Xây dựng game xếp gạch bằng opencv và python
-
Mã nguồn
import cv2
import numpy as np
from random import choice
def getColor():
lstColor = [[255,64,64],[255,165,0],[255,244,79],[102,255,0],[172,229,238],[148,87,235],[148,87,235],[241,156,187]]
return choice(lstColor)
def getInfo(piece):
if piece == "":
coords = np.array([[0, 0]])
elif piece == "I":
coords = np.array([[0, 3], [0, 4], [0, 5], [0, 6]])
elif piece == "T":
coords = np.array([[1, 3], [1, 4], [1, 5], [0, 4]])
elif piece == "L":
coords = np.array([[1, 3], [1, 4], [1, 5], [0, 5]])
elif piece == "J":
coords = np.array([[1, 3], [1, 4], [1, 5], [0, 3]])
elif piece == "S":
coords = np.array([[1, 5], [1, 4], [0, 3], [0, 4]])
elif piece == "Z":
coords = np.array([[1, 3], [1, 4], [0, 4], [0, 5]])
else:
coords = np.array([[0, 4], [0, 5], [1, 4], [1, 5]])
return coords, getColor()
def display(board, coords, color, next_info, held_info, score, SPEED):
# Generates the display
border = np.uint8(127 - np.zeros([20, 1, 3]))
border_ = np.uint8(127 - np.zeros([1, 23, 3]))
dummy = board.copy()
dummy[coords[:,0], coords[:,1]] = color
right = np.uint8(np.zeros([20, 10, 3]))
right[next_info[0][:,0] + 2, next_info[0][:,1]] = next_info[1]
dummy = np.concatenate(( border, dummy, border, right, border), 1)
dummy = np.concatenate((border_, dummy, border_), 0)
dummy = dummy.repeat(20, 0).repeat(20, 1)
dummy = cv2.putText(dummy, str(score), (325, 150), cv2.FONT_HERSHEY_DUPLEX, 1, [0, 0, 255], 2)
# Instructions for the player
index_pos = 300
x_index_pos = 300
dummy = cv2.putText(dummy, "A - left", (x_index_pos, index_pos), cv2.FONT_HERSHEY_DUPLEX, 0.6, [0, 0, 234])
dummy = cv2.putText(dummy, "D - right", (x_index_pos, index_pos+25), cv2.FONT_HERSHEY_DUPLEX, 0.6, [0, 0, 234])
dummy = cv2.putText(dummy, "S - drain", (x_index_pos, index_pos+50), cv2.FONT_HERSHEY_DUPLEX, 0.6, [0, 0, 234])
dummy = cv2.putText(dummy, "W - rotate", (x_index_pos, index_pos+75), cv2.FONT_HERSHEY_DUPLEX, 0.6, [0, 0, 234])
# dummy = cv2.putText(dummy, "J - rotate left", (45, 300), cv2.FONT_HERSHEY_DUPLEX, 0.6, [0, 0, 255])
# dummy = cv2.putText(dummy, "L - rotate right", (45, 325), cv2.FONT_HERSHEY_DUPLEX, 0.6, [0, 0, 255])
# dummy = cv2.putText(dummy, "I - hold", (45, 350), cv2.FONT_HERSHEY_DUPLEX, 0.6, [0, 0, 255])
cv2.imshow("Tetris", dummy)
key = cv2.waitKey(int(1000/SPEED))
return key
def getNextPiece():
next_piece = choice(["O", "I", "S", "Z", "L", "J", "T"])
return next_piece
SPEED = 1 # Controls the speed of the tetris pieces
# Make a board
board = np.uint8(np.zeros([20, 10, 3]))
# Initialize some variables
quit = False
place = False
drop = False
switch = False
held_piece = ""
flag = 0
score = 0
next_piece =""
current_piece = ""
# All the tetris pieces
if __name__ == "__main__":
next_piece = getNextPiece()
while not quit:
# Check if user wants to swap held and current pieces
if switch:
# swap held_piece and current_piece
held_piece, current_piece = current_piece, held_piece
switch = False
else:
# Generates the next piece and updates the current piece
current_piece = next_piece
next_piece = getNextPiece()
if flag > 0:
flag -= 1
# Determines the color and position of the current, next, and held pieces
held_info = getInfo(held_piece)
next_info = getInfo(next_piece)
coords, color = getInfo(current_piece)
if current_piece == "I":
top_left = [-2, 3]
if not np.all(board[coords[:,0], coords[:,1]] == 0):
break
while True:
# Shows the board and gets the key press
key = display(board, coords, color, next_info, held_info, score, SPEED)
# Create a copy of the position
dummy = coords.copy()
print("speed ",SPEED, "key ",key," ", ord("s"))
if key == ord("s"):
drop = True
elif key == ord("a"):
# Moves the piece left if it isn't against the left wall
if np.min(coords[:,1]) > 0:
coords[:,1] -= 1
if current_piece == "I":
top_left[1] -= 1
elif key == ord("d"):
# Moves the piece right if it isn't against the right wall
if np.max(coords[:,1]) < 9:
coords[:,1] += 1
if current_piece == "I":
top_left[1] += 1
# elif key == ord("j") or key == ord("l"):
# # Rotation mechanism
# # arr is the array of nearby points which get rotated and pov is the indexes of the blocks within arr
# if current_piece != "I" and current_piece != "O":
# if coords[1,1] > 0 and coords[1,1] < 9:
# arr = coords[1] - 1 + np.array([[[x, y] for y in range(3)] for x in range(3)])
# pov = coords - coords[1] + 1
# elif current_piece == "I":
# # The straight piece has a 4x4 array, so it needs seperate code
# arr = top_left + np.array([[[x, y] for y in range(4)] for x in range(4)])
# pov = np.array([np.where(np.logical_and(arr[:,:,0] == pos[0], arr[:,:,1] == pos[1])) for pos in coords])
# pov = np.array([k[0] for k in np.swapaxes(pov, 1, 2)])
# # Rotates the array and repositions the piece to where it is now
# if current_piece != "O":
# if key == ord("j"):
# arr = np.rot90(arr, -1)
# else:
# arr = np.rot90(arr)
# coords = arr[pov[:,0], pov[:,1]]
elif key == ord("w"):
# Rotation mechanism
# arr is the array of nearby points which get rotated and pov is the indexes of the blocks within arr
if current_piece != "I" and current_piece != "O":
if coords[1,1] > 0 and coords[1,1] < 9:
arr = coords[1] - 1 + np.array([[[x, y] for y in range(3)] for x in range(3)])
pov = coords - coords[1] + 1
elif current_piece == "I":
# The straight piece has a 4x4 array, so it needs seperate code
arr = top_left + np.array([[[x, y] for y in range(4)] for x in range(4)])
pov = np.array([np.where(np.logical_and(arr[:,:,0] == pos[0], arr[:,:,1] == pos[1])) for pos in coords])
pov = np.array([k[0] for k in np.swapaxes(pov, 1, 2)])
# Rotates the array and repositions the piece to where it is now
if current_piece != "O":
if key == ord("j"):
arr = np.rot90(arr, -1)
else:
arr = np.rot90(arr)
coords = arr[pov[:,0], pov[:,1]]
# Hard drop set to true
# drop = True
# elif key == ord("i"):
# # Goes out of the loop and tells the program to switch held and current pieces
# if flag == 0:
# if held_piece == "":
# held_piece = current_piece
# else:
# switch = True
# flag = 2
# break
elif key == 8 or key == 27:
quit = True
break
# Checks if the piece is overlapping with other pieces or if it's outside the board, and if so, changes the position to the position before anything happened
if np.max(coords[:,0]) < 20 and np.min(coords[:,0]) >= 0:
if not (current_piece == "I" and (np.max(coords[:,1]) >= 10 or np.min(coords[:,1]) < 0)):
if not np.all(board[coords[:,0], coords[:,1]] == 0):
coords = dummy.copy()
else:
coords = dummy.copy()
else:
coords = dummy.copy()
if drop:
# Every iteration of the loop moves the piece down by 1 and if the piece is resting on the ground or another piece, then it stops and places it
while not place:
if np.max(coords[:,0]) != 19:
# Checks if the piece is resting on something
for pos in coords:
if not np.array_equal(board[pos[0] + 1, pos[1]], [0, 0, 0]):
place = True
break
else:
# If the position of the piece is at the ground level, then it places
place = True
if place:
break
# Keeps going down and checking when the piece needs to be placed
coords[:,0] += 1
if current_piece == "I":
top_left[0] += 1
drop = False
else:
# Checks if the piece needs to be placed
if np.max(coords[:,0]) != 19:
for pos in coords:
if not np.array_equal(board[pos[0] + 1, pos[1]], [0, 0, 0]):
place = True
break
else:
place = True
if place:
# Places the piece where it is on the board
for pos in coords:
board[tuple(pos)] = color
# Resets place to False
place = False
break
# Moves down by 1
coords[:,0] += 1
if current_piece == "I":
top_left[0] += 1
# Clears lines and also counts how many lines have been cleared and updates the score
lines = 0
for line in range(20):
if np.all([np.any(pos != 0) for pos in board[line]]):
lines += 1
board[1:line+1] = board[:line]
score += lines*10
Mã nguồn này được kế thừa từ bài viết https://www.learnopencv.com/tetris-with-opencv-python/ và mình có modify lại theo sở thích cá nhân của mình. Còn một số bug mà mình chưa fix hết. Bạn đọc nào ghé ngang có đóng góp gì thì để lại comment giúp mình hen.


Created as a code-along with the Net Ninja's Youtube Angular 2 playlist
You can find the playlist here: Angular 2
Important Note: Much of the Ninja's tutorial was written with an earlier version of Angular 2 and the Angular 2 CLI which necessatated a lot of "looking things up" to make it all work now that Angular 2 final has been released.
You can find my notes here and the repo for this demo here.


MSG_GEN_PROCESS_CLOSE_APPLICATION, MSG_GEN_PROCESS_CLOSE_ENGINE, MSG_GEN_PROCESS_CLOSE_CUSTOM, MSG_META_QUIT
Just as loading an application is handled almost entirely by the system and GEOS classes, application shutdown is also fairly automatic. If the application intercepted
MSG_GEN_PROCESS_OPEN_APPLICATION
for its own purposes on startup, it likely has to do a little cleanup; otherwise, it won't have to worry about shutting down. (See Saving and Restoring State
for special information on using this message.)
Any object in the system may cause an application to shut down. Usually, shutdown occurs either when the system is being exited (when a user exits to DOS, for example) or when the user has closed the application. Therefore, the usual source of the shutdown directive is the User Interface.
An application begins shutting down when either its Process object or its Application object receives a
MSG_META_DETACH
. If you want to cause a shutdown manually, you should send
MSG_META_QUIT
to the application's GenApplication object; this will execute some default functions and then send the appropriate
MSG_META_DETACH
. Essentially, the same detach and destruction mechanisms used for any object are used for the entire application. The object receiving
MSG_META_DETACH
passes the message along to all of its children and to all the objects on its active list. (If a
MSG_META_DETACH
is used without
MSG_META_QUIT
, the application will create a state file.)
When they have all acknowledged the detach, the application acknowledges the detach and sets itself unusable. It automatically flushes its message queues before shutting down to avoid synchronization problems. You should not subclass the
MSG_META_DETACH
handler unless you have special needs for cleaning up or sending special detach messages to other objects or geodes. If you do subclass it, you must call the superclass at the end of your handler. Otherwise, the application will not finish detaching (see the GEOS Programming chapter).
Instead of intercepting
MSG_META_DETACH
, though, the application may intercept the mode-specific message it will also receive. Depending on the mode in which it was launched, the application will receive (via the Process object) either
MSG_GEN_PROCESS_CLOSE_APPLICATION
(for application mode) or
MSG_GEN_PROCESS_CLOSE_ENGINE
(for engine mode). There is no special shutdown message for shutting down to a state file; instead,
MSG_GEN_PROCESS_CLOSE_APPLICATION
is used.
When the system shuts down or task-switches, a different type of shutdown occurs. Applications (or other objects interested in this event) must register for notification on the notification list GCNSLT_SHUTDOWN_CONTROL (notification lists are described in the General Change Notification chapter). When the system shuts down or task-switches, the object will then receive a MSG_META_CONFIRM_SHUTDOWN
, at which time the object must call
SysShutdown()
.


Premonoidal categories
and a graphical view
of programs
Alan Jeffrey
School of Cognitive and Computing Sciences
University of Sussex
Brighton BN1 9QH
UK
alanje@cogs.susx.ac.uk


{% extends 'base.html' %} {% import 'macros.html' as macro %} {% block content %}
Get the latest news updates from around the world


You feel somewhat disoriented as you pass through...
This is the Interactive Fiction Archive at www.ifarchive.org.
You may be interested in...
The list of all directories is a table of contents of the entire Archive, in order. The list of files, sorted by date is a (long) index of every file. You can also see the entries of the past week, past month, past three months, or past year.
Or, go delving into the hierarchical tree shown here.
Symlink to if-archive/comp/files


{% extends "tv-layout.html" %} {% block body_classes %}dashboard{% endblock %} {% block page_title %} GOV.UK prototype kit {% endblock %} {% block content %} Budgeting Loans XXXXXXXXX Completion rate This week: 89% Public Beta: 87% 2% increase Average time to complete This week: 07:21 Public Beta: 07:15 00:06 increase Completions This week: 10,600 Public Beta: 39,800 Out of hours applications This week: 23%Public Beta: 33% Device usage This week:Mobile, Desktop, TabletPublic Beta: Mobile, Desktop, Tablet % % % Exit page % of exits Personal details 31.67% Contact details 11.89% Declaration 10.25% Loan ammount 9.02% Partner details 7.05% {% endblock %}
XXXXXXXXX
Public Beta: 87% 2% increase
Public Beta: 07:15 00:06 increase
Public Beta: 39,800
Public Beta: 33%
This week:Mobile, Desktop, Tablet
Public Beta: Mobile, Desktop, Tablet % % %
Personal details
31.67%
Contact details
11.89%
Declaration
10.25%
Loan ammount
9.02%
Partner details
7.05%


parallelDist: Parallel Distance Matrix Computation using Multiple Threads
A fast parallelized alternative to R's native 'dist' function to
calculate distance matrices for continuous, binary, and multi-dimensional
input matrices, which supports a broad variety of 41 predefined distance
functions from the 'stats', 'proxy' and 'dtw' R packages, as well as user-
defined functions written in C++. For ease of use, the 'parDist' function
extends the signature of the 'dist' function and uses the same parameter
naming conventions as distance methods of existing R packages. The package
is mainly implemented in C++ and leverages the 'RcppParallel' package to
parallelize the distance computations with the help of the 'TinyThread'
library. Furthermore, the 'Armadillo' linear algebra library is used for
optimized matrix operations during distance calculations. The curiously
recurring template pattern (CRTP) technique is applied to avoid virtual
functions, which improves the Dynamic Time Warping calculations while
the implementation stays flexible enough to support different DTW step
patterns and normalization methods.
| Version: |
0.2.4 |
| Depends: |
R (≥ 3.0.2) |
| Imports: |
Rcpp (≥ 0.12.6), RcppParallel (≥ 4.3.20) |
| LinkingTo: |
Rcpp, RcppParallel, RcppArmadillo |
| Suggests: |
dtw, ggplot2, proxy, testthat, RcppArmadillo, RcppXPtrUtils |
| Published: |
2018-12-12 |
| Author: |
Alexander Eckert [aut, cre],
Lucas Godoy [ctb],
Srikanth KS [ctb] |
| Maintainer: |
Alexander Eckert <info at alexandereckert.com> |
| BugReports: |
https://github.com/alexeckert/parallelDist/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/alexeckert/parallelDist,
https://www.alexandereckert.com/R |
| NeedsCompilation: |
yes |
| SystemRequirements: |
C++11 |
| Materials: |
README NEWS |
| CRAN checks: |
parallelDist results |
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=parallelDist
to link to this page.


+++ title = "Santiago the rafting guide" slug = "santiago-the-rafting-guide" date = 2007-05-23T12:00:00+10:00 tags = ["tourguides", "locals", "eccentric-encounters"] locations = ["peru/apurimac-river"] draft = false summary = """
One of the seasoned staff at Mayuc, Santiago is the instructor on my 6-person raft, for my three days on the Río Apurímac. He's a very cheerful guy, always cracking jokes and telling freaky stories. He's also a strict instructor, and (in our opinion) probably the best guide on the trip. We feel safe and lucky to have him. He also speaks excellent English, as well as a few token words of Hebrew (inevitable, given the number of Israelis around!).
""" +++One of the seasoned staff at Mayuc, Santiago is the instructor on my 6-person raft, for my three days on the Río Apurímac. He's a very cheerful guy, always cracking jokes and telling freaky stories. He's also a strict instructor, and (in our opinion) probably the best guide on the trip. We feel safe and lucky to have him. He also speaks excellent English, as well as a few token words of Hebrew (inevitable, given the number of Israelis around!).
{{< galleryphoto "2007-05-23-12-00-00--all-the-mayuc-boys-santiago-is-4th-in-line.jpg" >}}


Track 3: Strategies for Critical Mass
In 1969, Robert Neil Butler coined the term “ageism” to describe the widespread hostile prejudice faced by the elderly. However, ageism has now been altered to include the way mainstream society think of the youth. Asian American youth must now balance the challenges of youth while pursuing a track of advocacy. Many times, students may feel as though the spaces of activism are dominated by adults, and that their input is unnecessary. This workshop is aimed on giving youth activists/organizers the space to talk about their own experiences and strategies they have used to confront some of the tensions of operating within adult-dominated organizations and giving recommendations for other youth interested in carving their own activist spaces. Teaching about solidarity across age limits can be a way to educate people about activism and how it leads to current day issues.


American adopts Nigerian wife’s surname, Ekundayo
by Oluwakemi AbimbolaAn American man, Howard Sueing III Ekundayo, has revealed that he has adopted his Nigerian wife’s surname, Ekundayo.
Howard, whose Twitter bio reveals is an Engineering Manager at Netflix, tweeted on Sunday that he had recently got married to a Nigerian woman, one Bukky Ekundayo, and that he was changing his surname to hers, so they could pass it on to their children.
He tweeted, “I recently got married! I’m replacing my birth surname/suffix (Sueing III) with @SimplyBukks‘s (Ekundayo) so that we can pass it on to our future children. “Ekundayo” means “to turn tears to joy” in Yoruba. (sic)”
Howard has already changed his name on Twitter to reflect his new surname.
His decision has been well received by the Nigerian Twitter community.
A Twitter user, Uzoma Nwagba sharing a screenshot of Howard’s tweet, wrote, “Meet my college friend, Howard Sueing, an American. Howard was in love with us Nigerians he met (who wouldn’t be?). Howard has just married our Nigerian classmate, Bukky Ekundayo. He decided to take her surname, officially. Bold. Meet Howard Ekundayo. Congrats bro.”
Another user, ‘Sire @SiredOne said, He looks Nigerian already while Mike J @CodeInBlack wrote “That’s that love spirit. Congrats.”
See tweets:


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/ATP.20in.20the.20news.html ---
https://www.the-tls.co.uk/articles/kurt-godel-incompleteness-theorems/
{% endraw %}One way of describing the Incompleteness Theorems (1931) of the Austrian logician Kurt Gödel is to say that he proved, in the form of a mathematical theorem, that the possibility of a fully automated mathematics can never be realized.
Last updated: Apr 24 2020 at 19:02 UTC


Norway wealth fund, Prologis in US$2b real estate acquisition
OSLO, Nov 25 — Norway’s sovereign wealth fund and property investment group Prologis Inc have agreed to jointly buy a US$1.99 billion logistics real estate portfolio, Norges Bank Investment Management (NBIM) said in a statement.
“The portfolio consists of 127 properties located across multiple US markets, including Southern California, San Francisco Bay Area, Seattle and Dallas,” it added.
NBIM, which manages the world’s largest sovereign wealth fund with assets of some US$1.1 trillion, paid US$896 million for a 45 per cent stake in the portfolio, while Prologis will own the remaining 55 per cent and manage the properties.
The Norwegian fund invests in foreign bonds, stocks and real estate.
In real estate, it concentrates on two types of investment: logistics centres near major cities which benefit from the growth in online shopping, and office buildings in the centre of major cities such as New York, Tokyo, London and Paris.
The acquisition is part of Prologis Inc’s merger agreement to acquire Industrial Property Trust, Inc, NBIM added. — Reuters


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plotfunctions: Various Functions to Facilitate Visualization of Data and
Analysis
When analyzing data, plots are a helpful tool for visualizing data and interpreting statistical models. This package provides a set of simple tools for building plots incrementally, starting with an empty plot region, and adding bars, data points, regression lines, error bars, gradient legends, density distributions in the margins, and even pictures. The package builds further on R graphics by simply combining functions and settings in order to reduce the amount of code to produce for the user. As a result, the package does not use formula input or special syntax, but can be used in combination with default R plot functions. Note: Most of the functions were part of the package 'itsadug', which is now split in two packages: 1. the package 'itsadug', which contains the core functions for visualizing and evaluating nonlinear regression models, and 2. the package 'plotfunctions', which contains more general plot functions.
| Version: |
1.3 |
| Depends: |
R (≥ 2.10) |
| Suggests: |
knitr, sp |
| Published: |
2017-08-30 |
| Author: |
Jacolien van Rij [aut, cre] |
| Maintainer: |
Jacolien van Rij <vanrij.jacolien at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
plotfunctions results |
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=plotfunctions
to link to this page.


Greta Thunberg Has Perfect Response To Heckler At Climate Change Rally
by Lucy ConnollyGreta Thunberg has proven once again she is more than capable of handling herself, after shutting down a heckler at a climate change rally in North Carolina over the weekend.
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The 16-year-old climate activist was addressing a large crowd in Charlotte on Friday, November 8, when she was rudely interrupted by someone who clearly disagreed with Greta’s speech.
Echoing her earlier statements that climate change needs to be tackled ‘right now’, the Greta Thunberg told the 1000-strong crowd people like them are now ‘our greatest source of hope’ – although she did admit it ‘can be hard’ to find hope in times like this.

The 16-year-old was imploring fellow youth leaders to speak out in the fight against global warming when she was forced to halt her speech as someone near the stage interrupted her, as Yahoo News reports.
The unknown person started shouting Greta’s name before saying something to her, although it is unclear what the heckler actually said.
Greta then responded saying:
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I think if you want to speak with me personally, maybe you can do it later.
At this point, the crowd erupted into cheers with thousands of people chanting the activist’s first name, resulting in Greta having to pause for around 30 seconds before she could resume her speech again.
The 16-year-old, who has been travelling around North America to raise awareness about climate change, continued with grace with the rest of her speech,
She explained:
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While we young people may not be able to vote or make decisions today, we have something just as powerful. And that is our voices. And we need to use them.
It is we young people who are the future. There is not enough time to wait for us to grow up and become the ones in charge because we need to tackle the climate and ecological emergency right now.
This isn’t the first time Greta has had to deal with hecklers. In fact, it’s not even the most prominent heckler she’s ever had to deal with.
Nope, that title would belong to the one and only Donald Trump, who seemed so put out by Greta’s (very true and important) claims when she spoke at the United Nations Climate Summit that he chose to have a dig at her.
Instead of praising the 16-year-old for starting an important conversation about climate change when fully-grown world leaders – such as himself – have failed to do so, Trump instead sarcastically described Greta as a ‘very happy young girl looking forward to a bright and wonderful future’.
Which, far from being the incredible burn I imagine Trump believes it to be, is actually just further evidence that a 16-year-old girl is much more mature than a 73-year-old man who currently holds the most powerful position in the world.

Keep doing you, Greta.
A Broadcast Journalism Masters graduate who went on to achieve an NCTJ level 3 Diploma in Journalism, Lucy has done stints at ITV, BBC Inside Out and Key 103. While working as a journalist for UNILAD, Lucy has reported on breaking news stories while also writing features about mental health, cervical screening awareness, and Little Mix (who she is unapologetically obsessed with).


The food from the places that you loved most!
FoodieFun gives you the ability to keep track of what was order from your favorite restaraunts. Best of all you can keep track of your favorite items,rating them with a rating scale from 1 to 5 and commenting about what you liked best.
 With FoodieFun you're given a platform to keep a personal review of each restaraunt that you have visited and the ability to describe your personal experience.


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| Andrew Thai |
Social Chair |
<EMAIL_ADDRESS>|
| Joseph Le |
Technology Chair |
<EMAIL_ADDRESS>|


Inselect facilitates digitization by automating the capture of individual specimens from whole-drawer scans.
Inselect
About the Project
Whilst imaging a whole drawer of specimens is relatively quick, manually segmenting the specimens is a time consuming process. Inselect was developed to address this problem – it can crop individual images of specimens from whole-drawer scans and similar images that are generated through digitisation of museum collections.
Inselect combines image processing, barcode reading, validation of user-defined metadata and batch processing to offer a high level of automation that can greatly increase the efficiency of digitisation workflows.
Once the draw is scanned and processed through Inselect, a segmented image is provided which gives the user the option to resize, move, create or delete segmentation areas. The metadata for each specimen can then be manually modified and entered by the user.
Development
Inselect was developed by a team at the Natural History Museum, in collaboration with scientists from Stellenbosch University in South Africa. A workshop was held in September 2014 to develop a specification and prototype, which was then presented at the Taxonomic Database Working Group (TDWG) meeting in October 2014. The program was subsequently developed for wider release and testing amongst SYNTHESYS3 Taxonomic Access Facilities (TAFs) and has undergone continuous development and improvement based on feedback from user communities.
To date, Inselect has been downloaded 737 times. It is cross-platform (Windows and Mac OSX) and open source with modular architecture. To download or read more about the software, including a full list of user guides, please visit the website or github.


[t]Dear {$jobOwnerName|escape},[/t]
[t]Candidate {$applicantName} on job {$vacancyName} was changed by {$linemanagerName} on {$change_date_time}.[/t]
{if $r.type == "candidate"}
[t]Status[/t]
{else}
{$r.status_name}
{/if}
[t]changed from: {$r.from_status}, changed to: {$r.to_status}[/t]
[t]comments: {$lm_remarks}[/t]


Are you posting a
Homestay
or
Room
Showcase your unit well.
Take a Photo
Unit Rate
Enter a rate for your unit. Below are the reference rates of similar units nearby.
RM50
RM50
RM50
Air-Conditioner Water Heater Wifi


Series of classes used to generate SQL statements. Based around the notion that an SQL statement (SQLStatement) has a series of tables (SQLTable) involved, with a series of joins (SQLJoin), subject to a series of conditions, based on expressions (see org.datanucleus.store.rdbms.sql.expression. The tables involved in the statement can be grouped for convenience to represent objects and relations between objects (like in JDOQL/JPQL). This package has now replaced FetchStatement, and will also replace QueryStatement, StatementText and all code under org.datanucleus.store.mapped.expression.
Provides helpers to generate the SQLStatement. These include
Allows control over how tables in the SQL statement are aliased. This uses a plugin-point org.datanucleus.store.rdbms.sql_tablenamer and DataNucleus provides the following options


Ecstasy changed my life. I mean, I used to think if ecstasy were legal the world just might be a better place. I was given the confidence to do or say anything, to anyone. I could tell someone 'I Love You' and right at that moment I would know they loved me too.
Everyone is so willing to make your high as smooth as possible, people you dont even know. Strangers will look at you and as soon as they realize your rolling (and so are they), a special bond is formed. I never thought I would ever tire of ecstasy.
Although I never even started at taking one dose per night, it began to get ridiculous when I had to start taking five or six. I'm a little girl weighing merely 90 lbs, you'd think it'd only take 1 or 2. But with ecstasy I never want the high to end. After a while I started feeling gross the day after.
I started looking at myself like I was a tweek because I needed ex so bad the night before. Then I would start to think of all the things I did while rolling and it started to repulse me. I would remember the vibrating eyes and the crooked jaws that I once thought was so cute, but now it was confirmed to look so druggieish.
It wasnt only the fact that it took so many doses to get and stay high but it was more like the magic was gone. All the beauty of rolling left and it was replaced with truth. After a while I started seeing ecstasy for what it is, it's a drug, not some kind of cure or a warp hole to a better life.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Sample Srchhelp Screen
Click in the box, type the
AUTHOR
you want, and press Enter or Submit
Type the Author's or Artist's
LAST NAME first
, for example
Shakespeare
Shakespeare, William
Sinatra, Frank
Beethoven
Back to Opac Menu
Back to Main Menu


{{ observation.first_common }}
TSN: {{ observation.tsn }}
@ {{ observation.geom }}
This is an example of django-easy-pdf – Django PDF rendering the easy way. Check en.ig.ma for Django and Python development.
Scientific Name: {{ observation.name_sci }}
| Synonym | Second Common | Third Common | Family | Family Common | Phylum | Phylum Common |
|---|---|---|---|---|---|---|
| {{ observation.synonym }} | {{ observation.second_common }} | {{ observation.third_common }} | {{ observation.family }} | {{ observation.family_common }} | {{ observation.phylum }} | {{ observation.phylum_common }} |
| {% if forloop.counter|divisibleby:6 %} |


We optimize blockchain!
- seize customized applications
- incubate dynamic bandwidth
- enhance B2B e-services
- synergize real-time users
- recontextualize rich paradigms
- reinvent mission-critical metrics
- benchmark extensible communities
- enhance interactive action-items
- expedite compelling e-tailers
- innovate best-of-breed content
- engage one-to-one networks
- cultivate compelling niches


Complete Classroom Canada was established in 2002. We are a product-supply company. We supply materials and products to numerous companies around the world. Our expertise enables us to cater to you demands in a timely and cost efficient manner.
After a change in management, we revitalized the companies organization to better serve our customer’s needs.
Our goal as an organization is to provide products and services to our customers at the highest quality, on time and for the lowest cost.
Our promis is that we will do this in the most environmentally sustainable and reliable process.
The owner and director of Blue Water Sourcing Inc., Ms Omoh Raji graduated with a Masters degree in Education and a Post-Graduate diploma in Early Childhood Education from York University, Toronto Ontario Canada. As an Early Childhood Educator, she designed and implemented developmental appropriate activities that included opportunities for language development as well as hands-on exploration of the school environment. She has been teaching and was responsible for the creative writing and program management for young children for over fifteen years now. She is the author of “The Turtle’s Back” a children story book. She also provided a variety of learning experiences designed to meet children’s needs, utilized multicultural sensitive approaches and resources in the classroom setting. Happily married, blessed with children and presently manages Blue Water Sourcing Inc in Ottawa Ontario, Canada where she resides with her family.


Unique Researcher Identity (URI)
This project envisions the solution to the problem of unique researcher identity(URL). Our team is working on creating a unique research idenity which can be stored, tracked, shared and audited levaraging the block chain technologies. This would include the complex mapping of researcher's profile with his/her ideas, publications and activities.Following is the visaulization of how URI can be realized through the combination of existing solutions.


Note : If the tables are already created, it will indicate so on the console. Proceed to the step no. 4 in this case
Prerequisites
Make sure the tables enabled for 3NF with appropriate constraints applied, are created, as in the previous tutorial
Operation
- Click on the action button in the action panel
- It creates the store tables, if not already created, enabled for 3NF with constraints enforced
Result
The store tables with enabled for 3NF and constraints are created. If the tables already exist, it will indicate so


The host name which can be used by plugins to refer to this server e.g. in URLs sent in REST endpoints.
The host name or IP of the proxy server to use.
The TCP port of the HTTP proxy server, e.g. 80 or 8080.
A regular expression which is matched on a host name or IP address for which the proxy shall not be used.
A server is started on this port providing WebDriver conform endpoints. Clients shall use this port to request Selenium resources.
Selenium URL for clients: {{clientSeleniumURL}}
Specified the interval to use for checking each Selenium resource for its state.
If, during health check, a Selenium resource does not establish a connection within this amount of time, it is treated as DISCONNECTED.
If a client does not issue a Selenium request for this amount of time, the associated managed request is treated as ORPHANED, the current Selenium session is deleted, and the resource is reused.
{{ alert.message }}


Fake News Detection App
The news you provided was:
{% if prediction ==0%}
Reliable
{% elif prediction == 1%}
Unreliable
{% endif %}


--- layout: default title: Developer.bitcoin.com | Home --- {{> home-header}}
Build the foundation of knowledge needed to create world changing apps. Tutorials, blog posts, video streams and code snippets to help you go from being a hobbyist to a professional step by step.
BEGINWith the power of Bitcoin Cash, the ease of BITBOX, and the revolutionary features of cutting-edge Bitcoin.com technologies, you have the freedom to create your most innovative apps ever.
BEGIN


My website status
Please wait...
Server response time:
Please wait...
UpTime stats (%)
* Uptime refers to the period of time when my *website* is up and running.
Tweets by @*website*


New Chef Already have an account? Login here Email: Password: Chef's Name: Upload a photo! Choose file Chef's Dietary Restrictions: Are you kosher? Vegan? Any allergies? Let the other members of your household know! If you do not have any, enter 'none' Sign Up


Sea Point drowning: Second body found, two still missing
by Azarrah KarrimThe body of a 15-year-old girl has been found at Rocklands Beach in Sea Point by the National Sea Rescue Institute (NSRI).
"Police forensic personnel are on the scene after which the body will be taken by the health department's forensic pathology services.
"Trauma counselling services have been offered to the family," said police spokesperson Brigadier Novela Potelwa.
On Sunday, the NSRI received reports of four people in distress in the surf at Rocklands Beach.
After a rescue operation, one boy was found but declared dead after attempts to resuscitate him failed, News24 reported.
"The four casualties, who were originally in difficulty, were an 18-year-old male from Burundi, a 16-year-old male from the Congo, a 15-year-old male from Burundi and a 15-year-old female from Congo," NSRI spokesperson Craig Lambinon said in a statement late on Sunday evening.
Two others have still not been found.


Wanna find out if a triangle is equilateral, isosceles, scalene or even not a triangle at all?
Then try this out.
Simply put your three dimensions in the boxes below and click the 'Check It Out' button
The answer will pop up in no minute!
Side A:
Side B:
Side C:


No need for manually changing swap rates. Just turn it on.
We don't have any access to your account other than via API keys that allow only for basic swap operations.
Sometimes rates are off the charts - with our bot you will be sure that you will catch that spike


Looks like a bug.
On latest master: commit: 3cc8d2aaa0abec7253825b50a080988b0b37c27f
curl --data-binary '{"jsonrpc": "2.0", "id":"curltest", "method": "hdrsproof", "params": [1456592, 1456694 ] }' -H 'content-type: text/plain;' http://<IP_ADDRESS>:7771/
{"result":"success","prevht":1456570,"nextht":1456570,"prevtxid":"93a1c7d75fd84366c742bfdc3ab6a8c640df5d020113b55f3288b942e045feb3","prevtxidht":0,"prevtxlen":0,"nexttxid":"848b861cf11f30e4e62ac3567fa553ace89b1fbde7ffaefaa4f94926f3561928","nexttxidht":1456584,"nexttxlen":263774,"numhdrs":1,"headers":[{"height":1456570,"blockhash":"0055343953285e73c52d6fb5526ffb04260beac51d4b50ee6da82766f64b847d","hashPrevBlock":"000000007c95cf32cd13fb4692ad6fee85027fcd12139f02ed393a9579918bdd","hashMerkleRoot":"845ea63252ee2323217b44f9ff72975680abee693800665070465811102b5cba","nTime":1563961526,"nBits":486607149}],"lastpeer":"nodeid.30"}
hdrsproof call always returns only one block header - one below prevheight parameter.
Response always contains prevheight == next height, "numhdrs":1


| xgb.save {xgboost} | R Documentation |
Save xgboost model to a file in binary format.
xgb.save(model, fname)
model |
model object of |
fname |
name of the file to write. |
This methods allows to save a model in an xgboost-internal binary format which is universal
among the various xgboost interfaces. In R, the saved model file could be read-in later
using either the xgb.load
function or the xgb_model
parameter
of xgb.train
.
Note: a model can also be saved as an R-object (e.g., by using readRDS
or save
). However, it would then only be compatible with R, and
corresponding R-methods would need to be used to load it.
xgb.load
, xgb.Booster.complete
.
data(agaricus.train, package='xgboost') data(agaricus.test, package='xgboost') train <- agaricus.train test <- agaricus.test bst <- xgboost(data = train$data, label = train$label, max_depth = 2, eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic") xgb.save(bst, 'xgb.model') bst <- xgb.load('xgb.model') if (file.exists('xgb.model')) file.remove('xgb.model') pred <- predict(bst, test$data)


This component is providing the application navigation.
It can change itself to a hamburger menu when the width of the page is smaller than a given property.
The menu component accepts two types of slots:
slot="before" - this slot is rendered before the menu items.
slot="after" - this slot is rendered after the menu items.


Re-click on handler or click out of layer for close !
Demo #05 : Multi binding to open same popin/layer
The selector option must be a CSS class, it possible to have a multiple element HTML with a same CSS class. Same class, same container » open by link
One simple popin open by two different way !
Demo #06 : Add timer for prevent bad mouseover
In "layer" mode, if you have a button in the content of layer, a timer is necessary. The timer is already use for prevent bad hover and reduce opening unwanted» open with timer
Layer with timer
Demo #07 : Auto close case
This plugin has an option for auto close the popin targeted. start auto close


In the Fixed Size Array implementation of a Bag,
depicted below as ArrayBag1, what happens when a client tries to
add an element to the ArrayBag1 but the underlying array is full?
The element does not get added
The add method replaces the last element with the new element
The add method doubles its size and adds the new element


Welcome to The Hood. I am excited to have you in my platform. If you are receiving this email, it can be assumed that you already conversant with what the application offers. Incase of ant bugs, feel free to report them.


title = "Single Gallery for Department" url = "/dept/:perma/gallery/:id" layout = "department" description = "For content of a single gallery of department" is_hidden = 0 == function onStart() { $id = $this->param('id'); $gallery = $this['dept']->galleries()->find($id); if (!$gallery) { return Redirect::to('/404'); } $this['navtitle'] = $gallery->carousel->name; $this['gallery'] = $gallery; $this->addCss('assets/bower_components/lightgallery/dist/css/lightgallery.min.css'); $this->addJs('assets/bower_components/lightgallery/dist/js/lightgallery-all.min.js'); $this->addJs('assets/js/gallery.js'); } ==


Building, Compiling, etc
The best way of obtaining the source and building is via Subversion: svn co http://<IP_ADDRESS>/svn/projects/infotrope/acapd/ - I don't bother with releases all that often, primarily because hardly anyone else has shown interest. Sorry. Show me interest, and I'll show you a release. :-)
You should be able to get away with "make" in most cases. Failing that, try "make config help", read, learn, inwardly digest, then run ./configure with suitable options, and rerun make.
Prerequisites:
- Linux
- Previous renditions did apparently compile on FreeBSD, but since 0.1.x, I doubt this is the case anymore. For better or worse, the company that employs me standardized on Linux as a server platform some time ago, and this server requires various non-portable features, and has implemented them only to operate on Linux.
- Cyrus SASL
- Required for authentication. Previous versions did make an attempt at an alternate SASL library, but this effort never got far, and has since been abandoned.
- Expat
- Yes, I know, I've been meaning to try it on libxml for a while.
- Optional: OpenSSL
- OpenSSL is required if you want RFC2595 support. Some, possibly even most, of the work involved to get it working with GNU TLS is also there, but untested.
Once built, "make install" should put everything in more or less the right places.
Debian users on x86 or amd64 can rejoice, for there are packages synched to unstable:
deb http://dave.cridland.net/debian/ unstable main
deb-src http://dave.cridland.net/debian/ unstable main
. Just apt-get install infotrope-acapd.
Dave Cridland
Last modified: Tue Aug 10 16:47:47 BST 2004


Reservation = messageTitle ?>
= messageBody ?>
Start Date: = startDate ?>
End Date: = endDate ?>
Name: = clientName ?>
Coded by
Kurt Kaiser


BABY RECORD
BABY'S PROFILE
BABY'S GROWTH
IMMUNIZATION
DOCTOR'S ADVICE
EDIT INFO
IMMUNIZATION
VACCINE
Hepatitis A
Hepatitis b
Zoster
Influenza
Chicken pox
Pollo
Others...
DATE GIVEN
MM/DD/YYYY
ADMINISTRATED BY
clinic, doctor, etc
NEXT DOSE DATE
MM/DD/YYYY
Submit
Should be Empty:


When you work with us, you are part of a team of committed collaborators. All aspects of our software development
process are intergrated, interactive and agile.
Our approach unifies design, development and product management to create exceptional products.
We start each project by understanding your business goals, the user's needs and product requirements.
Then we help and translate them into effective and elegant solutions.
Before we sign a contract or write a line of code, our team will spend the necessary time needed to understand your product vision.
The outcome of this scoping session is a proposal or high level statement of work.
Based on your requirements, we can provide a complete offering or just the pieces you need.
Our design practice offers a full range of services including brand strategy, interaction and visual design and user experience testing.
Throughout your project, our designers create and implement visual design and workflows, solicit user feedback and work with you to ensure what gets build is what is needed
All engineers are fluent in the latest enterprise, mobile and web development technologies.
They collaborate with your team, to write and improve code on a daily basis, using proven practices such as test-driven development and pair programming.
Planning and development is iterative. Because we are constantly coding and testing, the products we build are always ready to go live.
This iterative process allows for changes as business requirements evolve.


Invoice (Sample for Calculation)
Navigation Controls by INTER-Mediator
id
issued
title
If the product id is no value, popup menu to select the product appears.
product
qty
unitprice (master)
amount
(
)
Tax Rate:
Total:


Creating Custom Widget Extensions
Once you have a custom widget plugin for Qt Designer, you can provide it with the expected behavior and functionality within Qt Designer's workspace, using custom widget extensions.
Extension Types
There are several available types of extensions in Qt Designer. You can use all of these extensions in the same pattern, only replacing the respective extension base class.
QDesignerContainerExtension is necessary when implementing a custom multi-page container.
| QDesignerTaskMenuExtension QDesignerTaskMenuExtension is useful for custom widgets. It provides an extension that allows you to add custom menu entries to Qt Designer's task menu. The Task Menu Extension example illustrates how to use this class. | |
| QDesignerContainerExtension QDesignerContainerExtension is necessary when implementing a custom multi-page container. It provides an extension that allows you to add and delete pages for a multi-page container plugin in Qt Designer. The Container Extension example further explains how to use this class. Note: It is not possible to add custom per-page properties for some widgets (e.g., QTabWidget) due to the way they are implemented. |
| QDesignerMemberSheetExtension The QDesignerMemberSheetExtension class allows you to manipulate a widget's member functions displayed when connecting signals and slots. | |
| QDesignerPropertySheetExtension, QDesignerDynamicPropertySheetExtension These extension classes allow you to control how a widget's properties are displayed in Qt Designer's property editor. |
Qt Designer uses the QDesignerPropertySheetExtension and the QDesignerMemberSheetExtension classes to feed its property and signal and slot editors. Whenever a widget is selected in its workspace, Qt Designer will query for the widget's property sheet extension; likewise, whenever a connection between two widgets is requested, Qt Designer will query for the widgets' member sheet extensions.
Warning: All widgets have default property and member sheets. If you implement custom property sheet or member sheet extensions, your custom extensions will override the default sheets.
Creating an Extension
To create an extension you must inherit both QObject and the appropriate base class, and reimplement its functions. Since we are implementing an interface, we must ensure that it is made known to the meta object system using the Q_INTERFACES() macro in the extension class's definition. For example:
class MyExtension: public QObject, public QdesignerContainerExtension { Q_OBJECT Q_INTERFACE(QDesignerContainerExtension) ... }
This enables Qt Designer to use the qobject_cast() function to query for supported interfaces using a QObject pointer only.
Exposing an Extension to Qt Designer
In Qt Designer the extensions are not created until they are required. For this reason, when implementing extensions, you must subclass QExtensionFactory to create a class that is able to make instances of your extensions. Also, you must register your factory with Qt Designer's extension manager; the extension manager handles the construction of extensions.
When an extension is requested, Qt Designer's extension manager will run through its registered factories calling QExtensionFactory::createExtension() for each of them until it finds one that is able to create the requested extension for the selected widget. This factory will then make an instance of the extension.
Creating an Extension Factory
The QExtensionFactory class provides a standard extension factory, but it can also be used as an interface for custom extension factories.
The purpose is to reimplement the QExtensionFactory::createExtension() function, making it able to create your extension, such as a MultiPageWidget container extension.
You can either create a new QExtensionFactory and reimplement the QExtensionFactory::createExtension() function:
QObject *ANewExtensionFactory::createExtension(QObject *object, const QString &iid, QObject *parent) const { if (iid != Q_TYPEID(QDesignerContainerExtension)) return 0; if (MyCustomWidget *widget = qobject_cast<MyCustomWidget*> (object)) return new MyContainerExtension(widget, parent); return 0; }
or you can use an existing factory, expanding the QExtensionFactory::createExtension() function to enable the factory to create your custom extension as well:
QObject *AGeneralExtensionFactory::createExtension(QObject *object, const QString &iid, QObject *parent) const { MyCustomWidget *widget = qobject_cast<MyCustomWidget*>(object); if (widget && (iid == Q_TYPEID(QDesignerTaskMenuExtension))) { return new MyTaskMenuExtension(widget, parent); } else if (widget && (iid == Q_TYPEID(QDesignerContainerExtension))) { return new MyContainerExtension(widget, parent); } else { return 0; } }
Accessing Qt Designer's Extension Manager
When implementing a custom widget plugin, you must subclass the QDesignerCustomWidgetInterface to expose your plugin to Qt Designer. This is covered in more detail in the Creating Custom Widgets for Qt Designer section. The registration of an extension factory is typically made in the QDesignerCustomWidgetInterface::initialize() function:
void MyPlugin::initialize(QDesignerFormEditorInterface *formEditor) { if (initialized) return; QExtensionManager *manager = formEditor->extensionManager(); Q_ASSERT(manager != 0); manager->registerExtensions(new MyExtensionFactory(manager), Q_TYPEID(QDesignerTaskMenuExtension)); initialized = true; }
The formEditor
parameter in the QDesignerCustomWidgetInterface::initialize() function is a pointer to Qt Designer's current QDesignerFormEditorInterface object. You must use the QDesignerFormEditorInterface::extensionManager() function to retrieve an interface to Qt Designer's extension manager. Then you use the QExtensionManager::registerExtensions() function to register your custom extension factory.
Related Examples
For more information on creating custom widget extensions in Qt Designer, refer to the Task Menu Extension and Container Extension examples.


London, city of Sherlock, Harry Potter, Dorian Gray and many others…
Our client is a mid size real estate company trying to enter into airbnb business in London. Their new revenue model is to build or purchase flats and houses, renovate them and then list on the website. They also want to expand the business to Budapest Hungary soon.
Our task is to help them on the pricing issue by using the historical data publicly available online.
library(doParallel)
library(ggplot2)
library(data.table)
library(caret)
library(glmnet)
library(dplyr)
library(ggthemes) #fivethirtyeight plots
library(skimr) #magical summaries
library(gridExtra) #combine plots
library(stargazer) #checked a few coeffs
library(knitr)
library(kableExtra) #html formating
registerDoParallel(cores = 4)
knitr::opts_chunk$set(warning = FALSE)
We need to get rid of some dublicates
data <- fread("data/airbnbdata/airbnb_london_workfile.csv", stringsAsFactors = TRUE)
data[, c("neighbourhood_cleansed", "cancellation_policy",
"usd_price_day", "property_type", "room_type") := NULL]
data[, neighbourhood_cleansed := NULL]
names(data)
## [1] "usd_cleaning_fee" "f_property_type"
## [3] "f_room_type" "f_cancellation_policy"
## [5] "f_bed_type" "f_neighbourhood_cleansed"
## [7] "p_host_response_rate" "n_accommodates"
## [9] "n_bathrooms" "n_review_scores_rating"
## [11] "n_number_of_reviews" "n_guests_included"
## [13] "n_reviews_per_month" "n_extra_people"
## [15] "n_minimum_nights" "n_beds"
## [17] "n_days_since" "d_24hourcheckin"
## [19] "d_airconditioning" "d_breakfast"
## [21] "d_buzzerwirelessintercom" "d_cabletv"
## [23] "d_carbonmonoxidedetector" "d_cats"
## [25] "d_dogs" "d_doorman"
## [27] "d_doormanentry" "d_dryer"
## [29] "d_elevatorinbuilding" "d_essentials"
## [31] "d_familykidfriendly" "d_fireextinguisher"
## [33] "d_firstaidkit" "d_freeparkingonpremises"
## [35] "d_freeparkingonstreet" "d_gym"
## [37] "d_hairdryer" "d_hangers"
## [39] "d_heating" "d_hottub"
## [41] "d_indoorfireplace" "d_internet"
## [43] "d_iron" "d_keypad"
## [45] "d_kitchen" "d_laptopfriendlyworkspace"
## [47] "d_lockonbedroomdoor" "d_lockbox"
## [49] "d_otherpets" "d_paidparkingoffpremises"
## [51] "d_petsallowed" "d_petsliveonthisproperty"
## [53] "d_pool" "d_privateentrance"
## [55] "d_privatelivingroom" "d_safetycard"
## [57] "d_selfcheckin" "d_shampoo"
## [59] "d_smartlock" "d_smokedetector"
## [61] "d_smokingallowed" "d_suitableforevents"
## [63] "d_tv" "d_washer"
## [65] "d_washerdryer" "d_wheelchairaccessible"
## [67] "d_wirelessinternet" "price"
data[, price := as.numeric(price)]
Since these properties will be renovated new listings, they will not have any review or review related scores. Later on we can build a new model for the existing ones. However, we will drop them for this initial study.
Cleaning of the properties will be outsourced to another company and it will be a standardized process. In the data when the cleaning fee is missing it is actually zero. And sometimes this feature is misused by the oweners. We will not use this feature as well.
data[, c("n_review_scores_rating", "n_reviews_per_month",
"n_number_of_reviews", "n_days_since", "p_host_response_rate", "usd_cleaning_fee") := NULL]
Do we have missing variables?
sapply(data, function(x) sum(is.na(x))) %>%
pander()
| f_property_type | f_room_type | f_cancellation_policy | f_bed_type |
|---|---|---|---|
| 0 | 0 | 0 | 0 |
| f_neighbourhood_cleansed | n_accommodates | n_bathrooms | n_guests_included |
|---|---|---|---|
| 0 | 0 | 237 | 0 |
| n_extra_people | n_minimum_nights | n_beds | d_24hourcheckin |
|---|---|---|---|
| 0 | 0 | 167 | 0 |
| d_airconditioning | d_breakfast | d_buzzerwirelessintercom | d_cabletv |
|---|---|---|---|
| 0 | 0 | 0 | 0 |
| d_carbonmonoxidedetector | d_cats | d_dogs | d_doorman | d_doormanentry |
|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 |
| d_dryer | d_elevatorinbuilding | d_essentials | d_familykidfriendly |
|---|---|---|---|
| 0 | 0 | 0 | 0 |
| d_fireextinguisher | d_firstaidkit | d_freeparkingonpremises |
|---|---|---|
| 0 | 0 | 0 |
| d_freeparkingonstreet | d_gym | d_hairdryer | d_hangers | d_heating | d_hottub |
|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 | 0 |
| d_indoorfireplace | d_internet | d_iron | d_keypad | d_kitchen |
|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 |
| d_laptopfriendlyworkspace | d_lockonbedroomdoor | d_lockbox | d_otherpets |
|---|---|---|---|
| 0 | 0 | 0 | 0 |
| d_paidparkingoffpremises | d_petsallowed | d_petsliveonthisproperty | d_pool |
|---|---|---|---|
| 0 | 0 | 0 | 0 |
| d_privateentrance | d_privatelivingroom | d_safetycard | d_selfcheckin |
|---|---|---|---|
| 0 | 0 | 0 | 0 |
| d_shampoo | d_smartlock | d_smokedetector | d_smokingallowed |
|---|---|---|---|
| 0 | 0 | 0 | 0 |
| d_suitableforevents | d_tv | d_washer | d_washerdryer | d_wheelchairaccessible |
|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 |
| d_wirelessinternet | price |
|---|---|
| 0 | 0 |
n_bathrooms (237), n_beds (167) have missing values.
We will use median value for the bathrooms assuming that 1 bathroom should be available. Number of beds should be equal to the number of accommodate for the missing ones.
data[, n_bathrooms :=
ifelse(is.na(n_bathrooms), median(n_bathrooms, na.rm = T), n_bathrooms)]
data[, n_beds :=
ifelse(is.na(n_beds), n_accommodates, n_beds)]
#a few observations with strange prices. Illogical and outscope numbers create noise to our business question.
data <- data[data$price > 10, ]
data <- data[data$price < 700, ]
#Below is the small gadget i wrote to study my variables in detail.
sapply(data, function(x) if (class(x) == 'factor') {unique(x)
} else if (class(x) != 'factor') {skim(x)})
#take another look
glimpse(data)
How is the average price changing district by district?
data[, .(count = .N,
avg_price = round(mean(price)),
median_price = round(median(price)),
min = min(price),
max = max(price)),
by = f_neighbourhood_cleansed][order(-avg_price)] %>%
filter(count > 1000)
## f_neighbourhood_cleansed count avg_price median_price min max
## 1 Kensington and Chelsea 3327 145 120 20 695
## 2 Westminster 5202 137 110 19 691
## 3 Camden 3623 112 95 15 666
## 4 Hammersmith and Fulham 2689 104 82 11 699
## 5 Wandsworth 2602 96 75 15 650
## 6 Islington 3478 94 80 11 654
## 7 Southwark 3241 86 69 11 699
## 8 Lambeth 3176 84 65 15 682
## 9 Brent 1537 81 60 15 669
## 10 Hackney 4492 80 65 11 500
## 11 Tower Hamlets 5431 77 60 12 650
## 12 Newham 1120 70 50 14 572
## 13 Haringey 1402 69 50 12 570
## 14 Lewisham 1448 61 48 15 400
Now time to select a random district… We will select a random district and compare it with the entire London. This will also help us to analyze several different districts without changing the code. However, I will set a seed below for reproducability of this one
#a dplyr way...
set.seed(22) #reproduce
selection_table <- data %>%
group_by(f_neighbourhood_cleansed) %>%
tally() %>%
filter(n > 1000) %>%
arrange(desc(n))
lottery_winner <- as.vector(sample(selection_table$f_neighbourhood_cleansed, 1))
lottery_winner
## [1] "Islington"
Hello My District… This is the area where Sherlock Holmes lived.
district <- data %>%
filter(f_neighbourhood_cleansed == lottery_winner)
## Distribution of price by type
plot1 <- ggplot(data, aes(price, fill = f_room_type)) +
ggtitle("Price Density, London") +
geom_density(alpha = 0.3) + theme_fivethirtyeight()
## Boxplot of price by room type
plot2 <- ggplot(data, aes(f_room_type, price)) +
ggtitle("Price in London") +
geom_boxplot()+ theme_fivethirtyeight()
plot3 <- ggplot(district, aes(price, fill = f_room_type)) +
ggtitle("Price Density, District") +
geom_density(alpha = 0.3) + theme_fivethirtyeight()
## Boxplot of price by room type
plot4 <- ggplot(district, aes(f_room_type, price)) +
ggtitle("Price in District") +
geom_boxplot()+ theme_fivethirtyeight()
grid.arrange(plot1, plot2, plot3, plot4, ncol=2, nrow=2)
Distributions are similar. We see the skewness to the right and how price changes by property type.
plot5 <- ggplot(data, aes(x = factor(n_accommodates), y = price, fill = factor(f_property_type))) +
geom_boxplot(alpha=0.8) +
scale_x_discrete(name = "Accomodate Persons") +
ggtitle("Price Per Accommodate in London") + theme_fivethirtyeight()
plot6 <- ggplot(data = data) +
geom_bar(data = data,
aes(x = factor(n_accommodates),
color = f_room_type, fill = f_room_type)) +
ggtitle("# Accomodations and property types London") +
xlab('Accommodates') + theme_fivethirtyeight()
grid.arrange(plot5, plot6, nrow=2)
Accommodate is an important feature we will need to pay attention.
There is a wide range of price. So there must be some factors which make differences. Now I will leave the path and enter into a random forest to see which variables it picks.
This will be a heuristic approach. Because I am lacking the domain knowledge in this business, I will use random forest to pick the most important (highly likely) variables for me. It is probabilistic, however, it can be a better start than knowing nothing at all.
# Fit random forest
RFtrainControl <- trainControl(method = "cv",
number = 2,
verboseIter = TRUE)
set.seed(1234)
RFmodel <- train(
price ~ .,
tuneLength = 1,
data = district,
method = 'ranger',
na.action = na.omit,
importance = 'impurity',
trControl = RFtrainControl)
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 9, splitrule = variance, min.node.size = 5 on full training set
varImp(RFmodel)
## ranger variable importance
##
## only 20 most important variables shown (out of 94)
##
## Overall
## n_accommodates 100.000
## f_room_typePrivate room 68.106
## n_beds 63.804
## n_bathrooms 47.317
## n_guests_included 24.744
## f_property_typeHouse 16.489
## n_minimum_nights 15.975
## d_tv 13.948
## f_cancellation_policystrict 13.741
## n_extra_people 13.692
## d_familykidfriendly 12.316
## d_laptopfriendlyworkspace 5.564
## d_cabletv 5.243
## d_dryer 5.018
## d_elevatorinbuilding 4.859
## d_indoorfireplace 4.723
## d_buzzerwirelessintercom 4.602
## d_carbonmonoxidedetector 4.592
## d_shampoo 4.329
## d_iron 4.285
Accommodates looks like the most important variable indeed. And for example being kid friendly might have some effect. Below we can see its plot, error bars added to emphasise the level of uncertainty.
library(psych)
stats <- describeBy(data$price,list(data$f_room_type,data$d_familykidfriendly), mat=TRUE,digits=2)
stats$se = stats$sd/sqrt(stats$n)
names(stats)[names(stats) == 'group1'] = 'Type'
names(stats)[names(stats) == 'group2'] = 'KidFriendly'
stats_2 <- describeBy(data$price,list(data$f_room_type,data$d_tv), mat=TRUE,digits=2)
stats_2$se = stats_2$sd/sqrt(stats_2$n)
names(stats_2)[names(stats_2) == 'group1'] = 'Type'
names(stats_2)[names(stats_2) == 'group2'] = 'TV'
limits = aes(ymax = mean + (1.96*se), ymin=mean - (1.96*se))
dodge = position_dodge(width=0.9)
apatheme=theme_bw()+
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
panel.border=element_blank(),
axis.line=element_line())
p7 <- ggplot(stats, aes(x = Type, y = mean, fill = KidFriendly))+
geom_bar(stat='identity', position=dodge)+
geom_errorbar(limits, position=dodge, width=0.25)+
ylab('Mean Price')+
theme_fivethirtyeight() + apatheme +
scale_fill_grey()
p8<- ggplot(stats_2, aes(x = Type, y = mean, fill = TV))+
geom_bar(stat='identity', position=dodge)+
geom_errorbar(limits, position=dodge, width=0.25)+
ylab('Mean Price')+
theme_fivethirtyeight() + apatheme +
scale_fill_grey()
grid.arrange(p7, p8, ncol=2)
Playing our card to be-kid-friendly, we can increase our profits. Maybe the families with small kids are ready to pay more than young couples and singles. We will try these interactions during modeling.
london <- data[complete.cases(data)]
district <- data.table(district)
district <- district[complete.cases(district)]
partitioning to training and test sets
training_ratio <- 0.7
set.seed(1234)
train_indices <- createDataPartition(y = london[["price"]],
times = 1,
p = training_ratio,
list = FALSE)
london_train <- london[train_indices, ]
london_test <- london[-train_indices, ]
set.seed(1234)
train_indices <- createDataPartition(y = district[["price"]],
times = 1,
p = training_ratio,
list = FALSE)
district_train <- district[train_indices, ]
district_test <- district[-train_indices, ]
fit_control <- trainControl(method = "cv", number = 10)
set.seed(1234) # The most simple one for benchmarking or comparing...
model_1_london <- train(price ~ n_accommodates,
data = london_train,
method = "lm",
trControl = fit_control)
set.seed(1234)
model_1_district <- train(price ~ n_accommodates,
data = district_train,
method = "lm",
trControl = fit_control)
set.seed(1234) # Adding some more variables from our exploration to see the effects
model_2_london <- train(price ~ n_accommodates + n_beds + f_room_type +
n_minimum_nights + n_guests_included + n_bathrooms,
data = london_train,
method = "lm",
trControl = fit_control)
set.seed(1234) #
model_2_district <- train(price ~ n_accommodates + n_beds + f_room_type +
n_minimum_nights + n_guests_included + n_bathrooms,
data = district_train,
method = "lm",
trControl = fit_control)
set.seed(1234) # load the variables from random forest varible importance list and some interactions and polynomials
model_3_london <- train(price ~ n_accommodates + n_beds + f_room_type + n_bathrooms + n_guests_included +
n_minimum_nights + f_property_type + n_extra_people + f_cancellation_policy +
d_tv + d_familykidfriendly + d_cabletv + d_laptopfriendlyworkspace + d_dryer +
d_elevatorinbuilding + d_indoorfireplace + d_buzzerwirelessintercom
+ d_carbonmonoxidedetector + d_hangers + d_internet +
n_accommodates^2 + n_beds^2 + f_property_type*d_familykidfriendly +
n_accommodates*d_tv + n_accommodates*d_familykidfriendly + n_accommodates*d_cabletv,
data = london_train,
method = "lm",
trControl = fit_control)
set.seed(1234) #load the variables from random forest varible importance list and some interactions and polynomials
model_3_district <- train(price ~ n_accommodates + n_beds + f_room_type + n_bathrooms + n_guests_included +
n_minimum_nights + f_property_type + n_extra_people + f_cancellation_policy +
d_tv + d_familykidfriendly + d_cabletv + d_laptopfriendlyworkspace + d_dryer +
d_elevatorinbuilding + d_indoorfireplace + d_buzzerwirelessintercom
+ d_carbonmonoxidedetector + d_hangers + d_internet +
n_accommodates^2 + n_beds^2 + f_property_type*d_familykidfriendly +
n_accommodates*d_tv + n_accommodates*d_familykidfriendly + n_accommodates*d_cabletv,
data = district_train,
method = "lm",
trControl = fit_control)
tune_grid <- expand.grid("alpha" = 1,
"lambda" = seq(0.028, 0.036, 0.001))
set.seed(1234) # lasso.
model_4_london <- train(price ~ .,
data = london_train,
method = "glmnet",
trControl = fit_control,
metric = "RMSE",
preProcess = c("center","scale"),
tuneGrid = tune_grid,
tuneLength=3)
tune_grid <- expand.grid("alpha" = 1,
"lambda" = seq(0.25, 0.35, 0.01))
set.seed(1234) # lasso
model_4_district <- train(price ~ .,
data = district_train,
method = "glmnet",
trControl = fit_control,
metric = "RMSE",
tuneGrid = tune_grid)
Cross Validation RMSEs
model_1_london_rmse_cv <- model_1_london$results[["RMSE"]]
model_2_london_rmse_cv <- model_2_london$results[["RMSE"]]
model_3_london_rmse_cv <- model_3_london$results[["RMSE"]]
model_4_london_rmse_cv <- min(model_4_london$results[["RMSE"]])
model_1_district_rmse_cv <- model_1_district$results[["RMSE"]]
model_2_district_rmse_cv <- model_2_district$results[["RMSE"]]
model_3_district_rmse_cv <- model_3_district$results[["RMSE"]]
model_4_district_rmse_cv <- min(model_4_district$results[["RMSE"]])
How do these models perform on test sets and the full sample? I have run all the predictions and listed them in a table below. These are RMSE scores:
| model | London.CV | London.FULL | London.Test | District.CV | District.FULL |
|---|---|---|---|---|---|
| 1 | 56.89 | 56.86 | 56.77 | 44.86 | 56.89 |
| 2 | 51.88 | 51.91 | 52.01 | 43.33 | 52.51 |
| 3 | 50.71 | 50.7 | 50.8 | 41.3 | 52.43 |
| 4 | 47.97 | 47.91 | 48.1 | 40.98 | 52.01 |
| District.Test |
|---|
| 44.87 |
| 41.26 |
| 41.38 |
| 40.45 |
lasso (number 4) is giving better RMSE results on test set, for both London and the district. I would use Lasso for both of them.
District is a representative sample of London. Price is distributed very similarly. However, when we try the model of the district to predict the entire London, we do a poor job. District is a small sample and its model cant capture the variation for the entire city.
Cross Validation and Test Set RMSE scores are almost identical. It is logical since the process of CV is to create several test sets and they are coming from the same data using random sampling.
We would use these models also for predicting 2018 prices for London. Because we think that 2018 will be similar to 2017. On the other hand Budapest prices are very different. I wouldn’t predict the price by using a model from London. It will overshoot first of all… Having said that, I can use the experience I gained from London to build new models for other cities. Especially during the process of picking the right coefficients or variables.
In my second iteration I would also use one of the variables I dropped at the beginning which are related to reviews. Probably reviews and review scores would have some effect if engineered carefully. Reviews, review scores are more touching to behavioural science and tend to contain more noise.
I would also be curious about how rating scores change over time.
Now we can grow a tree and see its results.
trctrl <- trainControl(method = "cv", number = 10)
set.seed(1234)
model_tree1_london <- train(price ~ .,
data = london_train, method = "rpart",
metric = "RMSE",
trControl=trctrl,
tuneLength = 20,
tuneGrid = data.frame(cp=seq(0.001, 0.01, 0.01))) #0.00001, 0.001, 0.0001
model_tree1_london
## CART
##
## 36090 samples
## 61 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 32481, 32481, 32481, 32481, 32480, 32482, ...
## Resampling results:
##
## RMSE Rsquared MAE
## 48.24238 0.5944131 29.1418
##
## Tuning parameter 'cp' was held constant at a value of 0.001
postResample(london_test$price, predict(model_tree1_london, newdata = london_test, type = "raw"))
## RMSE Rsquared MAE
## 48.7178292 0.5904734 29.4880805
A regression tree is giving quite promising results for the london test set. RMSE is already as good as the Lasso.
Lets get the result also for our district:
trctrl <- trainControl(method = "cv", number = 10)
set.seed(1234)
model_tree1_district <- train(price ~ .,
data = district_train, method = "rpart",
metric = "RMSE",
trControl=trctrl,
tuneLength = 20,
tuneGrid = data.frame(cp=seq(0.003, 0.0036, 0.0001)))
model_tree1_district
## CART
##
## 2436 samples
## 61 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 2193, 2193, 2192, 2192, 2194, 2191, ...
## Resampling results across tuning parameters:
##
## cp RMSE Rsquared MAE
## 0.0030 39.65540 0.6178117 25.71466
## 0.0031 39.65540 0.6178117 25.71466
## 0.0032 39.64336 0.6180021 25.75005
## 0.0033 39.64210 0.6180115 25.74834
## 0.0034 39.64587 0.6178014 25.79159
## 0.0035 39.64611 0.6176206 25.79206
## 0.0036 39.75832 0.6160279 25.86119
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was cp = 0.0033.
Here we can see RMSE change vs complexity parameter of the tree
plot(model_tree1_district)
postResample(district_test$price, predict(model_tree1_district, newdata = district_test, type = "raw"))
## RMSE Rsquared MAE
## 41.1274597 0.6239557 25.4295441
Again the tree is doing almost as good as lasso.
# Fit random forest to district
RFtrainControl <- trainControl(method = "cv",
number = 2,
verboseIter = TRUE)
set.seed(1234)
model_RF_district <- train(
price ~ .,
tuneLength = 1,
data = district_train,
method = 'ranger',
na.action = na.omit,
importance = 'impurity',
trControl = RFtrainControl)
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 9, splitrule = variance, min.node.size = 5 on full training set
RFmodel
## Random Forest
##
## 3478 samples
## 61 predictor
##
## No pre-processing
## Resampling: Cross-Validated (2 fold)
## Summary of sample sizes: 1739, 1739
## Resampling results across tuning parameters:
##
## splitrule RMSE Rsquared MAE
## variance 38.78331 0.6647095 24.45589
## extratrees 39.31677 0.6621379 24.93305
##
## Tuning parameter 'mtry' was held constant at a value of 9
## Tuning
## parameter 'min.node.size' was held constant at a value of 5
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were mtry = 9, splitrule =
## variance and min.node.size = 5.
postResample(district_test$price, predict(model_RF_district, newdata = district_test, type = "raw"))
## RMSE Rsquared MAE
## 38.3658077 0.6890767 24.1235225
This Random Forest RMSE is the best so far. It is giving better results than the others. However, it is difficult to explain it to our business stakeholders.
We can plot the trees and explain the linear regression coeffs more effectively to the people. RF model wont enjoy that much clarity. That’s because I would prefer to tweak and play with my linear models and lasso with an objective to get closer to the Random Forest.
Later on I can try the same coeffs to Budapest. I would use the model we built for entire London to work on Budapest. The model we used for the district is less likely to be generalized.
I would like to close this small paper with the plot below showing Lasso Model predictions to show the errors. Predictions are getting worse and worse as the real rental price increases: Predicting less than it is. Similar trend is visible for the predictions of other models as well.
ggplot(london_test, aes(price, model_4_london_pred_test - price, col = f_room_type)) + geom_point() +
geom_abline(intercept = 0, slope = 0) + theme_fivethirtyeight()


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One of the common questions for those who are planning a larger deployment is what they should expect and how they estimate the resource requirements. This page is intended to give you broad overview of how various things impact the resource requirements of Jenkins.
Factors that affect performance
Number of slaves
Each slave maintains a connection to a master, and to service these connections a master launchs several threads, and each thread is costly (2MB or so memory just for a call stack.)
Exactly how many threads each slave incurs on a master depends on the mode of connection. Most connection mechanisms require two threads per slave (SSH slaves and Java Web Start slaves), but if a JWS slave falls back to the communication over HTTP, it'll incur three. See "Obtaining a thread dump" for how to count the number of threads in your environment.
Degree of concurrent HTTP accesses to the master
If you expect a lot of users accessing Jenkins user interfaces, this adds additional CPU overheads to a master. The memory impact on additional concurrent users should be negligible.
When you expect a large number of concurrent users, watch out for the upper bound of the maximum number of HTTP request handling threads. Contrary to the intuition, you do not want to give too big a value, which tends to make contended locks even more contended. You want to keep this value relatively small, and let incoming requests wait in the queue as opposed to try to serve them all in parallel.
Number of jobs
Impact of a larger number of jobs can be seen in several places. One is the start-up time of Jenkins, especially noticable if you have $JENKINS_HOME in a disk that has a large seek time (spinning HDDs, as opposed to SSDs.) Another is the UI organization. You need to utilize views, folders, and other means to organize jobs, and avoid using the "All" view that'd render everything.
Number of builds
Starting around 1.450 to 1.500 Jenkins started lazy-loading builds and discard them from memory if unneeded, but various plugins are still being updated to take advantages of this. Therefore, you'll want to cap the number of builds each job occupies, by using the "discard old build" feature in Jenkins.
To prevent Jenkins from discarding build records only to load them back from the disk again, please give ample heap to Jenkins JVM.
 Different people have deployed Jenkins successfully in both ways, and you have to pick one or the other depending on your situations. Here are the common trade-offs.
- Your resource utilization will be higher with One Big Master, as you can use the same set of slaves to serve a large projects and even out their spiky load. With multiple masters, each Jenkins needs to have a reasonable number of slaves.
- Installing plugins, running maintenance scripts, backing up, upgrading, and so on are easier to do with One Big Master, since you only have one place to do it.
- When each team in your organization works autonomously and very differently, Multiple Masters allow individual teams to install additional set of plugins that suit them. This would help them use Jenkins better, feel their instances "closer", and tends to care more about when it reports a failure in a commit, for example.
- If One Big Master goes down, no one can do a build, while if one of Many Masters go down, it affects a smaller number of people.


'Pro-vaping lobby mimicking advertising used by the tobacco companies 50 years ago,' Cork doctor warns
by Áine KennyA Cork doctor has warned against the potential dangers of vaping.
Dr Nick Flynn told The Echo there are not enough studies on the long-term side effects of vaping. “While vaping may be safer than smoking, this doesn’t mean it is safe. There’s no scientific proof to say it’s completely safe.”
“Inhaling flavoured chemicals into your lungs is potentially dangerous,” he added. He says the phenomenon of young people vaping without ever smoking is a worrying trend.
They are vaping naively and becoming addicted to nicotine.
“If you switch to vaping after quitting cigarettes, then that is better. But the end goal has to be neither smoking or vaping.
“My concern is the pro-vaping lobby are mimicking the advertising used by the tobacco companies 50 years ago,” he says.
“These lobby groups can be quite aggressive on social media if you raise concerns. You are taking a chance with your health. A lot of recent studies show that vaping causes the endothelium, the cells which line the blood and lymphatic vessels, to become inflamed.
The study which came out on Wednesday says that vaping can also damage the lungs and brain.
This study was released by the European Heart Journal. It examined the effect of vaping on 20 vapers before they smoked and 15 minutes after. Their results showed an increase in heart rate and stiffened arteries, and the vaper’s endothelium stopped working properly.
The researchers also exposed 151 mice to vapour over one, three or five days, for 20 minutes six times a day. The mice’s lungs and brains appeared to become damaged.
“I would be very concerned that in ten years time we will have people in their 30s and 40s with serious lung conditions because of vaping in their teens and 20s,” added Dr Flynn.
Vapes are battery-powered devices which heat a flavoured liquid containing nicotine, propylene glycol and glycerin. This produces a vapour which can be inhaled.
- This story first appeared in The Echo on Friday, November 15
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Bangladesh PM arrives in Kolkata to attend Day/Night Test match
Board for Control of Cricket in India (BCCI) president Sourav Ganguly received Ms. Hasina at the airport.
by PTIBangladesh Prime Minister Sheikh Hasina arrived in Kolkata around 10.30 am on Friday to inaugurate India’s maiden Day/Night Test match at the iconic Eden Gardens stadium.
India is taking on Bangladesh in its first-ever pink- ball Test match, seven years after the International Cricket Council (ICC) gave its approval to the format.
Board for Control of Cricket in India (BCCI) president Sourav Ganguly received Ms. Hasina at the airport.
Several former leading cricketers, including Sachin Tendulkar, have arrived in the city to witness the historic match, which will kick off with Army paratroopers flying into the Eden Gardens to hand over a pink ball each to the two captains, just before the toss.
This will be followed by ringing of the customary Eden Bell by Ms. Hasina and West Bengal Chief Minister Mamata Banerjee.
Ms. Hasina and Ms. Banerjee are scheduled to hold a brief one-on-one meeting on the sidelines of the match.


|
PolyTycoon
1
UnityTransportTycoontypegame.
|
Public Member Functions | |
| ProceduralNeededSpace (Vector3Int usedCoordinate, TerrainGenerator.TerrainType terrainType, float noiseValue) | |
| ProceduralNeededSpace (NeededSpace neededSpace, Vector3Int offset, float noiseValue) | |
| Public Member Functions inherited from NeededSpace | |
| NeededSpace (Vector3Int usedCoordinate, TerrainGenerator.TerrainType terrainType) | |
| NeededSpace (NeededSpace neededSpace, Vector3Int offset) | |
Properties | |
| float | NoiseValue [get, set] |
| Properties inherited from NeededSpace | |
| Vector3Int | UsedCoordinate [get, set] |
Additional Inherited Members | |
| Static Public Member Functions inherited from NeededSpace | |
| static NeededSpace | Zero (TerrainGenerator.TerrainType terrainType) |
| Public Attributes inherited from NeededSpace | |
| TerrainGenerator.TerrainType | TerrainType => _terrainType |
| ProceduralNeededSpace.ProceduralNeededSpace | ( | Vector3Int | usedCoordinate, |
| TerrainGenerator.TerrainType | terrainType, | ||
| float | noiseValue | ||
| ) |
| ProceduralNeededSpace.ProceduralNeededSpace | ( | NeededSpace | neededSpace, |
| Vector3Int | offset, | ||
| float | noiseValue | ||
| ) |
|
getset |


the chat from which you want to subscribes to live location updates
callback that takes in a LiveLocation
boolean, if returns false then there were no valid live locations in the chat of chatId
Observable stream of messages
Adds participant to Group
Chat id<EMAIL_ADDRESS>Participant<EMAIL_ADDRESS>Load all messages in chat object from server.
contact detial as promise
Creates a new chat group
Group name
Contacts that should be added.
Demotes admin privileges of participant
Chat id<EMAIL_ADDRESS>Participant<EMAIL_ADDRESS>Forwards array of messages (could be ids or message objects)
Chat id
Array of messages ids to be forwarded
Retrieves all chats with messages
array of [Chat]
Retrieves all chats
array of [Chat]
Retrieves all contacts
array of [Contact]
Retrieve all groups
array of groups
Retrieves all Messages in a chat
any
Retrieves all new messages (where isNewMsg is true)
List of messages
Retrieves all unread messages (where ack is -1)
list of messages
Retrieves Battery Level
Retrieves chat object of given contact id
contact detial as promise
Retrieves chat object of given contact id
contact detial as promise
Retrieves the connecction state
Retrieves contact detail object of given contact id
contact detial as promise
Retrieves group admins
Group/Chat id<EMAIL_ADDRESS>Generates group-invite link
Invitation link
Returns group members [Contact] objects
Retrieves group members as [Id] objects
group id
Current host device details
Checks if a number is a valid whatsapp number
contact detial as promise
Retrieves chat picture
Chat id
Url of the chat picture or undefined if there is no picture for the chat.
Retrieves status of given contact
Retrieves all undread Messages
any
Retrieves WA version
Retrieves if the phone is online. Please note that this may not be real time.
Removes the host device from the group
group id
Load all messages in chat object from server.
contact detial as promise
Loads and Retrieves all Messages in a chat
any
Load more messages in chat object from server. Use this in a while loop
contact detial as promise
Stream of ParticipantEvent
Promotes participant as Admin in given group
Chat id<EMAIL_ADDRESS>Participant<EMAIL_ADDRESS>Removes participant from group
Chat id<EMAIL_ADDRESS>Participant<EMAIL_ADDRESS>Replies to given mesage id of given chat id
Chat id
Message body
Message id to reply to.
Sends contact card to iven chat id
Chat id
Sends file from path
Chat id
File path
Sends file base64 parameter should have mime type already defined
Chat id
base64 data
Sends image message
Chat id
Generates sticker from given image and sends it
image path
TODO: Fix message not being delivered Sends location to given chat id
Chat id
Latitude
Longitude
Sends text with tags
Experimental! Sends a text message to given chat even if its a non-existent chat
chat id<EMAIL_ADDRESS>text message
Sends message with thumbnail
Sets a chat status to seen. Marks all messages as ack: 3
chat id<EMAIL_ADDRESS>Sends a text message to given chat
chat id<EMAIL_ADDRESS>text message
Sends a video to given chat as a gif, with caption or not, using base64
chat id<EMAIL_ADDRESS>string xxxxx
string xxxxx
Sends a video to given chat as a gif, with caption or not, using base64
chat id<EMAIL_ADDRESS>base64 data:video/xxx;base64,xxx
string xxxxx
string xxxxx
Sets the chat state
Sets current user profile name
Sets current user profile status
Starts typing ('Typing...' state)
Stops typing
Chat id
Generated using TypeDoc


Courses
Colorado State University, 2015
Cultural Change, a political ecology perspective, upper-level undergraduate course
DePaul University, 2005-2008
Introduction to Sociology, core undergraduate course ● 2005-2007
Social Problems, undergraduate seminar ● 2005-2008
Gender and Society, upper-level undergraduate seminar ● 2006
Global Cities, upper-level undergraduate elective ● 2007
Green Chicago, Liberal Studies freshman program ● 2008
Environmental Sociology, upper-level undergraduate elective ● 2008


667_suibianfahui.py
class Solution:
def minDays(self, grid: List[List[int]]) -> int:
# 最多只需要2天!!!
rows, cols = len(grid), len(grid[0])
def isValid():
v = set()
cnt = 0
for sr in range(rows):
for sc in range(cols):
if grid[sr][sc] == 1 and (sr, sc) not in v:
cnt += 1
q = [(sr, sc)]
v.add((sr, sc))
for x in q:
r, c = x
for rr, cc in ((r + 1, c), (r - 1, c), (r, c + 1),
(r, c - 1)):
if 0 <= rr < rows and 0 <= cc < cols and grid[
rr][cc] == 1 and (rr, cc) not in v:
v.add((rr, cc))
q.append((rr, cc))
return cnt == 0 or cnt >= 2
# def calHq():
# hq = []
# for r in range(rows):
# for c in range(cols):
# if grid[r][c] == 1:
# cnt = 0
# for rr, cc in ((r + 1, c), (r - 1, c), (r, c + 1),
# (r, c - 1)):
# if 0 <= rr < rows and 0 <= cc < cols and grid[rr][
# cc] == 1:
# cnt += 1
# heapq.heappush(hq, (-cnt, r, c))
# return hq
day = 0
if isValid():
return 0
for r in range(rows):
for c in range(cols):
if grid[r][c] == 1:
grid[r][c] = 0
if isValid():
return 1
grid[r][c] = 1
return 2


---
layout: null
section-type: social
title: Main Social Phenomena
---
## Major Social Phenomena
4. How did the RTS coverage of important social phenomena like the sexual revolution,
women’s voting right, and terrorism evolved over time?
We have chosen to study some of the major social phenomenas of the 20th century.
Looking at the various plots below, we can see some trends appearing :
Note: Hover over the bar plots to see the co-occurrences for a given year.
A co-occurrence happens when words show up frequently together (un a cirtain year).
In some years, there is no dominant co-occurence. This is because we set a threshold and filter the words with co-occurrences
lower the threshold.
### Women's Voting Rights
For women's voting right, the co-occurrences peaks at 1971 when a referendum was held in Switzerland.
In the following years, presumably because it's more or less a resolved issue. However, there are some exceptions.
The last time the subject passed our threshold was in 2017, as the Saudi women gained right to vote in certain elections.
### Homosexuality
In the case of Homosexuality, we observe that there was absolutely no mention of the topic until 1960s! The interest in
the topic picks at 2003.
We observe that in the first appearances, the dominant co-occurences are "films" (movies), "livre" (books) showing that
artists were trying to talk about the subject. Whereas in the last years, although the number of occurrences has
not changed (even decreased over the last five years), the dominant co-occurrences are marriage, children and medicine
which shows the progress made on the subject.
### Terrorism
Finally, for terrorism, it's interesting to see that after every peak, there is an important dip in the occurrences,
in the following year.
The co-occurrences here give nice insights about major terrorist attacks over the last 50 years. You can see for example, amongst others:
1. Vietnam and US war in 1964
2. l'affaire Gordji and Mitterand in 1988
3. September 11 attacks (reflected in 2002)
4. Madrid attacks in 2004
5. Paris attacks in 2015


Twilight (1998)
Director: Robert Benton Cast: Paul Newman, Susan Sarandon, Gene Hackman, Stockard Channing, Reese Witherspoon, Giancarlo Esposito, James Garner, Liev Schreiber, M. Emmet Walsh Screenplay: Robert Benton, Richard Russo Producers: Arlene Donovan, Scott Rudin Runtime: 96 min. US Distribution: Paramount Rated R: Violence, language, sex
By Nathaniel R. Atcheson<EMAIL_ADDRESS>It's not a good sensation to walk away from a film feeling hungry for more. At the end of Twilight, when the image blacked out and I saw the word "Cast" peek up from the bottom of the screen, I was wondering where the other three or four reels had gone. Much to my dismay, it turns out that I saw the whole thing. Twilight is a mystery that runs just over 90 minutes; there are three extremely interesting and well-acted characters that are given a depressingly short amount of time to make their appearances and tell their stories. I can't think of a film that feels more rushed and unexplored as this one.
The first scene is unimpressive, and had me prepared for an overall bad film. Paul Newman stars as Harry Ross, who, in this opening scene, is in Mexico to retrieve a young woman (Reese Witherspoon) who is on vacation with her boyfriend (Liev Schreiber) without the consent of her parents. Things go wrong, and she accidentally shoots him in the leg. The scene is dry and poorly choreographed, and is plagued by an insipid and unoriginal music score.
Fortunately, things pick up a little. We are transported two years past the time depicted in the first scene; we learn that Harry lives with some old friends, wealthy actor/actress couple Jack and Catherine Ames (Gene Hackman and Susan Sarandon). Basically, Harry is Jack's personal private investigator. Jack sends Harry on a job that he describes as "safe"--he must deliver a package to a woman. When Harry arrives at the address, he finds a bleeding man who has been shot (M. Emmet Walsh), and he tries to kill Harry. Quickly, Harry finds himself caught up in a web of criminal activities, and his primary motive in attempting to bring the issues to closure revolves around his wanting to know if the Ames are responsible.
In the end, I found myself disappointed not in the film's execution, but in it's completely limited approach to a situation that is so complex and interesting. Perhaps the best thing about the film is Newman, who is a fantastic, seasoned, and completely engaging performer to watch on screen. He's great here--Harry is funny and well-written, and he has a life outside of what we see. Newman's performance is deep and sympathetic, and he had me hooked immediately--he plays his part like a pro.
Hackman and Sarandon are equally good, and are both given fascinating characters to embody. Jack, who is dying from cancer, is a very flawed individual, but Hackman elicits our sympathy and makes us like him despite some of the things he does. And Sarandon, as the woman who will do anything for her husband, is also excellent: she shows all of the depth and complexity that she did in Dead Man Walking, but in half the screen time. There is also a colorful supporting cast, including James Garner and Stockard Channing.
These characters are so well-written that I simply can't believe writer/director Robert Benton didn't do more with them. Twilight could have been a full hour longer without overstaying its welcome. I wanted to see these people in more situations; I wanted to see them interact with each other more. I wanted to see moments of emotional rage, and moments of quiet, probing tenderness. There are small bursts of all of these things in Twilight, but the moments are so short and yet so tasty that I felt kind of the same way I do when I eat just one potato chip on an empty stomach.
And it doesn't help that there are things here that I didn't like. The subplot involving a delusional man who thinks he is Harry's partner is intrusive and pointless. Much of the dialogue feels forced: in one scene, Witherspoon (who plays the Ames' daughter) meets Harry as he is watching Jack and Catherine cuddle on their bed. "This is their story," she says. "You and I just have bit parts." And while it's a nice comment and probably holds a lot of truth, it feels wrong as dialogue, and is certainly out of place from a girl who we're lead to believe is of questionable intelligence.
It's common to criticize a film because it feels long and tedious; I criticize Twilight because of the opposite. Benton rushes the fascinating story to a premature conclusion, and the result is a film that feels incomplete. The three hours of Titanic feel swift, but we are fulfilled and exhilarated when it's over. The 90 minutes of Twilight feel alarmingly brief--they whet our appetites for a main course that will never arrive.
** out of **** (5/10, C)
Visit FILM PSYCHOSIS at http://www.pyramid.net/natesmovies
Nathaniel R. Atcheson
The review above was posted to the
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Related links: index of all rec.arts.movies.reviews reviews


--- post_id: /presentation-in-milan-hackmeeting-2009 date: 2009-11-23 11:28 return_url: '2009-06-22-presentation-in-milan-hackmeeting-2009.html' name: 'harris' email<EMAIL_ADDRESS>comment: '
An expert is a person who has made all the mistakes that can be made in a very narrow field.
' ---