat(x,df)
scores.append(self.score(X,y))
self.scores = scores
df = dfs[np.argmax(scores)]
self.df = df
X = self.get_mat(x,df)
from sklearn.linear_model import LinearRegression
model = LinearRegression(fit_intercept=False)
model.fit(X,y)
self.model = model
def predict(self,x):
return self.model.predict(self.get_mat(x,self.df))
Let's try this for one day's worth of rounds to get a sense of what it does.
day = round_data[(round_data.Tournament_Year==2003) &
(round_data['Permanent_Tournament_#']==1) &
(round_data['Course_#']==101) &
(round_data.Round_Number==3)]
## helper function to represent the tee times as seconds
## since first tee time
def get_seconds(x):
x = pd.to_timedelta(x + ':00').apply(lambda x: x.seconds)
return x - x.min()
## get x and y
x = get_seconds(day.Tee_Time).values
y = day.Net_Result.values
## make a plot
plt.scatter(x,y);
plt.xlabel('Seconds Since First Tee Time');
plt.ylabel('Net_Result');
## define NaturalSpline object, fit it
ns = NaturalSpline()
ns.fit(x,y)
## number of degrees of freedom chosen using cv
ns.df
## predict x values
y_ = ns.predict(x)
## make a plot
plt.scatter(x,y);
plt.plot(x[np.argsort(x)],y_[np.argsort(x)],c='red');
## how many strokes easier does the model say teeing off
## first was than teeing off last on this day?
y_[np.argsort(x)[0]] - y_[np.argsort(x)[-1]]
So this model thinks teeing off last was almost two strokes harder than teeing off first. That is actually a lot.
## just for fun do an animation showing a bunch of the same sort
## of plots for different days
day_identifiers = ['Tournament_Year','Permanent_Tournament_#',
'Course_#','Round_Number']
xs, ys, y_s = [], [], []
for tup in round_data[day_identifiers].drop_duplicates().values[:50]:
year,tourn_num,course_num,round_num = tup
mask = (round_data.Tournament_Year==year) & \
(round_data['Permanent_Tournament_#']==tourn_num) & \
(round_data['Course_#']==course_num) & \
(round_data.Round_Number==round_num)
day = round_data.loc[mask]
x = get_seconds(day.Tee_Time).values
y = day.Net_Result.values
ns = NaturalSpline()
ns.fit(x,y)
xs.append(np.sort(x))
ys.append(y[np.argsort(x)])
y_s.append(ns.predict(x)[np.argsort(x)])
from matplotlib import animation, rc
from IPython.display import HTML
fig, ax = plt.subplots()
line, = ax.plot([], [], lw=2, c='red');
scatter = ax.scatter([],[])
def init():
line.set_data([], [])
scatter.set_offsets(np.column_stack([[],[]]))
return (line,scatter)
def animate(i):
ax.set_xlim((xs[i].min()-300, xs[i].max()+300))
ax.set_ylim((ys[i].min()-.5, ys[i].max()+.5))
line.set_data(xs[i], y_s[i])
scatter.set_offsets(np.column_stack([xs[i],ys[i]]))
return (line,scatter)
anim = animation.FuncAnimation(fig, animate, init_func=init,
frames=50, interval=2000, blit=True)
HTML(anim.to_html5_video())
It seems like maybe the morning does better than the afternoon? Let's investigate by looking at the distribution of correlation coefficients of time vs. performance over all the days.
coefs = []
for tup in round_data[day_identifiers].drop_duplicates().values:
year,tourn_num,course_num,round_num = tup
mask = (round_data.Tournament_Year==year) & \
(round_data['Permanent_Tournament_#']==tourn_num) & \
(round_data['Course_#']==course_num) & \
(round_data.Round_Number==round_num)
day = round_data.loc[mask]
x = get_seconds(day.Tee_Time).values
y = day.Net_Result.values
coefs.append(np.corrcoef(x,y)[0,1])
plt.hist(coefs,bins=30);
plt.axvline(np.mean(coefs),c='red');
Maybe there is a little something there but seems like nothing major. I won't bother looking at this hypothesis formally for now since that is not my goal this time.
Let's now fit a model for every day in the dataset and save the model's predition so we can then subtract it from Net_Result.
## fit model for each day and store the results in the DataFrame
round_data['day_model_pred'] = 0
for tup in round_data[day_identifiers].drop_duplicates().values:
year,tourn_num,course_num,round_num = tup
mask = (round_data.Tournament_Year==year) & \
(round_data['Permanent_Tournament_#']==tourn_num) & \
(round_data['Course_#']==course_num) & \
(round_data.Round_Number==round_num)
day = round_data.loc[mask]
x = get_seconds(day.Tee_Time).values
y = day.Net_Result.values
ns = NaturalSpline()
ns.fit(x,y)
y_ = ns.predict(x)
round_data.loc[mask,'day_model_pred'] = y_
## sanity check that all the predictions were filled
(round_data.day_model_pred==0).mean()
## plot a histogram
round_data.day_model_pred.hist(bins=30);
## finally, add Net_Result2 - Net_Result adjusted by the intra-day predictions
round_data['Net_Result2'] = round_data.Net_Result - round_data.day_model_pred
Now that we have the Net_Result2 variable - an outcome for each player's round that is adjusted according to their personalized expected performance and the performances of those playing around the same time during the same day - we can compute the same measure of variance within each group that we used in Part 1 - the average pariwise distance between the members of each group - and compare that with the distribution of this metric one would expect according to chance.
## define metric function
def compute_metric(sample):
from scipy.spatial.distance import pdist
return np.mean(pdist(sample[:,None]))
## compute metric by looking at average pairwaise distance within grouping
metrics = []
for tup,group in round_data.groupby(['Tournament_Year',
'Permanent_Tournament_#',
'Course_#','Round_Number',
'Tee_Time','Hole_Started_On']):
metrics.append(compute_metric(group.Net_Result2.values))
metric_unpermuted = np.mean(metrics)
metric_unpermuted
## extract values and number of twosomes and number of threesomes
result_values = round_data.Net_Result2.values
counts = round_data.groupby(['Tournament_Year',
'Permanent_Tournament_#',
'Course_#','Round_Number',
'Tee_Time','Hole_Started_On']).size()\
.reset_index().rename(columns={0:'Num_Groups'})\
.Num_Groups.value_counts()
num_twos,num_threes = counts[2],counts[3]
## indicies to split array at (no need for last index)
indicies = np.cumsum(np.concatenate([[2]*num_twos,
[3]*num_threes]))[:-1]
## permute 10,000 times and compute metric each time
metrics_permuted = []
for i in range(10000):
result_values = np.random.permutation(result_values)
samples = np.split(result_values,indicies)
metrics_permuted.append(np.mean([compute_metric(sample) for sample in samples]))
## make fancy plot of results
fig,ax = plt.subplots(1,1,figsize=(16,10))
plt.violinplot(metrics_permuted,vert=False);
plt.axvline(metric_unpermuted,lw=2.3,
label='Value that Occured in Reality',c='red');
plt.xlabel('Average of average pairwise distance within group',
fontsize=20);
plt.ylabel('Density',fontsize=20);
plt.title('''Distribution of Metric Occuring by Chance
Along with Value that Occured in Reality''',fontsize=23);
ax.tick_params(labelsize=16);
plt.legend(loc=2,prop={'size':16});
So this was unexpected. This is telling us that the metric that occured in reality is actually larger that what you'd expect by chance, in a statistically significant way. This is the exact opposite finding that I found in Part 1.
At first, I have to admit that I was confused by this result. Then I stopped to think about how I was doing the resampling of the data. This result actually made me come to the realization that resampling at the global level on the entire dataset is an invalid approach to what I am trying to get at - whether golfers' playing with each other causes their scores to be more closely bunched together than what you'd expect according to chance.
This strategy is invalid whether you are correcting for the difficult of the course for the day (and changing with time within the day) or not because there are factors that impact all the players playing on the same day, like the difficulty of the conditions. When I resampled at a global level, ignoring the fact that the results were recording on different days, there was a portion of the variance that I observed that was attributable to the difference in difficulty of the course and conditions on the different days. When I compared this distribution to the variance that occured in reality, I obtained the result that the variance within the group was much lower than expected. This effect could have been caused by players in the same group feeding off each other or from something less interesting - the fact that the difficulty of the course and conditions changes from day to day.
When I fit the models above to control for the difficulty of the conditions changing with time within each day, the overall difficulty of the course and conditions of the day where built into that model. Controlling for this decreased the variance in performance within the randomly generated pairings substantially.
To test truly test my hypothesis, I need to do the resamping within each day to see whether the variance within the pairings is less than what you'd expect according to random chance. I will do this with one day first and then I will do it for each day.
## the day currently in memory
day.head(3)
## return to just one day's worth of data
day = round_data[(round_data.Tournament_Year==2003) &
(round_data['Permanent_Tournament_#']==1) &
(round_data['Course_#']==101) &
(round_data.Round_Number==3)]
## same calculation as above but for the one day
metrics = []
for tup,group in day.groupby(['Tee_Time','Hole_Started_On']):
metrics.append(compute_metric(group.Net_Result2.values))
metric_unpermuted = np.mean(metrics)
metric_unpermuted
result_values = day.Net_Result2.values
counts = day.groupby(['Tee_Time','Hole_Started_On']).size()\
.reset_index().rename(columns={0:'Num_Groups'})\
.Num_Groups.value_counts()
num_twos,num_threes = counts[2],counts[3]
indicies = np.cumsum(np.concatenate([[2]*num_twos,
[3]*num_threes]))[:-1]
metrics_permuted = []
for i in range(1000):
result_values = np.random.permutation(result_values)
samples = np.split(result_values,indicies)
metrics_permuted.append(np.mean([compute_metric(sample) for sample in samples]))
fig,ax = plt.subplots(1,1,figsize=(16,10))
plt.violinplot(metrics_permuted,vert=False);
plt.axvline(metric_unpermuted,lw=2.3,
label='Value that Occured in Reality',c='red');
plt.xlabel('Average of average pairwise distance within group',
fontsize=20);
plt.ylabel('Density',fontsize=20);
plt.title('''Distribution of Metric Occuring by Chance
Along with Value that Occured in Reality''',fontsize=23);
ax.tick_params(labelsize=16);
plt.legend(loc=2,prop={'size':16});
This result says that the variance within the groups is about what you should expect according to random chance. Let's do this calculation for every day. I will store the percentile of where each value that occured in reality falls in the distribution of the metrics calculated for each resampling for each day.
## resample inside each day's results
from scipy.stats import percentileofscore
day_identifiers = ['Tournament_Year','Permanent_Tournament_#',
'Course_#','Round_Number']
round_data['day_model_pred'] = 0
percentiles = []
for tup in round_data[day_identifiers].drop_duplicates().values:
year,tourn_num,course_num,round_num = tup
mask = (round_data.Tournament_Year==year) & \
(round_data['Permanent_Tournament_#']==tourn_num) & \
(round_data['Course_#']==course_num) & \
(round_data.Round_Number==round_num)
day = round_data.loc[mask]
metrics = []
for tup,group in day.groupby(['Tee_Time','Hole_Started_On']):
metrics.append(compute_metric(group.Net_Result2.values))
metric_unpermuted = np.mean(metrics)
result_values = day.Net_Result2.values
counts = day.groupby(['Tee_Time','Hole_Started_On']).size()\
.reset_index()\
.rename(columns={0:'Num_Groups'})\
.Num_Groups.value_counts()
num_twos,num_threes = (counts[2] if 2 in counts else 0,
counts[3] if 3 in counts else 0)
indicies = np.cumsum(np.concatenate([[2]*num_twos,
[3]*num_threes]))[:-1]\
.astype(int)
metrics_permuted = []
for i in range(1000): ## run 1000 this time in interest of time
result_values = np.random.permutation(result_values)
samples = np.split(result_values,indicies)
metrics_permuted.append(np.mean([compute_metric(sample)
for sample in samples]))
percentiles.append(percentileofscore(metrics_permuted,
metric_unpermuted))
## make a histogram of the percentiles of each day's intra-group
## variance within the distribution that would happen by chance
plt.hist(percentiles);
So, it turns out that I was wrong! Seems like there is even weak evidence that the amount of variance within the groupings is higher than you'd expect from chance. So I will not bother to answer the further questions posed in the beginning because it seems like the effect I was hypothesizing doesn't exist after all!
From this post, I have the following take aways:


{I_TOURNEY_NAME} User Message
Hi {I_USERNAME},
a new password has been requested. In case you didn't request it, just ignore this message.
Otherwise go to {I_ACTIVATION_URL} and activate your new password.
New login-information:
Username: {I_USERNAME}
Password: {I_NEW_PASSWORD}


| Local
distributors available in these areas. (If you do not find a distributor in your area we will gladly ship directly to your door.) |
|||
| Des Moines, IA | Quad Cities | Chicago, IL | |
| Milwaukee, WI | Lincoln, NE | Sioux Falls, SD | |
| Kansas City, MO | St. Louis, MO | Green Bay, WI | |
| Minneapolis, MN | Indianapolis, IN | Louisville, KY | |
| Tallahassee, FL | Jacksonville, FL | Orlando, FL | |
| Miami, FL | Los Angeles, CA | San Diego, CA | |
| San Francisco, CA | Denver, CO | Phoenix, AZ | |
| Tucson, AZ | Flagstaff, AZ | Albuquerque, NM | |
| Memphis, TN | Nashville, TN | Knoxville, TN | |
| Chattanooga, TN | Boston, Ma | Concord, NH | |
| Got Juice? |
Who is Penny Juice?
/ What is Penny Juice? / Where
is Penny Juice?
Got Juice? / E-mail
/ HOME


This tournament is being played at the Kay Bailey Hutchinson
Convention Center in Dallas, TX. All times Central.
Wed 29 Jun |
|
| 4:00pm | Florida Suns 18 Boys 25-15, 25-18 |
| 6:00pm | Madfrog 18's Boys 25-14, 25-19 |
| 8:00pm | Adversity 18 Purple 15-25, 8-25 |
Thu 30 Jun |
|
| 4:00pm | Bones 18 Black 26-24, 23-25, 12-15 |
| 5:00pm | Orange Coast 18s 25-20, 14-25, 10-15 |
| 7:00pm | Louisville Fury 18 Orange 26-24, 25-20 |
Fri 1 Jul |
|
| 3:00pm | Tool City 18 Roche 22-25, 25-14, 15-12 |
| 5:00pm | Coastal 18-1N 25-19, 16-25, 8-15 |
| 8:00pm | BVP 18-Red 22-25, 22-25 |
Sat 2 Jul |
|
| 10:00am | Louisville Fury 18 Orange 25-23, 31-29 |
| 12:00pm | Kaizen Boys 18s Black 25-21, 25-17 |
| 1:00pm | Vortex18 Storm 27-25, 23-25, 5-15 |
Fri 1 Jul |
|
| 4:00pm | Rochester Pace Bootlegger 161 22-25, 22-25 |
| 5:00pm | Mountain View 16 Red 21-25, 22-25 |
| 7:00pm | Pac6 16s 20-25, 24-26 |
Sat 2 Jul |
|
| 4:00pm | Warren Sixpack 16-1 25-11, 25-20 |
| 6:00pm | Chicago Bounce 16 Red 25-18, 24-26, 17-19 |
| 8:00pm | Adversity 16 Purple 20-25, 25-16, 17-15 |
Sun 3 Jul |
|
| 8:00am | Ponce Pumas 25-21, 22-25, 10-15 |
| 9:00am | BRYC Boys 16 National 25-13, 14-25, 6-15 |
Mon 4 Jul |
|
| 8:00am | MVC B16-Kyle in quarterfinals on Court 15 |
| 10:00am | TBA in semifinals on Court 13 |
| 12:00pm | TBA in finals on Court 11 |
Thu 30 Jun |
|
| 9:00am | Ku`ikahi 14R RoShamBo 25-19, 25-19 |
| 11:00am | Bay to Bay 14-1 22-25, 23-25 |
| 1:00pm | Balboa Bay 14 Blue 6-25, 10-25 |
Thu 30 Jun |
|
| 4:00pm | Louisville Fury 14 Orange 25-15, 25-21 |
| 6:00pm | Indy Elite 14-1 25-23, 25-22 |
Fri 1 Jul |
|
| 9:00am | Spike and Serve Boys 14s 12-25, 25-21, 15-10 |
| 10:00am | 949 14 Black B-BT 16-25, 12-25 |
| 12:00pm | San Juan 14-1 5-25, 17-25 |
Sat 2 Jul |
|
| 8:00am | Panther 14 Gold 25-13, 25-16 |
| 10:00am | Indy Elite SVC 14-1 25-21, 25-12 |
Sun 3 Jul |
|
| 10:00am | Louisville Fury 14 Orange 25-14, 25-15 |
| 12:00pm | HVA 14 Young Guns 26-24, 14-25, 15-12 |
| 1:00pm | MVVC 14 Red 21-25, 21-25 |


Groton Place
I recognize this bridge, over the water lilies and dragonflies, I am so excited, tail wagging and nose twitching. Who will I meet today? Freedom is bounding through the wildflower meadow, grass twitching at my fur. I must sniff the stone, dog statue, it’s so real it makes me check again. Under the shady trees I splash into the cool clear river, oh please throw my stick one more time. Onwards my paws race, through the Rhododendron’s, chasing those delicious smells, onward I run - free.
Groton Place Dog Park Facebook
Top 10 Trail Videos - Groton Place


Glitched Writer is a lightweight javascript module for writing text, with highly customizable settings. For decoding, glitching, decrypting, scrambling, or simply spelling out text.


bnd.icc File Reference
(Revision: 3768)
#include "gecode/support/sort.hh"
Go to the source code of this file.
|
Namespaces |
| namespace | Gecode |
| namespace | Gecode::Int |
| namespace | Gecode::Int::Distinct |
Classes |
| class | Gecode::Int::Distinct::Rank |
| | Rank information. More...
|
| class | Gecode::Int::Distinct::MaxInc< View > |
| | Sort-order by increasing maximum. More...
|
| class | Gecode::Int::Distinct::MinInc< View > |
| | Sort-order by increasing minimum. More...
|
| class | Gecode::Int::Distinct::HallInfo |
| | Information on Hall intervals. More...
|
Defines |
| #define | GECODE_INT_MINSORTED(i) (i) |
| #define | GECODE_INT_MAXSORTED(i) (_maxsorted[i]) |
Functions |
| void | Gecode::Int::Distinct::pathset_t (HallInfo hall[], int start, int end, int to) |
| void | Gecode::Int::Distinct::pathset_h (HallInfo hall[], int start, int end, int to) |
| int | Gecode::Int::Distinct::pathmin_h (const HallInfo hall[], int i) |
| int | Gecode::Int::Distinct::pathmin_t (const HallInfo hall[], int i) |
| int | Gecode::Int::Distinct::pathmax_h (const HallInfo hall[], int i) |
| int | Gecode::Int::Distinct::pathmax_t (const HallInfo hall[], int i) |
| template<class View> |
| ExecStatus | Gecode::Int::Distinct::prop_bnd (Space *home, ViewArray< View > &x, int m) |
| | Perform bounds-consistent distinct propagation.
|
Define Documentation
| #define GECODE_INT_MINSORTED |
( |
i |
|
) |
(i) |
|
| #define GECODE_INT_MAXSORTED |
( |
i |
|
) |
(_maxsorted[i]) |
|


I'm an aspring web developer with a background in digital marketing. I've been taking a full-stack web development 'bootcamp' online in my spare time in order to fulfill my passion for building on the web.
I've namely worked with JavaScript, Ruby on Rails, HTML, and CSS. As my skillset grows, so too does my fascination for new technologies and frameworks.
In this project, I used Ruby and the Nokogiri web scraping gem to create a Command Line Interface application that scraped and formatted data from a webpage containing programming courses.
View Source Close ProjectBy using the MVC paradigm, I created a CRUD (Create, Read, Update, Destroy) app within the Sinatra Ruby framework. This app copies the core competencies of Twitter.
View Source Close ProjectTaking what I learned from the Twitter clone, I created this app in the Sinatra framework so that users could create an account and keep track of movies they've seen.
View Source Close ProjectBored? Maybe this brick breaker built in vanilla JavaScript can help. In this project, I learned how much logic is needed for a seemingly simple game. I particularly liked project as it taught me about abstracting physics and animation in HTML canvas.
View on Codepen Close ProjectA cornerstone of the programmer's portfolio. I definitely wanted to have a go at an app like this, and the design really brought this to life.
View on Codepen Close ProjectCan you crack the code? Follow the rules on the page to hone in the secret 4 number key which is generated at the beginning of each game.
View on Codepen Close Project


{% with object_user.user as object_user %}
{% include 'includes/user-info-as-table.html' %}
{% endwith %}
{{ object_user.get_identity }}资料
{% if user.is_doctor %}
{% with object_user as doctor %}
{% include 'includes/doctor-info-as-table.html' %}
{% endwith %}
{% elif user.is_patient %}
{% with object_user as patient %}
{% include 'includes/patient-info-as-table.html' %}
{% endwith %}
{% elif user.is_hospital %}
{% with object_user as hospital %}
{% include 'includes/hospital-info-as-table.html' %}
{% endwith %}
{% endif %}


@Target(value={TYPE,PACKAGE})
@Retention(value=RUNTIME)
@Inherited
public @interface OpenAPIDefinition
This is the root document object of the OpenAPI document. It contains required and optional fields.
| Modifier and Type | Required Element and Description |
|---|---|
Info |
info
Required: Provides metadata about the API.
|
| Modifier and Type | Optional Element and Description |
|---|---|
Components |
components
An element to hold a set of reusable objects for different aspects of the OpenAPI Specification (OAS).
|
Extension[] |
extensions
List of extensions to be added to the
OpenAPI model
corresponding to the containing annotation. |
ExternalDocumentation |
externalDocs
Any additional external documentation for the API
|
SecurityRequirement[] |
security
A declaration of which security mechanisms can be used across the API.
|
SecurityRequirementsSet[] |
securitySets
A declaration of which security mechanisms can be used across the API.
|
Server[] |
servers
An array of Server Objects, which provide connectivity information to a target server.
|
Tag[] |
tags
A list of tags used by the specification with additional metadata.
|
public abstract Info info
public abstract Tag[] tags
public abstract Server[] servers
public abstract SecurityRequirement[] security
Adding a SecurityRequirement
to this array is equivalent to adding a SecurityRequirementsSet
containing a single SecurityRequirement
to securitySets()
.
public abstract SecurityRequirementsSet[] securitySets
All of the security requirements within any one of the sets must be satisfied to authorize a request.
Including an empty set within this list indicates that the other requirements are optional.
public abstract ExternalDocumentation externalDocs
public abstract Components components


=== Test case: lasp_peer_to_peer_advertisement_counter_SUITE:heavy_peer_to_peer_state_based_test/1 (click for source code)
=== Config value:
[{watchdog,<0.452.0>},
<EMAIL_ADDRESS>{tc_group_properties,[]},
{tc_group_path,[]},
{data_dir,"/Users/anne-francevanswieten/lasp/_build/test/lib/lasp/test/lasp_peer_to_peer_advertisement_counter_SUITE_data/"},
<EMAIL_ADDRESS>=== Current directory is<EMAIL_ADDRESS>=== Started at 2017-05-02 17:44:32
*** User 2017-05-02 17:44:32.943 ***
Beginning test case heavy_peer_to_peer_state_based_test
*** User 2017-05-02 17:44:32.954 ***
Running peer_to_peer_ad_counter_state_based_test with nodes [server,client_1,
client_2,
client_3]
*** User 2017-05-02 17:44:32.954 ***
Launching Erlang distribution...
*** System report during lasp_peer_to_peer_advertisement_counter_SUITE:heavy_peer_to_peer_state_based_test/1 2017-05-02 17:44:33.012 ***
=PROGRESS REPORT==== 2-May-2017::17:44:33 ===
application: sasl
started_at: 'runner@MacBook-Pro-de-Anne-France'
*** System report during lasp_peer_to_peer_advertisement_counter_SUITE:heavy_peer_to_peer_state_based_test/1 2017-05-02 17:44:33.021 ***
=PROGRESS REPORT==== 2-May-2017::17:44:33 ===
application: syntax_tools
started_at: 'runner@MacBook-Pro-de-Anne-France'
*** System report during lasp_peer_to_peer_advertisement_counter_SUITE:heavy_peer_to_peer_state_based_test/1 2017-05-02 17:44:33.032 ***
=PROGRESS REPORT==== 2-May-2017::17:44:33 ===
application: compiler
started_at: 'runner@MacBook-Pro-de-Anne-France'
*** System report during lasp_peer_to_peer_advertisement_counter_SUITE:heavy_peer_to_peer_state_based_test/1 2017-05-02 17:44:33.042 ***
=PROGRESS REPORT==== 2-May-2017::17:44:33 ===
supervisor: {local,gr_sup}
started: [{pid,<0.479.0>},
{id,gr_counter_sup},
{mfargs,{gr_counter_sup,start_link,[]}},
{restart_type,permanent},
{shutdown,5000},
{child_type,supervisor}]
*** System report during lasp_peer_to_peer_advertisement_counter_SUITE:heavy_peer_to_peer_state_based_test/1 2017-05-02 17:44:33.045 ***
=PROGRESS REPORT==== 2-May-2017::17:44:33 ===
supervisor: {local,gr_sup}
started: [{pid,<0.481.0>},
{id,gr_param_sup},
{mfargs,{gr_param_sup,start_link,[]}},
{restart_type,permanent},
{shutdown,5000},
{child_type,supervisor}]
*** System report during lasp_peer_to_peer_advertisement_counter_SUITE:heavy_peer_to_peer_state_based_test/1 2017-05-02 17:44:33.048 ***
=PROGRESS REPORT==== 2-May-2017::17:44:33 ===
supervisor: {local,gr_sup}
started: [{pid,<0.483.0>},
{id,gr_manager_sup},
{mfargs,{gr_manager_sup,start_link,[]}},
{restart_type,permanent},
{shutdown,5000},
{child_type,supervisor}]
*** System report during lasp_peer_to_peer_advertisement_counter_SUITE:heavy_peer_to_peer_state_based_test/1 2017-05-02 17:44:33.049 ***
=PROGRESS REPORT==== 2-May-2017::17:44:33 ===
application: goldrush
started_at: 'runner@MacBook-Pro-de-Anne-France'
*** System report during lasp_peer_to_peer_advertisement_counter_SUITE:heavy_peer_to_peer_state_based_test/1 2017-05-02 17:44:33.057 ***
=PROGRESS REPORT==== 2-May-2017::17:44:33 ===
supervisor: {local,lager_sup}
started: [{pid,<0.490.0>},
{id,lager},
{mfargs,{gen_event,start_link,[{local,lager_event}]}},
{restart_type,permanent},
{shutdown,5000},
{child_type,worker}]
*** System report during lasp_peer_to_peer_advertisement_counter_SUITE:heavy_peer_to_peer_state_based_test/1 2017-05-02 17:44:33.058 ***
=PROGRESS REPORT==== 2-May-2017::17:44:33 ===
supervisor: {local,lager_sup}
started: [{pid,<0.492.0>},
{id,lager_handler_watcher_sup},
{mfargs,{lager_handler_watcher_sup,start_link,[]}},
{restart_type,permanent},
{shutdown,5000},
{child_type,supervisor}]
*** System report during lasp_peer_to_peer_advertisement_counter_SUITE:heavy_peer_to_peer_state_based_test/1 2017-05-02 17:44:33.061 ***
=PROGRESS REPORT==== 2-May-2017::17:44:33 ===
supervisor: {local,lager_sup}
started: [{pid,<0.494.0>},
{id,lager_crash_log},
{mfargs,
{lager_crash_log,start_link,
["log/crash.log",65536,10485760,[{hour,0}],5]}},
{restart_type,permanent},
{shutdown,5000},
{child_type,worker}]
*** User 2017-05-02 17:44:33.129 ***
Starting node: server
*** User 2017-05-02 17:44:33.132 ***
17:44:33.129 [info] Application lager started on node 'runner@MacBook-Pro-de-Anne-France'
*** User 2017-05-02 17:44:34.230 ***
Starting node: client_1
*** User 2017-05-02 17:44:35.298 ***
Starting node: client_2
*** User 2017-05-02 17:44:36.366 ***
Starting node: client_3
*** User 2017-05-02 17:44:37.435 ***
Loading lasp on node: 'server@MacBook-Pro-de-Anne-France'
*** User 2017-05-02 17:44:37.467 ***
Loading lasp on node: 'client_1@MacBook-Pro-de-Anne-France'
*** User 2017-05-02 17:44:37.497 ***
Loading lasp on node: 'client_2@MacBook-Pro-de-Anne-France'
*** User 2017-05-02 17:44:37.528 ***
Loading lasp on node: 'client_3@MacBook-Pro-de-Anne-France'
*** User 2017-05-02 17:44:38.347 ***
Starting lasp.
*** User 2017-05-02 17:44:39.469 ***
Custering nodes...
*** User 2017-05-02 17:44:39.469 ***
Joining node: 'server@MacBook-Pro-de-Anne-France' to 'client_1@MacBook-Pro-de-Anne-France' at port 57566
*** User 2017-05-02 17:44:39.470 ***
Joining node: 'server@MacBook-Pro-de-Anne-France' to 'client_2@MacBook-Pro-de-Anne-France' at port 57568
*** User 2017-05-02 17:44:39.474 ***
Joining node: 'server@MacBook-Pro-de-Anne-France' to 'client_3@MacBook-Pro-de-Anne-France' at port 57570
*** User 2017-05-02 17:44:39.474 ***
Joining node: 'client_1@MacBook-Pro-de-Anne-France' to 'server@MacBook-Pro-de-Anne-France' at port 57563
*** User 2017-05-02 17:44:39.479 ***
Joining node: 'client_1@MacBook-Pro-de-Anne-France' to 'client_2@MacBook-Pro-de-Anne-France' at port 57568
*** User 2017-05-02 17:44:39.482 ***
Joining node: 'client_1@MacBook-Pro-de-Anne-France' to 'client_3@MacBook-Pro-de-Anne-France' at port 57570
*** User 2017-05-02 17:44:39.483 ***
Joining node: 'client_2@MacBook-Pro-de-Anne-France' to 'server@MacBook-Pro-de-Anne-France' at port 57563
*** User 2017-05-02 17:44:39.484 ***
Joining node: 'client_2@MacBook-Pro-de-Anne-France' to 'client_1@MacBook-Pro-de-Anne-France' at port 57566
*** User 2017-05-02 17:44:39.485 ***
Joining node: 'client_2@MacBook-Pro-de-Anne-France' to 'client_3@MacBook-Pro-de-Anne-France' at port 57570
*** User 2017-05-02 17:44:39.487 ***
Joining node: 'client_3@MacBook-Pro-de-Anne-France' to 'server@MacBook-Pro-de-Anne-France' at port 57563
*** User 2017-05-02 17:44:39.488 ***
Joining node: 'client_3@MacBook-Pro-de-Anne-France' to 'client_1@MacBook-Pro-de-Anne-France' at port 57566
*** User 2017-05-02 17:44:39.490 ***
Joining node: 'client_3@MacBook-Pro-de-Anne-France' to 'client_2@MacBook-Pro-de-Anne-France' at port 57568
*** User 2017-05-02 17:44:39.491 ***
Lasp fully initialized.
*** User 2017-05-02 17:44:39.491 ***
Waiting for simulation to end
*** User 2017-05-02 17:46:49.508 ***
Simulation ended with success
*** User 2017-05-02 17:46:57.580 ***
Ending test case heavy_peer_to_peer_state_based_test
=== Ended at 2017-05-02 17:46:57
=== successfully completed test case
=== Returned value: ok
Test run history
| Top level test index


--- layout: post title: ">Solution Domain Architecture" date: 2007-05-19 21:10:00.000000000 +02:00 categories: - SDA SOA tags: [] status: publish type: post published: true meta: blogger_blog: andreasohlund.blogspot.com blogger_permalink: "/2007/05/solution-domain-architecture.html" author: login: andreas.ohlund email<EMAIL_ADDRESS>display_name: Andreas Öhlund first_name: Andreas last_name: "Öhlund" ---
>Nick Malik has a very interesting post about Solution Domain Architecture (SDA) which I believe is a great addition to the endless list of methods for Service Engineering. I especially like his teory that service reuse is most likely to happen with " 'top down' services that we know, in advance, that we are going to need. " and any reuse of bottom up services is "is a happy accident". This really highlights the need for SOA-governance in order to have a high degree of reuse of services in your service oriented architecture.


Before Change
]
else:
raise ValueError("Unrecognized Spec type: {}".format(spec))
return PathGlobs.create(directory, include=patterns, exclude=[])
def _recursive_dirname(f):
Given a relative path like "a/b/c/d", yield all ascending path components like:
After Change
patterns = set()
for spec in specs.dependencies:
if type(spec) is DescendantAddresses:
patterns.update(join(spec.directory, "**", pattern)
for pattern in address_mapper.build_patterns)
elif type(spec) in (SiblingAddresses, SingleAddress):
patterns.update(join(spec.directory, pattern)
for pattern in address_mapper.build_patterns)
elif type(spec) is AscendantAddresses:


SDE Learning Trail @ SDE4
SDE Learning Trail is a simple, easy to use web application to collect comfort feedback from building occupants and visitors while they interact, learn and engage with SDE4.
NZEB @ SDE
NZEB@SDE is Singapore's first purpose-built Net Zero Energy Building(NZEB). SDE4 was launched officially by Minister Heng Swee Keat on 30th of January, 2019.
First Users
During the SDE4 launch, users interacted with our app for the first time.


This year I managed to land a huge amount of presents, most of them books. Despite my excitement about reading them I can’t help but feel that acquiring new books in 2009 is like building a fantastic HD-DVD library. The fact remains that books are simply the best way to read text. Although I haven’t tried a Kindle, I’d love it to prove even better.
Here’s a complete list of my haul:
- Every Second Counts by Lance Armstrong
- More Information Than You Require by John Hodgman
- Areas of my Expertise by John Hodgman
- Infinite Jest by David Foster Wallace
- Emergency: This Book Will Save Your Life by Neil Strauss
- Our Choice by Al Gore
- The Camel Club by David Baldacci
- The Story of Danny Dunn by Bryce Courtenay
- Sophia Grantham by Barry Allan (my uncle)
- Black Sheep and Gold Diggers by Barry Allan
- The Chaser Annual 2009
- Electrolux dust buster
- Halo 3 ODST
- Tour de France Greats DVD
- Money tree, the non-magical plant
- Island Spa Yankee Candle, which I’m burning right now and is the most amazing smelling thing.
- Apple Wireless Keyboard
- Pair of casual shorts
- Party Ice Bucket
- RSPCA calendar
My list of things I want to buy has taken a serious hit, I’m very lucky.


twgl.js
- multiple render targets 3d texture
Sorry but your browser doesn't appear to support WebGL2


SCREAM A film review by David Landers Copyright 1997 David Landers
RATING: *** out of **** - 7.5 out of 10.0 CAST: David Arquette (Dewey Riley), Neve Campbell (Sidney Prescott), Courtney Cox (Gale Weathers), Rose McGowan (Tatum Riley), Skeet Ulrich (Billy Loomis), Drew Barrymore (Casey Becker) DIRECTOR: Wes Craven CERTIFICATION: R (USA) for excessive violence and language YEAR OF PRODUCTION: 1996
SCREAM is the present-day version of a horror film directed towards the teenage audience. And, like many of the same type of the past, it succeeded greatly (a sequel began filming while it was still in theaters). From horror and thriller director-supreme Wes Craven, SCREAM is a tense, nail-biter of a movie that, in the words of its director, "everyone really enjoys".
In my opinion, SCREAM is very similar to other works of Wes Craven (A NIGHTMARE ON ELM STREET comes to mind first). It is about, of course, a bloodthirsty killer who is out to kill, more than anyone, teenagers. The killer will not stop at anything to kill the one person he is really after, and like all other movies of its type, SCREAM plays this out very well.
The movie begins in the Becker household, where young Casey Becker (Drew Barrymore) is preparing her favorite movie-watching snack, popcorn. In the middle of her preparation, the phone rings, and a very rough, cautious male voice is on the other end. At first, the caller seems rather calm and friendly. That is, until Casey mistakingly sets off a hidden inner rage in the caller, who suddenly turns extremely violent. He makes very serious, frightening threats to Casey and continues to call after she repeatedly hangs up. The caller informs Casey that he wants her to play a game. The game is a very sick one indeed, in which Casey answers movie trivia questions. If she gets them wrong, the penalty is death. If she answers correct, well, the caller *says* he'll leave her alone. Unfortunately for Casey, she never escapes the killer alive.
The next day at, the small town is in shock. The high school is crawling with police officers and reporters, including the "tabloid twit" Gale Weathers (Courtney Cox). Gale Weathers is not liked at all by young Sidney Prescott (Neve Campbell), for a very serious reason.  Gale continually insisted that the accused killer was framed and that the real killer is still on the loose. However, Sidney doesn't believe a word of this and certainly "has it in" for Gale.
After the school day is over, the kids are basically still in shock, at least for the most part. Sidney, probably most cautious due to upcoming one-year anniversary of her mother's death, does not want to be alone in the house. Her father is on a business trip, so Sidney pleads with her friend Tatum (Rose McGowan) to come over. They agree that at seven o'clock Tatum will come over. Somehow, Sidney falls asleep. She is awakened a little past seven by the phone ringing. It's Tatum, informing her that she will be there very soon. However, when the phone rings again, it isn't Tatum, but the same guy who called Casey the night before, and supposedly attacked and killed her. Sidney, too, falls victim to the caller, but barely escapes.
After the attack, Sidney's boyfriend Billy Loomis (Skeet Ulrich) shows up quickly to comfort her. However, when Sidney notices that he is dressed in black (as was the attacker) and is carrying a cellular phone, she assumes the worst. Billy is arrested and held in jail, but later released because the phone records show that he did not call her house. Another interesting fact is that Sidney's father is missing and cannot be found. Dewey (David Arquette), a town deputy and brother of Tatum, takes it upon himself to look out for Sidney and to help find the killer. However, it becomes unclear who will find who first, as the killer stops at nothing to reach his apparent prime target.
>From here, the terrorizing continues, and it looks rather obvious that it won't cease until the killer is stopped - for good. However, the killer's identity is as yet unknown, and people can only assume. The rest of the events in the movie are equally as suspenseful, and the climax is very well crafted. If you are a fan of scary movies, as were most of the characters in this film, then you should enjoy SCREAM. And if you *really* enjoyed, fear not, a sequel will be out before you can, well, scream!
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Related links: index of all rec.arts.movies.reviews reviews


{% extends "home/base.html" %} {% load static %} {% load i18n %} {% block content %}
Introduce the new email with which you want to associate your GPCRmd account.
You need access to the new address to complete the process.
For more information on this form, see the docs.
{% trans 'New Adress:' %}
{{ err }}


Ayanna K. Thomas, PhD
As an Associate Professor of Psychology at Tufts University I spend my time investigating the subjective experience that accompanies memories. My research encompasses metacognition, memory distortion, eyewitness memory, and age related changes in memory. I received my Ph.D. in Cognitive Psychology from the University of Washington, and then spent three years as an NIA postdoctoral fellow at Washington University. I spent a final year at Wash U as a research scientist, studying changes in long-term memory as a function of aging. After teaching at Colby College for two years, I came to the Department of Psychology at Tufts University, where I established the Cognitive Aging and Memory Lab in 2007. Graduate and Undergraduate students interested in working in the Cognitive Aging and Memory Lab should contact me at<EMAIL_ADDRESS>My research has been funded by grants from the National Institute on Aging and National Institute of Neurological Disorder and Stroke, and has been featured in such popular media outlets as Psychology Today, The Initiative for Neuroscience and the Law, and the L.A. Times.


| Home | Trees | Indices | Help |
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Defines abstract samplers.
| Submodules | |
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| Classes | |
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AbstractSampler Abstract interface for sampling algorithms. |
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AbstractState Represents a point in phase-space. |
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| DimensionError | |
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EnsembleState Defines an Ensemble Monte Carlo state; it is a read-only collection of State objects. |
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State Represents a point in phase-space. |
| Variables | |
__package__ =
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| Home | Trees | Indices | Help |
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| Generated by Epydoc 3.0.1 on Tue Jul 4 20:19:04 2017 | http://epydoc.sourceforge.net |


Galate:
Yes... If you inject the energy from the refined crystal sample you just gave me into this magical equipment and activate it, you will be able to teleport to Hellbound Island without Beleth knowing.
(Galate activates the magical equipment using the refined crystal sample.)
Ah! You can safely move onto Hellbound Island now.
Here, take this as a small token of my appreciation...
Thank you very much.
If you want to go to Hellbound Island, please come see me.


Press any key to get the JavaScript event keycode
103
e.key
g
e.which
103
e.code
KeyG
e.location
0
(General keys)


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