| 1 |
coronavirus |
hubei |
italy |
patients |
model |
95 |
care |
fatality |
temperature |
drugs |
genome |
contact |
covid-19 |
R0 |
de |
| 2 |
disease |
province |
countries |
% |
predict |
CI |
patients |
rate |
correlation |
cells |
genomes |
market |
mental |
lombardy |
el |
| 3 |
respiratory |
china |
iran |
detection |
models |
interval |
experience |
% |
interest |
immune |
sars-cov-2 |
seafood |
media |
total population |
en |
| 4 |
2019 |
hubei province |
south |
symptoms |
peak |
estimated |
hospital |
cure |
factors |
drug |
host |
taiwan |
social media |
simulations |
children |
| 5 |
health |
february |
india |
fever |
method |
reproduction |
department |
mortality rate |
incidence |
cardiovascular |
phylogenetic |
united states |
social |
COVID−19 |
pediatric |
| 6 |
syndrome |
epidemic |
korea |
nucleic |
epidemic |
exported |
cancer |
death |
correlations |
lung |
molecular |
cluster |
public |
95%-CI |
con |
| 7 |
caused |
wuhan |
south korea |
clinical |
april |
travel |
staff |
case fatality |
humidity |
approved |
pangolin-cov |
animal |
health |
SIRD |
casos |
| 8 |
acute |
city |
country |
acid |
mathematical |
mainland china |
management |
CFR |
climate |
liver |
mutations |
united |
facemask |
tccpm |
gi symptoms |
| 9 |
world |
cases |
march |
positive |
based |
estimate |
COVID-19 |
ill |
daily |
HD |
sequences |
route |
H7N9 |
bound |
su |
| 10 |
china |
2020 |
growth |
chest |
prediction |
estimates |
dental |
aged |
significant |
trials |
ratg13 |
close |
avian |
+ |
las |
| 11 |
virus |
control |
cases |
swab |
data |
wuhan |
guidelines |
years |
p<0.001 |
injury |
protein |
animals |
supplies |
= |
GI |
| 12 |
sars-cov-2 |
COVID-19 |
spain |
samples |
time |
% |
departments |
critically |
meteorological |
COVID-2019 |
genetic |
droplets |
attention |
parameters |
los |
| 13 |
december |
provinces |
COVID-19 |
swabs |
predicted |
mainland |
protection |
cure rate |
COVID-19 |
therapeutic |
proteins |
transmitted |
sina |
wenzhou |
salud |
| 14 |
severe |
confirmed |
pandemic |
test |
SEIR |
95%CI |
treatment |
age |
variables |
ACE2 |
genomic |
clusters |
microblog |
number |
para |
| 15 |
outbreak |
measures |
social distancing |
RT-PCR |
trend |
cities |
medical |
mar |
° |
chloroquine |
epitopes |
transmission |
mental health |
interquartile |
paediatric |
| 16 |
2019-ncov |
january |
distancing |
CT |
SIR |
january |
hospitals |
mortality |
activity |
cytokines |
sequencing |
seafood market |
PTSS |
interquartile range |
como |
| 17 |
spread |
wuhan city |
exponential |
mild |
ARIMA |
serial |
emergency |
case |
examine |
heart |
strains |
person |
news |
based |
por |
| 18 |
organization |
number |
phase |
covid-19 pneumonia |
predictions |
- |
oncology |
critically ill |
meta-analysis |
cardiac |
intermediate |
huanan |
prevalence |
recovery |
mundial |
| 19 |
global |
population |
interventions |
pneumonia |
daily |
confidence interval |
expert |
severity |
weather |
inflammatory |
S1 |
bats |
disposal |
madrid |
sarscov‐2 |
| 20 |
2 |
days |
weeks |
throat |
situation |
sustained |
radiology |
- |
pearson |
clinical trials |
pangolin |
spatiotemporal |
disorders |
recovery rates |
se |
| 21 |
COVID-19 |
transmission |
2020 |
sars-cov-2 |
dynamic |
reporting |
guideline |
older |
highest |
plasma |
diversity |
oral |
sleep |
class |
fue |
| 22 |
world health organization |
cities |
number |
cough |
day |
chinese cities |
pandemic |
ratio |
cases |
patients |
spike |
source |
h7n9 avian influenza |
fade |
provincia |
| 23 |
pneumonia |
early |
affected |
laboratory |
future |
locations |
professionals |
workers |
students |
treatment |
human |
family |
public health emergencies |
twenty |
es |
| 24 |
infection |
NCP |
governments |
COVID-19 |
mathematical model |
basic |
lessons |
COVID19 |
search |
damage |
cell |
person-to-person |
shortage |
− |
diciembre |
| 25 |
public |
total |
days |
blood |
probability |
serial interval |
healthcare |
disease severity |
PM10 |
treat |
mutation |
kong |
shelter |
forty |
número |
| 26 |
coronavirus disease 2019 |
control measures |
lockdown |
imaging |
number |
23 |
suspected |
PIBA |
google |
pre-existing |
sars-cov-2 outbreak |
states |
caring |
french |
brote |
| 27 |
declared |
quarantine |
measures |
patient |
proposed |
dec |
radiotherapy |
instant |
RR |
chronic |
bat |
linked |
keywords |
r0 value |
nuevo |
| 28 |
named |
outbreak |
social |
hospitalized |
forecast |
number |
nosocomial |
IFP |
associations |
hydroxychloroquine |
orf1ab |
wuhan pneumonia |
hot |
per-day |
zoonosis |
| 29 |
international |
's |
units |
infection |
forecasting |
cordon sanitaire |
systems |
conditions |
correlated |
rdrp |
MHC |
huanan seafood market |
distress |
500 |
recomendaciones |
| 30 |
concern |
1 |
italian |
pathogens |
simple |
sanitaire |
protect |
moderate |
relative |
COVID-19 |
phylogenetic analysis |
home quarantine |
covid-19 outbreak |
scenario |
646 |
| 31 |
acute respiratory syndrome coronavirus 2 |
chinese |
deaths |
severe |
infected |
cordon |
radiation |
increment |
relationship |
antiviral |
residues |
network |
early |
found |
severe/critical |
| 32 |
coronaviruses |
feb |
european |
assay |
covid-19 |
jan |
working |
non-severe |
composite |
targets |
haplotype |
hong |
emergencies |
ratio |
resumen |
| 33 |
countries |
2019 |
mitigation |
fatigue |
fitting |
internationally |
procedures |
resource |
lag |
cardiac injury |
revealed |
0 |
psychological |
90 |
mundo |
| 34 |
globally |
spread |
intensive care units |
serum |
predicting |
cri |
planning |
pessimistic |
0.01 |
expression |
NYC |
contact history |
facemasks |
recovered |
la |
| 35 |
causing |
guangdong |
spread |
vertical |
forecasts |
cases |
general |
LYM% |
ARH |
TCM |
intermediate host |
ferrets |
reasons |
actual |
objetivo |
| 36 |
studies |
shenzhen |
policies |
admitted |
analysis |
volume |
special |
optimistic |
relative humidity |
targeting |
polyprotein |
wet |
list |
reported |
síntomas |
| 37 |
emerging |
results |
UK |
cases |
march |
data |
ICU |
numbers |
hypothesis |
cytokine |
genome sequences |
republic of korea |
fangcang |
total |
este |
| 38 |
pathogen |
controlled |
diffusion |
characteristics |
points |
SI |
risk |
elderly people |
searches |
single-cell |
amino |
macao |
public attention |
DAY-ZERO |
desde |
| 39 |
reported |
migration |
study |
HBV |
results |
reporting rate |
consensus |
recoveries |
ros |
traditional chinese medicine |
domain |
wholesale |
advance |
evolves |
familia |
| 40 |
wuhan |
27 |
canada |
2 |
2020 |
importations |
examinations |
kinetics |
rates |
storm |
genetic diversity |
ministry of health |
topics |
cells/mm3 |
dehydrogenase/glucose |
| 41 |
months |
movement |
active cases |
igm |
accuracy |
outbound |
west |
2003-SARS |
schools |
medicines |
analysis |
faced |
mental health problems |
45,000 |
ethical |
| 42 |
acute respiratory syndrome |
nationwide |
infected |
qingdao |
simulation |
findings |
diagnosed |
age-stratified |
england |
convalescent |
2019‐ncov |
cats |
posts |
exceed |
sputum production |
| 43 |
knowledge |
restrictions |
population |
report |
peak time |
2020 |
major |
0.9 |
temperate |
addition |
coverage |
identify |
fangcang shelter hospitals |
simulator |
cantidad |
| 44 |
pandemic |
data |
24th |
sputum |
website |
2019-ncov |
infection |
viral load |
mild covid-19 |
failure |
detecting |
human |
health authorities |
courses |
aislamiento |
| 45 |
east |
exposed |
hopkins |
nasopharyngeal swabs |
MCO |
demographic characteristics |
opinion |
medical |
reopening |
microenvironment |
introductions |
authorities |
crisis |
plots |
COVID‐2019 |
| 46 |
viruses |
closure |
beds |
tomography |
non-hubei |
undocumented |
prepared |
patients |
meteorological variables |
therapies |
isolates |
simplified |
pertinent |
considers |
pandemia |
| 47 |
MERS |
chinese government |
rate |
hospital |
outbreak |
destination |
surgery |
deaths |
P<0.001 |
γδ |
haplotypes |
category |
health emergency |
worth |
animales |
| 48 |
2019-ncov. |
spatial |
health |
IQR |
h value |
younger |
examination |
infected |
μg/m3 |
nelfinavir |
immunoinformatics |
sarbecovirus |
health professionals |
expected |
7.31 |
| 49 |
review |
provincial |
case |
anal |
infection |
evacuation |
preventive measures |
albeit |
16th |
cardiovascular diseases |
diagnosing |
subgenus |
mood |
population |
1117 |
| 50 |
’s |
traffic |
european countries |
gastrointestinal |
confirmed |
boltzmann |
learned |
3.6 |
wind |
bioinformatics |
sars-cov-2 genomes |
entry |
compiled |
infected |
otros |


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The 33 Disney Movies You Can Stream on Netflix With Your Kids in 2019 — Before They're All Gone!
by Alessia SantoroThere's something about Disney movies that make it so you're always in the mood to put one on, whether to watch seriously or to have on in the background of another activity. From animated classics and Pixar movies to live-action remakes and those movies everyone always forgets are Disney (National Treasure and the Star Wars films, anyone?), there's something for every member of the family (and then some) in Disney's catalog.
Ahead, find the Disney and Pixar movies you and your kids can stream on Netflix in 2019 — that is, until they all start disappearing one by one thanks to their held spots on Disney+. (And see all the other kids' films available on Netflix now.)
1 Coco
What it's about: "Music is his life, but it's forbidden in his family. To understand why, he'll have to speak to his ancestor in person."
Ages it's best suited to: 6 and up
Watch Coco here, before it leaves the service on Nov. 29!
2 Thor: Ragnarok
What it's about: "To save Asgard from a bloodthirsty goddess of death, the mighty Thor will have to battle his way to freedom and find a way back home."
Ages it's best suited to: 12 and up
Watch Thor: Ragnarok here, before it leaves the service on Dec. 4!
3 Star Wars: The Last Jedi
What it's about: "The last Jedi believes it's time for the Jedi to end. Hope may be lost, but one brave scavenger won't give up."
Ages it's best suited to: 10 and up
Watch Star Wars: The Last Jedi here, before it leaves the service on Dec. 25!
4 Christopher Robin
What it's about: "Christopher Robin — now a family man living in London — receives a surprise visit from his old childhood pal, Winnie-the-Pooh. With Christopher's help, Pooh embarks on a journey to find his friends — Tigger, Eeyore, Owl, Piglet, Rabbit, Kanga and Roo."
Ages it's best suited to: 7 and up
5 Saving Mr. Banks
What it's about: "Spurred on by a promise he made to his daughters, Walt Disney embarks on what would become a 20-year quest to obtain the movie rights to Mary Poppins. The author, P.L. Travers, proves to be an uncompromising curmudgeon who has no intention of letting her beloved characters become mangled in the Hollywood machine. However, when the books stop selling and she finds herself in need of money, Travers reluctantly agrees to consider Disney's proposition."
Ages it's best suited to: 12 and up
6 The Incredibles 2
What it's about: "Everyone's favorite family of superheroes is back . . . but this time Helen is in the spotlight, leaving Bob at home with Violet and Dash to navigate the day-to-day heroics of 'normal' life."
Ages it's best suited to: 8 and up
7 Cars 3
What it's about: "Sleek female race car Cruz Ramirez helps old-time champion Lightning McQueen make a comeback as he faces a new generation of high-tech racers."
Ages it's best suited to: 6 and up
8 Atlantis: The Lost Empire
What it's about: "This action-filled animated tale follows the exploits of a group of adventurers as they journey under the sea to the fabled kingdom of Atlantis."
Ages it's best suited to: 8 and up
Watch Atlantis: The Lost Empire here!
9 Bolt
What it's about: "This tale follows German shepherd Bolt, a canine actor who makes a cross-country trek when he's mistakenly shipped from Hollywood to New York City."
Ages it's best suited to: 5 and up
10 Tarzan
What it's about: "After being shipwrecked off the African coast, a lone child grows up in the wild, and is destined to become lord of the jungle."
Ages it's best suited to: 6 and up
11 Disney's The Nutcracker and the Four Realms
What it's about: "From Disney comes the re-imagined tale of The Nutcracker. When Clara's mother leaves her a key, she embarks on a journey to four secret realms—where she discovers her greatest strength could change the world."
Ages it's best suited to: 8 and up
Watch Disney's The Nutcracker and the Four Realms here!
12 Lilo & Stitch
What it's about: "Disney's song-filled animated comedy follows a lonely Hawaiian girl named Lilo who adopts a funny-looking dog and doesn't realize it's a wacky alien."
Ages it's best suited to: 5 and up
13 Meet the Robinsons
What it's about: "Young Lewis, who's always wondered why his mother put him up for adoption, may get his answer when Wilbur Robinson whisks him away to the future."
Ages it's best suited to: 7 and up
Watch Meet the Robinsons here!
14 The Emperor's New Groove
What it's about: "In this animated Disney adventure, a South American emperor experiences a reversal of fortune when his power-hungry adviser turns him into a llama."
Ages it's best suited to: 6 and up
Watch The Emperor's New Groove here!
15 Chicken Little
What it's about: "No one believes Chicken Little when he tries to warn of an alien invasion — so it's up to him and his misfit friends to save the world!"
Ages it's best suited to: 7 and up
16 A Wrinkle in Time
What it's about: "Years after their father disappears, Meg and her younger brother Charles Wallace cross galaxies on a quest to save him from the heart of darkness."
Ages it's best suited to: 8 and up
17 Princess Diaries 2: Royal Engagement
What it's about: "As the newly crowned princess of the tiny European country of Genovia, teen Mia Thermopolis learns an arranged marriage may be in store for her."
Ages it's best suited to: 8 and up
Watch Princess Diaries 2: Royal Engagement here!
18 Ralph Breaks the Internet
What it's about: "Video game bad guy Ralph and fellow misfit Vanellope von Schweetz must risk it all by traveling to the World Wide Web in search of a replacement part to save Vanellope's video game, 'Sugar Rush.' In way over their heads, Ralph and Vanellope rely on the citizens of the internet — the netizens — to help navigate their way, including an entrepreneur named Yesss, who is the head algorithm and the heart and soul of trend-making site BuzzzTube."
Ages it's best suited to: 8 and up
Watch Ralph Breaks the Internet here!
19 Solo: A Star Wars Story
What it's about: "Young Han Solo finds adventure when he joins forces with a gang of galactic smugglers and a 190-year-old Wookie named Chewbacca."
Ages it's best suited to: 10 and up
Watch Solo: A Star Wars Story here!
20 Black Panther
What it's about: "He's a warrior. A superhero. And a good man. But it's hard for a good man to be king. Doing what's right will take all his power."
Ages it's best suited to: 12 and up
21 Miracle
What it's about: "US hockey coach Herb Brooks unites a motley crew of college athletes and turns them into a force to be reckoned with at the 1980 Winter Olympics."
Ages it's best suited to: 8 and up
22 National Treasure
What it's about: "Modern treasure hunters search for a chest of riches rumored to have been stashed away by George Washington, Thomas Jefferson, and Benjamin Franklin."
Ages it's best suited to: 9 and up
• Brother Bear 2
• Pocahontas 2
• Pooh's Heffalump Movie
• Pooh's Grand Adventure
• George of the Jungle 2
• Secret of the Wings
• The Pirate Fairy
• Tinkerbell and the Legend of the NeverBeast
23 The Princess and the Frog
What it's about: "Hardworking and ambitious, Tiana dreams of one day opening the finest restaurant in New Orleans. Her dream takes a slight detour when she meets Prince Naveen, who has been turned into an amphibian by evil Dr. Facilier. Mistaking her for a princess and hoping to break the spell, Naveen plants a kiss on poor Tiana — thereby turning her into a frog as well. The pair hop along on an adventure through the bayous to seek the help of a powerful voodoo priestess."
Ages it's best suited to: 6 and up
Watch The Princess and the Frog here!
24 Race to Witch Mountain
What it's about: "Jack Bruno, a Las Vegas taxi driver, is swept up in the adventure of a lifetime when he meets Sara and Seth, a pair of extraterrestrial youths who possess paranormal powers. In order to prevent an invasion of Earth, Jack must help the youths reach their spaceship, which is buried within Witch Mountain. But the task will not be easy, for government operatives and an alien bounty hunter are fast on their trail."
Ages it's best suited to: 8 and up
Watch Race to Witch Mountain here!
25 Mary Poppins Returns
What it's about: "Decades after her original visit, the magical nanny returns to help the Banks siblings and Michael's children through a difficult time in their lives."
Ages it's best suited to: 6 and up


Courses taken
Math classes
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Calculus I, II, and III
-
Linear Algebra
-
Differential Equations
-
Foundations of Mathematics
-
Intro to Statistical Methods
-
Intro to Optimization Theory
-
Number Theory
-
Numerical Analysis
-
Abstract Algebra I & II
Computer Science classes
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Intro to Computer Science I and II
-
Intro to Bioinformatics
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Discrete Structures
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Assembly and Computer Organization
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Data Structures and Algorithms
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Database Systems
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Data Mining
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Artificial Intelligence
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Systems Programming
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Theory of Computing


Hello Dear,
This mail might come to you as a surprise and the temptation to ignore it as unserious could come into your mind but please consider it a divine wish and accept it with a deep sense of humility.
I am Mr. Gerald Zongo, the manager in charge of Auditing department of Bank of Africa (B.O.A) Ouagadougou Burkina Faso in West Africa, with due respect and Regard; I have decided to contact you on a business transaction that will be very beneficial to both of us at the end of the transaction.
During our investigation and auditing in this Bank, my department came across a very huge sum of money belonging to one of our deceased customer who died on Feb 29th 2002 of a ghastly motor accident and the fund has been dormant in his account with this bank without any claim of the fund in our Custody either from his family or relation before our discovery to this
development. Although personally,
I keep this information secret to enable the whole plans and idea be Profitable and successful during the time of execution, the said amount is Twelve million four hundred thousand United States dollars(US$12.4m), as it may interest you to know, I contacted you to be my partner and person to be reliable and capable to champion a business of such magnitude without any
problem.
Meanwhile all the arrangement to put claim over this fund as the next of kin to the deceased, get the required approval and transfer this money to a foreign account has been put in Place and directives and needed information will be relayed to you as soon as you indicate your interest and willingness to assist me and also benefit your self to this great business opportunity,
In fact I could have done this deal alone but because of my position in this country as a civil servant and we are not allowed to operate a foreign account and would eventually raise eye brow on my side during the time of transfer because I work in this bank.
I will not fail to inform you that this transaction is 100% risk free, on smooth conclusion of this transaction, you will be entitled to 30% of the total sum as gratification, while 5% will be set aside to take care of expenses that may arise during the time of transfer, while 65% will be for
me, please you have been advised to keep "Top Secret" as I am still in service and intend to retire from service after we conclude this deal with you.
I will be monitoring the whole situation here in this bank Until you confirm the money in your account, and ask us to come down to your country for subsequent sharing of the fund according to percentages previously indicated and further Investment, either in your country or any country you advice us to invest in and all other necessary vital information will be send to you
when I hear from you.
I suggest you get back to me As soon as possible and also include your personal phone/mobile and fax numbers for easy communication.
Yours faithfully,
Mr. Gerald Zongo
The manager in charge of
Auditing department of
Bank of Africa (B.O.A)
Ouagadougou Burkina Faso.
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The best ideas for taking a bite!
Have a great time with your squad and these awesome activities!
These are the best things to do when you are alone at home right now.
Either a romantic dinner or a trip to the zoo. We've got it all.


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This document describes the overall architecture for MyChat - a hypothetical chat/messaging system.
Date: 2021-03-13
Status: Draft
Objectives
The objectives of this architecture document are to outline the architectural elements for the MyChat messaging service. This architecture should provide enough clarity for developers to create more detailed design documents for each of the major architectural components (if required).
Scope
This document only provides a overall the high-level architecture for MyChat.
Assumptions
The following assumptions have been made in regards to this document:
- A known set of requirements have been created and accepted.
- A basic understanding of chat/messaging system architecture.
- A basic understanding of right-sized services.
Domains
The following are the top level domains:
Components
The MyChat service consists of the following components:
-
Account Management Service
- User Profile (name, email, phone, address, etc.)
- User Preferences (language, Timezone, etc.)
- Identity Service (SSO, MFA, etc.)
-
Chat Spaces
- Manage chat Spaces (create, delete, modify, add/delete users)
-
Notification Service
- Handle communications with users via (email, SMS)
- Notify if of new messages
- Invitation to join a chat Space
- Handle communications with users via (email, SMS)
-
Message Service
- SSE Emitter for chat spaces
- Handle input of messages to be queued for chat spaces
Component Interactions
Login
A user may authenticate to MyChat via SSO. The following details that interaction:
Once a user is logged in a token will be create that will be passed to all authenticated APIs.
SSE Events
SSE handshake:
Database Requirements
Databases schemas should be designed based on the type of required queries.
Account Management
The Account Management domain has the following database queries:
- Get account information by account ID
- Modify account information.
- Delete (may just flag a user as inactive unless deletion required by PII/GDPR)
Chat Spaces
The Chat Spaces domain has the following database queries:
- Get all Chat Spaces for a given account ID
- Add account ID to a given Chat Space
- Delete account ID from a given Chat Space
- Delete Chat Space for a given account ID
- Modify Chat Space attributes (TBD)
- Get messages starting from a given date from a Chat Space
- Delete messages starting from a given date from a Chat Space (depending on governance regulations)
Questions to be Resolved
- How do determine the SSE “capacity” of servers?
- Do we need to setup GEO-located servers to meet the latency requirements?
- Can we use a “wrapped” Webview for desktop applications?
- How do we manage the encryption keys for encrypted data (when to rotate, who owns the private key etc.)


Turbulent Media creates, produces and distributes quality Web, cross-platform projects with a focus on the gaming and television industries. Turbulent has recently achieved success with their website for the crowdfunded game Star Citizen (http://www.robertsspaceindustries.com)
Come early! Beer and pizza being served starting at 6PM, and meetup starts at 7PM.


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| These WEB pages of the transcriptions of Cogges Memorial Inscriptions and the photographs it contains, are Copyright © St. Mary's Church Cogges Parochial Church Council, 2005.
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Jack Dongarra opened the meeting by welcoming everyone and inviting everyone to introduce themselves. He then gave a brief summary of the previous BLAS workshops, and asked which subgroups should meet at this workshop. It was decided that the following subgroups would be discussed:
BLAS functionality was the first to be addressed. The discussion focused on the draft of the Functionality proposal that Barry Smith of ANL and Sven Hammarling of NAG had written and made available via the BLAS Forum homepage. Sven gave a brief overview of the proposal and invited comments and suggestions. Several attendees voiced their opinions on the proposal, and each of the lists of proposed routines were discussed, as well as possible additions. Revised lists were to be prepared that night and presented the following day. Anne Trefethen volunteered to expand the ``multiple instances'' section for the proposal.
No particular programming language will be primary in the discussion of functionality, instead we will discuss syntax and semantics for Fortran 77, Fortran 90, C, and C++. We shall provide reference implementations for the proposed BLAS routines in each language.
There are different ways of classifying functionality -- BLAS-like and tensor notation. Andrew Lumsdaine led a discussion explaining the proposed tensor notation. An open question for the forum is whether one or both of the classification schemes should be used.
After a break, the BLAS Lite were discussed. Two versions of the BLAS Lite were proposed. One (debugging) version with error-checking, and another (performance) version without error-checking. To provide good performance on all problem sizes, separate interfaces for stride 1 operations may be provided, as well as separate routines for small (block) problems, e.g., matrix multiplies of size 2, 3, 4, and 5.
Puri Bangalore raised the issue of specifying time and space (CPU and memory) complexity for various routines. This sort of specification is clearly useful for users of the routines, especially when the user might want to choose between various routines that provide similar functionality but which have different CPU/memory usage. However, it is not clear that many of the proposed BLAS functions admit implementations that have differing CPU/memory complexity. The obvious exceptions are xNRM2 (Euclidean vector norm) and matrix multiply. An open question here is whether we should give options for varying degrees of accuracy depending upon time and space considerations. Specifically, should a fast version of xNRM2 or a Strassen matrix multiply be provided?
Jack Dongarra opened the morning session with a summary of the previous day's discussions. The first topic of the morning was the discussion of the object-based BLAS interface, led by Andrew Lumsdaine.
During the discussion of the object-based interface, Andrew Lumsdaine mentioned that it may be important to consider the effects of new parts of the ANSI C++ standard, especially the Standard Template Library. He stressed the composition (templates) approach to building the library instead of the inheritance approach.
After a short break, Sven Hammarling spoke on the proposed revisions to the Functionality proposal. Specifically, updated tables of routines were presented.
Anne Trefethen gave input on the needed ``multiple instance'' routines.
The Lite BLAS were again discussed. Puri Bangalore opened the discussion by asking which data types, matrix types, and storage formats should be supported. Puri provided a list as a starting point and several attendees suggested additions.
The issue of error checking was introduced. Puri suggested that all functions should return an error code. Barry Smith pointed out that in the BLAS Lite there are very few errors that can occur. It was then suggested that each BLAS function might have two entry points, in similar fashion to MPI.
Puri then raised the thorny issue of indexing, in particular, whether indexing should be 0 based (as in C) or 1 based (as in Fortran). For dense linear algebra this is not a real problem, but it is a problem when user data maintains explicit indexing information (as in compressed sparse storage schemes). Suggested options for indexing were to support both via run-time or compile-time schemes or to make the index support language-dependent.
Next, Puri asked whether a stride argument is needed. Sven indicated that stride arguments are convenient and are required by some algorithms.
Puri also addressed guidelines to implementors and discussed these four issues:
The vendors expressed concern about the amount of work that these guidelines might impose upon them. It was clarified that this was intended to be mostly documentation, rather than additional implementation work. Puri suggested that the implementors were free to do what is best for their architecture, but as a library writer or user one would like to know what has been done, at least in terms of memory usage if extra memory is allocated within a function.
After a lunch break, Barry Smith spoke of sample implementations of the BLAS Lite. Proposed calling sequences for routines were discussed, as well as the necessity for reference implementations of some of the routines. Barry volunteered for ANL to implement the C and C++ versions of the routines. The University of Tennessee will provide Fortran 90 reference implementations for some of the routines.
Jack Dongarra wrapped up the meeting by suggesting that the Functionality proposal be finished by summer. Andrew Lumsdaine will also draft an Object-based BLAS proposal. Both proposals will be available on the Web page, and comments/suggestions from the user community are welcome.
The dates of the next forum meetings are:
The meeting was then adjourned by Jack Dongarra at 3:30 PM.
Attendees list for the May 20-21, 1996 BLAST Forum Meeting
Satish Balay ANL<EMAIL_ADDRESS>Puri Bangalore Miss. State Univ<EMAIL_ADDRESS>Andrew Cleary Univ. of TN<EMAIL_ADDRESS>Jack Dongarra Univ. of TN / ORNL<EMAIL_ADDRESS>Sven Hammarling NAG, UK / Univ. of TN<EMAIL_ADDRESS>Hidehiko Hasegawa ULIS, Tsukuba, Japan<EMAIL_ADDRESS>Satomi Hasegawa Hitachi<EMAIL_ADDRESS>Naoki Iwata NEC Systems Laboratory<EMAIL_ADDRESS>Chandrika Kamath DEC<EMAIL_ADDRESS>Guangye Li Cray Research<EMAIL_ADDRESS>Hsin-Ying Lin HP Convex Technology Ctr<EMAIL_ADDRESS>Andrew Lumsdaine Univ. of Notre Dame<EMAIL_ADDRESS>Joan McComb IBM Poughkeepsie<EMAIL_ADDRESS>Susan Ostrouchov Univ. of TN<EMAIL_ADDRESS>Antoine Petitet Univ. of TN<EMAIL_ADDRESS>Barry Smith ANL<EMAIL_ADDRESS>Anne Trefethen Cornell Theory Center<EMAIL_ADDRESS>Susan Ostrouchov and Andrew Lumsdaine agreed to take minutes for the meetings.


| 025:045 Khan | : |
Have you not seen how your Lord spread the shadow. If He willed, He could have made it still then We have made the sun its guide [i.e. after the sunrise, it (the shadow) squeezes and vanishes at midnoon and then again appears in the afternoon with the decline of the sun, and had there been no sun light, there would have been no shadow]. |
| 025:045 Maulana | : |
Seest thou not how thy Lord extends the shade? And if He pleased, He would have made it stationary. Then We have made the sun an indication of it, |
| 025:045 Pickthal | : |
Hast thou not seen how thy Lord hath spread the shade - And if He willed He could have made it still - then We have made the sun its pilot; |
| 025:045 Rashad | : |
Have you not seen how your Lord designed the shadow? If He willed, He could have made it fixed, then we would have designed the sun accordingly. |
| 025:045 Sarwar | : |
Have you not seen that your Lord increases the shadow. Had He wanted He would have made it stationary), and has made the sun their guide. |
| 025:045 Shakir | : |
Have you not considered (the work of) your Lord, how He extends the shade? And if He had pleased He would certainly have made it stationary; then We have made the sun an indication of it |
| 025:045 Sherali | : |
Dost thou not see how thy Lord lengthens the shade? And if HE had pleased, HE could have made it stationary. Then WE make the position of the sun an indicator thereof. |
| 025:045 Yusufali | : |
Hast thou not turned thy vision to thy Lord?- How He doth prolong the shadow! If He willed, He could make it stationary! then do We make the sun its guide; |
Concepts/Themes Covered: أركان الإسلام > التوحيد > توحيد الله تعالي > ربوبيته جلّ وعلا Pillars of Islam> Islamic> Oneness of Allah> His Divinity, the Great and Almighty أركان الإسلام > التوحيد > توحيد الله تعالي > وحدانيته Pillars of Islam> Islamic> Oneness of Allah> Singularity and Being Unique
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


We had a great visit to Gilroy High School to talk to Mr. Hungerford's class about nuclear science and install our device. Lots of fun with Geiger counters and some great questions from students! We are looking forward to future visits.
Fig 1. A group of interested students use a Geiger counter to investigate the radioactivity of various materials as Mr. Hungerford looks on.
Fig 2. A student on the look out for anything radioactive in the classroom.


Hi, my name is Saif Saeed I am 32 years old I am from Jordan .
Work Experience:
- I worked as a hairdresser.
- Student at LTUC-ASAC in a second-year software engineering and Software development course.
- Have an awesome job in Google.
- Swimming
- Chess
- Car Racingt
- Bodybuilding
- Archery
- Diving
- Hang Gliding
- Kick Boxing
- Volleyball
- Scuba Diving


Authorities seek Missouri woman after body found in freezer
by The Associated PressJOPLIN, Mo. (AP) — Authorities are searching for a woman after finding the corpse of a man in a freezer inside her southwest Missouri home.
Sixty-seven-year-old Barbara Watters, of Joplin, was charged Wednesday with abandonment of a corpse. Police said in a Facebook post that the man whose body was found Tuesday hasn’t been identified.
Police obtained a search warrant and located the body after learning Monday during an unrelated arson investigation that a body might be inside her home.
Police Chief Matt Stewart told The Joplin Globe that there were no obvious signs of foul play. An autopsy is needed to determine how the man died. Police haven’t indicated how long the body may have been in the freezer.
The Associated Press


SHORT CUTS A film review by Samuel Stoddard Copyright (c) 1997 by Samuel Stoddard
RATING: ***** (* to *****)
"Daddy, why can't I have a monkey?" -- Short Cuts, 1993
Set in the bustle of Los Angeles, this star-studded Robert Altman cinematic tapestry tells the interlocking stories of a host of characters. Tapestry is precisely the right word. Each character is a thread in said tapestry, who contributes to the big picture, interacts with other characters who also contribute to the big picture, but really have no conception as to what the big picture looks like.
We do. We see everything that happens to each of the characters in the few days the film spans. This omniscience gives us, the audience, a unique perspective. We can see the series of accidents and chance that change the course of people's lives forever. The characters persevere, try hard to make their lives work. Sometimes they do, sometimes they don't. Usually it's a mix of the two and often dictated by happenings completely out of their control. If each of the characters knew everything there was to know about all the others, how much more compassionate, less angry, might they all be, and how much simpler their lives? All the characters are linked to all the others in some direct or indirect way, but no one knows everybody. Roger Ebert surmised in his review how much things might change if they did. One character might never meet another character throughout the course of the film, but has plenty of precisely what the other needs. Maybe some of these perfect pairs will one day meet up -- several, undoubtedly, will not.
"Real life" films rarely interest me. This one fascinates me. I was not expecting to love it so -- I expected fine filmmaking, but not necessarily fine entertainment. Short Cuts does several things with smashing success: it is an upstanding example of cinematic art, it is engrossing entertainment, it is hilariously funny, it is poignant and moving, and it inspires an awe rarely evoked by every day life. Within the days that the film takes place, there are momental events that take place, which the characters will never forget as long as they live, and there are the routine events that occur and are forgotten. All of these are somehow made fascinating in some way. Frances McDormand's story, for instance, about her affair with a married man and a malicious ex, borders on hysteria. There are no punchlines -- it's pure situational humor, in the uncorrupted sense of the term -- and induced greater laughter in myself than most pure comedies can do. Andie MacDowell's story, on the other hand, evokes our utmost concern and compassion, more so than most pure dramas can. Short Cuts is surprising, shocking, and unpredictable in this fashion -- true to life, just when things seem settled and ordinary, something happens that snaps you attention. And it's a different kind of thing each time. It might be a sudden moment of humor or horror, or a facet of a character abruptly revealed that shows us that they're all real people after all, not the stereotypes that a few of them seem like at first.
Rare indeed is a film that runs over three hours yet leaves us wanting more. Most of the stories have resolutions of some sort, enough to provide a necessary level of satisfaction a viewer expects from a film. But the end of the film isn't the end of the stories -- no more so than the beginning of the film was the beginning of the stories. In real life, episodes in people's lives never really end -- they shape the people they happen to, are often recalled in sporadic moments, and always affect their futures. The film's open-ended conclusion is appropriate and necessary, but it is admittedly disconcerting to see the credits roll so soon. It takes some thought and reflection afterward to bring the film to a close in our own minds. Viewers put off by the seemingly abrupt end may not be inclined to do so and will therefore not fully appreciate it. But the characters must get on with their lives, and we must get on with ours.
--
Visit the At-A-Glance Film Reviews page at:
http://pubpages.unh.edu/~ss1/movies/
1500+ ratings, 600+ reviews, on films from all eras, 1918-1997.
--
|| Jesus saith unto him, I am the way, the truth, and the life: || || no man cometh unto the Father, but by me. John 14:6 ||
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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'Cowboy Killer?': Original 'Marlboro Man' Lives to Ripe Old Age of 90
Robert Norris, the first 'Marlboro Man', known for wearing a cowboy hat while lighting up a cigarette in an iconic advertisement, has died at age 90.
Robert Norris was an actor who was the "Marlboro Man" in cigarette advertisements before they were banned from television in the 1970s.
According to the Tee Cross Ranches he helped co-found, he passed away on 3 November 2019 in Colorado.
Robert Norris never smoked cigarettes, according to various media reports. His friends included screen luminary John Wayne.
 He called up Phillip Morris and quit that day.”
The Marlboro brand appeared in 1924 and was initially positioned to target a female audience of smokers. Everything changed in the early 1950s, when the brand began to “turn” towards a male audience. The idea to use the image of a cowboy came to Leo Burnet (executive advertising director of Philip Morris) in 1949, when he spotted eye-catching photographs in Life magazine, shot by Leonard McComb, and read the story of the Texas cowboy Clarence Hailey Long.


|
Machine Learning with R The R Bootcamp |
adapted from xkcd.com
In this practical, you’ll practice the basics of fitting and exploring regression models in R.
By the end of this practical you will know how to:
TheRBootcamp
R project.# Done!
Fitting_practical.R
in the 2_Code
folder.## NAME
## DATE
## Fitting practical
library()
, load the packages tidyverse
and caret
.# Load packages necessary for this practical
library(tidyverse)
library(caret)
college_train.csv
. Using the following template, load the dataset into R as college_train
.# Load in college_train.csv data as college_train
college_train <- read_csv(file = "1_Data/college_train.csv")
college_train <- read_csv(file = "1_Data/college_train.csv")
college_train
# A tibble: 500 x 18
Private Apps Accept Enroll Top10perc Top25perc F.Undergrad P.Undergrad
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Yes 1202 1054 326 18 44 1410 299
2 No 1415 714 338 18 52 1345 44
3 Yes 4778 2767 678 50 89 2587 120
4 Yes 1220 974 481 28 67 1964 623
5 Yes 1981 1541 514 18 36 1927 1084
6 Yes 1217 1088 496 36 69 1773 884
7 No 8579 5561 3681 25 50 17880 1673
8 Yes 833 669 279 3 13 1224 345
9 No 10706 7219 2397 12 37 14826 1979
10 Yes 938 864 511 29 62 1715 103
# … with 490 more rows, and 10 more variables: Outstate <dbl>,
# Room.Board <dbl>, Books <dbl>, Personal <dbl>, PhD <dbl>, Terminal <dbl>,
# S.F.Ratio <dbl>, perc.alumni <dbl>, Expend <dbl>, Grad.Rate <dbl>
# Convert character to factor
college_train <- college_train %>%
mutate_if(is.character, factor)
caret
, the computational nuances of training a model are defined using the trainControl()
function. As this session focuses on the basics of fitting, set method = "none"
for now and save the resulting object as ctrl_none
.# Set training resampling method to "none"
ctrl_none <- trainControl(method = "none")
Grad.Rate
) as a function of one feature, namely PhD
(percent of faculty with PhDs). Save the result as an object graduation_glm
. Specifically:form
argument to Grad.Rate ~ PhD
.data
argument to your training data college_train
.method
argument to "glm"
for regression.trControl
argument to ctrl_none
, the object you created above# graduation_glm: Regression Model
graduation_glm <- train(form = XX ~ XX,
data = XX,
method = "XX",
trControl = XX)
# graduation_glm: Regression Model
graduation_glm <- train(form = Grad.Rate ~ PhD,
data = college_train,
method = "glm",
trControl = ctrl_none)
summary()
function, by setting the function’s first argument to graduation_glm
. How do you interpret the output including the estimated parameters?# Show summary information from the regression model
summary(XXX)
# Show summary information from the regression model
summary(graduation_glm)
Call:
NULL
Deviance Residuals:
Min 1Q Median 3Q Max
-54.56 -13.38 1.34 14.13 41.58
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 40.4084 3.9472 10.24 < 2e-16 ***
PhD 0.3401 0.0525 6.47 2.3e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 350)
Null deviance: 188828 on 499 degrees of freedom
Residual deviance: 174167 on 498 degrees of freedom
AIC: 4352
Number of Fisher Scoring iterations: 2
glm_fit
.# Get fitted values from the graduation_glm
glm_fit <- predict(XXX)
# Get fitted values from the model
glm_fit <- predict(graduation_glm)
criterion
as the true graduation rates in the data.# Define criterion as Grad.Rate
criterion <- college_train$Grad.Rate
postResample()
, with the fitted values as the prediction, and the criterion as the observed values.Specifically:
pred
argument to glm_fit
(the fitted values).obs
argument to criterion
(the criterion values).# Model performance
postResample(pred = XXX,
obs = XXX)
# Regression Fitting Accuracy
postResample(pred = glm_fit,
obs = criterion)
RMSE Rsquared MAE
18.6637 0.0776 15.4097
postResample()
shows three values, RMSE, Rsquared und MAE. How do you interpret these; is the performance good or bad?# On average, the model fits are 12.8633 away from the true values.
# Whether this is 'good' or not depends on you :)
So far you have only used one feature (PhD
), to predict Grad.Rate
. Try again, but now use a total of three features, namely:
PhD
- the percent of faculty with a PhD.Room.Board
- room and board costs.S.F.Ratio
- student to faculty ratio.graduation_glm_three
which predicts Grad.Rate
using the three features. Specifically,…form
argument to Grad.Rate ~ PhD + Room.Board + S.F.Ratio
.data
argument to your training data college_train
.method
argument to "glm"
for regression.trControl
argument to ctrl_none
.# graduation_glm_three: Regression Model
graduation_glm_three <- train(form = XXX ~ XXX + XXX + XXX + XXX,
data = XXX,
method = "XXX",
trControl = XXX)
# graduation_glm_three: Regression Model
graduation_glm_three <- train(form = Grad.Rate ~ PhD + Room.Board + S.F.Ratio,
data = college_train,
method = "glm",
trControl = ctrl_none)
summary()
. What values were estimated for the parameters?summary(XXX)
summary(graduation_glm_three)
Call:
NULL
Deviance Residuals:
Min 1Q Median 3Q Max
-54.12 -12.40 0.69 11.44 44.59
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 36.320318 5.966050 6.09 2.3e-09 ***
PhD 0.208207 0.053036 3.93 9.9e-05 ***
Room.Board 0.004814 0.000792 6.08 2.4e-09 ***
S.F.Ratio -0.495331 0.210170 -2.36 0.019 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 316)
Null deviance: 188828 on 499 degrees of freedom
Residual deviance: 156590 on 496 degrees of freedom
AIC: 4302
Number of Fisher Scoring iterations: 2
predict()
and save them as a new object glm_fit_three
.# Save new model fits
glm_fit_three <- predict(XXX)
# Save new model fits
glm_fit_three <- predict(graduation_glm_three)
postResample()
to evaluate the performance of the model with three predictors. How well does the new model fit the data, relative to the one using only one predictor.# New model fitting accuracy
postResample(pred = XXX, # Fitted values
obs = XXX) # criterion values
# New model fitting accuracy
postResample(pred = glm_fit_three, # Fitted values
obs = criterion) # criterion values
RMSE Rsquared MAE
17.697 0.171 14.394
# The new MAE value is 11.779, it's better (smaller) than the previous model, but still not great (in my opinion)
graduation_glm_all
which predicts Grad.Rate
using all features in the dataset. Specifically:form
argument to Grad.Rate ~ .
.data
argument to the training data college_train
.method
argument to "glm"
for regression.trControl
argument to ctrl_none
.# graduation_glm_all: Regression Model
graduation_glm_all <- train(form = XXX ~ .,
data = XXX,
method = "glm",
trControl = XXX)
# graduation_glm_all: Regression Model
graduation_glm_all <- train(form = Grad.Rate ~ .,
data = college_train,
method = "glm",
trControl = ctrl_none)
summary()
. What do the parameter estimates tell you?summary(XXX)
summary(graduation_glm_all)
Call:
NULL
Deviance Residuals:
Min 1Q Median 3Q Max
-49.27 -10.69 0.25 10.41 49.36
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 26.320597 7.282672 3.61 0.00033 ***
PrivateYes 2.075873 2.477211 0.84 0.40245
Apps 0.001243 0.000722 1.72 0.08554 .
Accept -0.000965 0.001339 -0.72 0.47151
Enroll 0.006891 0.003663 1.88 0.06056 .
Top10perc -0.100378 0.110002 -0.91 0.36196
Top25perc 0.289288 0.085752 3.37 0.00080 ***
F.Undergrad -0.001247 0.000584 -2.13 0.03328 *
P.Undergrad -0.001296 0.000565 -2.29 0.02224 *
Outstate 0.001436 0.000383 3.75 0.00020 ***
Room.Board 0.001294 0.000917 1.41 0.15922
Books -0.000276 0.005208 -0.05 0.95781
Personal -0.001756 0.001270 -1.38 0.16731
PhD 0.060658 0.090671 0.67 0.50382
Terminal -0.066585 0.096828 -0.69 0.49200
S.F.Ratio 0.330961 0.241530 1.37 0.17124
perc.alumni 0.195720 0.078634 2.49 0.01315 *
Expend -0.000369 0.000257 -1.44 0.15098
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 251)
Null deviance: 188828 on 499 degrees of freedom
Residual deviance: 121047 on 482 degrees of freedom
AIC: 4202
Number of Fisher Scoring iterations: 2
glm_fit_all
.# Save new model fits
glm_fit_all <- predict(XXX)
# Save new model fits
glm_fit_all <- predict(graduation_glm_all)
postResample()
to evaluate the performance of the model with all predictors. Compare it to the previous two models with fewer predictors. Do you detect a pattern?# New model fitting accuracy
postResample(pred = glm_fit_all, # Fitted values
obs = criterion) # criterion values
RMSE Rsquared MAE
15.559 0.359 12.443
Now it’s time to do a classification task! Recall that in classification tasks, your are predicting a factor
. In this task, you will predict whether or not a college is Private or Public, which is stored in the feature Private
.
calss()
to make sure that the Private
is stores as a factor. If the output is factor
, you can proceed.# Look at the class of the feature Private
class(college_train$Private)
[1] "factor"
Private
as a new object called criterion
.# Define criterion as college_train$Private
criterion <- college_train$Private
train()
, create a logistic regression model called private_glm
predicting the criterion Private
using all other features. Specifically,…form
argument to Private ~ .
.data
argument to the training data college_train
.method
argument to "glm"
.trControl
argument to ctrl_none
.# Fit regression model predicting Private
private_glm <- train(form = XXX ~ .,
data = XXX,
method = "XXX",
trControl = XXX)
# Fit regression model predicting private
private_glm <- train(form = Private ~ .,
data = college_train,
method = "glm",
trControl = ctrl_none)
private_glm
object using the summary()
function. What to you make of the estimates?# Explore the private_glm object
summary(XXX)
# Explore the private_glm object
summary(private_glm)
Call:
NULL
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5443 -0.1048 0.0501 0.1859 2.4352
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.13e+00 2.05e+00 -1.04 0.29827
Apps 2.04e-04 2.22e-04 0.92 0.35798
Accept -2.26e-03 4.98e-04 -4.53 6.0e-06 ***
Enroll 4.04e-03 1.20e-03 3.38 0.00074 ***
Top10perc -1.00e-01 3.48e-02 -2.88 0.00395 **
Top25perc 7.44e-02 2.57e-02 2.90 0.00373 **
F.Undergrad -2.16e-04 1.59e-04 -1.36 0.17518
P.Undergrad -1.09e-04 1.28e-04 -0.85 0.39366
Outstate 8.36e-04 1.29e-04 6.50 7.8e-11 ***
Room.Board 7.42e-04 2.92e-04 2.54 0.01110 *
Books 3.57e-03 1.58e-03 2.26 0.02410 *
Personal -3.87e-04 3.27e-04 -1.18 0.23715
PhD -6.80e-02 3.09e-02 -2.20 0.02787 *
Terminal -3.55e-02 2.92e-02 -1.22 0.22350
S.F.Ratio -1.31e-01 6.75e-02 -1.93 0.05316 .
perc.alumni 5.50e-02 2.35e-02 2.33 0.01955 *
Expend -6.16e-05 9.82e-05 -0.63 0.53054
Grad.Rate 9.05e-03 1.09e-02 0.83 0.40814
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 625.35 on 499 degrees of freedom
Residual deviance: 191.56 on 482 degrees of freedom
AIC: 227.6
Number of Fisher Scoring iterations: 7
glm_fit
using the following code.# Get fitted values
glm_fit <- predict(XXX)
# Get fitted values
glm_fit <- predict(private_glm)
4.Use `confusionMatrix() to evaluate the performance of your classification model. Specifically:
data
argument to your glm_fit
values.reference
argument to the criterion
values.# Evaluate model performance
confusionMatrix(data = XXX, # This is the prediction!
reference = XXX) # This is the truth!
# Evaluate model performance
confusionMatrix(data = glm_fit, # This is the prediction!
reference = criterion) # This is the truth!
Confusion Matrix and Statistics
Reference
Prediction No Yes
No 135 23
Yes 24 318
Accuracy : 0.906
95% CI : (0.877, 0.93)
No Information Rate : 0.682
P-Value [Acc > NIR] : <2e-16
Kappa : 0.783
Mcnemar's Test P-Value : 1
Sensitivity : 0.849
Specificity : 0.933
Pos Pred Value : 0.854
Neg Pred Value : 0.930
Prevalence : 0.318
Detection Rate : 0.270
Detection Prevalence : 0.316
Balanced Accuracy : 0.891
'Positive' Class : No
# The overall accuracy is 0.942. Across all cases, the model fits the true class values 94.2% of the time.
Refit the classification model using fewer features.
How does using fewer features affect the model’s performance?
perc.alumni
). How good can your regression model fit this criterion? Which features contribute most?mod <- train(form = perc.alumni ~ .,
data = college_train,
method = "glm",
trControl = ctrl_none)
summary(mod)
Call:
NULL
Deviance Residuals:
Min 1Q Median 3Q Max
-25.66 -5.75 -0.47 5.35 32.59
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.94e+00 4.24e+00 1.40 0.16204
PrivateYes 2.55e+00 1.42e+00 1.79 0.07359 .
Apps -6.34e-04 4.16e-04 -1.53 0.12748
Accept -1.67e-03 7.68e-04 -2.17 0.03037 *
Enroll 6.96e-03 2.09e-03 3.33 0.00095 ***
Top10perc 4.87e-02 6.33e-02 0.77 0.44229
Top25perc 7.12e-02 4.98e-02 1.43 0.15383
F.Undergrad -3.86e-04 3.37e-04 -1.14 0.25364
P.Undergrad -9.67e-07 3.27e-04 0.00 0.99764
Outstate 1.13e-03 2.18e-04 5.20 2.9e-07 ***
Room.Board -1.79e-03 5.23e-04 -3.43 0.00067 ***
Books -6.58e-04 3.00e-03 -0.22 0.82623
Personal -2.24e-03 7.25e-04 -3.09 0.00215 **
PhD -2.75e-02 5.22e-02 -0.53 0.59842
Terminal 1.37e-01 5.54e-02 2.47 0.01385 *
S.F.Ratio -2.42e-01 1.39e-01 -1.74 0.08213 .
Expend 3.74e-05 1.48e-04 0.25 0.80083
Grad.Rate 6.48e-02 2.60e-02 2.49 0.01315 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 83.2)
Null deviance: 73707 on 499 degrees of freedom
Residual deviance: 40100 on 482 degrees of freedom
AIC: 3649
Number of Fisher Scoring iterations: 2
mod_predictions <- predict(mod)
hist(mod_predictions)
postResample(pred = mod_predictions,
obs = college_train$perc.alumni)
RMSE Rsquared MAE
8.955 0.456 7.075
hot
variable.# Add a new factor criterion 'hot' which indicates whether or not a schol receives at least 10,000 applications
college_train <- college_train %>%
mutate(hot = factor(Apps >= 10000))
mod_hot <- train(form = hot ~ .,
data = college_train,
method = "glm",
trControl = ctrl_none)
summary(mod_hot)
Call:
NULL
Deviance Residuals:
Min 1Q Median 3Q Max
-7.93e-05 -2.00e-08 -2.00e-08 -2.00e-08 7.02e-05
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.90e+01 2.07e+05 0 1
PrivateYes 6.66e-01 6.27e+04 0 1
Apps 2.22e-02 9.89e+00 0 1
Accept -1.00e-02 1.39e+01 0 1
Enroll 1.39e-02 2.92e+01 0 1
Top10perc -6.30e-01 1.55e+03 0 1
Top25perc 4.91e-01 1.33e+03 0 1
F.Undergrad -5.59e-04 4.64e+00 0 1
P.Undergrad -1.87e-05 7.01e+00 0 1
Outstate -1.29e-03 7.01e+00 0 1
Room.Board 4.18e-03 1.63e+01 0 1
Books -5.54e-02 9.04e+01 0 1
Personal -4.91e-03 4.36e+01 0 1
PhD -1.65e+00 2.23e+03 0 1
Terminal 4.50e-01 2.65e+03 0 1
S.F.Ratio -6.50e-01 2.39e+03 0 1
perc.alumni 1.29e-01 2.28e+03 0 1
Expend 7.07e-04 5.41e+00 0 1
Grad.Rate -2.25e-01 1.55e+03 0 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2.5364e+02 on 499 degrees of freedom
Residual deviance: 4.4673e-08 on 481 degrees of freedom
AIC: 38
Number of Fisher Scoring iterations: 25
mod_predictions <- predict(mod_hot)
plot(mod_predictions)
confusionMatrix(data = mod_predictions, # This is the prediction!
reference = college_train$hot) # This is the truth!
Confusion Matrix and Statistics
Reference
Prediction FALSE TRUE
FALSE 465 0
TRUE 0 35
Accuracy : 1
95% CI : (0.993, 1)
No Information Rate : 0.93
P-Value [Acc > NIR] : <2e-16
Kappa : 1
Mcnemar's Test P-Value : NA
Sensitivity : 1.00
Specificity : 1.00
Pos Pred Value : 1.00
Neg Pred Value : 1.00
Prevalence : 0.93
Detection Rate : 0.93
Detection Prevalence : 0.93
Balanced Accuracy : 1.00
'Positive' Class : FALSE
?glm.control
. To fix it just add the following code in your train()
function control = list(maxit = 75)
, and run it again.mod_hot <- train(form = hot ~ .,
data = college_train,
method = "glm",
trControl = ctrl_none,
control = list(maxit = 75))
summary(mod_hot)
Call:
NULL
Deviance Residuals:
Min 1Q Median 3Q Max
-6.59e-06 -2.10e-08 -2.10e-08 -2.10e-08 5.81e-06
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.89e+01 2.53e+06 0 1
PrivateYes 2.85e-01 7.39e+05 0 1
Apps 2.76e-02 1.18e+02 0 1
Accept -1.27e-02 1.70e+02 0 1
Enroll 1.76e-02 3.67e+02 0 1
Top10perc -8.17e-01 1.90e+04 0 1
Top25perc 6.40e-01 1.63e+04 0 1
F.Undergrad -6.99e-04 5.42e+01 0 1
P.Undergrad -9.70e-05 7.20e+01 0 1
Outstate -1.57e-03 8.63e+01 0 1
Room.Board 5.15e-03 2.04e+02 0 1
Books -6.88e-02 1.15e+03 0 1
Personal -6.71e-03 5.33e+02 0 1
PhD -2.07e+00 2.75e+04 0 1
Terminal 5.68e-01 3.29e+04 0 1
S.F.Ratio -8.19e-01 2.90e+04 0 1
perc.alumni 1.30e-01 2.79e+04 0 1
Expend 9.48e-04 6.60e+01 0 1
Grad.Rate -2.94e-01 1.89e+04 0 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2.5364e+02 on 499 degrees of freedom
Residual deviance: 3.0573e-10 on 481 degrees of freedom
AIC: 38
Number of Fisher Scoring iterations: 30
mod_predictions <- predict(mod_hot)
plot(mod_predictions)
confusionMatrix(data = mod_predictions, # This is the prediction!
reference = college_train$hot) # This is the truth!
Confusion Matrix and Statistics
Reference
Prediction FALSE TRUE
FALSE 465 0
TRUE 0 35
Accuracy : 1
95% CI : (0.993, 1)
No Information Rate : 0.93
P-Value [Acc > NIR] : <2e-16
Kappa : 1
Mcnemar's Test P-Value : NA
Sensitivity : 1.00
Specificity : 1.00
Pos Pred Value : 1.00
Neg Pred Value : 1.00
Prevalence : 0.93
Detection Rate : 0.93
Detection Prevalence : 0.93
Balanced Accuracy : 1.00
'Positive' Class : FALSE
glm.fit: fitted probabilities numerically 0 or 1 occurred
. This can happen if very strong predictors occur in the dataset (see Venables & Ripley, 2002, p. 197). If you added all predictors (except again the college names), then this problem occurs because the Apps
variable, used to create the criterion, was also part of the predictors (plus some other variables that highly correlate with Apps
). Check the variable correlations (the code below will give you a matrix of bivariate correlations). You will learn an easier way of checking the correlations of variables in a later session.# get correlation matrix of numeric variables
cor(college_train[,sapply(college_train, is.numeric)])
mod_hot <- train(form = hot ~ . - Apps -Enroll -Accept - F.Undergrad,
data = college_train,
method = "glm",
trControl = ctrl_none,
control = list(maxit = 75))
summary(mod_hot)
Call:
NULL
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5607 -0.1615 -0.0416 -0.0100 3.1035
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.58e+01 4.31e+00 -3.67 0.00025 ***
PrivateYes -5.53e+00 1.41e+00 -3.92 8.8e-05 ***
Top10perc 1.20e-02 2.62e-02 0.46 0.64566
Top25perc 5.44e-02 2.81e-02 1.94 0.05281 .
P.Undergrad 3.15e-04 1.40e-04 2.25 0.02461 *
Outstate 7.24e-05 1.29e-04 0.56 0.57454
Room.Board 8.30e-04 3.41e-04 2.43 0.01496 *
Books -3.34e-03 2.19e-03 -1.52 0.12795
Personal 3.00e-04 4.00e-04 0.75 0.45285
PhD 6.01e-02 5.48e-02 1.10 0.27241
Terminal -1.31e-03 5.97e-02 -0.02 0.98254
S.F.Ratio 2.32e-03 8.03e-02 0.03 0.97694
perc.alumni -2.78e-02 3.10e-02 -0.90 0.36911
Expend 4.47e-05 6.22e-05 0.72 0.47286
Grad.Rate 4.00e-02 1.88e-02 2.13 0.03318 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 253.64 on 499 degrees of freedom
Residual deviance: 118.81 on 485 degrees of freedom
AIC: 148.8
Number of Fisher Scoring iterations: 8
mod_predictions <- predict(mod_hot)
plot(mod_predictions)
confusionMatrix(data = mod_predictions, # This is the prediction!
reference = college_train$hot) # This is the truth!
Confusion Matrix and Statistics
Reference
Prediction FALSE TRUE
FALSE 459 17
TRUE 6 18
Accuracy : 0.954
95% CI : (0.932, 0.971)
No Information Rate : 0.93
P-Value [Acc > NIR] : 0.0175
Kappa : 0.587
Mcnemar's Test P-Value : 0.0371
Sensitivity : 0.987
Specificity : 0.514
Pos Pred Value : 0.964
Neg Pred Value : 0.750
Prevalence : 0.930
Detection Rate : 0.918
Detection Prevalence : 0.952
Balanced Accuracy : 0.751
'Positive' Class : FALSE
# Fitting and evaluating a regression model ------------------------------------
# Step 0: Load packages-----------
library(tidyverse) # Load tidyverse for dplyr and tidyr
library(caret) # For ML mastery
# Step 1: Load and Clean, and Explore Training data ----------------------
# I'll use the mpg dataset from the dplyr package in this example
# no need to load an external dataset
data_train <- read_csv("1_Data/mpg_train.csv")
# Convert all characters to factor
# Some ML models require factors
data_train <- data_train %>%
mutate_if(is.character, factor)
# Explore training data
data_train # Print the dataset
View(data_train) # Open in a new spreadsheet-like window
dim(data_train) # Print dimensions
names(data_train) # Print the names
# Step 2: Define training control parameters -------------
# In this case, I will set method = "none" to fit to
# the entire dataset without any fancy methods
# such as cross-validation
train_control <- trainControl(method = "none")
# Step 3: Train model: -----------------------------
# Criterion: hwy
# Features: year, cyl, displ, trans
# Regression
hwy_glm <- train(form = hwy ~ year + cyl + displ + trans,
data = data_train,
method = "glm",
trControl = train_control)
# Look at summary information
summary(hwy_glm)
# Step 4: Access fit ------------------------------
# Save fitted values
glm_fit <- predict(hwy_glm)
# Define data_train$hwy as the true criterion
criterion <- data_train$hwy
# Regression Fitting Accuracy
postResample(pred = glm_fit,
obs = criterion)
# RMSE Rsquared MAE
# 3.246182 0.678465 2.501346
# On average, the model fits are 2.8 away from the true
# criterion values
# Step 5: Visualise Accuracy -------------------------
# Tidy competition results
accuracy <- tibble(criterion = criterion,
Regression = glm_fit) %>%
gather(model, prediction, -criterion) %>%
# Add error measures
mutate(se = prediction - criterion,
ae = abs(prediction - criterion))
# Calculate summaries
accuracy_agg <- accuracy %>%
group_by(model) %>%
summarise(mae = mean(ae)) # Calculate MAE (mean absolute error)
# Plot A) Scatterplot of criterion versus predictions
ggplot(data = accuracy,
aes(x = criterion, y = prediction, col = model)) +
geom_point(alpha = .2) +
geom_abline(slope = 1, intercept = 0) +
labs(title = "Predicting mpg$hwy",
subtitle = "Black line indicates perfect performance")
# Plot B) Violin plot of absolute errors
ggplot(data = accuracy,
aes(x = model, y = ae, fill = model)) +
geom_violin() +
geom_jitter(width = .05, alpha = .2) +
labs(title = "Distributions of Fitting Absolute Errors",
subtitle = "Numbers indicate means",
x = "Model",
y = "Absolute Error") +
guides(fill = FALSE) +
annotate(geom = "label",
x = accuracy_agg$model,
y = accuracy_agg$mae,
label = round(accuracy_agg$mae, 2))
| File | Rows | Columns |
|---|---|---|
| college_train.csv | 1000 | 21 |
college_train
data are taken from the College
dataset in the ISLR
package. They contain statistics for a large number of US Colleges from the 1995 issue of US News and World Report.college_train
| Name | Description |
|---|---|
Private |
A factor with levels No and Yes indicating private or public university. |
Apps |
Number of applications received. |
Accept |
Number of applications accepted. |
Enroll |
Number of new students enrolled. |
Top10perc |
Pct. new students from top 10% of H.S. class. |
Top25perc |
Pct. new students from top 25% of H.S. class. |
F.Undergrad |
Number of fulltime undergraduates. |
P.Undergrad |
Number of parttime undergraduates. |
Outstate |
Out-of-state tuition. |
Room.Board |
Room and board costs. |
Books |
Estimated book costs. |
Personal |
Estimated personal spending. |
PhD |
Pct. of faculty with Ph.D.’s. |
Terminal |
Pct. of faculty with terminal degree. |
S.F.Ratio |
Student/faculty ratio. |
perc.alumni |
Pct. alumni who donate. |
Expend |
Instructional expenditure per student. |
Grad.Rate |
Graduation rate. |
| Package | Installation |
|---|---|
tidyverse |
install.packages("tidyverse") |
caret |
install.packages("caret") |
| Function | Package | Description |
|---|---|---|
trainControl() |
caret |
Define modelling control parameters |
train() |
caret |
Train a model |
predict(object, newdata) |
base |
Predict the criterion values of newdata based on object |
postResample() |
caret |
Calculate aggregate model performance in regression tasks |
confusionMatrix() |
caret |
Calculate aggregate model performance in classification tasks |


# File vendor/plugins/acts_as_taggable_on_steroids/generators/acts_as_taggable_migration/templates/migration.rb, line 2 def self.up create_table :tags do |t| t.column :name, :string end create_table :taggings do |t| t.column :tag_id, :integer t.column :taggable_id, :integer # You should make sure that the column created is # long enough to store the required class names. t.column :taggable_type, :string t.column :created_at, :datetime end add_index :taggings, :tag_id add_index :taggings, [:taggable_id, :taggable_type] end


CHAPTER 251
RENTAL MOTOR VEHICLE, TOUR VEHICLE, AND CAR-SHARING VEHICLE SURCHARGE TAX
Section
251-1 Definitions
251-2 Rental motor vehicle and tour vehicle surcharge tax
251-2.5 Car-sharing vehicle surcharge tax
251-3 Certificate of registration
251-4 Return and payments; penalties
251-5 Remittances
251-6 Annual return
251-7 Filing of returns
251-8 Assessment of surcharge tax upon failure to make
return; limitation period; exceptions; extension by
agreement
251-9 Overpayment; refunds
251-10 Appeals
251-11 Records to be kept; examination
251-12 Disclosure of returns unlawful; destruction of
returns
251-13 Collection by suit; injunction
251-14 Application of surcharge tax
251-15 Administration and enforcement; rules
251-16 Repealed
Note
Chapter heading amended by L 2014, c 110, §4.


Source code for hypertools.tools.describe
#!/usr/bin/env python
##PACKAGES##
from __future__ import division
from builtins import range
import warnings
import numpy as np
from scipy.spatial.distance import pdist
import scipy.spatial.distance as sd
import matplotlib.pyplot as plt
import seaborn as sns
from .reduce import reduce as reducer
from .._shared.helpers import format_data, memoize
[docs]def describe(x, reduce='IncrementalPCA', max_dims=None, show=True):
"""
Create plot describing covariance with as a function of number of dimensions
This function correlates the raw data with reduced data to get a sense
for how well the data can be summarized with n dimensions. Useful for
evaluating quality of dimensionality reduced plots.
Parameters
----------
x : Numpy array, DataFrame or list of arrays/dfs
A list of Numpy arrays or Pandas Dataframes
reduce : str or dict
Decomposition/manifold learning model to use. Models supported: PCA,
IncrementalPCA, SparsePCA, MiniBatchSparsePCA, KernelPCA, FastICA,
FactorAnalysis, TruncatedSVD, DictionaryLearning, MiniBatchDictionaryLearning,
TSNE, Isomap, SpectralEmbedding, LocallyLinearEmbedding, and MDS. Can be
passed as a string, but for finer control of the model parameters, pass
as a dictionary, e.g. reduce={'model' : 'PCA', 'params' : {'whiten' : True}}.
See scikit-learn specific model docs for details on parameters supported
for each model.
max_dims : int
Maximum number of dimensions to consider
show : bool
Plot the result (default : true)
Returns
----------
result : dict
A dictionary with the analysis results. 'average' is the correlation
by number of components for all data. 'individual' is a list of lists,
where each list is a correlation by number of components vector (for each
input list).
"""
warnings.warn('When input data is large, this computation can take a long time.')
def summary(x, max_dims=None):
# if data is a list, stack it
if type(x) is list:
x = np.vstack(x)
# if no max dims are specified, compute for all of them
if max_dims is None:
max_dims = x.shape[1]
# correlation matrix for all dimensions
alldims = get_pdist(x)
corrs=[]
for dims in range(2, max_dims):
reduced = get_pdist(reducer(x, ndims=dims, reduce=reduce))
corrs.append(get_corr(alldims, reduced))
del reduced
return corrs
# common format
x = format_data(x, ppca=True)
# if max dims is not set, make it the length of the minimum number of columns
if max_dims is None:
max_dims = np.min([xi.shape[1] for xi in x])
# a dictionary to store results
result = {}
result['average'] = summary(x, max_dims)
result['individual'] = [summary(x_i, max_dims) for x_i in x]
# if show, plot it
if show:
fig, ax = plt.subplots()
ax = sns.tsplot(data=result['individual'], time=[i for i in range(2, max_dims)], err_style="unit_traces")
ax.set_title('Correlation with raw data by number of components')
ax.set_ylabel('Correlation')
ax.set_xlabel('Number of components')
plt.show()
return result
@memoize
def get_corr(reduced, alldims):
return np.corrcoef(alldims, reduced)[0][1]
@memoize
def get_pdist(x):
return pdist(x, 'correlation')


FAIR Maturity Indicator - Collections
Status
Title
Description
Organization
Contact
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Execute
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{{collection.Organization}}


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On trial: Lives of Literature The Library has a new resource on trial: Lives of Literature – available until 30 November 2018
Sunday 21 October: Strathfield after hours space OPEN UPDATE: The after-hours facility at Strathfield Library will be open as normal on Sunday 21 October, from 8am-5pm.
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Spaced repetition systems are great tools. Sparesys minimizes time spent on the system.
This way you can get more return on your time investment.
Public courses - Private copies
You can create your own stacks of cards just for yourself. If you feel like it,
you can make them publically accessible. When you enroll in a public course, you
work on your local copy. You can add cards or edit them as you wish.
Sparesys learns about your learning
Some things are easier to remember than others. Why waste time repeating things often
that are easy to remember anyway? For public courses, sparesys keeps track on what
people remember easily and asks these cards less often.
You schedule your learning
You have to know your stack at a certain date? Tell sparesys, and it will optimize
your learning so that you succeed.


Russia to return seized naval boats to Ukraine on Nov 18
Russia's FSB has confirmed the naval vessels are being towed to a location agreed with the Ukrainian side for their handover.
Ukraine's tugboat Yany Kapu and two armored naval boats Berdyansk and Nikopol, seized by Russia near the Kerch Strait in November 2018, will be returned to Ukraine on Monday, October 18, according to the press service of the so-called "Russia's FSB Border Department in Crimea."
The FSB has confirmed the three naval vessels are being towed to a location agreed with the Ukrainian side for their handover, which will take place on November 18, Russia's Interfax news outlet reported on November 17, referring to the press service.
The transfer of vessels will take place in the neutral waters of the Black Sea. However, details of the procedure have not been reported, the publication said.
The Odesa-based Dumskaya news outlet earlier reported the transfer of ships would take place at high seas some 60 nautical miles from Odesa, near Cape Tarkhankut.
According to Dumskaya, Ukraine's Sapfir ("Sapphire"), Titan, and Haidamaka tugboats are now heading from the ports of Pivdenny, Chornomorsk, and Odesa to pick up the boats off the coast of Cape Tarkhankut.
As UNIAN reported earlier, the return of Ukrainian naval vessels was set to take place before the Normandy Four summit.
Personal weapons of Ukrainian crew, as well as radios and logs containing information on the crew and other data, will remain in Russia, the Kommersant outlet reported.


AIR FORCE ONE (1997) A film review by Steve Kong Copyright 1997 Steve Kong
On the surface Air Force One looks like a formula film, "Die Hard on a plane." But, as the movie starts, we see that it is different, though not much, from the formula.
Harrison Ford is the President of the US, James Marshall. And as the name somewhat suggest, he is somewhat like a cowboy.
In a team effort between the US and Russia, the leader of rebellious Kazakhstan is captured. The President is in Moscow giving a speech and taking a stand against terrorism. After the speech, the President boards Air Force One and this is where the fun starts.
The movie opens with the capturing of the Kazakhstan leader. This sequence opens the film with a bang, and sets the mood for the rest of the film. The film is packed with action, and surprisingly, most of it is well done and takes place on Air Force One. This is surprising because of the tight quarters that the surrounds the action. Some sequences that are of the wow-factor are the refueling plane, the many gunfights, the C-140 sequence, and the sequences with the F-15s.
As the plane is being hijacked, the President is put into an escape pod and ejected. But this would be too easy of a solution. The President, with the motivation to save his wife and daughter, stays on the plane, unbeknownst to the terrorists.
Gary Oldman plays the terrorist. He is a hard communist and wants to reinstate Communism to Russia. He and a team of men take over Air Force One and threaten the Vice President (Glenn Close) and Defense Secretary (Dean Stockwell). Their threat is that if the leader of Kazakhstan is not released, then one hostage will be eliminated each half-hour. The story moves quickly, and the action is spectacular. There are some minor details that bugged me, but not much. The terrorists are near blind and the President has a great shot. The load of weapons on Air Force One is incredible and somewhat easily accessible. And for one minor character we never do learn his motivation.
Ford, like always, is a joy to watch. He gives a great performance in Air Force One. Oldman does a good job at his part also. He is as much, and maybe a little more intense than Ford. Wolfgang Petersen, who directed Clint Eastwood in The Line of Fire, does a great job of building tension and creating dazzling action. There is one character, which I won't name, that is like the wrench in the wheel. And this character lives almost to the end of the movie. All of this time making you wonder what the character is going to do. I loved how this character was used, although sparingly, in the film.
Air Force One is a late comer to the summer action movie territory, but it makes up for this by being one of the most exciting films of this summer. The action in Air Force One never really goes into the yeah-right category, but is always in the wow-and-ouch category. Air Force One is a must-see movie. And when you walk out of the theatre, you too will be saying like me, "I'm proud to be an American!" -- steve kong<EMAIL_ADDRESS>spy on me at: http://mookie.relay.net/mookie/steve-cam.html movie reviews: http://hardboiled.home.ml.org/
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| Package | Description |
|---|---|
| ca.ualberta.cmput301f14t16.easya.Model | |
| ca.ualberta.cmput301f14t16.easya.Model.Data | |
| ca.ualberta.cmput301f14t16.easya.View |
| Modifier and Type | Method and Description |
|---|---|
java.util.List<QuestionList> |
MainModel.getAllQuestions() |
java.util.List<QuestionList> |
MainModel.getAllSavedQuestions() |
java.util.List<QuestionList> |
MainModel.getAllUserFavourites() |
java.util.List<QuestionList> |
MainModel.getAllUserQuestions() |
Question |
MainModel.getQuestionById(java.lang.String id) |
User |
MainModel.getUserByEmail(java.lang.String email) |
User |
MainModel.getUserById(java.lang.String id) |
| Modifier and Type | Method and Description |
|---|---|
java.util.List<QuestionList> |
Cache.getAllQuestions()
Updates the Cache with
QuestionList objects from the elastic
search database. |
java.util.List<QuestionList> |
Cache.getAllUserFavourites()
Acquires all
Question objects from the elastic search database
marked as favourite by the current user, and addes them to the Cache. |
java.util.List<QuestionList> |
Cache.getAllUserFavouritesFromCache()
Gets a list of
QuestionList objects containing all questions
marked as favourite by the user. |
java.util.List<QuestionList> |
Cache.getAllUserQuestions() |
Question |
Cache.getQuestionById(java.lang.String id)
Returns a
Question from the Cache with an ID matching the one
provided. |
Question |
Cache.getQuestionFromCache(java.lang.String id)
Returns a
Question from the Cache with an ID matching the one
provided. |
java.util.List<QuestionList> |
Cache.getQuestionListFromQuestionsCache() |
java.util.List<Question> |
Cache.getSavedQuestions() |
java.util.List<QuestionList> |
Cache.getSavedQuestionsList() |
User |
PMClient.getUser() |
User |
Cache.getUserByEmail(java.lang.String email)
Updates a
User object in the Cache from its entry in the elastic
search database. |
User |
Cache.getUserById(java.lang.String id)
Returns a
User from the Cache with an ID matching the one
provided. |
private User |
Cache.getUserFromCache(java.lang.String id)
Returns a
User from the Cache with an ID matching the one
provided. |
private java.util.List<QuestionList> |
Cache.getUserQuestionsFromCache(User u) |
| Modifier and Type | Method and Description |
|---|---|
private void |
QuestionViewAdapter.inflateAnswer(Answer a)
Populates the view with the data from the
Answer provided. |
private void |
QuestionViewAdapter.inflateQuestion()
Populates the view with the data from the
Question . |


Lightweight. Unobtrusive. Style-agnostic. Build your look on the web, not Twitter's – and build it fluid.
Layers CSS is a CSS framework aimed for practical use cases.  Check out v. 1.2 changelog and GitHub.
Columns are stacked from left to right, no wrappers are required. Mark the .last
column of the row, and use .clear
after rows (or .clear-after
the parent) if needed. Columns within columns work just fine. Include your customized breakpoints to progressively enhance the column layouts to fit each screen size.
Note! Browsers have to do rounding with fractional column/gutter widths, so you can't always control fluid grid per pixel.
Use fractional keywords as class names to control the width of each column, like this:
.column.half
.column.half.last
.column.third
.column.third
.column.third.last
.column.fourth
.column.fourth
.column.fourth
.column.fourth.last
You can also use traditional n/12 keywords to control widths:
.column.twelve
.column.six
.column.three
.column.three
.one
.column.two
.column.four
.column.five.last
You can .push-
columns to shift them further from their default position:
.column.three.push-one
.column.three.push-two.last
You can also start stacking from the .right
:
.column.right.five
.column.right.three.push-one
.reset
columns to remove gutter space:
.column.half.reset
.column.three.reset
.column.three.reset
.column.three.reset.push-one
.column.four.reset
.column.three.reset
column.half.push-fourth.reset
One part gets a fixed width, the other side fills the rest. Very handy for combining images with a fluid grid. Multiple combinations can be nested. The fluid column always comes first in markup.
You can adjust the size of the fixed-width in steps with media queries, or break the combination completely.
.column.fluid > .column-content
.column.fixed
You can start stacking this from the .right
as well:
.column.fluid.right > .column-content
.column.fixed.right
Use this CSS to adjust the width of the fixed column (default is 10em
):
/*Fixed column width*/
.column.fixed {
width: 10em;
}
.column.fluid > .column-content {
margin-right: 10em;
}
.column.fluid.right > .column-content {
margin-left: 10em;
}
The columns we used in the previous samples never break. We can extend them to present different layouts in viewports larger than specific breakpoints (which you can customize in the download section).
Here, users see 2 columns by default, 3 + 1 on .small-
screens and 4 on .large-
screens:
.column.half.small-third.medium-fourth
.column.half.last.small-third.medium-fourth
.column.half.small-third.small-last.medium-fourth
.column.half.last.small-full.medium-fourth.medium-last
Remember to mark the .last-
column for each screen size when you're not .resetting
gutters. You can also mark the .first-
column of each row if it changes between screen sizes.
Note! Don't skip levels: if you use .small-
and .large-
, also include .medium-
so the columns work as expected.
We can make quite complicated arrangements without nested columns or parents:
.column.small-third.medium-full.large-half
.column.small-twothirds.small-last.medium-half.large-fourth
.column.medium-half.medium-last.large-fourth.large-last
You can also .-break
columns (but this can't be undone on further levels):
.column.reset.fourth.medium-third.large-break
.column.reset.fourth.medium-twothirds.medium-last.large-break
.column.reset.half.last.medium-break
.rows
are also available. .row-content
is aligned to the center of its parent, and controlling its max-width results in a layout that scales down automatically but doesn't get too wide on large screens.
The default max-width of .row-content
is 70em
, but here it's set to 40em for clarity:
.row-content > .column.half
.row-content > .column.half.last
.buffer
gives the content some breathing room:
.row-content.buffer > .column.half
.row-content.buffer > .column.half.last
By default, .buffer
has less padding on the top (as seen above). This works well for natural article content, but you can use .even
buffers, too:
How
is it
going?
You can naturally use any element (like section
for example) for the rows. Use rows to build both the overall frame of your site and smaller pieces of page content.
Psst! You can automatically use these as mixins in LESS.
.clear
.clear-after
.hidden
display: none;
).dry
.border-box
, .content-box
.block
, .inline-block
, .inline
.absolute
, .fixed
, .relative
, .static
.keep-left
, .keep-right
, .keep-center
margin: auto
to keep it centeredYou can mark elements .hidden-under-small
, .hidden-over-medium
and so on with the progressive enhancements, according to your breakpoint settings.
Use a combination of em
and %
definitions to give your elements natural breathing room.
.reset
.squeeze
.push
Directions work as modifiers:
.reset-top, .reset-bottom
.squeeze-top
.push-top
.buffer
.no-buffer
Directions work as modifiers:
.buffer-left.buffer-right
.no-buffer-top
Sometimes you need containers that are not too wide even when there's screen space. limit-
classes come with your custom-set breakpoints and do just this. Multiple limit classes can be used, and they will work progressively.
They can be applied to .row-content
elements or used together with .keep-center
to keep the container centered on the screen.
.limit-small.keep-center
.limit-small.limit-medium.limit-large.keep-center
Unordered list: ul
Ordered list: ol
Definition list: dl
ul
/ol.plain
dl.plain
ul
/ol
/dl.inline
keeps the inline context. Whitespace works accordingly.
ul.inline.center
dl.inline.right
You can .collapse
lists to make them stack horizontally with block list items. .right
works, too. Very handy for menus:
Grey backgrounds added for clarity.
Default table
has collapsed borders, 100 % width and common-sense paddings for cells.
Grey backgrounds and borders added for clarity.
| Title | Title | Title | Title |
|---|---|---|---|
| Cell | Cell | Cell | Cell |
| Cell | Cell | Cell | Cell |
table.squeeze
has smaller cell paddings.
| Title | Title | Title | Title |
|---|---|---|---|
| Cell | Cell | Cell | Cell |
table.plain
collapses everything.
| Title | Title | Title | Title |
|---|---|---|---|
| Cell | Cell | Cell | Cell |
Use direction modifiers for th
and td
to change cell alignments. Text alignment defaults to left, vertical aligmnent to top.
| default | .center |
.right |
.middle (or .vertical-center ) |
.middle.center |
.middle.right |
.bottom |
.bottom.center |
.bottom.right |
Native input element styles are left alone. Input fields use the same, default box-sizing model as all other elements in all browsers so they're easy to customize.
.plain
elements are robbed of their OS-specific default styles, so they're a good jumping-off point to your own visuals.
Remember, .buffer
gives elements breathing room.
iframes
have 100 % width and lack borders by default, by the way.


On the bottom right corner, you will see a turquoise menu circle. This is fully functional and we have great
hopes for it in the future. This little circle can take over or entirely replace the nav bar on our
homepage!
We've added a google custom search bar to one of our experimental sub-pages. Feel free to visit


How do you adjust the check period and grace period for a task?
On the 'My Checks' page in the checks table, click on time in the periods colum. A modal will popup where you can adjust the period and grace period for the task.
How do you setup a check locally after creating it on healthchecks?
On the 'My Checks' page, hover over the fields in the Ping URL column, a alink to copy will appear at the end of the check URL. Clicking on the link will copy the check URL to the clipboard which you can then paste in you cron job
How do you delete/remove or pause a check ?
On the 'My Checks' page, hover over the row representing a check. A settings button appears in the last column which allows you to pause , remove or evn view the logs of the task.


--- layout: post status: publish published: true title: Asshat of the Year Nominee author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 1222 wordpress_url: http://ivyenvy.com/?p=1222 date: '2009-08-13 18:18:02 +0000' date_gmt: '2009-08-14 00:18:02 +0000' categories: - Wrigley Field - Cubs fans tags: - Asshat of the Week - Cubs fans - Douchebag - Wrigley Field - Twenty Sports Grille - Asshat of the Year - Shane Victorino - Loveable Losers - August - Guy throwing beer at Cubs game comments: - id: 359 author: Andy author_email<EMAIL_ADDRESS>author_url: http:// date: '2009-08-14 13:16:30 +0000' date_gmt: '2009-08-14 19:16:30 +0000' content: Well, Corey, I will be there, so I don't think you can completely deliver on the non-douchebag promise. But I do promise to not throw beer on anyone, well that is until the Cubs lose to the Natinals. - id: 360 author: Kurt author_email<EMAIL_ADDRESS>author_url: '' date: '2009-08-14 14:55:05 +0000' date_gmt: '2009-08-14 20:55:05 +0000' content: To clarify, I'm more of a post-98 Cubs fan, but thanks for the compliment. Most of my early days of fandom were spent with the same kind of d-bags in my dorm at ISU. - id: 361 author: Corey author_email<EMAIL_ADDRESS>author_url: http:// date: '2009-08-14 16:04:27 +0000' date_gmt: '2009-08-14 22:04:27 +0000' content: "I'm sorry Kurt, I don't mean to swipe 5 years of bad baseball from you. \ \r\n\r\nIt doesn't matter when you started following the Cubs, you are a good fan, my friend."  Of course, I'm speaking of the guy in the front row in the white shirt. The second he threw the beer at Shane Victorino, I was completely embarrassed to consider myself a Cubs fan. That photo right there shows exactly what people think about when they hear "Cubs fans". We are no longer the loyal supporters of the Loveable Losers, in their eyes. People think that Cubs fans are massive douchebags hanging out at the only bar in the world that holds 40,000 people.
I'm not saying that everyone that started following the Cubs after 2003 are idiots. Kurt is the best post-2003 fan I know. But the vast majority of them are like that little cross-section in the photo.   Yeah, it's a hot August evening in Chicago, but do your arms really sweat that much?
The fact is, Bartman acted without thinking and did something that many, if not most, Cubs fans would do. What this guy did was not a reaction. It was an aggressive douchey thing he did to make his frat buddies laugh.
 It was embarrassing to be a Cubs fan. It showed a lack of judgement. I miss those days.
In case you haven't heard, come hang out with us non-douchebag Cubs fans at Twenty Sports Grille in Moline on August 25th during the Cubs vs. Nationals game. We promise that a guy won't throw beer on you.


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Analyze any text!
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Hello, I'm Lilly! I was born in Tacoma, Washington and moved here to Cedar Rapids at the age of three. Ceramic art is my passion and my old pitbull Todo is my constant companion. My main goal now is to start a career in computer programming.
I've worked many different jobs including:


CodeChef College Chapters
CodeChef College Chapters are the programming clubs run and maintained by the official chapter leaders and mentored by CodeChef. They aim to make learning competitive programming accessible for students across the globe.
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