International Survey On Emotion Antecedents And Reactions (ISEAR) Short description Over a period of many years during the 1990s, a large group of psychologists all over the world collected data in the ISEAR project, directed by Klaus R. Scherer and Harald Wallbott. Student respondents, both psychologists and non-psychologists, were asked to report situations in which they had experienced all of 7 major emotions (joy, fear, anger, sadness, disgust, shame, and guilt). In each case, the questions covered the way they had appraised the situation and how they reacted. The final data set thus contained reports on seven emotions each by close to 3000 respondents in 37 countries on all 5 continents.
The following publications describe the procedures and report the major patterns of results: Wallbott, H.G., & Scherer, K. R. (1986). How universal and specific is emotional experience? Social Science Information, 24, 763-795. Matsumoto, D., Kudoh, T., Scherer, K. R., & Wallbott, H.G. (1988). Antecedents of and reactions to emotions in the US and Japan. Journal of Cross-Cultural Psychology, 19, 267-286. Wallbott, H.G., & Scherer, K. R. (1988). Emotion and economic development - Data and speculations concerning the relationships between economic factors and emotional experience. European Journal of Social Psychology, 18, 267-273. Scherer, K. R., & Wallbott, H.G. (1994). Evidence for universality and cultural variation of differential emotion response patterning. Journal of Personality and Social Psychology, 66, 310-328. Scherer, K. R. (1997). Profiles of emotion-antecedent appraisal: testing theoretical predictions across cultures. Cognition and Emotion, 11, 113-150. Scherer, K. R. (1997). The role of culture in emotion-antecedent appraisal. Journal of Personality and Social Psychology, 73, 902-922. Mikula, G., Scherer, K. R., & Athenstaedt, U. (1998). The role of injustice in the elicitation of differential emotional reactions. Personality and Social Psychology Bulletin, 24(7), 769-783.
For 16 of the 38 countries we were able to obtain transcriptions and English translations of the situation descriptions that the participants reported by writing a short text in their language in the space provided in the questionnaire. These data descriptions can be found in the last column of the Access file. All other information, i.e., background data, and the appraisal and reaction variables, are contained in the columns before that. The meaning of the variables can be lloked up in the following Word document: ISEAR Questionnaire & Codebook.doc This file contains brief explanations of what the variable names mean. In particular, a copy of the questionnaire is reproduced and the variable names, as well as the labels of the different values, are provided in direct correspondence to the questionnaire items. The complete quantitative data set for all of the approx. 3000 participants is provided in the form of an SPSS .sav data file ISEAR SPSS Data File (in the form of a .zip archive) Again, the variable names are explained in the Word document mentioned above. There is one row for each participant. In the first columns you will find the background variables, followed by seven times the set of variables, once for each emotion. Thus the variable name ints3_1 refers to the felt intensity for emotion 3 (anger) while ints5_1 refers to the intensity of emotion 5 (disgust), etc. We hope that this information is sufficient to find one's way through the data set. We would like to again acknowledge our thanks to all the many collaborators who have helped to gather this impressive and unique data set. They all have the status of co-authors of the papers, although, of course, the author line is not sufficiently long to hold all the names. We would like to reproduce the list of all collaborators on this webpahe to render homage to the time and effort that was spent by all of these colleagues to collect these data, often under difficult circumstances (it should be recalled that the world looked differently when the study was run! Which, by the way, should be considered in interpreting the data.)
Elisha Babad, Hebrew University of Jerusalem, Israel; Eva Baenninger-Huber, University of Zurich, Switzerland; Cleve Barlow, University of Auckland, New Zealand; Marek Cielecki, University of Warsaw, Poland; Cindy Gallois, University of Queensland, Australia; Jo Kleiven, Oppland Regional College, Norway; Jacques Cosnier, University II of Lyon, and Monique Alles-Jardel, University of the Provence, Aix-en-Provence, France; Britt-Marie Drottz, University of Göteburg, Sweden; Heiner Ellgring, Free University of Berlin, Germany; Alfonso Jimenez- Fernandez and Jose Miguel Fernandez-Dols, Autonoma University of Madrid, Spain (J.M. Fernandez-Dols especially for his help in collecting the data in Costa Rica, Honduras, Mexico, Guatemala, Venezuela, Chile, and El Salvador: Within these countries we extend our thanks to: Mirta Gonzalez (Costa Rica), Otto E. Gilbert (Guatemala), Isabel de Villanueva (Honduras), Rolando Diaz-Loving (Mexico), Ignacio Martin (El Salvador), Angelica Gonzalez and Gonzalo Zaror (Chile), and Pedro R. Rodriguez (Venezuela); Tsutomu Kudoh, University of Osaka, Japan; Hing-Keung Ma, Chinese University of Hong Kong, Hong Kong; David Matsumoto, University of California, Berkeley, USA; Silvia Maurer-Lane and Silvia Friedman, Catholic University of Sao Paulo, Brazil; Alastair Mundy- Castle, University of Lagos, Nigeria; Rauni Myllyniemi, University of Helsinki, Finland; Usha S. Naidu, Tata Institute of Social Sciences, Bombay, India; Vladimir Nesic, University of Nis, Yugoslavia; S. Nyandia-Bundy and R.P. Bundy, University of Zimbabwe, Zimbabwe; Robert F. Norton, American University of Beirut, Lebanon; Dimitra Papadopoulou and D. Markoulis, University of Thessaloniki, Greece; Karl Peltzer, University of Malawi, Malawi; Catherine Peng, University of Oxford, England, who collected the data in the People's Republic of China; Pio Ricci-Bitti and Dino Giovannini, University of Bologna, Italy; Luis Soczka and Constanza Paul, University of Porto, Portugal; Velina Topalova, Bulgarian Academy of Sciences, Bulgaria; Ad Vingerhoets and J. Hendriksen, Catholic University of Nijmegen, Netherlands; H. Wallbott, University of Giessen (now University of Salzburg); C. Westenholz-Bless, University of Zambia, Zambia. |


DPM: Federal welfare assistance to be credited by Nov 10
PUTRAJAYA, Nov 5 — The federal welfare assistance payments this month will be credited to the accounts of 484,828 recipients throughout the country from November 7 and is expected to be finalised by or before November 10, said Deputy Prime Minister Datuk Seri Dr Wan Azizah Wan Ismail.
She said the delayed payments were caused by technical issues over the procedure of transferring the money to the agencies.
“The allocation has been approved, but not yet credited into the accounts... it is time, but there was a technical glitch. The ministry apologises,” Dr Wan Azizah who is also women, family and community development minister, told reporters after delivering a keynote address at the Malaysia’s Halal Inclusion Roundtable and Masterclass here today
In a statement on Friday, Social Welfare Director Zulkifli Ismail said the late payments this month were due to technical problems.
Dr Wan Azizah said the ministry had improved the procedure to ensure that the problem did not happen again. — Bernama


[CHAPTER 334B]
UTILIZATION REVIEW AND MANAGED CARE OF MENTAL HEALTH,
ALCOHOL, OR DRUG ABUSE TREATMENT
Section
334B-1 Purpose
334B-2 Definitions
334B-3 Standards for review agents
334B-4 Waiver
334B-5 Confidentiality
334B-6 Penalty for violation
334B-7 Appeal by aggrieved party
334B-8 Annual report
Cross References
Diversion program for chemically dependent nurses, see chapter 334D.
Health care professionals, see chapter 451D.
Health planning and resources development and health care cost control, see chapter 323D.
Mental health and alcohol and drug abuse treatment insurance benefits, see chapter 431M.
Rights of recipients of mental health services, see chapter 334E.


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About Me
Why programming?
I made the decision to enroll in a coding bootcamp in order to gain a new skillset and initiate a transition in my career path. I feel that I have hit a bit of a ceiling in the roles I find myself employed in and am ready for a change.


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SSH and File transfer
Given your credentials via email, you can now create a ssh session to connect to your server at code.engineering.nyu.edu.
SSH Clients
If you are using macOS, the built in terminal will do, but iTerm is the recommended terminal emulator. If using Windows, you can use PuTTY, or MobaXterm.
Open up iTerm, and you should see the terminal window pop up:
Your username is your netid
. Hit enter and when the prompt comes, enter the password
. If successful, you will be dropped into a bash shell (More info about bash here). If you are adept, you can do everything straight from this shell. There are useful and powerful text editors avaialable on the server like vim. If you want to use something else not on the server, feel free to email me.
In your home directory, you should see a www
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File Transfer
If you are on macOS, FileZilla works great. If you are on Windows, MobaXterm actually comes with a sftp client which pops up when you start an ssh session.
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July 25
Therefore we ought to give the more earnest heed to the things which we have
heard, lest at any time we should let them slip.
Hebrews 2:1
Something very serious has been happening to the Body of Christ. We've often let the things which we have heard, the message of faith and righteousness, slip.
I'm not just talking about those believers who've been so surrounded by religion that they've never heard about the power that's theirs in Christ Jesus. I'm talking about those of us who have heard the Word and have known the thrill of living by faith.
In my own life, for example, there were times when I'd pray for healing. I'd say all the right words, "Thank God, I believe I receive..." and all that. But inside I'd be thinking, "I wonder why God isn't healing me?"
You see, even though I'd said that I believed I was healed, I hadn't really. I'd staggered at the promise of God, believing what my body told me instead of the Word of God.
I don't care how long you've been a believer or how long you've been practicing the principles of faith. You can easily slip into unbelief about the promises of God. And, when you do, it will cost you dearly.
"But if slipping into unbelief is soeasy," you may say, "how can we avoid it?" Hebrews 4:11 tells us: "Let us labour therefore toenterinto that rest, lest any man fall after the same example of unbelief."
We must labor! Not by working with our hands and feet or struggling to get God to do something, but by spending time in God's Word--hanging onto the promises of God by faith day after day. We must labor by hearkening to the Word and refusing to let it slip.
Don't get casual about the Word. Don't make the mistake of thinking, "Oh I know all that faith stuff. I know how to receive my healing."
Instead, dig more deeply in the Word than ever. Labor! Be diligent to keep your faith from slipping--and you won't have to fall!
Scripture Reading: Hebrews 10:23-39


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|
R for Data Science Basel R Bootcamp |
adapted from trueloveproperty.co.uk
In this practical, you’ll practice grouping and analysing data with dplyr
By the end of this practical you will know how to:
BaselRBootcamp
R project. It should already have the folders 1_Data
and 2_Code
. Make sure that the data files listed in the Datasets
section above are in your 1_Data
folder.# Done!
## NAME
## DATE
## Analysing Practical
Save it as a new file called analysing_practical.R
in the 2_Code
folder.
Using library()
load the tidyverse
and broom
packages for this practical listed in the Functions section above. If you don’t have them installed, you’ll need to install them, see the Functions tab above for installation instructions.
# Load packages
library(tidyverse)
library(broom)
library(tidyverse)
library(broom)
kc_house.csv
data. This dataset contains house sale prices for King County, Washington. It includes homes sold between May 2014 and May 2015. Using the following template, load the data into R and store it as a new object called kc_house
.kc_house <- read_csv(file = "XX/XX")
print()
, dim()
, summary()
, head()
, and View()
, explore the data to make sure it was loaded correctly.kc_house
# A tibble: 21,613 x 21
id date price bedrooms bathrooms sqft_living sqft_lot
<chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
1 7129… 2014-10-13 00:00:00 2.22e5 3 1 1180 5650
2 6414… 2014-12-09 00:00:00 5.38e5 3 2.25 2570 7242
3 5631… 2015-02-25 00:00:00 1.80e5 2 1 770 10000
4 2487… 2014-12-09 00:00:00 6.04e5 4 3 1960 5000
5 1954… 2015-02-18 00:00:00 5.10e5 3 2 1680 8080
6 7237… 2014-05-12 00:00:00 1.23e6 4 4.5 5420 101930
7 1321… 2014-06-27 00:00:00 2.58e5 3 2.25 1715 6819
8 2008… 2015-01-15 00:00:00 2.92e5 3 1.5 1060 9711
9 2414… 2015-04-15 00:00:00 2.30e5 3 1 1780 7470
10 3793… 2015-03-12 00:00:00 3.23e5 3 2.5 1890 6560
# … with 21,603 more rows, and 14 more variables: floors <dbl>,
# waterfront <dbl>, view <dbl>, condition <dbl>, grade <dbl>,
# sqft_above <dbl>, sqft_basement <dbl>, yr_built <dbl>,
# yr_renovated <dbl>, zipcode <dbl>, lat <dbl>, long <dbl>,
# sqft_living15 <dbl>, sqft_lot15 <dbl>
dim(kc_house)
[1] 21613 21
summary(kc_house)
id date price
Length:21613 Min. :2014-05-02 00:00:00 Min. : 75000
Class :character 1st Qu.:2014-07-22 00:00:00 1st Qu.: 321950
Mode :character Median :2014-10-16 00:00:00 Median : 450000
Mean :2014-10-29 04:38:01 Mean : 540088
3rd Qu.:2015-02-17 00:00:00 3rd Qu.: 645000
Max. :2015-05-27 00:00:00 Max. :7700000
bedrooms bathrooms sqft_living sqft_lot
Min. : 0.0 Min. :0.00 Min. : 290 Min. : 520
1st Qu.: 3.0 1st Qu.:1.75 1st Qu.: 1427 1st Qu.: 5040
Median : 3.0 Median :2.25 Median : 1910 Median : 7618
Mean : 3.4 Mean :2.11 Mean : 2080 Mean : 15107
3rd Qu.: 4.0 3rd Qu.:2.50 3rd Qu.: 2550 3rd Qu.: 10688
Max. :33.0 Max. :8.00 Max. :13540 Max. :1651359
floors waterfront view condition
Min. :1.00 Min. :0.000 Min. :0.00 Min. :1.00
1st Qu.:1.00 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:3.00
Median :1.50 Median :0.000 Median :0.00 Median :3.00
Mean :1.49 Mean :0.008 Mean :0.23 Mean :3.41
3rd Qu.:2.00 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:4.00
Max. :3.50 Max. :1.000 Max. :4.00 Max. :5.00
grade sqft_above sqft_basement yr_built
Min. : 1.00 Min. : 290 Min. : 0 Min. :1900
1st Qu.: 7.00 1st Qu.:1190 1st Qu.: 0 1st Qu.:1951
Median : 7.00 Median :1560 Median : 0 Median :1975
Mean : 7.66 Mean :1788 Mean : 292 Mean :1971
3rd Qu.: 8.00 3rd Qu.:2210 3rd Qu.: 560 3rd Qu.:1997
Max. :13.00 Max. :9410 Max. :4820 Max. :2015
yr_renovated zipcode lat long
Min. : 0 Min. :98001 Min. :47.2 Min. :-122
1st Qu.: 0 1st Qu.:98033 1st Qu.:47.5 1st Qu.:-122
Median : 0 Median :98065 Median :47.6 Median :-122
Mean : 84 Mean :98078 Mean :47.6 Mean :-122
3rd Qu.: 0 3rd Qu.:98118 3rd Qu.:47.7 3rd Qu.:-122
Max. :2015 Max. :98199 Max. :47.8 Max. :-121
sqft_living15 sqft_lot15
Min. : 399 Min. : 651
1st Qu.:1490 1st Qu.: 5100
Median :1840 Median : 7620
Mean :1987 Mean : 12768
3rd Qu.:2360 3rd Qu.: 10083
Max. :6210 Max. :871200
head(kc_house)
# A tibble: 6 x 21
id date price bedrooms bathrooms sqft_living sqft_lot
<chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
1 7129… 2014-10-13 00:00:00 2.22e5 3 1 1180 5650
2 6414… 2014-12-09 00:00:00 5.38e5 3 2.25 2570 7242
3 5631… 2015-02-25 00:00:00 1.80e5 2 1 770 10000
4 2487… 2014-12-09 00:00:00 6.04e5 4 3 1960 5000
5 1954… 2015-02-18 00:00:00 5.10e5 3 2 1680 8080
6 7237… 2014-05-12 00:00:00 1.23e6 4 4.5 5420 101930
# … with 14 more variables: floors <dbl>, waterfront <dbl>, view <dbl>,
# condition <dbl>, grade <dbl>, sqft_above <dbl>, sqft_basement <dbl>,
# yr_built <dbl>, yr_renovated <dbl>, zipcode <dbl>, lat <dbl>,
# long <dbl>, sqft_living15 <dbl>, sqft_lot15 <dbl>
# View(kc_house)
kc_house
data with names()
.names(kc_house)
[1] "id" "date" "price" "bedrooms"
[5] "bathrooms" "sqft_living" "sqft_lot" "floors"
[9] "waterfront" "view" "condition" "grade"
[13] "sqft_above" "sqft_basement" "yr_built" "yr_renovated"
[17] "zipcode" "lat" "long" "sqft_living15"
[21] "sqft_lot15"
rename()
.| New Name | Old Name |
|---|---|
| living_sqft | sqft_living |
| lot_sqft | sqft_lot |
| above_sqft | sqft_above |
| basement_sqft | sqft_basement |
| built_yr | yr_built |
| renovated_yr | yr_renovated |
living_sqm
, above_sqm
and basement_sqm
which show the respective room sizes in square meters rather than square feet (Hint: Multiply each by 0.093).kc_house <- kc_house %>%
mutate(living_sqm = living_sqft * 0.093,
XXX = XXX * XXX,
XXX = XXX * XXX)
kc_house <- kc_house %>%
mutate(living_sqm = living_sqft * 0.093,
above_sqm = above_sqft * 0.093,
basement_sqm = basement_sqft * 0.093)
total_sqm
which is the sum of living_sqm
, above_sqm
and basement_sqm
kc_house <- kc_house %>%
mutate(total_sqm = living_sqm + above_sqm + basement_sqm)
mansion
which is “Yes” when total_sqm
is greater than 750, and “No” when total_sqm
is less than or equal to 750.kc_house <- kc_house %>%
mutate(XXX = case_when(XXX > XXX ~ "XXX",
XXX <= XXX ~ "XXX"))
kc_house <- kc_house %>%
mutate(mansion = case_when(
total_sqm > 750 ~ "Yes",
total_sqm <= 750~ "No"))
table(kc_house$mansion)
. You should find 751 mansions and 20862 non-mansions!df$col
notation, calculate the mean price of all houses.mean(XXX$XXX)
mean(kc_house$price)
[1] 540088
summarise()
using the following template. Do you get the same answer? What is different about the output from summarise()
versus using the dollar sign?kc_house %>%
summarise(
price_mean = mean(XXX)
)
kc_house %>%
summarise(
price_mean = mean(price)
)
# A tibble: 1 x 1
price_mean
<dbl>
1 540088.
median()
function!kc_house %>%
summarise(
price_median = median(price)
)
# A tibble: 1 x 1
price_median
<dbl>
1 450000
max()
function!kc_house %>%
summarise(
price_max = max(price)
)
# A tibble: 1 x 1
price_max
<dbl>
1 7700000
kc_house <- kc_house %>%
arrange(desc(XXX))
kc_house
kc_house <- kc_house %>%
arrange(desc(price))
kc_house
# A tibble: 21,613 x 26
id date price bedrooms bathrooms living_sqft lot_sqft
<chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
1 6762… 2014-10-13 00:00:00 7.70e6 6 8 12050 27600
2 9808… 2014-06-11 00:00:00 7.06e6 5 4.5 10040 37325
3 9208… 2014-09-19 00:00:00 6.88e6 6 7.75 9890 31374
4 2470… 2014-08-04 00:00:00 5.57e6 5 5.75 9200 35069
5 8907… 2015-04-13 00:00:00 5.35e6 5 5 8000 23985
6 7558… 2015-04-13 00:00:00 5.30e6 6 6 7390 24829
7 1247… 2014-10-20 00:00:00 5.11e6 5 5.25 8010 45517
8 1924… 2014-06-17 00:00:00 4.67e6 5 6.75 9640 13068
9 7738… 2014-08-15 00:00:00 4.50e6 5 5.5 6640 40014
10 3835… 2014-06-18 00:00:00 4.49e6 4 3 6430 27517
# … with 21,603 more rows, and 19 more variables: floors <dbl>,
# waterfront <dbl>, view <dbl>, condition <dbl>, grade <dbl>,
# above_sqft <dbl>, basement_sqft <dbl>, built_yr <dbl>,
# renovated_yr <dbl>, zipcode <dbl>, lat <dbl>, long <dbl>,
# sqft_living15 <dbl>, sqft_lot15 <dbl>, living_sqm <dbl>,
# above_sqm <dbl>, basement_sqm <dbl>, total_sqm <dbl>, mansion <chr>
summarise()
.kc_house %>%
summarise(mil_p = mean(XXX > 1000000))
kc_house %>%
summarise(mil_p = mean(price > 1000000))
# A tibble: 1 x 1
mil_p
<dbl>
1 0.0678
filter()
to only select mansions!)kc_house %>%
filter(mansion == "Yes") %>%
summarise(
floors_mean = mean(floors),
bathrooms_mean = mean(bathrooms)
)
# A tibble: 1 x 2
floors_mean bathrooms_mean
<dbl> <dbl>
1 1.92 3.68
group_by()
to group the dataset by the mansion
column, then use the n()
function to count the number of cases.kc_house %>%
group_by(XXX) %>%
summarise(XXX = n())
kc_house %>%
group_by(mansion) %>%
summarise(N = n())
# A tibble: 2 x 2
mansion N
<chr> <int>
1 No 20862
2 Yes 751
summarise()
function!kc_house %>%
group_by(mansion) %>%
summarise(N = n(),
XXX = XXX(XXX))
kc_house %>%
group_by(mansion) %>%
summarise(N = n(),
price_mean = mean(price))
# A tibble: 2 x 3
mansion N price_mean
<chr> <int> <dbl>
1 No 20862 504024.
2 Yes<PHONE_NUMBER>.
group_by()
and summarise()
, create a dataframe showing the same results as the following table.| mansion | N | price_min | price_mean | price_median | price_max |
|---|---|---|---|---|---|
| No | 20862 | 75000 | 504024 | 441000 | 3100000 |
| Yes | 751 | 404000 | 1541915 | 1300000 | 7700000 |
built_yr
, and then calculate the mean number of living square meters. Be sure to also include the number of houses built in each year!kc_house %>%
group_by(built_yr) %>%
summarise(N = n(),
living = mean(living_sqm))
# A tibble: 116 x 3
built_yr N living
<dbl> <int> <dbl>
1 1900 87 161.
2 1901 29 164.
3 1902 27 179.
4 1903 46 140.
5 1904 45 149.
6 1905 74 183.
7 1906 92 168.
8 1907 65 177.
9 1908 86 158.
10 1909 94 177.
# … with 106 more rows
kc_house %>%
mutate(built_decade = floor(built_yr / 10)) %>%
group_by(built_decade) %>%
summarise(XX = XX,
XX = XX(XX))
brumhilda
that only contains data from houses in those zipcode (hint: use filter()
combined with the %in%
operator as follows:brumhilda <- kc_house %>%
filter(XXX %in% c(XXX, XXX, XXX, XXX))
brumhilda <- kc_house %>%
filter(zipcode %in% c(98001, 98109, 98117, 98199))
mean()
and median()
selling price (as well as the number of houses) in each zip code.brumhilda %>%
group_by(zipcode) %>%
summarise(price_mean = mean(price),
price_median = median(price),
N = n())
# A tibble: 4 x 4
zipcode price_mean price_median N
<dbl> <dbl> <dbl> <int>
1 98001 280805. 260000 362
2 98109 879624. 736000 109
3 98117 576795. 544000 553
4 98199 791821. 689800 317
mansion
(as well as zipcode
), and calculating the same summary statistics as before.brumhilda %>%
group_by(zipcode, mansion) %>%
summarise(price_mean = mean(price),
price_median = median(price),
N = n())
# A tibble: 8 x 5
# Groups: zipcode [4]
zipcode mansion price_mean price_median N
<dbl> <chr> <dbl> <dbl> <int>
1 98001 No 277589. 260000 359
2 98001 Yes 665667. 637000 3
3 98109 No 833528. 730500 106
4 98109 Yes 2508333. 2900000 3
5 98117 No 575626. 543000 551
6 98117 Yes<PHONE_NUMBER>50 2
7 98199 No 753625. 675000 305
8 98199 Yes 1762618. 1425000 12
brumhilda %>%
group_by(zipcode) %>%
summarise(price_mean = mean(price),
price_median = median(price),
floors_min = min(floors),
floors_max = max(floors),
N = n())
# A tibble: 4 x 6
zipcode price_mean price_median floors_min floors_max N
<dbl> <dbl> <dbl> <dbl> <dbl> <int>
1 98001 280805. 260000 1 2.5 362
2 98109 879624. 736000 1 3 109
3 98117 576795. 544000 1 3 553
4 98199 791821. 689800 1 3 317
t.test()
function and assign the result to waterfront_htest
. Fill in the XXXs in the code below, such that the dependent variable is price
and the independent variable is waterfront
waterfront_htest <- t.test(formula = XXX ~ XXX,
data = XXX)
waterfront_htest <- t.test(formula = price ~ waterfront,
data = kc_house)
waterfront_htest
object to see a printout of the main results.waterfront_htest
Welch Two Sample t-test
data: price by waterfront
t = -10, df = 200, p-value <2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1303662 -956963
sample estimates:
mean in group 0 mean in group 1
531564 1661876
waterfront_htest
object with names()
.names(waterfront_htest)
[1] "statistic" "parameter" "p.value" "conf.int" "estimate"
[6] "null.value" "alternative" "method" "data.name"
$
, print the test statistic (statistic
) from your waterfront_htest
object.waterfront_htest$statistic
t
-12.9
$
, print only the p-value (p.value
) from the object.waterfront_htest$p.value
[1] 1.38e-26
bedrooms
, bathrooms
, living_sqm
, waterfront
, lat
and long
best predict a house’s price (price
)? Answer this by conducting a regression analysis using the glm()
function and assign the result to price_glm
. Fill in the XXXs in the code below.price_glm <- glm(formula = XXX ~ XXX + XXX + XXX + XXX + XXX + XXX,
data = XXX)
price_glm <- glm(formula = price ~ bedrooms + bathrooms + living_sqm + waterfront + lat + long,
data = kc_house)
price_glm
object. What do you see?price_glm
Call: glm(formula = price ~ bedrooms + bathrooms + living_sqm + waterfront +
lat + long, data = kc_house)
Coefficients:
(Intercept) bedrooms bathrooms living_sqm waterfront
-65680578 -46146 17422 3157 790264
lat long
675209 -275008
Degrees of Freedom: 21612 Total (i.e. Null); 21606 Residual
Null Deviance: 2.91e+15
Residual Deviance: 1.09e+15 AIC: 594000
price_glm
object with names()
.names(price_glm)
[1] "coefficients" "residuals" "fitted.values"
[4] "effects" "R" "rank"
[7] "qr" "family" "linear.predictors"
[10] "deviance" "aic" "null.deviance"
[13] "iter" "weights" "prior.weights"
[16] "df.residual" "df.null" "y"
[19] "converged" "boundary" "model"
[22] "call" "formula" "terms"
[25] "data" "offset" "control"
[28] "method" "contrasts" "xlevels"
$
print the coefficients from your analysis.price_glm$coefficients
(Intercept) bedrooms bathrooms living_sqm waterfront lat
-65680578 -46146 17422 3157<PHONE_NUMBER>09
long
-275008
summary()
function, show summary results from your price_glm
object.summary(price_glm)
Call:
glm(formula = price ~ bedrooms + bathrooms + living_sqm + waterfront +
lat + long, data = kc_house)
Deviance Residuals:
Min 1Q Median 3Q Max
-1563708 -116127 -14138 84523 4180382
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.57e+07 1.41e+06 -46.51 < 2e-16 ***
bedrooms -4.61e+04 2.05e+03 -22.53 < 2e-16 ***
bathrooms 1.74e+04 3.07e+03 5.67 1.4e-08 ***
living_sqm 3.16e+03 2.95e+01 107.17 < 2e-16 ***
waterfront 7.90e+05 1.79e+04 44.14 < 2e-16 ***
lat 6.75e+05 1.12e+04 60.20 < 2e-16 ***
long -2.75e+05 1.14e+04 -24.15 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 5.06e+10)
Null deviance: 2.9129e+15 on 21612 degrees of freedom
Residual deviance: 1.0943e+15 on 21606 degrees of freedom
AIC: 594064
Number of Fisher Scoring iterations: 2
$residuals
element in your price_glm
object contains the residuals (difference from the predicted and true dependent variable values). Add this vector as a new column called residuals
to your kc_house
object using mutate()
kc_house <- kc_house %>%
mutate(residuals = price_glm$residuals)
ggplot(data = kc_house,
aes(x = residuals)) +
geom_histogram(col = "white") +
labs(title = "Residual regression")
ggplot(data = kc_house,
aes(x = residuals)) +
geom_histogram(col = "white") +
labs(title = "Residual regression")
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
kc_house %>%
summarise(resid_mean = mean(residuals))
# A tibble: 1 x 1
resid_mean
<dbl>
1 -0.000000151
residuals_abs
which shows the absolute value of the residuals (hint: use the abs()
function).kc_house <- kc_house %>%
mutate(residuals_abs = abs(price_glm$residuals))
ggplot(data = kc_house,
aes(x = residuals_abs)) +
geom_histogram(col = "white") +
labs(title = "Residual regression")
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
kc_house %>%
summarise(resid_abs_mean = mean(residuals_abs))
# A tibble: 1 x 1
resid_abs_mean
<dbl>
1 144018.
# On average, the model's fitted values are off by 144,018. So the model isn't terribly accurate on average.
mean()
, then sort the data in descending order) with arrange()
, then select the first row with slice()
. Once you find it, try searching for that zipcode on Google Maps and see if its location makes sense!kc_house %>%
group_by(zipcode) %>%
summarise(waterfront_p = mean(waterfront)) %>%
arrange(desc(waterfront_p)) %>%
slice(1)
# A tibble: 1 x 2
zipcode waterfront_p
<dbl> <dbl>
1 98070 0.203
price_to_living
that takes price / living_sqm
. Then, sort the data in descending order of this variable, and select the first row with slice()
! What id value do you get?kc_house %>%
mutate(price_to_living = price / living_sqm) %>%
arrange(desc(price_to_living)) %>%
slice(1)
# A tibble: 1 x 29
id date price bedrooms bathrooms living_sqft lot_sqft
<chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
1 6021… 2015-04-07 00:00:00 874950 2 1 1080 4000
# … with 22 more variables: floors <dbl>, waterfront <dbl>, view <dbl>,
# condition <dbl>, grade <dbl>, above_sqft <dbl>, basement_sqft <dbl>,
# built_yr <dbl>, renovated_yr <dbl>, zipcode <dbl>, lat <dbl>,
# long <dbl>, sqft_living15 <dbl>, sqft_lot15 <dbl>, living_sqm <dbl>,
# above_sqm <dbl>, basement_sqm <dbl>, total_sqm <dbl>, mansion <chr>,
# residuals <dbl>, residuals_abs <dbl>, price_to_living <dbl>
kc_house %>%
group_by(zipcode) %>%
summarise(price_mean = mean(price)) %>%
arrange(desc(price_mean)) %>%
slice(1:10)
# A tibble: 10 x 2
zipcode price_mean
<dbl> <dbl>
1 98039 2160607.
2 98004 1355927.
3 98040 1194230.
4 98112 1095499.
5 98102 901258.
6 98109 879624.
7 98105 862825.
8 98006 859685.
9 98119 849448.
10 98005 810165.
| built_yr | N | price_mean | price_max | living_sqm_mean |
|---|---|---|---|---|
| 1990 | 320 | 563966 | 3640900 | 234 |
| 1991 | 224 | 630441 | 5300000 | 244 |
| 1992 | 198 | 548169 | 2480000 | 223 |
| 1993 | 202 | 556612 | 3120000 | 226 |
| 1994 | 249 | 486834 | 2880500 | 209 |
| 1995 | 169 | 577771 | 3200000 | 224 |
| 1996 | 195 | 639534 | 3100000 | 240 |
| 1997 | 177 | 606058 | 3800000 | 234 |
| 1998 | 239 | 594159 | 1960000 | 241 |
kc_house %>%
filter(built_yr >= 1990 & built_yr < 1999) %>%
group_by(built_yr) %>%
summarise(N = n(),
price_mean = mean(price),
price_max = max(price),
living_sqm_mean = mean(living_sqm)) %>%
knitr::kable(digits = 0)
| built_yr | N | price_mean | price_max | living_sqm_mean |
|---|---|---|---|---|
| 1990 | 320 | 563966 | 3640900 | 234 |
| 1991 | 224 | 630441 | 5300000 | 244 |
| 1992 | 198 | 548169 | 2480000 | 223 |
| 1993 | 202 | 556612 | 3120000 | 226 |
| 1994 | 249 | 486834 | 2880500 | 209 |
| 1995 | 169 | 577771 | 3200000 | 224 |
| 1996 | 195 | 639534 | 3100000 | 240 |
| 1997 | 177 | 606058 | 3800000 | 234 |
| 1998 | 239 | 594159 | 1960000 | 241 |
living_glm
predicting the amount of living space (living_sqm
) as a function of bedrooms
, bathrooms
, waterfront
, and built_yr
. Explore the object with names()
and summary()
. Which variables seem to predict living space?living_glm <- glm(formula = living_sqm ~ bedrooms + bathrooms + waterfront + built_yr,
data = kc_house)
names(living_glm)
[1] "coefficients" "residuals" "fitted.values"
[4] "effects" "R" "rank"
[7] "qr" "family" "linear.predictors"
[10] "deviance" "aic" "null.deviance"
[13] "iter" "weights" "prior.weights"
[16] "df.residual" "df.null" "y"
[19] "converged" "boundary" "model"
[22] "call" "formula" "terms"
[25] "data" "offset" "control"
[28] "method" "contrasts" "xlevels"
summary(living_glm)
Call:
glm(formula = living_sqm ~ bedrooms + bathrooms + waterfront +
built_yr, data = kc_house)
Deviance Residuals:
Min 1Q Median 3Q Max
-705.7 -32.0 -4.9 25.7 566.1
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 219.6403 27.7140 7.93 2.4e-15 ***
bedrooms 23.1621 0.4532 51.11 < 2e-16 ***
bathrooms 71.3214 0.6294 113.31 < 2e-16 ***
waterfront 62.5121 4.1476 15.07 < 2e-16 ***
built_yr -0.1297 0.0143 -9.09 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 2750)
Null deviance: 157675161 on 21612 degrees of freedom
Residual deviance: 59420739 on 21608 degrees of freedom
AIC: 232503
Number of Fisher Scoring iterations: 2
chisq.test()
function allows you to do conduct a chi square test testing the relationship between two nominal variables. Look at the help menu to see how the function works. Then, conduct a chi-square test to see if there is a relationship between whether a house is on the waterfront and the grade of the house. Do houses on the waterfront tend to have higher (or lower) grades than houses not on the waterfront?# First look at a table
table(kc_house$waterfront, kc_house$grade)
1 3 4 5 6 7 8 9 10 11 12 13
0 1 3 29 238 2026 8958 6028 2590 1106 379 79 13
1 0 0 0 4 12 23 40 25 28 20 11 0
chisq.test(table(kc_house$waterfront, kc_house$grade))
Pearson's Chi-squared test
data: table(kc_house$waterfront, kc_house$grade)
X-squared = 300, df = 10, p-value <2e-16
# Analysing with dplyr and tidyr ---------------------------
library(tidyverse) # Load tidyverse for dplyr
library(broom) # For tidy results
library(rsq) # For r-squared values
# Load baselers data
baselers <- read_csv("1_Data/baselers.txt")
# No grouping variables
bas <- baselers %>%
summarise(
age_mean = mean(age, na.rm = TRUE),
income_median = median(income, na.rm = TRUE),
N = n()
)
# One grouping variable
bas_sex <- baselers %>%
group_by(sex) %>%
summarise(
age_mean = mean(age, na.rm = TRUE),
income_median = median(income, na.rm = TRUE),
N = n()
)
bas_sex
# Two grouping variables
bas_sex_ed <- baselers %>%
group_by(sex, education) %>%
summarise(
age_mean = mean(age, na.rm = TRUE),
income_median = median(income, na.rm = TRUE),
N = n()
)
# Advanced scoping
# Calculate mean of ALL numeric variables
baselers %>%
group_by(sex, education) %>%
summarise_if(is.numeric, mean, na.rm = TRUE)
# Examples of hypothesis tests on the diamonds -------------
# First few rows of the diamonds data
diamonds
# 2-sample t- test ---------------------------
# Q: Is there a difference in the carats of color = E and color = I diamonds?
htest_B <- t.test(formula = carat ~ color, # DV ~ IV
alternative = "two.sided", # Two-sided test
data = diamonds, # The data
subset = color %in% c("E", "I")) # Compare Diet 1 and Diet 2
htest_B # Print result
# Regression ----------------------------
# Q: Create regression equation predicting price by carat, depth, table, and x
price_glm <- glm(formula = price ~ carat + depth + table + x,
data = diamonds)
# Print coefficients
price_glm$coefficients
# Tidy version
tidy(price_glm)
# Extract R-Squared
rsq(price_glm)
| File | Rows | Columns | Description |
|---|---|---|---|
| kc_house.csv | 21613 | 21 | House sale prices for King County between May 2014 and May 2015. |
First 5 rows and 5 columns of kc_house.csv
| id | date | price | bedrooms | bathrooms |
|---|---|---|---|---|
|<PHONE_NUMBER> | 2014-10-13 | 7700000 | 6 | 8.00 |
|<PHONE_NUMBER> | 2014-06-11 | 7062500 | 5 | 4.50 |
|<PHONE_NUMBER> | 2014-09-19 | 6885000 | 6 | 7.75 |
|<PHONE_NUMBER> | 2014-08-04 | 5570000 | 5 | 5.75 |
|<PHONE_NUMBER> | 2015-04-13 | 5350000 | 5 | 5.00 |
| Package | Installation | Description |
|---|---|---|
tidyverse |
install.packages("tidyverse") |
A collection of packages for data science, including dplyr , ggplot2 and others |
broom |
install.packages("broom") |
Provides ‘tidy’ outputs from statistical objects. |
rsq |
install.packages("rsq") |
Returns r-squared statistics from glm objects. |
Wrangling
| Function | Package | Description |
|---|---|---|
rename() |
dplyr |
Rename columns |
select() |
dplyr |
Select columns based on name or index |
filter() |
dplyr |
Select rows based on some logical criteria |
arrange() |
dplyr |
Sort rows |
mutate() |
dplyr |
Add new columns |
case_when() |
dplyr |
Recode values of a column |
group_by(), summarise() |
dplyr |
Group data and then calculate summary statistics |
left_join() |
dplyr |
Combine multiple data sets using a key column |
Statistical Tests
| Function | Package | Hypothesis Test |
|---|---|---|
glm() , lm() |
base |
Generalized linear model and linear model |
t.test() |
base |
One and two sample t-test |
wilcox.test() |
base |
Wilcoxon test |
cor.test() |
base |
Correlation test |
chisq.test |
base |
Chi-square tests for frequency tables |
Other
| Function | Package | Description |
|---|---|---|
tidy() |
broom |
Converts statistical objects to ‘tidy’ dataframes of key results |
rsq() |
rsq |
Returns r-squared statistics from glm objects |
dplyr
vignetteSee the full dplyr vignette with lots of wrangling tips and tricks.
from R Studio


DCSync from RODCs?
It’s important to note that
we can’t DCSync
from a RODC since they don’t replicate data (technically they do replicate a few attributes, but are not meant to be replication partners).


Before you can save a subscription you need to preview the data that the report contains.
Look at the report-data in the reportfile, if it contained the expected data, answer "Yes" below, otherwise please answer "No" and correct the report parameters and preview again before saving the subscription.


Twindle
A better reading experience of twitter threads
Coming Soon. Please sign up to know more!!!
What does twindle offer ?
Twindle (Twitter + Kindle = Twindle) is an open source project made for the purpose of converting long Twitter threads into ePub's or PDF documents to allow users to read longscroll threads on a kindle or as a PDF on their device of choice.


- Administrator
- Industrial Engineer
- Software Engineer
- Lawyer
Name
Age
Profession
Experience
Telephone
Name
Age
Profession
Years Experience
Image
Telephone


Elastic Load Balancing V2 AuthenticateOidcConfig
The
AuthenticateOidcConfig
property type specifies request parameters when using an identity provider (IdP)
that is compliant with OpenID Connect (OIDC) to authenticate users.
AuthenticateOidcConfig
is a property of the Elastic Load Balancing V2 Action property
type.
Syntax
To declare this entity in your AWS CloudFormation template, use the following syntax:
JSON
{ "AuthenticationRequestExtraParams" :
{
, "AuthorizationEndpoint" :String
:String, ...
}String
, "ClientId" :String
, "ClientSecret" :String
, "Issuer" :String
, "OnUnauthenticatedRequest" :String
, "Scope" :String
, "SessionCookieName" :String
, "SessionTimeout" :Number
, "TokenEndpoint" :String
, "UserInfoEndpoint" :String
}
YAML
AuthenticationRequestExtraParams:
{
AuthorizationEndpoint:String
:String, ...
}String
ClientId:String
ClientSecret:String
Issuer:String
OnUnauthenticatedRequest:String
Scope:String
SessionCookieName:String
SessionTimeout:Number
TokenEndpoint:String
UserInfoEndpoint:String
Properties
AuthenticationRequestExtraParams
-
The query parameters (up to 10) to include in the redirect request to the authorization endpoint.
Required: No
Type: Map of string-to-string values
Update requires: No interruption
AuthorizationEndpoint
-
The authorization endpoint of the IdP. This must be a full URL, including the HTTPS protocol, the domain, and the path.
Required: Yes
Type: String
Update requires: No interruption
ClientId
-
The OAuth 2.0 client identifier.
Required: Yes
Type: String
Update requires: No interruption
ClientSecret
-
The OAuth 2.0 client secret.
Required: Yes
Type: String
Update requires: No interruption
Issuer
-
The OIDC issuer identifier of the IdP. This must be a full URL, including the HTTPS protocol, the domain, and the path.
Required: Yes
Type: String
Update requires: No interruption
OnUnauthenticatedRequest
-
The behavior if the user is not authenticated. The following are possible values:
-
deny
: Return an HTTP 401 Unauthorized error. -
allow
: Allow the request to be forwarded to the target. -
authenticate
: Redirect the request to the IdP authorization endpoint. This is the default value.
Required: No
Type: String
Update requires: No interruption
-
Scope
-
The set of user claims to be requested from the IdP. The default is
openid
.To verify which scope values your IdP supports and how to separate multiple values, see the documentation for your IdP.
Required: No
Type: String
Update requires: No interruption
SessionCookieName
-
The name of the cookie used to maintain session information. The default is
AWSELBAuthSessionCookie
.Required: No
Type: String
Update requires: No interruption
SessionTimeout
-
The maximum duration of the authentication session, in seconds. The default is 604800 seconds (7 days).
Required: No
Type: Number
Update requires: No interruption
TokenEndpoint
-
The maximum duration of the authentication session, in seconds. The default is 604800 seconds (7 days).
Required: Yes
Type: String
Update requires: No interruption
UserInfoEndpoint
-
The maximum duration of the authentication session, in seconds. The default is 604800 seconds (7 days).
Required: Yes
Type: String
Update requires: No interruption
See Also
-
AuthenticateOidcActionConfig in the Elastic Load Balancing API Reference version 2015-12-01


{% extends "layout.html" %} {% block page_title %} Money Claims Prototype {% endblock %} {% block proposition_header %} {% set my_account="2" %} {% include "includes/propositional_navigation.html" %} {% endblock %} {% block content %} {% include "includes/phase_banner_beta.html" %} Back Do you owe the claimant money? Yes, £1,500 Yes, a lower amount How much do you owe them? No, I don't owe them anything {% endblock %}


Fill Your Details
Personal information:
Ticket No:
Full name:
Train No:
Source:
Destination:
Phone No :
(enter the phone no. that you have with you at present)
Current Location:
Next Halt:
Complaint Details:
Type of Crime:
fight
murder
attempt to murder
crime against women other than rape
rape
kidnapping
robbery
drugging
riot
crime breech of trust
fight
other
other:
Crime Deatils(do specify necessary details eg. no. of person,reason behind crime etc):


--- title: 'Data Science with R: how do I start?' author: Jesse M date: '2018-01-03' slug: data-science-with-r-how-do-i-start categories: - data science - education - online learning - R - R4DS tags: - data science - education - online learning - R - R4DS ---
It always starts with a DM on Twitter, where someone shares with me their personal data science ambitions, where they currently are in their plans, and then they follow up with a request for me to help them figure out where to go next.
I love these messages—they’re an affirmation that the R community continues to grow and attract new members in part by creating a welcoming and supportive space for beginners, and that our community members are deemed approachable (enough) for someone brand new to R to reach out!
These messages used to be infrequent enough that I would spend time writing a tailored response to each individual, but over time the frequency has increased to a point where I can’t respond with as much thought and attention to detail as I would like. Rather than offer up a generic response, I’ve created this post for you, the data science beginner that wants to learn R!
I get it—we all have our favorite resources! This list is by no means meant to be comprehensive at all. It’s intended to be very biased towards the resources that I’ve personally used, but that doesn’t mean they’re the only resources out there!
You’ll also notice that this list leaves out things like Machine Learning and Artificial Intelligence, and that’s intentional. This list is aimed at someone literally starting out with data science and R—ML/AI will be there for them later on in their learning path.
I had a couple of amazing Chemistry professors in undergrad, and I will never forget how one of them phrased learning:
The first time you encounter a piece of information is like going to an open house. You don’t know if you’re going to rent that house, buy that house, let alone if you’re even going to like that house.
The next few times you encounter that same information is like renting a house—you’re committed for a relatively short amount of time, but you’re not necessarily in it for the long haul. You don’t own it, and you can’t really make any changes.
Once you’ve really learned something, you’ve bought the house. It’s yours. You can knock down walls and landscape the yard and you don’t have to ask permission to do so, because you own the house.
When you first encounter something in data science and/or R, it’s 100% OK to forget it 20 minutes later. At first blush, you don’t know if you’ll ever need this information again! So why go through an online course or a textbook once, expecting to extract all of the information?
Instead, try getting comfortable with the fact that you’re going to forget a lot of things when you first start out—but the more that you read and re-read and learn and practice and apply what you’ve been learning, the more you’re going to remember.
It’s so easy to use perfection as procrastination—you don’t have the correct plan for documenting your learning, you don’t have the right color pen, the music is too loud, whatever. These are all excuses.
So what if you don’t document every moment of your learning process [on a personal blog that you created using blogdown](https://bookdown.org/yihui/blogdown/? So what if your notes are scattered in a couple of different notebooks and half-used GitHub repos? Who cares if you even take notes?!
These are all potentially life-altering decisions that I am in no way qualified to help you answer. These are, however, great conversations to have with people who know you well.
You have something to say—you’re alive on this planet commanding a tiny metal box to do your data science bidding, and that’s awesome. Now tell the world!


Quick Jump: Starting the Game | Ones | BS | Calzone | Exact Bids | Show & Roll | Ludacris Speed | League Scoring
Liar's dice, also known as Perudu, can be played with two or more players. For a game to be considered sanctioned by the L.I.A.R. (League of Incrandulous Argumentative Rollers) at least four players must be playing with a maximum of six. Seven players are allowed in the event of a majority vote. At least half of the players (rounded down for odd numbers) must be league players in order for the game to be sanctioned.
Each player starts with a cup and five dice
To see who goes first, all players roll their dice in the open. The player with the most sixes rolled goes first. In the event of a tie, ones act as a tie breaker. If no ones are rolled between tied players, or the same number of ones is rolled, those players will each re-roll to start the process again. The starting player has the choice to start the round clockwise or counter clockwise.
When multiple games are played in one sitting, the previous game's winner goes first by default, and the round must transition around the table in the opposite direction of the previous game.
Starting the game:
Each player shakes the cup and then upends it onto the table using it to conceal his/her dice from other players.
Having looked at his/her dice the first player makes a call based on how many dice of a certain number there may be under all the cups. He/she has to bear in mind 2 factors:
1. The total quantity of dice on the table e.g if there are six players (total of thirty dice) the law of averages says that there should at least five of any number; five sixes, for example. The odds for the other numbers are more or less the same. Dependent on what you have in your hand can dictate what you 'bid'. So if you have three fives that means that you could call heavy on the fives. But then of course you can lie!
2. All 'ones' are wild so can be used as any number. This factor along with the open license to bluff distorts all the 'laws of averages' and opens the door further to skill, guile and a big slice of luck. This also means, that the average number of dice is about the total number of dice divided by 3. So with 30 dice, there are likely at least 10 sixes, meaning you think there are AT LEAST 10 dice that are either ones or sixes combined.
Once the call is made the next player can do one of two things. He/she can either accept the bid and make and increasing bid or end the round with a call of BS or Calzone; to be explained shortly. If the bid is accepted (deemed plausible) the player must raise the call by either calling the same quantity of a higher number or a greater quantity of any number. For example, if a bid of 10 fives is made, the following bid could be 10 sixes (same quantity, higher number) or 11 of any number. (Back to Top)
Ones:
Because 'ones' are wild, it means that they help every bid but no other numbers help a bid of ones. For this reason, if a player wishes to bid on ones, they may bid half the quantity of the previous bid of another number. So if 10 sixes are bid, the following player might choose to bid 5 ones. You must round up for odd numbers, so a bid of 11 of some number would require a following bid of at least 6 ones if ones are to be bid on.
If the person who bids before you makes a bid on ones, such as 5 ones, and you wish to increase the bid, you may obviously bid a number higher than 5 ones. This could be 6 or more ones, or 11 of ANY other number. To bid a number other than ones, after ones have been bid, you must double the number of ones bid and add one. Looking at the example above, 5 ones are bid so you would double five to get 10 and add 1 to get 11 of whatever number you wish to bid. As noted above, this only applies if you wish to increase the bid to a number other than ones in this scenario. (Back to Top)
BS:
If you disagree with the bid made before you, you can yell "BS". This immediately ends the game and all dice must be revealed. The total number of dice matching the persons bid, and all ones, are counted. If there are less then the number of dice bid, then you were right and the player who made the bid loses the difference. For example, if the bid was 10 sixes, and the total actual number of sixes AND ones combined was only 8, that player would lose 2 dice. If you are wrong and total actual number of sixes AND ones combined was 12, you would lose 2 dice. (Back to Top)
Exact bids:
Often times after BS is called on a bid, the dice will all be counted to reveal that the bid was spot on and exactly correct. When this happens, the person who said BS AND everyone else lose 1 die, with the exception of the person who made the exact bid. (Back to Top)
Calzone:
Similar to BS, yelling "calzone" also ends the game. It means you think that the bid made before you is exactly right. All players reveal their dice and the total number of dice matching the persons bid, and all ones, are counted. However, the person who made the bid is safe no matter what and cannot lose dice. If you are correct, and the bid made before you is exactly right, every other player except you and the person who made the bid, lose 1 die. If you are wrong and there are more OR less actual dice then bid, you lose the difference either way. Use calzone carefully, as it can, and often will, burn the roof of your mouth. (Back to Top)
NOTE: Calzone may not be called when there are only two people left in the game.
Show and roll:
When it's your turn to make a bid, you always have the option to show the dice you have that match the bid you're making. For example, if you bid 10 fives, you can show the other players the ones and/or fives you have under your cup by placing them in the center of the table or designated "S&R" spot. You must then re-roll the rest of your dice which may increase your odds of getting more of the number you want. These dice remain hidden, and the other dice stay in full view, and cannot be changed for the remainder of that round. You can do this on as many of your turns as you like, but it is by no means required. It simply gives the rest of the players more information, and can reduce the chances of the player next you to you calling BS on your bid. (Back to Top)
Ludacris Speed
Ludacis is a version of the game played much, much faster. All of the rules are the exact same, with one difference. When playing "Ludacris" or "Ludacris Speed", a player only has 5 seconds to make his/her bid. The scoring for League Ludacris games is adjusted to account for the added difficulty, and is described below. (Back to Top)
When playing the game
it is perfectly cromulant to lie, bluff, intimidate, insult, wildly accuse, befriend, defriend, glare at, and wink at other players.  You win and lose dice until such time that there is only one person remaining with any dice and he or she is the winner of that game.League Scoring:
Earn 1 point per person you beat in a match, lose 1 per person you lose to, i.e. if you place first in a game of 5 people, you get 4 points (because you beat 4 people) i.e. if you place third of 6 people, you get 1 point (because you beat 3 people and lost to 2) Points cannot be shared, distributed, donated, or sold.
In event of a tie, the people who tie average their points. Ludacris games only pay out points to the winner at half value. If a player forfeits, it is treated as an out at the time of the forfeit. So, if a player forfeits when there are in fourth place, they get the forth place points. Games that have a time limit of 18 minutes or under, are automatically Ludacris, unless a unanimous vote decides otherwise. A game over 18 minutes can also be ruled as Ludacris if a unanimous vote is passed by the current players. All votes must take place before game play begins.
Boom.
If you're not tying ol' yeller, I'm gonna call you no matter what. Unless you show some digits, ofcourse.
Yea...we know we took this too far.


Chaining is based on the following JavaScript syntaxical capability:
var myfunc = function() {
return {x : 10};
};
alert(myfunc().x); // 10
When you take this principle further:
var obj = {
funcA = function() {
return this;
},
funcB = function() {
return this;
},
funcC = function() {
alert(this.x);
return this;
}
x : 10
};
alert(obj.funcA().funcB().funcC().x); // alerts 10 twice
Eventually you end up with something similar to IM/jQuery:
var obj = {
elements : []
hide = function() {
for (var x = 0; x < this.elements.length; x++) {
this.elements[x].display = 'none';
}
return this;
},
};
obj.elements = [elementA, elementB];
obj.hide(); // hides both elements
How IM does it
IM has a special 'chain constructor' simple called im, that makes a new IM object and attaches all chaining functions on it. You can pass a single element, a bunch of nodes (in a array) or a CSS selector to the construtor so you have a chain that actually does things on DOM elements. Different from the short code sample above, IM stores it's elements on the chain as numbered properties, as if it was an array (but the chain is always just a plain object).
var chain = im(); // empty chain
alert(im.show); // will alert function as the chain has the whole IM chain API as properties
var spans = im('span'); // a CSS selector
var single = im(element); // a DOM element
var multi = im([elementA, elementB, elementC]); // multiple elements
multi.hide() // hide the multiple elements
/*
chains 'pretend' to be arrays, as IM stores elements on numbered properties and keeps track of
it's own length property.
*/
alert(multi.length); // alerts 3
alert(multi[0]); // alerts first element
// the safe way to get an element from the fake array:
alert(multi.el(10)); // returns undefined
// or when you want to continue chaining:
multi.item(0).hide(); // hides first element of multi chain.
While chaining, you can change the selection of nodes that you work on, and you can jump back to previous 'states' of the chain:
im('p') // get all paragraphs
.item(0).hide() // change selection to first one, and hide it
.end() // jump back to previous state: all paragraphs
.bind('click', function(){ // set an event handler
alert('hi');
})
.find('span') // find all spans within all paragraphs
.css({color : '#000'}) // and change their color
.end() // jump back to all paragraphs
.css({border : '1px solid red'}); // and give them a nice border color


Result-oriented software development agency
We do consulting, business analysis, product design and full-cycle software development. We support our customers to reach the best results in the domain.
Web & mobile apps development, digital marketing and market promotion. We work efficiently with your goals in mind.


Work Day Scheduler
A simple calendar app for scheduling your work day
9 A.M.
10 A.M.
11 A.M.
12 P.M.
1 P.M.
2 P.M.
3 P.M.
4 P.M.
5 P.M.


Custom elements Gauge displays an indicator within a range of values. Custom element Gauges can be used in a table or matrix to show the relative value of a field in a range of values in the data region, for example, as a KPI.


Some of the festivities that will be around
We want to be able to contribute to Christ kingdom through our wedding and feel like if you don't want to buy us stuff (that we may not need), then you might want to help enable us to go on missions and contribute to people in real need.
We are planning to go on a mission trip together to Northern China in 2020.
To support us, donate via the paypal link below and put a note of 'China mission'
If you do, we will keep you up to date with pictures and blogs during the mission trip.


In todays job environment,computers are a necessary part of the job.However,the visually impaired usually face problems as they have difficulty seeing the letters on the keys of the keyboard.
The "Soundboard" reads out the word keyed in by the user and notifies the user if there is a certain spelling mistake made. This enables them to be assured that they have typed in the correct words and also allows them to perform tasks like anyone else.
The "Soundboard" reads out the word that was typed everytime the user hits a punctuation key or the space bar.It has a built in dictionary that is able to identify words and alert the user if the word they typed does not exist in the dictionary.
This product will be effective in helping the visually impaired as it enables them to express themselves without having troubles seeing the letters on their keyboard. It also helps them to find jobs, most of which require working with computers. Hence, this keyboard wil be an advantage towards the visually impaired and is able to help them in their lives.


---
layout: page
title: "Projects"
subtitle: "Open Source Projects from Craig Davis and Friends"
header-img: "img/headers/saws.jpg"
---
- Subcultured: Interesting Articles and Long Form Content
- This is an experiment in aggregated content as a side effect of "read it later" apps. It's got a small group of interesting contributors.
- Gifts for Medics
- Durable and practical gifts for First Responders, Paramedics and EMTs
- Slim Framework Unit Testing Example
- Integration testing a Slim PHP site with PHP Unit and without a webserver
- Markdown Resume Builder
- Build your resume with simple Markdown tools. What started as an experiment in CSS and print styles turned into something useful!


About:Enthusiastic software engineer with 3+ years experience participating in the complete SDLC of successfully launched applications. Eager to join ABC Inc. to deliver mission-critical technology and business solutions to Fortune 500 companies and some of the most recognized brands in the world. In my previous roles, reduced downtime by 15% and warranty costs by 30%; identified and resolved a process bottleneck that reduced coding efficiency by up to 25%.
| College/University | Graduation | Year |
| NIET, NOIDA | B.E(Computer Science) | July 2015-July-2019 |
| IIT,Powai | M.tech(Computer Science) | July 2019-July 2021 |


Siyuan Qiao
Research Scientist, Google
I am currently a Research Scientist at Google. Before joining Google, I obtained my Ph.D. from the Department of Computer Science, Johns Hopkins University, advised by Bloomberg Distinguished Professor Alan Yuille. I obtained my B.Sc. degree in 2016 from Shanghai Jiao Tong University.
My research interest is computer vision with focuses on image recognition with limited data, object detection, and instance segmentation. I’ve been really fortunate to have worked with great research scientists in YITU, Baidu IDL, Adobe, and Google.
news
| Mar 2, 2022 | 2 papers accepted to CVPR 2022. |
|---|---|
| Feb 28, 2021 | 3 papers accepted to CVPR 2021. |
| Dec 10, 2020 | Check out our ViP-DeepLab on Depth-aware Video Panoptic Segmentation. |
| Jun 3, 2020 | Check out our DetectoRS: 55.7% box AP, 48.5% mask AP, and 50.0% PQ on COCO. |


a journal
read
about
submit
Inferior Planets - Issue 1
FLASHBACK II by Joshua Young
Wardrobe Department by Chuck Young
Flame by Sarah Xerta
5 Poems by Maggie Woodward
3 Poems by Stacey Teague
Sometimes a metaphor is worth dying for by Mallory Smart
3 Poems by Jeremy Radin
4 Poems by Alexis Pope
Animal by Jessica Poli
upon receiving a snake vertebra as a gift by Danielle Perry
2 Poems by Joe Nicholas
1 Poem by Luis Neer
3 Poems by Jamie Mortara
2 Poems by Christopher Morgan
Art by Roy Milburn
winter waiting ufos by C.M. Keehl
WE CAN HAVE A SECTIONAL WITH A CHAISE by Kati Goldstein
3 Poems by Zooey Ghostly
2 Poems by Kenyatta JP Garcia
2 by Carolyn DeCarlo
Dear M by Jenny MacBain-Stephens and Meg Tisinger
3 Poems by Dalton Day
3 Poems by Isabelle Davis
2 Poems by Zachary Cosby
2 Poems by Catch Business
We Made Contact by Vanessa Borjon
Invisible Backpacks by Tyler Barton
4 Poems by Raul Alvarez
VOID BLESS by June Altamura


--- title: Instant Answers and Other Features category: Features order: 699 ---
DuckDuckGo uses over 100 sources to provide answers to your searches, without the need to click on a result. Just look for the instant answer above the results:
They're open source, so any developer can contribute new Instant Answers or fix current ones." This also means we have a wide variety of Instant Answers!
Of course, things like calculations and factual information are covered but what's more interesting are the surprises you'll find as instant answers:
From checking the status of a flight to finding words that rhyme together, we've got an instant answer for it.
 Check out DuckDuckHack.com for more info.
You can search other sites directly from DuckDuckGo, just by using our available !bangs.
For example, if you wanted to search on Amazon.com for, "shoes" you could simply type: !amazon shoes
...which would automatically take you to Amazon.com and search for shoes...
Many of the !bangs even have short versions. Use 'a' for Amazon or 'w' for Wikipedia.
For example: !a shoes or !w shoes
There are thousands of sites covered and you can even submit your own!
If you're using our browser extension or, if you have DuckDuckGo as your browser's default search-engine, the !bang commands will even work from the address bar or search bar.
You can quickly search other sites without leaving the DuckDuckGo results page. To do that, click on, More from exampledomain.com DuckDuckGo will then show perform your search on that domain and show results on the search page. Like this:
Perform a search:
Hover over the search result and select the, More from ....option:


The Purdue IEEE Remotely Operated underwater Vehicle (ROV) Team exists as a committee under the Purdue IEEE Student Organization. The mission of the ROV team is to foster technical and professional skills of its members by designing, constructing, and testing an innovative underwater vehicle to compete in the MATE Center International ROV Competition. All ROV team members are required to follow the rules established in these bylaws in addition to the IEEE Constitution.
The ROV team, at minimum, shall be composed of a captain (who shall be IEEE committee chair), electrical team lead, mechanical team lead, and software team lead. Other, non-team lead positions may be appointed and removed at the captain’s leisure.
Attendance at a majority of team meetings and continuous participation with meaningful contributions throughout the design, construction, testing, and documentation phases of the ROV are expected of all members. Additionally, attendance at a majority of technical team meetings to which the respective member belongs is expected. Infrequent attendance, low number or quality of contributions made, disruptive behavior, or lack of IEEE dues payment may be cited as reasons to not be recognized as a member, selected to represent the team at outreach events, or allowed to attend competitions.
While decisions should usually be made with the advice and consent of the team, the captain shall have the ability to make or delegate all final team decisions. However, if there is a disagreement, a unanimous vote of team leads shall result in a vote over the decision being taken by the whole team. A majority of voting team members will override any captain’s decision.
The captain shall have the power to decide access to team resources in a manner consistent with these bylaws. Additionally, the captain will be responsible for the approval of team purchases. This process may be delegated by the captain as needed. Only with approval will a purchase be eligible for reimbursement by the IEEE Treasurer.
While members of the team may be removed or demoted by the captain, the captain may lose his or her position only in the same voting procedure listed above.
In the event of a temporary captain vacancy he or she may appoint a captain in his or her absence. If the vacancy is longer than one month, an election should occur as soon as reasonably possible in the same way as defined below. The captain may appoint an acting captain for any length of time during his or her captaincy with a majority approval of the team leads.
Before the beginning of each new season, there shall be a vote for captain. A season shall consist of no more than one international competition. A captain must have been a member of the team for at least four months. All current members of the team at the time of the election shall have one vote. Eligibility for voting shall be decided by the captain with regards to the aforementioned bylaws. A majority of voting team members shall elect the captain. In the event no majority exists, the member with the fewest votes shall be removed from the ballet and another vote will occur. If there is still no majority when there are only two members left, the previous captain shall decide the new captain.
Any amendments to the bylaws shall be proposed and approved by the captain. The bylaws shall pass with a majority of voting team members or by a unanimous team lead vote.


Below are the lists of the particpants or recipents of the Google Africa Reskilling Scholarship in partnership with Andela Learning Community and Pluralsight. If you participated in the program feel free to add your credential by creating an account or send me a message if you get stuck


GimmeMotifs_1 (Gimme)
Motif and consensus
|
||||
| nynrCCAsyAGrkGGCrsyn | Database match |
|---|
Significance and enrichment
|
Positional Preference Plot
(Fullscreen)GimmeMotifs_2 (Gimme)
Motif and consensus
|
||||
| CCAGCGmCCyCTrCwG | Database match |
|---|
Significance and enrichment
|
Positional Preference Plot
(Fullscreen)GimmeMotifs_3 (Gimme)
Motif and consensus
|
||||
| CTGCCATCTTGTGGnGGmTy | Database match |
|---|
Significance and enrichment
|
Positional Preference Plot
(Fullscreen)GimmeMotifs_4 (Gimme)
Motif and consensus
|
||||
| CmTCTAGT | Database match |
|---|
Significance and enrichment
|
Positional Preference Plot
(Fullscreen)GimmeMotifs_5 (Gimme)
Motif and consensus
|
||||
| GCGCTGCG | Database match |
|---|
Significance and enrichment
|
Positional Preference Plot
(Fullscreen)GimmeMotifs_6 (Gimme)
Motif and consensus
|
||||
| GGyGGCGCCG | Database match |
|---|
Significance and enrichment
|
Positional Preference Plot
(Fullscreen)GimmeMotifs_7 (Gimme)
Motif and consensus
|
||||
| CGGCAG | Database match |
|---|
Significance and enrichment
|
Positional Preference Plot
(Fullscreen)GimmeMotifs_8 (Gimme)
Motif and consensus
|
||||
| CTAGCGGC | Database match |
|---|
Significance and enrichment
|
Positional Preference Plot
(Fullscreen)GimmeMotifs_9 (Gimme)
Motif and consensus
|
||||
| ssAGCG | Database match |
|---|
Significance and enrichment
|
Positional Preference Plot
(Fullscreen)GimmeMotifs_10 (Gimme)
Motif and consensus
|
||||
| GCGTTT | Database match |
|---|
Significance and enrichment
|
Positional Preference Plot
(Fullscreen)GimmeMotifs_11 (Gimme)
Motif and consensus
|
||||
| CGCGCATGCG | Database match |
|---|
Significance and enrichment
|
Positional Preference Plot
(Fullscreen)GimmeMotifs_12 (Gimme)
Motif and consensus
|
||||
| CnGCAGCA | Database match |
|---|
Significance and enrichment
|
Positional Preference Plot
(Fullscreen)GimmeMotifs_13 (Gimme)
Motif and consensus
|
||||
| GCwGCwGCwGCwGCwGCw | Database match |
|---|
Significance and enrichment
|
Positional Preference Plot
(Fullscreen)GimmeMotifs_14 (Gimme)
Motif and consensus
|
||||
| TGTGCAAA | Database match |
|---|
Significance and enrichment
|


Trello Webhook Management
Register New Webhook
Model ID to Watch:
Name for Webhook:
Register Trello Webhook
Delete Existing Webhook
Webhook ID:
webhookid
-
url
- Watched Model ID:
idmodel
Webhook ID:
Delete Webhook


Never miss another deadline again!
Better Professor is an easy-to-use deadline management app that allows you to set custom reminders for yourself and your students.
Sign Up Log InBetter Professor is an easy-to-use deadline management app that allows you to set custom reminders for yourself and your students.
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<<<<<<< HEAD
Anusha. (Class on March 13, 2014)
Read papers
Besag J & Diggle PJ. 1977. Simple Monte Carlo Tests for Spatial Pattern. Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 26, No. 3, pp. 327-333.
Lancaster J and Downes BJ. 2004. Spatial Point Pattern Analysis of Available and Exploited Resources. Ecography, Vol. 27, No. 1, pp. 94-102.
Crowley PH. 1992. Resampling methods for computation-intensive data analysis in ecology and evolution. Annual Review of Ecology, Evolution and Systematics, 23:405-47.
we first clarified that the term Monte Carlo does not only refer to Markov Chain Monte Carlo (MCMC) methods. A Monte Carlo process is any random draw process. Markov Chain Monte Carlo methods, on the other hand, only relate to the preceeding time step. However, note that because the preceeding time step is always correlated with its corresponding previous time step, you could get very long correlation times. MCMC methods can sometimes be used to get likelihoods- when you don't know a likelihood function, but would like to sample from it, you might use MCMC. This is also relevant because this is in contrast to Bayesian methods, where you don't have a final distribution you can draw from.
The Besag and Diggle paper focused on general Monte Carlo methods. Monte Carlo processes can be very useful when full randomisation tests would be useful but are computationally difficult. They can also be used to test for spatial randomness in data. Here, the null would be complete spatial randomness, and you would use a test statistic to test this. For example, a test statistic proposed by Clarke and Evans (1954) uses nearest neighbor distances. A large sum of the nearest neighbor distances shows a tendency towards regularity, while a small sum shows clustering.
The next test using Monte Carlo methods is testing for pattern transference, for example, from small-scale migration to larger scales.
The third test was for space-time interaction. They give the example of disease outbreak, where to test if there is an outbreak, you need to see a large number of infected individuals in the SAME time step- you cannot collapse the data over time or over space and deduce an outbreak. Monte Carlo methods can help with this.
The fourth and last test is for patterns of scale, where you partition the area into blocks of different sizes and test for patterns over various combinations of blocks.
The Crowley paper contrasted different randomisation methods (monte Carlo, jacknife, bootstrap; and their applications in Ecology and Evolution). We first discussed when one would use jacknife over bootstrap methods.  So the bootstrap gives you more information than the jacknife does. The bootstrap also simulates a rerun of your experiment, while the jacknife is a more independent test.
However, you might want to use jacknife rather than bootstrap if you are running species distribution models (SDMs). When you bootstrap in SDMs, you might be assuming that your environmental variables are coming from a bigger set of variables, in which case you are testing the effect of your variables as opposed to that bigger set. So doesn't make as much sense as doing a jacknife- where you are testing the effect of any one variable at a time on the distribution.
We then reviewed a few concepts (just to be clear!): A parametric bootstrap is when you fit your data to a distribution and sample from the distribution rather than from the original data (which is a non-parametric bootstrap, of course). We defined power- the probability that you will correctly reject the null hypothesis. This is also the opposite of a Type II error. Non-parametric methods are rank-based methods, like Wilcoxon's signed-rank test. These methods have low power. Parametric tests, on the other hand, have high power given that their assumptions are met.
When you do randomisation tests, you get high power (which also means a high false alarm rate), high rate of Type I errors, and low rate of type II errors. If you violate the assumptions of the t-test, your power increases, but you no longer know what your level of sginificance is. This is linked to why experimental design in frequentist statistics is subjective- depending on the design of your experiment, your thresholds of significance change. Thus, you need to have considerable transparency when you are publishing significance levels after performing frequentist statistics.
We then continued by discussing the Lancaster and Downes (2004) paper and parts of the Crowley (1992) paper in parallel, as their themes overlapped. The Lancaster and Downes paper addressed spatial point pattern analysis, given patchiness vs regularity of resource availability.
The first problem is that of autocorrelation. It does not allow you to easily write down likelihoods, as they would be functions of other likelihoods. Here arises the difference between independent variables, and conditionally independent variables. In this case, the spatial structure of covariates and responses is important. You only have to account for spatial autocorrelation when you create a joint probability of all likelihoods- when there is conditional independence. For example:
P(brown hair AND blue eyes) = P(brown hair)*P(blue eyes)
is true only if the two terms on the right are independent. Otherwise, you don't know the interaction term where they intersect. In these situations of conditional probability, randomisation methods come in handy. In our partcular interest here- with spatial autocorrelation- you would use MCMC methods, because even though you can't write down the likelihood function, you can sample from the likelihood distribution. The Knox test is one technique, where you hold space constant and permute time, and the other way around. This technique is not used much, because specifying a cut-off is difficult.
We then moved to the exciting issue of edge correction. Drawing an amorphous form on the board, which was then christened a lake, we discussed different ways of correcting for sampling from edges. To determine how resource availability and species presence are related, for instance in stated lake, we might sample a point at random from within the lake. We would then draw a circle of radius 't' with that ,point at the center, and count how many points in total that circle encompasses. The Lancaster and Downes paper focuses on how you can correct for the inevitable bias of sampling points near the edge of this lake- the circles around those points would encompass much fewer points than those at the center, simply because half the circle would fall outside the habitat edge.
Using this formula, you can describe a cumulative frequency distribution of all point-to-point distances: \[ K(t) = n^{-2}A\sum_{i=i}^{n} \sum_{j\neq i}^{n}w_{ij}I_{t}(u_{ij}) \]
This K(t) function is often represented as a linearised L function: \[ L(t) = \sqrt{K(t)/\pi} - t \]
Anusha. (Class on March 13, 2014)
Read papers
Besag J & Diggle PJ. 1977. Simple Monte Carlo Tests for Spatial Pattern. Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 26, No. 3, pp. 327-333.
Lancaster J and Downes BJ. 2004. Spatial Point Pattern Analysis of Available and Exploited Resources. Ecography, Vol. 27, No. 1, pp. 94-102.
Crowley PH. 1992. Resampling methods for computation-intensive data analysis in ecology and evolution. Annual Review of Ecology, Evolution and Systematics, 23:405-47.
we first clarified that the term Monte Carlo does not only refer to Markov Chain Monte Carlo (MCMC) methods. A Monte Carlo process is any random draw process. Markov Chain Monte Carlo methods, on the other hand, only relate to the preceeding time step. However, note that because the preceeding time step is always correlated with its corresponding previous time step, you could get very long correlation times. MCMC methods can sometimes be used to get likelihoods- when you don't know a likelihood function, but would like to sample from it, you might use MCMC. This is also relevant because this is in contrast to Bayesian methods, where you don't have a final distribution you can draw from.
The Besag and Diggle paper focused on general Monte Carlo methods. Monte Carlo processes can be very useful when full randomisation tests would be useful but are computationally difficult. They can also be used to test for spatial randomness in data. Here, the null would be complete spatial randomness, and you would use a test statistic to test this. For example, a test statistic proposed by Clarke and Evans (1954) uses nearest neighbor distances. A large sum of the nearest neighbor distances shows a tendency towards regularity, while a small sum shows clustering.
The next test using Monte Carlo methods is testing for pattern transference, for example, from small-scale migration to larger scales.
The third test was for space-time interaction. They give the example of disease outbreak, where to test if there is an outbreak, you need to see a large number of infected individuals in the SAME time step- you cannot collapse the data over time or over space and deduce an outbreak. Monte Carlo methods can help with this.
The fourth and last test is for patterns of scale, where you partition the area into blocks of different sizes and test for patterns over various combinations of blocks.
The Crowley paper contrasted different randomisation methods (monte Carlo, jacknife, bootstrap; and their applications in Ecology and Evolution). We first discussed when one would use jacknife over bootstrap methods.  So the bootstrap gives you more information than the jacknife does. The bootstrap also simulates a rerun of your experiment, while the jacknife is a more independent test.
However, you might want to use jacknife rather than bootstrap if you are running species distribution models (SDMs). When you bootstrap in SDMs, you might be assuming that your environmental variables are coming from a bigger set of variables, in which case you are testing the effect of your variables as opposed to that bigger set. So doesn't make as much sense as doing a jacknife- where you are testing the effect of any one variable at a time on the distribution.
We then reviewed a few concepts (just to be clear!): A parametric bootstrap is when you fit your data to a distribution and sample from the distribution rather than from the original data (which is a non-parametric bootstrap, of course). We defined power- the probability that you will correctly reject the null hypothesis. This is also the opposite of a Type II error. Non-parametric methods are rank-based methods, like Wilcoxon's signed-rank test. These methods have low power. Parametric tests, on the other hand, have high power given that their assumptions are met.
When you do randomisation tests, you get high power (which also means a high false alarm rate), high rate of Type I errors, and low rate of type II errors. If you violate the assumptions of the t-test, your power increases, but you no longer know what your level of sginificance is. This is linked to why experimental design in frequentist statistics is subjective- depending on the design of your experiment, your thresholds of significance change. Thus, you need to have considerable transparency when you are publishing significance levels after performing frequentist statistics.
We then continued by discussing the Lancaster and Downes (2004) paper and parts of the Crowley (1992) paper in parallel, as their themes overlapped. The Lancaster and Downes paper addressed spatial point pattern analysis, given patchiness vs regularity of resource availability.
The first problem is that of autocorrelation. It does not allow you to easily write down likelihoods, as they would be functions of other likelihoods. Here arises the difference between independent variables, and conditionally independent variables. In this case, the spatial structure of covariates and responses is important. You only have to account for spatial autocorrelation when you create a joint probability of all likelihoods- when there is conditional independence. For example:
P(brown hair AND blue eyes) = P(brown hair)*P(blue eyes)
is true only if the two terms on the right are independent. Otherwise, you don't know the interaction term where they intersect. In these situations of conditional probability, randomisation methods come in handy. In our partcular interest here- with spatial autocorrelation- you would use MCMC methods, because even though you can't write down the likelihood function, you can sample from the likelihood distribution. The Knox test is one technique, where you hold space constant and permute time, and the other way around. This technique is not used much, because specifying a cut-off is difficult.
We then moved to the exciting issue of edge correction. Drawing an amorphous form on the board, which was then christened a lake, we discussed different ways of correcting for sampling from edges. To determine how resource availability and species presence are related, for instance in stated lake, we might sample a point at random from within the lake. We would then draw a circle of radius 't' with that ,point at the center, and count how many points in total that circle encompasses. The Lancaster and Downes paper focuses on how you can correct for the inevitable bias of sampling points near the edge of this lake- the circles around those points would encompass much fewer points than those at the center, simply because half the circle would fall outside the habitat edge.
Using this formula, you can describe a cumulative frequency distribution of all point-to-point distances: \[ K(t) = n^{-2}A\sum_{i=i}^{n} \sum_{j\neq i}^{n}w_{ij}I_{t}(u_{ij}) \]
This K(t) function is often represented as a linearised L function: \[ L(t) = \sqrt{K(t)/\pi} - t \]
So, if you are dealing with datasets with complex edges that you want to compensate for, these are some possible tools to use. Note that it is not always prudent to do edge corrections. In some cases, it does not change the results at all, in others it in fact confounds the results. Thus it should only be performed when there is a biological/ecological basis to use edge corrections.
a12944dee22b5e0295e069f6fbf9e585318fe919


The ZUSE Graphomat Z64 was a flatbed drawing machine of high precision. Its engineer, famous computer pioneer Konrad Zuse, had originally intended it to be used for the production of maps and for land registration purposes.The Graphomat Z64 is a punch tape or punch card controlled plotter. This machine was presented in 1961 on the Hannove exhibition.
Early computer artists Georg Nees and Frieder Nake created their first computer art pieces on the Graphomat.
Frieder Nake, 13/9/65 Nr. 2 ("Hommage à Paul Klee")
The Z64 didn’t come with any software—someone needed to program it from scratch. Nake’s boss asked him if he could do it. “This short moment changed my life,” says Nake. In learning to program the Z64, he started to grasp the creative capabilities of computers—and thus began a long career as a computer artist and academic.
Georg Nees, Würfel-Unordnung (Cubic Disarray)
LOGO is an educational programming language, designed in 1967 by Daniel G. Bobrow, Wally Feurzeig, Seymour Papert and Cynthia Solomon. Today the language is remembered mainly for its use of turtle graphics, in which commands for movement and drawing produced line graphics either on screen or with a small robot called a turtle.
The first working Logo turtle robot was created in 1969. A display turtle preceded the physical floor turtle. Modern Logo has not changed too much from the basic concepts before the first turtle. The first turtle was a tethered floor roamer. At BBN Paul Wexelblat developed a turtle named Irving that had touch sensors and could move forwards, backwards, rotate, and ding its bell.
The Aspen Movie Map was a revolutionary hypermedia system developed at MIT by a team working with Andrew Lippman in 1978 with funding from ARPA.
Created in the late 1970’s, the Aspen Movie Map was a groundbreaking interactive virtual tour of the real-world city of Aspen, Colorado. Users could navigate the streets, go inside selected buildings, and change the seasons between fall and winter.
The Aspen Movie Map predated Google Street View by 34 Years
Inventing Interactive - Aspen Movie Map ArticleHyperCard is an application program and programming tool for Apple Macintosh and Apple IIGS computers, that is among the first successful hypermedia systems before the World Wide Web. It combines database capabilities with a graphical, flexible, user-modifiable interface.
Hypermedia, a term derived from hypertext, extends the notion of the hypertext link to include links among any set of multimedia objects, including sound, motion video, and virtual reality. It can also connote a higher level of user/network interactivity than the interactivity already implicit in hypertext.
HyperCard is based on the concept of a "stack" of virtual "cards". Cards hold data, just as they would in a Rolodex card-filing device. Each card contains a number of interactive objects, including text fields, check boxes, buttons, and similar common GUI (Graphical user interface) elements. Users "browse" the stack by navigating from card to card, using built-in navigation features, a powerful search mechanism, or through user-created scripts.
Hypercard included a high level programming language called Hypertalk, which allowed users to add interactivity to their hypercard stacks
Hypercard influenced the creation of HTML and the modern internet
Hypermedia continued to evovle...
Released in 1985, a publicly available linear animation software package that let users create interactive video tours, animations and more on personal computers.
Macromind Videoworks eventually became Macromedia Director, which became the leading authoring application for interactive media in the era of the multimedia CD-ROM


{% load nonce %} {% if request.user.extra.mobile or activation %}
Your mobile phone is the quickest ticket to posting on the go. Enter your mobile number in the field below and click the button to send the activation message to your phone.
U.S. Only In international format (+1204134567) If the U.S., add +1 before your area code


Field reference for HMDA LAR data
Why so many rows and fields? Don't be alarmed–there's a lot of information in HMDA LAR data, but you can make your queries as basic or as detailed as you want.
Use this page as a cheat sheet when you are ready to add color to some basic queries. You can also skim this table to get an idea of the kinds of information you can find in these records.
If you are new to HMDA LAR data, that's not a problem. Watch this video to get started, and then check out these resources to learn about the nitty gritty details of the data.
HMDA Loan Application Register fields
We recommend viewing this table in landscape mode on your mobile device.
| Field name | Description | Data type | Indexed? |
|---|


Ian is a simple tool to manage your development environment and your coding projects.
Us, developers, love to work with the terminal. More reliable and simpler than most GUIs, this “black screen” allows also automation through scripting and blazing fast manipulation.
But even with these capabilities, we still execute pretty repetitive tasks and have troubles to make our local environment portable. Sometimes, we’re looking in our history to retrieve a database URI, change from one project directory to another one just to execute a command, and so on.
Ian was designed and created to solve these problems.
ian propose your to store and persist your projects and env configuration in order to interact with them more easily. You can (among others things):
… Which bring us to customization and social interactions (yes, for those who still doubt about it, we are human too) !
We decided to offer the maximum customization possible to make you able to interact with your environment the way you like it. In brief, we give you a set of default commands that we find useful in general and we let you add any subcommands you’d like to execute.
Projects depend on a huge number of factors: languages, platforms, frameworks, host ressources, etc With ian, through an interactive setup you can provide the infos necessary to specific the context of execution of all the project commmands.
Using ian share, you can share your ian configuration files
In the next page, we’ll go though the setup together :)


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oak-prepare-shutdown
NAME
oak-prepare-shutdown: make for a fast and safe MySQL shutdownSYNOPSIS
Prepare and shutdown the MySQL server:oak-prepare-shutdown --user=root --ask-pass --socket=/tmp/mysql.sock && /etc/init.d/mysql stopPrepare and shutdown the MySQL server, RedHat style:
oak-prepare-shutdown --user=root --ask-pass --socket=/tmp/mysql.sock && service mysql stopSame as above, use defaults file:
oak-prepare-shutdown --defaults-file=/home/myuser/.my-oak.cnf && service mysql stop
DESCRIPTION
This tools makes for a faster and safe shutdown process:
- It flushes InnoDB buffer pool pages until no further progress can be achieved for the duration of 10 consequent seconds
- When server acts as a slave, it gracefully stops replication while verifying no slave temporary tables remain open
Flushing of InnoDB pages is performed by setting innodb_max_dirty_pages_pct to 0. InnoDB follows by attempting to reach that target. On an active server, it will most probably never reach 0. oak-prepare-shutdown follows up on the rate of page flush, and terminates when it concludes no more gain is expected.
Interrupting this tool with Ctrl-C results with restoring original settings and termination with error.
Any abnormal termination of this tools results with innodb_max_dirty_pages_pct still being set to 0. This may seriously degrade the performance of your running database. Make sure to manually restore to original settings, e.g. via:
mysql> SET GLOBAL innodb_max_dirty_pages_pct = 90;
OPTIONS
--ask-passPrompt for password.
--debugPrint stack trace on error.
--defaults-file=DEFAULTS_FILERead from MySQL configuration file. Overrides --user, --password, --socket, --port.
Configuration needs to be in the following format:
[client]
user=my_user
password=my_pass
socket=/tmp/mysql.sock
port=3306
MySQL host (default: localhost)
-p PASSWORD, --password=PASSWORDMySQL password
-P PORT, --port=PORTTCP/IP port (default: 3306)
-S SOCKET, --socket=SOCKETMySQL socket file. Only applies when host is localhost
-u USER, --user=USERMySQL user
-v, --verbosePrint user friendly messages
ENVIRONMENT
Requires MySQL 5.0 or newer, python 2.3 or newer. python-mysqldb must be installed in order to use this tool. You canapt-get install python-mysqldbor
yum install mysql-python
SEE ALSO
LICENSE
This tool is released under the BSD license.Copyright (c) 2008 - 2010, Shlomi Noach All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the organization nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.


| Modifier and Type | Constant Field | Value |
|---|---|---|
public static final int |
APPLE_CLASS_ID |
11 |
public static final int |
EMPTY_CLASS_ID |
1 |
public static final int |
FLYTRAP_CLASS_ID |
12 |
public static final int |
HAZARD_CLASS_ID |
3 |
public static final int |
THORN_CLASS_ID |
3 |
public static final int |
TREASURE_CLASS_ID |
10 |
public static final int |
UNINITIALIZED_CLASS_ID |
0 |
public static final int |
UNKNOWN_CREATURE_CLASS_ID |
18 |
public static final int |
WALL_CLASS_ID |
2 |
| Modifier and Type | Constant Field | Value |
|---|---|---|
public static final String |
SYNTAX_HELP |
"java Darwin [-3D | -2D] [-nosecurity] mapname Class0 Class1 ..." |
| Modifier and Type | Constant Field | Value |
|---|---|---|
public static final long |
ATTACK_COST |
800000L |
public static final long |
DELAY_COST |
100000L |
public static final long |
EMIT_PHEROMONE_COST |
200000L |
public static final long |
FOG_MOVE_FORWARD_PENTALTY_COST |
200000L |
public static final long |
KILL_TIME |
250000000L |
public static final long |
LOOK_COST |
200000L |
public static final long |
MILLISECONDS |
1000000L |
public static final long |
MOVE_BACKWARD_COST |
700000L |
public static final long |
MOVE_FORWARD_COST |
400000L |
public static final long |
MUD_PENALTY_COST |
500000L |
public static final long |
MZ_TIME_LIMIT |
6000000000L |
public static final int |
NS_ASCENSIONS_TO_WIN |
3 |
public static final long |
NS_TIME_LIMIT |
50000000000L |
public static final long |
OBSERVE_COST |
200000L |
public static final int |
SCALE_2D |
17 |
public static final long |
SECONDS |
1000000000L |
public static final long |
TURN_COST |
600000L |
| Modifier and Type | Constant Field | Value |
|---|---|---|
public static final int |
ASCENSION_SCORE |
3 |
public static final int |
DOMINATION_SCORE |
3 |
public static final int |
EXHAUSTION_MAJORITY_SCORE |
2 |
public static final int |
EXHAUSTION_SURVIVAL_SCORE |
1 |
public static final int |
LOSE_SCORE |
0 |


Trading Card Transactions
Submit a transaction
Add Node
Trading Card Transactions
Show local chain
Mine new block
Resolve chain with network
Sender
Recipient
Card(s)


True or False: the set {1, 2, 3, 4} and the set {4, 3, 1, 2} are the same
True
- False: even though the same elements are in both sets, the ordering makes them different
- False: {1, 2, 3, 4} and {4, 3, 2, 1} would be the same but since the second set is {4, 3, 1, 2}, they are different


{% macro displayPitches(pitch_list) %} {% for pitch in pitch_list %}
{{ pitch.pitch | truncate(30) }}


In computer science, a double-ended queue (dequeue, often abbreviated to deque, pronounced deck) is an abstract data type that generalizes a queue, for which elements can be added to or removed from either the front (head) or back (tail).
Deque interfaces can be implemented using various types of collections such as LinkedList
or ArrayDeque
classes. For example, deque can be declared as:
Deque deque = new LinkedList<>();
or
Deque deque = new ArrayDeque<>();
You can find more details about Deque here.
In this problem, you are given $N$ integers. You need to find the maximum number of unique integers among all the possible contiguous subarrays of size $M$.
Note: Time limit is $3$ second for this problem.
Input Format
The first line of input contains two integers $N$ and $M$: representing the total number of integers and the size of the subarray, respectively. The next line contains $N$ space separated integers.
Constraints
$1 \le N \le 100000$
$1 \le M \le 100000$
$M\le N$
The numbers in the array will range between $[0,10000000]$.
Output Format
Print the maximum number of unique integers among all possible contiguous subarrays of size $M$.
Sample Input
6 3
5 3 5 2 3 2
Sample Output
3
Explanation
In the sample testcase, there are 4 subarrays of contiguous numbers.
$s1=\langle 5, 3, 5 \rangle$ - Has $2$ unique numbers.
$s2=\langle 3, 5, 2 \rangle$ - Has $3$ unique numbers.
$s3=\langle 5, 2, 3 \rangle$ - Has $3$ unique numbers.
$s4=\langle 2, 3, 2 \rangle$ - Has $2$ unique numbers.
In these subarrays, there are $2, 3, 3, 2$ unique numbers, respectively. The maximum amount of unique numbers among all possible contiguous subarrays is $3$.


You can earn shekels by showing up to meetings or as a prize.
If you have a preferred name that you'd like to be listed as; let a club officer know.


Work Experience
Contact
<EMAIL_ADDRESS>09267239752
youtube.com/c/regelpakshetgaming
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Skills
Regel Industry 2016


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• 🏆 Link SNS profile to keep scores/settings/items.
• 🖼 Adjust graphics display if your box is too slow.